We show that for \(1\) separated subsets of \(\mathbb{R}^{2}\), the natural Marstrand type slicing statements are false with the counting dimension that was used earlier by Moreira and Lima and variants of which were introduced earlier in different contexts. We construct a \(1\) separated subset \(E\) of the plane which has counting dimension \(1\), while for a positive Lebesgue measure parameter set of tubes of width \(1\), the intersection of the tube with the set \(E\) has counting dimension \(1\). This is in contrast to the behavior of such sets with the mass dimension, in regards to slicing, where the slicing theorems hold true.
A certain residue representation of the inverse binomial coefficients makes them amenable to Egorychev method for the reduction of sums by analytic methods, wherein the main idea is to identify parts of the summands as residues of analytic functions. We illustrate the use of such residue representation on some instances varying in complexity, including a generalization of an identity by Sung Sik U and Kyu Song Chae in [13].
This paper uses exponential sum methods to show that if \(E \subset \mathcal (\mathbb{Z}/p^r)^n \setminus (p)^{(n)}\) has a sufficiently large density and \(j\) is any unit in the finite ring \(\mathbb{Z}/p^r\) then there exist pairs of elements of \(E\) whose dot product equals \(j\). It then applies this to the problem of detecting \(2-\) simplices with endpoints in \(E\).
In this paper, we derive some new combinatorial inequalities by applying well known real analytic results like Hölder’s inequality, Young’s inequality, and Minkowiski’s inequality to the recursively defined sequence \(f_n\) of functions \[\begin{align*} f_0(x) & = \chi_{(-1/2, 1/2)} (x), \nonumber \\ f_{n+1}(x) & = f_n(x+1/2)+ f_n(x-1/2), n \in \mathbb{N}\,\cup \,\{0\}. \end{align*}\] Towards this goal, we derive the closed form of the aforementioned sequence \((f_n)_{n\in \mathbb{N}\,\cup \,\{0\}}\) of functions and show that it is a sequence of simple functions that are linear combinations of characteristic functions of some unit intervals \(I_{n,i},\, i=0,1, …, n\), with values the binomial coefficients \(\binom{n}{i}\) on each unit interval \(I_{n,i}\). We show that \(f_n \in L^p(\mathbb{R})),\, 1\leq p \leq \infty\). Besides applying real analytic methods to formulate some combinatorial inequalities, we also illustrate the application of some combinatorial identities. For example, we use the Vandermonde convolution (or Vandermonde identity), in the study of some properties of the sequence of functions \((f_n)_{n\in\mathbb{ N}\cup \{0\}}\). We show how the \(L^2\) norm of \(f_n\) is related to the Catalan numbers.
We study Lambert series generating functions associated with arithmetic functions \(f\), defined by
$$L_f(q)=\sum_{n\ge1}\frac{f(n)q^n}{1-q^n}=\sum_{m\ge1}(f*1)(m)q^m.$$
These expansions naturally generate divisor sums through Dirichlet convolution with the constant-one function and provide a useful framework for enumerating ordinary generating functions of many multiplicative functions in number theory. This paper presents an overview of key properties of Lambert series, together with combinatorial generalizations and a compendium of formulas for important special cases. The emphasis is on formal and structural aspects of the sequences generated by these series rather than on analytic questions of convergence. In addition to serving as an introduction, the paper provides a consolidated reference for classical identities, recent connections with partition-generating functions, and other useful Lambert series expansions arising in applications.
We study edge partitions of a bipartite graph into induced-2K2-free bipartite graphs, i.e. into Ferrers (chain) graphs. We define fp(G) as the minimum number of parts in such a partition. We prove general lower and upper bounds in terms of induced matchings and Dilworth widths of neighborhood posets. We compute the parameter exactly for paths and even cycles, and we exhibit separations showing that the induced-matching lower bound and the width upper bound can both be far from tight. We also record a simple host-induced conflict-graph lower bound, present a 0–1 matrix viewpoint, and add some complexity remarks.
We study the Equivalent Local Sequence Problem (ELSP), which consists in recovering an explicit sequence of local complementations that transforms a graph into a locally equivalent one. Focusing on directed Paley graphs, we establish that local complementations commute and induce a free action of an elementary abelian 2-group. The stabilizer condition is reformulated as a system of convolution equations and analyzed through Fourier techniques over finite fields, leading to a proof of stabilizer triviality. As a consequence, each graph in the orbit admits a unique subset encoding, and ELSP reduces to solving a linear inversion problem over 𝔽2. This characterization completely resolves ELSP for directed Paley graphs, provides a polynomial-time inversion algorithm and highlights structural features that may support future developments in cryptographic frameworks and quantum graph-state models.
Given a configuration of pebbles on the edges of a connected graph G, an edge pebbling move is defined as the removal of two pebbles off an edge and placing one on an adjacent edge. The domination cover edge pebbling number of a graph G is the minimum number of pebbles required such that the set of edges that contain pebbles form an edge dominating set S of G, for the initial configuration of pebbles can be altered by a sequence of pebbling moves and it is denoted by ψe(G) for a graph G. In this paper, we determine ψe(G) for Generalized Petersen graph, Jewel graph and Triangular snake graph.
Let W = {w1, w2, w3, …, wk} be an ordered set of vertices in a connected graph G. The representation of a vertex v ∈ V(G) with respect to W is the k-tuple r(v|W) = (d(v, w1), d(v, w2), …, d(v, wk)), where d(v, wi) is the length of the shortest path from v to wi. If each vertex in G is uniquely identified by the distance vector, r(v|W) = (d(v, w1), d(v, w2),…,d(v, wk)), then W is called a resolving set for G. If the resolving set is also independent, it is referred to as an independent resolving set. The independent metric dimension of G, denoted by idim(G), is the smallest cardinality of an independent resolving set. This study explores the independent metric dimension of the circulant graphs Cn(1, 2), Cn(1, 2, 3), Cn(1, 2, 3, 4) for sufficiently large n.
In this paper, given a homeomorphism f of a compact metric space X, we show that the set of all asymptotic average shadowable points of f is an open and invariant set and f has the asymptotic average shadowing property if and only if the set of all asymptotic average shadowable points of f is X if and only if any Borel probability measure μ of X has the asymptotic average shadowing property.
In this paper we study group divisible designs (GDDs) with block size 4 and two groups of different sizes when λ2 = 1. We obtain necessary conditions for the existence of such GDDs and prove that these necessary conditions are sufficient in several cases. Further, we present general constructions using resolvable designs.
In 1940, Birkhoff posed an open problem of counting all finite lattices on n elements. Recently, Bhavale counted all non-isomorphic lattices on n elements, containing up to four reducible elements, and having nullity up to three. Further, Aware and Bhavale counted all non-isomorphic lattices on n elements, containing up to five comparable reducible elements, and having nullity up to three. In this paper, we count all non-isomorphic lattices on n elements, containing five reducible elements, and having nullity three.
The unit graph of a commutative ring with a non-zero identity is a graph with vertices as ring elements, and there is an edge between two distinct vertices if their sum is a unit. This study investigates the decomposition of the unit graph by examining its induced subgraphs and analyze key graph invariants, such as connectivity, diameter, and girth, for a finite local ring. We further decompose the unit graph of certain finite commutative rings into fundamental structures, such as cycle and star graphs.
A mapping of the set of undirected simple (loopless) graphs to itself is a linear operator if it maps the edgeless graph to the edgeless graph and maps the union of graphs to the union of their images. A linear operator preserves a set if it maps that set to itself. We study linear operators that map sets defined by the restriction of their chromatic number. For example the set of all graphs whose chromatic number is at least \(k\) for some fixed \(3\leq k\leq n\). We show these linear operators must be vertex permutations.
The first Zagreb index of a graph \(G\) is defined as \(\sum\limits_{u \in V} d_G^2(u)\), where \(d_G(u)\) is the degree of vertex \(u\) in \(G\). The algebraic connectivity of a graph \(G\) is defined as the second smallest eigenvalue of the Laplacian matrix of \(G\). Using Wagner’s inequality, we in this paper first obtain an upper bound for the algebraic connectivity that involves the first Zagreb index of a graph. Following the ideas of obtaining the upper bound, we present sufficient conditions involving the first Zagreb index and the algebraic connectivity for some Hamiltonian properties of graphs.
We present a proof of a conjecture of Goh and Wildberger on the factorization of the spread polynomials. We indicate how the factors can be effectively calculated and exhibit a connection to the factorization of Fibonacci numbers into primitive parts.
For a graph \(G\), let \(la(G)\) denote the linear arboricity of \(G\) and \(\Delta(G)\) denote the maximum degree of \(G\). The famous linear arboricity conjecture was made by Akiyama, Exoo, and Harary [Covering and packing in graphs. IV. Linear arboricity] in 1981. It asserts that \(la(G) \leq \Bigl\lceil\frac{\Delta(G)+1}{2}\Bigr\rceil\). In this paper, we prove the linear arboricity conjecture for products of a path and a complete graph, and for products of a path and a tree.
Let \(G\) be a connected graph. The center function defined on \(G\) yields a set of vertices that minimizes the maximum distance from the given input vertices. Through axiomatic characterization of the center function, we identify the specific axioms that characterize its behavior on connected graphs. Universal axioms encompass the properties satisfied by the center function on all connected graphs. However, for some graphs, the center function cannot be fully characterized using universal axioms alone. To address this, a set of graph class-specific axioms, known as non-universal axioms, was introduced. In the case of book graphs (Cartesian product of star graph \(K_{1,n}\) and path \(P_2\)), the center function cannot be adequately characterized using known universal axioms. Therefore, in this context, we find an axiomatic characterization of the center function on book graphs using the universal axioms and one newly introduced Cycle Consensus \((CC)\) axiom.
A supermagic labeling (often also called vertex-magic edge labeling) of a graph \(G(V,E)\) with \(|E|=q\) is a bijection from \(E\) to the set of first \(k\) positive integers such that the sum of labels of all incident edges of every vertex \(x\in V\) is equal to the same integer \(c\). An existence of a supermagic labeling of Cartesian product of two cycles, \(C_{n}\Box C_m\) for \(n,m\geq4\) and both \(n,m\) even and for any \(C_n\Box C_n\) with \(n\geq3\) was proved by Ivančo. Ivančo also conjectured that such labeling is possible for any \(C_n\Box C_m\) with \(n,m\geq3\). We prove his conjecture for all \(n,m\) odd that are not relatively prime.
Let \(G=(V,E)\) be a graph. For a vertex \(u\) of \(V(G)\), its closed neighborhood, \(N[S]\), is defined as \(N[u]=\{u\}\cup\{v|v\in V(G), v\neq u, u\) and \(v\) are adjacent in \(G \}\). A vertex subset \(S\) of \(V(G)\) is called a subversion strategy of \(G\) if each of the vertices in \(N[S]\) is deleted from \(G\). By \(G/S\) we denote the survival subgraph \(G-N[S]\). A subversion strategy \(S\) is called a cut strategy of \(G\) if \(G/S\) is disconnected, or is a clique, or is empty. In this paper, we revise the definition of neighbor-isolated scattering number, which was introduced by Aslan, as \(NIS(G)=\max\{i(G/S)-|S|\}\), where \(S\) represents a cut strategy of \(G\) such that every component of \(G/S\) is an isolated vertex or a clique, and \(i(G/S)\) represents the number of the components of \(G/S\). We discuss the relationship between this parameter and the structure of graphs. Some tight bounds and extremal graphs with given order and neighbor-isolated scattering number are determined.
Let \(k\) be an odd prime and choose \(s\in\mathbb{Z}_k^\times\) with \(s^2\not\equiv \pm1\pmod{k}\) (hence \(k\ge7\)). We give a deterministic, purely algebraic construction of compound pandiagonal (Nasik) magic squares of order \(k^{2}\) with consecutive entries \(\{0,1,\dots,k^{4}-1\}\). The input is the \(k\times k\) Modular Inverse Shift (MIS) kernel \(M_s(i,j)=si+s^{-1}j\in\mathbb{Z}_k\), a classical linear Latin square. Our contribution is not a new Latin-square object, but a closed-form integration of: (i) orthogonality of \((M_s,M_s^{\mathsf T})\), (ii) toroidal diagonal-regularity, and (iii) a two-level base-\(k\) digit superposition producing a \(k^2\times k^2\) square with closed-form evaluation of entries. We encode four \(\mathbb{Z}_k\)-digits coming from \((M_s,M_s^{\mathsf T})\) at both the block level and the within-block level, obtaining an explicit formula \(P_s(I,J)\in\{0,\dots,k^{4}-1\}\). Orthogonality yields bijectivity, while a carry-sensitive diagonal decomposition proves that every broken diagonal of both slopes sums to the magic constant. Finally, evaluating block sums shows that the induced \(k\times k\) block-sum array is itself pandiagonal magic, establishing the compound property.
We provide a hierarchy of “nonconventional ergodic theorems” in quantum setting involving operators and unitaries acting on the Hilbert space of the Gelfand-Naimark-Segal covariant representation associated to a reference \(C^*\)-dynamical system. The first two levels correspond to the Mean Ergodic Theorem by J. von Neumann involving a unitary, and the ergodic theorem by Kovács and Szúcs relative to unitarily implemented automorphisms of von Neumann algebras, respectively. As a consequence, we provide multiple correlation results concerning the ergodic behaviour of “three-operator” expectations.
We study a search problem on capturing a moving target on an infinite real line. Two autonomous mobile robots (which can move with a maximum speed of 1) are initially placed at the origin, while an oblivious moving target is initially placed at distance d from the origin. The robots can move along the line in either direction, but the target is oblivious, cannot change direction, and moves either away from or toward the origin at a constant speed v. Our aim is to design efficient algorithms for two robots to capture the target. The target is captured only when both robots are co-located with it. The robots communicate only face-to-face (F2F), meaning they can exchange information only when co-located. We design algorithms under various knowledge scenarios regarding d, v, and the target’s direction of movement. We analyze competitive ratios, i.e., the capture time versus the optimal full-knowledge scenario, and show that our strategies use at most three direction changes.
We present a theorem which characterizes the class of line graphs of directed graphs. The characterization is an analogue of both the characterization of line graphs by Krausz (1943) and of directed line graphs of directed graphs by Harary and Norman (1960). Our characterization simplifies greatly in the case that the graph is bipartite. This and another result which we present draws attention to the special case of bipartite line graphs of directed graphs. As a result we explore the problem of finding the complete list of forbidden subgraphs for the class of bipartite line graphs of directed graphs. It appears, however, that this problem is extremely difficult. We do find two infinite families of forbidden subgraphs as well as several other illustrative examples.
As a generalization of vector spaces over finite fields, we study vector spaces over finite commutative rings, and obtain Anzahl formulas and a dimensional formula for subspaces. By using these results, we discuss normalized matching (NM) property of a class of subspace posets.
IOur focus is on the set of lower-triangular, infinite matrices that have natural operations like addition, multiplication by a number, and matrix multiplication. With respect to addition this set forms and abelian group while with respect to matrix multiplication, the invertivle elements of the set form a group. The set becomes an algebra (non-commutative in fact) with unity when all three operations are considered together. We indicate important properties of the algebraic structures obtained in this way. In particular, we indicate several sub-groups or sub-rings. Among sub-groups, we consider the group of Riordan matrices and indicate its several sub-groups. We show a variety of examples (approximately 20) of matrices that are composed of the sequences of important polynomial or number families as entries of certain lower-triangular infinite matrices. New, significant relationships between these families can be discovered by applying well-known matrix operations like multiplication and inverse calculation to this representation. The paper intends to compile numerous simple facts about the lower-triangular matrices, specifically the family of Rionian matrices, and briefly review their properties.
In a graph, a vertex is said to dominate itself and all its neighbors. A subset \(S\subseteq V(G)\) is a double dominating set of a graph \(G\), if every vertex of \(V\) is dominated by at least two vertices of \(S\). The double domination number denoted by \(\gamma_{2\times(G)}\) is the minimum cardinality of a double dominating set. Graph operations are fundamental in graph theory and have various applications across different fields including network analysis, parallel computing and electrical circuit design. This paper studies the problem of finding the double domination number under unary and binary operations of graphs. We investigate the double domination number of graphs under unary operations such as inflation and cubic inflation. Also, we introduced two new unary operations inspired from inflation operation and studied the impact of these operations on double domination number. Further, we explore the double domination number of edge corona and neighborhood corona of two graphs. Additionally, we study the double domination number of various corona operations of two graphs combined with subdivision of a graph and \(R-\)graph.
In Theorem 8.7 of [22] eight centralities on trees are presented that all coincide with the median. In this paper we explore a functional extension for three of these Centralities, viz. the Centroid, the Security Center, and The Telephone Center of a tree. In the functional extension model, instead of using the whole vertex set to determine `central’ vertices we allow any multiset of vertices to determine the central vertices. The centroid and security center allow straightforward functional extensions, and both coincide with the well-kown median function. The functional extension of the Telephone center is a different story, and we present three versions, each of which catches most but not all the features of the original Telephone center. These all have a close relationship with the median function. As a bonus we obtain a deeper insight in the median function on trees.
A block graph is a graph in which each block (maximal biconnected subgraph) is a clique. A block graph can, equivalently, be seen as a tree of cliques where the blocks form complete subgraphs connected to each other in a tree-like, hierarchical structure. Such graphs provide a good conceptual and computational framework in designing, analyzing, and optimizing resilient and efficient power networks. In view of the above applications, this paper investigates the \(k\)-fault tolerant power domination number of block graphs. Also, we obtained a lower bound for \(k\)-fault tolerant power domination number when an edge in the graph is contracted.
We define \(3\)-PBIBDs to simplify the notation used in one of Hanani’s celebrated papers, where he developed important tools for the constructions of \(3\)-designs. A special case of the \(3\)-PBIBD, a \(3\)-GDD\((n,2,4;\lambda_1,\lambda_2)\), was recently studied in [5] and [6]. In this note, we develop necessary conditions for the existence of another special case, denoted \(3'\)-GDD\((n,3,4;\) \(\mu_1,\mu_2)\), and provide several constructions for infinite families of these designs. We show that the necessary conditions are sufficient for the existence of \(3'\)-GDD\((n,3,4;\mu_1,\mu_2)\) when \(\mu_2=0\) and \(\mu_1\) is arbitrary. In particular, when \(\mu_1\) is even and \(\mu_2 = 0\), such designs exist for all \(n\); and when \(\mu_1\) is odd and \(\mu_2 = 0\), they exist for even \(n\). We also provide instances of nonexistence.
A Kempe chain in a colored graph is a maximal connected component containing at most two colors. Kempe chains have played an important role historically in the study of the Four Color Problem. Some methods of systematically applying Kempe chain color exchanges have been studied by Alfred Errera and Weiguo Xie. A map constructed by Errera represents an important counterexample to some implementations of these methods. Using the ideas of Irving Kittell, we determine all colorings of the Errera map which form such a counterexample and describe how to color them individually. We then extend our results from the Errera map to a family of graphs containing the Errera map in a specific way. Being able to color this family of graphs appears to address many cases which prove difficult for the previous systematic color exchange methods.
We use Rosa-type labelings to decompose complete graphs into unicyclic, disconnected, non-bipartite graphs on nine edges. For any such graph \(H\), we prove there exists an \(H\)-design of \(K_{18k+1}\) and \(K_{18k}\) for all positive integers \(k.\)
In this paper, we continue investigation of decompositions of complete graphs into graphs with seven edges. The spectrum has been completely determined for such graphs with at most six vertices. The spectrum for bipartite graphs is completely known for graphs with seven or eight vertices. In this paper we completely solve the case of disconnected unicyclic bipartite graphs with seven edges by studying the remaining graphs with nine or ten vertices.
Let \(G\) be a disconnected tripartite unicyclic graph on seven edges with two or more connected components. We prove that \(G\) decomposes the complete graph \(K_{n}\) whenever \(n\equiv0,1\pmod{14}\) using labeling techniques.
Let \(G\) be a graph. We introduce the balanced antimagic labeling as an analogue to the antimagic labeling. A \(k\)-total balanced antimagic labelling is a map \(c\colon V (G)\cup E(G) \to \{1,2,\ldots,k\}\) such that: the label classes differ in size by at most one, each vertex \(x\) is assigned the weight \(w(x)={c}(x)+\sum\limits_{x\in e}{c}(e)\), and \(w(x)\neq w(y)\) for \(x\neq y\).
We present several properties of balanced antimagic labeling. We also derive such a labeling for complete graphs and complete bipartite graphs.
For a subgraph \(G\) of the complete graph \(K_n\), a \(G\)-design of order \(n\) is a partition of the edges of \(K_n\) into edge-disjoint copies of \(G.\) For a given graph \(G\), the \(G\)-design spectrum problem asks for which \(n\) a \(G\)-design of order \(n\) exists. This problem has recently been completely solved for every graph \(G\) with less than seven edges, with the lone exception of \(G \cong K_3 \cup 2K_2,\) the disconnected graph consisting of a triangle and two isolated edges. In this article, we solve this problem by proving that a \(K_3 \cup 2K_2\)-design of order \(n\) exists if and only if \(n \equiv 0 \; \textrm{or} \; 1 \pmod{5}\) and \(n\geq 10.\)
A question involving a chess piece called a prince on the \(8\times 8\) chessboard leads to a concept in graph theory involving total domination in the Cartesian products of paths and cycles. A vertex \(u\) in a graph \(G\) totally dominates a vertex \(v\) if \(v\) is adjacent to \(u\). A subset \(S\) of the vertex set of a graph \(G\) is a total dominating set for \(G\) if every vertex of \(G\) is totally dominated by at least one vertex of \(S\). If \(S\) is a total dominating set of a graph \(G\), then \(S(v)\) denotes the number of vertices in \(S\) that totally dominate \(v\). A total dominating set \(S\) in a graph \(G\) is called a proper total dominating set if \(S(u) \ne S(v)\) for every two adjacent vertices \(u\) and \(v\) of \(G\). It is shown that \(C_n \ \Box \ K_2\) possesses a proper total dominating set if and only if \(n\ge 4\) is even and the graph \(C_n \ \Box \ P_m\) possesses a proper total dominating set for every even integer \(n \ge 4\) and every integer \(m \ge 3\). Furthermore, \(C_3 \ \Box \ P_m\) possesses a proper total dominating set if and only if \(m = 3\). If \(n\ge 5\) is an odd integer and \(m\equiv 3 \pmod 4\), then \(C_n \ \Box \ P_m\) has a proper total dominating set. If at least one of \(n\) and \(m\) is even, then \(C_n \ \Box \ C_m\) has a proper total dominating set. The graphs \(C_n \ \Box \ C_m\) are further studied when both \(n\) and \(m\) are odd. Other results and questions are also presented.
For a graph \(F\) and a positive integer \(t\), the vertex-disjoint Ramsey number \(VR_t(F)\) is the minimum positive integer \(n\) such that every red-blue coloring of the edges of the complete graph \(K_n\) of order \(n\) results in \(t\) pairwise vertex-disjoint monochromatic copies of subgraphs isomorphic to \(F\), while the edge-disjoint Ramsey number \(ER_t(F)\) is the corresponding number for edge-disjoint subgraphs. These numbers have been investigated for the three connected graphs \(K_3\), \(P_4\) and \(K_{1, 3}\) of size 3. For two vertex-disjoint graphs \(G\) and \(H\), let \(G+H\) denote the union of \(G\) and \(H\). Here we study these numbers for the two disconnected graphs \(3K_2\) and \(P_3+P_2\) of size 3. It is shown that \(VR_t(3K_2)= 6t+2\) and \(VR_t(P_3+P_2)= 5t+1\) for every positive integer \(t\). The numbers \(ER_t(3K_2)\) and \(ER_t(P_3+P_2)\) are determined for \(t \le 4\) and bounds are established for \(ER_t(3K_2)\) and \(ER_t(P_3+P_2)\) when \(t \ge 5\). Other results and problems are presented as well.
Every red-blue coloring of the edges of a graph \(G\) results in a sequence \(G_1\), \(G_2\), \(\ldots\), \(G_{\ell}\) of pairwise edge-disjoint monochromatic subgraphs \(G_i\) (\(1 \le i \le \ell\)) of size \(i\) such that \(G_i\) is isomorphic to a subgraph of \(G_{i+1}\) for \(1 \le i \le \ell-1\). Such a sequence is called a Ramsey chain in \(G\) and \(AR_c(G)\) is the maximum length of a Ramsey chain in \(G\) with respect to a red-blue coloring \(c\). The Ramsey index \(AR(G)\) of \(G\) is the minimum value of \(AR_c(G)\) among all red-blue colorings \(c\) of \(G\). Several results giving the Ramsey indexes of graphs are surveyed. A galaxy is a graph each of whose components is a star. Results and conjectures on Ramsey indexes of galaxies are presented.
An edge-coloring of a graph \(G\) with natural numbers \(1,2,\ldots\) is called a sum edge-coloring if the colors of edges incident to any vertex of \(G\) are distinct and the sum of the colors of the edges of \(G\) is minimum. The edge-chromatic sum of a graph \(G\) is the sum of the colors of edges in a sum edge-coloring of \(G\). In general, the problem of finding the edge-chromatic sum of an \(r\)-regular (\(r\geq 3\)) graph is \(NP\)-complete. In this paper we provide some bounds on the edge-chromatic sums of various products of graphs. In particular, we give tight upper bounds on the edge-chromatic sums of tensor, strong tensor, Cartesian, strong products and composition of graphs. We also determine the edge-chromatic sums and edge-strengths of the Cartesian products of regular graphs and paths (cycles) with an even number of vertices. Finally, we determine the edge-chromatic sums and edge-strengths of grids, cylinders, and tori.
Generalised nice sets are defined as subsets of edges of the extended Fano plane satisfying a certain absorbing property. The classification up to collineations, purely combinatorial in nature, provides 245 generalised nice sets. In turn, this gives rise to new Lie algebras obtained by modifying the bracket of homogeneous elements on an initial \(\mathbb Z_2^3\)-graded Lie algebra.
Let \(\alpha(n)\) denote the number of perfect square permutations in the symmetric group \(S_n\). The conjecture \(\alpha(2n+1) = (2n+1) \alpha(2n)\), provided by Stanley [4], was proved by Blum [1] using generating functions. However, several structural questions about these special permutations remained open. This paper presents an alternative and constructive proof for this conjecture, which highlights the deeper interplay between cycle structures and square properties. At the same time, we demonstrate that all permutations with an even number of even cycles in both \(S_{2n}\) and \(S_{2n+1}\) can be categorized into three disjoint types that correspond to each other.
In this paper, we generalize the \(k\)-Jacobsthal sequences and call them the generalized \((k,t)\)-Jacobsthal \(p\)-sequences. Also, we obtain combinatorial identities. Then, the generalized\((k,t)\)-Jacobsthal \(p\)-matrix is used to factorize the Pascal matrix. Finally, using the Riordan method, we obtain two factorizations of the Pascal matrix involving the generalized \((k,t)\)-Jacobsthal \(p\)-sequences.
An open-dominating set \(S\) for a graph \(G\) is a subset of vertices where every vertex has a neighbor in \(S\). An open-locating-dominating set \(S\) for a graph \(G\) is an open-dominating set such that each pair of distinct vertices in \(G\) have distinct set of open-neighbors in \(S\). We consider a type of a fault-tolerant open-locating dominating set called error-detecting open-locating-dominating sets. We present more results on the topic including its NP-completeness proof, extremal graphs, and a characterization of cubic graphs that permit an error-detecting open-locating-dominating set.
In this paper, we explore some interesting applications of the matrix tree theorem. In particular, we present a combinatorial interpretation of a distribution of \((n-1)^{n-1}\), in the context of uprooted spanning trees of the complete graph \(K_{n}\), which was previously obtained by Chauve–Dulucq–Guibert. Additionally, we establish a combinatorial explanation for the distribution of \(m^{n-1}n^{m-1}\), related to spanning trees of the complete bipartite graph \(K_{m,n}\), which seems new. Furthermore, we extend this study to the graph \(K_{n}\setminus \{e_{1,n}\}\), obtained by deleting an edge from \(K_n\), and derive a new identity for the number of its uprooted spanning trees.
The domatic number of a graph is the maximum number of pairwise disjoint dominating sets admitted by the graph. We introduce a game based around this graph invariant. The domatic number game is played on a graph \(G\) by two players, Alice and Bob, who take turns selecting a vertex and placing it into one of \(k\) sets. Alice is trying to make each of these sets into a dominating set of \(G\) while Bob’s goal is to prevent this from being accomplished. The maximum \(k\) for which Alice can achieve her goal when both players are playing optimal strategies, is called the game domatic number of \(G\). There are two versions of the game and two resulting invariants depending on whether Alice or Bob is the first to play. We prove several upper bounds on these game domatic numbers of arbitrary graphs and find the exact values for several classes of graphs including trees, complete bipartite graphs, cycles and some narrow grid graphs. We pose several open problems concerning the effect of standard graph operations on the game domatic number as well as a vexing question related to the monotonicity of the number of sets available to Alice.
Sparse magic squares are a generalization of magic squares and can be used to the magic labeling of graphs. An \(n\times n\) array based on \(\mathcal{X}\)\(=\{0,1,\cdots,nd\}\) is called a sparse magic square of order \(n\) with density \(d\) (\(d<n\)), denoted by SMS\((n,d)\), if each non-zero element of \(\mathcal{X}\) occurs exactly once in the array, and its row-sums, column-sums and two main diagonal sums is the same. An SMS\((n,d)\) is called pandiagonal (or perfect) denoted by PSMS\((n,d)\), if the sum of all elements in each broken diagonal is the same. A PSMS\((n,d)\) is called regular if there are eactly \(d\) positive entries in each row, each column and each main diagonal. In this paper, some construction of regular pandigonal sparse magic squares is provided and it is proved that there exists a regular PSMS\((n,6)\) for all positive integer \(n\equiv 5 \pmod{6}\), \(n>6\).
Two graphs are said to be \(Q\)-cospectral (respectively, \(A\)-cospectral) if they have the same signless Laplacian (respectively, adjacency) spectrum. A graph is said to be \(DQS\) (respectively, \(DAS\)) if there is no other non-isomorphic graphs \(Q\)-cospectral (respectively, \(A\)-cospectral) with it. A tree on \(n\) vertices with maximum degree \(d_1\) is called starlike and denoted by \(ST(n, d_1)\), if it has exactly one vertex with the degree greater than 2. A tree is called double starlike if it has exactly two vertices of degree greater than 2. If we attach \(p\) pendant vertices (vertices of degree 1) to each of pendant vertices of a path \(P_n\), the the resulting graph is called the double starlike tree \(H_n(p,p)\). In this article, we prove that all double starlike trees \(H_n(p,p)\) are \(DQS\), where \(p\geq 1, n\geq 2\) and \(p\) denotes . In addition, by a simple method, we show that all starlike trees are \(DQS\) excluding \(K_{1,3}=ST(4,3)\).
In the present paper, we are interested in the distribution of the elements lying along the Raab direction in the binomial coefficients triangle. More precisely, we prove that the sequence \(\{\binom{n-rk}{k}\}_{0\leq k \leq \lfloor n/(r+1)\rfloor}\) is asymptotically distributed according to a Gaussian law. We also provide some experimental evidences.
We determine the maximum number of edges of a graph without containing the 2-power of a Hamilton path. Using this result, we establish a spectral condition for a graph containing the 2-power of a Hamilton path. Furthermore, we characterized the extremal graphs with the largest spectral radius that do not contain the 2-power of a Hamilton path.
We introduce the ID-index of a finite simple connected graph. For a graph \(G=(V,\ E)\) with diameter \(d\), we let \(f:V\longrightarrow \mathbb{Z}\) assign ranks to the vertices. Then under \(f\), each vertex \(v\) gets a string, which is a \(d\)-vector with the \(i\)-th coordinate being the sum of the ranks of the vertices that are of distance \(i\) from \(v\). The ID-index of \(G\), denoted by \(IDI(G)\), is defined to be the minimum number \(k\) for which there is an \(f\) with \(|f(V)|=k\), such that each vertex gets a distinct string under \(f\). We present some relations between ID-graphs, which were defined by Chartrand, Kono, and Zhang, and their ID-indices; give a lower bound on the ID-index of a graph; and determine the ID-indices of paths, grids, cycles, prisms, complete graphs, some complete multipartite graphs, and some caterpillars.
We prove that the class of trees with unique minimum edge-vertex dominating sets is equivalent to the class of trees with unique minimum paired dominating sets.
We investigate properties and structure of \(zero \ divisor \ graph\) of endomorphism ring of direct product of cyclic groups \(\mathbb{Z}_n\). We provide a method to determine the number of zero divisors of \(End(\mathbb{Z}_2 \times \mathbb{Z}_{2p})\), for some prime \(p\). We proved that minimum distance between any two vertices of \(zero \ divisor \ graph\) of \(End(\mathbb{Z}_m \times \mathbb{Z}_m)\) is 2.
Let \(G = (V, E)\) be a graph. The Gutman-Milovanović index of a graph \(G\) is defined as \(\sum\limits_{uv \in E} (d(u) d(v))^{\alpha}(d(u) + d(v))^{\beta}\), where \(\alpha\) and \(\beta\) are any real numbers and \(d(u)\) and \(d(v)\) are the degrees of vertices \(u\) and \(v\) in \(G\), respectively. In this note, we present sufficient conditions based on the Gutman-Milovanović index with \(\alpha > 0\) and \(\beta >0\) for some Hamiltonian properties of a graph. We also present upper bounds for the Gutman-Milovanović index of a graph for different ranges of \(\alpha\) and \(\beta\).
Suppose \(G_1=(V_1, E_1)\) is a graph and \(G_2=(V_2, E_2)\) is a strong digraph of \(G_1\), where \(V_1\) and \(V_2\) represent the vertex sets, \(E_1\) and \(E_2\) represent the edge sets. Let \(u\) and \(v\) be any two vertices of \(G_2\). The strong distance \(sd(u,v)\) is the minimum value of edges in a strong subdiagraph of \(G_2\) that contains \(u\) and \(v\). The minimum strong diameter of \(G_2\) is defined as the maximum eccentricity \(se(u)\) from \(u\) to all other vertices in \(G_2\). In this paper, we propose different strong orientation methods to explore the minimum strong diameter of the strong product graph of \(K_{m_1,m_2,\ldots,m_k}\otimes P_n\), where \(K_{m_1,m_2,\ldots,m_k}\) and \(P_n\) represent respectively complete multipartite graph and path. In addition, based on strong orientation methods, a new algorithm is proposed to model the presence or absence of a minimum strong diameter in a strong product graph. Simulation experiments show a trend of simultaneous decrease and concentration in the minimum strong diameter of the strong product graph, as the value of parts in \(K_{m_1,m_2,\ldots,m_k}\) increases while the length of \(P_n\) remains constant.
We consider a joint ordered multifactorisation for a given positive integer \(n\geq 2\) into \(m\) parts, where \(n=n_1~\times~\ldots~\times~n_m\), and each part \(n_j\) is split into one or more component factors. Our central result gives an enumeration formula for all such joint ordered multifactorisations \(\mathcal{N}_m(n)\). As an illustrative application, we show how each such factorisation can be used to uniquely construct and so count the number of distinct additive set systems (historically referred to as complementing set systems). These set systems under set addition generate the first \(n\) non-negative consecutive integers uniquely and, when each component set is centred about 0, exhibit algebraic invariances. For fixed integers \(n\) and \(m\), invariance properties for \(\mathcal{N}_m(n)\) are established. The formula for \(\mathcal{N}_m(n)\) is comprised of sums over associated divisor functions and the Stirling numbers of the second kind, and we conclude by deducing sum over divisor relations for our counting function \(\mathcal{N}_m(n)\). Some related integer sequences are also considered.
In this work we study the acyclic orientations of graphs. We obtain an encoding of the acyclic orientations of the complete \(p\)-partite graph with size of its parts \(n_1,n_2,\ldots,n_p\) via a vector with \(p\) symbols and length \(n=n_1+n_2+\ldots+n_p\) when the parts are fixed but not the vertices in each part. We also give a recursive way to construct all acyclic orientations of a complete multipartite graph, this construction can be done by computer easily in order \(\mathcal{O}(n)\). Furthermore, we obtain a closed formula for non-isomorphic acyclic orientations of both the complete multipartite graphs and the complete multipartite graphs with a directed spanning tree. Moreover, we obtain a closed formula for the number of acyclic orientations of a complete multipartite graph \(K_{n_1,\ldots,n_p}\) with labelled vertices. Finally, we obtain a way encode all acyclic orientations of an arbitrary graph as a permutation code. Using the codification mentioned above we obtain sharp upper and lower bounds of the number of acyclic orientations of a graph.
In this work, we defined almost neo balancing numbers and determined the general terms of them in terms of balancing and Lucas-balancing numbers. We also deduced some results on relationship with triangular, square triangular, Pell, Pell-Lucas numbers and these numbers. Further we formulate the sum of first \(n\)-terms of these numbers.
In this paper, we prove a surprisingly simple formula that counts connected cycle-free families of set partitions, labelled free cacti and coloured Husimi graphs in which there are no blocks of the same colour that are incident to one another. We also provide a formula that enumerates noncrossing connected, cycle-free pairs of partitions.
Recently, it was shown that the Gallai-Ramsey number satisfies \(gr(F_{3,2}, K_3, K_3)=31\), where \(F_{3,2}\) is the generalized fan \(F_{3,2}:=K_1+2K_3\). In this paper, we show that the star-critical Gallai-Ramsey number satisfies \(gr_*(F_{3,2}, K_3, K_3)=27\). We also prove that the critical colorings for \(r_*(K_3, K_3)\), \(gr(F_{3,2},K_3,K_3)\), and \(gr_*(F_{3,2},K_3,K_3)\) are unique.
Given a network modeled as a graph, a detection system is a subset of vertices equipped with “detectors” that can uniquely identify an “intruder” anywhere in the graph. We consider two types of detection systems: open-locating-dominating (OLD) sets and identifying codes (ICs). In an OLD set, each vertex has a unique, non-empty set of detectors in its open neighborhood; meanwhile, in an IC, each vertex has a unique, non-empty set of detectors in its closed neighborhood. We explore one of their fault-tolerant variants: redundant OLD (RED:OLD) sets and redundant ICs (RED:ICs), which ensure that removing/disabling at most one detector retains the properties of OLD sets and ICs, respectively. This paper focuses on constructing optimal RED:OLD sets and RED:ICs on the infinite king grid, and presents the proof for the bounds on their minimum densities; \(\left[\frac{3}{10}, \frac{1}{3}\right]\) for RED:OLD sets and \(\left[\frac{3}{11}, \frac{1}{3}\right]\) for RED:ICs.
Exploring the vulnerability of any real-life network helps designers understand how strongly components or elements of the network are connected and how well they can function if there is any disruption. Any chemical structure can also be considered as a network in which the atoms correspond to the vertices, and the chemical bonds between the atoms correspond to the edges. Let \(G=(V, E)\) represent any simple graph with vertex set \(V\) and edge set \(E\). The vulnerability measure used in this paper is the paired domination integrity, defined as the minimum of the sum of any paired dominating set \(S\) of a graph \(G\) and the order of the largest component in the induced subgraph of \(V-S\). The minimum is found by considering all possible paired dominating sets of \(G\). In this paper, we obtain the paired domination integrity of the comb product of paths and cycles. In addition, we extend the study of graph vulnerability to chemical structures.
Let \(k, b, n\) be positive integers such that \(b\geq 2\). Denote by \(S(k,b,n)\) the numerical semigroup generated by \(\left\{b^{k+n+i}+\frac{b^{n+i}-1}{b-1}\mid i\in\mathbb{N}\right\}\). In this paper, we give formulas for computing the embedding dimension and the Frobenius number of \(S(k,b,n)\).
Given a connected graph \(G=(V,E)\) of order \(n\ge 2\) and two distinct vertices \(u,v\in V(G)\), consider two operations on \(G\): the \(k\)-multisubdivision and the \(k\)-path addition. Let \(msd_{\gamma_c}(G)\) and \(pa_{\gamma_c}(G)\) denote, respectively, the connected domination multisubdivision and path addition numbers of \(G\). In both operations, \(k\) represents the number of vertices added to \(V(G)\), resulting in a new graph denoted by \(G_{u,v,k}\). We prove that \(\gamma_c(G) \le \gamma_c(G_{u,v,k})\) for \(k = msd_{\gamma_c}(G) \in \{1,2,3\}\) in the case of \(k\)-multisubdivision, where \(uv \in E(G)\). Additionally, we show that \(\gamma_c(G) – 2 \le \gamma_c(G_{u,v,k})\) for \(k = pa_{\gamma_c}(G) \in \{0,1,2,3\}\) in the case of \(k\)-path addition, where \(uv \notin E(G)\), and provide both necessary and sufficient conditions under which these inequalities hold.
This paper introduces two novel sequences: the \(k-\)-division Fibonacci–Pell polynomials and the \(k-\)-division Gaussian Fibonacci–Pell polynomials. Building on the well-known Fibonacci and Pell sequences, these new sequences are defined using a division-based approach, enhancing their combinatorial and algebraic properties. We present explicit recurrence relations, generating functions, combinatorial identities, and Binet-type formulas for these sequences. A significant contribution of the study is the factorization of the Pascal matrix via the Riordan group method using the proposed polynomials. Two distinct factorizations are derived, highlighting the algebraic structure and combinatorial interpretations of the \(k-\)-division polynomials. The work not only generalizes known polynomial sequences but also provides new insights into their matrix representations and applications.
This paper provides new lower bounds for van der Waerden numbers using Rabung’s method, which colors based on the discrete logarithm modulo some prime. Through a distributed computing project with 500 volunteers over one year, we checked all primes up to 950 million, compared to 27 million in previous work. We point to evidence that the van der Waerden number for \(r\) colors and progression length \(k\) is roughly \(r^k\).
We numerically investigate typical graphs in a region of the Strauss model of random graphs with constraints on the densities of edges and triangles. This region, where typical graphs had been expected to be bipodal but turned out to be tripodal, involves edge densities \(e\) below \(e_0 = (3-\sqrt{3})/6 \approx 0.2113\) and triangle densities \(t\) slightly below \(e^3\). We determine the extent of this region in \((e,t)\) space and show that there is a discontinuous phase transition at the boundary between this region and a bipodal phase. We further show that there is at least one phase transition within this region, where the parameters describing typical graphs change discontinuously.
Richard Rado’s work in Ramsey Theory established conditions under which monochromatic solutions to a linear system must occur. In this paper, we find exact values for a linear system involving the equation \(x_1 + x_2 + c = x_0\) and two colors: Let \(c\) and \(k\) be integers with \(-1 \leq c \leq k\). Then the 2-color Rado number for the system \[\begin{array}{lcl} x_1 + x_2 + c &=& x_0, \\ y_1 + y_2 + k &=& y_0, \end{array}\] is infinite if \(c\) and \(k\) have opposite parity, and has a value of \(4k+5\) if \(c\) and \(k\) have the same parity. We also extend this to the continuous result where we color the real numbers.
In this article, we begin to investigate prime labelings of the zero-divisor graph of a commutative ring. A graph \(G\) with \(n\) vertices admits a prime labeling if the vertices can be labeled using distinct positive integers less than or equal to \(n\) such that any two adjacent vertices have labels which are relatively prime. We are able to construct several infinite families of commutative rings which will have prime labelings for their zero-divisor graphs. We also find infinite families of commutative rings which do not have prime labelings for their zero-divisor graphs. We then continue the process of determining which commutative rings will have prime labelings for their zero-divisor graphs by resolving the question for all rings with 14 or fewer vertices in their zero-divisor graph. We conclude with several unresolved questions that could be interesting to pursue further.
Some methods of constructions of square tactical decomposable regular group divisible designs are described. These designs are useful in threshold schemes. An L_2 design is also identified as square tactical decomposable. This completes spectrum of the solutions of entire L_2 designs listed in Clatworthy [2] using matrix approaches.
Lightweight design is widely used for optimizing machinery. This paper proposes an algebraic–topology–based structural optimization method that formulates a mathematical model for mechanical equipment. The model is solved via sequential linear programming and recast as a function-optimization problem addressed by a Memetic algorithm combining a Newtonian local search, adaptive multipoint crossover, and stochastic variability. Using a bridge crane main girder as a case study, we minimize cross-sectional area with selected sectional parameters as design variables and constraints from the Crane Design Manual. With population size 100 and 300 maximum iterations, the optimized girder achieves a 13% weight reduction. Static and dynamic analyses confirm that strength and stiffness satisfy safe working conditions.
Given a directed graph, the Minimum Feedback Arc Set (FAS) problem asks for a minimum (size) set of arcs in a directed graph, which, when removed, results in an acyclic graph. In a seminal paper, Berger and Shor [1], in 1990, developed initial upper bounds for the FAS problem in general directed graphs. Here we find asymptotic lower bounds for the FAS problem in a class of random, oriented, directed graphs derived from the Erdős-Rényi model \(G(n,M)\), with n vertices and M (undirected) edges, the latter randomly chosen. Each edge is then randomly given a direction to form our directed graph. We show that \[Pr\left(\textbf{Y}^* \le M \left( \frac{1}{2} -\sqrt{\frac{\log n}{\Delta_{av}}}\right)\right),\] approaches zero exponentially in \(n\), with \(\textbf{Y}^*\) the (random) size of the minimum feedback arc set and \(\Delta_{av}=2M/n\) the average vertex degree. Lower bounds for random tournaments, a special case, were obtained by Spencer [13] and de la Vega [3] and these are discussed. In comparing the bound above to averaged experimental FAS data on related random graphs developed by K. Hanauer [8] we find that the approximation \(\textbf{Y}^*_{av} \approx M\left( \frac{1}{2} -\frac{1}{2}\sqrt{\frac{\log n}{\Delta_{av}}}\right)\) lies remarkably close graphically to the algorithmically computed average size \(\textbf{Y}^*_{av}\) of minimum feedback arc sets.
An alternative proof of A.B. Evans’ result on the existence of strong complete mappings on finite abelian groups is presented. Applications of strong complete mappings in computing the chromatic numbers of~certain Cayley graphs are discussed.
Let \(G = (V, E)\) be a simple graph. A subset \(D \subseteq V\) is called a dominating set of \(G\) if every vertex in \(V\) is either in \(D\) or has a neighbour in \(D\). A subset \(D \subseteq V\) is called an equitable dominating set of \(G\) if for every vertex \(v \in V \setminus D\), there exists a vertex \(u \in D\) such that \(uv \in E(G)\) and \(\lvert d_G(u) – d_G(v) \rvert \leq 1\). The minimum cardinality of an equitable dominating set of \(G\), denoted by \(\gamma^{e}(G)\), is called the equitable domination number of \(G\). In this paper, we study the equitable domination number of certain graph operators such as the double graph, the Mycielskian, and the subdivision of a graph.
This paper studies the Pell-Narayana sequence modulo \(m\). It starts by defining the Pell-Narayana numbers and examining their combinatorial relationships with well-known sequences and functions, including Eulerian, Catalan, and Delannoy numbers. Building on this, the concept of a Pell-Narayana orbit is introduced for a 2-generator group with generating pair \((x, y) \in G\), which allows the analysis of the periods of these orbits. The results include explicit calculations of the Pell-Narayana periods for polyhedral and binary polyhedral groups, depending on the choice of generating pair \((x, y)\), along with a discussion of their properties. Furthermore, the paper determines the periodic lengths of Pell-Narayana orbits for the groups \(Q_8, Q_8 \times \mathbb{Z}_{2m},\) and \(Q_8 \times_\varphi \mathbb{Z}_{2m}\) for all \(m \geq 3\).
A graph \(G=(V,E)\) is \(H\)-supermagic if there exists a bijection \(f\) from the set \(V\cup E\) to the set of integers \(\{1,2,3,\dots,|V|+|E|\}\), called \(H\)-supermagic labeling such that the sum of labels of all elements of every induced subgraph of \(G\) isomorphic to \(H\) is equal to the same integer and all vertex labels are in \(\{1,2,3,\dots,|V|\}\). We present a \((K_4-e)\)-supermagic labeling of the triangular ladder \(TL_{2n}\) for any \(n\geq2\).
Slilaty and Zaslavsky (2024) characterized all single-element extensions of graphic matroids in terms of a graphical structure called a cobiased graph. In this paper we characterize all orientations of a single-element extension of a graphic matroid in terms of graphically defined orientations of its associated cobiased graph. We also explain how these orientations can be canonically understood as dual to orientations of biased graphs if and only if the underlying graph is planar.
We consider a combinatorial question about searching for an unknown ideal \(\mu\) within a known pointed poset \(\lambda\). Elements of \(\lambda\) may be queried for membership in \(\mu\), but at most \(k\) positive queries are permitted. We provide a general search strategy for this problem, and establish new bounds (based on \(k\) and the degree and height of \(\lambda\)) for the total number of queries required to identify \(\mu\). We show that this strategy performs asymptotically optimally on the family of complete \(\ell\)-ary trees as the height grows.
Graph theory serves as a central and dynamic framework for the design and analysis of networks. Convex polytopes, as fundamental geometric entities, encompass a rich variety of mathematical structures and problems. The basic theory of convex polytopes involves the study of faces, normal cones, duality—particularly polarity—along with separation and other elementary concepts. A convex polytope can be described as a convex set of points within the \(n\)-dimensional Euclidean space \(\Re^{n}\). Among the various dimensions, the partition dimension is the most challenging, and determining its exact value is an NP-hard problem. In this work, we establish bounds for the partition dimension of convex polytopes \(T_\nu\), \(R_\nu\), and \(U_\nu\).
In \(1941\) Dushnik and Miller introduced the concept of dimension of a poset. In \(2020\) Bhavale and Waphare introduced the concept of an RC-lattice as a lattice in which all the reducible elements are lying on a chain. In this paper, we introduce the concept of a complete fundamental basic block and prove that its dimension is at the most three. Consequently, we prove that the dimension of an RC-lattice on \(n\) elements is at the most three. Further, we prove that an RC-lattice is non-planar if and only if its dimension is three.
For a graph, a smallest-last (SL) ordering is formed by iteratively deleting a vertex with smallest degree, then reversing the resulting list. The SL algorithm applies greedy coloring to a SL ordering. For a given vertex coloring algorithm, a graph is hard-to-color (HC) if every implementation of the algorithm results in a nonoptimal coloring. In 1997, Kubale et al. showed that \(C_{8}^{2}\) is the unique smallest HC graph for the SL algorithm. Extending this, we show that for \(k\geq4\), \(k+4\) is the smallest order of a HC graph for the SL algorithm with \(\chi\left(G\right)=k\). We also present a HC graph for the SL algorithm with \(\chi\left(G\right)=3\) that has order 10.
Cut vertices are often used as a measure of nodes’ importance within a network. These are nodes whose failure disconnects a connected graph. Let \(N(G)\) be the number of connected induced subgraphs of a graph \(G\). In this work, we investigate the maximum of \(N(G)\) where \(G\) is a unicyclic graph with \(n\) nodes of which \(c\) are cut vertices. For all valid \(n,c\), we give a full description of those maximal (that maximise \(N(.)\)) unicyclic graphs. It is found that there are generally two maximal unicyclic graphs. For infinitely many values of \(n,c\), however, there is a unique maximal unicyclic graph with \(n\) nodes and \(c\) cut vertices. In particular, the well-known negative correlation between the number of connected induced subgraphs of trees and the Wiener index (sum of distances) fails for unicyclic graphs with \(n\) nodes and \(c\) cut vertices: for instance, the maximal unicyclic graph with \(n=3,4\mod 5\) nodes and \(c=n-5>3\) cut vertices is different from the unique graph that was shown by Tan et al. [The Wiener index of unicyclic graphs given number of pendant vertices or cut vertices. J. Appl. Math. Comput., 55:1–24, 2017] to minimise the Wiener index. Our main characterisation of maximal unicyclic graphs with respect to the number of connected induced subgraphs also applies to unicyclic graphs with \(n\) nodes, \(c\) cut vertices and girth at most \(g>3\), since it is shown that the girth of every maximal graph with \(n\) nodes and \(c\) cut vertices cannot exceed \(4\).
The honeymoon Oberwolfach problem HOP\((2m_1,2m_2,\ldots,2m_t)\) asks the following question. Given \(n=m_1+m_2+\ldots +m_t\) newlywed couples at a conference and \(t\) round tables of sizes \(2m_1,2m_2,\ldots,2m_t\), is it possible to arrange the \(2n\) participants at these tables for \(2n-2\) meals so that each participant sits next to their spouse at every meal, and sits next to every other participant exactly once? A solution to HOP\((2m_1,2m_2,\ldots,2m_t)\) is a decomposition of \(K_{2n}+(2n-3)I\), the complete graph \(K_{2n}\) with \(2n-3\) additional copies of a fixed 1-factor \(I\), into 2-factors, each consisting of disjoint \(I\)-alternating cycles of lengths \(2m_1,2m_2,\ldots,2m_t\). The honeymoon Oberwolfach problem was introduced in a 2019 paper by Lepine and Šajna. The authors conjectured that HOP\((2m_1,2m_2,\ldots,\) \(2m_t)\) has a solution whenever the obvious necessary conditions are satisfied, and proved the conjecture for several large cases, including the uniform cycle length case \(m_1=\ldots=m_t\), and the small cases with \(n \le 9\). In the present paper, we extend the latter result to all cases with \(n \le 20\) using a computer-assisted search.
We introduce and investigate a new class of graph maps, called backward edge-preserving maps. These are the maps between the vertex sets of graphs such that the pre-image of every edge in the target graph is an edge in the source graph. We give a complete structural characterization of such maps for both general and connected graphs. We also explore the relationship between backward edge-preserving maps, metric maps, and graph homomorphisms, and establish criteria for the intersection of these classes. Furthermore, we study a related class of graph cohomomorphisms and establish conditions under which a map can simultaneously be a homomorphism and a cohomomorphism.
A directed star is a star digraph in which the centre is either a sink or a source. With every directed star, we associate a weight \(w\left(\alpha\right)\). Let \(D\) be a digraph; and \(C\), a spanning subgraph of \(D\); in which every component is a directed star. Then \(C\) is called a directed star cover of \(D\), and the weight of \(C\) is \(w(C)=\prod_{\alpha}w\left(\alpha\right)\), where the product is taken over all the components \(\alpha\) of \(C\). The directed star polynomial of \(D\) is \[E\left(D;\mathbf{w}\right)=\sum w(C),\] where the sum is taken over all the directed star covers of \(D\). The paper establishes properties of star polynomials and establishes relationships between star polynomials, source polynomials (where all components are source stars), and sink polynomials (where all components are sink stars). Furthermore, it presents methods to compute these polynomials for general digraphs. We derive formulae for the star polynomials of rooted products of digraphs. Moreover, we establish applications of these polynomials to several domination parameters and obtain results regarding these polynomials and independent sets in undirected graphs.
Let \(G\) be an undirected graph. The \(k\)-strong labeling (coloring) number of \(G\), \(\chi_k(G)\), is the minimum number of labels (colors) necessary to label all the vertices of \(G\) so that no two vertices that are exactly of distance \(k\) apart have the same label. \(\chi_1\) is the usual chromatic number. In this article it is shown that any linear operator on the set of graphs on \(n\) vertices thar preserves \(\chi_k\) is a vertex permutation.
Cartesian-product networks combine well-studied graphs to create new structures with inherited properties, making them valuable for interconnection networks and parallel algorithms. Cycle decompositions of these networks are crucial for fault tolerance and adaptive routing. In this paper, we address the hypercube version of the Oberwolfach problem, focusing on decompositions of \(Q_n\) into cycles of equal or unequal lengths. We present an algorithm that enumerates all possible cycle types in \(Q_n\) and determine which decompositions are feasible or infeasible for \(Q_4\). Using an inductive approach, we extend these results to \(Q_n\) by leveraging distinct perfect matchings of \(Q_4\), yielding a variety of cycle decompositions. Additionally, we present results on factorizations of \(Q_n\) when \(n\) is a power of \(2\). These findings enhance the understanding of cycle structures in hypercubes and their applications to interconnection networks.
We investigate the combinatorial structure of edge-disjoint triangle packings in the complete graph \(K_n\). Two triangles are said to be edge-disjoint if they share no common edges, though they may share at most one vertex. For a given \(n\), let \(T_n\) denote the total number of subsets of triangles in \(K_n\) that are pairwise edge-disjoint, including the empty set, and let \(T_n^k\) denote the number of \(k\)-element sets of such triangles. In this article, we establish: (i) a general recurrence relation for \(T_n\) that enables asymptotic analysis, yielding the growth \(\log T_n = \Theta(n^2 \log n)\) for large \(n\); (ii) exact closed-form formulas for the number of edge-disjoint pairs (\(T_n^2\)), triples (\(T_n^3\)), and quadruples (\(T_n^4\)) of triangles in \(K_n\) for \(n \geq 6\). These results extend classical work on Steiner Triple Systems and provide new tools for analyzing triangle packings in complete graphs.
This paper fits in the intersection between two disparate areas of combinatorics. Namely, graph theory and the combinatorics of Catalan words. A Catalan word with n parts is defined as a word w = w1w2⋯wn over the set of positive integers in which w1 = 1 and 1 ≤ wk ≤ wk − 1 + 1 for k = 2, 3, …, n. In order to study the intersection of the two areas, a specific type of graph called a grid graph is defined for each Catalan word. The main thrust of the paper is investigating the degrees of vertices in grid graphs. For each of the possible fixed degrees i ∈ {1, 2, 3, 4}, we find generating functions DFi(x) where the coefficient of xn is the total number of vertices of degree i in all grid graphs with n parts.
This paper introduces Hadamard-type \(t\)-Fibonacci-Lehmer (HTFL) sequences, a new hybrid construction combining Lehmer and Fibonacci recurrences. We establish their fundamental properties, including simple periodicity, and extend the definition to finite groups, with a detailed study of the Heisenberg group. Building on these results, we propose two Diffie–Hellman-style key exchange protocols based on upper-triangular unipotent matrices parameterized by HTFL sequence terms. Our work thus connects sequence theory, group theory, and cryptography in a novel way. While the algebraic framework and periodicity analysis are rigorous, we present the cryptographic constructions primarily as a conceptual foundation. We also discuss potential security considerations and outline directions for strengthening these schemes under formal hardness assumptions. This study demonstrates that HTFL sequences provide a fertile ground for both combinatorial investigations and future cryptographic applications.
A split graph is a graph in which the vertices can be partitioned into an independent set and a clique. We show that every nonsplit graph has at most four split maximal proper edge induced subgraphs. The exhaustive list of fifteen classes of nonsplit graphs having a split maximal proper edge induced subgraph is determined in this paper.
A kernel \(J\) of a digraph \(D\) is an independent set of vertices of \(D\) such that for every \(z\in V(D)\backslash J\) there exists an arc from \(z\) to \(J.\) A digraph \(D\) is said to be kernel-perfect if every induced subdigraph of it has a kernel. We characterise kernel-perfectness in special families of digraphs, namely, the line digraph, the subdivision digraph, the middle digraph, the digraph \(R(D)\) and the total digraph. We also obtain some results on kernel-perfectness in the generalised Mycielskian of digraphs. Moreover, we find some new classes of kernel-perfect digraphs by introducing a new product on digraphs.
The first, second Zagreb connection indices and modified first Zagreb connection index are defined as \(Z{C_1}(G)={\sum\limits_{{v\in V(G)}} {{\tau _G}^2(v)} }\), \(ZC{}_{2}(G)=\displaystyle\sum_{uv\in E(G)}^{}\tau{}_{G}(u)\tau{}_{G}(v)\) and \(ZC{}_{1}^{\ast }(G)=\displaystyle\sum_{v\in V(G)}^{}d{}_{G}(v)\tau{}_{G}(v)\), respectively. In this paper, we consider the maximum values of \(Z{C_1}(G)\), \(Z{C_2}(G)\), \(Z{C_1}^{*}(G)\) of \(n\)-vertex trees with fixed matching number \(m\) and the extremal graphs are also characterized.
An improper interval (edge) coloring of a graph \(G\) is an assignment of integer colors to the edges of \(G\) satisfying the condition that, for every vertex \(v \in V(G)\), the set of colors assigned to the edges incident with \(v\) forms an integral interval. An interval coloring is \(k\)-improper if at most \(k\) edges with the same color all share a common endpoint. The minimum integer \(k\) such that there exists a \(k\)-improper interval coloring of the graph \(G\) is the interval coloring impropriety of \(G\), denoted by \(\mu_{int}(G)\). In this paper, we provide a construction of an interval coloring of a subclass of complete multipartite graphs. Additionally, we determine improved upper bounds on the interval coloring impropriety of several classes of graphs, namely 2-trees, iterated triangulations, and outerplanar graphs. Finally, we investigate the interval coloring impropriety of the corona product of two graphs, \(G\odot H\).
A decomposition \(\mathcal{C}\) of a graph \(G\) is primitive if no proper, nontrivial subset of \(\mathcal{C}\) is a decomposition of an induced subgraph of \(G\). The existence of primitive decompositions has been studied for several decompositions, including path and cycle decompositions for complete and cocktail party graphs. In this work, we classify the existence of primitive star decompositions for complete graphs.
In this paper, we study the distribution on \([k]^n\) for the parameter recording the number of indices \(i \in [n-1]\) within a word \(w=w_1\cdots w_n\) such that \(|w_{i+1}-w_i|\ \leq 1\) and compute the corresponding (bivariate) generating function. A circular version of the problem wherein one considers whether or not \(|w_n-w_1|\ \leq 1\) as well is also treated. As special cases of our results, one obtains formulas involving staircase and Hertzsprung words in both the linear and circular cases. We make use of properties of special matrices in deriving our results, which may be expressed in terms of Chebyshev polynomials. A generating function formula is also found for the comparable statistic on finite set partitions with a fixed number of blocks represented sequentially.
Let \(G=(V,E)\) be a graph of order \(n\) without isolated vertices. A bijection \(f\colon V\rightarrow \{1,2,\dots,n\}\) is called a local distance antimagic labeling, if \(w(u)\not=w(v)\) for every edge \(uv\) of \(G\), where \(w(u)=\sum_{x\in N(u)}f(x)\). The local distance antimagic chromatic number \(\chi_{ld}(G)\) is defined to be the minimum number of colors taken over all colorings of \(G\) induced by local distance antimagic labelings of \(G\). The concept of Generalized Mycielskian graphs was introduced by Stiebitz [20]. In this paper, we study the local distance antimagic labeling of the Generalized Mycielskian graphs.
A graph is called \(t\)-existentially closed (\(t\)-e.c.) if it satisfies the following adjacency property: for every \(t\)-element subset \(A\) of the vertices, and for every subset \(B \subseteq A\), there exists a vertex \(x \in A\) that is adjacent to all vertices in \(B\) and to none of the vertices in \(A \setminus B\). A \(t\)-e.c. graph is critical if removing any single vertex results in a graph that is no longer \(t\)-e.c. This paper investigates \(2\)-e.c. critical Cayley graphs and vertex-transitive graphs, providing explicit constructions of \(2\)-e.c. critical Cayley graphs on cyclic groups. It is shown that a \(2\)-e.c. critical Cayley graph (as well as vertex-transitive graphs) of order \(n\) exists if and only if \(n \geq 9\) and \(n \notin \{10, 11, 14\}\). Additionally, this paper examines the numbers of \(2\)-e.c. (critical) vertex-transitive graphs among all vertex-transitive graphs for small orders, and presents detailed observations on some \(2\)-e.c. and \(3\)-e.c. vertex-transitive graphs.
For a family \(\mathcal F\) of graphs, a graph \(G\) is said to be \(\mathcal F\)-free if \(G\) contains no member of \(\mathcal F\) as an induced subgraph. We let \(\mathcal G_{3}(\mathcal F)\) be the family of \(3\)-connected \(\mathcal F\) -free graphs. Let \(P_{n}\) and \(C_{n}\) denote the path and the cycle of order \(n\), respectively. Let \(T_{0}\) be the tree of order nine obtained by joining a pendant edge to the central vertex of \(P_{7}\). Let \(T_{1}\) and \(T_{2}\) be the trees of order ten obtained from \(T_{0}\) by joining a new vertex to a vertex of \(P_{7}\) adjacent to an endvertex, and to a vertex of \(P_{7}\) adjacent to the central vertex, respectively. We show that \(\mathcal G_{3}(\{C_{3}, C_{4}, T_{1}\})\) and \(\mathcal G_{3}(\{C_{3}, C_{4}, T_{2}\})\) are finite families.
We model each 4\(\mathrm{\times}\)4 magic square by encoding its 16 integers as magnitudes of repelling positive point charges on a fixed 2D lattice, evolved under Coulomb forces with linear damping and a harmonic pinning to anchor sites. We simulate all 880 magic squares and compare them with equally sized ensembles of random permutations of \(\{1,{\dots},16\}\). Three readouts differentiate the ensembles. (i) Final positions: magic cases form sixteen tight, index-specific clusters on an annulus, whereas random cases show broader arcs and central accumulation. (ii) Displacement–correlation structure: across cases, many pair-of-pairs of inter-index displacements in magic squares are near-linearly dependent; the random ensemble exhibits only moderate relationships, with |r| and R\(^2\) distributions shifted to weaker correlation. (iii) Center potential: the Coulomb-type potential at the geometric center collapses to a single value at the anchors for all magic squares and remains narrowly distributed after dynamics, while random squares remain broad. Sensitivity analysis reveals a broad damping–stiffness region with high convergence, indicating that the results are robust to parameter choice.
Tolerance graphs, introduced by M. C. Golumbic and C. L. Monma in 1982, are a generalization of interval graphs. In this paper, we propose an application of coloring of tolerance graphs together with machine learning, in particular supervised learning, in solving problems of airport gate assignment during a pandemic of an airborne disease. The idea is to minimize a contact between passengers at the gates, in order to slow down a spread of the disease. This application includes calculating the chromatic number of a graph and finding a clique of a given size in the graph. As a result, we obtain the minimum number of gates needed under the given assumptions and the corresponding gate assignment. Further, we propose an application of list coloring, a generalization of a coloring, of tolerance graphs in solving these problems. Besides the theoretical approach, the corresponding algorithms are given. The algorithms developed may take into account several parameters, such as the number of passengers on a flight, the number of newly infected people per 1000 inhabitants. A similar approach can be taken for classroom assignment during a pandemic, scheduling meetings, etc.
This paper addresses the enumeration of two types of zero-sum sequences. The first type is defined by constraints on both the number of terms and the values they may assume; the second type is constrained by the number of terms and the total absolute value of the sequence. For both cases, exact enumeration formulas are derived in terms of restricted partition functions, and asymptotic estimates are obtained. In the final section, an algebraic analogue of restricted compositions is introduced, and its enumerative properties are analyzed.
A Fibonacci cordial labeling of a graph \(G\) is an injective function \(f: V(G) \rightarrow \{F_0, F_1, \dots,\\ F_n\}\), where \(F_i\) denotes the \(i^{\text{th}}\) Fibonacci number, such that the induced edge labeling \(f^*: E(G) \rightarrow \{0,1\}\), given by \(f^*(uv) = (f(u) + f(v))\) \((\bmod\ 2)\), satisfies the balance condition \(|e_f(0) – e_f(1)| \le 1\). Here, \(e_f(0)\) and \(e_f(1)\) represent the number of edges labeled 0 and 1, respectively. A graph that admits such a labeling is termed a Fibonacci cordial graph. In this paper, we investigate the existence and construction of Fibonacci cordial labelings for several families of graphs, including Generalized Petersen graphs, open and closed helm graphs, joint sum graphs, and circulant graphs of small order. New results and examples are presented, contributing to the growing body of knowledge on graph labelings inspired by numerical sequences.
An S-packing k-coloring of a graph \(G\) (with \(S=(s_1,s_2,\dots)\) is a non-decreasing sequence of positive integers) is a mapping \(f\) from \(V(G)\) to \(\lbrace 1,\dots , k \rbrace\) (the set of colors) such that for every two distincts vertices \(x\) and \(y\) in \(V(G)\) with \(f(x)=f(y)=i\) the distance between \(x\) and \(y\) in \(G\) is bigger than \(s_i\). The S-packing chromatic number \(\chi_S (G)\) of \(G\) is the smallest integer \(k\) such that \(G\) has an S-packing k-coloring. Given a set \(D\subset \mathbb{N}^*\), a distance graph \(G(\mathbb{Z}, D)\) with distance set \(D\) is a graph with vertex set \(\mathbb{Z}\) and two distincts vertices \(u\) and \(v\) are adjacents if \(| u-v | \in D\). In this paper, for \(S=(s,s+1,s+1,\dots)\) with \(s \geq \left\lceil \frac{t}{2} \right\rceil\) we give a lower bound of \(\chi_S (G(\mathbb{Z}, \lbrace 1, t\rbrace))\), and a lower bound of \(\chi_d (G(\mathbb{Z}, \lbrace 1, t\rbrace))\) with \(d \geq \left\lceil \frac{t}{2} \right\rceil\), for \(S=(s_1,s_2,\dots, s_i,a,a,\dots)\) with \(a \geq \max( 1 , t-2 )\) we give an upper bound of \(\chi_S (G(\mathbb{Z}, \lbrace 1, t\rbrace))\), and we determine the exact values of \(\chi_S (G(\mathbb{Z}, \lbrace 1, t\rbrace))\) and also of \(\chi_d (G(\mathbb{Z}, \lbrace 1, t\rbrace))\) for \(s\geq \max (\left\lceil \frac{t}{2} \right\rceil, t-3)\) and \(d \geq \max (\left\lceil \frac{t}{2} \right\rceil , t-2)\). And we give a lower and an upper bound of \(\chi_S (G(\mathbb{Z}, \lbrace 1, t\rbrace))\) for \(S=(1,s,s,\dots)\) with conditions on \(s\) and \(t\), which in the cases \(s\geq \max (t-2,\left\lceil \frac{t}{2}\right\rceil)\) we determine the exact values of \(\chi_S (G(\mathbb{Z}, \lbrace 1, t\rbrace))\).
For a graph \(G=(V,E)\), a pair of vertex disjoint sets \(A_{1}\) and \(A_{2}\) form a connected coalition of \(G\), if \(A_{1}\cup A_{2}\) is a connected dominating set, but neither \(A_{1}\) nor \(A_{2}\) is a connected dominating set. A connected coalition partition of \(G\) is a partition \(\Phi\) of \(V(G)\) such that each set in \(\Phi\) either consists of only a singe vertex with the degree \(\mid V(G)\mid-1\), or forms a connected coalition of \(G\) with another set in \(\Phi\). The connected coalition number of \(G\), denoted by \(CC(G)\), is the largest possible size of a connected coalition partition of \(G\). In this paper, we characterize graphs that satisfy \(CC(G)=2\). Moreover, we obtain the connected coalition number for unicycle graphs and for the corona product and join of two graphs. Finally, we give a lower bound on the connected coalition number of the Cartesian product and the lexicographic product of two graphs.
The lower deg-centric graph of a simple, connected graph \(G\), denoted by \(G_{ld}\), is a graph constructed from \(G\) such that \(V(G_{ld}) = V(G)\) and \(E(G_{ld}) = \{v_iv_j: d_G(v_i,v_j) < \deg_G(v_i)\}\). This paper presents the Roman domination number of lower deg-centric graphs. Also, investigate the properties and structural characteristics of this type of graph.
Let \(K = K(a,p;\lambda_1,\lambda_2)\) be the multigraph with: the number of vertices in each part equal to \(a\); the number of parts equal to \(p\); the number of edges joining any two vertices of the same part equal to \(\lambda_1\); and the number of edges joining any two vertices of different parts equal to \(\lambda_2\). The existence of \(C_4\)-factorizations of \(K\) has been settled when \(a\) is even; when \(a \equiv 1 \ (\mbox{mod } 4)\) with one exception; and for very few cases when \(a \equiv 3 \ (\mbox{mod } 4)\). The existence of \(C_z\)-factorizations of \(K\) has been settled when \(a \equiv 1 \ (\mbox{mod } z)\) and \(\lambda_1\) is even; when \(a \equiv 0 \ (\mbox{mod } z)\); and when \(z=2a\) where both \(a\) and \(\lambda_1\) is even. In this paper, we give a construction for \(C_z\)-factorizations of \(K\) for \(z \in \{ 4,4a \}\) when \(a\) is even.
In 1940, Birkhoff raised the open problem of computing of all posets/lattices on \(n\) elements up to isomorphism for small \(n\). Many authors tried to solve this problem by providing algorithms such as nauty. In 2020, Gebhardt and Tawn given an orderly algorithm for constructing unlabelled lattices of given size and explicitly obtained the number of lattices on up to \(20\) elements. In 2020, Bhavale and Waphare introduced the concept of nullity of a poset as the nullity of its cover graph. Recently, Bhavale and Aware counted lattices having nullity up to two. Bhavale and Aware also counted all non-isomorphic lattices on \(n\) elements, containing up to three reducible elements, having arbitrary nullity \(k \geq 2\). In this paper, we count up to isomorphism the class of all lattices on \(n\) elements containing four comparable reducible elements, and having nullity three.
In this paper, we consider (\(a,b\))- parity factors in graphs and obtain a toughness condition for the existence of (\(a,b\))-parity factors. Furthermore, we show that the result is sharp in some sense.
The most serious physical disability in children is caused by cerebral palsy (CP), a frequent mobility disease in children. Early diagnosis is essential for an early intervention and it helps in potential recovery of infants at high risk. Diagnosis of cerebral palsy is crucial at an early stage since it allows monitoring and therapy sooner. Children with cerebral palsy are prone to high error and may cause underestimation in the values of Hypothalamic–Pituitary–adrenal that is often detected in children with limitations in mobility. Accelerometer-based motion sensors have been acknowledged as the standard for accurately measuring PA in children and adolescents. GAIT aims to map these readings and create a 3-Dimensional model to map coordinates and perform analysis, however finding the severity and the type of Cerebral Palsy is a task due to a lack of classification models. The paper aims to deploy a classification model to predict the presence, intensity and severity of the condition in infants / adults by using the coordinate dataset provided by the GAIT Lab datasets available within the institute’s GAIT Lab. Alongside this, also focusing on deploying an infant cerebral palsy prediction model that can predict the condition in early stages and be used for treatment.
This paper contributes to the classification of non-trivial \(2\)-designs with block size \(5\) admitting a block-transitive automorphism group. Let \({\cal D=(P,B)}\) be a non-trivial 2-\((v,5,\lambda)\) design and \(G\) be a block-transitive automorphism group of \(\mathcal{D}\). The main aim of this paper is to determine all pairs \((\mathcal{D},G)\) when Soc(\(G\)) is a sporadic simple group.
In mathematics education research, mathematics task sets involving mixed practice include tasks from many different topics within the same assignment. In this paper, we use graph decompositions to construct mixed practice task sets for Calculus I, focusing on derivative computation tasks, or tasks of the form “Compute \(f'(x)\) of the function \(f(x)=\) [elementary function].” A decomposition \(D\) of a graph \(G=(V,E)\) is a collection \(\{H_1, H_2, …, H_t\}\) of nonempty subgraphs such that \(H_i=G[E_i]\) for some nonempty subset \(E_i\) of \(E(G)\), and \(\{E_1, E_2, …, E_t\}\) is a partition of \(E(G)\). We extend results on decompositions of the complete directed graph due to Meszka & Skupień to construct balanced task sets that assess the Chain Rule.
Let \(G\) be a graph of order \(n\) and let \(A\) be an additive Abelian group with identity 0. A mapping \(l : V(G) \to A \setminus \{0\}\) is said to be a \(A\)-vertex magic labeling of \(G\) if there exists a \(\mu \in\) \(A\) such that \(w(v) = \sum\limits_{u \in N_G(v)} l(u) = \mu\) for all \(v \in V\) and \(\mu\) is called a magic constant of \(\ell\). The group distance magic set of an \(A\)-vertex magic graph \(gdms(G,A)\) is defined as \(gdms(G,A):= \{ \lambda: \lambda \text{ is a magic constant of some $A$-vertex magic labeling} \}\). In this paper, we investigate under what conditions \(gdms(G,A)\) is a subgroup of \(A\). We also introduce the concept of the reduced group distance magic set, \(rgdms(G, A)\), which can be used as a tool to determine \(gdms(G, A)\).
Let \(2\le k\in\mathbb{Z}\). We say that a total coloring of a \(k\)-regular simple graph via \(k+1\) colors is an efficient total coloring if each color yields an efficient dominating set, where the efficient domination condition applies to the restriction of each color class to the vertex set. We prove that Hamming shells of star transposition graphs and Hamming cubes have efficient total colorings. Also in this work, a focus is set upon the graphs of girth \(2k\) and \(k\). Efficient total colorings of finite connected simple cubic graphs of girth 6 are constructed. These are of two specific types, namely: (a) those whose 6-cycles use just 3 colors with antipodal monochromatic pairs of vertices or edges; (b) those whose 6-cycles do not respect item (a) so they use four colors. An orthogonality property holds for all graphs of type (a). Such property allows further edge-half-girth colorings in the corresponding prism graphs.
A \(\{2\}\)-dominating function (\(\{2 \}\)DF) on a graph \(G=(V(G),E(G))\) is a function \(f : V(G) \rightarrow \{0,1,2 \}\) such that \(f(N[v]) \geq 2\) for every \(v \in V(G)\), where \(N[v]\) is the closed neighourhood of \(v\). The \(\{2\}\)-domination number of \(G\) is the minimum weight \(\omega(f) = \sum\limits_{v \in V(G)} f(v)\) among all \(\{2 \}\)-dominating functions on \(G\). In this article, we prove that if \(G\) and \(H\) are graphs with no isolated vertex, then for any vertex \(v \in V(H)\) there are six closed formulas for the \(\{2\}\)-domination number of the rooted product graph \(G \circ_v H\). We also characterize the graph \(G\) and \(H\) that satisfy each of these formulas.
The chemical graphs are graphs that have no vertex with degree greater than 4. The sigma index of a graph \(G\) is defined by \(\sum_{uv\in E(G)} (deg_{G}(u)-deg_{G}(v))^{2}\), where \(deg_{G}(u)\) stands for the degree of vertex \(u\) in \(G\). In this work, we present lower and upper bounds on the sigma index for chemical trees with a given order and number of pendent vertices. Furthermore, we solve the problem of minimizing sigma index for chemical graphs of order \(n\) having \(m\) edges and \(p\) pendent vertices.
Sports movement recognition is vital for performance assessment, training optimization, and injury prevention, but manual observation is slow and inconsistent. We propose a compact framework that fuses deep learning with biodynamic analysis: convolutional neural networks (CNNs) extract spatial cues from video, a biodynamic encoder derives joint angles, torques, velocities, and forces, and temporal convolutional networks (TCNs) capture sequential dependencies. Using a simulated multimodal dataset of athletic activities, our method outperforms baseline CNN and LSTM models, achieving higher precision (91.5), recall (93.2), and accuracy (92.7). Gains are largest for complex biomechanics (e.g., throwing, kicking), with up to a 10% accuracy increase from biodynamic integration. These results highlight the value of multimodal fusion and provide a scalable path toward real-time, AI-driven sports performance monitoring, with potential extensions to niche sports (fencing, gymnastics, pole vaulting, javelin).
Let \(S\) be an independent set of a connected graph \(G\) of order atleast \(2\). A set \(S' \subseteq V(G)-S\) is an \(S\)-fixed geodetic set of \(G\) if each vertex \(v\) in \(G\) lies on an \(x-y\) geodesic for some \(x\in S\) and \(y\in S'\). The \(S\)-fixed geodetic number \(g_s(G)\) of \(G\) is the minimum cardinality of an \(S\)-fixed geodetic set of \(G\). The independent fixed geodetic number of \(G\) is \(g_{if}(G) = min \left\{g_s(G)\right\}\), where the minimum is taken over all independent sets \(S\) in \(G\). An independent fixed geodetic set of cardinality \(g_{if}(G)\) is called a \(g_{if}\)-set of \(G\). We determine bounds for it and characterize graphs which realize these bounds. Also, the relations with the vertex geodomination number, vertex independence number and vertex covering number of graphs are studied. Some realization results based on the parameter \(g_{if}(G)\) are generated. Finally, two algorithms are designed to compute the independent fixed geodetic number \(g_{if}(G)\) and their complexity results are analyzed.
Enumerative study of RNA secondary structures is one of the most important topics in computational biology. However, most of the existing results are concerned with a single type of structural motifs and are asymptotic. Hairpins and stacks are among the most important motifs in secondary structures. Certain subsets of secondary structures characterized by the number of contained hairpins and the way how these hairpin loops are organized, for instance, cloverleaves (Waterman 1979), have been enumerated in a variety of works, mostly asymptotically. In this paper, we generalize these enumerations and combinatorially obtain exact formulae counting general RNA secondary structures by the joint distribution of hairpins and stacks.
Let \(G\) be a graph with vertex set \(V(G) = \{v_1, v_2, \dots, v_n\}\). We associate to \(G\), a matrix \(P(G)\) whose \((i, j)\)-th entry is the maximum number of vertex-disjoint paths between the corresponding vertices if \(i\neq j\), and is zero otherwise. We call this matrix the path matrix of \(G\), and its eigenvalues are referred to as the path eigenvalues of \(G\). In this paper, we investigate the path eigenvalues of graphs resulting from certain graph operations and specific graph families.
Null graphs (respectively, 1-regular graphs) are the only regular graphs with local antimagic chromatic number 1 (respectively, undefined). In this paper, we proved that the join of 1-regular graph and a null graph has local antimagic chromatic number 3. As a by-product, we also obtained many families of (possibly disconnected or regular) bipartite and tripartite graph with local antimagic chromatic number 3.
Let \(G = (V, E)\) be a graph with vertex set \(V\) and edge set \(E\). A vertex set \(S \subset V\) is a perfect dominating set if every vertex in \(V – S\) is adjacent to exactly one vertex in \(S\). A perfect dominating set \(S\) is furthermore: (i) an efficient dominating set or a \(1\)-efficient dominating set if no two vertices in \(S\) are adjacent, (ii) a total efficient dominating set or a \(2\)-efficient dominating set if every vertex in \(S\) is adjacent to exactly one other vertex in \(S\), and (iii) a \(1,2\)-efficient dominating set if every vertex in \(S\) either adjacent to no vertices in \(S\) or to exactly one other vertex in \(S\). In this paper we introduce the concept of \(1,2\)-efficiency in graphs and apply it to the existence of \(1,2\)-efficient sets in grid graphs \(G_{m,n}\), that is, graphs resembling chessboards having a rectangular array of \(m \times n\) vertices arranged into \(m\) rows of \(n\) vertices, or \(n\) columns of \(m\) vertices. It is well known that almost no grid graphs are \(1\)-efficient, and relatively few grid graphs are \(2\)-efficient. However, in this paper, we show that all but a relatively small percentage of grid graphs are \(1,2\)-efficient.
onsidering regions in a map to be adjacent when they have nonempty intersection (as opposed to the traditional view requiring intersection in a linear segment) leads to the concept of a facially complete graph: a plane graph that becomes complete when edges are added between every two vertices that lie on a face. Here we present a complete catalog of facially complete graphs: they fall into seven types. A consequence is that if \(q\) is the size of the largest face in a plane graph \(G\) that is facially complete, then \(G\) has at most \(\left\lfloor 3q/2\right\rfloor\) vertices. This bound was known, but our proof is completely different from the 1998 approach of Chen, Grigni, and Papadimitriou (Planar map graphs, Proc. 30th ACM Symp. Th. of Computing, 514–523). Our method also yields a count of the 2-connected facially complete graphs with \(n\) vertices. We also show that if a plane graph has at most two faces of size 4 and no larger face, then the addition of both diagonals to each 4-face leads to a graph that is 5-colorable.
A bowtie graph is the union of two edge disjoint 3-cycles which share a single vertex. A mixed bowtie is a partial orientation of a bowtie graph. In this paper, we consider decompositions of the complete mixed graph into mixed bowties consisting of a union of two isomorphic copies of mixed triples.
In this paper, we study the signless Laplacian eigenvalues of the subgraph \(Z^*(\Gamma(\mathbb{Z}_n))\) of the total graph of the integers modulo \(n\), \(\mathbb{Z}_n\), for certain values of \(n\). We also identify specific values of \(n\) for which the graph is \(Q\)-integral. Finally, we discuss the normalized Laplacian spectrum of \(Z^*(\Gamma(\mathbb{Z}_n))\).
We obtain several results towards the proof that the necessary conditions are sufficient for the existence of a GDD\((n_1 = 1, n_2 = n, 4; \lambda_1, \lambda_2)\) where \(\lambda_1 \ge \lambda_2\). We also have some general results including the constructions for larger block sizes as well as when the first group size \(n_1\) is not 1 or \(\lambda_1 < \lambda_2\).
We introduce a new arc in directed graphs of integers. Our arcs are determined by the values of the popular arithmetic functions such as the divisor function \(\tau\), the prime divisors counting functions \(\omega\) and \(\Omega\), and the sum of digits function \(s_b\), evaluated at the multiples \(N\) of a particular integer. Among other things, we determine the positive integers that have arcs to all except a finite number of positive integers. We also propose some possible research problems at the end of this article.
Let \(G\) be a plane graph with vertex, edge, and region sets \(V(G), E(G), F(G)\) respectively. A zonal labeling of a plane graph \(G\) is a labeling \(\ell: V(G)\rightarrow \{1,2\}\subset \mathbb{Z}_3\) such that for every region \(R\in F(G)\) with boundary \(B_R\), \(\sum\limits_{v\in V(B_R)}\ell(v)=0\) in \(\mathbb{Z}_3\). We extend this to general abelian groups, defining a \(\Gamma\)-zonal labeling as a labeling \(\ell:V(G)\rightarrow \Gamma\setminus \{0\}\) such that for every region \(R\in F(G)\), \(\sum\limits_{v \in V(B_R)}\ell(v)\) is \(0\). We explore existence of \(\Gamma\)-zonal labelings for various families of graphs. We also introduce two variations: generative and strong \(\Gamma\)-zonal labelings. A generative \(\Gamma\)-zonal labeling is one in which the elements used to label the vertices generate the group \(\Gamma\). A strong \(\Gamma\)-zonal labeling is a labeling in which the additive order of \(\ell(v)\) is equal to \(\deg(v).\) Examples and existence results are provided for both variations. It is shown that strong \(\Gamma\)-zonal labelings have a connection to edge colorings that generalizes the connection between zonal labelings and proper edge \(3\)-colorings of cubic maps.
In this paper, \(k\)-domination is considered for the king’s, queen’s, knight’s, and bishop’s graphs for square boards of any dimension size. We also consider \(k\)-tuple total domination for the queen’s and bishop’s graphs for square boards as well.
Finite games in normal form and their mixed extensions are a corner stone of noncooperative game theory. Often generic finite games and their mixed extensions are considered. But the properties which one expects in generic games and the existence of games with these properties are often treated only in passing. The paper considers strong properties and proves that generic games have these properties. The space of mixed strategy combinations is embedded in a natural way into a product of real projective spaces. All relevant hypersurfaces extend to this bigger space. The paper shows that for all games in the complement of a semialgebraic subset of codimension at least one all relevant hypersurfaces in the bigger space are smooth and maximally transversal. The proof uses the theorem of Sard and follows an argument of Khovanskii.
A proper \(k\)-coloring \(\alpha\) of a graph \(G\) induces a partition \(\Pi = \{C_1, C_2, \dots, C_k\}\), where \(C_i = \{v \in V(G) \mid \alpha(v) = i\}\). The color code of a vertex \(v \in V(G)\) with respect to \(\Pi\) is defined as the tuple \(c_{\Pi}(v) = (d(v, C_1), d(v, C_2), \dots, d(v, C_k))\), where \(d(v, C_i)\) represents the distance from \(v\) to the set \(C_i\). A proper \(k\)-coloring \(\alpha\) is called a locating \(k\)-coloring of \(G\) if \(\alpha\) induces a partition \(\Pi\) such that for any two distinct vertices \(u, v \in V(G)\), it holds that \(c_{\Pi}(u) \neq c_{\Pi}(v)\). The locating chromatic number of \(G\), denoted \(\chi_L(G)\), is the smallest \(k\) for which a locating \(k\)-coloring of \(G\) exists. In this paper, we establish a connection between the locating \(k\)-coloring of \(C_n(1,2,\dots,t) + K_m\) and the union of graphs \(\bigcup_{i=1}^p C_{n_i} + K_m\), leveraging properties of simple cycles in directed graphs. Using this connection, we determine the locating chromatic number of \(C_n(1,2,\dots,t) + K_m\) for \(t = 2\) and \(n \in [6, 28]\), as well as for \(t = 3\) and \(n \in [8, 24]\).
We characterize line digraphs of polytrees, including several of their well-known subclasses. For a given undirected tree, we characterize its orientations with weak line digraphs, and count the exact number. Furthermore, we find the minimum, maximum, and average sizes of these line digraphs. We provide an explicit formula for the number of weak components in line digraphs of polytrees in terms of the inner sources and sinks. Additionally, we count the average number of weak components in them among all orientations of a fixed tree. Finally, we propose an algorithm for finding weak components in line digraphs of polytrees.
Let \(G\) be a loopless connected graph. A graph \(G\) is reduced if it contains no collapsible subgraph. Catlin (posted by Chen and Lai [9]) conjectured that every connected reduced graph is either 2-colorable or 3-colorable. A weaker conjecture states that the independence number of a connected reduced graph \(G\) is at least one-third of its number of vertices. In this paper, we establish a lower bound on the independence number in reduced graphs. As an application, we examine the independence number conjecture for reduced graphs with a given upper bound on the number of vertices. Also, we investigate the chromatic number of reduced planar graphs under given conditions.
Graph pebbling is a network optimization method modeling the movement of resources in transit. A pebbling move on a connected graph \(G\) removes two pebbles from a vertex, places one on an adjacent vertex, and discards the other, with the loss analogous to packet loss in communication networks. The generalized version, \(t\)-pebbling, defines the \(t\)-pebbling number \(f_t(G)\) as the smallest integer such that, from any distribution of \(f_t(G)\) pebbles, \(t\) pebbles can be moved to any vertex \(v\) via a pebbling sequence. A graph satisfies the \(2t\)-pebbling property if \(2t\) pebbles can be transferred to \(v\) when \(2f_t(G)-q+1\) pebbles are distributed, where \(q\) is the number of occupied vertices. This paper establishes a lower bound for the rooted product of two graphs \(G\) and \(H\), sharp when one factor is a path, complete graph, or star. Further results on pebbling in triangle-free graphs are also obtained, including verification of the \(2t\)-pebbling property for rooted products involving such graphs.
This note derives asymptotic upper and lower bounds for the number of planted plane trees on \(n\) nodes assigned labels from the set \(\{1, 2, \dots, k\}\) with the restriction that on any path from the root to a leaf, the labels must strictly decrease. We illustrate an application to calculating the largest eigenvalue of the adjacency matrix of a tree.
Let US be the class of all ultrametric spaces generated by labeled star graphs. We prove that compact US-spaces are the completions of totally bounded ultrametric spaces generated by decreasingly labeled rays. We characterize the ultrametric spaces which are weakly similar to finite US-spaces and describe these spaces by certain four-point conditions.
It is known that null graphs are the only (regular) graphs with local antimagic chromatic 1 and 1-regular graphs are the only regular graphs without local antimagic chromatic number. In this paper, we first use matrices of size \((2m+1) \times (2k+1)\) to completely determine the local antimagic chromatic number of the join of null graphs \(O_m\) and 1-regular graphs \((2k+1)P_2\) for all \(k\ge 1, m\ge 2\). We then make use of other matrices of same size to obtain the local antimagic chromatic number of another family of tripartite graphs. Consequently, we obtained infinitely many (possibly disconnected) regular tripartite graphs with local antimagic chromatic number 3.
Let \(2\le k\in\mathbb{Z}\). We say that a total coloring of a \(k\)-regular simple graph via \(k+1\) colors is an efficient total coloring if each color yields an efficient dominating set, where the efficient domination condition applies to the restriction of each color class to the vertex set. We prove that Hamming shells of star transposition graphs and Hamming cubes have efficient total colorings. Also in this work, a focus is set upon the graphs of girths \(2k\) and \(k\). Efficient total colorings of finite connected simple cubic graphs of girth 6 are constructed. These are of two specific types, namely: (a) those whose 6-cycles use just 3 colors with antipodal monochromatic pairs of vertices or edges; (b) those whose 6-cycles do not respect item (a) so they use four colors. An orthogonality property holds for all graphs of type (a). Such orthogonality property allows further edge-half-girth colorings in the corresponding prism graphs.
Graph Theory was started by Euler after solving the famous Konigsberg bridge problem. The Graph Coloring is among one of the famous topic for research since it has many beautiful theorems on optimization and its applications in numerous fields of science. The Pi coloring is the coloring of graph parts without a recurring pattern. As a result, it is defined as a function from a set of graph elements with similar properties to the power set of colors, so that each set receives a different color set from the power set. In consequence, Incident Vertex Pi coloring of a graph is defined as the coloring of incident vertices for every single edge with Pi coloring. Incident Vertex Pi coloring of the complete graph is \(n\), wheel graph, star graph and double star graph is \(n+1\), diamond, friendship graphs is \(\Delta +1\), and double fan graph is \(\Delta +2\). In this research, we derived the Incident Vertex PI coloring of Star and Double Star graph’s Middle graph, Total graph, Line graph, and Splitting graph.
For a graph \(G\) with a (not necessarily proper) vertex coloring, a set \(D\subseteq V(G)\) is a polychromatic dominating set of \(G\) if it is a dominating set and each vertex in \(D\) is a different color. The polychromatic domination number of \(G\), \(\rho(G)\), is the minimum number of colors such that, for any \(\rho(G)\)-coloring (with exactly \(\rho(G)\) colors) of the vertices of \(G\), there exists a polychromatic dominating set of \(G\). This paper begins the exploration of the polychromatic domination number. In particular we give tight upper and lower bounds for \(\rho(G)\) both of which are functions of the minimum degree of \(G\).
A novel approach to building strong starters in cyclic groups of orders \(n\) divisible by 3 from starters of smaller orders is presented. A strong starter in \(\mathbb{Z}_n\) (\(n\) odd) is a partition of the set \(\{1,2,\dots,n-1\}\) into pairs \(\{a_i,b_i\}\) such that all pair sums \(a_i+b_i\) are distinct and nonzero modulo \(n\) and all differences \(\pm(a_i-b_i)\) are distinct and nonzero modulo \(n\). A special interest to strong starters of odd orders divisible by 3 is motivated by Horton’s conjecture, which claims that such starters exist (except when \(n=3\) or \(9\)) but remains unproven since 1989. We begin with a starter of order \(p\) coprime with 3 and describe an algorithm to obtain a Sudoku-type problem modulo 3 whose solution, if exists, yields a strong starter of order \(3p\). The process leading from the original to the final starter is called triplication. Besides theoretical aspects of the construction, practicality of this approach is demonstrated. A general-purpose constraint-satisfaction (SAT) solver z3 is used to solve the Sudoku-type problem; various performance statistics are presented.
Let Pn + 1, Cn and Sn represent a path, cycle, and star with n edges, Qn denote the n-dimensional hypercube graph. The (ℋ1, ℋ2)−multidecomposition of G for graphs ℋ1, ℋ2, and G is a decomposition of G into copies of ℋ1 and ℋ2, where there is at least one copy of ℋ1 and at least one copy of ℋ2. In this paper, we prove that the graph Qn is (Sn − 2, C4)−multidecomposable for n ≥ 4 and (Sn − 4, P5)−multidecomposable for n ≥ 5.
The Erdős-Anning Theorem states that an integer distance set in the Euclidean plane must have all of its points on a single line or is finite. However, this is not true if we consider area sets. That is, if \((x_1,y_1)\) and \((x_2,y_2)\) are any two vectors contained in the integer lattice, then the area of the parallelogram determined by the two vectors is an integer, showing that the points do not have to lie on a line. We prove a finite field version of these results for \(d=2\) and \(d=3\), showing that if \(E \subset \Bbb{F}_q^d, q=p^2\), where \(p\) is an odd prime and the distance set of \(E\) is \(\Bbb{F}_p\), then the size of \(E\) is at most \(p^d\). Furthermore, we prove that if the area set of \(E\) is a subset of \(\Bbb{F}_p\), then the size of \(E\) is at most \(p^2\) in two dimensions.
Let Fn denote the n-th Fibonacci number defined by Fn = Fn − 1 + Fn − 2 if n ≥ 2, with F0 = 0 and F1 = 1. In this paper, we find determinant identities for several Toeplitz–Hessenberg matrices whose nonzero entries are derived from the sequence kn + m for various fixed m, where kn = Fn − 1. These results may be obtained algebraically as special cases of more general formulas involving the Horadam numbers and the generating functions for the associated sequences of determinants. Equivalent multi-sum identities featuring sums of products of kn terms with multinomial coefficients may be given, which follow from Trudi’s formula. Connections are made to several OEIS entries that have arisen previously in other contexts, perhaps most notably the Padovan number sequence. Finally, we provide combinatorial proofs of our identities involving kn by enumerating (or finding the sum of signs of) various classes of tilings containing squares, dominos, trominos and a special type of tile which can be of arbitrary length.
In this paper, we study additional aspects of the capacity distribution on the set ℬn of compositions of n consisting of 1’s and 2’s extending recent results of Hopkins and Tangboonduangjit. Among our results are further recurrences for this distribution as well as formulas for the total capacity and sign balance on ℬn. We provide algebraic and combinatorial proofs of our results. We also give combinatorial explanations of some prior results where such a proof was requested. Finally, the joint distribution of the capacity statistic with two further parameters on ℬn is briefly considered.
Let \(k\ge 1\) be an integer. Let \(G=(V,E)\) be a connected graph with \(n\) vertices and \(m\) edges. Suppose fires break out at two adjacent vertices. In each round, a firefighter can protect \(k\) vertices, and then the fires spread to all unprotected neighbors. For \(uv\in E(G)\), let \(sn_{k}(uv)\) denote the maximum number of vertices the firefighter can save when fires break out at the ends of \(uv\). The \(k\)-edge surviving rate \(\rho'_k(G)\) of \(G\) is defined as the average proportion of vertices saved when the starting vertices of the fires are chosen uniformly at random over all eages, i.e., \(\rho'_k(G)=\sum\limits_{uv\in E(G)}sn_{k}(uv)/nm\). In particular, we write \(\rho'(G)=\rho'_1(G)\). For a given class of graphs \(\mathcal{G}\) and a constant \(\varepsilon>0\), we seek the minimum value \(k\) such that \(\rho'_k(G)>\varepsilon\) for all \(G\in \mathcal{G}\). In this paper, we prove that for Halin graphs, this minimum value is exactly 1. Specifically, every Halin graph \(G\) satisfies \(\rho'(G)> 1/12\).
We consider a ring \(R_{u^3} = \mathbb{F}_2+u\mathbb{F}_2+u^2\mathbb{F}_2+u^3\mathbb{F}_2, u^4=0\). We discuss the structure of self-dual codes, Type I codes and Type II codes over the ring \(R_{u^3}\) in terms of the structure of their Torsion and Residue codes. We construct Type I and Type II optimal codes over the ring \(R_{u^3}\) for different lengths.
Magic squares are known to mankind since ages. With their eye catching properties, they have attracted and inspired researchers to further explore and work with them. The present paper is written with an aim to explore the usefulness of magic squares in the construction of partially balanced incomplete block (PBIB) designs. In this regard, we have proposed a new method for the construction of magic squares and studied their properties. We have also established a link between these properties and some existing association schemes. We have then introduced four new association schemes using these properties which have been later used for the construction of PBIB designs.
We consider a vertex-coloring problem where the amount one pays for using a color is a function of how many times the color is used. For a cost-function \(f\), we define the \(f\)-chromatic number of graph \(G\) as the minimum cost of a (proper) coloring of \(G\), and focus on the case that the marginal costs \(f(i+1)-f(i)\) are non-increasing. We provide bounds for general graphs, for specific classes of graphs, and for some operations on graphs. We also consider the number of colors used in an optimal coloring, and for example, characterize the trees where the bipartite coloring is not always optimal.
Let \(q\) be a positive integral power of some prime \(p\) and \(\mathbb{F}_{q^m}\) be a finite field with \(q^m\) elements for some \(m \in \mathbb{N}\). Here we establish a sufficient condition for the existence of primitive normal pairs of the type \((\epsilon, f(\epsilon))\) in \(\mathbb{F}_{q^m}\) over \(\mathbb{F}_{q}\) with two prescribed traces, \(\text{Tr}_{{\mathbb{F}_{q^m}}/{\mathbb{F}_q}}(\epsilon)=a\) and \(\text{Tr}_{{\mathbb{F}_{q^m}}/{\mathbb{F}_q}}(f(\epsilon))=b\), where \(f(x) \in \mathbb{F}_{q^m}(x)\) is a rational function with some restrictions and \(a, b \in \mathbb{F}^*_q\). Furthermore, for \(q=5^k\), \(m \geq 9\) and rational functions with degree sum 4, we explicitly find at most 13 fields in which the desired pair may not exist.
Let \(G = (V(G), E(G))\) be a simple connected graph. The inverse sum indeg index of \(G\), denoted by \(\text{ISI}(G)\), is defined as the sum of the weights \(\frac{d(u)d(v)}{d(u) + d(v)}\) of all edges \(uv\) of \(G\), where \(d(u)\) denotes the degree of a vertex in \(G\). In this paper, we first present some lower and upper bound for \(ISI\) index in terms of graph parameters such as maximum degree, minimum degree and clique number. Moreover, we compute \(ISI\) index of several graph operations like join, cartesian product, composition, corona and strong product of graphs.
We consider the eternal distance-2 domination problem, recently proposed by Cox, Meger, and Messinger, on trees. We show that finding a minimum eternal distance-2 dominating set of a tree is linear time in the order of the graph by providing a fast algorithm. Additionally, we characterize trees that have eternal distance-2 domination number equal to their domination number or their distance-2 domination number, {along with trees that are} eternal distance-2 domination critical. We conclude by providing general upper and lower bounds for the eternal distance-k domination number of a graph. We construct an infinite family of trees which meet said upper bound and another infinite family of trees whose eternal distance-k domination number is within a factor of 2 of the given lower bound.
We apply the splitting operation defined on binary matroids (Raghunathan et al., 1998) to \(p\)– matroids, where \(p\)-matroids refer to matroids representable over \(GF(p).\) We also characterize circuits, bases, and independent sets of the resulting matroid. Sufficient conditions to yield Eulerian \(p\)-matroids from Eulerian and non-Eulerian \(p\)-matroids by applying the splitting operation are obtained. A class of connected \(p\)-matroids that gives connected \(p\)-matroids under the splitting operation is characterized. In Application, we characterize a class of paving \(p\)-matroids, which produces paving matroids after the splitting operation.
For a connected graph \(G=(V,E)\) of order at least two, a \(u-v\) chordless path in \(G\) is a \(monophonic\) \(path\). The edge monophonic closed interval \(I_{em}[u,v]\) consists of all the edges lying on some \(u-v\) monophonic path. For \(S'\subseteq V(G),\) the set \(I_{em}[S']\) is the union of all sets \(I_{em}[u,v]\) for \(u,v\in S'.\) A set \(S'\) of vertices in \(G\) is called an \(edge\) \(monophonic\) \(set\) of \(G\) if \(I_{em}[S']=E(G).\) The edge monophonic number \({m_1}(G)\) of G is the minimum cardinality of its edge monophonic sets of \(G\). In this paper the monophonic number and the edge monophonic number of corona product graphs are obtained. Exact values are determined for several classes of corona product graphs.
A bipartite labeling of a tree of order \(n\) is a bijective function that identifies the vertices of \(T\) with the elements of \(\{0, 1, \dots, n-1\}\) in such a way that there exists an integer \(\lambda\) such that the set of labels on the stable sets of \(T\) are \(\{0,1, \dots, \lambda\}\) and {\(\lambda + 1, \lambda +2. \dots, n-1\}.\) The most restrictive and versatile bipartite labeling is the variety called \(\alpha\text{-labeling}\). In this work we present a new construction of \(\alpha\text{-labeled}\) trees where any two adjacent vertices of a path-like tree, or a similar caterpillar, can be amalgamated with selected vertices of two equivalent trees.
A set \(D\) of vertices of a graph \(G=(V_G,E_G)\) is a dominating set of \(G\) if every vertex in \(V_G-D\) is adjacent to at least one vertex in \(D\). The domination number of a graph \(G\), denoted by \(\gamma(G)\), is the cardinality of a smallest dominating set of \(G\). A subset \(D\subseteq V_G\) is called a certified dominating set of \(G\) if \(D\) is a dominating set of \(G\), and every vertex in \(D\) has either zero or at least two neighbours in \(V_G-D\). The cardinality of a smallest certified dominating set of \(G\) is called the certified domination number of \(G\), and it is denoted by \(\gamma_{\rm cer}(G)\). A vertex \(v\) of \(G\) is certified critical if \(\gamma_{\rm cer}(G -v) < \gamma_{\rm cer}(G)\), and a graph \(G\) is vertex certified domination critical or \(\gamma_{cer}\)–critical if the removal of any vertex reduces its certified domination number. In this paper, we give examples and properties of certified critical vertices and vertex certified domination critical graphs. As an example of an application of certified critical vertices, we give a constructive characterisation of trees for which the smaller partite set is a minimum certified dominating set.
In this paper, we consider Ramsey and Gallai-Ramsey numbers for a generalized fan \(F_{t,n}:=K_1+nK_t\) versus triangles. Besides providing some general lower bounds, our main results include the evaluations of \(r(F_{3,2}, K_3)=13\) and \(gr(F_{3,2}, K_3, K_3)=31\).
Seymour’s Second Neighborhood Conjecture (SSNC) asserts that every finite oriented graph has a vertex \(v\) whose second out-neighborhood is at least as large as its first out-neighborhood. Such a vertex is called a Seymour vertex. In this note, we introduce pseudo-Seymour set such that Seymour’s conjecture becomes: Every oriented graph has a singleton pseudo-Seymour set. We prove that any oriented graph has a pseudo-Seymour set \(S\) with \(|S|=2\). Furthermore, we show that there are pseudo-Seymour sets of any size at least 2. We define \(\rho\)-Seymour vertex where \(0 < \rho \leq 1\), and give an approach such that finding \(\rho=1\) is equivalent to the existence of Seymour vertex. Attempting to maximize its value, we prove that for any oriented graph with minimum out-degree \(\delta\), there is \(\rho=\frac{2}{3}(1+\frac{1}{2\delta})\).
In this paper, given a homeomorphism \(f\) of a compact metric space \(X\), we show that the set of all asymptotic average shadowable points of \(f\) is an open and invariant set and \(f\) has the asymptotic average shadowing property if and only if the set of all asymptotic average shadowable points of \(f\) is \(X\) if and only if any Borel probability measure \(\mu\) of \(X\) has the asymptotic average shadowing property.
This paper is concerned with the pullback attractors for the Kirchhoff type BBM equations defined on unbounded domains. Sobolev embeddings are invalid on unbounded domains. We obtain the pullback asymptotic compactness of such non-autonomous BBM equations by using the method of uniform tail-estimates.
We say that a graph \(G\) has a path-system with respect to a set \(W\) of even number of vertices in \(G\) if \(G\) has vertex-disjoint paths \(P_1,P_2, \ldots, P_m\) such that (i) each path \(P_i\) connects two vertices of \(W\) and (ii) the set of end-vertices of the paths \(P_i\) is exactly \(W\). In particular, \(m=|W|/2\). Moreover, if \(G\) has a path-system with respect to every set \(W\) of even number of vertices in \(G\), we say that \(G\) has a path system. We prove the following theorems: (i) if \(G\) is an \(r\)-edge-connected \(r\)-regular graph, then for any \(r-1\) edges \(e_1,\ldots, e_{r-1}\), \(G-\{e_1,\ldots, e_{r-1}\}\) has a path-system, (ii) every \(k\)-connected \(K_{1,k+1}\)-free graph has a path-system, and (iii) if a connected bipartite graph \(G\) with bipartition \((A,B)\) satisfies \(|A| \le 2|B|\), \(|N_G(X)| \ge 2|X|\) or \(N_G(X)=B\) for all \(X\subseteq A\), and \(|N_G(Y)| \ge |Y|\) or \(N_G(Y)=A\) for all \(Y\subseteq B\), then \(G\) has a path-system with respect to every set \(W\) of even number of vertices of \(A\).
For a graph \(G\), an Italian dominating function (IDF) is a function \(f: V(G) \rightarrow \{0,1,2\}\) such that all vertices labeled with 0 must have at least two neighbors assigned the label 1 or at least one neighbor assigned the label 2. The weight of \(f\), denoted by \(w(f)\), is calculated by summing all the labels assigned by the function. Let \(f\) be an IDF on \(G\) with a minimum weight, denoted as \(\gamma_I(G)\). If \(S\) is the set of vertices where \(f(v) > 0\), then an Inverse Italian Dominating Function (IIDF) \(f'\) is defined as an IDF on \(G\) such that \(f'(v) = 0\) for all \(v \in S\). The notation \(\gamma_{iI}(G)\) represents the Inverse Italian Domination Number of the graph \(G\), which is the minimum weight among all IIDFs on \(G\). In this paper, we find \(\gamma_{iI}(G)\) of graphs and characterize the graphs for which \(\gamma_I(G) = 2\) and \(3\), as well as those with \(\gamma_{iI}(G) = 2\) and \(3\). Additionally, we provide a characterization of trees and graphs that achieve the largest possible \(\gamma_{iI}(G)\).
The strongly chordal graph literature has recently expanded to include the sequentially smaller classes of \(s\)-strongly chordal graphs for \(s = 1, 2, 3,\ldots\) (and the limiting class of majorly chordal graphs). These stronger classes preserve — while simultaneously intensifying — the conventional chords-of-cycles inspiration of chordal graph theory. This leads to characterizing corresponding \(s\)-strongly chordal bipartite graphs and majorly chordal bipartite graphs. Our new analysis does this by using chains of quadrangles, with each adjacent pair of quadrangles having a unique edge in common. This leads to constructive characterizations that exploit a somewhat unexpected resemblance to earlier characterizations of \(s\)-strongly chordal graphs involving chains of triangles sharing common edges to characterize \(s\)-strongly chordal tripartite (and, similarly, multipartite) graphs.
In this paper, we present a method for constructing a new sequence, which we call \(k-\)division sequence and denoted by \(h_n(k)\). Using Fibonacci and Pell sequences, we define the \(k-\)division Fibonacci-Pell sequence obtain its properties, and prove that this sequence is periodic. Then, as an application of the sequence, we define \(1-\)division Fibonacci-Pell sequence on finite groups and study it in the groups \(G_m\) and \(H_{(u,l,m)}\) groups.
We consider the minimum weight and smallest weight minimum-size dominating set problems in vertex-weighted graphs and networks. The latter problem is a two-objective optimization problem, which is different from the classic minimum weight dominating set problem that requires finding a dominating set of the smallest weight in a graph without trying to optimize its cardinality. In other words, the objective of minimizing the size of the dominating set in the two-objective problem can be considered as a constraint, i.e. a particular case of finding Pareto-optimal solutions. First, we show how to reduce the two-objective optimization problem to the minimum weight dominating set problem by using Integer Linear Programming formulations. Then, under different assumptions, the probabilistic method is applied to obtain upper bounds on the minimum weight dominating sets in graphs. The corresponding randomized algorithms for finding small-weight dominating sets in graphs are described as well. Computational experiments are used to illustrate the results for two different types of random graphs.
Let \(G\) be a plane graph. If two edges are adjacent and consecutive on the boundary walk of a face of \(G\), then they are said to be facially adjacent. We call \(G\) facially entire \(k\)-colorable if there is a mapping from \(V(G)\cup E(G)\cup F(G)\) to a \(k\) color set so that any two facially adjacent edges, adjacent vertices, adjacent faces, and incident elements receive different colors. The facial entire chromatic number of \(G\) is defined to be the smallest integer \(k\) such that \(G\) is facially entire \(k\)-colorable. In 2016, Fabrici, Jendrol’ and Vrbjarová conjectured that every connected, loopless, bridgeless plane graph is facially entire \(7\)-colorable. In this paper, we give a positive answer to this conjecture for \(K_4\)-minor-free graphs. More specifically, we shall prove that every \(K_{4}\)-minor-free graph is facially entire \(7\)-colorable.
The Markov graph of a self-map on a combinatorial tree is a directed graph that encodes the covering relations between edges of the tree under the map. This work explores the dynamical structure of self-maps on trees with weakly connected Markov graphs. The main result of the paper is a complete characterization of self-maps on finite sets that yield weakly connected Markov graphs for all trees. Additionally, we describe the dynamical structure of self-maps whose Markov graphs take specific forms, including complete digraphs, cycles, paths, in-stars, and out-stars.
In this paper, we construct two infinite families of graphs \(G(d,c)\) and \(G^+(d,c)\), where, in both cases, a vertex label is \(x_1x_2\ldots x_c\) with \(x_i\in\{1,2,\ldots, d\}\). We provide a tight lower bound on the metric dimension of \(G^+(d, c)\). Moreover, we give the definition and properties of the supertoken graphs, a generalization of the well-known token graphs. Finally, we provide an upper bound on the metric dimension of supertoken graphs.
Let \(A\) be a commutative ring with nonzero identity and \(n\geq 2\) be a positive integer. With the ring \(R=A\times\cdots\times A\) (\(n\) times), one can associate graphs \(TD(R)\) and \(ZD(R)\) respectively called the total dot product graph and the zero-divisor dot product graph of \(R\). In this paper, we study some topological properties of these two dot product graphs of \(R.\) In particular, it is shown that, the zero-divisor dot product graph \(ZD(R)\) is a projective graph if and only if \(R\) is isomorphic to \(\frac{Z_2\left[x\right]}{\left\langle x^2+x+1\right\rangle}\times\frac{Z_2\left[x\right]}{\left\langle x^2+x+1\right\rangle}.\) Moreover, we prove that no total dot product graph can be projective. With these observations, we classify all commutative rings for which dot product graphs \(ZD(R)\) and \(TD(R)\) have crosscap two.
Let \(p > 5\) be a prime positive integer, \(m\) and \(s\) be positive integers. We classify the negacyclic codes of length \(5p^s\) over \(R= \mathbb{F}_{p^m}+u\mathbb{F}_{p^m}\), with \(u^2=0\) using the factorisation of cyclotomic polynomials, and we investigate their Hamming distances.
Dominator coloring is a fascinating type of proper coloring where vertices are assigned colors so that every vertex in the graph is within the closed neighborhood of at least one vertex from each color class. The smallest number of colors needed for a dominator coloring is called the dominator chromatic number. In this paper, a new graph product called the closed extended neighborhood corona of two graphs is introduced and its dominator chromatic number for any pair of connected graphs is determined. Also, the dominator chromatic numbers for the extended corona of a path with any graph and a cycle with any graph are derived. Additionally, the dominator chromatic number for the closed neighborhood corona of any two graphs is established.
The article investigates the domination polynomial of generalized friendship graphs. The domination polynomial captures the number of dominating sets of each cardinality in a graph and is known to be NP-complete to compute for general graphs. We establish the log-concave and unimodal properties of these polynomials, and determine their peaks. Furthermore, we analyze the distribution of the zeros of aforesaid polynomial and identify their region in the complex plane. Several open problems are proposed for future exploration.
The generalized Petersen graph \(G(n,k)\) is a cubic graph with vertex set \(V(G(n,k))=\{v_i\}_{0 \leq i < n} \cup \{w_i\}_{0 \leq i < n}\) and edge set \(E(G(n,k))=\{v_i v_{i+1}\}_{0 \leq i < n} \cup \{w_i w_{i+k}\}_{0 \leq i < n} \cup \{v_i w_i\}_{0 \leq i < n}\) where the indices are taken modulo \(n\). Schwenk found the number of Hamiltonian cycles in \(G(n,2)\), and in this article we present initial conditions and linear recurrence relations for the number of Hamiltonian cycles in \(G(n,3)\) and \(G(n,4)\). This is attained by introducing \(G'(n,k)\), which is a modified version of \(G(n,k)\), and a subset of its subgraphs which we call admissible, and which are partitioned into different classes in such a manner that we can find relations between the number of admissible subgraphs of each class. The classes and their relations define a directed graph such that each strongly connected component is of a manageable size for \(k=3\) and \(k=4\), which allows us to find linear recurrence relations for the number of admissible subgraphs in each class in these cases. The number of Hamiltonian cycles in \(G(n,k)\) is a sum of the number of admissible subgraphs of \(G'(n,k)\) over a certain subset of the classes.
Perfect codes in the \(n\)-dimensional grid \(\Lambda_n\) of the lattice \(\mathbb{Z}^n\) (\(0<n\in\mathbb{Z}\)) and its quotient toroidal grids were obtained via the truncated distance in \(\mathbb{Z}^n\) given between \(u=(u_1,\cdots,u_n)\) and \(v=(v_1, \ldots,v_n)\) as the graph distance \(h(u,v)\) in \(\Lambda_n\), if \(|u_i-v_i|\le 1\), for all \(i\in\{1, \ldots,n\}\), and as \(n+1\), otherwise. Such codes are extended to superlattice graphs \(\Gamma_n\) obtained by glueing ternary \(n\)-cubes along their codimension 1 ternary subcubes in such a way that each binary \(n\)-subcube is contained in a unique maximal lattice of \(\Gamma_n\). The existence of an infinite number of isolated perfect truncated-metric codes of radius 2 in \(\Gamma_n\) for \(n=2\) is ascertained, leading to conjecture such existence for \(n>2\) with radius \(n\).
A graph \(G=(V,E)\) is said to be a \(k\)-threshold graph with thresholds \(\theta_1<\theta_2<…<\theta_k\) if there is a map \(r: V \longrightarrow \mathbb{R}\) such that \(uv\in E\) if and only if the number of \(i\in[k]\) with \(\theta_i\le r(u)+r(v)\) is odd. The threshold number of \(G\), denoted by \(\Theta(G)\), is the smallest positive integer \(k\) such that \(G\) is a \(k\)-threshold graph. In this paper, we determine the exact threshold numbers of cycles by proving \[\Theta(C_n)=\begin{cases} 1 & if\ n=3, \\ 2 & if\ n=4, \\ 4 & if\ n\ge 5, \end{cases}\] where \(C_n\) is the cycle with \(n\) vertices.
Let G = (V, E) be a simple connected graph and W ⊆ V. For v ∈ V, the representation multiset or m-code of v is the multiset rm(v) = {d(v, w) ∣ w ∈ W}. If no two vertices in G have equal m-codes, then W is called an m-resolving set of G. The multiset dimension md(G) of G is the minimum possible cardinality of an m-resolving set of G, if such a set exists. If G does not possess an m-resolving set, then we say that G has infinite multiset dimension. In this paper, we show that all cylindrical graphs Pm ▫ Cn, where m, n ≥ 3, have finite multiset dimension. In particular, we show that md(Pm ▫ Cn) ≤ 4 if m ≥ 6 and n ≥ 3, or if m ≥ 3 and n ≥ 12. Moreover, if m ≥ 3 and n ≥ 8m + 1, we show that Pm ▫ Cn has multiset dimension 3.
In 2020 Bhavale and Waphare introduced the concept of a nullity of a poset as nullity of its cover graph. According to Bhavale and Waphare, if a dismantlable lattice of nullity k contains r reducible elements then 2 ≤ r ≤ 2k. In 2003 Pawar and Waphare counted all non-isomorphic lattices on n elements having nullity one, containing exactly two reducible elements. Recently, Bhavale and Aware counted all non-isomorphic lattices on n elements having nullity two, containing up to three reducible elements. In this paper, we count up to isomorphism the class of all lattices on n elements having nullity two, containing exactly four reducible elements.
In the era of big data, classical computing techniques face challenges in handling large and complex datasets. Quantum computing offers a transformative solution, especially in terms of real-time data processing speed. This study compares the performance of quantum and classical algorithms for large-scale data tasks. Results show that quantum algorithms achieve up to 70% faster processing and 30% greater computational efficiency, with scalability and an accuracy rate of 95% outperforming classical methods. Despite current limitations such as decoherence and error rates, ongoing advancements in quantum hardware and error correction highlight the potential of quantum computing to revolutionize data processing.
In this paper we introduce a natural mathematical structure derived from Samuel Beckett’s play “Quad”. We call this structure a binary Beckett-Gray code. We enumerate all codes for \(n \leq 6\) and give examples for \(n=7,8\). Beckett-Gray codes can be realized as successive states of a queue data structure. We show that the binary reflected Gray code can be realized as successive states of two stack data structures.
Graph invariants, often regarded as topological indices, play a pivotal role in understanding and quantifying the structural properties of graphs. Among these, the line completion number has emerged as a significant measure of a graph’s edge connectivity and topology. In 1992, Bagga et al. defined a generalization of line graphs, namely super line graphs, and introduced the concept of the line completion number as a topological index of a graph. They calculated the line completion number for several classes of graphs, showcasing its utility in understanding graph structure. The line completion number of a graph, is the smallest index such that the super line graph becomes a complete graph. This index encapsulates the interplay between edge relationships and structural complexity, making it a versatile tool for characterizing graphs. Building upon this foundation, we analogously introduce the concepts of super point graphs and the point completion number, as vertex-centric topological indices. We establish a relationship between the point completion number and the line completion number, further extending the framework of graph invariants. Additionally, we compute the point completion numbers for various graph classes and analyze their structural implications. Our findings emphasize the significance of completion numbers as robust descriptors for graph topology, with potential applications in network analysis, chemistry, and other domains.
In IoT-managed power systems, equipment or communication failures can result in missing or abnormal power quality data, making data restoration increasingly important. Traditional repair methods often struggle to capture complex data relationships and suffer from low accuracy. This paper proposes a power quality data restoration approach based on a low-rank matrix completion algorithm to enhance repair accuracy and efficiency. The system consists of three main steps: data preprocessing, matrix completion, and result validation. Z-score normalization is applied to raw data, and Singular Value Decomposition (SVD) is used for low-rank approximation in matrix filling. Cross-validation and error metrics are employed to assess performance. Experimental results show that at a 10% missing rate, the mean square error is approximately 0.1. The proposed method demonstrates superior performance over traditional approaches, particularly at low missing rates, offering reliable support for monitoring and control in power IoT systems.
The current changes in China’s population structure and dynamics have led to profound challenges in population planning, forecasting, decision-making, and early warning. To address the issues of predicting age- and gender-specific population retention, migration, and birth rates, a combination model of Multilayer Perceptron (MLP) and Random Forest (RF) is constructed using stacking techniques, with a discrete population development equation as the base model. The MLP-RF model is employed to perform regression training on population data, resulting in a novel ensemble approach to population forecasting. The study uses the data from the sixth and seventh national censuses of Hebei Province, reconstructing population data for 2010-2020. After data training and error evaluation, it is demonstrated that the ensemble forecasting model has excellent predictive capabilities for population retention, migration, and birth-related issues.
In the current energy-constrained era, promoting electric vehicles (EVs) is a necessary trend. However, the simultaneous and uncoordinated charging of diverse EVs can negatively impact the power grid. This paper proposes a scaled EV orderly scheduling model, comprising charging demand simulation and a scheduling algorithm. Monte Carlo simulation, based on charging probability models, is used to generate EV cluster entry information and preprocess parameters. Two control strategies are proposed for clean energy dispatch and EV-based grid operation, accounting for user behavior-induced load variations. A microgrid optimization model is developed, with economic cost weights calculated. The model is solved using an improved PSO algorithm (APSO). Results show the APSO achieves better performance, with hourly average exchange loads of 2.7092 P/kW (vs. 1.9979 P/kW for PSO). Under 30–80% user responsiveness, microgrid management and environmental costs are reduced to 28,618.439 yuan and 7,864.685 yuan, respectively.
This paper investigates human-computer communication within the framework of deep learning and identifies three key features of such interaction. A cross-cultural empathy feature aliasing model based on Graph Neural Network-Attention Mechanism-Bi-directional Gating Unit (GCN-Attention-BiGRU) is proposed, with categorical cross-entropy and L2 regularization as the loss function. By integrating IoT and deep learning, an adaptive interaction model is developed and evaluated through experiments. Results show high mean scores for empathy (4.537), relevance (4.447), and fluency (4.499) across 60 samples, indicating effective empathy feature extraction. Additionally, the proposed model demonstrates greater efficiency and adaptability compared to traditional interaction models, enhancing cross-cultural empathy in human-computer communication.
In a society governed by the rule of law, constitutional interpretation forms the foundation of judicial practice. This paper focuses on the role of constitutional hermeneutics in shaping judicial practice in China. Using data from 2010 to 2020, an evaluation index system and fuzzy comprehensive evaluation method are employed to assess the development quality of China’s judicial practice. A multi-period DID regression model further examines the impact of constitutional hermeneutics. Results show that development scores ranged from 86.04 to 92.22, reflecting steady improvement in fairness, efficiency, and effectiveness. Constitutional hermeneutics significantly enhanced judicial practice (P < 0.01), with the positive effects of value supplementation and loophole filling confirmed through robustness tests.
Given two graphs \( G_1 \) and \( G_2 \), the size Ramsey number \( \hat{r}(G_1, G_2) \) refers to the smallest number of edges in a graph \( G \) such that for any red-blue edge-coloring of \( G \), either a red subgraph \( G_1 \) or a blue subgraph \( G_2 \) is present in \( G \). If we further restrict the host graph \( G \) to be connected, we obtain the connected size Ramsey number, denoted as \( \hat{r}_c(G_1, G_2) \). Erd\H{o}s and Faudree (1984) proved that \( \hat{r}(nK_2, K_{1,m}) = mn \) for all positive integers \( m, n \). In this paper, we concentrate on the connected analog of this result. Rahadjeng, Baskoro, and Assiyatun (2016) provided the exact values of \( \hat{r}_c(nK_2, K_{1,m}) \) for \( n = 2, 3 \). We establish a more general result: for all positive integers \( m \) and \( n \) with \( m \ge \frac{n^2 + 2pn + n – 3}{2} \), we have \( \hat{r}_c(nK_{1,p}, K_{1,m}) = n(m + p) – 1 \). As a corollary, \( \hat{r}_c(nK_2, K_{1,m}) = nm + n – 1 \) for \( m \ge \frac{n^2 + 3n – 3}{2} \). We also propose a conjecture for the interested reader.
A subset \(S \subset V(G)\) is called a captive dominating set of a graph \(G\) if \(S\) is a total dominating set and every vertex \(v \in S \) is adjacent to at least one vertex which is not in \(S\). Furthermore, a captive dominating set \(S\) is termed a minimal captive dominating set if no proper subset \( S’ \subset S \) qualifies as a captive dominating set. The minimum size of such captive dominating set in \(G\) is referred to as the captive domination number of \(G\), denoted by \( \gamma_{ca}(G)\). This paper investigates the relationship between the captive domination number and the order of a graph. We establish bounds on the captive domination number and present results for specific graph families obtained through various graph operations.
Let\(G\) be an undirected graph. A tree partition of\(G\) is a set of trees whose edge sets are disjoint and whose union is the edge set of\(G\). The minimum cardinality of such a tree partition is called the tree partition number of\(G\). We show that for various types of trees allowed in the tree partition, that the only linear operators that preserve the tree partition number are vertex permutations.
The Mostar index \( \text{MoI} \) of a finite and connected graph \( G \) is a measure of asymmetry, focusing on the edge-based structure of the graph. For an edge \( xy \) in \( G \), let \( \gamma_{xy} \) and \( \gamma_{yx} \) denote the cardinalities of the sets of vertices closer to \( x \) and \( y \) respectively, then the Mostar index is defined as: \( \text{MoI}(G) = \sum_{xy \in E(G)} |\gamma_{xy} – \gamma_{yx}| \) where the summation is taken over all edges \( xy \in G \). This edge-wise difference reflects how asymmetrically the graph is structured around each edge and summing these differences across all edges yields the Mostar index for the graph. In this article, we compute the \( \text{MoI} \) for certain classes of bicyclic graphs that are of particular interest due to their moderately complex structure, lying between acyclic and polycyclic graphs. We classify bicyclic graphs into three distinct types, namely \( \mathcal{B}^{1}(m,n),\; \mathcal{B}^{2}(l,m,n) \) and \( \mathcal{B}^{1}(l,m) \), based on their cycle arrangements and then provide explicit formulas for calculating the exact value of the Mostar index.
Studying the influencing factors of logical reasoning ability can not only help teachers to find out the effective way to cultivate students’ logical reasoning ability, but also provide methodological and theoretical references for the relevant research in the area of artificial intelligence-driven program design education, which is of certain research value. The article firstly introduces the theory of structural equation modeling and the principle of algorithm used in model analysis. Then, taking the students of School S and School T as an example, we designed and distributed relevant collection questionnaires, and analyzed the data using SPSS to understand the overall status of students’ logical reasoning ability and the level of each dimension. Then we make reasonable assumptions about the factors affecting students’ logical reasoning ability, establish a structural equation model of the factors affecting logical reasoning ability, and analyze the effects and paths between the factors and on the logical reasoning ability. Finally, according to the experimental results, we propose targeted teaching reform methods. The results of the study show that: teacher’s activities, learning interest, learning attitude, classroom environment have a positive effect on students’ logical reasoning ability, in which the effect of classroom environment on logical reasoning ability is 0.48. Enhancing the teacher’s power and promoting the diversified development of students is an effective way to improve logical reasoning ability.
Personalized service is a targeted initiative for digital resource libraries to improve the quality of service and better play the function of culture and education. This paper proposes a digital book personalized recommendation algorithm based on artificial intelligence technology. After acquiring the borrowing data and pre-processing, the reader’s portrait is visualized with factor analysis and cluster analysis methods respectively. The traditional Slope one algorithm is weighted and the collaborative filtering algorithm is improved. Combine the user profile with collaborative filtering to realize the personalized recommendation of digital books. User similarity calculates four types of readers such as pragmatic, youthful, recreational and curious. This paper’s algorithm outperforms CFRA and RABC algorithms under each parameter, with the highest recommendation accuracy and novelty, and realizes personalized library services.
There is an increasing demand for assisted training techniques in the sport of sparring. In this paper, a sparring multiple recognition and analysis system is designed and fabricated for the movements of sparring sports and used to recognize and analyze the players’ technical movements using the collected data and the model built using deep neural networks. The CNN-LSTM network is applied to extract the feature classification of the preprocessed sparring inertia data, and then the DTW algorithm is combined with the spatial distance classification method to realize the matching and recognition of sparring behaviors by stretching and compressing transformations of the time axis, effectively eliminating the distortion error in the time domain and obtaining the similar path with the shortest cumulative distance of the effective matches between different sequences. Experiments on the application of this paper’s system were conducted in two groups of sparring players, and after 12 weeks of training intervention, the average confrontation striking speed of the experimental group progressed from 0.36 seconds before the experiment to 0.32 seconds after the experiment, and the average performance of the control group progressed from 0.38 seconds before the experiment to 0.36 seconds after the experiment, which indicates that although the traditional resistance training also has a positive impact on the training effect of sparring training, the training effect of this paper’s system is more obvious The systematic training effect of this paper is more obvious. This paper makes an innovative exploration for the combination of sports programs such as sparring and cutting-edge information technology.
The continuous development of neural network makes the automated style migration technology also rise to a new height. This paper selects digital media art as the research field, constructs Cycle GAN, a cyclic consistent generative adversarial network structure applied to digital media art, on the basic framework of GAN, and optimizes it by adding bilinear interpolation and attention mechanism, so as to build up a style migration model for digital media art. In the style migration simulation experiment, the IS test values of this paper’s model on the photo2vangogh and photo2monet datasets are 5.32 and 6.03, and the FID test values are 97.52 and 75.55, which are better than the other comparative models. Similarly, the optimized performance of FID, SSIM and PSNR values on the dataset is also better than other comparative models, and the style migration performance of the model is verified. Using the model of this paper to design a digital topography with Chinese traditional ink painting as the content, we explore the correlation between the design attributes of the style migration design works in digital media art and the audience’s cognitive evaluation and overall perception. Among the design attributes, “plot relevance” (4.375) and “atmosphere rendering” (3.38) have the highest T-value, which is the most important influence on audience perception.
The implementation of tax incentives is a powerful measure to reduce the burden of enterprises, build a new development pattern, and expand reform and opening-up. Some enterprises in nine provinces from 2010 to 2023 are sampled to verify the role of tax incentives in reducing the tax burden by using the double difference model. The weight coefficients are introduced as learning factors for the population center of mass, and the SWC-PSO algorithm is proposed to improve the shortcomings of PSO, which has low convergence accuracy and is prone to fall into local extremes, and to realize the mathematical planning for minimizing the tax burden of enterprises. After controlling the variables of tax policy and enterprise nature, the regression coefficient reflecting the enterprise tax burden is significantly negative at 1% level, and the tax burden of enterprises receiving tax incentives is significantly reduced, which proves the role of tax incentives in reducing the enterprise tax burden. After using SWC-PSO for planning, the sample units have a total of 1,779,919,000 yuan of tax relief, and the business tax rate of a construction project decreases from 3.35% to 0.42%, which indicates that the improved algorithm in this paper can plan the strategy of minimizing the tax burden of enterprises more efficiently.
With the promulgation of relevant policies, virtual power plant market transactions are facing major adjustments, in order to promote the smooth entry of virtual power plants into market-oriented transactions and improve the economic benefits of virtual power plants, this paper proposes a virtual power plant market transaction model. The traditional virtual power plant resources are mathematically modeled, blockchain technology is introduced to build a decentralized trading framework, and fuzzy neural networks are combined to predict the power load of the virtual power plant. Then the decision-making model of virtual power plant participation in spot market trading is constructed by using two-stage stochastic planning theory with the goal of maximizing expected return. The results show that the prediction effect of the fuzzy logic-based virtual power plant market trading model is 2.925% higher than that of the traditional BP algorithm model, and its accuracy and stability are significantly improved. In addition, the distributed energy storage aggregated by the virtual power plant as well as the dynamic demand response rate is fast, the regulation is flexible, the short-time power throughput capability is strong, and it can accurately track the FM instructions. The cumulative FM capacity and FM mileage provided by the virtual power plant account for 84% and 99% of the total FM capacity demand in the system, respectively, making it highly competitive in the FM market. And under the premise of balancing riskiness and profitability, the bidding scheme of virtual power plant derived in this paper is more effective.
The power optimization of wind farms and the optimal control of wind turbines require high-precision power ultra-short-term prediction for each wind turbine. In order to improve the performance of ultra-short-term prediction of wind power, this paper couples the LSTM model with the Logistic model and combines it with Graph Convolutional Neural Network (GCN) to construct the ultra-short-term prediction model of wind power based on Logistic-LSTM-GCN, and test and analyze the prediction performance of the model. Comparing the LASSO, XGboost, LSTM, GRU and TCN-LSTM models, the MAE and RMSE of this paper’s model are the lowest among all the models, which are 3.34% and 5.89%, respectively, and the R² is the highest, which is 79.76%. And the MAE and RMSE predicted by the model with inputs of four-dimensional spatio-temporal feature matrix are smaller than the model with inputs of one and two dimensions, and the R² value is larger than that of one and two-dimensional model. It indicates that the Logistic-LSTM-GCN model based on spatio-temporal information can extract the spatio-temporal information of wind farms more effectively, which improves the accuracy of wind cluster power prediction. In addition, with the increasing time step, the error indicators MAE, MAPE and RMSE are gradually increasing. Taking a time step of 4s for prediction, the prediction error of the model is minimized when considering multivariate variables such as wind speed, wind speed decomposition component, yaw error, wind direction, and rotor speed. This indicates that the multivariate LSTM, logistic and GCN coupled model can significantly improve the performance of ultrashort-term prediction of wind power.
Some athletes’ lack of basic knowledge of exercise mechanism, mode, method, process and intensity has led to frequent occurrence of athletic risk events such as injury, disease and even sudden death, which seriously affects the physical and mental health of athletes and even threatens their lives. In this study, the data of athletes’ injury and disease risk characteristics were collected, and the feature selection method of Least Absolute Value Convergence and Selection Operator (LASSO) combined with Boruta’s algorithm was used to preprocess the data in order to eliminate redundant features. In terms of model construction, the prediction results of support vector machine, logistic regression, random forest algorithm and deep forest algorithm were integrated by using Stacking algorithm to construct the prediction model of athletes’ injury risk. After the predictive performance of the model is examined, it is used as an intervention for injury rehabilitation to carry out comparative experiments. The results show that the fusion model can effectively extract the feature importance of injury risk factors and predict the risk probability, and the prediction effect is better than that of a single model. Meanwhile, the intervention results show that the model has excellent effects on injury rehabilitation. This study can accurately predict injuries and illnesses, prevent the occurrence of injury and illness risk events in athletes, ensure the successful realization of sports goals, and play a role in assisting injury and illness rehabilitation.
The field of artificial intelligence provides a new practical path for the inheritance and protection of non-heritage art. This paper proposes an innovative morphological design method for rattan weaving art based on fractal theory, and the grasshopper plug-in is selected to establish a parametric design model. The fractal graphics generated by the iterative function system are used as the input graphics, and the GrabCut algorithm and VGG16 neural network are combined to propose a graphic rendering method based on style migration containing elements of the cultural symbols of the Maonan Flower Bamboo Hat, and to realize the inheritance of the cultural symbols of the Maonan Flower Bamboo Hat. In the high preference survey, the A1 and A4 features of the sun hat in the questionnaire results are consistent with the preference results derived from the fractal design, and the questionnaire results of the handbag and handkerchief are also consistent with the preference results derived from the fractal design. It shows that the product form design method of Maonan flower bamboo hat cultural symbols based on fractal theory and style migration can play a certain role in promoting cultural inheritance.
With the continuous development of deep learning technology and the increasing maturity of rural tourism market, this paper obtains tourism user-generated content data through customized crawler technology, describes the data flow diagram of single-user crawling and the data flow diagram of database batch crawling module. A sentiment index covering multiple dimensions is constructed to mine the deep-seated features of tourist behavior. Fusing effective features in tourism data by using multiple topological maps, using graph convolution network to capture multiple spatial features of scenic spots and recurrent neural network to capture temporal features of traffic, to complete the analysis and prediction of tourists’ behavior. Taking Jiangxi Wuyuan Huangling rural attraction market as an example for empirical analysis, the importance of historical flow and search volume under all time windows is as high as 111 and 117 respectively, proving that these two features have a significant impact on predicting the target variables. The model in this paper is highly fitted to the predicted value of actual passenger flow at 12 time points, especially in the 9th month, the predicted value is 402, which is 401 from the actual value, which is an important reference value for rural tourism management and marketing strategy.
Goal-oriented dimension is a new angle to solve the problem of universities’ performance assessment. Firstly, designs an input-output index system, and calculates the Malmquist Index of the performance utilizes the panel data. Then, the non-parametric KDE graph is used in this research for further discussion of the differences of TFP changes. Meanwhile, a non-parametric KDE analysis is carried out respectively for TECHCH, EFFCH, PTEC, and SECH indexes. The Malmquist-KDE index model shows the results as follows: TFP is on a declining curve; the increased range of EFFECH is relatively smaller, while the annual growth of PECH and SECH are slow; the decrease of TFP is caused by the decrease of TECHCH; the general distribution gradually moves leftward, reflecting a fact that the TFP changes are decreasing progressively; the TFP change rate demonstrates obvious a skewed distribution; the patterns in the graph gradually shift from thin and tall ones into short and thick ones. Conversely, the changes of external factors force universities to improve their operations actively.
The purpose of this study is to solve the energy efficiency problem of small agricultural base stations, propose an optimal design scheme based on RF power amplification, and verify its effectiveness through simulation experiments. In order to achieve the research purpose, this paper first defines the objectives and principles of energy efficiency optimization design, and puts forward the energy efficiency optimization technology based on RF power amplification. On this basis, a complete set of energy efficiency optimization design scheme for small agricultural base stations is designed. And by building a simulation platform, set the parameters close to reality, and simulate the operation state of the base station in different scenarios. The simulation results show that the stability of the algorithm in this paper is considerable under different loads. Even if the load is large, the stability of this method can reach above 89%. The proposed energy efficiency optimization scheme can significantly reduce the energy consumption of the base station and improve the overall energy efficiency performance under different load and interference conditions. This result proves the effectiveness and superiority of the scheme and provides strong support for practical application.
For building construction enterprises, civil engineering project schedule assurance is the embodiment of project performance ability, and project cost control is the root of project profitability. This paper researches the cost-schedule control method based on BIM and critical path earned value method, and establishes a complete set of dynamic cost-schedule analysis and control method including plan preparation, process evaluation and result correction. This paper takes Project F as an example, integrates project management in the BIM platform and optimizes the plan through construction simulation, so that the construction plan is closer to the actual demand, establishes the Earned Value Method for distinguishing the critical path and embeds it into the BIM platform, reflects the progress with the Earned Value parameters of the critical path, reflects the cost with the Earned Value parameters of the whole project, analyzes the problems of the critical path and the project and proposes cost-schedule corrective measures in a targeted way. The critical path and project problems are analyzed, and cost-schedule corrective measures are proposed, so as to realize the fine management of project cost-schedule. Through the case study, it is proved that based on BIM critical path earned value method can achieve schedule and cost coordination and dynamic control and realize 91.8% cost reduction, good civil engineering project management efficiency and change the status quo of civil engineering project cost management.
Information security is the most concerned issue in modern communication, with the continuous development of new computing technologies, classical cryptography has been difficult to effectively guarantee information security, quantum key distribution technology through the theory of quantum mechanics to ensure the absolute security of key distribution. Therefore face recognition system oriented optimization using quantum key distribution, this paper is based on the advantages of OQKD technology such as easy to implement, low overhead, high security, optimization for commercial privacy queries in the system. On the basis of the quantum key distribution regional network of trust relay, a new type of quantum key distribution experimental network structure based on switching nodes which is more flexible, energy-saving and efficient is proposed. Finally, the method of this paper is comprehensively verified through modeling simulation, and the simulation results show that the average call loss is 3.67% when the quantum key generation rate is increased to 20Kbps, which is significantly reduced. Moreover, the network call loss can be reduced to less than 11% when the method of this paper is adopted in the same situation, and the network call loss is even smaller. It shows that the call loss of the network will be greatly reduced when the key generation rate is increased with a fixed amount of voice traffic.
Machine learning provides new perspectives and methods for company M&A valuation due to its powerful data processing and prediction capabilities. This paper analyzes the prediction steps based on the decision tree algorithm, i.e., decision tree generation, attribute selection, decision tree construction, and accuracy metrics, and obtains the relevant data of AB after merger and acquisition through data mining. The model and SHAP framework are utilized to predict the financial risk, financial performance, and enterprise value of the two post-merger companies. The precision, recall, and F1 scores of this paper’s model range from 91.25 to 93.81, which has a good performance of company M&A valuation. This paper’s model predicts that in 2024, the key indicator of AB’s financial crisis is Gross margin, which has an importance of 0.297, and the possibility of AB’s financial crisis increases when the value of Gross margin is between -0.0279 and -0.0014. The accuracy of the financial performance prediction of this paper’s model is more than 0.97, which can accurately value the company’s performance. The model in this paper predicts the enterprise value of AB in 2024 to be 52.14yuan/share, respectively.
Virtual teaching and research community is an effective way for teachers to realize communication and cooperation among themselves, to improve their professional level and to promote their career development. Under the framework of teaching and research community community construction, the relevant factors that resound the development of virtual teaching and research community construction were extracted by questionnaire survey method, CRITIC-assigned to them, and the factors with larger weights were taken as the key factors, and the multiple linear regression method was utilized to explore their influence on teachers’ professional development. The analysis found that the key factors with larger weights are teaching and research team building (0.3234) and teaching and research motivation (0.2683), and the regression coefficients of both of them in the regression results of teachers’ professional knowledge and professional skills are 0.18, 0.158, and 0.089, 0.059, respectively, and the significance of all of them is less than 0.05. Therefore, the teaching and research team building and teaching and research motivation are not only crucial to virtual teaching and research community operation, but also have a positive effect on teachers’ professional development.
In order to explore and promote the strategy of students’ active health behaviors, this paper designs a personalized scientific guidance system architecture for active health promotion based on a three-tier service architecture model, using students’ sports literacy big data processing technology to construct a sports mobilization effect information system. Second, a sports prescription generation model is designed. The model adopts a multifactor fusion approach to recommend personalized exercise programs based on the different exercise abilities, different physical conditions, and personal exercise preferences of the exercisers. Under the condition of satisfying multiple constraints such as the physical condition, parameter range and exercise ability of the exerciser, the particle swarm optimization algorithm is used to optimize the exercise parameters, and the topological structure is further used to adjust the broadness of the distribution of the solution set in the objective space. The improved particle swarm optimization algorithm is compared, and the experimental results show that the improved TS-PSO algorithm converges faster, the solution accuracy is higher, and the parameter optimization using this algorithm generates a personalized exercise prescription that is more suitable for the exerciser. The exercise prescription generation model studied in this paper provides a new idea for the improvement of the effect of sports mobilization under the perspective of active health.
Due to its heavy reliance on imports, the futures and spot markets of China’s upstream and downstream soybean products are vulnerable to the impact of the international market. In order to guarantee the security of the soybean industry, China introduced corresponding agricultural subsidy policies in 2008, 2014 and 2018, respectively. In order to test the impact of the subsidy policy on the development of the soybean industry, this paper utilizes an empirical mathematical planning model to evaluate the implementation effect of the subsidy policy for soybean producers ex ante, and explores the defects of the agricultural subsidy policy by simulating the production decisions of farmers. It also measured the efficiency of soybean subsidy, the efficiency of agricultural machinery purchase subsidy and the efficiency of agricultural insurance premium subsidy using a three-stage DEA model. In the empirical research part, the constructed numerical method of soybean producer subsidy policy unfolds the effect assessment. The empirical results show that the implementation of the soybean producer subsidy policy increases the proportion of soybean planting and soybean total factor productivity by 9.47% and 17.43%, respectively, and that the soybean producer subsidy policy has a facilitating effect on the expansion of soybean planting and total factor productivity. Accordingly, five policy recommendations are put forward with a view to promoting the healthy development of the soybean industry.
When a manufacturing enterprise adopts lean manufacturing system for multi-species production and processing of products, the workshop production scheduling problem (i.e., production scheduling) is a major factor affecting the production efficiency of products. Aiming at the shortcomings of the standard simulated annealing algorithm, which is easy to fall into the local optimum due to the influence of stochastic factors, this paper designs an improved simulated annealing algorithm with tempering and slow-cooling functions, and an event-driven priority coefficient search for solving the dynamic scheduling optimization model of the production line. At the same time for specific cases of simulation and parameter testing of the algorithm, and respectively with manual scheduling results, the performance of the basic algorithm before the improvement of experimental comparison and analysis, to find the optimization effect of the improved optimization scheduling algorithm. Compared with the manual scheduling method, this paper’s method significantly optimizes the two objectives of total weighted delay time and production energy consumption. Compared with the basic SA algorithm, the accuracy of chromosome encoding of this paper’s method is improved by 233.33% and the computing workload is reduced by 79.51%, which verifies the feasibility and efficiency of this algorithm’s optimization scheme.
In the operation of storage system, improper scheduling of shuttle and hoist will waste resources and affect the picking efficiency, so it is of great significance to optimize the operation scheduling of storage system. Based on queuing theory, this paper constructs a queuing model of ring RGV system and proposes queuing model assumptions of hoist system to analyze the reasonableness of storage layout. The operation activity scheduling mechanism is designed to execute the warehousing activities strictly in accordance with the established operation order. Agree on the ring track RGV operation rules, calculate the distance between any two points on the track, and ensure the shortest distance of the warehousing operation. Merge the shortest operation path and the shuttle car operation equilibrium rules to construct a dynamic scheduling decision model. Through the storage resources in and out of storage management and scheduling module, improve the measuring equipment intelligent storage system, apply the system to the actual storage operations, analyze the operational efficiency. After the implementation of the strategy proposed in this paper, the optimal scheduling result is 36min, the execution time of different types of work is different, and the operation time of equipment J1-J4 is 15min, 23min, 17min, 34min respectively. The pickup execution efficiency of the strategy used in this paper is improved by 66.38%, and the pickup efficiency is improved by 10% when the number of equipment is less than 300 pieces. The scheduling strategy proposed in this paper has a higher priority when facing a small number of devices.
The proposal of “Belt and Road” has helped these countries along the route to stimulate the development vitality and cooperation potential of their respective domains, which fits well with their common needs and opens a new window of opportunity for their complementary advantages and open development. This paper improves the construction of the new regional cooperation mechanism of the Belt and Road from three aspects: power mechanism, coordination mechanism, benefit distribution mechanism and compensation mechanism. The double difference method is utilized to assess the economic benefits generated under the Belt and Road regional economic cooperation mechanism. The assessment results show that the country with the highest import and export trade dependence of China is New Zealand, which reaches 18.5611, and as the dominant country of the Belt and Road, China’s two-way investment in other countries has the highest scale of $124,705.9 million, but the index of investment closeness is -1, which indicates that the capital flow between the two sides is mainly a unidirectional investment from China to other countries.
This paper combines the demand structure mechanism and the current account mechanism to show that an increase in the share of the elderly population affects the appreciation of the real exchange rate. Based on the formula of the internal real exchange rate and the “Balassa-Samuelson effect”, the transmission mechanism of population aging on the real exchange rate is established. Combining the results of the discussion on the savings rate and investment rate, an analytical model of the impact of population aging on the balance of trade is proposed. Panel models are built with sample data from full sample countries, super-aging countries, deeply aging countries, mildly aging countries and nonaging countries respectively, and heterogeneity analysis is carried out for each type to test the multilevel regression results of population aging on the real exchange rate. The control variables are brought in separately for the benchmark regression of population aging on the size of trade surplus and current account balance. The empirical results of the data regression show that the old age dependency ratio is an important influence on the real effective exchange rate. And if the country is in the stage of super-aging and mild aging, aging leads to the depreciation of the real effective exchange rate. In the stage of deeply aging and non-aging countries, aging leads to real effective exchange rate appreciation. Meanwhile population aging positively affects the trade surplus at the provincial level mainly by affecting the level of savings.
The dance teaching method that effectively combines motion capture and posture estimation can effectively differentiate itself from the traditional dance teaching mode, realize the teaching interaction between the 3D virtual world and the real technology, and help to improve the quality of dance movement teaching. In this paper, HRNet network is selected under the framework of human posture estimation for the extraction of key points of human posture, and SPIN algorithm is composed of two parts, namely, regression network and SMPLify, to complete the three-dimensional expansion of human posture information. Design the flow of the dance visual movement tracking decomposition method, and perform feature point labeling and labeling parameter setting for multiple key points and key movement parts in the dance movement. Select the specific parts of the dancer as the motion tracking points, record and record the dancer’s action images, and track and fit the dance action trajectory using the 3D visual motion tracking decomposition method proposed in this paper. Compare the dance trajectory fitting effect of this paper’s method with that of the degree-of-freedom vector method and the tracking differentiator method, and obtain the performance of the three-dimensional visual motion tracking decomposition method. Analyze the students’ physical flexibility, balance ability, and the completion of complex movements after a two-month dance teaching. After the dance teaching utilizing dance movement posture analysis, the students’ body flexibility (shoulder) and balance ability improved by 12.8cm, 18.74s (left), and 22.2s (right), respectively.
The article applies recurrent neural networks to multi-intelligent body collaborative autonomous systems and uses optimized RNN algorithms for multi-objective detection and path planning of intelligent bodies. The multi-intelligent body multi-target detection and path planning model optimized based on recurrent neural network is constructed to realize multi-target detection and tracking of intelligent bodies and multi-intelligent collaborative path planning. Simulation experiments are designed with a mobile robot as the research object to analyze the trajectory tracking and path planning effects of the multi-target detection and path planning model in this paper. The error between the actual trajectory and the reference position of the robot trajectory tracking is continuously reduced, and reaches complete coincidence at the 127th reference tracking point. The actual speed and acceleration errors of the robot are infinitely close to 0. The accuracy of this paper’s algorithm in multi-objective path planning is 100%, the average arrival time is 20.02s, and the probability of collision is 0%, which is much better than other algorithms. The algorithm in this paper has the highest path smoothing validity for planning in three environments. In the 30 × 83 warehouse map, the total path length of this paper’s algorithm is shortened by 13.00% and 10.77%, and the total path cost is shortened by 9.71% and 11.52% compared with the Wd-SIPP algorithm for the number of collaborative robots in a single group of three and five, respectively. In 100*100 storage map, the total path length is shortened by 10.32% and 11.67%, and the total path cost is shortened by 7.34% and 12.09%, respectively.
The article preliminarily studies the structure of flexible interconnection system of MV distribution network, and understands the application scenario and equipment composition of the flexible system. For the purpose of reducing SOP loss, transformer loss and line loss, the operation of the MV flexible interconnected distribution network is optimized, the operation optimization model of the flexible interconnected distribution network is constructed, and the fault enumeration method is adopted as the reliability assessment method of the flexible interconnected system. Through experimental simulation, the stability, reliability and dynamic characteristics of the MV flexible interconnection system are explored respectively, and the system protection control strategy is proposed. For the same constant power load step, the larger the voltage loop proportional parameter is, the more stable the system tends to be, and the larger the voltage loop integral parameter and the station circuit parameter are, the more unstable the system tends to be. The maximum mutation value of the system constant power load gradually decreases when the station load power gradually increases. The reliability of the MV flexible interconnection system increases with the increase of SOP capacity. In the medium voltage flexible distribution interconnection system. The system damping, oscillation frequency and overshoot are significantly reduced and the peak time is increased when the DC voltage sag factor is increased.
Based on the complexity and nonlinear characteristics of market volatility, this paper proposes a market volatility prediction model that combines MA filtering method, autoregressive moving average (ARMA), and long-short-term memory (LSTM) neural network. And the back-propagation (BP) neural network is utilized to quantitatively solve the problem of corporate strategy formulation, and a corporate strategy formation model is established to determine the corporate strategic choice through the corporate strategic environment and strategic capabilities. In the ablation experiment, the combined model MA-ARMA-LSTM reduces its MSE, RMSE, MAE and MAPE by 0.0007, 0.0131, 0.0074 and 1.57%, respectively, compared to the ARMA model. Compared with common market volatility prediction models, the combined model has the smallest error in each assessment index. The output of BP neural network for corporate strategy selection is consistent with the expert ranking, which is verified to be in line with the actual business situation, indicating that the method in this paper can provide a reasonable corporate strategy.
This paper constructs a multi-agent simulation model to study and prevent juvenile delinquency. A multi-agent reinforcement learning model is constructed according to reinforcement learning theory to simulate the behavioral decision-making process of minors in different social environments. By introducing the NashQ algorithm, it simulates the minors’ strategic choices when facing the temptation of crime. In the simulation experiments, the NashQ algorithm meets the convergence requirements of the model, and only 1/3 of the training times are needed to achieve the stability of the simulated environment. Among them, family factors, school factors and social factors all affect the stability of the prevention effect. Good family environment, high quality teaching conditions and healthy social atmosphere can effectively prevent juvenile delinquency.
From World War II to the Cold War (1945-1991), the U.S. military-industrial complex went through a process from its rise to its full expansion, which had a profound impact on the global political and economic landscape. In this paper, computer simulation techniques are used to construct a vector autoregressive model (VAR) to quantitatively analyze the impact of the military-industrial complex on the U.S. economy. Smoothness and cointegration treatment and Granger causality test are done on the collected sample data. After that, the VAR model between three sets of variables, namely, military expenditure as a share of GDP, consumption as a share of GDP, and investment as a share of GDP, is designed. Using impulse response function and variance decomposition to analyze the data, we get that the rise of the U.S. military-industrial complex can effectively promote the growth of the economy in the long term, and the development of the economy can also promote the development of the military-industrial complex, but the promotion effect is not obvious.
The existence of a large number of multi-source heterogeneous hosts and application service types in various zones of the power monitoring system leads to difficulties in extracting comprehensive host attack trace data and the problem of fine-grained deep threat detection. This study combines network attack traces extracted from multi-source logs and stores them in attack trace styles. An attack event description model based on key attributes and behavior sequences is constructed. Based on the vulnerability scoring system, an algorithm is designed to map a general attack graph into an absorbing Markov chain attack graph, which provides a computational basis for the analysis of network attacks by calculating the state transfer probability matrix of the attack graph. Finally, the performance of this paper’s method for multi-dimensional data feature extraction is explored in a python experimental simulation environment. The simulation results show that the average mapping time of LSTM model for 7 vulnerabilities is 117ms, while the average mapping time of this paper’s algorithm is improved by 37ms compared to the LSTM model.Meanwhile, the accuracy, stability, average false detection rate and positive and negative recall rate also achieve good results, which verifies the validity of this method in the practice of power monitoring system management.
The specialty of soil and water conservation and desertification control has gradually become a hot and popular discipline, and the educational practitioners of this specialty must also follow the trend and actively carry out educational reform. This paper utilizes genetic algorithm to conduct in-depth research on the problem of class scheduling, and on the basis of traditional genetic algorithm, an improved adaptive genetic algorithm is proposed to be applied to the class scheduling system. Through the adaptive adjustment of genetic parameters to improve the convergence accuracy of the genetic algorithm and accelerate the convergence speed, and finally after chromosome conflict detection and repeated iterative operations, the final optimal scheduling program is obtained. The improved adaptive genetic algorithm is applied in the course scheduling system of soil and water conservation planning and design in colleges and universities. After experimental verification, the improved new adaptive genetic algorithm, under the setting of different rules of scheduling conditions, under the setting of different rules of scheduling conditions, the fulfillment rate of students’ class selection reaches 100%, and the mean value of the overall rule fulfillment rate reaches 94.1%, and the overall fulfillment rate of the scheduling efficiency is improved to 96% by applying it to the intelligent class scheduling system. Finally, the professional classes were tested on the knowledge of soil and water conservation planning and design, and the remaining eight dimensions of professional knowledge were accompanied by questionnaires, and the achievement data of the test were statistically analyzed using SPSS22.0. The analysis results show that the test scores are quasi-normally distributed, and the actual pass rate of each question in the test paper is roughly close to the preset difficulty, which proves that the test paper is of good quality and the algorithm designed by the institute can basically meet the requirements.
In the context of information is mostly trivial, messy and disordered, under the context of information fragmentation, the creation path of new media art is also being affected by it. Based on the color sensual imagery, this paper adopts the gray correlation analysis method to research on the creation of new media art. Through the questionnaire survey, the cluster analysis algorithm is used to filter the color semantics, and the five most representative color imagery semantics are selected as the imagery scale in the quantitative space. Combined with the grey correlation analysis method to construct a new media art creation perceptual evaluation model, the new media art creation works as the object of color design practice, the constructed color design evaluation model well reached the product color scheme with the color screening, confirmation and evaluation of the preferred goals. The design practice based on the evaluation model of new media art creation. The results show that, combined with the gray correlation analysis, the color design evaluation model of new media art creation constructed under the intentional color system can effectively improve the color design efficiency of the work scheme, and give an intuitive and accurate reference standard for the selection of the color scheme of the work.
The development of digital technology has made the use of machine learning algorithms to protect cultural heritage has become a trend. In this paper, based on the random forest algorithm, the conservation model of tomb mural cultural heritage is recognized. The mural paintings in the tomb of Prince Zhanghuai are used as the data source to construct the tomb mural painting dataset, and the images in the dataset are processed, augmented and labeled. The features such as color, texture and shape in the mural images are extracted as one of the input information of the cultural heritage protection model of the tomb murals. Based on the random forest algorithm, a pattern recognition model for the protection of cultural heritage of tomb frescoes is constructed, and the feature vectors obtained from the feature extraction are used to calculate the split points of the decision tree. The classification results of multiple decision trees are weighted and averaged to obtain the final recognition results. The recognition accuracies of this paper’s model on the training set, test set and validation set are 99.45%, 95.46% and 92.58%, respectively. This is a significant improvement over other existing algorithms. Meanwhile, the algorithm consumes significantly less time than the ResNet18 deep residual network model before and after data enhancement, and is able to efficiently accomplish the task of recognizing the protection of cultural heritage of tomb chamber murals.
This paper discusses the application of virtual reality technology in enhancing college students’ selfefficacy and proposes an iterative optimization algorithm based on learning experience. By analyzing self-efficacy, the application of virtual reality technology machines in education, and combining relevant theories and empirical studies, the structural equation model of virtual reality technology influencing college students’ self-efficacy is constructed. The original structural equation model is optimized by using algorithms such as stochastic gradient descent method and stochastic average gradient, and the effectiveness of the algorithms is verified through experiments. This paper concludes that virtual reality technology can significantly improve college students’ self-efficacy, and the proposed iterative optimization algorithm can effectively improve the prediction accuracy and fit of the original structural equation model.
The rapid development of natural language processing technology makes machine translation play an increasingly important role in cross-lingual information exchange. In this paper, we propose an English long text translation paradigm based on the self-attention mechanism and introduce various improvement strategies to enhance the model performance. The model’s ability to process English long text is improved by introducing multi-head attention and hierarchical self-attention modules. The long text translation paradigm is optimized by using techniques such as residual linkage, layer normalization and dynamic memory network. A series of experiments are conducted to verify the effectiveness of the improved model on the English long text translation task. The English long text translation paradigm constructed in this paper outperforms the Transformer model and other related variants on both CPU and GPU. And Transformer outperforms this paper’s model in terms of n-gram accuracy in real translation experiments. The BLEU scores of the improved model on News and other datasets are significantly improved compared with the original baseline model, which verifies the effectiveness of the improvement strategy of this paper and provides a reference for the solution of the problem of English long text translation.
The lateral quantitative control of paper machine is the key to the quality control of paper machine. In this paper, the paper machine transverse quantitative control system is introduced, for the transverse quantitative control and the model exists of strong coupling, large time lag, multi-dimensional number of characteristics, combined with the predictive control theory, put forward based on the dynamic matrix control and Gram polynomials of the intelligent prediction method. Meanwhile, the speed chain control device of the paper machine control system is designed based on the self-immunity controller and simulated and analyzed. The simulation results show that the slope of the model size and computation in this paper’s method is 1.97, which is smaller than that of the traditional MPC’s 2.85, and has more computational efficiency without affecting the predictive control effect, which is more suitable for online operation. At the same time, the speed chain control system applying the self-resistant control algorithm is better than the traditional PID control in terms of steady state performance, dynamic performance and anti-disturbance performance. The method proposed in this paper facilitates the predictive control and speed chain anti-disturbance of the lateral dosing control system of paper machine and promotes the improvement of paper quality.
In recent years, hyperspectral imaging technology has a large application prospect in quality inspection in the tobacco industry. The study is based on near infrared spectroscopy technology and partial least squares regression method to establish mathematical analysis model of tobacco adulteration ratio of four components, such as expanded tobacco, stalked tobacco, large threaded tobacco and small threaded tobacco, and carry out internal and external inspection. At the same time, TLBO algorithm is used in the optimization of ELM tobacco purity grade determination model to realize the design of tobacco purity monitoring method, and then build the real-time monitoring system of tobacco blending ratio and purity. Tobacco with different purity grades were selected for experimental testing and model comparison analysis. The results show that the constructed PLS model can accurately predict the adulteration content of the four components in tobacco, and the correlation coefficients between the predicted and actual values are above 0.95 (p < 0.01), and the relative deviation of the prediction is below 3%.The accuracy of the TLBO-ELM model for identifying tobacco with different grades of purity is 88%, and the classification accuracy in the validation set is improved by 9.32% compared with the ELM model, which is within the acceptable range. It shows a better classification effect than PLS-DA in an acceptable range, which proves that the proposed method can be used for discriminating and monitoring the purity of tobacco. The monitoring system in this paper can be used in the analysis of tobacco blending ratio and purity detection.
The combination of technology and art in film and television special effects can greatly enhance the visual impact of film and television animation, and improve its commercial and artistic value. The study elaborates on the generation of special effects in 3D modeling technology in film and television production, and based on the application of rigid body special effects, it proposes a highly efficient rigid body crushing mode for optimization in response to the problems such as low real-time performance in rigid body crushing simulation. The model is a particle-based real-time simulation method of object crushing under the impact of external forces, using the discrete unit method to represent the inter-particle force, and proposes an inverse crushing mechanism, which realizes the particle-based DEM simulation on the GPU. Experimental results show that the simulation method of rigid body crushing constructed in this paper can meet the simulation requirements in different scene scales, and the rendering rate in small-scale and large-scale scenes is 90~155FPS and 40~50FPS, respectively, which is not only realistic but also real-time, and can meet the requirements of film and television production.
As the world’s No. 1 sport with wide popularity and high degree of attention, there exists a great application demand and development potential for applying artificial intelligence to soccer sports training. In this paper, Yolov5s-CBAM target detection network is utilized to identify the human body posture of target athletes in soccer sports training, and HRNet network is used to detect the location information of key points of target human skeleton and identify the skill movements of soccer players. Subsequently, the TDS-Fast DTW algorithm is applied to evaluate the skill movements to establish a skill recognition and evaluation system for soccer sports athletes. It is verified that the soccer player skill movement recognition model proposed in this paper outperforms other comparative models, with the checking rate reaching 99.12%, and the evaluation scores of the model on the skill movements of the athletes are not different from those of the manual evaluation scores (P>0.05). It is also found that the application of the system in actual soccer training matches can fully meet the needs of soccer training. The system in this paper can accurately assess the technical movements of soccer sports athletes to meet the needs of scientific training, and at the same time, it can meet the needs of coaches to timely grasp the understanding of the level of technical movements of soccer athletes and improve the quality of training.
Taijiquan is a kind of sport that can be used as a national ϐitness program, and its effect on the training effect of adolescent physical coordination has important research value. In this paper, particle swarm optimization algorithm is applied to the optimization of taijiquan training program, and independent samples test and analysis of variance (ANOVA) are used to investigate the quantitative impact of taijiquan training on adolescents’ physical coordination. The results show that the particle swarm optimization algorithm can effectively improve the effect of taijiquan training, and the algorithm convergence and other properties have obvious superiority compared with other algorithms. At the same time, after the experiment, all the physical coordination test indexes of the experimental group were signiϐicantly improved compared with the pre-test and the control group, which explains the important role of taijiquan training in the physical coordination training of adolescents.
Based on the wide application of collaborative filtering algorithm in the current field of graduate employment, this paper introduces it into the employment recommendation mechanism of senior college students and takes it as one of the auxiliary means to formulate the employment policy for senior college students. By studying the implementation effect of employment policy, so as to explore the adaptability of employment policy. Through the time series prediction method based on neural network, the prediction model of employment policy adaptability of higher vocational tertiary students is constructed. Compare the prediction performance of this paper’s prediction model with other models, predict the employment policy implementation effect through this paper’s model, and finally, construct an evaluation system of employment policy prediction results to evaluate the model prediction results. The prediction fit of the model of this paper is 0.8644, and the average relative prediction error is 0.35%, which is the best performance among all prediction models. In the prediction of the employment of higher vocational college students in province A, the number of employment of higher vocational college graduates is positively correlated with the average annual income level and the market share of graduates, and negatively correlated with the total number of gaps between faculty and students in the institutions and the amount of education expenditure. The overall score of the employment policy implementation effect predicted by the prediction model in this paper is 88.8, which is a good evaluation result.
The study constructs a prediction model to predict the mental health status of innovative entrepreneurs. The real data of mental health assessment of innovative entrepreneurs in S province in 2023 is chosen as the data source. The recursive random forest feature elimination method is used to select the features of the mental health status prediction model. The pre-selection-elimination mechanism was used to construct the mental health state prediction model. The prediction models constructed by support vector machine algorithm, decision tree algorithm and random forest algorithm were trained and evaluated respectively. The AUC value and accuracy corresponding to the random forest algorithm are 0.9126 and 86.39%, respectively, which are better than the other two comparison models. Among the 17 mental health characteristic variables selected in this paper, emotional stress and self-acceptance degree have the greatest influence on the prediction model based on the random forest algorithm.
Social media as a new type of media has become an important channel for people to obtain information and communicate, which brings new opportunities and challenges for English education. In this study, the Markov chain model is improved by using the weighted ward system clustering method and the fuzzy set theory to improve the prediction performance of the model. Then the ARIMA model and the improved Markov chain model are combined to construct an improved time series prediction model to realize the prediction of the interaction efficiency of social media in English education. The performance of the improved prediction model is superior compared to other comparative models, providing reliability for the subsequent prediction results. The prediction results show that the interactive efficiency of social media interaction data in English education shows an upward trend over time, and the number of readings and playbacks of English courseware resources as well as video resources increases from 18477 and 18147 to 88629 and 84571 in six months. The predicted results of this study indicate that social media has good interactive efficiency in English education, which can be utilized in the future to expand the dimension of education, build an English education platform, expand the teaching space and extend educational thinking, and play a percolating role in English education.
Since the 21st century, the rapid development of artificial intelligence technology, artificial intelligence in many fields have achieved remarkable research results and applications, the integration of AI technology and music has also gradually become an emerging research field. In this paper, first of all, the generation principle of vocal interpretation AI model is studied, in order to realize the digital conversion of vocal interpretation this paper constructs a converter model so as to facilitate the application of artificial intelligence algorithm model. In this paper, in order to match the generated opera vocal music with the given opera performance background, the rhythmic relationship between opera and vocal interpretation is established, and the relationship between motion salience and note intensity is constructed. On this basis, the generator model is changed to a model with a loop structure, and the music theory is mathematically modeled to propose an adversarial network model based on improved multi-track sequence generation. Finally, for the prediction problem in the vocal interpretation AI model, this paper is optimized based on support vector regression. Through empirical analysis, the improved model in this paper has a smaller gap with the real dataset on the metrics of pitch use, pitch shift, note interval and polyphony rate within the track. Meanwhile, the TD distances of this paper’s improved model on the three datasets are 0.655, 0.784, and 0.685, respectively, which is the smallest in the experimental data, and the quality of the improved model’s vocal music generation is excellent. The pitch distribution of this paper’s improved model and the original vocal data basically match, indicating that this paper’s model has better effect on pitch adjustment. In addition, the improved model of this paper generates vocal music with better musicality effect, which has higher musicality while avoiding the generation of more invalid notes. The research work of the paper proves the feasibility of the AI model for opera vocal interpretation and provides a new solution for the current field of vocal music generation.
The application of technologies such as big data, mobile Internet, artificial intelligence and so on has triggered a major change in the field of education and promoted the classroom reform in colleges and universities. Taking deep learning theory as the research perspective, this paper constructs a college English teaching model based on deep learning, and applies the model to actual teaching practice, with a view to promoting students’ English learning level and enhancing their intercultural communication ability. Among them, the K-means algorithm improved by the whale optimization algorithm is also used to cluster and stratify the English proficiency of students in a class to illustrate the specific application of deep learning in English teaching. The results classified the sample students into four categories, A, B, C and D. The English level of students in category A is the highest and the largest, accounting for 35.56%, and teachers can design differentiated teaching based on the results of student stratification. After carrying out the experiment of the teaching model, the practicing students’ English scores improved by 4.01%, and at the same time, they gained 18.87%~28.45% and 18.82%~39.01% of competence in the personal domain and the communicative domain, respectively, which confirms the effect of the constructed English teaching model on the enhancement of the students’ English learning level and cross-cultural communicative competence.
Teaching curriculum design is centered around the three dimensions of affective attitudes and values, processes and methods, and knowledge and skills, which fit with the affective learning model composed of emotion, learning and cognition. This paper brings affective analysis into art curriculum design and proposes a learner affective model for teaching art courses driven by multiple teaching objectives. Through multi-objective optimization, we give an interactive decision-making method based on a hierarchical affective cognitive model to simulate learners’ affective decision-making under multi-objective-driven teaching. Analyze the teaching process of incorporating affective learning strategies in an art course, and examine the interrelationship between affective engagement and learners’ knowledge construction in three rounds of learning activities. To analyze the impact of affective learning strategies on students’ learning outcomes. The experimental group (affective learning strategy group) significantly outperformed the corresponding creativity abilities of students in the control group in the three components of surprise, originality and challenge after the teaching of the art course, and the affective learning strategy succeeded in stimulating students’ creativity. The combination of affective learning model and curriculum design can enhance the effectiveness of art education.
Based on the view that artistic style is mainly reflected in sculpture and painting, the sculpture and painting style of Giacometti is analyzed in depth. Starting from the scope of application of big data technology, the theoretical knowledge based on information theory is proposed to explore the differences in the styles of Giacometti and his contemporaries, and the basic concepts used in the processing are defined, including Shannon entropy, conditional entropy, and interactive information. Redundancy, orderliness, and complexity are set as eigenvalues that can characterize the style of art works, and the eigenvalues of the style of Giacometti and contemporaneous artists are analyzed. The minimum, maximum, and average values of the complexity of Picasso’s works are 207, 991, and 596, respectively, while the values of the three indexes of the complexity of Giacometti’s art works are 446, 990, and 718, respectively, and on the whole, the complexity of Picasso’s works is smaller than that of Giacometti’s works. This paper comprehensively reveals the stylistic differences between Giacometti and his contemporaries through the analysis of quantitative characteristic indexes.
In the context of big data, with the accelerated development of digital technology, enterprises are facing the pressure of digital transformation, and at the same time, big data computing system provides technical support for the digital transformation of enterprises. In this paper, we propose a data analysis system based on iterative computing for the digital transformation of enterprises. In order to avoid the resource consumption caused by unnecessary repeated calculations in iterative computing, this paper proposes optimization based on Spark fault-tolerant mechanism and constructs an enterprise data analysis system based on iterative computing model, which provides technical support for enterprise digital transformation. On this basis, this paper also provides optimization strategies in terms of organizational structure and cultural coordination for enterprise transformation, which provides an effective path for realizing comprehensive digital transformation of enterprises. Through the test of this paper’s iterative computing data analysis system, the speed of Spark optimization based on this paper is increased by nearly 2 times, which illustrates the usefulness of this paper’s optimization based on Sparl fault-tolerant mechanism. Meanwhile, the cache misses of the data analytics system are in the range of 46% to 60%, which provides better performance performance in terms of cache hits and time overhead. In this paper, we provide practical and feasible transformation paths for enterprise digital transformation from three aspects, including digital technology, enterprise organizational structure and culture, and promote the development of enterprise digital transformation.
Product awareness can be spread to a wider audience through advertisements, and the introduction of social media platforms has made it easier for marketers to spread brand advertisements and fully attract consumers to generate corresponding purchasing behaviors. Based on fuzzy set theory, the article establishes a fuzzy evidence theory through evidence-weighted fusion, and calculates the utility value of social media advertisements in order to achieve the optimal evaluation of social media advertisements. Then, it explores the influence of social media advertisements on consumers’ purchasing behavior with OLS-LR model, and combines the VAR model to study the dynamic correlation between different types of social media advertising channels and consumers’ purchasing behavior. Without considering the control variables, the regression coefficient of social media advertising on consumer purchasing behavior is 0.438, which is significant at 1% level. With the fourth-order VAR model, CICs social media advertisements have a significant short-term effect on consumer purchasing behavior, while FICs advertisements show a long-term effect. Based on the fuzzy evidence theory, the utility value of social media advertisements can be calculated, and based on the sorting of the calculation results, the construction of optimization paths of social media advertisements can be realized, which provides a new research basis for improving the efficiency of corporate advertising and marketing.
This paper establishes a solution model for resource scheduling optimization in university laboratories, and sets the corresponding constraints and objective functions. The genetic algorithm under the heuristic algorithm is used to solve the resource scheduling optimization problem. On this basis, the pyramid model is constructed, the population evolution and variant strategy are proposed respectively, the model genes are labeled with scheduling cost adaptation, and the genes are generated in series. The framework of scheduling algorithm is proposed, and the dynamic scheduler is constructed to realize the scheduling of university laboratory resources. Through simulation experiments and algorithm analysis, the effectiveness of the use of the model is verified. The experimental results show that when the number of simulation is 10 times, the fitness of the population is 20, 100 and 200 respectively. After the implementation of scheduling for college laboratory resources, the utilization rate of laboratory equipment is increased by 16.3%, 34.6% and 18.4% respectively.
The aim of this paper is to improve the advertisement display effect and realize accurate placement in the market. Firstly, the convolutional neural network is used to select the advertisement keywords, and optimize the click rate, conversion rate and so on when the number of iterations reaches a certain value. Next, the established hierarchical analysis model is used to conduct a comprehensive evaluation of online advertisement release forms, and select the advertisement form that best suits the needs of the enterprise and the market environment. The weight of the webpage and the similarity between the center of mass of the webpage and the advertisement are used to calculate the final score, and the advertisements are sorted to achieve the improvement of the display effect and placement accuracy of the advertisements. The final analysis found that for short-term user behavior, the weight of text link ad clusters is as high as 0.66, which can improve the accuracy of ad placement. For long-term user behavior, the multi-objective optimization algorithm can accurately identify and assign high weights when users continue to visit specific web pages, for example, the cluster of web banner ads reaches 0.64. Meanwhile, it can be adapted to different application scenarios, and the weight of text link ads cluster is significantly increased from 0.14 to 0.758 when the freshness factor is increased from 0 to 1. The optimal F1 value of the advertisement delivery effect is 97.24, which is the highest F1 value of AIGC. The AIGC ad placement strategy provides a new method for the intelligent development of the advertising industry.
In order to study the role of digital economy on the transformation of regional economic structure, firstly, the mechanism of the role of datatized economy on the change of regional economic form is elaborated, and on the basis of the analysis of theoretical model, the structure of the distribution of capital factors in each industry and the ideal factor are determined. Determine the index system and weights of regional economic structure transformation through the selection of weight indicators, and complete the measurement of the data-based economic situation under the construction of the index system of data-based economic situation. Two hypotheses are proposed that digital industrialization can have an ideal effect on the structural transformation of local development but the shape of the effect is inverted U-shape, and that industrial digitalization can have an ideal effect on the structural transformation of local development. The empirical analysis finds that the Moran’s I index of structural transformation of local development from 2008 to 2020 is prominent in the 1% case, and the FP and UE within, central, eastern, western regions of China and the level of structural transformation of local economy is prominent in the 1% case. It is concluded that there is a prominent spatial isotropic relationship between the datadriven economy on regional economic structural change resilience in the whole region, and the constructed research model has a good robustness.
With the development of Internet of Things (IoT) technology, improving the interactivity of IoT communication teaching has become an important research content. This paper firstly constructs the IOT communication teaching system on the basis of service layer, network layer and teaching layer, through which the teaching information is ensured to be delivered timely and accurately. Secondly, the group intelligence algorithm teaching interactivity is optimized and designed to optimize the teaching environment, network, and teaching layer to get the optimized server resource allocation scheme to achieve the optimization of different levels in the teaching of Internet of Things communication. When the number of iterations reaches 20 and 45, the adaptability of this paper’s algorithm is maintained between 100-10-1, and the optimization of the algorithm improves the student participation, the depth of understanding of knowledge, the accuracy of data, the speed of transmission, the efficiency of management, and the teaching effect by 28.6%, 41.7%, 4%, 100%, 18.8%, and 20%, respectively. In the delay analysis, when the number of terminals is 10, 20, and 30 respectively, the delay of the teaching system in this paper is the lowest among all the compared systems, which is 10ms, 40ms, and 230ms respectively.This study can lay the foundation for improving the quality and effect of IoT communication teaching and promote the cultivation of teaching interactivity between teachers and students.
In order to satisfy consumers’ needs, enterprises must conduct in-depth research on consumers’ purchasing behaviours and design and develop marketing strategies based on the characteristics of consumers’ needs. The article takes 4P marketing theory and SOR model as the guide, and establishes a consumer purchase intention model in combination with the consumer behaviour model. The questionnaire is designed from the product value, price range, channel optimisation, and promotional efforts of the enterprise marketing strategy, and the validity of the questionnaire is tested by principal component analysis. Then meta-analysis method was used to explore the correlation of each variable, and the SEM model was combined to explore the influence path of corporate marketing strategy on consumer purchase intention. The Q-value of the hypothesised relationship of consumer purchase intention ranges from 446.137 to 814.535 and is significant at 1% level, and the correlation coefficients of each variable in the model with consumer purchase intention are more than 0.35. The indicators of model fit, CMIN/DF and RMSEA, are 1.076 and 0.015 respectively, and the path coefficient of the value of the product in the marketing strategy on the purchase intention is the largest at 0.076. The path coefficient of product value on consumers’ purchase intention in marketing strategy is 0.369, and the development of enterprise marketing strategy needs to actively expand marketing channels and design differentiated product and service programmes, so as to enhance consumers’ recognition of the enterprise brand to stimulate their purchase intention.
With the proposal of sustainable development of energy, countries begin to develop from fuel vehicles to new energy vehicle market. Firstly, we construct a consumer purchase behavior recognition model based on XG Boost algorithm, simulate the gradient enhancement process of purchase behavior recognition, obtain the approximation value based on function calculation to become the learning target of the overflow value, and at the same time, give higher learning weight to the samples with unsatisfactory accuracy in the last round, and after continuous iteration, gradually correct the purchase behavior recognition bias. According to the number of purchase behavior features identified correctly, the number of features that do not have purchase behavior features, and the number of features that are not identified, invalid users are eliminated to improve the accuracy of the algorithm. The Cronbach’s alpha coefficients of the four factors are found to be 0.891, 0.895, 0.813, and 0.800, all of which are greater than or equal to 0.800, indicating that the factors are internally consistent. And the relationship values between the factors and purchase intention are 0.439, 0.406, 0.430, 0.387, which are all greater than 0. Therefore, there is a prominent relationship between all four dimensions of consumer purchase behavior factors and consumption impulse, and the identification of purchase behavior patterns has a guiding role in electric energy vehicle marketing strategy.
In order to be able to accurately identify user behavior and emotional tendency, this paper firstly adopts the neural network structure to build the emotion analysis model, and divides the model into four parts to analyze the text and emotion in social media, and obtains the information of semantics and emotion-related content in social media text. Secondly, from the semantic and emotional symbol content of the text in social media, the public emotional tendency model is built, and the sharing content and behavior of a large number of users in social media are analyzed. Finally, the association rule mining algorithm is used to extract the text and emotional symbols in social media, to improve the accuracy of the user’s emotional tendency analysis model, and to be able to accurately derive the user’s behavioral habits. In order to verify the analytical effect of the model, the model was tested, and the training speed of the BLSTM model was fast, and the training time was 1.5 hours in the first iteration of the test with a data set of 1 million. The model is more accurate in analyzing the user’s positive emotions, with accuracy and precision around 85% and 90% respectively, and the results obtained are more accurate, meet the user’s needs, and enhance the user’s experience.
The ontological issues such as the concept, features, and attributes of smart contracts written in code and running on the blockchain have been the focus of research in the academic community. In this paper, we first construct a smart contract illegal behavior determination model based on the C4.5 decision tree algorithm, which realizes accurate prediction and determination of illegal behaviors existing in smart contract transactions by extracting multiple attribute features of smart contract transaction data. Then, the correlation between smart contract features and contract risk is analyzed by Pearson coefficient, and the risk assessment evaluation system of smart contract performance is constructed by using hierarchical analysis. Finally, the fulfillment path of smart contract is proposed by synthesizing all the analysis results. Among the 24 randomly selected samples, the total prediction probability of the illegal behavior determination model based on the C4.5 decision tree algorithm reaches 95.83%, which is able to effectively identify the illegal behavior of smart contracts. The Pearson chi-square value between smart contract features and contract risk is 224.6317, and the Sig.(two-tailed) value is 0.000, indicating that there is a significant correlation between the two. By constructing a smart contract risk assessment index system, this paper designs a dynamic monitoring model of smart contract fulfillment risk level, and proposes a smart contract fulfillment path from the aspects of reasonable allocation of legal responsibility and legal regulation of contract fulfillment.
In order to be able to study in-depth image recognition technology for the detection of emergencies, this paper firstly adopts the image processing technology image processing, removes the noise in the image, improves the clarity of the image, and reduces the distortion of the image. Secondly, the signal in the image is extracted, and the network transmission algorithm is used to detect the signal in the image and calculate the corresponding transmission energy value. Finally, a standard threshold is set according to the calculation results, and once the transmission energy exceeds the threshold, it is an abnormal event. The analysis of the emergency event detection model based on image recognition technology shows that the image contrast effect is good, around 8.5 points, indicating that the image quality obtained based on image recognition technology is good. For the third emergency detection, the value based on image recognition technology is 93.3%, the detection results are more accurate, the response speed is faster, the fastest can reach about 1.1s, can real-time feedback on the results of the detection of the emergency situation in a timely manner to deal with the emergency situation, to reduce the loss of personnel, and to improve the efficiency of the management of the smart community emergencies of public health events.
The integration of modern information technology and civil litigation promotes the electronic civil litigation, online litigation as a kind of litigation behavior, promoting the development of traditional trial mode. This paper starts from analyzing the relationship and conflict between civil e-litigation and traditional civil litigation, and organizes the relationship between online trial mode and traditional court trial, and the relationship between civil online trial mode and traditional trial mode respectively. Based on the influencing factors of civil trial, the time proof consensus algorithm and data security transmission algorithm are respectively proposed to combine the network nature of online litigation and blockchain storage data information to optimize the online litigation electronic evidence storage. Summarizing the litigation efficiency of online trial and traditional trial under different control variables, from the point of view of the complexity of the case, the litigation efficiency of online trial mode for more complex cases is significantly higher than that of traditional trial mode. For non-complex cases, the efficiency increases but the difference is not significant. The online litigation mode is a part of the civil online trial mode and serves the traditional civil trial mode.
In order to accurately assess the financial status of a company and identify potential anomalies, this paper first implements unsupervised classification of financial transaction data based on Support Vector Machines, which automatically classifies the data into normal and abnormal categories. Histograms are introduced in combination with LightGBM to quickly fuse data from multiple sources. The most suitable first layer is selected by different algorithms, and the outputs of these algorithms are combined with industry-wide common abnormal features as inputs for LightGBM’s second layer identification. With this two-layer structure, the model not only takes into account the industry characteristics, but also the common anomaly features. Empirical results show that in the accuracy of smart financial statement generation, the sensitivity of this paper’s model iterates to 99.99% at 41.25% specificity, and the accuracy of this paper’s model is as high as 0.98 when dealing with financial private information, macroeconomic, and market information.In the identification of financial transaction anomalies, the number of anomalous weeks is identified to be 24, 29, 34, and 36, and the fusion of multi-source data effectively identifies the large amount of financial transactions, fluctuating transactions and other suspicious abnormal transactions.
Tunnel gas and shallow natural gas overflow have been a major problem plaguing the safe construction of tunnels and one of the main types of common diseases in tunnel engineering. The article chooses the tunnel construction of Funci Highway as the research object, and collects the rock and gas data in the research area on the basis of analyzing the distribution characteristics of shallow natural gas. Based on the AVO analysis technique, the PP wave reflection coefficient is approximated as a linear combination of longitudinal wave velocity, transverse wave velocity, density and other elastic constants to construct a pre-stack AVO inversion model to analyze the shallow natural gas distribution in the Funci Highway Tunnel construction. The porosity of the rock layer in the tunnel construction area ranges from 4.5% to 12%, with an average porosity of 8.93% and a maximum permeability of 0.004 μm². The longitudinal wave impedance distribution of the non-reservoir surrounding rock ranges from 1.48 to 2.01, and the error between the longitudinal wave velocity and density obtained by the inversion and the original logging curves is up to only 2.04%. Combined with the logging data, it can realize the comprehensive evaluation of the oil and gas geological environment of Funci Highway tunnel construction, and provide data support for ensuring the safety of Funci Highway tunnel construction.
This paper examines the differences and convergence of regional real estate markets based on panel statistics of 28 provinces, autonomous regions and municipalities directly under the central government in China from 2010 to 2023. Relevant variables such as urban construction land area, population and economic growth are set and the data are processed. The data show that the degree of industrial convergence and circulation costs have a positive spatial correlation and an upward trend from 2015 to 2021. From the viewpoint of regional real estate market divergence, the proportion of the real estate industry in GDP has remained above 5% since 2015, and this proportion is larger in the eastern region, for example, it was 8.74% in Beijing in 2015, but it has slightly decreased in some provinces and cities. The proportion in central and western provinces and cities has been rising faster year by year. The extreme deviation and standard deviation coefficient of the eastern region are relatively large, with the extreme deviation of the eastern region being 4.35% and the standard deviation coefficient being 1.45529 in 2021, indicating that the internal development is not balanced. From the analysis of convergence, the rate of convergence in the absolute convergence test is 3.66%, and the rate of convergence in the conditional convergence test is 2.89%, with a half-life of about 23.8 years. It indicates that the regional real estate market differences are shrinking, showing a trend of convergence, but the convergence process is relatively slow, which provides an important basis for an in-depth understanding of the characteristics of the regional real estate market.
With the increasing complexity of the financial market, corporate financial fraud events occur frequently, posing a serious challenge to investors and market regulators. Aiming at the limitations of traditional financial fraud recognition methods, this paper constructs a financial fraud recognition model MCN based on the topological data analysis method. The model consists of two parts: the Mapper algorithm and one-dimensional convolutional neural network (1DCNN), which combines the global topology extracted by the Mapper algorithm with the local features of the IDCNN to realize the effective identification of financial fraud samples. In order to evaluate the recognition performance of the model, this paper controls the topological feature extraction method unchanged and the classifier unchanged respectively, and compares the performance of the MCN model with other financial fraud recognition models. The results show that the Acc and F1-score of the MCN-based financial fraud recognition model in this paper are 98.69% and 97.64%, respectively, which are better than other models in both perspectives, proving the superiority of the financial fraud recognition model based on topological data analysis constructed in this paper, and thus providing powerful technical support for the regulation of the financial market and the risk management of enterprises.
This paper applies smart technologies to urban rain garden design and analyzes the hydrological effects based on urban smart rain garden technologies. The SWMM model is used to simulate runoff water quantity and quality under the environment of long-term and continuous rainfall events in urban areas. Building area A is selected as the case study object of this paper, and its geographic location and precipitation data are analyzed to preliminarily explain the hydrological conditions of the case study area. Based on the SWMM model, the model pipe network generalization and other operations are carried out to establish the SWMM model of the study area. The SWMM model is calibrated in terms of the calibration of the model’s parameters and the feasibility of the structured network SWMM model to verify the validity of the SWMM model of the study area and its catchment delineation method. Based on the urban smart rain garden technology, the LID module is added to the SWMM model of the study area and the hydrological effects under different scenarios such as combined LID are analyzed. Each LID measure can have a certain reduction effect on the combined runoff coefficient and total runoff. The combined LID measures in this paper have the best reduction effect, with the reduction rate of the integrated runoff coefficient over 35% and the total runoff over 50% in the 2h rainfall event. The combined LID scheme has the best reduction effect on the flood peak, and the reduction rate can reach more than 40% in both 2h rainfall events.
In this paper, the image parameters are preprocessed by the gray scale histogram statistical image parameters, which reflect the gray scale distribution information of the plant images, using the zero-mean normalization formula. According to different lighting conditions, the plant image is segmented, and the texture feature information in the plant image is extracted by using the improved grayscale covariance matrix. The hyperspectral linear mixing model is constructed, and the MVSA algorithm meta-decomposes the mixing model to solve the solution optimization problem. Using the natural gravity embossing method, produce plant embossed flowers and analyze the features and spectral curves of different parts of the embossed flowers to evaluate the comprehensive use of the embossing method proposed in this paper. The ROI images of 1200 embossed pattern petals were calculated to obtain the sample spectral matrix of embossed petals, in which the reflectance of the central petal was the highest among the three parts at a wavelength of 450 nm, with a reflectance of 0.46487, and then decreased, and then gradually increased to one place after the wavelength was equal to 694, with a reflectance of 0.8. The reflectance of the Shaanxi Weixiang (Weixia), the single side-embossed Yuanbaosi (Yuanbao maple), the hammered elm (fruits), and the pachypodium (Green) obtained a full score of 35 in the comprehensive evaluation after drying, which is a perfect embossed plant material, and all the plant materials embossed using the method proposed in this paper averaged above 30, and the comprehensive effect of plant embossing was good.
Since the financial crisis, the economies of all countries have been affected by the recession triggered by global events, and the uncertainty brought by the changes in economic policies has also become a risky shock, and the uncertainty of economic policies has been climbing worldwide. This paper firstly briefly analyzes the mechanism of economic policy and financial market, in order to comprehensively study the changes of market economic liquidity, this paper starts from the return of the market economy, and adopts the symbolic time series analysis method to analyze the prediction of the financial market by taking the stock market as an example. Then construct the regression model, and then study the impact of economic policy uncertainty on market liquidity. The regression coefficient of economic policy uncertainty is 0.064, which is significant at 1% level. Secondly, when GDP growth rate and inflation level are added as control variables, the regression coefficient of economic policy uncertainty obtained is 0.108, which is still significant at 1% level, implying that a rise in economic policy uncertainty brings about a decline in market liquidity. This study provides an effective analytical tool for the impact of economic policies on market liquidity. It also provides a basis for the government to improve market liquidity and enhance market vitality.
The study adopts a detection followed by tracking paradigm. In the detection stage, the BiFormer dynamic sparse attention module is embedded in the YOLOv8 network model, while the original nearest neighbor interpolation upsampling is improved by replacing it with the lightweight upsampling operator CARAFE. In the target tracking stage, a multi-vehicle steering trajectory tracking algorithm based on particle filtering is proposed, and the particle filtering algorithm is improved by combining the target motion direction weighted resampling algorithm. The two improved algorithms are combined for multi-vehicle detection and tracking in tunnel scenarios, and the average tracking accuracy can reach 97.3%. Compared with the traditional YOLOv8 combined with particle filtering algorithm for tracking, the method in this paper is more advantageous.
Curriculum Civics refers to the integration of Civics elements into the teaching of professional courses, so that courses other than Civics courses can also play the role of Civics teaching. In this paper, we study a knowledge mapping-based content generation technology for teaching course Civics and Politics, so that the knowledge of Civics and Politics courses can be integrated and visualized. The knowledge points, concepts, definitions and other information of the course Civics and Politics are extracted in the form of Civics and Politics knowledge triples. Through the extraction of the knowledge entity of curriculum Civics and politics, the relationship between semi-structured data and unstructured data is extracted to realize the integration of knowledge and content generation. After achieving content generation, the generated content is personalized through a deep reinforcement learning recommendation algorithm based on diversity optimization. Taking the two courses of Engineering Cost Management and Engineering Economics in the engineering management specialty as an example, it is found that the proposed knowledge graph construction method has an accuracy rate of 96.2%, which is able to effectively establish the knowledge association between the civic elements and the elements of professional knowledge, and realize the mining and generation of the civic elements. Meanwhile, the DDRL-Base recommendation algorithm achieves the optimum in accuracy, recall and F1 value indexes, and optimizes the problems such as cold start and sparse data in resource matrix, which improves the effect of recommending the Civics and Politics teaching content of the course.
The technical analysis of conventional tennis sports basically focuses on individual studies, with less research on the basic theory of tennis, and the theoretical analysis of tennis trajectory is even rarer. In this study, based on the calculation equations of the main forces during tennis movement, the dynamics analysis of tennis serve movement is carried out, and the three-dimensional trajectory equations of tennis serve are established. Then, based on the ODE dynamics engine technology, the simulation platform of tennis serve is built to realize the simulation and visualization analysis of tennis trajectory. Since the simulation system beat frequency is 1000Hz, the time difference between tennis simulation and actual movement is the smallest, so the frequency of 1000Hz is chosen for the simulation study of tennis serve trajectory. The simulation results show that under the same hitting height and ball angle, the larger the initial velocity of the tennis ball is, the farther the X-axis landing point is from the center line. In addition, under the consideration of air resistance and Malnus force, the difference between the Y-axis landing point of tennis ball when the initial serve angle is 30° and 60° is 1.81098 m. The present study provides a certain reference for the in-depth study of the serving strategy of tennis ball, and at the same time, it also provides a certain theoretical basis for the improvement of the tennis players’ training method and technical playing style.
Flipped classroom teaching puts forward new requirements on the enthusiasm of students’ independent learning, however, the traditional independent learning lacks scientific aids and cannot meet the individual needs of students in the process of self-study. Therefore, this paper exploits the neural network technology in intelligent computing technology to extract the deep implicit semantic representation, combines the implicit semantic indexing (LSI) to improve the traditional collaborative filtering algorithm, and explores an optimized implementation path of the flipped classroom teaching mode. The improved ICF algorithm outperforms the comparison algorithm in terms of recommendation accuracy, average recall, and average coverage in the three datasets. The computational time consumed is reduced by 44.85%, 57.34%, and 73.68%, respectively, compared with UCF. Incorporating the learning resource recommendation model constructed in this paper in a traditional flipped classroom, it is found that the post-test scores of the experimental class in Moral Education are significantly higher than those of the control class (p<0.01), and its post-test scores are significantly higher than its pre-test scores (p<0.01). The collaborative filtering algorithm optimized by intelligent computing technology facilitates students' personalized independent learning, innovates the general flipped classroom teaching mode, and receives the expected results.
The study applies machine vision technology to the production and operation process of energy enterprises, and constructs a fire detection model based on improved YOLOv4 from the real-time monitoring of fire emergency safety scenarios. Based on the original YOLOv4 algorithm, the model lightens the feature extraction and feature fusion networks, and introduces CA attention mechanism in the bottom layer of the feature extraction network to improve the accuracy of target detection. An intelligent fire alarm system is built on this basis as a response method for emergency security scenarios. Comparison with the basic YOLOv4 algorithm reveals that the improved YOLOv4 algorithm reduces the parameter amount by 45.97%, improves the FPS by 27.75, and improves the mAP value by 14.10%, which achieves a better detection accuracy on the basis of greatly reducing the amount of computation and parameter count, and also achieves a better Loss value and mAP in the comparison with other detection methods. Intelligent Fire Alarm The system integrates intelligent detection, intelligent alarm, intelligent alarm receiving and intelligent alarm dispatching, and can complete the fire alarm process within 6s. In summary, it shows that the method proposed in this paper can be used in real-time monitoring of emergency security scenarios and can provide timely warning at the early stage of security hazards.
In recent years, study travel has become a popular way to expand teaching outside the classroom. Based on the trajectory of the development of study travel, the article conducts an in-depth study of the current development of study travel in the context of the new era, and explores the 4.0 model of regional study travel development. Introducing big data and new technologies into study travel and designing a digital platform for study travel. Construct the evaluation index system of study travel, and evaluate the study travel 4.0 mode through questionnaires. Detect the study effect of the study travel 4.0 mode by comparing the impact of the study travel 4.0 mode and the traditional study travel mode on students’ disciplinary literacy. The comprehensive score of the evaluation of the study trip was 4.17, and the study trip 4.0 mode achieved excellent evaluation results. The experimental group and the control group did not show significant differences before the experiment, and significant differences were produced after the experiment. The experimental group’s scores on each dimension of geographic literacy increased by 6.35, 5.56, 7.57, 5.01, 7.89, 5.75, and 38.13 points after the experiment, showing significant differences (p<0.05), while none of the control group's scores increased by more than 1.5 points, with p-values of greater than 0.05. The research and study trip 4.0 model has a significant positive effect on improving students' disciplinary literacy. At the same time, under the background of regional study tours, the cultural innovation strategy is put forward.
The rapid development of the economy in recent years has brought convenience to enterprises, but also made the competition between enterprises more intense, enterprises want to stand firm in the fierce competition not only to improve financial performance, but also from a multi-dimensional integrated perspective. For this reason, this paper launched a multidimensional financial comprehensive evaluation research for enterprises. Based on the Harvard analytical framework, the study firstly emphasizes the financial performance of enterprises and at the same time combines the social responsibility perspective to screen the indicators. Then the quantitative evaluation method of this paper is proposed, i.e. the entropy weight method and gray correlation method are combined to analyze the development status of multidimensional financial performance from an objective point of view. Then the entropy weight method and gray correlation method model are introduced respectively, and the modeling method of combining the two applied in this paper is explained. Finally, by analyzing and evaluating the results of the sample company M, it can be obtained that (1) the results of the correlation degree of company M from 2017 to 2022 are 0.722, 0.473, 0.398, 0.389, 0.426, and 0.496 respectively, and the results of the multidimensional financial synthesis evaluation of company M during these six years are optimal in 2017. (2) The overall performance of the financial capital status of Company M from 2017 to 2022 is gradually deteriorating. (3) Overall, the performance of Company M’s responsibility to its employees is evolving from 2017 to 2022. (4) The company’s performance of responsibility to consumers and government during the six years from 2017 to 2022 is good, but ecological responsibility is at a medium level and has some room for development. This paper provides a multidimensional and comprehensive evaluation of the financial indicators of the company from a scientific point of view, which provides some reference for investors and business managers.
The article is based on Cite Space software for bibliometric analysis of the impact of artificial intelligence on economic development. Literature information comes from CNKI Knowledge Network database, identifying the hotspots and characteristics of the research related to artificial intelligence and economic development from the perspective of the number of articles issued, core authors, keywords, etc., and comprehensively analyzing 3,340 pieces of literature during the period from 2013 to 2023. The study shows that the number of published articles on the research on the impact of artificial intelligence on economic development increases year by year, and by 2021, the number of published articles is more than 600. Most authors publish related articles in the range of 3-7 articles, and there are fewer collaborations between authors. There are 16 keywords that appear more than 30 times in the field of the impact of AI on the economy between 2013~2023, which is statistically accounted for the total of 15.41%. The keyword clustering is divided into 7 cluster classes, and the clustering module Q=0.781, S=0.877, which has a high feasibility degree. The keyword with the highest intensity of emergence (3.91) in the field of research on the impact of artificial intelligence on economic development after 2018 is “research and development applications”.
Rapid and accurate assessment of power network loss in the power system has become a key research topic for the vast and diverse dataset of power grid operation. This study integrates data mining techniques with typical scenario modeling concepts and innovatively designs a distribution area power network loss rate multi population parallel spectral clustering evaluation strategy that incorporates distribution characteristics. Firstly, clustering attributes are determined for power network loss evaluation, and a power network loss evaluation framework based on clustering algorithms is proposed. Based on power flow calculation, the distribution characteristics and indicator system of each node’s output are analyzed; Secondly, in order to improve the clustering accuracy of power network loss evaluation, spectral clustering algorithm is introduced, and automatic algorithm design is carried out to address the issue of manually setting the initial number of clusters and cluster centers. Then, multi cluster partitioning and parallel computing methods are used to significantly improve the computational efficiency of spectral clustering algorithm; Finally, to verify the practicality of this method, a provincial power grid was selected as a case study. The results showed that this method not only has high accuracy in evaluating power network loss, but also has excellent computational efficiency, demonstrating good feasibility in practical engineering applications.
Transportation electromechanical engineering has an important role in the process of transportation engineering construction. After studying the basic situation and problems of the current transportation electromechanical engineering, the author selects the Q city subway line 1 as a test of the effect of quality control of its transportation electromechanical engineering construction, so as to assess its performance. Ten monitoring points are randomly selected, and four groups of control quantities between the monitoring points are detected, and their qualification rate is judged by the gap between the detection results and the standard value. Then, optimize the transportation electromechanical engineering by using big data and emerging detection technology, etc. Construct the performance evaluation index system of traffic electromechanical engineering to evaluate the performance of the optimized traffic electromechanical engineering scheme. The pass rate of the preoptimized traffic electromechanical engineering in the four groups of control volume testing is 100%, 22%, 80% and 70%, respectively, and the construction performance is poor. The scores of the equipment layer indicators of the optimized traffic engineering scheme were all above 80 points, and 19 indicators scored more than 90 points. The subsystem index scores are between 87.1 and 96.2 points, and the comprehensive score of traffic E&E engineering performance is 92.2 points, which shows that the optimized traffic E&E engineering has achieved more excellent performance evaluation results.
In the face of the requirements of the financial management system, enterprises need to accelerate the digital transformation of finance and realize the “data-driven” management decision-making operation mechanism. The article constructs a new quality productivity-based finance-driven enterprise digital transformation path, and makes it clear that enterprises need to play a new type of labor objects, labor materials and laborers to achieve digital transformation. Based on this transformation framework, a system dynamics approach is used to construct an enterprise financial dynamic planning system, which consists of five parts: a financial analysis subsystem, a target gap adjustment subsystem, an income statement subsystem, a balance sheet subsystem, and a production and operation subsystem, and analyzes the driving factors that affect value growth. The feasibility of the model is determined through the methods of structure test and sensitivity test on the dynamic financial planning model. Taking Group A as a case study object, the financial data for the five-year period from 2019-2023 are analyzed, and the operation of the enterprise is reflected through the financial indicators of each system, which proves the validity of the model and promotes the realization of the digital transformation of the enterprise, which contributes to the management of enterprise value.
Encouraged by the strategy of rural revitalization, rural areas in many places are exploring the development path of characteristic industries. The article embeds the multi-objective optimization model into the development of rural characteristic industries to optimize the current rural industrial development path. The multi-objective optimization model of rural characteristic industry development is constructed, and the ACO-PSO algorithm is used to solve the model, in order to realize the organic unity of economic, social and ecological benefits of rural industry development. The multi objective optimization model is used to optimize the industrial development of village S. The total regional output value of village S in 2035 is 2.08 times of that in 2025. The proportion of output value of primary industry and secondary industry decreases by 20.29% and 18.50% respectively. The proportion of tertiary industry output value increases by 38.79%, and the industrial structure becomes more and more reasonable. After the multi-objective optimization, Village S changes the development mode at the expense of resources and the environment, and maintains the survival of the ecological environment by appropriately slowing down the economic development. After the multi objective optimization, the total output value of the primary industry and the per capita income of farmers in Village S increased by 17,412 and 205.76 yuan respectively. The total output of tourism in the tertiary industry is 465,222,000 yuan, which is 126% higher than that before optimization.
As the most popular mode of out-of-school education in recent years, study tour plays an important role in the comprehensive ability improvement and overall development of students. Based on the path planning problem of study tour, this paper proposes a travel route optimization model with time optimization as the goal orientation, aiming to plan the time-optimal path for students in the study tour process. The particle swarm algorithm is used to improve the genetic algorithm for solving the travel route optimization model. The effectiveness of the optimization model and the hybrid algorithm is verified through the analysis of an actual case of a study tour, and the experimental results are substantially optimized compared with the traditional planning path, reducing the time spent by 2.2 days. Then we use qualitative comparative analysis method to explore the efficiency improvement of the curriculum of study tours, and obtain four grouping paths, which can cover more than 85% of the cases. The research in this paper not only helps to enrich the academic research of cross disciplines in the form of “travel + education”, but also provides theoretical basis and practical reference for the development of study tours to a certain extent.
Particle swarm algorithm, as a kind of population intelligent optimization algorithm, shows great potential in solving multivariate and nonlinear optimization problems due to its simple and efficient characteristics. The article constructs a concrete ratio optimization model in construction engineering technology, which is supported by particle swarm algorithm as the main technology. The model also integrates the least squares support vector regression algorithm, which makes it not only simple ratio optimization, but also has the function of concrete performance prediction. The relative error of the model in predicting the physical properties of concrete is small, less than 5%, which improves the reliability of concrete proportioning. The concrete samples generated by the model with five different ratios have better physical properties for daily needs. In the durability test, the concrete sample with proportion 4 showed the best performance in terms of mass loss rate and impermeability, which were 3.52% (after 400 cycles) and 156.44C (after 56d), respectively. And all the concrete samples used were in the range of proportional qualification and the cost was 5.99% to 28.61% lower than the comparison method.
Blockchain theory and its key technologies are developing rapidly, and the industrial internet combined with blockchain technology is driving the realization of safe and reliable comprehensive connectivity in multiple fields. In this paper, we propose a resource optimization allocation method for industrial internet that integrates edge computing and blockchain to reduce the task computing energy consumption and computational overhead of the system while improving the efficiency of the consensus process. This optimization problem is constructed as a Markov decision process, and a deep reinforcement learning algorithm is used to solve the optimal resource scheduling strategy under a single edge node. The effectiveness of the proposed resource optimization allocation method for industrial internet fusing edge computing and blockchain is verified by simulation validation. The method is able to obtain better and smoother convergence under the premise of harvesting high total rewards, effectively reduces the computational energy consumption and computational overhead of the device, and at the same time effectively improves the consensus efficiency of the blockchain.
This paper firstly constructs a coupled evaluation index system based on three primary indicators, seven secondary indicators and 29 tertiary indicators for agriculture, culture and tourism. Then the entropy weight-TOPSIS method and the coupling coordination degree model are selected to measure the development of agriculture, culture and tourism industry and the coupling level in Yijun County respectively. Finally, 23 spatially related villages in Yijun county area are selected to reveal the reasons for their spatial differences with spatial measurement model, and analyze the factors affecting the coupled and coordinated development of agricultural, cultural and tourism industries in Yijun county area. From the comprehensive evaluation results, the trend of the development level of agriculture, culture and tourism in 2017-2023 was generally upward, in which the agriculture industry had the highest growth between 2022 and 2023, with an increase of 0.0445. After analyzing the factors influencing the development of the coupled agriculture, culture and tourism industries in the Yijun county region by applying the spatial Durbin model, it was found that the general budget expenditures, human capital, infrastructure construction, fixed asset investment and education investment in the region at a significant level of 0.01 correlation of 0.211, 0.03, 0.082 and 0.085, and education investment in the region at a significant level of 0.1 correlation of 0.211. These five factors significantly affect the Yijun County region agriculture, culture and tourism industry, and deepen the development of integration of tourism industry.
In recent years, intelligent control has realized rapid development in the field of electrical engineering, the article initially studied the principle of electrical intelligent control, accordingly built the electrical intelligent control system, and designed the system hardware, the system module is divided into the main control module, the expansion module, the digital input and output module and the mounting rail. Based on the working principle of fuzzy control, design the software of the electrical intelligent control system, and optimize the traditional fuzzy controller by using fuzzy adaptive hybrid genetic algorithm, so as to improve the fuzzy control accuracy of the electrical intelligent control system in this paper. The electrical control system of this paper is applied to greenhouse greenhouse temperature and humidity control, substation air conditioning energy consumption control and subway station illumination control, and the control effect of the electrical intelligent control system of this paper is known through three experimental data. The system of this paper can effectively deal with the dissimilar data in the greenhouse temperature control experiment. Under the steady state environment, the temperature deviation of this paper’s fuzzy control method and conventional single structure fuzzy control is within 0.1℃ and 1℃ respectively, and the humidity deviation is within 5%RH and 10%RH respectively. Obviously, the fuzzy control method in this paper has higher control accuracy. In the substation air conditioning energy consumption experiment, the annual power consumption of this paper’s electrical intelligent control system and the traditional ventilation and air conditioning system are 32,660 degrees and 45,620 degrees, respectively. The electrical intelligent control system in this paper can save 22,000 yuan per year. The output illuminance of the subway station of the fuzzy control system in this paper increases with the comfort of the light environment and the density of the crowd, which achieves the expected effect.
In today’s rapid development of information technology, the big data industry has ushered in explosive growth, and big data analysis has become an important research topic in the cross-cutting field of computing. This study constructs a big data prediction base model based on deep learning, and uses the improved butterfly optimization algorithm with OGRU model to realize feature selection and classification processing of big data. Then the Adam algorithm is used to optimize the parameters of the model, and finally the classification and prediction model of big data based on deep learning is constructed. Simulation and empirical analysis results show that the model proposed in this paper has excellent classification and prediction performance, and can meet the efficiency requirements of big data classification and prediction. The prediction errors of distribution network load data and smart charging pile operation data are lower than 9% and 16%, respectively, which have high practical application value. This study is of great significance to the research related to big data classification and prediction in different fields, and provides an effective method for data prediction in complex scenarios such as industrial as well as power grid scheduling.
Big data is an important foundation in social economy, science and technology, life and other fields, which also becomes a strategic emerging industry and has a crucial impact on the development of enterprises. As a new business model, its development is greatly limited due to the huge amount of data and difficult management. At present, there are many problems in power trading enterprises, such as backward management and low efficiency. The development of big data and blockchain technology would provide new management models for power trading enterprises and eliminate data inconsistency. It can improve data quality and help improve work efficiency, so as to reduce operating costs. Therefore, this paper introduced big data and blockchain based on fuzzy algorithm into the research of digital transformation of enterprise management. Blockchain technology provided technical support for enterprise data management. By starting from the concept of big data and blockchain, this paper would study and analyze how to promote the digital transformation of enterprise management. The research results showed that big data and blockchain based on fuzzy algorithm could promote the digital transformation of power enterprise management and improve the digital transformation process of power enterprises. This was about 11% higher than the digitalization process of traditional enterprises, and the satisfaction score was about 14.7% higher. Through data governance, the speed of digital transformation of power enterprise management was improved.
With the rapid expansion of high-speed railway network, the real-time monitoring of trackside equipment becomes particularly important. To detect trackside equipment information more accurately, a YOLO-R algorithm grounded on the improved You Only Look Once v3 (YOLOv3) algorithm is proposed, and the trackside equipment identification and detection model is constructed. By introducing feature pyramid network and adaptive Bessel curve network, the new model can effectively identify and locate different types of trackside equipment such as switch machine, derailer, and shaft counter. The experiment findings denote that the new model is superior to the existing technology in all aspects of on-orbit equipment recognition and detection, the computer resource occupancy rate is only 22%, the image recognition accuracy rate is more than 98%, and the processing speed is up to 200 images/second. This research not only raises the automation level of trackside equipment monitoring, but also provides a powerful technology for railway safety operation.
Generative artificial intelligence represented by ChatGPT has attracted wide attention in the field of education because of its powerful generative ability, both personalized learning, understanding the learner’s motivation, and providing personalized tutoring and feedback for education. With the advent of the Education 2.0 era, smart classroom has become a strategic choice for the construction of education modernization, and is widely used in higher education and vocational education. Generative AI enlightens students’ engineering thinking, computational thinking, design thinking and systems thinking, which not only helps students to master their professional courses, understand what they have learned, and improve their academic performance, but also assists teachers in updating their course content, keeping abreast of students’ learning trends, improving their teaching efficiency, and simplifying their work. However, generative AI is faced with expertise gaps and uncertainty about the existence of generated content in its application, as well as ethical issues, and this study proposes that the needs and values of education should be respected, with the aim of efficient and convenient services, and that data-driven and ethical ethics should be emphasized in future development. Smart classroom and enlightened thinking with the application of generative AI is a new way of thinking about educational change, which can help teachers and students to effectively carry out multiple interactions, enable teachers to better understand students, play the role of human beings in education, and truly allow technology to be used for teaching and promote classroom teaching reform.
Ultra-low energy buildings for building energy efficiency development, compared with traditional buildings have obvious advantages. This paper simulates ultra-low-energy residential buildings in severely cold regions through Software PHES, and calculates the energy-saving results of ultra-lowenergy residential buildings. The carbon emission factor method is analyzed, and the carbon emission factor is calculated at different stages in the life cycle of the building. Select ultra-low-energy residential buildings in cold regions for modeling, input meteorological parameters, indoor environmental parameters and internal disturbance settings, building envelope, and combine with heat recovery system to simulate the operation of ultra-low-energy residential buildings in cold regions. Analyze the indoor and outdoor temperature and humidity values of traditional houses and compare them with those of ultra-low-energy-consumption houses to verify the advantages of ultralow-energy-consumption residential buildings. Calculate the energy-saving efficiency of ultra-lowenergy residential buildings. Using the 9# residential building of Ruihu·Yunshanfu in Datong as a practical verification case, this ultra-low energy residential building has a total life-cycle carbon emission of 171.078 tCO₂/a, with a unit area carbon emission of 16.415 kgCO₂/m²·a. Compared to the energy-saving design standards implemented in 2016, the carbon emission intensity is reduced by 60.02%, fully confirming the carbon reduction benefits of ultra-low energy residential buildings in severe cold regions.
In recent years, the development of study activities is in full swing. In order to study the eco-education effect in national park study activities, this paper introduces Bayesian network and constructs an ecoeducation effect assessment model based on Bayesian inference. In the comparison of the absolute error of the assessment value with other assessment models, the assessment accuracy of the Bayesian inference assessment model in this paper is obtained. After constructing the ecological education effect assessment index system and completing the assignment, the level of ecological education that should be achieved in the national park study activities is obtained through Bayesian inference diagnosis. Finally, according to the results of education effect assessment, the probability of each indicator being in various states is obtained by simulation using Monte Carlo method. The mean absolute error of the Bayesian assessment model is 0.26 points, which is smaller than other comparative assessment models and has the highest assessment accuracy. The model’s ecosystem principles, anthropogenic intervention impacts, ecological disasters and ecological protection measures should be guaranteed to reach 75.6, 64.8, 67.9 and 69.4. The ecological operation rules (59.4→79.8), climate change (50.6→70.2), biodiversity reduction (52.2→69.8), and pollution prevention and control (56.4→78.3) have the highest accuracy for the ecosystem principle, anthropogenic intervention impacts, ecological disasters and ecological protection measures, respectively. , anthropogenic intervention effects, ecological disasters and ecological conservation measures, and ecological education effects had the greatest impact. The overall score of ecological education effect was 84.1, and the scores of ecosystem principle, human intervention impact, ecological disaster and ecological protection measures were 83.8, 85.2, 83.0 and 84.2.
For enterprises, development is ultimately reflected in the task completion performance of employees, and in order for employees to create higher task performance, it is necessary to consider not only their education and knowledge level, but also their emotional management ability. This study first collects data related to employees’ emotion management ability and task completion efficiency improvement through questionnaires, and then analyzes the statistical data by using the potential impact identification model designed based on Bayesian neural network model to obtain the potential impact probability of each dimension of emotion management ability on task completion efficiency improvement. The analysis of the forward and reverse inference probabilities of the Bayesian network model indicated that the most important potential influence factor leading to the improvement of task completion efficiency was the emotion expression ability, with a forward and reverse inference probability of 36.2% and 59.4%, respectively, followed by the emotion regulation ability and emotion acceptance ability. The results of this study reveal the important potential influence of emotion management ability on task completion efficiency enhancement, and the formulation of task completion efficiency enhancement strategies based on the perspective of emotion management ability can effectively enhance employee task performance, which in turn promotes the overall development and competitive advantage of enterprises.
With the continuous improvement of positioning accuracy of high-power fiber lasers and industrial robots, the use of robots for laser processing has been widely applied in the field of industrial manufacturing. This article designs a laser cutting robot and control method, using ABB-IRB120 dual robotic arms, specifically applied to the cutting of railway sleeper steel bars. The robot vision system can automatically recognize the steel bars of railway sleepers, and the overall cutting process is controlled by a safe and reliable PLC. The follow-up system is controlled by STM32 and integrates a dual loop competition algorithm to establish a control model namely “feedforward compensation PID+sliding mode control”. The visualization simulation experiment results of trajectory tracking analysis have verified that the model has the advantages of fast response and high control accuracy. The experimental results show that the robot can achieve high-speed, stable, and precise cutting of rail sleepers, and can meet the needs of cutting various types of rail sleeper steel bars.
To overcome the obstacle of ranging by using the same type of ultrasonic transducer in two medium such as water and air, whose sound wave transmission characteristics are of significantly different, this paper proposed a dual medium ultrasonic ranging scheme, with the application background of cast-in-place piles’ borehole diameter measurement. Based on the analysis of media’s influence mechanism on ultrasonic ranging performance, a high-sensitivity weak signals conditioning circuit is constructed, with front-end amplifier, bandpass filter, demodulator, back-end amplifier and lowpass filter, which enables the ultrasonic transducer dedicatedly designed for underwater ranging can work in the air. On this basis, by designing a dual channel signal conditioner and increasing the ultrasonic emission power, the problem of ultrasonic ranging in air and underwater using unique type of transducer is solved, and the media in which ranging is ongoing can be distinguished at the same time. To verify the scheme’s effectiveness, an experimental platform is built and ranging experiment is conducted in both air and water. The result proves that the expected ranging range and accuracy can be achieved under both media conditions, which lays theoretical foundation and provides engineering approach for similar scenarios.
AI (Artificial Intelligence) technology and multimedia technology are changing with each passing day, and have gradually involved in various fields. At the same time, the use of these technologies in the field of education has also effectively promoted the assessment of English classroom teaching. Since good classroom teaching is inseparable from evaluation, then scientific and reasonable teaching assessment of English classroom teaching can be guaranteed. At present, there is a lack of English teaching index assessment system for AI and multimedia technology. Therefore, this paper conduced in-depth research on improving the assessment system of English classroom teaching, and expounded on AI and multimedia technology. This paper built an assessment system for English classroom teaching based on AI and multimedia technology, and innovated and improves the assessment system. The experiment showed that 85% of the teachers were satisfied with the assessment system of English teaching indicators based on AI and multimedia technology, and 70% of the teachers were satisfied with the assessment system of traditional English teaching indicators. The new system can help to promote a more objective and scientific assessment of English classroom teaching.
With the rapid development of science and technology, culture, education and other fields, people’s demand for library and information materials is increasing, and the traditional library and information management can no longer meet this demand. However, at present, the security of library and information management has become an important issue to be solved. Library and information management should also prevent external intrusion to ensure that users and administrators manage within their authority, so as to stop unauthorized operations in time, and timely detect and stop illegally changed documents. This paper aimed to study the effectiveness of artificial intelligence (AI) security and library and information management in the Internet of Things (IoT). This paper proposed RSA (Rivest-Shamir-Adleman, RSA) algorithm to encrypt books and information. However, the algorithm had limitations. Therefore, the Elliptic Curve Cryptography (ECC) algorithm has been adopted again. ECC is currently the most effective and feasible solution for large-scale distributed open networks. The scheme has adopted the characteristics of hierarchical group management, fewer keys stored in nodes, less calculation of key update, and historical group key storage mechanism, which met the needs of file management. The experimental results in this paper showed that when the size of the tested document was 15M, the time for RSA and ECC to encrypt the document was 42ms and 40ms respectively. When the document size was 90M, the time for RSA and ECC to encrypt the document was 502ms and 256ms respectively. It can be seen that the encryption time of the two algorithms is similar when the document is small. However, as the document becomes larger and larger, the encryption time of the two algorithms has been widened. Keywords: Library and Information Management, Rivest Shamir Adleman, Artificial Intelligence, Internet of Things, Elliptic Curve Cryptography
Artificial intelligence technology has brought new breakthroughs to the field of machine translation.
Through the introduction of cloud computing data aggregation algorithms, this paper proposes two
translation methods, namely rules and corpus. At the same time, the translation system is studied
with English as the research object. Based on the statistical translation method, the basic framework
of the English translation system (ETS) is designed, including a preprocessing module, a source
language matching module, a statistical decoding module, and a target translation generation module.
And by introducing the k-means algorithm and the optimized k-means++ algorithm, ETS was studied.
Combined with cloud computing technology, the ETS had a powerful data storage platform. Finally, a
simulation experiment was carried out to test the performance of the system from three aspects: the
average number and type of translation results, the success rate of translation in different languages,
and the speed of online translation. First, the comparison method of the two algorithms was used to
test them separately. The data showed that with the increase of vocabulary, the average number and
types of translation results in the ETS have also increased. The system developed by k-means++
algorithm was 5.03 items higher than the average number of translation results of the system
developed by k-means algorithm, and 1.93 items higher than the average number of categories.
When testing the success rate of translation in six languages, the data showed that the average
success rate of English translation in different languages remained at 94.34%. It was concluded that
the success rate of using k-means++ was higher than that of k-means algorithm, and the k-means++
algorithm could make the translation system produce better results when running. Finally, the online
translation speed of the common ETS and the ETS based on cloud computing technology were tested.
The average online translation speed of the system under cloud computing technology was 40.46b/s
under different translated text volumes, while the average online translation speed of the common
system was 26.47b/s. It indicates that the efficiency of the ETS on the basis of cloud computing
technology is high and the data processing capability is strong, which makes the system far more
efficient than the ordinary translation system in operation and has obvious superiority.
The continuous development of power market puts forward new requirements for power grid operation and power supply quality. Under this background, if electric power enterprises want to achieve sustainable development, they must strengthen management and technological innovation to improve their competitiveness. Currently, the distribution network construction is still dominated by the traditional cable transmission method, which has many drawbacks and can hardly meet the requirements of modern power production for communication capability. Digital twin technology is an advanced intelligent control method, which can effectively integrate information in complex systems. It can use data-driven to achieve real-time monitoring, fault detection and analysis functions, which can better help users improve the efficiency and security of power use. The intelligent communication network has the characteristics of good real-time, strong scalability and can quickly adapt to different environments, different devices and application scenarios. By building a complete set of distributed automatic control system, the goal of stable, reliable, efficient and energy-saving power system can be achieved. This paper presented the relevant calculation formulas of energy consumption and time delay in smart grid, and the effectiveness of the formula was verified through simulation. By combining the principle of heterogeneous sensors, a new intelligent integrated management system for remote monitoring of distribution lines was designed to realize a series of functions such as centralized meter reading, load forecasting, inspector positioning, online diagnosis, etc. of the intelligent dispatching center, which provided a theoretical basis for the optimal dispatching of the smart grid. This paper compared the traditional distribution network monitoring system with the distribution network digital twin monitoring system based on intelligent communication network. The results showed that the time delay and bit error rate of the optimized detection system had been significantly reduced, and the success rate of packet reception had increased by 10.4%; in addition, it could achieve higher accuracy and security and reduce operation
The combination of the content of Civics and professional courses in colleges and universities is one of the important contents of general education in colleges and universities in recent years. The article introduces machine learning algorithms into this field to explore the optimization path of western economics course civics in colleges and universities. After developing the resources of western economics course civics, the content generation model of western economics course civics is constructed by using the content generation algorithm based on pre-training model and keywordawareness, respectively. Then the text generation performance of the proposed content generation model is examined. The results of the teaching experiments of the experimental group and the control group are compared to explore the effectiveness of this paper’s machine-learning-based content optimization and practice path of western economics course civics on improving students’ performance. The F1 values of this paper’s content generation model on the ROUGE-1, ROUGE-2, and ROUGE-L indicators are 39.06%, 24.79%, and 36.65%, respectively, which is the optimal performance among all models. The students in the experimental group and the control group had the same level of Civics in Western Economics course before the experiment. After the experiment, the two groups produced a score difference of about 5 points on the 8 content dimensions, and the p-values were all less than 0.05. The experimental group’s postexperimental performance in course civics were all significantly improved (p0.05). The content optimization and practice path of western economics course Civics based on machine learning can significantly improve the learning effect of students.
Curriculum Civics reform in physical education should keep pace with the times and actively explore modern technical means. This study addresses the problem of regulating the elements of Civic and political education in physical education, and establishes a mathematical model of multi-objective optimization and regulation by comprehensively considering the various factors and constraints involved in the problem. In order to further optimize the regulation results, an improved two-population genetic algorithm is used to solve the model. Taking the physical education course of a university as an example to analyze, the design algorithm of this paper is compared with the experiments, and the improved two-population genetic algorithm completes the convergence in 300 iterations, and the degree of adaptability is improved by 2.04%, which has the characteristics of strong global search ability and fast convergence speed, which proves that the improved two-population genetic algorithm has a certain degree of superiority and validity. The utilization rate of the elements of ideology and politics education in the experimental solution results reaches 0.87, and other factors meet the actual needs of sports teaching, and the method of this paper can realize the intelligent regulation of the elements of ideology and politics education in sports teaching.
In the process of increasing the service capacity of digital infrastructure, the complex data generated by data terminals grows rapidly, which puts forward higher requirements for complex data task scheduling preprocessing. In this paper, based on particle swarm algorithm and improved artificial fish swarm algorithm, a hybrid particle swarm multi-objective optimization scheduling algorithm applicable to task scheduling and processing of complex data sets is designed. Then we design a reasonable expression method for the particle position and adaptation value algorithm in the multiobjective optimization algorithm, and put forward the pre-search strategy of the particle swarm algorithm to improve the search performance of the particles in the algorithm. Finally, the algorithm is equipped to construct a task scheduling and processing model for complex data sets. The results show that the hybrid particle swarm optimization algorithm established in this paper outperforms the comparison model in terms of load balancing and processing time, and is able to keep the system CPU utilization between 0.350-0.491 in the simulation experimental environment. It is also found that the application of the task scheduling and processing model in this paper can increase the power of photovoltaic and wind power generation in the grid system and reduce the operating cost of the grid system. This study provides an effective reference method for the processing of data and task scheduling in various types of complex systems, and brings new ideas and directions for research in related fields.
This project defines and generalizes the groundwater flow and soil deformation in geotechnical engineering by combining the hydrogeological conceptual model. Based on the fluid-solid coupling theory, a coupled model of groundwater flow and soil deformation is constructed, and the SUB program package in MODFLOW simulation software is selected to numerically simulate and analyze the relationship between groundwater flow and soil deformation in the study area. In layer2 and layer3, the trend of groundwater level decline and soil compression is shown, and the other layers4~layer9 also show the same situation, due to the over-exploitation of groundwater, resulting in serious decline of the soil in the study area, which reveals the causal relationship between groundwater flow and soil deformation at present.
Random forest algorithm is a kind of integrated learning algorithm with strong universality, high prediction accuracy and not easy to overfitting, and strong stability in stock index prediction application. This study constructs a stock index prediction model based on the random forest algorithm, and predicts the stock index futures price state according to the iteration of the decision tree in the random forest algorithm. Then we propose to use the regular term and ARMA-GARCH time series forecasting model to optimize the overfitting and large forecasting errors in the Random Forest model to achieve the construction of stock index forecasting optimization model. It is verified that the average absolute error of the random forest optimization model proposed in this paper is only 0.0316 in stock index forecasting, and the robustness in stock index forecasting is excellent. The empirical application results of stock index forecasting show that the accuracy of this paper’s model for CSI 300 and CSI 500 indexes is above 90%, and the total return of the strategy during the backtesting period is relatively high. The practical application of the stock index forecasting model proposed in this study has the value of further research, which can provide reference and guidance for investors.
Financial risk has a greater impact on the operation and development of enterprises, and accurate prediction of financial risk has become an industry demand, so as to better help enterprises avoid possible financial risk. The article establishes an enterprise financial risk prediction model based on the random forest algorithm, and fills in the oversampling of financial data through the SMOTENC algorithm, and realizes the downsizing of financial data by combining with the KPCA algorithm. Based on the enterprise financial risk characterization index system, the financial data of 358 listed enterprises were selected to carry out model validation and application analysis. The accuracy of corporate financial risk prediction based on Random Forest can reach up to 94.17%, and the average value of the overall time efficiency of the model is 0.68%, which is faster than the comparison algorithm in terms of financial data processing capability. Based on the results of financial risk prediction, the changes in corporate profitability, operating ability, solvency and development ability can be analyzed in depth, providing data support for enterprises to formulate preventive measures for corporate financial risk.
The article uses web crawling to obtain public opinion data after the Sichuan Luding MS6.8 earthquake and preprocesses this data. Aiming at the limitations of the traditional LDA topic model, an improved topic model based on LDA, TT-LDA, is proposed. the BERT model is used to encode the public opinion data, and on the basis of the BERT embedding, the BiLSTM model is used for contextualized word representation for deep feature extraction to complete the modeling of public opinion sentiment evolution. Combining the crawled data and the model, we analyze the public opinion after the Sichuan Luding MS6.8 earthquake. Three days after the earthquake, positive sentiment, neutral sentiment, and negative sentiment increase to 488498, 466832, and 516560, respectively, a total of 1471890 sentiment data, and after time evolution, the sentiment polarity intensity increases from -0.178 to – 0.886, indicating that when the official announcement of the number of casualties of the accident is made, the netizens’ negative sentiment fully erupts to show the post-earthquake public opinion sentiment evolution process.
E-commerce classroom teaching is an important means to improve the quality and teaching effect of e-commerce teaching, and effective interaction in teaching is an important carrier of e-commerce teaching classroom activities. This study combines pan-reinforcement learning and reinforcement Q learning algorithms to recognize and analyze speech data in e-commerce teaching classroom, and uses head posture estimation algorithm to recognize interactive behaviors in e-commerce teaching classroom video, and combines the video and speech interaction data to get the e-commerce teaching interactive behavior recognition model. The model is then equipped with web application technology to design a visual analysis system for e-commerce teaching interaction, and the optimization strategy of e-commerce teaching interaction is realized with the assistance of this system. The results of the study show that the interactive behavior recognition model proposed in this paper can accurately identify the interactive behavior of teachers and students in each course of e-commerce teaching. It is also found that after the implementation of interaction optimization strategy in college e-commerce teaching classroom, the frequency of effective interaction behaviors of teachers and students increases from 351 to 391 times, and the meaningless classroom silence time is reduced. And the learners’ cognition of knowledge is also improved under the influence of the improvement of the effect of interactive behavior. The visual analysis system of teaching interaction proposed in this paper based on reinforcement learning algorithm is of great significance for optimizing the effective interactive behaviors of teachers and students in e-commerce teaching and improving the degree of students’ knowledge cognition.
Green economy is an important factor to measure the quality of economic development. In order to explore the current green economy resource allocation, this paper embeds methods such as DEAMalmquist model and Tobit regression model into the study of green economy resource allocation, explores the green economy resource allocation efficiency of 30 provinces in China by constructing a multilevel model of green economy resource allocation, and analyzes China’s green economy during the period of 2021-2023 through the results of the measurement of the Static, dynamic and level changes of resource allocation efficiency. Tobit regression analysis of the influencing factors of green economy resource allocation efficiency is carried out to optimize the current resource allocation based on the influencing factors. The green economy resource allocation efficiency increases year by year in 2021-2023, and the resource allocation effect improves continuously, with the mean value of the comprehensive efficiency of 0.712, 0.762, and 0.809, respectively. The green economy resource allocation efficiency in Beijing, Shanghai, Jiangsu, and Zhejiang is the highest, and the allocation structure is the most reasonable. Chongqing, Gansu, Qinghai, Ningxia and Xinjiang are less efficient in green economy resource allocation. The per capita GDP and the ratio of education expenditure to GDP have a positive impact on the effect of green economy resource allocation, with an impact of 1.246 and 0.489, respectively.
In recent years, with the rapid development of information technology, the traditional single-threaded processing method can no longer meet the rapid growth of digital media data volume. In this paper, based on the digital media data processing system based on BS structure, the GPGPU parallel processing architecture is used for optimization. The access efficiency of massive parallel multithreading is ensured by executing a multilevel storage architecture composed of behavior decision unit, branch merge unit and branch recovery stack. The study designs the computational resource pool as well as the storage resource pool to form an infrastructure solution to the data processing problem. The query performance of the digital media data processing system using the GPGPU microarchitecture with multithreaded parallel processing is improved by about 81% and 69% or so compared to the Ocelot and prototype systems, respectively. And the average execution time for performing dynamic data allocation is 5.17s less than that of the original system. It shows that the optimized digital media data processing system has better data processing efficiency.
The problems of debt value and optimal capital structure of enterprises are the main issues in corporate finance research. Under the ESG rating mechanism, the article first utilizes the real option theory to study the optimal capital structure and investment and financing decision-making methods of enterprises. Then it puts forward model assumptions and combines the jump diffusion model for the construction of enterprise project investment and financing decision-making model and the dynamic planning adjustment of capital structure. Finally, through specific numerical experiments, the influence process of each variable in the model on the enterprise investment and financing decision is analyzed, and the agency problem is analyzed.Through the experiment, it can be obtained that when the residual value after stopping production, the risk-free interest rate, the variable production cost and the tax rate are set to γ = 1, r = 0.1, ξ = 0.1, θ = 0.2, respectively, with the increase of the frequency of the jump, the investment price of the positive-jump model gradually decreases, and the investment price of the negative-jump model gradually increases, which can be obtained that the reasonable simulation estimation of the relevant parameters has an important impact on the enterprise’s investment strategy, so the enterprise should make a more accurate assessment of the parameters, otherwise they will lose part of the benefits or lose good investment opportunities.
Analyzing the interaction strategies of international relations helps to understand and predict changes in the international landscape, so as to develop and optimize international interaction strategies. Firstly, a single-layer multi-temporal network is modeled for political events, scientific cooperation and international trade in international relations, and added to a multi-layer aggregation network. On this basis, a simulation and analysis method for simulating international relations interaction strategies based on deep learning and multi-intelligence body reinforcement learning methods is proposed. Applying the method of this paper to the arithmetic simulation analysis, it is found that the international relations in the last 10 years have shown the small-world characteristics, and cooperation and conflict coexist. Economic dependence is an influencing factor of conflict between two countries, when the economic and trade links are close, the two countries are less prone to conflict, so the optimization of the international relations interaction strategy should focus on the economic and trade relations.
Objective and comprehensive teaching effectiveness assessment is a strong guarantee for the quality of English teaching in colleges and universities. This paper establishes an index system for evaluating the teaching effectiveness of English in colleges and universities from the levels of students, teachers and classroom teaching. The particle swarm algorithm is used to optimize the convolutional neural network, the SGD formula is used to improve the calculation accuracy, the Adam optimizer is improved to improve the model operation efficiency, and the optimization algorithm of convolutional neural network, PSO-CNN, is proposed. The PSO-CNN algorithm is introduced into the system and the logic design is carried out to realize the evaluation of the teaching effect of English teaching and to build up the English teaching effect evaluation system based on the intelligent algorithm. Evaluation system based on intelligent algorithm. The performance of the system is examined and analyzed with the help of PCA method, which shows that the cumulative contribution rate of the first six indicators, such as learning acquisition and teaching ability, reaches 91.08%. In the mean square error of model training, the PSO-CNN algorithm applied in this paper’s system has a lower mean square error than other algorithms after 35 iterations, and has better evaluation accuracy. In the application practice of English majors in a higher education institution in B city, after applying the system of this paper to evaluate the effectiveness of English teaching, the English test scores of the regular and experimental classes with improved teaching were significantly improved.
Traditional learning path planning methods often fail to meet the individualized needs of learners. In this paper, a dynamic learning path planning method based on neural network is studied by constructing a student model. Firstly, the construction method of the student model is designed, and the Item Response Theory (IRT) is used as a test method for the cognitive level of students, which realizes the dynamic acquisition of student information. A neural network-based cognitive collaborative filtering model was constructed, which models learners’ learning behaviors and interests, and customizes personalized dynamic learning paths for learners after assessing their cognitive levels and learning difficulties. The collaborative filtering algorithm in this paper performs better than the other four algorithms in terms of accuracy and coverage, and the accuracy and coverage rate of the generated knowledge point sequences reach 98.9% and 93.6% respectively, and the performance of the students in the experimental group has been significantly improved under the application of the dynamic learning path generation model of blended teaching in this paper, indicating that the effectiveness and feasibility of the personalized learning path generation model in this paper are excellent and are expected to be further promoted.
The monitoring of training load and recovery cycle of Wushu Sanshou athletes is a long-term and fundamental work for sports teams. The article introduces the parameters of resting heart rate, ventricular muscle contractility, arterial wall and maximal oxygen uptake VO2max as monitoring indexes, and designs a real-time monitoring method of physical training load data based on graph convolution network. Subsequently, through the flow level variables (BFL, TLQ, BRQ), flow rate variables (BFLI, BFLD, TLQI, BRQI), auxiliary variables (TT, TI, RT, RM), exogenous variables (RYN), and the causal relationship between the elements of each variable of the Wushu sparring training function monitoring system, we constructed a nonlinear system of the training load and recovery cycle of the Wushu sparring athlete Dynamics model. Using the real-time monitoring model of this paper to monitor the wushu sparring athletes, in the third minute of the experiment, the real-time monitoring system predicted that the heart rate was 90, and the adjusted heart rate using the model of this paper was 90, which was consistent with the actual monitored heart rate. It can be concluded that the model of this paper can well monitor the training load of martial arts sparring athletes. Through experimental simulation, the article concludes that both the strong physical fitness program and the strong training program can be beneficial to the training of wushu sparring athletes.
Least Squares Support Vector Regression (LSSVR) machine has the advantages of small sample, nonlinearity and high dimensionality, which can solve the problem of predicting the compressive strength of green concrete with oil palm shell aggregate. In this paper, the error sum of squares instead of the error sum is used as the objective function, IFFA is used to find the optimization of the kernel function parameters and penalty factors of LSSVR, and the PWLCM-based chaotic search is used to initialize the population, and ultimately the improved auricular fox algorithm is realized for the optimization of the least squares support vector regression algorithm, which makes it have strong fitting and generalization abilities, and significantly reduces the burden of computation, thus improving the Computational efficiency. Application of the designed combined algorithm for compressive strength prediction of concrete reveals that the R², MAPE and RMSE values obtained by this paper’s model on the training dataset are 98.71%, 5.92% and 1.0823 MPa, respectively. The correlation coefficients predicted by the model are much closer to 1 as compared to that of the baseline model, which suggests that this paper’s model possesses a superior generalization capability, making it more effective in dealing with complex and invisible data. The adopted method is practical and innovative, and is of guiding significance for practical engineering.
Starting from the essence of dynamic programming algorithms, the terminology in dynamic programming algorithms, the applicability conditions of the algorithms, and common sub-problem models are summarized. The Belman optimal algorithm is used to split the multilevel problems in dynamic planning into simple single-level problems and solve them one by one, combined with the function approximation structure to approximate the performance index function, to construct the adaptive dynamic planning algorithm, and to apply it in the resource balancing optimization of integrated teaching. The results show that the adaptive dynamic programming algorithm has better resource balancing effect than other algorithms, and the number of convergence and running time are reduced by 6-53 times and 48.92-90.34 seconds respectively. The introduction of the adaptive dynamic programming algorithm improved the resource balancing accuracy of university teaching and learning management by 4.0%-17.4% in each subject group. As the number of resources increased, the time consumption required when balancing resources decreased by 50%-83.33% for test groups 3, 4 and 5, and the efficiency of the test improved by 75%-100%. This shows that the algorithm proposed in this paper is effective when dealing with balancing online and offline teaching resources in higher education.
Grasping economic behavior is a foothold of market optimization, this paper combines game theory ideas with deep learning technology to explore the laws of economic behavior. Firstly, different payment strategies are considered and payment matrices are constructed, and replicated dynamic equations are used to describe the dynamic adjustment process of the game and simulate the game process of market economic transactions. The MS-RCNN model that can be used to predict economic behavior is constructed by extracting data features using CNN and processing the feature matrix using GRU. The results of the game simulation simulation show that when the government regulation of the market is in place, it is easier for the trading parties to reach a deal. In addition the MS-RCNN model can more accurately reflect the fluctuation of the market when making long-term and short-term predictions, and the predicted price is closer to the real market price. Therefore a better understanding of economic behavior through game theory and its prediction through deep learning helps to achieve the optimization of market strategies.
The change of landscape pattern is closely related to the quality of ecological environment, and the study of urban and rural landscape pattern, especially three-dimensional landscape pattern, is of great significance for urban-rural integration spatial planning. Based on the theory of landscape pattern, this study constructs a numerical simulation method for the characteristics of urban and rural threedimensional landscape pattern, and explores the formation of optimization strategies for the threedimensional development of urban and rural areas. Taking Chengdu City as an example, firstly, based on multi-source remote sensing data, the landscape pattern index method and gradient analysis method are utilized to explore the spatial and temporal coupling characteristics of urban and rural three-dimensional landscape patterns. Then the CA-Markov coupling model is used to predict the landscape pattern of future land use, so as to provide a reference for decision-making. The results of the study show that the landscape type changes in Chengdu City from 2005 to 2020 are dominated by the transformation between cultivated land, forest land and construction land, and the reasons for the changes are closely related to the urban development plan. In addition, the accuracy indices of the CAMarkov model all reached more than 80%, and the simulation results were reliable. The model prediction results show that construction land and cropland are the largest transformed landscape types, with a large-scale increase in the landscape area of construction land and a large-scale decrease in the landscape area of cropland. Spatially, the degree of fragmentation of the landscape pattern in Chengdu City gradually decreases, the landscape patches are more regularized, and the overall pattern shows a highly aggregated trend. The research results of this paper can be used as a reference for the optimization policy of three-dimensional landscape pattern in urban and rural areas, and provide data support and innovative ideas for the innovative development of urban and rural three-dimensional landscapes
As a core course of Business English majors, Business English translation plays a crucial role in the cultivation of Business English talents, and how to realize the assessment of translation efficiency in teaching has become a hot topic nowadays. This paper builds up a translation efficiency assessment index system in the teaching of business English translation around five aspects: vocabulary, syntax, context, society, and translator’s factors. Random forest and Lasso regression methods were used to select 15 feature variables including sentence order and collocation between words. The multiple regression linear model was chosen to construct a model for assessing translation efficiency in business English translation teaching, and the model was estimated and tested. The least squares method was used for estimation, and all the parameters were significant (Sig<0.05) except for the variables compound sentences, sentence structure and situational intermingling. The distribution of the residuals of the model approximates to the normal distribution, which satisfies the assumption of normality and the assumption of independence, and possesses a good fit and some explanatory power.
This paper optimizes the K-means clustering algorithm based on the RFM model improved by the entropy weight method and then using the distribution between the samples, and adopts the combination of both density and distance to accurately classify the cross-border e-commerce customers. Finally, the capsule network recommendation model is used as the benchmark model, and the CCN4SR model is designed to accurately recommend goods to customers. The results show that cross-border e-commerce customers are categorized into five-star to one-star customer groups, which focus on “return on investment, pursuit of social value, the pursuit of cost-effective, the pursuit of low prices, while having their own different consumer preferences”. The capsule network outperforms CNN on both training and test sets, and its precision, recall and F1 value are above 92% on the test set, which shows that the capsule network is well adapted in the ϐield of implicit feedback recommendation.
There are many mature traditional navigation algorithms, but most of them are insufficient in the function of environment perception and understanding, and reinforcement learning can give robots the ability to learn and make decisions. This paper proposes a robot reinforcement learning navigation algorithm and optimal control strategy based on deep reinforcement learning. Firstly, Markov decision modeling for local planning of the robot navigation system is implemented, and then a POMDP belief space dimensionality reduction algorithm based on the NMF update rule is proposed to address the situation of excessive dimensionality and combined with PRM to achieve global reinforcement learning planning. Finally, considering the external information interference problem, a power controller based on the TD3 algorithm is designed to ensure that the robot navigation system can accurately track the signals even under the external interference environment.The position error of the robot under the TD3 controller tends to be close to 0, which is much lower than that of the robot under the PD controller. The experimental results of this paper show that the designed TD3 controller can effectively improve the trajectory tracking accuracy of the robot navigation system and better realize the optimization of the robot tracking control function.
Cataract, as an extremely common visual impairment disease, seriously affects the normal work and life of patients, and the optimization of cataract IOL model is of extraordinary significance to the diagnosis and treatment effect. The article collects ocular biological measurements of cataract surgery patients as experimental data, explores the radial basis function (RBF) neural network belonging to the field of artificial intelligence in the process of IOL calculation, and then introduces genetic algorithms to optimize the RBF neural network, and constructs the cataract IOL calculation model based on GA-RBF. The experimental results show that after combining the improved cataract IOL calculation model for telemedicine, the patient’s hospitalization days were shortened by 3.06 d, and the hospitalization cost decreased by $1,383.7, meanwhile, the patient’s satisfaction increased by 4.69%.
This paper investigates and analyzes the optimal allocation of educational resources and the expansion and innovation of the content system of e-commerce English courses in vocational education institutions in Fujian and Taiwan, and proposes methods and strategies for the optimal allocation of educational resources and the innovation of the course system. The evaluation index system of educational resources allocation was established, the factor analysis method was used to establish the educational resources allocation measurement model of vocational colleges and universities, and the K-Means clustering algorithm introducing profile coefficients was applied to cluster vocational colleges and universities on the level of educational resources allocation. The study classified 42 vocational colleges in Fujian and Taiwan into four categories, and based on the results of cluster analysis and factor ranking, the four categories of vocational colleges put forward suggestions for optimizing the allocation of their own educational resources allocation level. The results of the curriculum system innovation practice show that after the teaching design of the e-commerce English curriculum system innovation, the performance of the experimental class is significantly higher than that of the control class, increasing from 22.84 to 26.81 points. It shows that the teaching design of ecommerce English course system innovation is suitable for the needs of English teaching and can provide important guidance for teachers of e-commerce English in vocational colleges and universities when they are teaching.
This paper combines the multifactorial influence of the actual situation, adds the objectives of user interest preference and traditional music overseas communication budget into the influence maximization model, and constructs the Multi-Objective Influence Maximization Model (MOIM) of Chinese traditional music overseas communication to deal with the problem of objective inconsistency in the process of music communication. After that, the seed node selection algorithm of MOEA/D based on decomposition strategy is proposed to improve the search optimization strategy of seeds in the MOIM model. The cross-variance operator designed in the algorithm optimizes the set of solutions generated by the chromosome in the iterative process and finally obtains the Pareto non-dominated solution. The results show that the distribution of Pareto optimal solutions for each graph in the three datasets of TFM, TCC and TCO is very uniform when T=300, and the distribution of Pareto optimal solutions is more uniform with the increase of the number of iterations. The more influential nodes in the multi-objective optimization model of this paper, the higher the cost. The influence and cost of the seed set need to be considered in the overseas dissemination of music, and the seed set should be selected to maximize the influence within the budget. When the network structure and user behavior conform to different characteristics, the MOEA/D model can also get the corresponding undominated solution.The MOEA/D model integrally optimizes the influence index and cost index, so it provides a more flexible set of decision-making solutions for the overseas dissemination of Chinese traditional music.
In the garment production industry, garment cutting size matching plan is an important step in the process, which plays a decisive role in production management and cost control. In this paper, we first model the size matching problem of garment cutting, then use the improved fast particle swarm algorithm (APSO) to optimize the multi-objective optimization solution, and finally verify the performance of the APSO algorithm and the actual effect of garment size matching with cases. Comparing the test results of APSO, PSO and LDWPSO algorithms in the six test functions of Griewank, Ackle, Levy, Rastrign, Schwefel and Sphere, it can be seen that: with the improvement of the problem dimensions, the APSO algorithm used in this paper can still maintain a better optimization accuracy, and the optimization accuracy and stability are significantly improved compared with the PSO and the LDWPSO algorithms. LDWPSO algorithms. In the actual case, the APSO algorithm is more reasonable in the size combination and the number of layers of fabric, for four different types of apparel orders have obtained a superior optimal solution set, cutting production error is far less than the enterprise requirements. At the same time, compared with other optimization methods, the APSO algorithm has better optimization accuracy and solving efficiency, and can obtain a more superior cutting and bed splitting scheme. The algorithm proposed in this paper can effectively optimize the cutting size matching process, reduce fabric waste and production equipment investment, and has good application value and reference significance.
The global urbanization process is rapidly increasing, and a reasonable and scientific analysis of the relationship between urban land expansion and land resources plays an important role in the rational allocation and coordinated development of land resources. This paper constructs a spatio-temporal geographic weighted regression model coupled with geospatial and temporal coordinates, and incorporates temporal and spatial non-stationarity into the model. Then, using the method of hypothesis testing, the temporal non-stationarity and spatial non-stationarity of the spatio-temporal geographic weighted regression model are examined, and at the same time, the multiple covariance test and the variance expansion factor method are proposed to carry out further statistical inference of the model. As the degree of urban sprawl increases, the land resources weaken year by year from the center to the surrounding area.The global Moran’s I for the three periods from 2003 to 2023 are 0.6289, 0.7159, and 0.7368, respectively, which show a trend of increasing year by year. It shows that land resources are strongly influenced by urban expansion, and the spatial distribution of land resources shows spatial aggregation. Several variables, such as building volume rate, population size, regional economic development, regional cultural level, infrastructure construction and urban fallow area, have significant effects on the spatial differentiation of land resources. The above differentiation characteristics provide insights into the rationalization of urban expansion and the scientific allocation of land resources.
In the process of sharing accounting information using cloud computing technology, the integrity of the data is related to the security of the transmission and utilization of accounting information. For this reason, this paper studies the algorithm optimization based on the multi-branch path tree LBT. Multi-branch path tree LBT adopts distributed data storage method to reduce the number of hash operations. The data integrity auditing scheme is designed for different phases of cloud auditing, and the dynamic update process of cloud data is optimized to improve the data integrity verification effect. This algorithm can still maintain a high challenge success rate after more than 300 challenge data blocks, and the total overhead of the experimental computation does not exceed 8 ms, and the verification efficiency is also better than the comparison algorithm. Therefore, the research idea of this paper has validity and has improved effect on data integrity verification in the process of cloud computing smart accounting informatization.
This paper deeply analyzes the innovative application and intelligent upgrading steps of Artificial Intelligence Generated Content (AIGC) in Civic and Political Education. Based on metadata, we construct an automated generation model of Civics education resources, divide the meta-properties of education knowledge resources, set up a knowledge tracking model DT-BKT to obtain students’ mastery of Civics knowledge, adopt personalized recommendation model to realize the high adaptability of education resources based on students’ Civics learning, and combine the functions of each model to build a Civics education content intelligent generation and adaptability system. Knowledge tracking experiments show that the AUC and R2 indexes of the DT-BKT model in this paper are better than those of other comparative models, and it can better simulate the response of learners on the dataset. Facing different groups of learners is able to recommend Civics courses that meet the learners’ abilities. For active learners and potential learners, the average difficulty of the recommended client layer is higher by 0.08~0.15 and 0.06~0.085 respectively, while the overall difficulty difference for inactive learners is between -0.01~0.015, and the recommended difficulty is in line with the characteristics of the learner groups.
In this paper, a K⁃Means clustering algorithm based on improved differential evolution (AGDE⁃KM) is proposed to design the adaptive operation operator, design the multi-variation strategy and introduce the weight coefficients in the variation stage to regulate the searching ability of the algorithm and accelerate its convergence speed. The Gaussian perturbation crossover operation based on the best individual of the current population is introduced, and the optimal solution output from the improved differential evolution algorithm is used as the clustering center to realize the cluster analysis of students’ sports performance data. Afterwards, the hierarchical recognition algorithm and support vector machine are used to recognize students’ sports patterns, and the wavelet transform algorithm is used to extract and select the students’ sports feature quantities, so as to improve the accuracy of students’ sports pattern recognition in sports teaching. In the process of physical education teaching, AGDE ⁃ KM algorithm is more pertinent to the clustering effect of students’ sports performance, and its explanatory degrees of Calinski-harabasz metrics, profile coefficients, and Dunn metrics are 860.0276, 0.3928, and 0.0486, which are 19.0382, 0.0435, and 0.0099. In addition, the AGDE⁃KM algorithm achieves 95.7625%, 99.75%, and 99.85% of the mean value of step recognition accuracy for different testers in the 50m, 800m, and 1000m events, respectively, which is a good recognition effect.
Music conductors rely on the visual impact of gestures and emotions for the interpretation and expression of musical works. In this paper, we utilize spatio-temporal two-stream convolutional neural network and replace the original VGG-16 network with ResNet-34 network with deeper network structure to construct a conductor recognition model for improving music conductor level. The Dropou optimization is applied in the fully connected layer to reduce the overfitting phenomenon, and the network structure is designed to fuse the temporal and spatial networks in advance with the feature maps, in view of the defects that the network structure of dual-stream convolutional neural network is shallow and the temporal and spatial networks do not learn the temporal and spatial information correlation. After the construction is completed, the model is applied in the teaching of a music college. The spatio-temporal information fusion convolutional neural network proposed in this paper is compared with other existing methods, and it is found that the optimized design helps the convolutional neural network to learn better, and better emotion and action effects can be obtained. It has better recognition accuracy on the dataset and obtained the highest accuracy of 74.3% on the CoST dataset. The results of the dimensions of music perception ability of the conductor students in the experimental class are better than the reference class, and the dimensions of pitch and intensity are more than 20% ahead of the control class, which proves that the model in this paper is more powerful to promote the development of music perception of the conductor students.
In the context of rapid research and development of unmanned equipment products, how can we better design an environment sensing system suitable for unmanned equipment combat missions and combat tasks from the perspective of actual combat has become an important research topic. This paper explores the optimization scheme of unmanned equipment environment sensing system based on blockchain technology, proposes PBFT (DTPBFT) consensus algorithm based on C4.5 decision tree optimization, and combines with the full homomorphic encryption algorithm to put forward the shared data encryption scheme of unmanned equipment environment sensing system. The experimental results show that the classification accuracy of C4.5 decision tree is as high as 94.37%, which is better than other classification algorithms, indicating that the use of C4.5 decision tree can effectively improve the accuracy of the classification of the consensus nodes and the security of the PBFT algorithm. In the case of the same number of nodes, the throughput size of the DTPBRT algorithm proposed in this paper is always higher than that of the PBFT algorithm, and the consensus latency is higher than that of the PBFT algorithm only when there are Byzantine nodes inside the system, but the DTPBRT algorithm is able to effectively remove the Byzantine nodes inside the system, which verifies the superiority of this paper’s algorithm. Comprehensive encryption and decryption time-consuming and throughput data, this paper’s scheme in general can realize high data sharing efficiency and ensure the security of data sharing, which can provide technical support for the data security of unmanned equipment environment sensing system.
Teaching optimization algorithm is a new type of group intelligence algorithm, which simulates the teaching process of teachers, and this paper improves the algorithm to realize the improvement of music teachers’ teaching ability. Aiming at the shortcomings of the teaching optimization algorithm which is easy to mature prematurely, has low solution accuracy and converges to the local optimum, this paper proposes a teaching optimization algorithm which integrates the improved Tennessee whisker search. The algorithm combines Tent mapping and inverse learning strategy to initialize the population and improve the quality of the initial population. Tennessee whisker search is performed on teachers to improve their teaching ability. Incorporating the hybrid variation operator into the individual student variation formula allows the algorithm to quickly jump out of the local optimum dilemma. The experimental results show that the hybrid teaching optimization algorithm based on BASTLBO proposed in this paper has good solution accuracy and robustness in finding the optimum on different types of optimization problems. The algorithm in this paper can achieve better teaching ability results than the unimproved TLBO algorithm and the teaching optimization algorithm incorporating the hippocampus strategy, and the objective function on two different indexes is reduced by 8.75% and 7% compared with that of the TLBO algorithm, respectively, and the hybrid teaching multi-objective optimization model designed in this paper has stronger practicality.
Accurately capturing the behavioral factors of different types of customer groups and adopting targeted service strategies is the key to business competition in the hotel industry. In this paper, we combine the variance Boston matrix and PSO-based K-means algorithm to achieve hotel customer attribute segmentation based on customer behavior, customer value and word-of-mouth reliability, and then use deep learning algorithms to construct a hotel customer behavior prediction model. The feature fusion layer and SENet are incorporated into the residual network in order to utilize the feature expression ability of different layers and the spatial coding ability between different channels to enhance the hotel customer behavior predictive ability. Downloading the public dataset from the online wine travel platform for example analysis, it is found that the classification of this paper’s algorithm before customer segmentation has a correct rate of 83.75%, which is higher than the rest of the baseline models. After customer segmentation this paper’s algorithm achieves the highest recall rate in all customer categories, and the recall rate is as high as 84% on category 1 customer groups, and the superiority of the designed algorithm is verified. This study facilitates hotel management to target customer service and retention according to different customer groups.
In order to improve the consistency of on-chain-off-chain interaction of private data supported by blockchain and reduce the redundancy of data storage performance, this paper applies an efficient data interaction method of prefix hashing with improved red-black tree index to store public indexes and improve the efficiency of retrieval and interaction of blockchain data. Under the idea of generalization, anonymous region (AR) is used to hide the real location of participating nodes and protect the privacy of realized nodes. To reduce the computational overhead of the selection process, a cooperative sensing location privacy preserving optimization mechanism, LPPOM, is proposed. The scheme in this paper has a slow growth of data size on the chain with higher storage efficiency, larger throughput, and shorter query time (0.1899ms). The time cost consumed when the number of privacy chains is 15, 30, and 60 only increases by 0.2309-0.4855ms compared to the single chain system, indicating that the scheme scales well. When the file size is within 200 and the number of encrypted attributes is less than 4, its total encryption time meets the user’s privacy data encryption needs (between 66.1765-236.7081ms). The IPFS read/write module is able to satisfy the people’s daily use needs under the public network conditions, and its read/write speed is between 0.1568 and 0.2639MB/ms (file <100M).
This paper firstly starts from the thermodynamic theory, based on the classical heat transfer theory, and adopts the finite difference dichotomy method for mathematical modeling, and uses the secondorder center difference format to discretize the space, and solves the non-Fourier heat conduction equation. After completing the algorithmic solution of thermodynamic theory and finite difference method, the two are combined to deeply analyze and discuss the thermodynamic behavior of highspeed mechanical devices represented by high-speed rotating bearings. When the bearings operate at high speed, with the increase of stiffness, the pressure change in the middle and rear part of the bearings gradually flattens out, the temperature gradually rises, and the relative bearing capacity of the bearings decreases. The increase in the number of bearings also brings about an increase in the pressure at the centerline of the bearings, and the temperature of the air film corresponds to the increase in the average pressure, and there is a risk of over-temperature. In the thermodynamic characterization, the work done by the air film under compression and the heat generation due to viscous shear will lead to an increase in the temperature of the air film, which will lead to the temperature rise of the bearings, and will have a very great impact on the bearing performance.
Water resource is a high degree of unity between quantity and quality, once the water body suffers from pollution will make the water resources more scarce, and karst groundwater resource is one of the main water resources in the seismic area. In this paper, we chose Baiquanquan area in the low-mountain hilly area at the eastern foot of the south section of Taihang Mountains in H province as the research object, set 25 sampling points and collected 20 groups of karst groundwater samples and 5 groups of surface water samples, and carried out the reliability test by the ion balance method to control the error within ±5%. Based on the karst groundwater samples, the general characteristics of its hydrochemistry were analysed, and its hydrochemical characteristics were explored by cluster analysis. The causes of hydrochemical ions in karst groundwater were investigated by Gibbs plot, chlor-alkali index and saturation index, and the related factors affecting the hydrochemical characteristics of karst groundwater were investigated by factor analysis. The hydrochemical cations and anions in karst groundwater were mainly composed of Ca2+ and HCO3–, and the average concentrations of the two were 132.15 mg/L and 193.66 mg/L, respectively. The cast points of karst groundwater all fell between the dolomite and calcite areas, and their Mg2+/Ca2+ values ranged from 0.11 to 0.75, and the contribution of the F1 factor composed of Ca2+, Mg2+, SO42-, TDS, HCO3– was the maximum of 38.91%. Karst groundwater in the seismic area will be affected by rock weathering, human activities, etc., which will affect the flow path of karst groundwater, and then have an impact on the hydrochemical composition of karst water.
In the context of building a new type of energy system, pumped storage projects have been widely adopted as a form of energy storage with the most mature technology and the most economical investment. In this paper, a hybrid pumped storage project online monitoring system based on multisensors is proposed, and an online monitoring database is designed and constructed. Based on the data in the online monitoring database, the soil erosion of the hybrid pumped storage project is calculated and analyzed by combining the CSLE model. Then the attention mechanism is combined with BiLSTM model to construct the landslide risk prediction model of hybrid pumped storage project. The soil erosion during the construction of the hybrid pumped storage project is mainly distributed on the construction land, garden land, grassland and cropland, among which the construction land has the largest area of soil erosion (132.19 km²), followed by the area of soil erosion of cropland (29.24 km²). The MAPE is between 0.002% and 0.005% when predicting landslide risk deformation of hybrid pumped storage project using CNN-BiLSTM-ATT model. And using the model in this paper can minimize the error of rainfall on the prediction of landslide risk deformation and realize the safe and stable construction and operation of hybrid pumped storage projects.
Based on the relevant theoretical foundation, a preliminary natural disaster public opinion risk evaluation index system is formulated, which consists of 4 first-level indicators, 15 second-level indicators, and 49 third-level indicators, and correlation analysis is utilized to screen the preliminary evaluation indexes, and after the screening of the indexes, the final system consists of 4 first-level indicators, 14 second-level indicators, and 23 third-level indicators. Indicator system. Taking a certain province as the research object, we synthesize the hierarchical analysis algorithm and fuzzy theory to explore the risk of natural disaster public opinion in a certain province. The natural disaster public opinion risk assessment result based on fuzzy theory is 69.48, while the corresponding affiliation matrix Sa = (0.2195, 0.3385, 0.1444, 0.1264, 0.1713), according to the principle of maximum affiliation degree, concludes that the natural disaster public opinion risk of the province is at the level of IV, and in order to improve the natural disaster public opinion risk management, the target strategy is proposed. This study has important reference value to promote the rationalization of natural disaster public opinion risk management, so that it can better prevent natural disaster public opinion risk.
In natural orchard environments, tangerines are susceptible to being shaded by foliage and to overlapping with multiple fruits. Varying weather conditions can cause inconsistent levels of illumination, and these unstable factors combined with complex backgrounds can diminish the efficiency of tangerine recognition and localization. Consequently, this paper utilizes images of tangerines captured under various weather conditions within a tangerine orchard as a dataset, and a method based on the YOLOv8n object detection algorithm is proposed. The dataset was trained using BiFPN, MCA attention mechanism, and PConv. An improvement in the algorithm resulted in an accuracy rate of 94.4% for tangerine target detection, a recall rate of 92.7%, an F1 score of 93.5%, and a mAP of 98.3%, with each metric showing an increase of 0.7%, 0.6%, 0.7%, and 1.3% respectively over the original model.
As the throat of transportation system, bridge structure is a lifeline project related to the coordinated development of society and economy. Based on fuzzy mathematical theory, this paper adopts Gaussian subordinate degree function to quantitatively characterize the damage detection information of articulation joints, combines Latin hypercubic sampling method and response surface method, and proposes a reliability assessment method for bridges integrating transversely distributed damage information, and analyzes the bridge of a certain structure as an engineering case, compares the failure probability corresponding to the model outputs and the real damage degree under different damage degrees, and analyzes the change of reliability indexes with the damage degree under articulation joint damage and main plate damage conditions, respectively. Under the conditions of articulation joint damage and main plate damage, the changes of reliability indexes with the damage degree are analyzed. The results show that the narrower the width of the damage interval is, the closer the failure probability is to the value corresponding to the real damage degree, and the reliability of the bridge decreases with the increase of the articulation damage and the main plate damage, which illustrates the objectivity and reasonableness of the method proposed in this paper.
Intelligent power preservation automation terminal is an important part of the smart grid, which plays an important role in improving the power supply quality of the power system. In this study, the CAB module is utilized to extract the multimodal features of visible images and infrared images during distribution network line operation. The weighted feature maps of each part of the distribution network operation are obtained by fusing different modal features through the average fusion strategy to realize line quality monitoring and fault detection and localization. The batch normalization layer and Relu function are also used to improve the image feature quality extraction performance of the model, which is then piggybacked on the model to construct a digital platform for intelligent power protection. The empirical analysis of the case found that the power supply reliability rate of L power supply company increased from 85.060% to 99.87% after the application of the smart power protection digital platform, the average power supply restoration time of non-faulty sections in the grid can be shortened to 3.24 minutes, and the line loss rate in the distribution network has been reduced to a certain extent. This study carries out the exploration of the practical application of the intelligent power preservation digital platform system, which lays the foundation for the stable operation of the distribution network and the improvement of power supply reliability.
Distribution network line project acceptance is a key link in the quality control of distribution network line project, an important factor affecting the safe and stable operation of the distribution network, which directly determines the level of safe operation of the distribution network. In this paper, for the distribution network line manual acceptance time-consuming and laborious, rare quality defects found rate identification rate is low and other issues, to carry out visual positioning and image recognition based on the distribution network drone automated acceptance technology research. In order to optimize the spatial positioning, attitude sensing and target tracking of the UAV, five coordinate systems, including the world coordinate system, body coordinate system, and photocentric coordinate system, are selected for spatial transformation. Based on the visual localization of the UAV, the path planning algorithm for UAV distribution line inspection combined with the path acquisition scheme is proposed. Gaussian denoising and histogram equalization are performed on the UAV inspection collected images, and Sarsa reinforcement learning algorithm is applied to train the samples to improve the automatic identification capability of safety hazards and other security hazards in the distribution network inspection. Visualization and analysis of UAV distribution line inspection path. Combine the distribution network defects dataset for optimal training strategy selection for distribution networks. The automatic identification algorithm for distribution network defects proposed in this paper achieves a mAP value of 79.60% in the target detection experiment. And in multiple dynamic path planning, the UAV nodes are able to accomplish the path planning tasks in different environments.
With the construction and development of new power system, grid business presents high reliability, high security protection, high flexibility, massive access level characteristics, 5G as the frontier technology of wireless network access, with high speed, wide connectivity, low latency features and advantages, and diversified grid business communication needs are highly compatible. Based on the characteristics of 5G communication technology, this paper analyzes its practicality in the power system. The main two protocols of the current autonomous network routing protocol for power system are proposed, and the inter-cluster routing optimization of OLSR is carried out by using AO algorithm. Simulate the predation behavior of skyhawk, develop the search strategy in the optimization process of AO algorithm, and construct the mathematical model of AO optimization algorithm. A quasi-inverse solution is used on the basis of the inverse solution to further increase the population diversity and convergence speed of the AO algorithm, while an adaptive weight factor strategy is used to balance the global search and local exploration capabilities of the AO algorithm. Simulation experiments are utilized to investigate the performance of the IAO algorithm as well as the PDR and delay in the mobile scenario of the power system. Comparing the PDR of the three protocols at different expected delivery distances, IOLSR still maintains a delivery rate of about 28% at a distance of 350m-500m. The optimized IOLSR shows further reduction in delay compared to OLSR in most of the cases with an average delay of 10829.43ns.
Wide-area protection systems are capable of eliminating or mitigating the consequences of disturbances by obtaining multi-point information about the grid system through measurement and communication techniques, and power system control and protection systems. In this study, distribution data in the grid system is collected and preprocessed, the distribution state of the grid system is estimated using data fusion methods, and an optimization method for distribution state estimation based on distributed computing methods is proposed. Then the grid wide-area protection system is designed by combining the grid system fault diagnosis method. Simulation and example analysis results show that the grid wide-area protection system based on data fusion and distributed computing has good performance in processing grid data and detecting and localizing grid faults, and the maximum localization error of faulted line points is maintained within 0.770%. In addition, this grid wide-area protection system is able to accurately detect a certain circuit fault in a regional grid system where faults are frequent, avoiding large-scale power outages and ensuring the stable operation of the grid system. This study has important scientific significance and application value for grid multivariate data fusion modeling and real-time fault detection, and provides an effective widearea protection scheme for the grid system.
Reservoir dams are highly susceptible to unstable conditions under the long-term action of water flow. In this paper, we mainly investigate the effect of water flow on the stability of reservoir dams under the conditions of complex boundaries. Through the advection orthogonal curve generation network and the use of the adjustment factor on the sparsity of the grid range of values, to achieve the mesh division of the reservoir dam body. The control volume method is used to discretize the control equations of the planar two-dimensional water flow model under the complex boundary conditions, and the SIMPLEC method is used for numerical calculations at the same time. For the treatment of complex boundaries, this paper reduces the error between the simulated and real water margins by comparing and selecting the diagonal Cartesian method. The limit equilibrium method is introduced and combined with the strength reduction method to solve the problem together and comprehensively analyze the stability of the reservoir dam under the action of water flow. Reservoir dam A is selected as the object of numerical calculation in this paper, and the input information of the model is set by setting parameters and selecting working conditions. The model is used to calculate the stability of the reservoir dam under the action of water seepage and water pressure. Compared to the static condition, the value of displacement and deformation of the dam body is increased by about 52.00% under the water flow action condition. The stability of the reservoir dam body under the action of water flow infiltration and pressure decreases significantly.
Fractal geometry is an emerging discipline that has developed rapidly in recent decades, and its study of irregular geometric shapes can be used to describe objects in nature that cannot be described by traditional geometry, and it has a broad space for development and application prospects. In this paper, the theory of fractal geometry is applied to industrial design to realize the refinement and analysis of surface features. The study includes an in-depth analysis of the theory of fractal geometry, the Koch curve as an example to illustrate the principle of fractal geometry. The study also investigates different dimension calculation methods, such as Hausdorff dimension, box dimension, correlation dimension, information dimension, generalized dimension, and self-similarity dimension of fractal geometry, and proposes a dimension calculation method for the refinement of structural surface features for industrial design. After the fractal geometry surface feature refinement simulation analysis, the porosity of the fractal map based on this paper’s method ranges from 16% to 38%, and the comparison with the Serpinski method proves that the presently selected fractal model is more effective in the refinement of structural surface features for industrial design. As shown by the surface feature simulation results, there is indeed a certain degree of similarity between the roughness topography of the real structural surface of the two surface processing methods in industrial design and the roughness topography simulated by the fractal function. The above study proves that the method of refining the structural surface features of industrial design based on fractal geometry in this paper is scientific and feasible.
This topic discusses the problem of maximizing the interests of multiple participants in the trading market based on game theory. Taking the electric power market as a study case, an interest maximization model of multi-party trading strategy in the electric power market is constructed, and the ADMM algorithm is used to solve the model. The rationality and effectiveness of the described model are verified through experimental analysis and arithmetic simulation. Compared with other algorithms, the ADMM algorithm in this paper has a faster convergence speed, and the benefits of the grid company, the benefits of the new entities and the benefits of the users under different numbers of users are all closest to the ideal Nash equilibrium state, which shows the superiority of the ADMM algorithm in this paper. The constructed model is used to solve the conflict of interests among the grid company, new entities and users, and the existence and uniqueness of the game equilibrium is proved through analysis and derivation, and has good convergence results. After the optimization of the strategy in this paper, the revenue of the added entity increases by 6.76%, the power purchase cost of the users decreases by 10.29%, and the consumption surplus increases by 4.50%. Through price-guided output, the load curve is realized to shift peaks and fill valleys, so that the grid company, the added entities and the users get higher benefits.
The large number of accesses of distributed power supplies changes the distribution network from a passive network to an active network with small and medium-sized power supplies all over the network, which brings important impacts on all aspects of the distribution network, such as planning, operation, and power quality. The study analyzes the types of distributed power nodes and the traditional trend model of distribution network, studies the changes of voltage and network loss after the integration of distributed power sources into the distribution network, and analyzes the impact of different numbers, capacities and access locations of distributed power sources on the reactive power optimization of the distribution network by means of IEEE33 nodes. Analyze the impact of distributed power supply on distribution network. Firstly, the characteristics of distributed power supply are analyzed, distributed photovoltaic and distributed wind power operation models are established, and the influencing factors of the two power supply outputs are analyzed to generalize the distributed power supply output model. The basic principle of weighted least squares state estimation and its algorithmic process are introduced, and on its basis, an equation-containing constrained state estimation model for dealing with zero-injection nodes in the distribution network is introduced, and finally, the feasibility and validity of the proposed constrained state estimation model’s state estimation method for the distribution network are verified through the analysis of an example of the IEEE 33-node system. Combining the sequence quadratic programming method and the idea of trust domain, the trust domain sequence quadratic programming method is proposed, and the use of the effective set method to quickly solve the sub-quadratic programming problem after downsizing is the key that the algorithm in this paper can solve the optimization problem relatively quickly. The optimization results show that distributed power sources participate in active distribution network reactive power optimization and cooperative control to provide a theoretical basis, and the effectiveness of the method is verified through simulation cases.
The distribution network system is directly connected to users, and its faults directly affect people’s daily life and economic activities. In order to realize rapid fault location, isolation and self-healing, this paper carries out a new energy distribution system reliability assessment based on sequential Monte Carlo simulation algorithm. Comparing the use of sequential simulation algorithms, non-sequential simulation algorithms and pseudo-sequential simulation algorithms in the state analysis of distribution network systems, the sequential Monte Carlo method is selected to establish a simulation model for distribution network reliability assessment. According to the “2-3-6” architecture system commonly used in distribution networks, a new energy distribution network self-healing system data communication scheme is designed. The Monte Carlo simulation method is used to model and analyze the reliability of new energy coupled components and to evaluate the reliability indexes. The simulation environment is designed and the RTDS model is used to test the self-healing system of intelligent devices in the distribution network. In the new energy distribution network data scheme designed in this paper, the distribution network self-healing technology is able to remove the fault time within 95ms and complete the fault point elimination work. And when the DG can make the far power side and the switch is located in the near power side, the distribution self-healing system is able to remove the fault point within 60ms time and complete the self-healing operation of the distribution network fault.
With the prosperous development of the Internet, the open network environment has also become a breeding ground for hackers, especially in the huge network system, the multi-stage, large-scale and coordinated network attacks have brought great trouble to the traditional defense means. This study designs a network security expert system based on multi-source heterogeneous data based on the characteristics of huge volume of multi-source heterogeneous network security data, heterogeneous format, and diverse semantics. The system contains five hierarchical structures: perception layer, event layer, alarm layer, attack context and attack pattern layer, and attack scenario layer. Petri nets are used for network security risk analysis and assessment to overcome the shortcomings of traditional defenses that become difficult to handle after modeling the attack scenarios. Incorporating the D-S evidence theory, the outputs of multiple decision engines are applied to the network security posture assessment to analyze the network condition from a global perspective and further enhance the effect of network attack classification. In the simulation experiments of simulated attacks, the monitoring information of the network security expert system has autocorrelation coefficients within two times standard deviation ( \(\pm\)0.1) after the 0th-order differencing, which indicates that the system is able to accurately assess the potential values of network attacks, such as scanning, brute-force cracking, DoS, and Web.
In the realm of supercapacitor energy storage, multi-element transition metal-oxides with high theoretical specific-capacitance values have been extensively explored. However, their poor electrical conductivity and cycling stability limit their applications. In this study, \(CoMoO_4\)@\(Co_3O_4\)/NF was formed by loading \(Co_3O_4\) on the nickel foam (NF) surface as a substrate by solvent co-precipitation method and annealing treatment first, and then growing \(CoMoO_4\) on the surface of \(Co_3O_4\) by hydrothermal reaction and calcination. \(Co_3O_4\) nanosheets, which are derived from ZIF-67, offer more active sites and simpler ion/electron transport paths. The electrochemical characteristics of the composite electrode can be substantially boosted by the synergistic effect between \(Co_3O_4\) as the inner layer and \(CoMoO_4\) as the outer layer in the \(CoMoO_4\)@\(Co_3O_4\)/nickel foam hierarchical composite structural materials. When combined with activated carbon (AC) to form an asymmetric supercapacitor, it exhibits a capacitance normalized to unit area of 0.669 F \(cm^2\) at 1 mA \(cm^2\). Furthermore, the assembled asymmetric supercapacitor demonstrates an energy per unit volume of 209.29 mWh \(cm^{-2}\) at the current flux of 0.75 mW \(cm^{-2}\), and upholds 89% of its Initial surface capacitance after 6000 cycles.
In this study, a complete two-dimensional anatomical model of the rabbit heart was constructed by Zhang et al. The positive effects of external noise stimulation on cardiac pacemaking and conduction have been studied using computer simulations. Our results show that: 1) the slower pacemaking rate or even oscillation cessation, which is induced by the removal of \(i_{Na}\) from the sinoatrial node or a decrease in the active sinoatrial node cell population, can be recovered by adding external noise. 2) Compared to the effect of \(i_{Na}\) removal alone, the combined effect of \(i_{Na}\) and cell death had a greater impact on slowing down the pacemaking rate. These phenomena indicate that external stimuli play an important role in controlling the initiation and conduction of sinoatrial node pacemaker activity, and that the aging heart may experience obstruction of sinus node conduction and even sudden cardiac death due to decreased cell membrane \(i_{Na}\) or partial myocardial cell death. Our results provide insights into the intrinsic mechanisms underlying electroshock healing in some heart diseases.
Due to its advantages in energy conservation, emission reduction and recyclability, air-supported membrane structure (ASMS) has been widely implemented in various applications, including large stadiums, conference centers, warehouses and temporary buildings. Compared to traditional building, the structural and material properties of ASMS are special which significantly affect smoke diffusion and flow behavior during fire scenarios. Moreover, the design methods for smoke management system of ASMS buildings are still lacked and further research is required. In this study, a numerical model of a coal storage bin was built. The impact of several key design parameters, including such as height of natural smoke vent, fire source location and makeup air methods, on the performance of ASMS building`s smoke management system were evaluated. Gas temperature, CO concentration, visibility, smoke exhausting quantity, critical pyrolysis temperature and critical heat radiation intensity were selected as evaluation indexes. The results indicated that better natural smoke exhaust efficiency was achieved when the natural smoke vents were located at the height between 80% and 100% of the building’s total height. Setting natural makeup air vent in the wall of smoke bay 3 can achieve the best cooling effect. However, compared to natural makeup air, the natural smoke exhaust efficiency of mechanical makeup air was much better. When the rate of mechanical makeup air reached 120% of the required natural smoke exhausting quantity, it has significant improvements in temperature reduction, visibility and natural smoke exhausting quantity which are crucial for ensuring safe evacuation during fire events. These findings provide a design refer for enhancing fire protection and construction practices in ASMS buildings.
The development of modern economy encourages the development of enterprises. As an economic accounting system, the financial integration system is not only an indicator to evaluate the income of enterprises, but also an indicator to evaluate the business status of enterprises. In the modern information environment, how to carry out the financial fusion system information of modern information environment is the focus of enterprises. The information contained in the financial integration system provides complete accounting information for the application of communication network and computer technology by combining modern technical means, and effectively analyze accounting and make reasonable economic plans. In the process of dynamic design of financial integration system, it can avoid accounting risks and significantly improve the economic benefits of the company. Firstly, the paper expounded the analysis of the influencing factors of mobile Internet on accounting information system, and briefly analyzed the relevant factors of financial integration construction. It considered the significance of financial integration construction and proposed effective ways to solve problems. After that, the dynamic planning algorithm was introduced into the financial integration construction design. Finally, the investigation and calculation were carried out. The new accounting information system designed by using dynamic programming algorithm and mobile Internet technology could improve the security and efficiency of enterprises by 38.6%.
The expansion of information-based education and the growing need for computer laboratories have made their day-to-day management a key part of teaching and learning management activities. The traditional manual management method is inefficient and difficult to handle the increasingly complex management tasks. The emergence of optimized management systems has effectively improved the effectiveness and quality of computer laboratory management work, but the operational efficiency and security of management systems are also facing enormous challenges. To improve the real-time and security of management systems while efficiently completing management tasks, this paper conducted in-depth research on the design of computer laboratory optimization systems using nonlinear engineering algorithms. To verify its effectiveness, this article tested the system from three perspectives: functional implementation, running load, and security performance. The experimental results showed that at the functional implementation level, the average response time of the system for each test case requirement was 1.0767 seconds. It can be seen from the specific results that the demand response speed of the computer laboratory optimization management system based on nonlinear engineering is fast, which can efficiently handle the daily management tasks of the laboratory and promote the improvement of the quality and level of teaching management.
There are many precious cultural relics collected in the art museum. If it is not properly managed, it will have a great impact on the economy and culture of the country. The construction of a digital art museum that represents the history and culture of the country is very important for the protection, research and dissemination of history and culture. Legacy plays a very important role. Therefore, the current art museum is no longer just a simple display, but more for people to appreciate and understand cultural knowledge, technology and feelings. Under the background and environment of informatization, the main forms of contemporary digital art museums are virtual exhibitions and virtual experiences. From the perspective of art museums, the authenticity of virtual reality is more significant, which can improve the audience’s viewing and experience of various exhibitions. Therefore, under the support of digital technology, the museum combines various exhibits and technologies to present a brand-new “virtual” experience. Based on virtual reality technology, this paper combined digital technology and intelligent technology to establish a virtual display platform for digital art museums with rich content and open sharing. The optimization of the loading mechanism of the scene model can effectively shorten the loading time. When the distance between the viewpoint and the exhibits is within 2m, the optimized shortening time is 509ms. The system can display the virtual art gallery very well. Users can browse the cultural relics exhibits from different perspectives in the scene, so as to realize the real-time roaming of the virtual art gallery scene.
In response to the problems of traditional visual design that overly relies on designer experience, poor design effectiveness, and low efficiency, this article studied visual design based on digital technology. In this paper, linear stretch visual design image enhancement method and histogram equalization visual design image enhancement method are used to enhance the image of advertising visual design image, packaging visual design image, beauty visual design image, cultural visual design image. Peak signal to noise ratio (PSNR) was used to analyze the effect of digital technology in visual design. The conclusion was drawn that the linear stretching enhanced visual design image method has better results. This article utilized the linear stretching visual design image enhancement method to enhance 20 visual design images, and analyzed the effectiveness of the linear stretching visual design image enhancement method using a questionnaire survey. The survey results indicated that after processing with the linear stretching visual design image enhancement method, the evaluation scores of the enhanced images by the respondents in School S, School T, and School A were 8.28, 8.76, and 8.47, respectively, which were higher than the original image evaluation scores. The visual design method based on digital technology is beneficial for improving the visual effect of images, and is of great significance for the dissemination of visual images.
With the development of science and technology, the application of flight manipulators has received extensive attention. The flying manipulator has broad application prospects, such as the maintenance of high-voltage towers, the storage and retrieval of elevated goods in warehouses, and the delivery of express and takeout goods. Before the actual application of the flight manipulator, due to the complex task requirements and nonlinear environment, it is necessary to continuously optimize the Trajectory Planning and Control (hereinafter referred to as TPC) of the flight manipulator. In order to improve the recognition and positioning accuracy of the robotic arm on the surface of the aircraft, and achieve precise control of the autonomous motion and operation of the robotic arm on the surface of the aircraft, this paper studies the TPC of the flight robotic arm based on deep learning, image moment and vector product methods, establishes a bearing return function model based on deep learning, and a Jacobian matrix of the flight robotic arm based on image moment and vector product methods. Through the experimental research on TPC of the flight manipulator, it was proved that the DL trajectory planning method could reduce the collision risk of the flight manipulator by 4.79% compared with the traditional trajectory planning method, and could improve the task completion speed of the flight manipulator by 4.66%. The application of DL to the TPC of the flight manipulator could improve the trajectory planning effect of the flight manipulator.
Financial analysis is a method of analyzing the overall operating status of an enterprise based on financial information, which can help managers judge the company’ s operating risks and adjust the company’ s operating conditions in a timely manner, so as to better achieve business management. This paper aims to study the design of financial indicator analysis system through big data. This paper proposes to find the best clustering center by means of fuzzy identification algorithm, determine the quality of the company’ s operating status, calculate the company’ s overall operating indicators, and determine the company’ s risk level and improvement direction. The experimental results of this paper show that the fuzzy identification algorithm can help the enterprise to determine the overall state of the enterprise’ s operation, improve the financial risk identification ability by 20%, and better realize the enterprise’ s financial analysis and processing.
Fresh items have become an essential necessity for modern people, and the daily diet structure is growing more and more rich as people’s attention to health increases. One of the characteristics of fresh products is that they are hard to retain at room temperature. As a result, IoT logistics technology assistance is frequently needed in logistics linkages including distribution, transportation, and warehousing. Through the scientific and logical planning of the route of fresh food logistics distribution vehicles, this paper aims to effectively lower the overall economic cost of logistics distribution, guarantee the freshness of the fresh food distribution process, satisfy the various individualized needs of customers for delivery time, and enhance logistics distribution. security. This study suggests an enhanced ant colony algorithm in artificial intelligence that can efficiently determine the shortest path. This algorithm can be used to find the best route for new logistics distribution and lower transportation losses. It is based on 5G Internet of Things technology. The ant colony method prior to the enhancement had the longest optimization time of 25. 06 seconds in the 8 search process, according to the experimental data presented in this study. The enhanced ant colony algorithm had the longest optimization time of 17. 89 seconds. In finding the optimal path, after the improvement, the ant colony algorithm takes less time. In the comparison of transportation costs, the cost of the improved ant colony algorithm is reduced by about 1, 100 yuan, the vehicles required are less than those of the ant colony algorithm before the improvement, and the decay rate is also reduced a lot. It can be seen that the improved ant colony algorithm is more suitable for the analysis of the optimal path of fresh logistics distribution.
Students in adolescence are not mature in mind, thought, ability and other aspects, which are easily affected by various emotional behaviors. Positive emotional behavior contributes to students’ mental health and academic progress. Negative emotional behavior would lead to psychological problems and academic frustration. If it is not paid attention to, students may act out of control under the control of negative emotions, thus resulting in serious mental illness, which is not conducive to the education and management of students. Due to the rapid development of social information network and science and technology, the analysis of students’ emotional behavior and educational management by pure human intervention has fallen behind, and it is impossible to timely feedback, track and predict students’ status. This paper introduced the general direction and achievements of human-computer interaction research, and discussed the combination of big data and human-computer interaction. The method of applying human-computer interaction technology to students’ emotional behavior analysis and education management was studied. The pure human intervention method was compared with facial emotion recognition, voice emotion recognition, human-computer body feeling interaction and virtual scene education methods under human-computer interaction technology. Five experimental groups were designed to conduct research in three aspects of emotional behavior analysis, education and learning, and supervision and management. It was found that the average accuracy of facial emotion recognition for emotional behavior analysis was 88.0%; the average course learning efficiency of virtual scene education used for students’ educational learning was 82.8%, and the total progress was up to 99.81%; the average success rate of human-computer somatosensory interaction for supervision and management was the highest, which was 68.1%.
With the rapid development of China’s economy and the continuous improvement of its international status, “Chinese fever” is quietly emerging all over the world, and the teaching of Chinese as a second language has become an independent discipline in China. There is a lot of room for exploration in research. In the past few decades, with the rapid development of science and technology, “intelligence” has become the development trend of the whole society. The potential of smart devices has become more and more widely used, which has also inspired users’ love and ultra-fast adaptability to devices. This paper uses wireless network communication to study language recognition and its type induction in second language teaching, and proposes a resource allocation mechanism based on bilateral induction. Different expressions are designed with different priorities, and after transformation, the follow-up type induction research can be carried out smoothly, and finally the optimal induction allocation scheme is obtained. The research results show that in the teaching type induction, there are 684 new words in volume I, 778 new words in volume II, and 1462 total words in volumes I and II, and they are all summarized. Compared with traditional methods, the search and extraction speed is increased by 45%. Teaching type induction is more effective in the comprehensive use of multiple teaching methods in primary teaching, but it is still inseparable from traditional teaching methods. Therefore, in the specific teaching practice, we should choose a more suitable teaching method according to the individual factors of the teaching content and teaching objects.
As AI technology matures, computational intelligence has also been more widely used. Computational intelligence is an important branch of AI. Because of its global search, efficient parallel and other characteristics, it has become a new method to solve complex optimization problems and has received more and more attention. In the meantime, driven by other technologies such as big data, education has gradually broken away from the traditional teaching methods, broken the traditional time and space constraints, and opened a new chapter. With the reform of the national curriculum instructional patterns, the exploration and practice of educational informatization in the educational circle is rising, and the instructional patterns of intelligent service aided design curriculum is also coming. In this paper, a teaching mode of intelligent service aided design with human-computer interaction (HCI) as the core was proposed. Based on the available results, it is an important reference value for its application and promotion in practice. Through the empirical analysis of the course instructional patterns of intelligent service aided design of HCI under computational intelligence, the classroom instructional patterns method of intelligent service aided design of HCI in the intelligent era proposed in this paper has improved 12.7% in promoting students’ understanding of teaching content compared with traditional methods and has increased by 19.7% in improving students’ full satisfaction with course teaching. Besides, in terms of overall teaching effectiveness, it has improved 22.9% compared to the traditional. It illustrates that the teaching methods presented in this paper can better serve students’ development and improve their overall quality, and also meet the teaching needs of teachers, so that teachers can better carry out teaching activities. At the same time, the curriculum teaching mode of human-computer interaction intelligent service aided design in the intelligent era was discussed, which was conducive to promoting the gradual maturity of the development of computational intelligence and making its application in teaching more complete.
In order to provide various disturbance voltage waveforms for the test of power quality event detection and compensation device, it is necessary to develop a device that can simulate power grid faults. VSC power disturbances generator (VSC-IG, Interruption Generator based on Voltage Source Converter) is the importance of theoretical study and compensation device of power quality test tools, the flexibility to produce all kinds of disturbance voltage waveform, And reduce the harmonic pollution to the power grid. In this paper, the power injected by AC power supply into VSC-IG is controlled to stabilize the DC voltage, and a PI controller parameter tuning method considering the change of resistance parameters is proposed to optimize the dynamic performance of the controller. The SPWM rule sampling method is adopted as the underlying control strategy to realize the generation of disturbance voltage waveform. PSCAD/EMTDC platform is used to build the VSC-IG simulation model and carry out the simulation research. The VSC-IG device is designed and implemented by using the physical prototype hardware platform of 30kVA back-to-back converter in dynamic modeling laboratory. The experiment verifies the main circuit structure and control strategy of VSC-IG in this paper, and also verifies the function of digital controller, which lays a foundation for further research on power quality.
With the continuous development of the internet age, more and more art images are taking on digital forms, resulting in a new way of survival for art image digitization. However, the digitization process of art images is affected by various factors, resulting in poor results and low digital quality of art images. Therefore, this article conducted research on the digitization of art images based on metadata, and utilized BP (Back Propagation) neural network for metadata processing and analysis to achieve metadata visualization and interactive design. Animation production software was then utilized for image compression, transparent display, and modeling, and finally interactive display technology was used to display the dynamic design of art images. 4000 user feedback data and art image metadata from four age groups were collected and named A art image set. Starting from the visual communication effect, accuracy, and fidelity of art images, the differences in dynamic design of A art image digitization were compared. The experimental results showed that 2820 people were satisfied with the visual communication effect of dynamically designed art images, with a satisfaction rate of 70.5%. Only 1070 people in the control group were satisfied. The metadata accuracy of dynamically designed art images was greater than 80%, and the average accuracy was close to the median line, with small overall fluctuations. The deviation value between dynamically designed art image data and standard images is small, and the overall fidelity is relatively high. In short, the evaluation effect of digital dynamic design of art images is very good.
Short-term traffic speed prediction in Intelligent Transportation System (ITS) provides an important idea for solving traffic problems. To capture the spatio-temporal properties of traffic speed prediction, we proposes a Graph Convolutional Network-Gated Recurrent Units with Attention (GCN-GRUA) mode for expressway. The Graph Convolutional Network (GCN) and Gated Recurrent Unit (GRU) were used to extract the spatial and temporal features of traffic speed, and the attention mechanism was introduced to improve the prediction performance of the model. Experimental results from the real traffic data set of Qingyin Expressway show that the proposed model has a significant improvement in prediction accuracy compared with GCN, GRU and GCN-GRU models. In addition, the importance of speed characteristic variables and exogenous variables on the traffic speed prediction accuracy show that the speed data with the closest time interval has the greatest influence on the traffic speed prediction, followed by the daily cycle characteristics of traffic speed. As the prediction time increases, the relative importance of the velocity characteristic variable remains above 0.6, while the relative importance of the exogenous variable keeps rising.
As a new product of artificial intelligence, big data is widely used in daily life. Due to its appearance, people’s lives are more convenient and efficient, but at the same time, there are certain security risks, namely the leakage of private information, especially the financial information problem brought about by financial informatization has a more serious leakage problem. In order to effectively reduce the problems caused by the leakage of financial information privacy, this paper attempted to establish a model of related protection measures for financial big data information security by establishing a three-dimensional encrypted information model of big data or by using differential privacy method and using their own. The three-dimensional encrypted information model of big data overcame the defect that financial information is easy to be broken, while the differential privacy model overcame the defect of inaccurate protection of financial information, both of which can play a better protective role in different applications. The experimental results showed that in the process of accessing financial data information, with the increase of access frequency, the number of sensitive locations changes from 40 to 46. This also meat that a non-sensitive position becomes a sensitive position, which blurs the original sensitive position and achieves the effect of protecting the real sensitive position.
The expansion of the big data network has continuously increased the demand for student education and management, and the student education management system has been designed and developed, but the current student education management system does not take care of the core issues of management – teachers and students. As a result, the current management system cannot effectively manage student education information. In this paper, the biggest purpose of applying big data network to the design and development of student education management system is to solve management problems and improve education efficiency. The main algorithms involved in the network increase the development feasibility of the management system, and the design level of the student education management system is analyzed in order to provide a theoretical basis for the later design and development experiments of the management system under the big data network. Through experiments, it is found that the research on the design and development of the management system based on the big data network can effectively improve the quality of online education by 5.39%, which timely found the students who are left behind in learning, and actively followed up the students’ learning situation.
The development of economy is inseparable from the construction of traffic buildings. Especially in the current road construction, asphalt mixture is mainly used for pouring. Over time, the asphalt mixture is disturbed by other external factors, resulting in a decrease in the performance of the asphalt mixture. Under this background, this paper mainly studied the mesoscopic angle of the construction asphalt mixture through image processing technology, and analyzed the mesoscopic structural characteristics of the construction asphalt mixture. This paper took the void structure as the research index, and performs image enhancement, image denoising, image sharpening, image segmentation and image edge detection on the collected images of building asphalt mixture in turn. In terms of image enhancement, the image after histogram equalization is clearer in texture, distinct in layers and more prominent than the original image. In terms of image denoising, the median filter method is used, and the noise reduction effect is obviously better than other methods. In terms of image sharpening, the contour of the image sharpened by the Laplacian operator is clearer. In the aspect of image segmentation, the threshold segmentation method has obvious image void boundary and detail information, which is conducive to extracting void information. In terms of edge detection, the image lines under the Canny operator are complete, which greatly reduces the loss of edge information. On this basis, the void structure model was constructed and tested experimentally. The results showed that the average equivalent diameter, average perimeter, and average contour area of the voids in each layer have roughly the same trends as the layers increase. Not only that, the detected void ratio was about 8.14%, which was only 1.17% different from the actual void ratio. This showed that the void structure model constructed under the image processing technology has a significant effect on the porosity detection, and this result brought certain guiding suggestions for the follow-up study of mesostructure characteristics.
As a kind of humanistic culture, art has existed in people’s daily life long ago. Based on the research of the relevant literature in the art field, this paper found that there were some problems in the art field at present. Combining with the relevant problems, this paper proposed a remote storage system of art painting resources based on artificial intelligence. The system mainly included network security part and image scanning and recognition part. According to these two parts to achieve the purpose of safe remote storage of painting resources, this paper has carried out corresponding tests on the network security rate and image recognition rate of the system. Under the condition of ensuring the normal operation of the system, the data was compared and analyzed with the traditional painting resource storage method. The system method surpassed the traditional method in most performance with 100% security rate and 100% integrity rate. However, based on the particularity of the system method, it has not been accepted by most people at present.
With the application and development of generative AI technologies such as ChatGPT in the field of education and teaching, higher requirements have been put forward to improve the digital ethical literacy of pre-service teachers. However, there are still impediments to the current development of digital ethical literacy among pre-service teachers. Therefore, based on the social cognitive theory, this study aims to discuss the individual-level, behavioral-level, environmental-level, and social-level factors and their relationships that affect pre-service teachers’ digital ethics literacy. A total of 524 pre-service teachers in China were used as the study population. The study found that the factors influencing pre-service teachers’ digital ethics literacy include seven dimensions: personal values and digital ethics awareness at the individual level, digital ethics education competence and digital technology use skills at the behavioral level, resources and environment of the school and related educational policies at the environmental level, and social recognition at the social level. Among them, there are some interactions between the individual and behavioral dimensions, the environment and individual dimensions, the environment and behavioral dimensions, the individual, behavioral and environmental dimensions, and the social and individual behavioral dimensions two by two, and they play a positive influence on improving the digital ethical literacy of the pre-service teachers, but the interactions between the social and environmental dimensions are not significant. On this basis, the improvement of digital ethical literacy of pre-service teachers is discussed to provide some references for the related research on improving digital ethical literacy of pre-service teachers.
Along with the vigorous development of artificial intelligence in all walks of life, artificial intelligence technology has become an inevitable trend in the reform and development of physical education. First of all, this study takes constructivist learning, motor skill learning, and blended learning as the theoretical basis, and selects the three major goals of synergy, intelligence, and wholeness as the basis, and preliminarily constructs the theoretical framework of the health-promoting teaching (SCT) model for the professional disciplines of physical education in colleges and universities. Then, using the gray correlation model, the system characteristics behavioral sequence and the related factors behavioral sequence were established, and the similarity and dissimilarity of the development trends of the two sequences of factors indicator sequences were measured to confirm their correlation degree. Finally, through the implementation condition elements and equipment function elements obtained from the gray correlation analysis, the SCT model applicable to physical education courses was designed, and the effect of the application of the SCT model in physical education classes and its impact on the physical fitness of physical education students were explored. It is found that the SCT model is characterized by timely feedback, strong relevance and abundant resources, so the improvement of all the scores of the students in the experimental class is better than that of the control class, and the physical fitness of the students is also improved to a certain extent. It shows that the teaching effect of SCT mode is better than traditional teaching mode, and can be used in general multimedia classroom, which has certain universality and promotion value.
In order to meet the needs of high-quality development of the civil engineering industry, it is necessary to carry out corresponding teaching reforms in the level drawing course as a core basic course. The purpose of this paper is to explore whether the case teaching of leveling drawing can effectively improve students’ ability of leveling drawing. By analyzing the level drawing course, the case teaching method of level drawing is designed. Students of a higher vocational school were selected as the experimental objects, and the questionnaire survey was used to understand the current learning status of the students’ drafting and to carry out the teaching practice, and the statistical analysis method was used to explore the teaching effectiveness. After the teaching practice, the students’ learning attitude, skill mastery and teaching satisfaction increased by 46.24% as a whole, which was significantly different from the learning status quo before the practice (p < 0.01). Meanwhile, there was an improvement of 8.52% and 5.57% in learning achievement over the pre-practice and comparison students, respectively. The results indicate that case teaching of leveled drafting can effectively improve students' learning attitudes, develop students' skill mastery, enhance teaching satisfaction, and it has a significant role in promoting students' learning outcomes in leveled drafting. This study confirms the value of case teaching of plain drawing in professional practice and has positive significance for improving the quality of education.
This paper constructs the SAM agile iterative model according to the direction of online art course design, firstly by collecting art teaching related information and initiating the cognitive system of art teaching, and then entering into the iterative design phase to accomplish the development goal of the online art course incrementally through continuous iteration. Finally, after the double iteration phase, the software process enters the delivery phase to complete the design of the online art teaching course. The effect of the online art teaching course and its impact on delivery are analyzed in conjunction with the dynamic key-value memory network model based on the forgetting curve. The results of the memorization ability of art knowledge experiments in the pre-test have a mean value of 35.259, and the post-test has a mean value of 53.1254, while the Sig value of the paired test is 0.000, 0.000<0.05, which indicates that the effect of using the online course for art learning based on the Ebbinghaus forgetting curve is more significant on the learning of art knowledge than other applications. The regression results of the full sample model showed that overall instructors' use of big data aids for online art instruction significantly affects instructional delivery, t=1.245, P=0<0.05, which is significantly positive at the 1% level, indicating that the more adequate the use of these instructional methods, the higher the probability that students will rate their satisfaction with the instructional delivery.
This paper analyzes the current teaching development direction of intelligent simulation context, and argues that the development and updating of semi-open human-computer dialogue systems based on scenes and topics need to rely on the division of labor between machines and human beings. In this regard, the development strategy of intelligent teaching of English dialogue interaction is proposed by combining the speech corpus annotation system designed based on artificial intelligence technology and the dialogue interaction teaching strategy derived from the interactive teaching model. The corpus annotation model of multi-layer perceptual machine is designed, which consists of real-time interaction module, core technology and algorithm module and data storage module. Draw a mind map of the framework for analyzing the effectiveness of dialog teaching, and develop a dialog interactive teaching strategy for pre-class dialog, in-class dialog and after-class dialog by invoking the interactive teaching model. Analyze the annotation results of the intelligent annotation model of speech corpus in LDC corpus and UN corpus. Observe and organize the teaching implementation effect of the dialogue interactive teaching strategy, and illustrate the pedagogical feasibility of the English dialogue interactive intelligent teaching development strategy proposed in this paper in combination with the learning achievement and questionnaire results. In the classroom English conversation, the zero feedback of dialog teaching is only 1.60%. The linguistic feedback of dialogue is reflected in the proportion of 33.87% of combing and summarizing, 33.55% of judging the correctness and error, and 30.99% of extending and pursuing questions. The effectiveness of English conversation classroom is improved.
In this paper, we design a cloud-integrated financial robot based on artificial intelligence technology to provide advanced financial analysis and decision support for financial institutions. The robot platform is embedded with a large amount of financial domain knowledge and data, which can provide uninterrupted financial services to customers using a dialog engine. At the same time, it is equipped with the attention mechanism – long and short-term memory neural network model, in investment transactions and credit risk prediction, which can bring a new digital intelligence experience for financial institutions. The standard and similar sentence recognition accuracy of the article robot dialog engine can be stabilized at more than 90%, and the average access time of the user’s access request is about 0.15s. The importance distribution of financial credit risk indicator features is 5~24, and when the number of features takes the value of 10, the risk prediction accuracy of the robot in this article is the highest, 97.98%. When the prediction model is trained to 50~70 epochs, the Loss value of the financial robot converges to 0.15~0.17. The accuracy of the model chosen in this paper for risk prediction as well as stock prediction is 95.35 and 96.2% respectively. And the absolute difference between the predicted stock price and the true value of the model in this paper is 0~0.28 yuan. Combined with DMI strategy for stock trading, the return is 30.7%. The financial robot improves the user experience and increases the value of risk control as well as stock prediction for financial institutions.
In this paper, seven dimensional variables of marketing mode development and product quality improvement are selected under the 5G network security theory. In order to clarify the relationship between the two, the questionnaire was used to obtain the quantitative values of the seven dimensional variables, and then the Pearson correlation analysis was used to test whether the dimensional variables could be used in the regression analysis. After passing the test, multiple linear regression is used to interpret the relationship between the development of agricultural e-commerce marketing mode and product quality improvement. The regression equation of strengthening brand awareness on product quality is B1=0.511+0.439*B4+0.653*B5+ 0.327*B6+0.297*B7, which demonstrates the relationship between product quality enhancement and strengthening brand awareness in the development of agricultural products e-commerce marketing model in a more intuitive way. This study makes people have a more new cognition of the role relationship between the two, which is of great significance for practicing the goal of synergistic development of the two.
Computer multimedia technology has brought unprecedented innovation to the film and television production industry. Multimedia technology in film and television post-production mainly focuses on two aspects of image processing and audio processing, this paper selects the skin color enhancement and voice enhancement for further research. Adaptive skin color enhancement method is proposed, IMCRA-OMLSA audio enhancement method is selected, and relevant experiments are designed to compare this paper’s method with other classical skin color enhancement and voice enhancement methods respectively, and the effectiveness of this paper’s method in skin color enhancement and audio enhancement is examined through the results of subjective and objective evaluation. The accuracy and F1 value of this paper’s adaptive skin color detection method are 0.961 and 0.945, respectively, and the performance of skin color detection is good. The adaptive skin color detection method in this paper has the best performance with a comprehensive evaluation score of 6.81. In the objective evaluation of speech enhancement, the PESQ, STOI, WSS, and RMSE values of IMCRA-OMLSA method in this paper are 2.03, 72.36, and 38.06, respectively, which are all optimal results. On subjective evaluation, the MOS value of IMCRA-OMLSA method is 1.88 which is the highest value.IMCRA-OMLSA method has the best performance for speech enhancement.
In order to improve the intelligent processing capability of the server of the electric power information platform, the intelligent control platform of electric power informatization based on intelligent data analysis is designed. Taking the regional electric power headquarters as the base point, deploying the electric power informatization intelligent management and control workbench, connecting the necessary systems for electric power operation through the telecommunication management network (TMN), and completing the platform hardware structure design. Divide the platform monitoring function into four parts: query instruction issuance, feedback data reception, data parsing, and data storage, and monitor power data in real time. Deploy data collection algorithms on the data collection server to collect power data such as power harmonics, effective voltage and current, active and reactive power, and harmonic distortion. And Deep Belief Network (DBN) is used to train the anomaly detection model, which realizes the detection of abnormal behavior of the system. Determine the experimental methods and steps, and test the results: the server of the intelligent control platform for electric power informatization designed in this project passed the pressure test of the number of clicks per second and throughput of 100 and 500 simulated users, and has superior traffic processing capability. Application test of the platform, through the test to achieve the design requirements of the system’s various functional modules, in the distribution network line and equipment operation status monitoring, fault precision judgment, fault time statistical analysis and daily repair and other work has achieved certain results.
In today’s increasingly complex and dynamic network structure, cloud computing brings great convenience to computer users and meets people’s requirements for rapid computer data processing. The article firstly analyzes the cloud computing architecture and the security threats existing in the cloud environment, and explains the importance of cloud computing access control mechanism to ensure data security, starting from the traditional access control. Then it introduces the multi-authority attribute access control scheme based on blockchain and elliptic curve improvement, on the basis of which it proposes a blockchain-based cloud data security sharing model and a blockchain transaction privacy protection scheme, which both meets the user data privacy protection needs and realizes privacy computing. Finally, the security of the two schemes is analyzed, and compared with other schemes with the same mechanism. The results show that the blockchain-based cloud data security sharing scheme has better performance and scalability, which shows a stable linear growth of 1x, and the time load introduced by this scheme while enhancing the security of the encrypted data sharing system is acceptable compared to the other schemes to satisfy the application scenarios with large-scale access requests. At the same time, the blockchain transaction privacy protection scheme ensures data privacy while the average time consumed meets the user’s requirements for fast response.
In order to improve the accuracy of automatic detection of malicious code, this paper focuses on the “texture” features of malicious code and the characteristics of different types of malicious code, which are also different, and uses them for the automatic detection of unknown malicious code by using the four machine learning algorithms of KNN, RF, NB and SVM to perform single-feature detection and multi-feature (GLCM, LBP and ngram feature merging) detection respectively. Four machine learning algorithms, namely KNN, RF, NB, and SVM, are used to perform single-feature detection and multi-feature (GLCM, LBP, and n-gram feature merging) detection respectively, and analyze the accuracy of the spatial relationship feature-oriented malicious code detection scheme. A multi-version oriented data protection model is proposed for the data storage space, data version, quantity management and recovery requirements involved in service emergency response. The relative performance errors between its data protection scheme and the plaintext scheme and the simple add noise scheme are analyzed. In all four machine learning algorithms, the detection rate of fused features is higher than that of single features, and the maximum difference can reach more than 60%. When takes the value of 9 or 3, the data privacy protection algorithm, the plaintext algorithm, and the noise-only addition algorithm in this paper have similar accuracy rates. With proper noise selection, this paper’s scheme has good performance in real simulation.
As an important part of the excellent traditional culture of the Chinese nation, Chinese Wushu condenses the wisdom of the Chinese nation, contains the genes of Chinese culture, and has important communication value. Based on the big data Hadoop technology, the article proposes a content recommendation design scheme for all-media communication of wushu cultural communication, and introduces the LFM model and MBGD algorithm to construct an intelligent recommendation model of wushu cultural communication content under the framework. Then, based on Lasswell’s 5W model, the fsQCA method was utilized to explore the relevant factors affecting the effect of martial arts cultural communication. When the number of hidden factors of LFM-MBGD intelligent recommendation model is 55, its RMSE is 0.92, and the HR@K value of the model can reach 62.12%. The consistency level of the existence and non-existence states of each conditional variable of the communication effect of wushu culture is less than 0.8, and the overall coverage rate and the coverage rate of each path are higher than 0.85.The wushu culture communication system driven by intelligent technology can start from building an online resource base of wushu culture, broadening the communication paths of wushu culture, sounding the laws and regulations of wushu culture communication, and building the brand of wushu to improve the communication effect of wushu culture.
In this study, the ecological environment landscape pattern index was selected to construct the ecological environment landscape data representation model under the self-organized feature mapping network (SOM) technique. The input data to the model were monitored under unsupervised conditions and made more sensitive to specific characterization information in the neuronal structure (Hebb), which resulted in different groups of regular data. The moving window method shows that the landscape index is unstable under the 1000-3000m window and the magnitude of change begins to decrease at the 4000m scale. The data tends to stabilize at 5000m scale, and the stability of the data decreases at 6000-7000m, and the anomalous data increases at this time. In terms of landscape level, the aggregation and connectivity of the overall landscape of the study area increased and landscape fragmentation, complexity and diversity decreased under the 4000m window. The land use change model based on SOM network can well reflect the law of land use change in the sample area, which greatly expands the spatial analysis research method of land use change.
STEM education emphasizes the in-depth integration between the knowledge of different disciplines, which is based on real problem solving, aims to establish an organic link between education and life, and takes the cultivation of composite talents with a sense of innovation and hands-on ability as the fundamental purpose. Aiming at the current problems of STEM education, the development path of STEM+ education based on digital visual virtual reality is proposed. Then, combining the DEMATEL method and Interpretive Structural Modeling (ISM), the dynamic factors affecting the development of STEM+ education are explored. Finally, the fuzzy set qualitative comparative analysis (fs/QCA) method was used to analyze the group path of STEM+ education high-quality development. The results of the analysis of motivational factors show that the governmental promotion among the extrinsic motivational factors has a high centrality and is a deep factor that drives the development of STEM+ education. Synergistic motivational factors play the largest role among the three dimensions and are the key to ensure the development of STEM+ education. Endogenous motivational factors are the direct motivational factors for the development of STEM+ education and need to be focused on control. The analysis of the grouping paths in region C, for example, shows that there are two high-level grouping paths and three non-high-level grouping paths, multiple grouping paths with different paths, and high-level grouping and non-high-level grouping are in an asymmetric state. There are some differences in the grouping paths in the east, center and west, and the three regions’ high-quality development of STEM+ education cannot be separated from the support of state factors and response factors. This paper provides a path reference for realizing high-level STEM+ education high-quality development.
Accurate short-term load forecasting of distribution networks can ensure the normal life and production of the society, effectively reduce the cost of power generation, and improve the economic and social benefits. Aiming at the multivariate information that affects the power load, this paper utilizes factor analysis to reduce the dimensionality of the original influencing factors, and obtains the main influencing factors with the highest contribution rate, so as to guarantee the accuracy of the neural network prediction. On this basis, the neural network structure is improved by combining AlexNet and GRU, and the short-term load prediction model of distribution network is finally constructed. The relevant charge data of N village in 2023-2024 is used as a research sample to analyze the main influencing factors of its short-term load change, and three main influencing factors affecting the load change in the short term are identified as temperature, air pressure, and humidity factor. Based on the real data of N-village distribution network to carry out prediction simulation experiments, the load short-term prediction curve of this paper’s model has a better fitting degree and good stability, and the values of the prediction result evaluation indexes MRE, RMSE and MAE are smaller than those of the other comparative models, which are basically able to maintain a prediction accuracy of more than 90%.
Aiming at the problem of large prediction error caused by the complex background of macroeconomic prediction, this paper proposes a macroeconomic prediction model based on time series clustering. The model adopts sparse self-encoder to deeply mine the features of the input vectors, constructs a bidirectional threshold cyclic unit network, and predicts the preliminary trend of the macroeconomy, and proposes a time series deep clustering algorithm that integrates the multi-scale feature extraction and clustering objectives of time series data into the same network. A sample generation strategy based on data augmentation and a multiclassification assistance module are used to extract the invariant patterns contained in the time series data to obtain a better representation for targeting time series clustering. Comparing this paper’s model with different forecasting models, the RMSE metrics are 0.0038 and 0.003 for the two time horizons, which are better than the other two models. The prediction range of this paper’s model for future GDP is 5.8%-5.9%, which is smaller than the GDP prediction range of the ARIMA model, indicating that this paper’s model is suitable for the realistic application of macroeconomic forecasting.
Computer image-assisted design, as a product born in the era, provides more inspiration and creativity for art design. Based on the study of the basic theory of color design and the theory of color harmonization, an intelligent color matching model integrating visual aesthetics based on conditional generation adversarial network is proposed. Then a candidate graphic layout generation method based on visual saliency is proposed, which not only considers the visual saliency of each element in the image, but also considers how to generate candidate text regions under the constraints of aesthetic rules. In the visual analysis analysis experiment, under different color transformations, the F-value of the subject’s gaze time was 2.548, with significance P=0.051, which is not significant. The F-value of average gaze point is 6.398, significance P=0.002, significant difference is obvious. From this, it can be concluded that the artistic innovation design method proposed in this paper can make the subject’s point of interest change with a large difference, and the color that highlights the target object can significantly attract people’s attention, which is a feasible artistic innovation design scheme.
Through the investigation of Chinese reading comprehension ability, the evaluation index system of Chinese reading comprehension ability is constructed, combining the hierarchical analysis method (AHP) and the data characteristic method (CRITIC) to combine the indexes to assign weights, and then using the fuzzy comprehensive evaluation model to calculate the indexes to quantify Chinese reading comprehension ability. After that, the indicators affecting Chinese reading comprehension ability in language education were screened and sorted out using a binary logistic regression model, and the Chinese reading comprehension ability education was optimized based on machine learning. This paper constructs a systematic evaluation model of Chinese reading comprehension in colleges and universities with 5 first-level indicators and 22 second-level indicators, and obtains the final score of the system of 87.73 points, the fuzzy comprehensive score of the five first-level indicators of “reading ability, general comprehension ability, deep comprehension ability, evaluation appreciation ability, and comprehensive application ability” is between 86.63 points and 88.68 points, and the fuzzy comprehensive score of 22 second-level indicators such as vocabulary, language comprehension ability and logical reasoning ability is between 80.68 points and 90.38 points. The final score of each indicator was 88.67, and the model was evaluated extremely well. In addition, the empirical analysis showed that all the indicators had a significant effect on Chinese reading comprehension (P < 0.05), and the language education should be optimized in terms of vocabulary mastery and the cultivation of critical thinking.
Reasonable and scientific supplier selection and resource allocation is a prerequisite for enterprises to optimize the quality of supply chain and avoid business risks. In this paper, we select multiple supplier evaluation indexes, use decision tree algorithm to train and calculate the hierarchy of suppliers to determine the supplier options that can be selected. Then the main body of procurement resource planning decision-making is divided into three types: purchaser, database vendor, and customer, to establish a multi-objective model for optimal allocation of procurement resources, and the model is optimized by genetic algorithm to solve the optimal allocation scheme of procurement resources. The supplier selection method based on decision tree can realize the optimal selection of suppliers by constructing a decision tree and transforming it into If-then classification rules. The procurement solutions based on genetic algorithm are 10.44%, 4.31%, and 5.14% higher than B, C, and D solutions, respectively, for better allocation of procurement resources.
As the most intuitive visual phenomenon of animated films, color has emotional characteristics that are closely related to the viewers’ emotional experience. From the perspective of chromaticity and psychology, we explain the method of color emotion quantification, calculate the fuzzy affiliation degree and grey correlation degree for the uncertainty and fuzziness between color and emotion mapping, put forward the method of fuzzy grey correlation for emotion mapping in animated movies, and carry out the experiment of color emotion mapping in animated movies. Through the experiment, it is found that the character color schemes of warm, cold and neutral colors are suitable for the design of character color emotion experience in animated movies. Taking the animated film “Ne Zha: The Descent of the Magic Boy” as the research object, the correlation between color emotion mapping and character matching is further explored. Most of the H-value color blocks in Ne Zha are distributed between 0-60, which indicates warm and neutral tones, and the distribution of S-value and V-value color blocks shows a clear trend of decreasing color saturation, while the overall luminance remains basically stable. The whole film takes the proportion of red, blue, color purity changes and other aspects of color design to achieve the position of the characters, the character of the transformation of the transformation of the matching and implied.
Aiming at the allocation of teaching resources for school affairs scheduling, a decision-making model for school affairs scheduling is designed based on a multi-objective optimization model. The “conflict detection and repair” module is added after the “initial population generation” operation in the traditional genetic algorithm, which decouples the scheduling model and meets the needs of scheduling decision-making. The designed method is compared with the standard genetic algorithm and stochastic two-point crossover genetic algorithm on the data set, and then the efficiency of resource allocation for school scheduling is improved by solving an example problem. The average faculty satisfaction with scheduling is 2.8, which is about 17% higher than the second place NPGA. Applying the algorithms to a college scheduling project, the feasible solutions of the algorithms in this paper satisfy all the various constraints, and the results of the three-stage style algorithm in the self-selected course scheduling mode yield better solutions than the baseline algorithm based on the course set in any of the arithmetic cases. This paper provides an informative solution path for the allocation of school scheduling resources, which can satisfy the course allocation needs of the three parties: teachers, students and schools.
Speech-text multimodal large model as a key tool in the operation of the power industry, its fault prediction performance directly affects the operational safety of mechanical equipment, this paper designs a detailed scheme for the optimization of its performance. Firstly, the structural design of the unimodal model is discussed, and the audio classifier based on Wav2Vec2 and the text classifier based on BERT are used to pre-train the model. Based on the above foundation, a multimodal model is introduced, with the cross-attention mechanism as the fusion strategy, so that the different modal information in the deep neural network is fused with each other, thus improving the accuracy and robustness of the recognition task. After completing the fault feature extraction task, on the premise of introducing the relevant theory of BNN, the structure of BBN is optimized, and after fusing the HC algorithm, BIC and annealing idea, the fault diagnosis method based on the improved BBN network is constructed by combining the fault feature extraction method in the electric power industry and the optimized BBN method. The effectiveness of the method is verified through simulation experiments. The prediction accuracy of this paper’s method for nine categories of fault data is above 90% at a high level, and the prediction accuracy of faults in some categories can reach 100%. The multimodal model fusion strategy proposed in this paper significantly improves the performance of fault feature recognition, in addition, the fault diagnosis method based on the improved BBN reduces the computational volume of the model and improves the fault prediction ability of the model.
With the accelerated pace of life and outdoor running constrained by the environment and other factors, the consumption in treadmill is on the rise, and at the same time, the design of treadmill is more and more concerned. Starting from the customer demand, the user demand analysis method is formed by synthesizing KJ method, rough set theory, KANO theory and AHP, and combining with the prediction theory of destructive innovation technology. And the design requirements and their weights derived from the QFD model are used as the criteria for PUGH decision evaluation to select the optimal treadmill design solution. Finally, the treadmill design scheme is applied specifically. In the planned turnover analysis after the treadmill is put into operation, the turnover scale is increased from 0.73 billion yuan in 2016 to 180 million yuan in 2020. After the experimental test, both the percentile 10% of the female human body and the percentile 90% of the male human body in the treadmill to carry out some of the necessary actions are in a more comfortable state, at the same time, the various joints of the force and angle are in a reasonable range. The design program of this paper’s method outputs better evaluation results, and meets the user’s expectations.
The development of off-grid wind power to hydrogen systems is crucial for promoting renewable energy, reducing dependence on fossil fuels, and achieving sustainable energy development. However, the volatility of wind power can lead to problems such as shortened service life of batteries and electrolyzers. This study proposes an optimized scheduling strategy for off-grid wind power hydrogen generation systems, considering the degradation of batteries and electrolyzers, with a focus on the impact of battery state of charge (SOC) overrun and electrolyzer overload on system operation. A voltage degradation model for electrolyzers was established by analyzing different operating conditions, aiming to improve utilization capacity and reduce degradation costs. Additionally, a degradation model for energy storage batteries was developed, considering factors such as cycle depth, cycle number, and SOC overrun, to optimize charging and discharging operations, extend battery life, and reduce degradation costs. The effectiveness of the proposed scheduling strategy was verified through detailed simulation analysis, demonstrating improved wind power consumption capacity, slowed degradation of batteries and electrolyzers, and ultimately enhanced economic benefits for the system.
Taking Xi’an metro station as an example, we analyze the artistic methods of metro public space to explain and spread the ancient rhyme culture, modern industrial civilization, modern revolutionary culture and other regional cultures, and put forward the concept of further innovative design in terms of increasing the occupancy, enlarging the pattern, highlighting the cultural characteristics of the ancient capital, and strengthening the comprehensive utilization of the metro public space in multilevel and multi-carrier. The study first establishes a multi-objective metro public space configuration model based on genetic algorithm to realize the reasonable layout of metro public space. Then it proposes a style migration method of regional cultural elements based on the improved circular consistency generating adversarial network, and realizes the color migration under multiple reference objects to realize the interface and color design of the entrances and exits of the metro public space, the station hall level and the platform level. The results of user experience show that the overall public space of Xi’an Metro Line 4 has an intermediate centrality of 8.8216 and an intermediate centrality potential of 0.5859, and its overall suitability is relatively balanced.
In order to promote the development of medical rehabilitation industry, the study deeply analyzes flexible wearable devices and utilizes joint moment estimation based on skeletal muscle model in order to calculate the joint moments of elbow and wrist joints, so as to carry out the design of flexible pneumatic wrist joint system. And a fuzzy-PI dual-mode control strategy is used in the position control of the flexible pneumatic wrist joint to construct an intelligent flexible rehabilitation device for the wrist joint. The wrist joint rehabilitation equipment is systematically tested to analyze its practical application effect. The response speed of the fuzzy-PI dual-mode control method is faster than that of the traditional PID control strategy, and it can effectively reduce the vibration noise. The accuracy of the hybrid recognition method in this paper is 97%, which is better than the single recognition model. The average time taken by the wrist rehabilitation device on the seven tasks of lifting, grasping, undertaking, pulling, pushing, probing down and probing up is between 2.06 and 2.67 seconds. The output moments of the wrist and elbow positions were 17.1 and 11.6 N.m respectively for the human body-worn wrist joint rehabilitation device with 50N driving force output, and the joint output moments decreased significantly, and the joint comfort of the human body was improved greatly.
Currently, visible and infrared image fusion (VIF) technology has a wide range of applications in road safety monitoring, anti-surveillance, etc. However, the traditional image fusion algorithms in the feature fusion process will have limitations such as part of the information is lost, etc. For this reason, this paper proposes an infrared visible image fusion algorithm based on the double-branching and decomposition of the results. The algorithm firstly adopts the dense block method, extracts visible image features, and uses a feature pyramid network to extract infrared features. The algorithm firstly adopts the dense block method to extract the visible image features, and uses the feature pyramid network to extract the infrared features, then, based on the deep learning network structure to extract the image information of different modalities, and designs the fusion network constrained by the three loss functions of the gradient loss, intensity loss and decomposition loss, so as to obtain a good fusion effect of the image. The experimental results show that the proposed algorithm achieves the optimal value in five indexes, and reaches sub-optimal value in one index, indicating that the proposed algorithm fuses the images with the optimal value and sub-optimal value. At the same time, the proposed algorithm retains the main thermal radiation information of infrared images better than other algorithms such as DenseFuse and IFCNN, which is superior to some extent.
Aiming at the RRT* algorithm in unmanned aerial vehicle (UAV) path planning, there are problems such as poor target bias, slow convergence speed, and tortuous path. This paper introduces an improved Bi-Informed-RRT* algorithm (BPD-APF-Informed-FARRT*), integrating a dual-path balancing operation strategy, a partition-biased sampling strategy, an artificial potential field guidance approach, and a fuzzy adaptive step-size strategy. To begin, the third point between the start and target points is chosen as the middle point, allowing four random trees to be generated at the same time at the start, target, and middle points, hence resolving the delayed convergence issue. Second, the artificial potential field method and the partition-biased sampling strategies are employed in both path generation and optimization phases to guide the placement of new nodes, tackling issues with poor target bias. Then, to address the intricacies of global environments, a fuzzy adaptive step size adjustment strategy is incorporated to boost the exploration efficiency of the growing tree in complex obstacle scenarios. Finally, leveraging the principle of triangular inequality, redundant nodes are removed, and the path is refined using the B-spline curve. Path planning simulation experiments were performed using MATLAB software. The results show that BPD-APF-Informed-FARRT* has more significant advantages in many ways compared with the Bi-RRT*, Informed-RRT*, and Bi-InformedRRT* algorithms. This improved algorithm is a practical and feasible method for solving similar problems.
Globally, tackling climate change and promoting low-carbon development has become a universally accepted course of action. self20century90Since the 1990s, human understanding of climate change has gradually deepened, and a scientific theoretical system and systematic governance framework have been constructed. In order to deeply understand the research status and development stage of carbon emission reduction path, this paper systematically combs and analyzes the relevant literature. This study was selected based on the Web of Science core collection database 2006 Year to 2023 Published by global scholars 8679 the English documents were used as research samples, and CiteSpace software was used to conduct an in-depth visual analysis of the number of published papers, the group of core authors, the distribution of research institutions, published journals, keywords and key areas of research. The results reveal that research in the field of carbon emissions in1991Before 200 years, it was still in its infancy, and then the number of literature increased year by year. The main research institutions are distributed in China, the United States and the United Kingdom, and the research in this field involves environmental science, engineering, environmental science, economics and other disciplines. In addition, climate change and energy development and rational utilization are the two major research hotspots in this field. The study results indicate that: (1) During the study period, the research of carbon emission reduction path received high attention, and the publication volume of relevant literature continued to increase and the growth rate increased significantly. (2) Through the analysis of the author group, it is found that a clear core author group has not been formed in the field of carbon emission reduction path. (3) The main research forces are concentrated in China, the United Kingdom, the United States and other countries, among which 13 research institutions including the Chinese Academy of Sciences and Tsinghua University have shown significant research ability and influence, and the cooperation between the research institutions and the authors is very close.(4) In terms of published journals, yes10The international journals show high attention to the field of carbon emission reduction, covering environmental science and ecology, energy and fuel, environmental engineering, economics and public management and other fields. Through the visual analysis of keywords, it is found that energy transition, microstructure research and carbon emission are the key objects of current research. Based on the analysis results and the actual situation, this paper puts forward the focus and direction of carbon emission reduction path research, aiming to provide theoretical and practical reference for the realization of carbon emission reduction targets.
The rapid development of artificial intelligence technology provides new tools to optimize the design and application of site-specific integrases and drive innovation in this field. In this study, a site-specific integrase generation model based on artificial intelligence was designed. The learning effect of the model to generate site-specific integrase is improved by mining sequence data of site-specific integrase with feature selection and discretization, and then using generative adversarial network as a framework to extract the detail information of protein sequences by using convolutional layer, and extracting the global features of sequences by using self-attention layer. In addition, to address the degradation problem during training, a residual structure module is constructed and spectral normalization is used to ensure training stability. Meanwhile, Gumbel Softmax Trick is used to solve the problem of non-returnable gradient of discrete data generated by the model. The sequence of the site-specific integrase generated by the model showed 92% identity with the training set, which has better sequence quality. In terms of amino acid composition, the Pearson value with the natural amino acid composition was greater than 0.8, and the two were highly correlated. The site-specific integrase can increase the expression of bax protein and decrease the expression of bax-2 protein and Ki67 protein in lung cancer patients, which is favorable for patient treatment. It can up-regulate the expression of ovarian STAR, CYP11A1, CTP19A1, and 3β-HSD genes and promote steroidogenesis in ewes. The alkane content of the group of strains incorporating site-specific integrase was 57.25%~63.00% lower than that of those without the enzyme in a high concentration of petroleum pollution environment.
Based on the excellent achievements of deep learning technology in image recognition in several fields, then the convolutional neural network is expected to play its superior performance in the recognition of micro features of Chinese herbal medicines. The object of analysis in this paper is the microscopic feature images of Chinese herbal medicines, and the residual network will be improved in order to improve the recognition accuracy of the network model on the microscopic images of herbs. On the basis of the traditional CNN network model, CBAM based on mixed domain attention mechanism is added, and residual connection is introduced to increase the transfer of gradient and information flow, preserve image feature data and reduce feature loss. Improved from the traditional residual structure to moving inverted bottleneck convolution (MBConv), the SE module and SAM module are added to the MBConv stage respectively to optimize the feature extraction performance and improve the accuracy of the classification of microscopic features of traditional Chinese medicine. The effect of the addition of the attention mechanism on the network model is analyzed, and the network model is examined in conjunction with the constructed dataset of powdered microscopic images of commonly used Chinese medicinal herbs.The average accuracy of the Attention-TCM-Net network model on the test set reaches 96.47%, which is an improvement of 0.85 percentage points compared with that of the ResNet34 network, and meanwhile, the convergence of the model is significantly better than other models.
With the gradual standardization, professionalization and standardization of the senior care service industry, the construction of the talent team to grasp the senior care service has become an important part of the high-quality development in the senior care service. It is worth noting that at the present stage, there is a serious shortage of nursing staff in China’s elderly care institutions, unreasonable distribution of age structure, disproportionate gender ratio, low cultural level, lagging comprehensive quality, and weak professional ability and skills. To address the above problems, based on the study of relevant information and using the questionnaire survey method, survey interviews were conducted on the nursing staff of the all-weather work mode in the nursing institutions of Hospital C and Hospital F, as well as the nursing staff of the shift work mode in the nursing center of Hospital J and the nursing institution of Hospital L, in order to grasp the personnel arrangements, the nursing staff team, and other professional trainings of the four nursing institutions. Further, comparing the 24/7 care work pattern of the nursing institutions with the shift system work pattern and the difference in burnout found that the shift system work pattern is relatively better and can reduce the burnout of caregivers in their caregiving work. Elderly caregiver work is labor-intensive content, and the burnout resulting from a heavy workload will increase the instability and uncertainty of the caregiver’s career. In order to avoid this phenomenon, we should strengthen the detection and prevention of burnout among nursing staff in nursing institutions, and actively carry out heart health counseling, goal planning and professional knowledge training for nursing staff in the shift system, so as to comprehensively improve the comprehensive quality of nursing staff in the shift system, and thus contribute to the high-quality development of nursing institutions.
Control techniques of Smart windows using Multi-parameter neural feedforward systems as a control strategy shows great potential in improving not only the energy efficiency geometrically but also the building’s indoor environmental quality. In this study, a new smart window control is developed that is based on neural networks which are able to implement multi control strategies in various conditions with regard to temperature, humidity, light and air quality. This allows for a further development of the system: firstly, it thoroughly presents the model, which facilitates the understanding of the mathematic modeling of windows’ dynamic position and, at the same time, shows how the neural network works. The structure comprises a perception layer, which provides perception of the environment, processing layer for analysis and decision making on the input data, and the last action layer that performs windows’ actuation and gives feedback on the action implemented. In terms of the system’s control efficiency, timing, energy consumption and seeking users’ satisfaction, the performance of this control system outperforms other existing systems in empirical application. The control accuracy attained in the proposed system is 97.8%. What is more interesting about this approach is the energy efficiency which stands at 94.3%, this is only the bare minimum, estimation says it surpasses the rest by a great deal. The successful realization of this control system is an important step toward the development of smart buildings that can be relied on for excellent results.
As the digital economy develops, the use of digital methods for the storage of financial documents is being commonly adopted. To protect the data security of financial documents and ensure the traceability of data, this article designed a secure storage and traceability system for financial documents based on blockchain. Firstly, a blockchain platform with a private chain that had better security and scalability was selected, and the data structure and smart contract design were then carried out. Secondly, an identity authentication and permission management mechanism was established, and data storage and transaction processing modules were designed. Asymmetric encryption was used to secure data, and the digital signatures were combined to ensure the integrity of financial documents. Finally, the traceability function was achieved through the immutability of blockchain technology. The task of storage and traceability was accomplished by designing a secure storage and traceability system for financial documents in conjunction with the blockchain technology. In the experiment, it only took about 50 seconds to process traceability tasks with an interval of 50 people, which was shorter than the traditional system’s 180 seconds; it also maintained an accuracy rate of over 90% in traceability tasks with an interval of 100 people; in the face of 1000 network attacks in a short period of time, the financial management system based on blockchain technology was only invaded 20 times, while the traditional financial system was invaded 200 times. This system, in terms of time, traceability accuracy, and data security, were all improved over the traditional system. The design of a secure storage and traceability system for financial documents based on blockchain technology is conducive to strengthening the security of data and the accuracy of traceability.
This study aims to build a framework called Tucker Reasoning Learning Method to train the upper layer knowledge graph (KG) making explainable and reasonable decisions. The numerical experiments show that the accuracy is 84%. The contributions are as follows: (i) It overcomes in-explainable problems of using deep learning method; (ii) It has more feedback rings and reasonable paths than decision tree method; (iii) Compared with RESCAL’s application in reasoning domain, it enhances 22 percentage points. It is suitable for application scenarios like financial, justice, and medical decision-making, which require explainable and reasoning paths. This study builds a framework called Tucker Reasoning Learning Method to train the upper layer knowledge graph to make explainable and reasonable decisions. The method has the accuracy of 84%, which enhances 22 percentage points compared to the SOTA methods.
Like money or gold, data has emerged as a new class of economic commodity. Big data is now a factor of production on par with other material resources, having permeated every aspect of today’s economy and society. Social development inevitably leads to population aging, which affects all facets of social life, particularly social and economic development. Nevertheless, systematic and thorough study on how population aging affects economic development is still lacking. The economic and fiscal policy trade-offs of aging on economic growth are the main emphasis of this article, which is based on big data techniques. This study examines the effects of population aging on economic development from the perspectives of economic growth, social security, and financial pension expenses, based on an analysis of the current state of population aging and its drivers. It was designed to address the aging of province A’s population and discovered that it not only caused the share of the working-age population to decrease, but also decreased the resources available to the labor force. The proportion of tax revenue in total fiscal revenue will continue to be over 82% by 2021, with 73% of the population being between the ages of 15 and 64. The scale of fiscal pension expenditures in Province A has shown a clear upward trend.
In recent years, tourism has rapidly developed into a key economic sector, with travel route recommendation algorithms playing a vital role in enhancing tourists’ experiences. These algorithms often utilize large amounts of travel data, social networks, and interest hotspots maps to recommend optimal routes. Social networks, as online platforms for communication and content sharing, help connect people, while interest hotspots maps visualize popular topics on social media. This paper proposes a tourism route recommendation algorithm based on social networks and interest hotspots maps, combining tourist preferences and scenic spot data. By analyzing tourist needs and scenic spot conditions, the algorithm improves route recommendations, reducing analysis time and increasing accuracy. Research results show that, before using this algorithm, tourists rated travel time, routes, and attractions at 87.25, 86.84, and 88.62 points, respectively. After using the travel route recommendation algorithm, tourists’ satisfaction was 95.76 points, 96.48 points and 92.89 points respectively. These results can showed that the travel route recommendation algorithm can improve the satisfaction of tourists, and that the research of travel route recommendation algorithm based on social networks and interest hotspots map was of practical value. This also provided a new research path for tourism route recommendation technology.
With the continuous growth of the national economy and the expansion of market demand, traditional finance has gradually turned to digital transformation, and the emergence of digital finance has brought new breakthroughs to the economy. With the continuous development of the times, in order to meet the needs of the market, digital finance and commercial investment are constantly integrated. Therefore, this paper selects the returns and risks of digital financial investment as the research topic, and predicts the investment returns of the five major online banks by analyzing the digital financial portfolio investment return prediction system. The machine learning algorithm is introduced to optimize the digital financial portfolio investment return prediction system. The investment return rate is predicted by the optimized digital financial portfolio investment return prediction system, and then compared with the actual investment return rate. The experimental results show that the predicted value of the traditional digital financial portfolio investment return prediction system for the online bank financial management return rate differs from the actual return rate by 1%-2%, while the predicted value range of the digital financial portfolio investment return prediction system for the online bank financial management return rate is the same as the fluctuation range of the actual return rate. From the experimental data, it can be seen that the digital financial portfolio investment return prediction system based on machine learning can effectively improve the prediction ability of the digital financial portfolio investment return prediction system, making the predicted value closer to the actual value and increasing the reliability of the prediction. This paper provides reference value for the optimization and improvement of the digital financial portfolio investment return prediction system and contributes to the development of digital finance. Keywords:
Visual design and security and communication network semantic technology are hot topics in recent years. As a typical representative of visual design and security and communication network semantic technology, visual design and traditional cultural element environment design have attracted many scholars’ attention. With the rapid development of modern computer technology, contemporary visual design methods have also changed. It has great development space in conveying information and aesthetic feeling. The combination of Chinese traditional culture and visual design is not only the inheritance of Chinese traditional culture, but also the trend of visual design in the new era. China’s traditional culture is a very valuable resource, which not only has a wide range of themes but also has rich connotations. It is incomparable to any other country. After thousands of years of development, China’s traditional culture has no doubt about its artistic value. As a new design method, dynamic visual design is rising with the development of market economy. With the passage of time, all aspects of human life have encountered a variety of dynamic visual design. In further exploration, it was found that the highest score of users’ visual perception of static visual design was only 6. The integration of cultural elements was also very low, and user satisfaction had not changed for a long time. The visual experience of dynamic visual design can often reach full score, but the integration degree of cultural elements is as high as 97%. High user satisfaction has laid a foundation for the inheritance of Chinese traditional culture. It can be seen that the latter has a broader development prospect and can better meet the requirements of the times. The research in this paper has important guiding significance for the application of visual design and security and communication network semantic technology.
Low earth orbit satellites can help narrow the digital divide and provide low latency and high-speed Internet connections. However, they are extremely fast and cannot stay over a single place. In addition, during the process of circumnavigating the ground, it can only be exposed to a fixed location for a short period of time. In response to the above issues, a collaborative spectrum sensing algorithm based on fuzzy integration is proposed, and an intelligent algorithm is obtained by combining alliance game algorithm. Finally, a multi-satellite low orbit satellite collaborative spectrum sensing method based on multi-satellite cooperative beamforming and intelligent algorithm is designed. The research results indicated that the correct detection probability of intelligent algorithms was positively correlated with the signal-to-noise ratio. At a signal-to-noise ratio of -11dB, the probability of correct detection reached a steady state of 1. Under strong interference conditions, when the number of participating satellites in the array was 10, the detection probability of the research method approached 1, and the optimal satellite array power utilization rate obtained was 93.4%. The above results indicate that the research method can reduce the impact of strong ground interference signals and fully tap into the spatial resources available for low orbit satellites.
With the acceleration of economic globalization and industrialization process, the processing technology of natural gas and oil is being more and more challenged and influenced. This elevates the likelihood of oil and gas leakage impacting the surrounding environment during the loading and unloading processes. To enhance the safety of oil and gas handling, an index system has been developed which is based on an improved correlation analysis algorithm and a hierarchical analysis method, as well as a correlation analysis network model of risk source. The results proved that in the night experiment, the accuracy of the correlation rule of the improved algorithm increased from 90% to 95%, and the error value was even close to 0, while the traditional algorithm fluctuated between-1 and 1. In general, the proposed evaluation system and model effectively improve the prediction and identification probability of operation safety in the oil and gas processing process.
Introduction: The physical health of students is an indispensable part of the education system. Objectives: The existing methods for evaluating physical fitness and health lack sufficient analysis of test data. Methods: Therefore, the study proposed an improved student physical health evaluation algorithm using K-means and decision tree algorithms. The initial cluster center of K-means was determined using cuckoo optimization, and the median distance of data points was used instead of the mean. The minimum Gini coefficient was used as the optimal binary value for the decision tree algorithm. Results: Experiments showed that the root mean square error of each item in the improved K-means algorithm was on average 0.056 lower than that of the fuzzy C-means algorithm. The recall rate and F1 value were on average 0.084 and 0.093 higher, respectively. The accuracy of clustering analysis was 3.3% and 5.1% higher than that of the FC-MC algorithm and SC algorithm, respectively. The decision tree algorithm approached convergence after 200 iterations, with the maximum values being 1.4%, 6.3%, and 13.5% higher than other algorithms. In the randomly selected class, the contribution of male students’ sitting forward bending, long-distance running, and pull-up projects to the total score was relatively low and need to be prioritized for improvement. Conclusion: From this, the proposed physical health evaluation method can effectively minimize the impact of extreme value data on the calculation outcomes, raise the accuracy of clustering analysis and evaluation, and accurately determine the overall and individual physical weakness items of the class.
A method for automatic recognition and anomaly detection of electrocardiogram signals based on deep neural network structure search has been proposed. Firstly, the raw ECG signals are converted into various image representations, including Gram angle field, recursive mapping, Markov transition field, etc., which enables the deep learning model to better handle these complex signal features. Meanwhile, this study utilizes convolutional neural networks for feature extraction and learns the complex relationships between features through fully connected layers. The results demonstrated that the improved method achieved a maximum accuracy of 98.5% and an average accuracy of 94.0% on the PhysioNet MIT-BIH dataset. Additionally, on the PTB dataset, the average recall rate of the improved method reached 98.4%, surpassing the performance of traditional neural networks and Canny algorithm. The experimental results indicate that the research method effectively optimizes the key patterns’ recognition ability in electrocardiogram signals and has excellent performance in detection results. This study offers a more reliable tool for early diagnosis and health management of human health diseases.
Traditional green building performance evaluation methods usually rely on static design data and a single evaluation indicator, and lack dynamic monitoring and multi-dimensional data integration, which makes it difficult to connect data at different stages, resulting in a large gap between prediction and actual performance. This paper applies digital construction technology to construct a green building performance prediction and evaluation system that integrates data across stages and dynamically, thereby improving the accuracy and reliability of the evaluation. First, according to Building Information Modeling (BIM) technology, a digital building model containing data such as energy efficiency, environmental impact, and resource utilization is constructed. By deploying Internet of Things (IoT) sensors, energy consumption, temperature, humidity, and air quality in the building are monitored in real-time. The data is transmitted to the cloud platform for centralized processing and visualization, and compared with the design data in the BIM model to provide timely feedback on performance differences. Using big data analysis, support vector machine (SVM), and particle swarm optimization (PSO), data from the full life cycle are analyzed to predict building performance and optimize it. Finally, the LCA (Life Cycle Assessment) method is utilized to comprehensively consider the environmental impacts of buildings such as carbon footprint and resource consumption, and combined with multi-objective decision analysis tools to optimize the green building design and operation plan. The experiment shows that the energy efficiency comparison difference of green buildings is within 10kWh/m², and all indicators are accurately predicted, providing a scientific basis for the design and operation of green buildings.
Power distribution and automation stand to gain greatly from the widespread use of connected devices made possible by the advent of the Internet of Things (IoT). The reliability of a SCADA (Supervisory Control and Data Acquisition) system has been extensively shown in the substation environment. The fundamental problem with distribution automation is the lack of distribution-side management, mostly from the field’s geographically dispersed workforce. As a result of their dispersed locations, there has been inadequate tracking of their distribution channels. The smart grid is a power system incorporating evolutionary computing, bidirectional communication, two-way electrical flow, and real-time monitoring. Hence, this paper Internet of Things based Integrated Smart Grid Distribution Management System (IoT-ISGDMS) with fog computing has been presented that addresses issues such as power quality assurance, pole transformers health, and customer consumption in distribution automation. In this paper IoT-ISGDMS uses fog computing which analyzes distribution automation in real-time, making this possible. As a first step, IoT-ISGDMS uses intelligent acceptance systems (IAS) to improve coordination between smart grids and other electronic infrastructures. The second step is to perform comprehensive data analysis, automatically recognize any possible problems, and offer more intelligent fault detection and diagnosis to cut down on time and money spent on maintenance. In conclusion, as the degree of system intelligence rises safeguarding data privacy and the safety of networks will become critical priority areas.
In higher education, physical education courses and ideological and political education are often carried out independently, lacking effective integration and interaction. This paper explores the informationization reform of physical education courses in universities and evaluates its impact on online ideological and political education. First, interdisciplinary integration and informationization methods are used to optimize the physical education course design and integrate ideological and political education content. Then, an online learning platform is constructed to break the limitations of time and space, encourage students to deeply understand the ideological and political education content in physical education teaching, and provide real-time feedback and personalized learning support. Multimedia technology is also adopted to enhance students’ understanding and internalization of sportsmanship and ideological and political education concepts. Students’ learning behavior and ideological and political education absorption in physical education courses are individually evaluated through data analysis. Finally, the profound impact is evaluated through experiments. The results indicate that the informationization reform of physical education courses in universities significantly improves students’ participation and knowledge mastery and effectively promotes the improvement of students’ ideological and political literacy, with the average score increasing by about 14%. These results provide strong empirical support for future teaching design and also provide valuable experience for further exploring the deep integration of physical education courses and online ideological and political education.
As society progresses and science and technology evolve, the need for skilled professionals in the country continues to rise. Ideological and political education (IPE) in courses, crucial for fostering students’ overall development, has shifted from theoretical exploration to practical implementation. Sports colleges should incorporate ideological and political elements into professional courses like sports and computers based on their unique characteristics, aiming to develop high-quality, multidisciplinary talents that align with national requirements. Professional courses are often disconnected from IPE in traditional sports college teaching methods. There is excessive focus on knowledge and technology instruction, while insufficient attention is given to guiding students in developing correct values through the courses. Additionally, there is a lack of well-targeted course design and a comprehensive evaluation system. In response to the above problems, this paper studies and constructs a framework that integrates IPE with basic computer application knowledge, and designs a variety of course designs and teaching methods. In addition, a progressive assessment is designed to introduce the Convolutional Neural Networks (CNN) model and the Bidirectional Encoder Representations from Transformers (BERT) model in the early stage of teaching to conduct preliminary assessments of students’ basic computer skills and ideological and political qualities. In the later stage of the course, the Long Short-Term Memory (LSTM) network model is introduced to analyze student learning behavior and assess overall student quality based on prior evaluations. The experiment shows that the course design studied in this paper can enable students of the School of Physical Education to learn basic computer knowledge while also receiving certain ideological and political quality education. The designed teaching method is better than traditional offline teaching, online teaching and online and offline mixed teaching. When conducting comprehensive quality assessment, a 40% excellent rate can be achieved. Students are randomly selected to track their growth analysis. After the application of this teaching design, 90% of the students’ comprehensive quality assessment remains the same or improves. The findings suggest that the teaching model discussed in this paper not only improves students’ computer skills but also plays a crucial role in their JIANG ideological and political education, effectively achieving the intended teaching goals. The success of the innovative teaching design has been confirmed through practical application, providing a feasible teaching model for sports universities and other specialized institutions.
With the rapid development of video surveillance and multimedia applications, video data is requiring higher bandwidth demands for its transmission, storage, and retrieval. This paper presents a novel approach to video processing based on skeletal information and the recognition of identities. The skeletal data enables the extraction of skeletal data features from video frames and integrates this with the recognition of identities in such a way that the video data gets segmented into skeletal data, identity information, and other relevant data. A multimodal approach like this one spans a broad range in data transmission volume, optimizes bandwidth use, and significantly improves storage efficiency and increases retrieval speed. Experimental results have verified that the proposed method is able to transmit information with efficacy even in complex scenarios and further enable significant improvement in the accuracy and speed of performing storage and retrieval tasks. Such improvements turn into an effective solution for real-time monitoring, behavior analysis, and identity recognition applications featuring strong robustness and adaptability.
Rural population loss is a common phenomenon in northeast China, even in the whole country and all over the world, has significantly hindered economic and social development in rural areas, leading to a weakening of growth momentum and even stagnation. In view of this, this paper focuses on Jilin Province, a typical region, and uses key data such as rural resident population, rural employed population, and job supply in the region from 2008 to 2021. Through the comprehensive application of spatial autocorrelation analysis methods and the geographical detector model, it deeply analyzes the spatio-temporal evolution patterns of the rural occupational and residential function-efficiency at the county scale in Jilin Province, the trade-off and synergy relationships, and the driving mechanisms behind them. The results show that: the synergy level of the rural occupational and residential function-efficiency index in Jilin Province has gradually increased over time; the index shows a steady upward trend and spatial clustering characteristics; the index is influenced by a variety of driving factors, and the mechanisms of these factors vary. These findings will help the government formulate sustainable rural development policies and provide a useful reference for promoting comprehensive rural revitalization and development.
With the development of big data (BD) technology, tourism route planning of historical blocks relies on a large amount of real-time data. The existing research data sources are limited and difficult to integrate, which cannot meet the personalized needs of tourists. This paper combined BD and intelligent algorithms to realize personalized tourism route planning of historical blocks. By collecting tourists’ behavioral data, scenic spot spatial data and real-time traffic information, the paper built tourist portraits and used the neural collaborative filtering algorithm to make personalized scenic spot recommendations. It used genetic algorithms (GAs) to optimize routes, taking into account factors such as tourists’ interests, distances between scenic spots, and traffic conditions. With the help of the real-time data streaming platform Apache Kafka, the paper dynamically adjusted routes to deal with sudden traffic or crowded attractions, thereby improving the tourist experience. The experimental results analyze the consumption preferences and behavioral characteristics of different tourists. Tourist 1002 spent 500 yuan on shopping, and high-end shopping malls and food courts were recommended for him. Tourist ID 1005 preferred “snacks and coffee” in terms of dining, and showed no interest in souvenir consumption. This tourist preferred to stay in leisure places for a longer time rather than a compact travel route. The neural coordination filtering algorithm + GA performed well in terms of total travel time of 4.2 hours, total walking distance of 7.8 kilometers, and traffic congestion coefficient of 0.35, which was better than other algorithms, showing its significant advantages in digital tourism route planning in historical blocks. This method combines BD and intelligent algorithms to improve the tourist experience through personalized recommendations and route optimization, optimize the traffic management of scenic spots, flexibly respond to emergencies, promote the intelligent and refined management of historical district tourism, and provide innovative ideas for future tourism route planning.
Based on accounting informatization, this paper constructs a financial risk prediction system by applying the CNNs (Convolutional Neural Networks)- BiLSTM (Bi-directional Long Short-Term Memory)-Attention model to accurately identify and classify various risk types in enterprise FM (financial management), and improve the accuracy and efficiency of financial risk prediction. CNN was used to extract local features in financial data, BiLSTM was used to capture time dependencies, and finally the importance of financial indicators was weighted and fused through the Attention mechanism. During the training process, the Adam optimizer and cross entropy loss function are used for optimization, and appropriate learning rates and training rounds are set to ensure the stability and performance of the model. The experimental results show that when the epochs is 50, the accuracy of risk classification is 98.9% and the loss value is 0.012. In the analysis of each data level, the average response time of the proposed system and the traditional system is 1.80s and 7.17s respectively. The system in this paper shows obvious advantages in response time and prediction accuracy. The response time is greatly shortened, and it can provide effective support in real-time decision-making. This paper model has significant application prospects in financial risk prediction, and can provide enterprises with efficient and accurate risk warnings, which has important theoretical significance and practical value. Keywords: Enterprise Financial Management, Risk Classification, Financial Data, Accounting Informationization, Convolutional Neural Networks
The model that evaluates the integrated condition of the ecological environment of the lake Taihu is created in conjunction with remote sensing satellite images, ground monitoring data, and other such geo-sourced information. This paper provides a comprehensive assessment framework integrating water quality measures, vegetation indices, and atmospheric conditions to assess-temporal and spatial variations of lake ecosystems. Analysis of five years (2019-2023) of monitoring data reveals significant spatial heterogeneity in water quality parameters, with distinct increase in degradation within the northern and western parts of the lake. Characterised pan-regional eutrophication indicators show clear zonation patterns which are largely distributed in areas of increased human use and zonal hydrodynamic conditions. Seasonal analysis indicates distinct differences in water quality parameters prompting an increase in algal bloom within the summer months. Target areas are designated and analysed in this study and are reflective of critical conditions that require immediate management control measures german Meiliang Bay and the Western Zone. Methodological testing reflects a congenial result resulting in models with high accuracy (R² > 0.89) and reliability within diverse temporal and spatial range. Data obtained partially or largely complement ecological management policy and enable such policies to be formulated where monitoring the health of a lake’s ecosystem and addressing its restoration is key.
The intelligent transformation of the apparel design industry needs to simultaneously meet the requirements of both efficiency improvement and personalization promotion. This paper proposes an intelligent design framework that integrates curve theory, garment prototyping and meta-learning technology. It optimizes the design of apparel by using the smoothness constraints of interpolation curves, the flexibility expression of parametric curves, and the local optimization characteristics of Bspline curves. Combine the prototype-based thinking model with meta-learning method to solve the generalization problem under small sample data and improve the model adaptation speed. The practical efficiency enhancement level and application value of the methods in this paper are verified through practice and testing, etc. The results show that the parametric design can realize the fast garment styling change of single parameter and multi-parameter. The optimization algorithm combining prototyping and meta-learning always takes less than 20 seconds in the 10-parameter range adjustment experiments, which is faster than the comparison algorithm. In the comprehensive fuzzy evaluation of experts and consumers, “very satisfied” and “good” account for 63.99% and 58.42%, respectively. The method based on technology fusion in this paper can significantly improve the design efficiency and user satisfaction of clothing personalization.
With the global informationization boom, information security has become a problem for all of us. In order to be able to effectively detect the physical health status of criminals in prison and ensure the data security of the process, an image encryption method is designed to effectively protect the monitoring information. The process is based on generative adversarial network with generator and discriminator for image generation and data discrimination processing respectively, and optimizes the feature transmission process of image with the help of residual network. The key is generated by chaotic sequence method during the image transmission process. The encrypted image is transmitted to the staff port and the destination image is obtained after the decryption process of data key. The results of the study indicated that the decryption accuracy of the GAN algorithm in the dataset test increases gradually with the iteration process. The accuracy of the image after the completion of the iteration reached 98.69%, indicating that the algorithm has a good restoration effect for recovering the image after transmission. The structural similarity of the data image after the GAN algorithm processing decryption can reach 0.988. The peak signal-to-noise ratio index of the image was 37.78dB, which indicates that the clarity of the image after encrypted transmission is high. The research method can provide an effective theoretical support for the encrypted transmission of video images.
The sorting residue of copper clad laminate stacked on site contains polybrominated diphenyl ethers (PBDEs) and novel brominated flame retardants (NBFRs). If not properly treated, they will be discarded into the surroundings and cause secondary pollution. The PBDEs and several NBFRs were detected in the sorting residue of copper clad laminate (SRCCL) of the storage yard. The ∑9PBDEs and ∑5NBFRs concentrations ranged from 2.71 to 122.83mg/kg. Different storage yards displayed three composition patterns of PBDEs, indicating that their sources were different, with domestic and imported ones. All results indicate that untreated SRCCL dumping sites are an important source of PBDEs and their emissions.
The emergence of ride-hailing services has revolutionized the transportation industry for passengers, prompting taxi services to evolve from the conventional method of street-hailing to a combined “online-offline” operational approach. In this new model, taxis combine on-street pickups with platform-based orders. When market supply and demand are imbalanced, leading to excess orders, taxis prioritize street-hailing for faster customer acquisition. Meanwhile, ride-hailing platforms address surging passenger demand by offering subsidies to attract more vehicles to participate in online dispatching. This study focuses on the strategic choices of ride-hailing platforms and taxis during order overflow scenarios. An evolutionary game model is constructed to simulate taxi street hailing behavior under such conditions. Simulations are conducted to generate interpolation-based probability curves, including the probability of taxis accepting offline orders and the probability of regional orders being served. These findings offer recommendations for ride-hailing platforms on designing subsidy strategies in response to changes in regional order density. Additionally, the study examines how factors such as order distance, passenger-seeking costs, and platform commission rates influence taxis’ order acceptance strategies.
Aiming at the problem that military equipment resources are easily affected by high-frequency random disturbances such as emergency order insertion, abnormal processing quality, equipment operation failure, etc. in the process of processing task execution in the cloud manufacturing environment, which causes the quality of service (QoS) of product processing to fail to meet the personalized needs of customers, a dynamic selection method of equipment resources in the cloud manufacturing environment is proposed. According to the running characteristics of cloud manufacturing services, a dynamic evolution model of service quality towards the process of processing task execution under cloud manufacturing environment is constructed. Taking the state vector and control vector in the dynamic evolution model as node variables, combined with Bayesian network, a decision model for dynamic selection of military equipment resources under random disturbance is established. By solving the model, the corresponding scheme of the optimal QoS value is obtained, and the dynamic selection of military equipment resources is realized. The experimental results show that this method can effectively and dynamically select military equipment resources, reduce the price and time cost of military equipment manufacturing, and improve the reliability of product processing, platform satisfaction and comprehensive QoS score.
This study examines the relationship between career aspiration and career adaptability, and focuses on the mediating effect of coping efficacy. A total of 377 students of higher vocational education were measured using the career aspiration scale, the coping efficacy scale and the career adaptability scale, and the study showed these results. (1) There were significant differences in career aspiration in terms of whether they are student leaders , coping efficacy in terms of whether they were student leaders, and career adaptability in terms of whether they were student leaders, whether they are the only child of their parents，whether they have received career counseling, and whether they participated in parttime jobs, internships or social practices. And there were also significant differences in career aspiration and career adaptability in terms of the interaction of gender and whether they have received career counseling. (2) Career aspiration and its three dimensions were positively correlated with coping efficacy and coping efficacy was also positively with career adaptability and its four dimensions. (3)career aspiration was a significant direct positive predictor of career adaptability. When the mediating variable coping efficacy was included, career aspiration could still significantly predict career adaptability, coping efficacy mediated the relationship between career aspiration and career adaptability. Career education can enhance career adaptability of students in higher vocational education by improving their coping efficacy.
Ammonia synthesis is vital for fertilizer production, but the traditional Haber-Bosch process is energyintensive and environmentally burdensome due to its high-temperature and high-pressure operations. Plasma-catalytic ammonia synthesis offers a sustainable alternative, generating large datasets under various experimental conditions. To optimize energy efficiency, we established a database with 305 data points and 7 experimental parameters, each linked to its corresponding energy efficiency. We employed an Extreme Gradient Boosting (XGBoost) regression tree model, achieving an average R² value of 0.9434 for predictions. Bayesian Optimization (BO), using Gaussian Process Regression as a surrogate model, systematically explored the experimental parameter space. It utilized XGBoost predictions to identify parameter combinations that maximized energy efficiency. After 50 iterations, the optimal parameters were identified: 6.4 g catalyst mass, 50 mm grounding electrode length, nickel metal catalyst, Al₂O₃ catalyst support, 5 W power, 160 ml·min⁻¹ flow rate, and a 1:2 feed ratio. Under these conditions, the energy efficiency of plasma-catalytic ammonia synthesis improved to 1.49 g·kW·h⁻¹, a 22.1% increase from the highest value of 1.22 g·kW·h⁻¹ in the dataset.
With the gradual improvement of the resilience and vitality of the tourism market, promoting the high quality development of the tourism industry with the new development concept has become an important fundamental issue for the sustainable growth of the regional green economy. The article measures and analyzes the level of high-quality development of Guizhou’s tourism industry from 2012 to 2021 on the basis of constructing an evaluation index system for high-quality development of tourism, using methods such as entropy value method and gray correlation analysis. The results found that: the average value of the development index of Guizhou’s tourism high-quality development subsystem is ranked in the order of GD, ED, ID, SD, OD and CD, the level of green development and effective development of Guizhou’s tourism industry is higher, while the level of coordinated development of the tourism industry and the level of openness are insufficient; HQD, ID, GD, OD, SD and ED show a fluctuating upward trend, while CD is in a fluctuating downward state, and the tourism high-quality development system of Guizhou has gone through a fluctuating upward trend. Guizhou tourism high-quality development system has experienced three stages of evolution, namely, “stable rise, rapid rise and fluctuating rise”, and the level of Guizhou tourism high-quality development and the development level of its various sub-systems have been affected by the New Crown Epidemic to varying degrees, with a greater impact on the level of open development of the tourism industry. GDP, per capita park green space area and tourism high-quality development index correlation is larger, while the total amount of SO2 emission and tourism high-quality development index correlation ranked at the bottom, tourism industry R & D funding is the most important factor affecting the level of high-quality development of Guizhou’s tourism industry, and the total amount of SO2 emission has the smallest impact on it. On this basis, countermeasures for the high-quality development of Guizhou’s tourism industry are proposed.
In this paper, the vibration characteristics of the helical cylindrical gear split-torque transmission system with diaphragm coupling misalignment are studied. Firstly, the 14-DOF nonlinear simulation model of the helical cylindrical gear split-torque transmission system are established. To improve the model accuracy, time-varying mesh stiffness, random backlash, mesh error and bending deformation of shaft are considered respectively. Secondly, according to the nonlinear simulation model, the differential equations are established, and the differential equations are sovled with the time-varying stiffness of diaphragm coupling misalignment. Finally, the relationship between the phase of bolt group in diaphragm coupling and the asymmetric property of the split-torque transmission system is determined by numerical methods. The results show that the asymmetric property of split-torque transmission system could be effectively improved by changing the phase of bolt group in diaphragm coupling. The method is proven effectiveness by a modification work involved in this paper, and have reference significance for solving engineering problems.
Due to the complexity of genome structure and technical conditions, wheat genome structure variation has not yet been comprehensively and accurately detected and evaluated for genetic effects. The aim of this study is to construct a method based on deep learning algorithm to accurately detect genomic structure variation in wheat. The method converts genomic data into image form by genomic structure variation image generation algorithm. A gene structure variation prediction model is constructed based on deep learning, and efficient and accurate structure variation prediction is realized by automatically extracting and analyzing the variation features in the image. The experimental results show that this method has better detection performance than other structural variation detection methods based on third-generation sequencing data, especially in the structural variation detection of the “Sequencing and Assembly of Spring Wheat Genome in China” project, and the accuracy, precision, and recall rate of this method are all over 90%. This study provides a novel deep learning framework for efficiently detecting structural variants in the wheat genome, and provides powerful technical support for genetic improvement and breeding research of wheat.
This paper analyzes and evaluates high school examination questions based on machine learning. The study first introduces Bloom’s classification method and constructs a categorized dataset of high school exam questions according to three steps of data collection, data annotation and data analysis. Then an automatic assessment model (WoBERT-CNN) based on WoBERT and Text-CNN is designed. The semantic similarity of word vector mapping is used to label the cases for determination, the improved WoBERT encoder is used to represent the text in word vectors, Text-CNN is used as a text classifier to extract the textual semantic features, and the features are integrated and screened, so as to realize the automatic classification of the cases in Bloom’s taxonomy. Finally, based on the deep representation framework, the text information of the test questions is deeply mined and utilized to establish the relationship between the text of the test questions and the actual difficulty, and to realize the difficulty prediction of the test questions.The classification accuracy of the WoBERT-CNN model reaches more than 92%.The prediction error range of the H-MIDP model on the score rate of the test questions is between 1.3% and 3.2%, which is not too far from the real value. In conclusion, the automatic assessment model and difficulty prediction model designed in this paper can be applied in the analysis and evaluation of high school test questions, helping the high school test paper proposition and talent cultivation strategy.
Aiming at the limitations of the sample weight updating mechanism of the traditional Adaboost algorithm, the article proposes three improved algorithms based on the joint weight updating mechanism to solve the problems of sample distribution imbalance, etc. The MW_UA algorithm is centered on the updating of the proportion of the sample weight, the OW_UA algorithm realizes the updating of the weight of the sample set based on the classification effect of the initial samples, the MAR_UA algorithm employs sample The MAR_UA algorithm uses the sample Margin to quantify the degree of difficulty of sample classification and then obtain the corresponding sample weights. The performance test experiments and prediction simulation experiments of the improved algorithm are based on the MWSP and Caltech datasets. The experimental results show that the average accuracy and F1 score of MAR_UA algorithm in the two datasets are over 90%, which is the best performance among all the improved algorithms. The algorithm also shows optimal prediction error convergence performance in both datasets, and the training error can be converged to the minimum within 40 times of training. When the algorithm is applied to the simulation experiment of pedestrian recognition, it has the best recognition effect in the sunny environment, with a detection rate of 94.1%. In addition, the error between its predicted and real values of offshore wind speed is no more than 0.2 m/s, and the ERMS and EMA are reduced by 63.52% and 55.5%, respectively, compared with the traditional Adaboost. This study optimizes the weight updating mechanism of the joint Adaboost algorithm using various methods, which can provide new ideas for the optimization research of the weight updating mechanism.
With the maturity of digital display technology, its application scope is also more and more extensive, and there are more and more application cases in the protection and inheritance of minority hand-weaving skills. This paper builds a general framework for the design of the digital inheritance system for the handloom weaving techniques of the Miao family in southern Sichuan, and applies three-dimensional modeling technology, three-dimensional animation technology, digital imaging technology and interactive interface design to complete the preliminary establishment of the digital display system for the handloom weaving techniques of the Miao people in southern Sichuan. Combined with the information dissemination characteristics of mobile intelligent terminals, relevant improvement programs are proposed. At the same time, the optimization and improvement of the digital display system is further improved to meet the needs of users. Comparing the users’ experience and perception of the digital display system, the system designed in this paper is superior to R-Space in terms of functional scope and technology, and the average score of the system designed in this paper is 4.193, which is higher than the score of 3.985 of the R-Space system, and the system designed in this paper has a higher score. At the same time, the user’s satisfaction with the system’s interactivity is more stable in the three aspects of login start, system home page, and Chuannan humanities resources. In the login start, the scores of very satisfied, more satisfied, and general are 2, 3, and 2.5 respectively, which indicates that the user’s experience of this paper’s system is better.
With the purpose of exploring the mechanism of change in Chinese relational clauses, this paper firstly includes transitive verbs, intransitive verbs and adjectives in the study of relational clauses, and carries out a comparative analysis from the perspectives of syntactic form, semantic expression, and distribution of thesis elements, and finds that relational clauses constituted by transitive verbs are indeed the most typical members of Chinese relational clauses. Then, we examine its performance in the type of relativization, main clause syntactic position of core words, vitality pattern, and structural features, and conclude that the argument elements of the relational clauses present a vitality contrast pattern and have a simpler structure with an average of about 4 syllables, while the distribution of the central words of the Chinese relational clauses conforms to the order of the noun-dominant syntactic position. Finally, ERP technology is used to explore the processing advantages of subject-relative clauses and to regulate the vitality and denotation of the verbal thesis elements of the clauses, and it is found that the difference in processing difficulty between subject and object-relative clauses increases when the subject of the clauses is a vital noun and the object is a non-vital noun.
The field of urban scene image segmentation is a crucial task in the field of computer vision. Aiming at the problems of large parameter count and insufficient image segmentation accuracy of the traditional DeepLabV3+ model, an improved lightweight DeepLabV3+ model is designed. The overall performance of the model is improved by replacing the Xception backbone network with MobileNetV2, introducing the band pooling module and the densely connected null pyramid module in ASPP, and using the GD-FAM multi-feature fusion module in the fusion stage. Using Cityscapes as the dataset, the model experiment results show that compared with the traditional Deeplabv3+ model, this paper’s method increases the target category IoUs of urban scenes such as pedestrians, cyclists, and columns by 3.1%, 4.41%, and 6.74%, respectively. Therefore, the segmentation effect of the model in this paper is significantly better than the segmentation effect of other models. The mIoU of the MobileNetV2 backbone network is 4.91% higher than the baseline model. The loss function change curve of the model shows that it tends to converge after 100 iterations. In summary, the overall segmentation performance of the improved model is significantly improved.
In this paper, the non-chiral boundary of the mixed kdv-mkdv equation is transformed into a chiral boundary by the construction of auxiliary functions, and a new linear difference format is constructed for the chiral boundary problem. Based on the traditional difference format, explicit and implicit differences are used alternately to construct a class of explicit-implicit (E-I) and implicit-explicit (I-E) alternating difference formats, and the unconditional stability of the numerical solutions is proved by taking advantage of the symmetric discrete numerical advantage of this class of alternating difference formats. The exact solution of the kdv-mkdv equation and its dynamical behavior are explored in the calculations using the semi-fixed separation of variables method combined with the phase diagram method for planar dynamical systems. Various types of exact solutions of the equations are obtained under special parametric conditions, and the existence problem of isolated wave solutions of the kdvmkdv equations is analyzed in conjunction with the exact solutions of the equations. Numerical examples verify the accuracy and feasibility of the constructed differential format, indicating the existence of isolated wave solutions for the KdV-mKdV equation.
The construction of university English teaching resources is an inevitable requirement to adapt to the development of the times and educational reform. Based on the concept of knowledge and classification, this paper puts forward the theory of Rough set, and applies the idea of partition to the data simplification based on Rough set. Based on the applicability of the partition strategy, the partition idea is added in the process of attribute simplification to achieve the purpose of reducing the complexity of the data simplification algorithm about Rough set. After deriving the decision table, the attribute approximation algorithm based on the attribute order and the partition method is given, i.e., the efficient knowledge approximation method based on the partition method for Rough set. Analyze the performance of Rough set efficient knowledge reduction method based on partitioning method in multiple datasets. To build a knowledge acquisition system platform for university English teaching resources using the efficient knowledge reduction method based on the Rough set of the partition method. In the Heart dataset, the classification accuracies of DIDS method, IV-FS-FRS method, and this paper’s method are 0.5936, 0.5536, and 0.6689, respectively, and this paper’s method outperforms the classification accuracies of DIDS method, IV-FS-FRS method 0.0753, and 0.1153, respectively. The knowledge acquisition system platform of university English teaching resources constructed by using this algorithm has operational advantages in instance analysis.
Based on the common problems of the original fuzzy testing technique and the needs of RESTful API fuzzy testing, this paper proposes a white-box fuzzy testing method of REST API based on graph resource nodes for RESTful API software interface testing by using EvoMaster as a basic tool. The effectiveness of the fuzzy testing technique in this paper is analyzed. 21 apps with millions of downloads obtain more than 65,000 web request data and more than 8.5GB HAR files, and an average of 2,966 web request data is collected for each app. The REST interface filtering method of this paper’s fuzzy testing approach effectively and accurately targets interface objects for fuzzy testing. The number of generated requests of the REST API white-box fuzzing test method based on graph resource nodes in this paper is much lower than that of other tools, and the efficiency of vulnerability discovery is much higher than that of other tools. The test method in this paper improves the number of lines of code covered in six hours by an average of 53.86% over other tools. The test method in this paper can identify more vulnerabilities and can cover all the vulnerabilities found.
Inadequate writing skills can prevent learners from improving their writing performance and interfere with their subsequent writing performance in authentic scenarios. The article’s research focuses on the effects of metacognitive regulation on students’ authentic writing performance in a web-based constructivist learning environment, which relies on constructivist learning environments to better present the authentic writing problems learners face in their studies and lives. In this paper, we adopt the method of randomized group sampling to conduct a single-group pre-test and post-test experiment on 40 students in a public high school. It also chooses students’ writing learning achievement as the dependent variable, and students’ metacognitive regulation level and writing selfefficacy as the independent variable and mediator variable, respectively, and explores the degree of influence of metacognitive regulation level on students’ writing learning achievement through multiple linear regression. The results showed that there was no significant difference between pretest 1 and pretest 2, while posttest 1 and posttest 2 were much higher than pretest 1 and pretest 2. There was a significant positive effect of students’ level of metacognitive regulation on students’ learning achievement in writing (0.459), and there was a significant mediating effect of students’ writing selfefficacy between students’ level of metacognitive regulation and students’ learning achievement in writing. Relying on the web-based constructivist learning environment can significantly enhance students’ metacognitive regulation level and provide a new teaching path to promote students’ writing learning achievement.
In the context of artificial intelligence technology, the current academic research on the relationship between exosome molecular screening and the role of gene probes is relatively weak. Accordingly, this paper formulates a modeling study of exosome molecule screening and its relationship with gene probes under the framework of multi-objective genetic algorithm. The multi-objective genetic algorithm is applied to realize the screening of secretory body molecules, and after the completion of the screening work, the mechanism of the role between exosome molecules and gene probes is investigated by constructing a regression model, and the above theoretical knowledge is applied to empirically analyze the research scheme of this paper. The regression coefficients of exosome molecules and gene probes showed significant correlation at 0.05 level, indicating that the mechanism of action between the two is monotonically increasing, which well reveals the influence of exosome molecules on gene probes.
Traditional landscape design methods have low efficiency, poor subjectivity and insufficient goal optimization. This paper proposes a landscape design optimization and spatial layout method based on artificial intelligence (AI) algorithms to achieve scientific and efficient landscape design through the combination of collected information data and algorithms. The optimization design of landscape facility paths and spatial dimensions is carried out by adopting a heuristic polygonal layout algorithm, establishing a data model based on the database and scene templates, and combining the landscapes in the polygonal space after landscape matching. The optimal sequence of the landscape is obtained by using the scoring function, and then combined with the particle swarm algorithm to realize the optimization of the landscape layout. The Hypervolume index is stable to about 0.815 in 30 generations, which has a good quality of Pareto optimal solution set. In this paper, the algorithm formulates three groups of landscape design optimization and spatial layout planning schemes for different situations, making full use of the land that is utilized for a certain place. The implementation of the sustainable development scenarios improves the local environmental and social benefits significantly, and the average annual growth rate of employment in related industries reaches 3.16%. Satisfaction survey results show that local residents are most satisfied with the green environment and cultural atmosphere after the implementation of the program, respectively 80.03, 79.35, through the smart management to improve the local environmental quality and cultural atmosphere.
As a large tourism province, Hainan Province produces carbon emissions from the tourism industry that should not be underestimated. In view of the problems reflected, this paper designs a research program based on the data envelopment analysis model to evaluate the carbon emission efficiency of Hainan’s tourism industry and optimize the emission reduction path. The tourism industry of 10 regions in Hainan Province is taken as the object of this study, and the DEA-SMB model is designed. Drawing on existing research results, nine carbon emission efficiency evaluation indicators for the tourism industry in Hainan are set, in addition to improving the principle of measuring carbon emissions and energy consumption in the tourism industry. In order to better promote the green, lowcarbon and sustainable development of the tourism industry, it is proposed to adopt the DEA-SMB model to optimize the carbon emissions of the tourism industry in Hainan Province. Finally, the 2005- 2024 Hainan Tourism Statistical Yearbook is taken as the main data source of this study, and combined with related research data, the optimization effect of carbon emission efficiency and emission reduction path of Hainan tourism industry is explored. In the optimization process of tourism carbon emissions in 10 regions of Hainan Province, the model of this paper has a particularly prominent effect on the optimization of tourism emission reduction in region C, and its emission reduction efficiency is increased to 64.21%, which verifies the tourism emission reduction effect of the model of this paper, and also reflects that there is still a huge room for improvement in local tourism emission reduction projects in Hainan Province.
The all-round penetration of artificial intelligence technology has brought about a drastic change in the educational landscape, and the teaching system of colleges and universities relies on artificial intelligence technology to expand its own boundaries, leading to interdisciplinary knowledge fusion between dual colleges and universities. With the support of AI technology, a teaching system design idea of interdisciplinary knowledge integration is proposed, and a teaching innovation system of interdisciplinary knowledge integration between dual colleges and universities is established. Taking the learners’ interdisciplinary knowledge point response situation as an entry point, input modeling is carried out for the learners’ interdisciplinary knowledge points, forgetting coefficient, etc., and the dual colleges’ interdisciplinary knowledge tracking SA-BiGRU model is established by combining BiGRU and the attention mechanism, and simulation verification is carried out to verify its effectiveness. Taking a vocational college in province G as an example, a dual college interdisciplinary teaching comparison experiment was designed in combination with the teaching innovation system, so as to verify the effectiveness of the interdisciplinary knowledge integration teaching innovation system. The results show that the AUC and ACC of the SA-BiGRU model can reach up to 0.837 and 0.841 respectively in interdisciplinary knowledge tracking, and the learners’ interdisciplinary knowledge reserve and ideological literacy level have been improved by 1.36 and 1.82 points respectively compared with that before the experiment. Relying on artificial intelligence technology can promote interdisciplinary knowledge integration, provide a new research direction for the development of interdisciplinary intelligence in BiGR, and lay the foundation for the cultivation of highly skilled and qualified applied talents.
Smart campus relies on IoT technology to realize teaching management, location monitoring, business processing and other teaching and management activities, this paper draws on the characteristics of the development of smart campus, and builds a decision support system for educational management of smart campus by applying the conditions of IoT technology. The IoT multi-sensor is used to collect educational management data, and the Grobes criterion is applied to exclude the data with too large an error, and the consistency test is performed on the collected data. The least squares method and variance calculation are combined to process the multi-sensor data to optimize the data fusion accuracy. Comparison tests were conducted to analyze the fusion accuracy and variance of the observed data under different methods. Distribute questionnaires online and offline to analyze the feasibility of the construction of IoT in smart campus. Collate the ratings of teachers and students on the educational management decision support system of the smart campus, in which the ratings of teachers and students on the educational management decision part of the school are concentrated in the range of 0.7 to 0.8, and the overall rating of the educational management decision support system of the smart campus is 86.453 points.
Energy, as one of the larger contributing industries to greenhouse gas emissions, has an urgent task to reduce emissions, and standardizing the carbon footprint and trading mechanism of the energy market is an important concern for the development of the current energy industry. Under the guidance of the principle of green, low-carbon and sustainable development of the energy market, this paper first uses heterogeneous blockchain and federated reinforcement learning to design a decentralized energy trading mechanism model. It is found that the model fails to realize the intelligent detection and control of carbon footprint, in this regard, on the original model, the carbon footprint origin algorithm is introduced. Combining the above models and algorithms, the current interactive energy market is explored and analyzed. Consumer user 5 has the largest net benefit, with a specific value of 15.05 million yuan, and comprehensive energy supplier 3 has the largest net benefit, with a value of 37,467,000 yuan, indicating that this paper’s model implements the principle of green, low carbon and sustainable development of energy while meeting the energy needs of consumers and suppliers, maximizing the interests of each other in the process of energy trading, which proves that this paper’s research has excellent practical application value.
OMO teaching mode based on artificial intelligence big model is one of the important future research directions and application landing forms in the future education field. The learning path recommendation algorithm based on big language model is constructed by integrating Transformer architecture, neural network architecture and self-attention mechanism. Combining it with the course knowledge graph, it links the learners with the knowledge system and visualizes the results of the intelligently planned learning path. The study shows that compared with several other algorithms, the personalized learning path recommendation algorithm based on AI big model has better convergence speed and stability. The optimal solution for learning path planning is found after only about 90 iterations. Taking “Chemical Process and Control Simulation” as the target course, the method in this paper gives the learning path and course. Through the questionnaire survey, the mean value of the four dimensions of pre-class pre-study, classroom exploration, post-class enhancement, and learning satisfaction is more than 3 points, which indicates that the OMO model and the teaching model of the artificial intelligence big model have a better experience.
Accurate distribution system topology is of great significance for distribution network planning operation and analysis. This project constructs a distribution system network model, applies graph convolutional network and graph attention network in graph neural network, and designs the topology identification method of distribution system. On this basis, a reconfiguration model of the distribution system is given, and the network structure after topology identification is used for trend calculation, and the model reconfiguration is realized by using the extensive learning quantum evolutionary algorithm. Through experimental analysis of several test systems, it is found that the topology identification F1 values of this paper’s method are all above 0.9, which are 5.64% to 29.64% higher than other methods, confirming the good accuracy and robustness of the GNN topology identification model. In addition, the CLQIEA method can give the correct distribution system reconfiguration optimization scheme, which reduces the network loss to a larger extent and improves most of the node voltage values, and the network loss decreases by 31.91% and 56.11%, and the voltage values are improved by an average of 1.95% and 1.23% in the two test systems, which makes the power supply of the distribution system of a higher quality, and the operation of the power supply system is more economical, which is important for the distribution automation and the power supply department’s optimal scheduling is of great significance.
On the basis of ensuring the balance between supply and demand of the power grid, fully realizing the automatic control of the air conditioning system can make the energy consumption of the air conditioning operation reduce significantly, thus realizing the purpose of energy saving. This paper combines a variety of technologies to establish an intelligent air conditioning measurement and control system, realizes terminal communication through the CoAP protocol, and designs the corresponding system hardware as well as the real-time data acquisition method for air conditioning equipment. Based on the PID principle, the temperature and humidity control strategy of air conditioning equipment based on expert PID is proposed. In order to better ensure the energy-saving control efficiency of air-conditioning equipment, this paper fully considers human thermal comfort and the interaction between supply and demand of the power grid, establishes a comprehensive optimization control model with the objectives of user power consumption and human comfort, and passes through the PSO algorithm in order to obtain the optimal control results. Simulation found that when the initial temperature is lower than the set value, the expert PID control strategy will adaptively realize the air conditioning temperature and humidity adaptive regulation to ensure that the indoor temperature is within a reasonable range. The total power consumption of the grid is reduced by 90.18kW compared with that before optimization, and the maximum value of human comfort evaluation is improved by 11.39%. Relying on the intelligent air conditioning control system, the adaptive control of temperature and humidity can be effectively realized and the indoor air quality can be better ensured, and a reliable control strategy can also be provided to ensure the balance between supply and demand of the power grid.
This paper follows the active reactive power cooperative control strategy of station voltage autonomy, combines the operation scenarios of the autonomous control strategy within the group, and establishes the reactive power optimization objective function of the low-voltage distribution network to improve the voltage quality and reduce the active loss, which takes into account the installation location of reactive power compensation device, and the constraints include the system power balance constraints and voltage quality constraints. In order to solve the reactive power optimization model of low-voltage distribution network containing distributed photovoltaic, the uniformity of the population distribution of the MPA algorithm is initialized using Bernoulli mapping, the inertia weight function and elite strategy of nonlinear attenuation are introduced to enhance the optimization capability of the MPA algorithm in the iterative process, and the eddy-current and fish aggregation effects are applied to widen the scope of optimization search. The network loss and voltage amplitude of the proposed strategy are analyzed to compare the changes of node voltage, voltage offset, objective function value and branch circuit active loss before and after the voltage autonomous reactive power control of low voltage stations. After adopting the optimization strategy of voltage autonomous reactive power control for LV stations, the branch circuit active loss of LV distribution network decreases with the increase of the proportion of distributed PV, and the branch circuit active loss of LV distribution network can be reduced by up to 60%.
Bitumen is a high-quality raw material for the preparation of carbon materials due to its high carbon and low ash characteristics, and its use in the preparation of supercapacitor electrode materials plays a significant role in the enhancement of the economic benefits of the entire coal chemical process. In this paper, the raw materials and experimental equipment required for this study were selected to prepare porous carbon samples under the guidance of the raw material pretreatment process. After completing the preparation of porous carbon samples, the finite element analysis software ANSYS was used to investigate the effect of bitumen pretreatment on the structure and electrochemical properties of porous carbon. With the rising air oxidation time, the peak ratio of porous carbon showed a trend of decreasing and then increasing, with specific values of 2.627, 1.958, 2.083, and 2.486, which was the same trend as that of the XRD test results, suggesting that the asphalt pretreatment has a moderating effect on the structure of porous carbon. The study in this paper further recognizes the effect of asphalt pretreatment on the structure and electrochemical properties of porous carbon, which provides a reference for research and development and innovation in materials chemistry.
Asphalt mixture is a multiphase composite material composed of aggregates, asphalt, fillers and other materials of different properties, in which the coarse aggregate forms the main bearing structure, and the fine aggregate fills the voids formed by the coarse aggregate to improve the structural stability. In this paper, computerized tomography is used to obtain the preliminary tomographic images of asphalt mixture specimens, and the image is effectively segmented through the grayscale thresholding method, and the scanning results are refined. Using voxel-based three-dimensional reconstruction method, the three-dimensional finite element model of asphalt mixture is reconstructed, and the corresponding fine structural characterization index is proposed to prepare asphalt mixture specimens and study the fine structural characteristics of asphalt mixture. The distribution characteristics of the contact connectivity tree of the asphalt mixture are analyzed, with 61.54% of the primary and middle order trees in gradation 1, 92.31% in gradation 2, 83.33% in gradation 3 and 58.82% in gradation 4. It shows that the higher the percentage of second-order connectivity tree, the worse the skeleton contact connectivity, which is not conducive to the improvement of asphalt mixture shear strength. For the four different gradation specimen slice images, the generated areas of each order tree were statistically analyzed. Most of the primary order tree areas were distributed between 100 mm²-300 mm², the intermediate order tree areas were basically distributed between 200 mm²-400 mm², and the high order trees were distributed between 400 mm²-500 mm². The area distribution of high-order tree of grade 4 is more uniform and concentrated, which has better load transfer chain and rutting resistance.
Traditional digital signatures are often publicly verifiable, and in certain applications with privacy preservation requirements, the signer does not want the sensitive information it signed to be redelivered by a dishonest verifier. Aiming at the problem that traditional chameleon signatures (CS) cannot resist quantum computer attacks, this paper proposes a lattice-based authentication CS scheme. Based on the analysis of the lattice difficulty problem and the security vulnerability of the CS scheme, it is pointed out that it does not satisfy the third-party unforgeability and the signer rejectability, and a new lattice-based identity CS scheme is established, which is verified under the stochastic predicate machine model, and the storage and transmission efficiency of the scheme is analyzed. The results show that the newly designed identity-based CS scheme on the lattice can effectively resist quantum computer attacks, can sign messages of arbitrary length, and possesses more lightweight storage and transmission efficiency. The optimized chameleon signature scheme has better security and also provides a new solution for digital signatures to resist quantum computer attacks.
Appropriate use of emotions as a means to intervene in students’ sports behaviors in physical education can promote individuals to form correct concepts of sports and physical exercise. In this paper, in order to construct an emotion intervention model, a cross-temporal adaptive graph convolution network (CST-AGCN) model for whole-body limb emotion recognition is proposed by using the method of spatio-temporal graph convolution. The model was applied to the first stage of negative emotion intervention, after which the appropriate intervention strategy was selected from the intervention strategy library. Then the system was used to assist the teacher in completing some of the intervention initiatives. Finally, based on the empirical study and the system, the learners’ classroom status after the intervention was analyzed again. In addition the study also designed strategies related to enhancement of students’ mental health to further promote students’ physical and mental health. After applying the emotional intervention model and mental health enhancement strategies to the second year (1) class of Secondary School S, this group of students showed significant differences in subjective experience, emotional vitality, body value, interpersonal perception, and dilemma coping, and their mental health was significantly improved. Physical education scores were 7.96 points higher compared to the traditional teaching class, and anxiety decreased significantly. It indicates that the intervention model and mental health enhancement strategies in this study can reduce students’ anxiety behavior and have a more significant relief of students’ negative emotional symptoms such as anxiety and depression, thus promoting the quality of physical education teaching.
This paper constructs a scientific and systematic model for evaluating the quality of Civic and Political teaching in physical education courses with the core concept of establishing morality and combining the intrinsic requirements of collaborative parenting between physical education courses and Civic and Political education. The evaluation indicators use the hierarchical analysis method to assign weights to the established indicators, and at the same time, the consistency test is carried out to ensure that the weights are assigned reliably. The evaluation model is applied to a sports college, scored by questionnaire survey, and combined with the fuzzy comprehensive evaluation method to realize the rating division of the teaching quality of the college. At the beginning of the study, the first-level indicator “Chinese sportsmanship” was rated by experts as low, with a mean value of 2.4, so it was revised to “professionalism”. The importance of the indicator “ideal belief” compared with other level 1 indicators in the evaluation model ranges from 2.24 to 2.65, with the highest weight of 0.308. A university implemented the evaluation model in this paper, and the quality of the university’s comprehensive sports ideology teaching was rated at 4.36 points, which is a good rating. Among them, most students rated the secondary index under “ideal belief” as excellent. The results of the study can be used as a theoretical basis and a practical tool to promote the design and evaluation of the Civics teaching in college sports courses.
The recent frequent occurrence of students’ psychological crisis events has drawn widespread attention to mental health education in colleges and universities. Based on students’ behavioral data, we use big data and data mining technology to model and analyze students’ daily behaviors, complete the construction of students’ social intimacy features based on Dijkstra’s algorithm, use the C4.5 decision tree improvement algorithm based on variable-precision rough set to realize the identification of students’ psychological problems, and analyze the intervention paths of students’ psychological problems and the evaluation of the results of the intervention. The proposed method can recognize students’ psychological problems more accurately, and the recognition accuracy of different levels of psychological problems reaches more than 72%, which is significantly higher than other classification methods. Learning anxiety, loneliness tendency and terror tendency of students in the intervention group were significantly reduced after the psychological intervention (P < 0.05), and the overall factor scores decreased by 9.85%, and the level of mental health was answered to be improved, which reflected the effectiveness of the proposed mental health intervention. The experiment proves that the model in this paper can effectively identify students with psychological abnormalities, and the proposed intervention path for students' psychological problems has a positive impact on the development of students' mental health.
Aiming at the problems of poor point cloud data fusion in traditional MLP models, this paper proposes a multimodal 3D target detection network based on KANs. A KANDyVFE encoder incorporating a fusion layer is designed with KANs as the backbone, and a self-attention mechanism is used to dynamically fuse point cloud features. Two datasets, KITTI and WaymoOpen, are selected as 3D target detection datasets to explore the performance level of the algorithm through controlled experiments. Based on ablation experiments, the effectiveness of the KANDyVFE encoder and the self-attention fusion module is verified. The proposed algorithm achieves 80.72% and 80.23% 3DmAP and 3DmAPH on the WaymoOpen dataset for LEVEL_1, which is 2.14% and 2.17% better than the closest BtcDet method, and achieves the same advanced performance on LEVEL_2. When the KANDyVFE encoder module is not used, the 3DmAP and 3DmAPH are only 72.36% and 74.35%, respectively, and the addition of the KANDyVFE encoder and the self-attention fusion module achieves 91.33% and 92.09% for 3DmAP and 3DmAPH, respectively. The experimental results validate the effectiveness of KANs in point cloud applications, and the ablation experiments further demonstrate the performance improvement brought by the designed modules.
Thanks to the wave of digital economic globalization, the business development of cross-border e-commerce platforms is in full swing. This paper aims to promote the development of e-commerce personalization and launch the research of consumer behavior characteristics. This paper utilizes the concept of entropy in information theory to modify the weights of user feature vectors, so as to make up for the inadequacy of the K-Means algorithm in expressing ambiguous clustering information. Combined with the data samples, the consumer behavior prediction model is established. For the dynamic clustering of customer groups, construct the customer segmentation model based on the improved K-Means algorithm. Combined with the time series prediction model, complete the formation of the spatio-temporal data mining model of consumer behavior. The model is used to mine the consumer behavior dataset of a cross-border e-commerce platform, and the clustering analysis yields four precise consumer group portraits. In this paper, by mining and analyzing the characteristics of consumer spatio-temporal data, the cross-border e-commerce platform is provided with more accurate user insights and marketing optimization solutions.
This paper takes ten economies as examples to analyze and assess the current situation of their international trade development through RCA, MS and TC indexes. On the basis of Porter’s “diamond model” theory, a comprehensive evaluation index system of international trade competitiveness is set up in combination with the actual situation. The entropy value method is used to measure the comprehensive index of international trade competitiveness, and the influence of various influencing factors on international trade competitiveness is empirically studied based on the principal component multiple regression analysis. The results show that the U.S. international trade competitiveness is far ahead, with an average score of 3.67 in 2020-2024, and the lowest score is Singapore, with a score of only -2.17. The degree of explanation of international trade competitiveness of the four factors reaches 98.9%, and all of them have a promotional effect on the international trade competitiveness, in the following order: factors of production>enterprise strategy and competition>related industries>demand factors.
The construction and opening of high-speed railroads have brought new development opportunities to China’s ethnic regions, which are economically backward but rich in tourism resources. From the perspective of the impact of high-speed rail on regional tourism, this paper briefly analyzes the homogenization effect and accessibility effect that the construction of high-speed rail brings to the corresponding region. Accordingly, it puts forward the relevant research hypotheses on the impact of high-speed rail on regional tourism and analyzes the current situation of tourism market development in China’s A ethnic region. Under this premise, the model of high-speed railroad influence on tourism development level is designed and relevant research variables are selected. Based on the model, the empirical analysis of the impact of high-speed railroad on tourism in ethnic region A is launched. The study points out that the opening of high-speed railroad significantly promotes the total tourism income of ethnic region A at the 1% level, i.e., the opening of high-speed railroad has a positive positive effect on the tourism development of ethnic regions.
Aiming at the complexity of mental health assessment for students in colleges and universities, this paper proposes an innovative framework that integrates social sentiment analysis and multi-branch neural networks. A multilevel mental health assessment system is constructed through cross-modal feature interaction CNN+BiGRU with heterogeneous graph structure modeling. In the model design, image feature extraction is pre-trained by five-branch CNN structure ViT, text features are fused by dynamic word embedding with multi-scale convolution, and a virtual node and metapath-driven heterogeneous graph neural network H-GNN is introduced to strengthen the global relationship modeling. Experiments show that the model achieves 89.7% and 91.2% accuracy on Twitter-15 and Twitter-17 datasets, respectively, and the F1 values are improved by 3.24% and 2.32% from the optimal baseline BICCM. In the actual college mental health monitoring, the model successfully captured the time-series fluctuations of depression index and anxiety level, and found that the rational-perceptual dimension was highly correlated with the examination cycle, with 0.69 during the midterm examination and 0.68 during the final examination. Through the ten-fold cross-validation comparison experiments, the model significantly outperforms the cutting-edge models, such as MIMNBERT, EF-NET and so on on the weighted average index, with an average accuracy rate of 99.02% and F1 value of 98.08%. The study shows that the framework provides a highly accurate and interpretable technical solution for mental health risk early warning, which is especially suitable for dynamic monitoring scenarios in universities.
Taking the perspective of new quality productivity, this study explores the promotion effect of the intermingling of intelligent computing and traditional culture on the cultivation of innovative talents, and constructs an evaluation system containing four primary indicators and 14 secondary indicators of educational activities, student practice, collaborative innovation and teaching resources. The cloud integration model is used to deal with the ambiguity and randomness of the complex system, and the network hierarchy analysis method ANP is used to determine the weights of the indicators and reveal the dynamic association of each element. It is found that: the indicator B2 of student practice category has the highest weight of 0.329, in which the number of awards of C5 innovation competition and the number of C4 students’ project participation are the core driving factors, with the weights of 0.103 and 0.078, respectively. the cloud integration model verifies the scientificity of the evaluation system. The evaluation value of the traditional culture innovation talent evaluation system constructed in this paper is 0.798, and the integrated cloud model belongs to “very good” grade. However, the mapping intervals of C14 Resource Library Call Frequency and C13 Teacher Integration Background are low, 0.346 and 0.413 respectively, which need to be adjusted and optimized. The innovative talent cultivation program of colleges and universities constructed in this study can make up for the shortcomings in traditional talent cultivation performance evaluation, has certain practicality and effectiveness, and helps to improve the quality of traditional culture innovative talent cultivation.
Under the background of economic transformation and high-quality development strategy, the coordinated development of regional economy and precise regulation of fiscal policy have become a hot spot of concern. This paper constructs the evaluation index system of regional economic high quality development and evaluates the level of economic high-quality development in the recent 10 years by using the entropy weight-TOPSIS method. Combined with Dagum Gini coefficient and spatial autocorrelation test, we study the spatial correlation of economic high-quality development among regions. The benchmark regression model and mediation effect model are constructed to calculate the effect of fiscal policy on the level of regional economic high-quality development, and to judge the effectiveness of fiscal policy regulation path. The study shows that the 30 provinces in China can be divided into different regions according to the level of high-quality economic development, and each region presents different types of characteristics such as “high – high”, with significant differences. The variables in the benchmark regression model and the mediation effect model are correlated at the 1% level, and pass the smoothness test when the difference is of the 0th order. Fiscal policy at the regional level has a positive contribution to the level of high-quality economic development, but at the same time is affected by the original level of development of each region.
The article takes the defect detection and recognition of railroad track as the main research point, and extracts, preprocesses and corrects the railroad track surface image by introducing image segmentation algorithm. Gabor function, K-means clustering method and conditional iterative pattern algorithm are embedded in the original Markov random field model to construct the improved two layer graph model for railroad track defect segmentation. The recall, precision, mean average precision, and loss function of the improved Markov defect segmentation model are significantly better than those of the original model, and the mean average precision of the defect segmentation model is increased to 95.7% after the Gabor function, K-means clustering method, and conditional iterative pattern algorithm are applied. The improved Markov defect segmentation model fused with clustering features in this paper can better meet the classification and identification of railroad track defects.
In recent years, with the rapid development of artificial intelligence, big data, machine learning and other technologies, human society is entering a more and more intelligent society, and the interaction between humans and machines becomes more and more common. In this paper, image processing operations are added on the basis of Kinect’s original acquisition of gong dance images, which reduces the influence of external light, background and other factors, and makes the human capture efficiency increase dramatically, and a spatio-temporal graph is constructed on the basis of the continuous human posture key point data, which describes the distribution of the human posture key points in different dataset types. Aiming at the problems existing in the traditional spatio-temporal map convolutional network, a multi-dimensional attention mechanism is designed to guide the model to reasonably allocate the weight resources in three dimensions: space, time and channel, respectively. Experiments are conducted on NTU-RGB+D, Kinect skeleton and Taiji datasets, respectively, which show that the AGCN-STC proposed in this paper has better recognition performance on all three datasets, and the recognition accuracy is improved by 0.9 percentage points compared with AM-GCN. Two actors are used as samples for visual measurement and quantitative analysis to compare the differences between the performance gestures of the two ornaments. Finally, based on the results of the study, we propose a transmission path for the Guanzhong gong dance, which is a reference for the cultural transmission of the Guanzhong gong dance.
In the process of social development of Tang Dynasty, literary works behind the depth of interpretation and expression, systematized spiritual concepts. In this paper, the text data of Tang Dynasty literary works are processed by word division and de-discontinued words, and it is intended to use Transformer model to realize the word vector transformation of text data, and put the word vector into Text-CNN network for iterative training to realize the text feature extraction. By means of text feature screening, the cultural value assessment system of Tang Dynasty literary works is formed, and a comprehensive evaluation model of cultural value is designed under the role of convolutional neural network and text features, and using the model of this paper, the cultural value of Tang Dynasty literary works is assessed. The accuracy rate of cultural value classes “Ⅱ”, “Ⅲ” and “Ⅴ” is 1, while the accuracy rate of cultural value classes “I” and “Ⅳ” have accuracy rates of 0.98 and 0.96, indicating that the model in this paper can accurately assess the cultural value in Tang Dynasty literary works.
The equalization and rationalization of educational resource allocation is of great significance to the coordinated development of education. The study takes the educational resources of 13 districts and counties in Y city in 2023 as an example, and proposes to use the BP neural network-based educational resource allocation evaluation system to analyze it. The results show that only three districts and counties have “very good” and “good” levels of educational resource allocation. Accordingly, this paper constructs a multi-objective optimization model to improve the level of educational resource allocation, reduce the differences between counties, and improve the utilization rate of educational resources. The weights corresponding to the eight indicators of the educational resource allocation evaluation index system are solved by the entropy weight method, after which the preset values of the three objective functions and the weights accounted for by the eight indicators are brought into the model and the artificial raindrop algorithm is used to find the optimal solution. After finding the optimal solution of educational resource allocation, the BP neural network-based educational resource allocation evaluation system is used again to evaluate it, and at this time, the educational resource allocation of a total of 12 districts and counties belongs to the “very good” and “good” grades. The study shows that the optimization method of educational resource allocation designed in this paper can reasonably plan educational resources and realize the coordinated development of education.
With the explosive growth of the variety and quantity of multimedia information in the Internet of Things (IoT) environment, its security problem is becoming more and more prominent. Therefore, this paper constructs APODAC dynamic access control model. The information processing of massive data of IoT is carried out through the fusion technology of multiple media features. Based on the real-time access behavior sequence of IoT, a fuzzy reasoner is used to analyze the degree of risk and assess the network security posture. Based on the degree of risk, IoT access rights are dynamically adjusted. The simulation experiment results show that the fuzzy reasoning method in this paper has a 4.4% higher risk detection rate for IoT network and a 10.5% decrease in false alarm rate compared to the traditional SVM method. In risk behavior oriented dynamic access control, the APODAC model proposed in this paper still outperforms the other 2 models in terms of response time for both higher number of access requests and smaller amount of access request data.
Since the strategic plan for rural revitalization was put forward, the related contents of public facilities have been continuously written into national policies. Promoting the high-quality construction of rural public facilities has become a hot topic of research in China’s rural areas. In this paper, optimization ideas and frameworks are proposed for the layout of rural public service facilities. Using the mixed integer planning model, the optimal solution of facility layout is obtained by calculating the distance between facilities to realize the optimization of rural public facility layout. Moran’s I index in global spatial autocorrelation is used to analyze the degree of spatial autocorrelation of rural public basic facilities accessibility. Construct a multivariate linear model to assess the impact of mixed integer planning applications on rural residents’ sense of social governance effectiveness. Evaluating the efficiency of rural basic public service facility accessibility coverage, the number of rural clinics is much larger than other facilities, with 26 facilities, and the number of middle schools, township general hospitals, and post offices is smaller, all with only two, indicating that there are certain problems in the configuration and spatial layout of public service facilities in a certain rural area nowadays. The application of the mixed integer planning model has a significant impact on rural governance in terms of human development index, public services, social security, public safety and social participation, with regression results of 0.075, 0.068, 0.125, 0.083 and 0.164, respectively.
The power grid, as a unified whole composed of various links of generation, transmission, transformation, distribution and use, needs mutual coordination and unified scheduling in terms of operation characteristics. This paper establishes a power 3D engine based on lightweight 3D engine technology, and builds a panoramic display platform for grid one map generation information on this basis. In order to explore the feasibility of this platform on grid optimization and scheduling, this paper establishes a two-layer optimization and scheduling model of the grid by taking the minimization of the operation cost as the objective function of the upper layer, and combining the supply-side and demand-side balancing objectives of the lower microgrid. The PSO algorithm is improved by introducing the immune mechanism, linear adjustment, and linear combination, and the HPSO algorithm is used to solve the grid two-layer optimal dispatch model. The simulation shows that the economic cost and environmental cost are reduced by 51.78% and 23.07%, respectively, and the total cost is reduced by 8.66*106 yuan after considering the uncontrollable residential electricity load. Relying on the One Grid Map platform can realize the accurate analysis of the peaking capacity and climbing residual capacity of the grid at typical time periods, providing reliable data support to meet the peak shaving and valley filling of the grid. Combined with the lightweight 3D engine technology, the panoramic display platform of production information of one map of power grid helps to obtain real-time grid operation and environmental information, realize monitoring and comprehensive analysis of the system, and make real-time decisions and interactions for optimal dispatching of power grid.
The integration of industry and education refers to the in-depth integration of industry and education, which emphasizes the cultivation of students’ practical ability and vocational quality. The integration of industry and education brings a new development direction for higher vocational education, and at the same time puts forward higher standards and requirements for higher vocational English teachers. In this paper, a new recursive Bayesian network structure algorithm is proposed based on RAI algorithm and CS algorithm, which mainly learns the Bayesian network structure by calling two functions recursively. Then based on the application effect evaluation model of recursive Bayesian network, the index system of classroom teaching evaluation is given based on the characteristics of classroom teaching, and the application effect is evaluated. The experimental results show that the optimization of the Bayesian network model can significantly improve the classification recognition reliability of the classifier, and taking the appearance score as a random effect, it can be found that the teacher’s appearance difference has a significant effect on the teaching evaluation. The results of the study are of great significance to the construction of scientific English classroom construction as well as teaching quality evaluation system.
This paper introduces the decision tree algorithm into the field of preschool education to categorize the styles of children in preschool education. The learning activities of children with different styles are deeply analyzed by the total number of detections, the task score and the total game time in the small train by counting activity. Decision tree algorithm is utilized to integrate online preschool education resources and used in practice so as to assist teaching. The teaching experiment method is used to test its educational effect. Kindergarten children were categorized into 3 types: extroverted, negative emotional and effortful control children. Effort-control style children performed well in play detection behavior, play task score and total play time. In the teaching experiment, children in the experimental group obtained very significant improvements in small muscle activity, art, music and rhythm, blocks, natural science and mathematical thinking, while the control group also improved, but their changes were not significant. Decision tree algorithm has better results in assisting preschool education.
In today’s society, hospitals are treated with images generated by medical examination equipment for disease diagnosis, and high-resolution images can greatly improve the accuracy of doctors’ disease diagnosis. The study constructs an ultrasound image dataset US-Dataset suitable for the task of super resolution reconstruction of ultrasound images. Based on this ultrasound image dataset, a degradation model is proposed, which in turn constructs ultrasound image matching pairs containing high – low resolution images for training the model proposed in this paper. To improve the perceptual quality of endometrial images, a super-resolution reconstruction model UN-SRGAN based on generative adversarial network is proposed in this paper. The network structure of this model consists of a generator and a discriminator. To validate the effectiveness of the model proposed in this paper, it is evaluated on Accuracy, Precision, Recall, Specificity, and F1-score metrics. The proposed model achieves the lead on PSNR and SSIM metrics and subjective quality evaluation, and the UN-SRGAN model has an accuracy of 0.9721, which is better than the other models, verifying the effectiveness of the model.
Soil microorganisms and mineral ions play a crucial role in the material cycle and energy flow. Different types of sandy, loamy and gravelly soils were selected as experimental sample plots, and the mineral element and microbial diversity of the soils were analyzed by using the curve method with spiked recovery analysis measurement and Illumina high-throughput sequencing technology. Then, principal component analysis and Pearson correlation analysis were applied to extract the factors affecting phosphorus and sulfur cycling by soil mineral ions and microorganisms, and the results showed that the mineral ions in the three different types of soils were mainly Na+, K+, Mg+, and Ca+. The top ten dominant bacterial phyla in relative abundance in different types of soils were Ascomycetes, Actinobacteria, and so on. The eigenvalues of the first four principal components in the principal component analysis of phosphorus-sulfur cycle influencing factors were greater than 1. Therefore, four principal components were selected: soil water content, soil Mg+ content, soil actinomycetes content, and soil Ca+ content.
Soil microorganisms are the main drivers in maintaining soil health. This paper focuses on the process of soil mineral ions and microorganisms involved in regulating the phosphorus-sulfur cycle, and systematically investigates the repair and improvement mechanism of soil microorganisms. Relying on an experimental area of a typical grassland in Inner Mongolia, we set up experiments with different nitrogen addition treatments, and combined with one-way analysis of variance (ANOVA) to investigate the distribution of soil phosphorus and sulfur fractions under various scenarios. Then, structural equation modeling was applied to explore the dynamic role between microbial action and phosphorus-sulfur cycle under N addition. Under different nitrogen addition scenarios, Ca10-P accounted for the largest proportion of inorganic phosphorus fractions, which were all greater than 40%. The percentage of inorganic sulfur in the soil was relatively small, less than 3% of total sulfur, and the response of inorganic and total sulfur to the gradient of nitrogen addition, nitrogen frequency, and different grassland management practices was not obvious. Fungal communities were important drivers of changes in functional genes for interleaf phosphorus and sulfur cycling at different N application levels, i.e., N fertilizer application altered the interleaf fungal communities by affecting soil physicochemical properties, which significantly regulated the interleaf bacterial communities, phosphorus and sulfur cycling functional gene abundance, and pathogenic fungal abundance.
Civil engineering disasters mostly occur in mountainous areas, and it is difficult to comprehensively monitor them using traditional technology, while this drawback can be avoided by utilizing UAV inclined photogrammetry technology. In this paper, with the support of the relevant experimental equipment, we obtain the images of civil engineering disasters with the help of this technology, and in order to avoid the influence of the interference factors in the images on the research results, we propose to use the K-means algorithm to pre-process the images. After completing the image processing, the improved YOLOV4 target detection algorithm is used to complete the design of the intelligent detection model of civil engineering disasters, and the processed images are input into the model for iterative training, so as to realize the intelligent management and early warning of civil engineering disasters. A region in Yunnan Province is taken as an example to explore and analyze the example. As of 2022, it is found that 180 landslides actually appeared in the region, while the model detected 172 landslides, resulting in the model’s civil engineering disaster detection accuracy of 95.56%, which is within the permissible range, proving that the model has a good application efficiency, and can provide certain help and innovative guidance for the relevant units of civil engineering disaster management.
In recent years, the deepening of reform and opening up, the deepening of the socialization of college management, the trend of students’ thinking is more and more diversified leading to the frequent occurrence of college students’ behavior. This paper is based on Spark’s parallel H-mine cluster computing to mine the behavioral characteristics data of students in frequent item sets. Using the K-Means clustering algorithm optimized by information entropy and density, the clustering and classification process is carried out according to the central value of the obtained behavioral features. Construct the class model of student behavioral features, realize student behavior prediction by K-nearest neighbor algorithm, and build the early warning model of student behavior prediction based on Spark cluster. The results of clustering analysis show that the average number of times a class of students, the second class of students, and the third class of students eat at breakfast is 120.07, 107.66, and 118.25, respectively, and the first class of students has the most number of times of breakfast meals, which shows that this class of students has better eating habits. The number of students studying on March 24, 2023 is predicted by the model based on the K nearest neighbor algorithm, and the trajectory of the real value and the predicted value The number of students with relative error less than 0.2 accounted for 86.42%, indicating that the model is good at predicting the number of students as a whole.
AI technology in the development and application of traditional texture recovery and reproduction, deep learning models for traditional texture information and color information consistency migration is still deficient, this paper by using the visual Transformer network advantage and visual Transformer network Transformer encoder structure optimization. That is to say, in the Transformer encoder, the multi-head self-attention module and feed-forward network module are called to process the input data and extract the image features, and then join the edge preservation smoothing technology to remove the strong edge information, preserve some weak edges and local colors, and generate the image texture information with the input texture. The color interpolation method is used to achieve the consistency of texture color texture and image texture migration. The result images of Dong brocade texture style migration show that the image texture migration model based on visual transformer is more capable of generating images with the best style loss value and the best content loss value, and is able to obtain more than 70% of user preference.
For a long time, the cultivation and assessment of the practical application ability of piano in music education has been an important issue that people are constantly concerned about and trying to solve. The research uses the evaluation method based on fuzzy neural network to conduct the study, first of all, from the basic skills, performance skills as well as creative skills in three aspects of the construction of the students’ piano skills level index system, through the objective weight entropy weighting method to determine the weight of the index system on the students’ piano skills were assessed and analyzed, and got the indexes of the importance of the order of the subjective weighting order of the creation of skills (C, 0.471) > performance skills (B, 0.384) > basic skills (A, 0.145). 0.384) > basic skills (A, 0.145). After the selection of sample data, standardization of sample data and simulation training of the network model, the experimental results show that the application of the fuzzy neural network model for the evaluation of piano skill level is effective and feasible. The temporal accuracy and cognitive accuracy of piano playing were fused to quantitatively assess the brain function. The experimental results show that the brain function scores obtained with this method can effectively indicate that the students’ brain function increases with the increase of practice time and decreases with the increase of difficulty.
Artificial Intelligence AI composition is one of the hot topics that have been debated in recent years. In this paper, we first extract monophonic and chordal features from MIDI digital music files. Then the WaveNet intelligent music generation model is used as a carrier to optimize its multilayer convolutional network structure. The audio files are fed into the optimized WaveNet model, and the final training parameters are obtained after several rounds of iterative training. After the model completes the training, music sequences are automatically generated. The results show that the optimized WaveNet model for training leads to a significantly higher accuracy rate in the validation set than before optimization. Compared to other models, the method in this paper generates music using a larger variety of notes, improving the quality of the music theory and chord aspects. Compared with the composite scores of human compositions, the percentage of WaveNet model compositions with scores of 4 and above is about 20.3%, and the percentage of scores of 3 and above is 30.5%. Therefore, the overall level of the compositions generated by the model in this paper is good.
In the era of digital media, with the help of media empowerment, Chinese medicine culture dissemination completes the innovation from the two dimensions of disseminators and media channels, which brings new opportunities to Chinese medicine culture dissemination. Aiming at the problem of large time overhead of traditional greedy algorithm in the optimization of nodes of TCM culture dissemination network, NPG algorithm is used to optimize the influence of starting nodes, computational efficiency and selection strategy. On the basis of optimization, the propagation probability is calculated to determine that time, content and social relationship can be used as the basis for judging the propagation path, and the path coefficients are analyzed with the help of structural equations. The path coefficient of social relationship→time→Chinese medicine culture dissemination is 0.173, i.e., under the role of time, there is a significant direct effect between social relationship and Chinese medicine culture dissemination, and time plays the role of mediating effect in the reconstruction of dissemination path. The research in this paper promotes the sustainable development of Chinese medicine culture through the improvement of Chinese medicine culture communication network.
In today’s society, a single intelligent body does not meet the needs of complex tasks, and coordinated control of multiple intelligences becomes an important solution. In this regard, this paper carries out the research on the coordinated control strategy of multiple intelligences supported by deep reinforcement learning technology. Aiming at the problems of uneven task distribution and unsatisfactory decision consistency arising from the collaborative decision making of multiple intelligences under the software system architecture, a hierarchical multi-intelligence collaborative decision-making algorithm based on the AC framework is proposed to realize the information exchange and decision-making collaboration among intelligences, so as to improve the efficiency of coordinated control. However, with the increase of the number of multi-intelligents, the algorithm will have the problem of upper and lower level non-smoothness, in order to solve this problem, a multi-intelligents collaborative algorithm based on role parameter sharing is designed. Finally, the research scheme of this paper is evaluated and analyzed from multiple dimensions. When the number of intelligences increases by 5, the reward value of this paper’s algorithm does not show a decreasing trend, which indicates that this paper’s algorithm is able to handle the control coordination problem in the case of a small number of intelligences. When the number of intelligences increases by 15, the original method shows a decreasing trend, while in the multi-intelligence body collaboration algorithm based on the sharing of role parameters, the performance is very bright, which ensures the coordinated control effect of multi-intelligence bodies under the software system architecture.
The Chineseization of Marxism is one of the important topics of concern to Chinese social sciences. The study summarizes the main manifestations of the cultural identity of Marxist Chineseization, and estimates the potential growth rate of the Chinese economy using the extended Kalman filter algorithm from the dimension of material culture construction. Then based on CiteSpace, it conducts bibliometric measurements to explore the relationship between the Chineseization of Marxism and traditional Chinese culture. The measurement results of the model can better reflect the growth trend of the Chinese economy, and the economy will experience a period of medium-speed growth in the future, which should be seized to deepen the economic restructuring and promote the cultural identity of Marxist Chineseization by safeguarding the construction of material culture. The research literature on both the Chineseization of Marxism and traditional Chinese culture shows a general upward trend, especially from 2012-2021, with an increase of 3.06 times. The Chineseization of Marxism and Chinese culture have a deep-level fit, and the essence of Marxist ideology should be connected with the essence of Chinese traditional culture, so as to promote cultural identity and enhance cultural self-confidence in the process of the Chineseization of Marxism.
In order to improve the automation and intelligence level of underground fluid sampling, this paper proposes a kind of underground fluid automatic sampling device, and carries out the structural design of the sampling device, the control system design and the field experiment test. According to the action process and movement characteristics of the underground fluid stratified sampling device, the control system needs to use multiple electromagnetic control valves to control the switching of the oil circuit of different actuators respectively. In order to improve the control state and response speed of the stratified sampling device system, a fuzzy identification algorithm is chosen to identify the control model, and the MIMO robust generalized predictive controller is used as the robust adaptive controller of the system to realize the low-flow and low-disturbance acquisition of underground fluids at the same monitoring point and at different depths. In the field sampling, the average values of DO at sampling depths of 1m, 2m, 3m, 4m, and 5m for manual sampling, vertical sampling, and fuzzy adaptive device sampling under the 1-2 sampling plumb line were 7.98mg/L, 7.86mg/L, 8.25mg/L, 7.83mg/L, and 7.77mg/L, respectively. The deviation of dissolved oxygen content at the same sampling point in the three ways is small and the trend of change is consistent at different depths. It shows that the fuzzy adaptive stratified sampling device system designed in this paper can be applied to the sampling of subsurface fluids with dissolved oxygen as the detection target.
In this paper, finite element simulation of heat transfer process is carried out using Cu composites reinforced with TiB2 of different particle sizes. Based on the FEA data, the BP neural network algorithm is integrated and optimized by the MEA algorithm to establish the FEA-MEA-BP performance prediction model. The results of thermal conductivity analysis show that the correction factor of the simulated thermal conductivity value of TiB2/Cu composites can be calculated using the finite element method as 2.3. Compared with the actual value measured by the LINSEISLFA1600 laser thermal conductivity meter, the fluctuation of the simulated thermal conductivity results from the experimental results is no more than 10% between 50~200°C, and the simulation performance has a high degree of accuracy. Taking 304L stainless steel as a sample, the RMSE, MAE and R² are improved to different degrees compared with other models, so the performance of the FEA-MEA-BP model is excellent in terms of the accuracy of prediction.
The article is based on the need for music education innovation in colleges and universities to optimize the traditional piano skill training through Monte Carlo algorithm. Taking the finger as the research entry point, based on the physiological structure of the hand, the reduced-density Monte Carlo method is used to carry out the mechanical design of the finger trainer and plan the finger training movement mode. Through kinematic simulation experiments to understand the feasibility of the piano finger training device in this paper. Analyze the error sensitivity of position and posture on the finger training device. Finally, the teaching experiment method is utilized to explore the training effectiveness of the Monte Carlo-based piano finger training device in this paper. This paper has good usability. When the position error of the mechanism varies in the range of -40mm~40mm, the position error gradually decreases in the X-axis and Z-axis, and the position error in the direction of Y-axis remains stable. The attitude error of the mechanism gradually increases with the increase of the X-axis rotation angle. The output accuracy gradually increases during the rotation from -5° to 5° around the Y-axis. The angular attitude around the Z-axis has no significant effect on the output accuracy. The two groups did not have significant differences in the four dimensions of piano playing skills before the experiment. After the teaching experiment, the experimental group was much better than the control group, and the posttest results of the two groups produced significant differences, and the pre-test and post-test results of the experimental group possessed very significant differences. The Monte Carlo optimization-based piano finger training device has a significant effect on the improvement of students’ piano skills.
Driven by artificial intelligence and deep learning technology, this study proposes an intelligent course recommendation system for art and design education. By constructing XMMC, a joint extraction model of knowledge entities and relations based on deep learning, the accurate analysis of course knowledge structure is realized. Key features such as user preference, content semantics and social influence are extracted by combining multi-feature ranking models such as collaborative filtering, topic modeling and course hotness. Finally, based on the deep reinforcement learning algorithm DDPG, a dynamic recommendation strategy is designed to optimize the recommendation effect. The experiments are based on Coursera Course, Caltech-UCSD Birds 200 and Education Recommendation datasets, and the results show that the improved DDPG model achieves 49.11%, 70.05% and 59.23% course coverage on the three datasets, respectively, which is better than the traditional algorithms Time SVD and CDAE with significant improvement. We constructed the art education course category with the number of topics as 5. In the practical application, the recommended list generated by the system is highly consistent with the course heat analysis, in which the course “Introduction to 3D Modeling and Blender” ranks the first with 6729 average playbacks, which verifies that the recommendation strategy can effectively improve the fitness of the pushed content and the current course progress of the students. It verifies that the recommendation strategy can effectively improve the compatibility between the pushed content and the students’ current course progress.
Mining the dynamic association between psychological state changes and sports performance is one of the core tasks of physical education towards scientific teaching. In this paper, the data of psychological change indexes of student athletes were collected by scales and the indexes variability was tested. Combined with the principal component analysis to extract the principal component factors of the psychological change index data, construct the correlation coefficient matrix, and calculate the multiple linear regression equations of psychological change and sports performance. The gray correlation model based on the whitening weight function was used to analyze the gray correlation between psychological change and athletic performance, and calculate the influence of the two.Among the 9 psychological indicators, 4 dimensions, such as social evaluation anxiety, had a significant difference with P<0.01. P<0.05 for 2 dimensions such as competition preparation anxiety, there was a difference. In the principal component analysis, the negative and positive psychological dimensions were extracted as principal components, including the 7 psychological indicator components excluding the 2 dimensions. Judging from the regression coefficients and gray correlation calculation results, the 3 psychological indicators of cognitive state anxiety, state self-efficacy, and injury anxiety had the greatest influence on sports performance. Targeted alleviation of cognitive and injury anxiety and improvement of self-confidence can optimize students' sports performance.
In this study, a multimodal analysis framework based on GCN is constructed to address the needs of interaction behavior analysis and creativity assessment of programming games for 0-6 years old children. A stack noise reduction self-coding neural network is used to recognize human gestures in images, and the feature representation of interaction behaviors is realized based on GCN, and the effectiveness of the method is proved by the experimental results on the two-player interaction behavior library. Construct a creativity evaluation system applicable to programming game scenarios for young children, and recruit 80 students aged 0-6 years old to carry out experiments. The students were classified using the GCN-based interaction behavior analysis model, and the weights of creativity evaluation indexes were determined by AHP. The fuzzy comprehensive evaluation method was used to evaluate and score the factors of creativity of the three categories of students, and the test results were verified with the help of the gray correlation method. The comprehensive evaluation scores of the three types of students are 2.006, 3.507 and 5.026, respectively, in which the creativity level of excellent learners is the highest and reaches the excellent grade. The normalized gray comprehensive correlation vector (0.3224, 0.3727, 0.3049) is close to the AHP weight vector (0.328, 0.357, 0.315) with a good assessment effect, and the research results provide a new technical path for behavior analysis and creativity development assessment in early childhood programming education.
The study of student behavior data is a necessary way to enhance the digitalization process of the curriculum system of business administration. This paper collects students’ online platform learning behavior and other data, and carries out data cleaning and other pre-processing on them. Using the density function and entropy discretization algorithm to divide the continuous student data into intervals, and study the course learning characteristics of students in different attribute intervals. On this basis, optimize the curriculum system of business administration majors in colleges and universities, and judge its application value through comparative experiments. Obtain students’ satisfaction data on the curriculum system of business administration majors in colleges and universities, and analyze the direction of continuous improvement. Through preprocessing and descriptive analysis, it can be judged that the student behavior data conforms to the characteristics of continuous data, and can be classified using the discretization algorithm. The student behavior data are discretized into 3 major categories and 11 subcategories, and the attribute characteristics of each category can represent the behavioral characteristics and curriculum needs of different students. The mean value of the overall satisfaction of the optimized business administration curriculum system is 3.567 points, and the scores of all dimensions are higher than those before optimization, and there is no gender difference in satisfaction (P>0.05). The entropy-based discretization algorithm can effectively support the optimization of business administration professional course system.
This paper discusses the application of AI color analysis technology in oil painting teaching, combined with experiments to verify its effect on improving teaching quality. Firstly, the core algorithm of AI color analysis technology is analyzed, and the implementation scheme of digital image sharpening preprocessing is proposed based on the RGB color model, and the edge and color information of the image is extracted based on the improved Canny operator. Improved GAN completes the reconstruction of the oil painting image, and the characteristic colors of the oil painting are extracted using the optimized K-means clustering algorithm. The oil painting images are selected for color feature analysis, and the color matching scheme is improved based on the color feature results to construct the color analysis process based on AI technology in oil painting teaching. Finally, students from art colleges were selected as the research subjects, and a control experiment was designed to investigate the effect of AI color analysis in teaching. The p-value of the five factors of the experimental group and the control group’s post-test scores of creativity of modeling, application of color, color richness, emotional tendency of color and expression of the theme are all less than 0.05, and the average scores of the experimental group in these five aspects, 3.66, 3.74, 3.85, 3.77, 3.34, are all significantly larger than those of the control group, which indicates that the experimental group using AI color analysis to assist teaching has significantly widened the gap between the control group and the experimental group in terms of the use of color. It shows that the experimental group using AI color analysis to assist teaching has a significant gap with the control group in the use of color.
This study focuses on the computational analysis of business data, constructs a market prediction framework that integrates K-means clustering, feature standardization and improved N-BEATS model, and verifies its effect on the accuracy of enterprise strategic decision-making based on multi-source data. The study selects real-time transaction data and weather data from 800 merchants under Alibaba, extracts key features through standardization and correlation analysis, and improves the model by introducing topological features and multi-attention mechanism, which significantly optimizes the time series prediction accuracy and reduces the RMSE by 18.6%. The empirical analysis for tissue paper category shows that the forecast error rate of the time series decomposition method is only 0.58%, which is better than the traditional trend method and seasonal index method. Through the regression analysis of 328 business managers’ questionnaires, data-driven analysis β=0.617, p<0.001 and innovative forecasting β=0.594, p<0.001 have a significant positive effect on strategic decisionmaking accuracy and consensus.
In the context of digital economy, the cultivation of rural revitalization talents urgently needs interdisciplinary collaboration and intelligent support. Taking Ningbo Future Rural College as a practice carrier, this study proposes an interdisciplinary platform construction model that integrates knowledge graph and deep reinforcement learning. The dynamic semantic association network is constructed through knowledge graph representation learning (TTransE), which is combined with a hierarchical reasoning intelligent framework to realize personalized learning and career recommendation. The experiment is based on 850 student sample data, and uses principal component analysis (PCA) dimensionality reduction with Pearson’s correlation coefficient to validate feature relevance and construct a breadth-first association knowledge graph. The model achieved the highest 97.79% accuracy with 90.89% F1 value in entity recognition, and the skill assessment score was improved to 80.69, which was significantly higher than the control group’s 67.10. The model significantly improved students’ thinking ability (8.58 vs. 6.20), skill level (7.96 vs. 5.64), and innovation and adaptability (7.84 vs. 5.71) in this paper. The application of the proposed research methodology in Ningbo Future Rural College is effective and has certain promotion value for rural revitalization talent cultivation across specialties, which can help professional decision makers to formulate and implement cultivation policies.
The human specimen, due to its non-renewable nature, requires the liquid exchange process in adaptive regulation mode to realize precise control. In this paper, a stochastic parallel perturbation based gradient descent algorithm (SPGD) is introduced into the real-time control system for human specimen liquid exchange. The SPGD algorithm is used to assist the real-time control system to monitor the liquid concentration and regulate the liquid exchange power in real time, so as to keep the liquid exchange speed fast and stable and reduce the risk of specimen damage. The advantages of SPGD algorithm and real-time control system in the process of human specimen liquid exchange are verified through several experiments. The results show that there is a correlation between different liquid concentrations, real-time power and liquid exchange speed, and the combination of SPGD algorithm and real-time monitoring of the changes between the three can improve the stability of liquid exchange. The SPGD algorithm with the introduction of stochastic parallel perturbations reaches an evaluation function value of 0.83 at 31 iterations and a convergence accuracy of 0.9698 after 150 iterations. The convergence speed and accuracy are better than the unimproved SPGD algorithm. During the real-time control process, the relative error of pipetting is not more than 1.5%, and the repeatability deviation is less than 1%. Within the time range of 0-120ms, the real-time control system introducing SPGD algorithm can give the changing power of liquid exchange, which guarantees the specimen safety in the process of liquid exchange.
In this study, a data-driven assessment framework integrating multi-criteria decision making, association rule mining and fuzzy clustering methods is proposed to address the scientific and objective needs of rule extraction and optimization in employee performance assessment. The TOPSIS model is improved by triangular fuzzy numbers to realize the objective ranking of business performance. The Apriori algorithm is improved to mine the association rules between competency and performance. The empirical results show that Employee 3 is ranked in the excellent grade with 101.32% task completion rate and 0.8323 relative proximity. The questionnaire results of competency quality had a significant impact on appraisal with a confidence level of 84.3%, while technical title and education were not sufficiently correlated with a confidence level of <30%. The fuzzy decision tree model generated 25 classification rules with a confidence level higher than 63.2%. And combined with the work attitude index with a weight of 0.2913 to complete the comprehensive performance assessment, the results show that the overall performance score of the employees in this enterprise is 0.81362, which is a good grade. This study makes the performance appraisal more objective, precise and efficient, and at the same time expands the application scope of data mining technology in enterprise management.
In this paper, we construct a multi-level network based on corporate mobility relationships to quantify human resource attributes. The cuckoo search algorithm (CS) is chosen to enhance the global optimization capability of human resource management scheme. Combine CS and XGBoost to construct CS-XGBoost algorithm, and realize the optimal solution of HRM scheme through hyperparameter optimization and other steps. The multi-project human resource management of construction enterprises is taken as an example to verify the auxiliary value of CS-XGBoost algorithm in the generation of optimal management scheduling scheme. Empirical studies show that the algorithm can obtain the optimal solution in about 450 iterations. In multi-project scheduling management, the optimal duration can be reduced to 510 days, which is better than the comparison algorithm. With the introduction of demand prioritization requirements, the algorithm can effectively balance the differences in project duration, project cost and employee working time. The CS-XGBoost algorithm can be used to quickly realize the optimal decision-making of enterprise human resource scheduling management, save costs and improve efficiency.
In the era of artificial intelligence, online learning of English courses in colleges and universities has gradually become one of the mainstream learning modes. Based on the traditional teaching methods, this paper carries out the research on the optimization of English teaching path in colleges and universities. A micro-learning unit clustering model is constructed with four modules: data preprocessing, learning pattern mining, learning path diagram construction and micro-learning unit clustering. The model analyzes the learning state of learners through sequence pattern mining technology, and conducts orderly planning of learning resources based on learners’ characteristics. On this basis, this paper defines the online learning path planning problem and online learning path planning according to the continuity characteristics of learning knowledge points, and constructs the online learning path planning model. At the same time, the dynamic planning algorithm is selected to carry out the optimization of path planning. Based on the learning status of different learners, the optimal online learning path is planned to realize the optimization of English teaching path. Compared with similar classical algorithms, the online learning path planning model has the highest matching degree of 0.8 between the planned paths and the learning states of users under different learning resources conditions, which verifies the superiority of this paper’s model in the optimization of English teaching paths in colleges and universities.
The evaluation of labor education under the modernization of education should establish a long-term evaluation mechanism of labor education to achieve the goal of educating people by labor to build morality, labor to enhance intelligence, labor to strengthen the body, labor to cultivate beauty, and labor to innovate. In this paper, we use fuzzy clustering algorithm to construct labor education evaluation mechanism based on teacher evaluation standard. The results of this model for labor education evaluation are basically the same as those of manual evaluation, and can be used for the evaluation of the quality status of labor education. Based on this, the study plans in detail the preimplementation preparation, specific implementation steps and continuous optimization process of the evaluation mechanism. It also analyzes the implementation path of the labor education evaluation mechanism based on the fuzzy clustering algorithm by taking the example of Z elementary school in city A. The overall evaluation score of the quality of labor education in Z elementary school is 4.013, and there are still many areas that need to be improved. The evaluation mechanism of labor education based on fuzzy clustering algorithm was run in this school for 8 weeks, and the educational effect was continuously optimized through the incentive mechanism. Finally, the second-level fuzzy judgment method is introduced to further optimize the mechanism. Based on the new evaluation mechanism of labor education, individual student development can be evaluated, curriculum quality can be assessed, and operable solutions can be provided for the improvement of the quality of school labor education.
The aim of this paper is to construct a data model applicable to youth sports training in the IoT environment and develop efficient pattern recognition algorithms to achieve accurate analysis and assessment of youth sports training status. The features of youth sports training data collected by IoT technology are extracted through a combination of deep learning and feature decomposition. The feature vectors obtained from feature extraction are inputted into the Long Short-Term Memory (LSTM) network to generate the data model of youth sports training in this paper and predict the state of youth sports training. The prediction results are input as features into the Support Vector Machine (SVM) algorithm, and these features are extracted using the Empirical Modal Decomposition (EEMD) method, and at the same time, the hierarchical idea is utilized to realize the recognition of youth sports training patterns. The results of the study showed that the errors of the results of predicting youth sports training states using the LSTM model were mostly within 0 ± 0.5. The prediction accuracies of the model on the test set for the three athletic training state metrics were 96.80%, 99.40%, and 98.80%, respectively. Meanwhile, the performance of the SVM model for youth athletic training state pattern recognition using the SVM model was significantly superior, with 100% accuracy on the test set for four models, including pattern 2.
In this study, we first collected and preprocessed data from 500 basketball players between the ages of 13-35 years old in the same-court rivalry training in Northwest China, after which we utilized the Global Chaos Bat Algorithm (GCBA) for the mental training anxiety emotion feature extraction, and analyzed the correlation between each feature and the anxiety emotion through the Pearson coefficient. Finally, the LightGBM-based emotion prediction model was constructed, and the SHAP value was introduced to evaluate the feature importance of the model. The results show that the LightGBM model performs better and has higher prediction accuracy, which is as high as 96.68%; the interpretation results of the SHAP algorithm indicate that the gender and age of the basketball players are the main real-world factors for assessing their anxiety in same-court rivalry training. In addition, their game scores, opponents’ strengths and injury histories during the same-court rivalry training were the main intrinsic factors for their anxiety. In conclusion, the psychological state of basketball players can reflect the severity of their training anxiety, and it further reveals the relationship between the psychological characteristics of basketball players and their training anxiety.
In this paper, leisure tourism is taken as the entry point of the research, and the fused location key point features are added and integrated with the multidimensional features of time, location and space to construct an accurate portrait of social media tourism users. On the basis of tourism user profiles, a two-step clustering algorithm is combined to carry out cross-cultural analysis of social media data, to explore and excavate the performance of users’ tourism preferences under the cross-cultural ability of social media. Meanwhile, in order to realize the prediction of leisure tourism preference, a combined model based on BP neural network and ARIMA is proposed to improve the accuracy of leisure tourism preference prediction by fully considering the linear and nonlinear laws of tourism statistics. The ARIMA-BP combination prediction model is applied to predict the leisure tourism preference in the future from 2027-2034. During the period 2027-2029, the number of leisure tourism tourists maintains a high annual growth rate of more than 15%, while the growth rate slows down after 2029, with an average annual growth rate of 4.44%. In 2033, the number of leisure tourism tourists will reach 1,691,280,000, and the leisure tourism preference of tourism users has been significantly strengthened.
During the operation of transmission lines, there are sudden failures and a large number of slowdeveloping, preventable “gradual” failures, which have seriously threatened the safe and stable operation of the transmission system. Based on analyzing the multidimensional environmental factors affecting line safety, the study proposes a method for identifying the operating state of transmission lines based on the AdaBoost integrated learning algorithm, and develops a set of transmission line hidden danger monitoring system. A decision pile based on Ginin indicators is used as a weak classifier, and the hidden danger monitoring results and their confidence levels are output by training and weighted summation of multiple weak classifiers. Using historical data for validation experiments, the proposed method achieves an accuracy of 95.92% in recognizing the operating state of transmission lines, which is a more superior performance compared with traditional machine learning methods. The system can basically realize the hidden danger monitoring of transmission lines, so as to assist the field operation and maintenance personnel of transmission lines to carry out fault investigation, and reduce the transmission line tripping due to the development of hidden danger into fault.
School-enterprise integration is an effective way to improve the running level of higher vocational colleges and universities and stimulate the innovation vitality of enterprises. This paper takes the higher vocational hotel management profession as the research object, combines IPO model and intuitionistic fuzzy hierarchical analysis method (IFAHP) to realize the construction of schoolenterprise integration performance evaluation index system, and utilizes the fuzzy comprehensive evaluation method (FCE) to carry out specific application of this evaluation system. On this basis, the fuzzy set qualitative comparative analysis (fsQCA) was used to explore the specific path of schoolenterprise collaborative education in higher vocational colleges. The empirical study shows that the constructed evaluation system of school-enterprise integration has high reliability and operability, which is conducive to horizontal and vertical comparisons of higher vocational colleges and universities, and is also applicable to the authorities of higher vocational colleges and universities and the third-party evaluation organizations for the performance evaluation of school-enterprise integration. At the same time, it also indicates that the realization of high-performance schoolenterprise collaborative parenting programs in higher vocational hotel management majors cannot be achieved through a single variable, but rather through the form of conditional grouping to play a key role. There are five paths to improve the effectiveness of university-enterprise collaborative parenting in colleges and universities, and in the paths, the enterprise scale, the pre-project input and the project implementation process are the core conditions to improve the effectiveness of university-enterprise collaborative parenting in higher vocational colleges and universities, and the combination of changes of the three plays a decisive role in different condition grouping states.
In this paper, Kernel density estimation method is used to analyze the distribution characteristics of continuing education resources and reveal the distribution pattern of resources in different communities. On this basis, CCR model and BCC model are introduced to optimize the DEA model of data envelopment analysis and evaluate the resource allocation of continuing education institutions. The resource allocation optimization and dynamic planning system of continuing education is further constructed, and the system dynamics simulation method is used to simulate the optimization process of resource allocation, which provides a scientific basis for the governance of community education. The results show that: continuing education resource input is polarized in quantity, its performance level is not high, regional differences are significant, and scale efficiency is a key factor restricting quality improvement. This paper constructs a system dynamics model for the quality and user use of educational information resources, and in view of the difficulties of optimization and dynamic planning of the allocation of continuing education information resources, it is proposed that the managerial and digital educational resource platform construction-based inputs such as teachers’ information technology application ability, assessment system construction, etc. should be improved to promote the high-quality and balanced development of continuing education informatization.
Aiming at the bridge monitoring system, some of the monitoring data are abnormal due to equipment failure and environmental impacts. In this paper, the time-frequency domain convolutional neural network method is applied to the calculation of monitoring data and the risk assessment of bridge structure. The data collected by the acceleration sensor is firstly sliced and sampled and visualized. Then wavelet analysis is used to preprocess the cluttered data, and Wigner-Ville distribution and Fast Fourier Transform are introduced to extract time-frequency features from the collected data. A convolutional neural network is proposed and the network is trained on dual channel images fusing time and frequency domain images. By analyzing the spectrogram and and time-frequency diagram of the bridge monitoring data, the method of this paper classifies the bridge health condition into three kinds: no disease, slight disease and disease, which can accurately determine the health condition of different bridges, and the assessment accuracy of the risk assessment model based on the fusion of time-frequency domain information reaches 97.78%, which indicates that the high efficiency and feasibility of the bridge inspection data computation and the risk assessment model in this paper can meet the actual engineering construction needs of bridge inspection.
As the global climate change problem is getting more and more serious, carbon emission quota allocation is more and more emphasized by countries all over the world, while the traditional carbon quota allocation program has the problem of single objective. In order to improve the scientificity and acceptability of the carbon quota allocation scheme, this paper constructs indicators and forms multiobjective functions to formulate the carbon quota allocation scheme from the three perspectives of efficiency, fairness and sustainability, and builds a multi-objective optimization model for carbon quota allocation and decision support. Aiming at the solution problem of the carbon quota allocation model, an improved hybrid swarm algorithm based on Gaussian perturbation, tournament selection strategy and proposed Newtonian local optimization search operator (L-BFGS) is proposed. The model is used to explore the quota allocation scheme for cities in the Bohai Economic Rim in 2030. In the three single-target pre-allocation schemes based on the principles of efficiency, fairness, and sustainability, the difference between the cities with the largest and smallest quotas is 319 Mt, 289 Mt, and 256 Mt, respectively, which lacks scientificity and rationality. In contrast, the allocation results of the multi-objective pre-allocation scheme proposed by the carbon quota allocation model in this paper are relatively balanced and the difference is small, which can eliminate the conflict between multiple principles.
This paper establishes a multi-objective optimization model for the process of yoga course ideology and politics in colleges and universities through the group paper technology, and adopts an improved genetic algorithm to solve the model. Integrating the online and offline hybrid teaching mode, the paper’s group paper technology is placed in the online assessment to realize the all-round reform of yoga course Civics teaching. The research results show that the improved genetic algorithm (IGA) in this paper has a higher grouping efficiency and quality compared with the traditional genetic algorithm (GA), and saves about 52.7% in the grouping time. At the same time, using the social network analysis method to analyze the online-offline hybrid teaching mode of this paper, we are able to derive the student objects that need to be focused on based on the results of the analysis of the centrality of the point degree and the centrality of the middle of the point degree. The experimental class adopting the teaching mode of this paper has an excellent class performance rate of 100% under the test of group paper technology, which fully demonstrates that the teaching reform method proposed in this paper for the ideology of yoga course in colleges and universities has significant practical application effects.
This paper constructs an overall framework for vulnerability mining, covering the whole process from code collection to vulnerability remediation. The word vector technique is used to transform code fragments into vector form, thus preserving the semantic information of the code. A vulnerability mining system based on semantic graph of source code is further designed, which generates a semantic graph of code by constructing an abstract syntax tree (SAT), and analyzes the semantic graph by using graph neural network to accurately locate potential vulnerabilities. At the same time, a vulnerability repair method based on thought chain is proposed. The results show that the model in this paper can accurately mine the vulnerabilities of web service software, and it consumes short latency and has strong stability. The results of web service software vulnerability detection show that the accuracy rate of the model always stays above 85% under different network structures. In addition, this paper obtains that the integration degree centrality measure and 60 iteration rounds have the best effect on the detection of vulnerabilities of the model. Finally, the vulnerability repair experiments show that at Beams=15, the model in this paper repairs each vulnerability function with a PPP metric of 61.52% and an average time of 3.168 seconds, which is the best for vulnerability repair.
Because of their inherent vulnerability, small States are in a relatively weak position in global economic relations. In order to survive and develop, regional cooperation is an important way for small countries to safeguard and promote their national interests. Taking Malta as an example, this paper utilizes the international trade data of some major countries along the “Belt and Road” from 2004 to 2018, and adopts the social network analysis method to construct the trade network of 37 countries in each year, and explores the formation of the trade network of the “Belt and Road” through the gravity model. The gravity model explores the major trade subgroups formed in the “Belt and Road” trade network and reveals the cooperation and interaction paths of small countries in the global economic system. Among them, Malta’s position and role in the countries along the “Belt and Road”, Malta still participated in global economic cooperation through Russia in 2004, and in 2018, in the more closely linked global economic network system, Malta strengthened its economic ties with China, which has a core degree of 0.897, further enhancing its own position in the global economic network. The economic scale and institutional differences between countries have different impacts on the trade of different products, and small countries have limited resources, so it is more important to formulate different economic development strategies according to different influencing factors and product types, and strengthen trade cooperation with neighboring countries.
The professional development of physical education teachers is the direction and basic requirement of modern education development, which is better promoted by strengthening the tracking and prediction of the trajectory of the professional development of physical education teachers. In this paper, a combined ARIMA-LSTM model is established to visualize the PE teachers’ professional development trajectory by predicting their professional development scores, using the advantages of ARIMA model in handling linear time series data, while combining the powerful ability of LSTM network in capturing the long-term dependency of data. Three physical education teachers were randomly selected as research subjects to predict their PE teacher professional development trajectories. The root mean square error (RMSE) and mean absolute percentage error (MAPE) values were used as the assessment indexes of the model, and the MAPE and RMSE of the ARIMA-LSTM model were less than those of the ARIMA model and the LSTM model for the physical education teachers in No. 1 and No. 3. And on the prediction of physical education teacher No. 2, the MAPE comparison of ARIMA-LSTM model and LSTM model increased by 1.12%, but the RMSE decreased by 73.4563, and the prediction curve of the professional development score was close to the original sequence, and the ARIMA-LSTM model still showed better prediction effect.
In order to improve the accuracy of enterprise financial risk early warning and realize the risk control of enterprise capital chain under digital transformation, this paper adopts the Long Short-Term Memory (LSTM) neural network algorithm to establish the enterprise financial risk early warning model. First analyze the enterprise financial risk early warning indicators, use factor analysis for indicator screening, determine the indicator weights through the Delphi method and the improved hierarchical analysis method, and select the indicators with high importance to construct the enterprise financial risk early warning feature samples. Then after LSTM neural network training, the enterprise financial risk early warning model is obtained, and the model is evaluated for performance and practical use. The experiment proves that the accuracy of the LSTM neural network model on the training set and test set is 91.48% and 88.62% respectively, which indicates that the model can effectively predict the enterprise financial risk. By comparing with the commonly used enterprise financial risk warning algorithms, the algorithm in this paper has the highest warning accuracy, shorter prediction time, and better warning performance in dealing with large-scale enterprise samples. This study provides an effective financial risk early warning method for enterprises, which can help them better carry out capital chain wind control in the process of digital transformation.
Vocal music is an art about the perception and expression of sound. Successful vocal music teaching is to cultivate students’ unique singing personality, so this paper constructs a personalized vocal music teaching mode with the help of AI algorithm. Subsequently, it describes the problem of service object learners under personalized learning path recommendation, proposes a personalized learning path recommendation strategy based on ant colony optimization algorithm, and verifies the recommendation effect of personalized path through simulation experiments. Then the cognitive diagnosis model based on KM-VDINA is proposed to diagnose students’ vocal music knowledge under personalized learning path. The article concludes through experiments that the personalized vocal music teaching model based on AI algorithms requires the integration of online and offline teaching, while focusing on the integration of teaching inside and outside the classroom. The vocal music learning path of most students can be expressed as (000000)→(100001)→(101001)→(101100)→(111100)→(101110)→(111111). Students have multiple trajectories to master the attributes of vocal music knowledge, so teachers can explain the attributes of knowledge that are easier to master according to the actual situation, and then explain the attributes of knowledge that are difficult for students to master.
The purpose of this paper is to study the multidimensional computational optimization problem of enterprise organization structure. Based on the value dimension, this paper designs a kind of enterprise organizational structure which contains six dimensions such as demand deϐinition, sales and so on. And the optimization model of enterprise organizational structure is studied by taking a demand-pull enterprise as an example. In the iterative process of the algorithm, the particle swarm optimization (PSO) algorithm and ant colony optimization (ACO) algorithm are run alternately to form the PSACO algorithm to solve the optimization model of enterprise organizational structure constructed in this paper. The experimental results show that the PSACO algorithm used in this paper can maintain stable and good convergence performance under different demand sizes. The enterprise organizational structure optimization model and solving algorithm in this paper can achieve ideal resource allocation scheme and effect, and can still achieve good solving effect in large-scale enterprise organizational structure optimization adjustment. It provides good decision support for the optimization and improvement of enterprise organizational structure in the era of digital economy, and has important application value.
In this paper, we first constructed a mathematics network course goal achievement index system with 5 primary indicators, 16 secondary indicators and 70 evaluation points to provide a scientific basis for course design. After that, based on the radial basis function (RBF) neural network structure, the fruit fly optimization algorithm (FOA) is introduced to dynamically optimize the parameters of the RBF model, and the dynamic FOA-optimized RBF neural network model is put forward to predict the degree of achievement of the course objectives. The results show that the model in this paper has good convergence and prediction accuracy, and its error on the four course math network goal attainment is only about 0.4%, with excellent model accuracy and simulation effect. Combined with the prediction results, considering the shortcomings of the current teaching, a blended teaching model based on mathematics majors is constructed, and the advantages of the teaching scheme in this paper are elaborated, which provides support for the teaching reform of mathematics courses.
With the development of transportation systems, there is an increasing demand for real-time understanding of traffic scenes using image segmentation algorithms. Therefore, this paper carries out an in-depth study on how image segmentation algorithms for complex traffic scenes can meet the detection requirements of real-time while maintaining accuracy. The article first proposes a lightweight semantic segmentation method based on IDEL_DeepLabV3+, which lightens the IDE_DeepLabV3+ network and optimizes the loss function to improve the positive and negative sample imbalance problem. Then an improved image multi-texture detection method based on Faster RCNN is proposed to improve the detection performance of complex traffic scenes. Finally, the performance of the algorithm designed in this paper is tested through experiments. The performance of the deformable convolution, attention mechanism and feature pyramid improved model is tested and verified, the AP value of the deformable convolution is increased from 41.36 to 47.26, the mAP value of the overall model of the scSE attention mechanism is increased by 0.84%, and the final AP value of the weighted bi-directional feature pyramid network reaches 45.4. The improved DeepLabv3+ network achieves a high AP value of 75.03% in terms of the evaluation index mIOU by 75.03% is better than the original network’s 72.26%, so it can be said that we experimentally verified that our improved method enhances the segmentation accuracy of DeepLabv3+ network. The experimental results show that the proposed method in this paper improves the image segmentation accuracy while guaranteeing the segmentation speed, which effectively improves the segmentation effect.
Traditional non-heritage music art is gradually declining, driven by cultural heritage education, this paper studies the future inheritance trend of non-heritage music art. In this paper, the infectious disease dynamics model and complex network propagation theory are used to construct a mathematical model of the inheritance of non-heritage music art, an improved non-dominated sorting genetic algorithm is proposed, and the trend characteristics of the inheritance of non-heritage music art are simulated by solving the parameters of the model through the improved NSGA-Ⅱ algorithm which introduces the congestion calculation method and the crossover strategy. The improved NSGA- Ⅱ algorithm shows better convergence speed of optimization search and uniformity of solution distribution on single peak function and three ZDT functions. Its SP and IGD indexes are much better than the comparison algorithm, with values less than one-half of the comparison algorithm. Taking the heat of non-heritage music and art inheritance in Baidu index for several days as the simulation object, it is found that the simulation of the mathematical model of non-heritage music and art inheritance in this paper has a maximum heat value of 115,000 and the real maximum heat value of 117,241 are not much different from each other, which confirms that this paper’s non-heritage music and art inheritance mathematical model has a better fitting effect and reasonableness. The work of this paper has injected new vitality into the innovation of non-heritage music art inheritance.
Social network is a special social factor in the development of cooperatives, and the influence of the degree of social network embeddedness cannot be ignored in order to realize the high-quality development of cooperative economy. In this paper, we first use the entropy power method to measure and characterize the social network embeddedness, and then use the OLS regression model to analyze the influence mechanism of social relationship network embeddedness on the mechanism of wage income distribution and access to employment information of cooperative members, and explore the moderating role of environmental dynamics. The experimental results show that there is a certain strength gap in the external relationships of the social network of rural cooperative members, and the level of social relationship network embeddedness among samples from different regions is polarized. At the same time, the internal and external embeddedness of the social network of cooperative members has a positive effect on the efficiency of employment information acquisition, and there is a mediating role of the wage income distribution mechanism between the two. In addition, environmental dynamics moderates the two paths of action between social relationship network embeddedness and wage income distribution mechanism and employment information acquisition efficiency, but the moderating effect of environmental dynamics on capital income distribution mechanism and employment information acquisition efficiency is not significant. This study has certain guiding significance for the innovative development of cooperative economy.
Reasonable allocation of enterprise marketing resources can ensure that different target markets can be taken into account, but also to ensure that the newly developed markets can be cultivated, so as to maximize the economic benefits of limited resources. The article first combines the principles of marketing resource allocation, constructs a dynamic planning model of marketing resource allocation, and proposes a hybrid genetic algorithm improved by simulated annealing algorithm to solve the marketing resource allocation model. The effectiveness and superiority of the algorithm is tested through simulation and comparison experiments. And take an electrical appliance company as an example, based on the marketing resource allocation model to find the optimal program of the model, to explore the production and sales decision-making that is beneficial to the company. The results can be obtained, with the marketing resource allocation model set in the marketing department of the profit ratio from 10% to 25%, the total profit of the product is increasing, only the pursuit of product sales profit is maximized when the total profit can be obtained is about 17,956,500 yuan.
The essence of music is the carrier of human emotion expression, with the continuous deepening of music science and technology research, how to realize more accurate music emotion recognition has become the focus of public attention. This paper constructs a music emotion recognition model based on discrete emotion space (WLDNN_SAGAN). After pre-processing the collected audio data of vocal performances, the attention mechanism is introduced to weight and fuse the extracted low-level and middle-high-level music emotion features, and then the fused feature information is inputted into the WLDNN_SAGAN network to classify music emotions. The experimental results show that the model in this paper will improve the recognition accuracy of different emotions. Compared with the comparison model, the accuracy of this paper’s model reaches 60% and above on three DIFFERENT datasets. The emotional vein of Chinese folk song performance identified by the model is lightness towards sadness and sacredness, which is consistent with the historical facts of Chinese folk song creation. In conclusion, the emotional expression of vocal performance can be enhanced by understanding the cultural connotation, applying singing techniques and body language.
This study integrates fuzzy logic with DuPont ratio analysis reϐlecting ϐinancial ratios to construct enterprise proϐitability prediction model. The main indicators of DuPont analysis system are processed by principal component analysis (PCA) algorithm to obtain the calculation method of the mean value of enterprise comprehensive proϐitability indicators. The BP neural network is used to construct the enterprise proϐitability index model, and the momentum term is introduced into the model to improve the convergence speed of the BP neural network. The Takagi-Sugeno type fuzzy neural network is utilized to construct the enterprise development ability index model, and the enterprise proϐitability prediction model is constructed by combining the output structure of BP neural network. The relevant data of 792 listed enterprises in a certain industry in China’s A-share market are selected as the research objects of this paper, and the data are inputted into BP neural network and Takagi-Sugeno fuzzy neural network to obtain the output results of the model, and the output results are used as the input data of the ϐinal proϐitability prediction model to forecast the proϐitability of the enterprise in the next ϐive years. The experimental results show that the model in this paper can effectively realize the prediction of corporate proϐitability, which is signiϐicantly conducive to the sustainable development of enterprises and the adjustment and improvement of strategic policies.
The rapid development of artiϐicial intelligence algorithms makes them play an important auxiliary role in college English education. This paper deeply analyzes the application of artiϐicial intelligence algorithms in English education in colleges and universities, and constructs a method of analyzing students’ behavior in college and university English classrooms with computer vision as the algorithmic representative, which assists teachers to understand the state of students in the whole classroom.YOLOv7 network carries out multi-target detection in the classroom and improves the network for the deϐiciencies in the classroom environment. The improved K-means algorithm is then introduced to improve the DeepSORT multi-target tracking algorithm. Obtain the surveillance video data in the English classroom of Q college and build the dataset by itself, and design different experiments to verify the effectiveness of this paper’s algorithm respectively. Finally, the classroom behavior analysis method based on computer vision in this paper is applied to teaching practice to explore the practical application effect of the method. The results show that the improved method of this paper can signiϐicantly improve the performance of the target detection and tracking model, and the application of this paper’s method to the classroom time can accurately capture the classroom state of different students, and assist teachers in formulating different teaching strategies according to different classroom stages.
This paper focuses on the demand for intelligent assistance in English business writing scenarios and proposes an intelligent assistance system for English business writing based on image recognition algorithm and language model. The system is able to quickly extract image information related to the writing topic through the similarity vocabulary matching technology combined with the image retrieval recognition function based on CBLSTM-Attention model. The language model is utilized to make accurate vocabulary recommendation and expression for the writing scene and user input content, and finally construct the overall framework of the intelligent assistive system based on English business writing. The system performs well in terms of vocabulary matching accuracy and writing efficiency improvement, with an average matching accuracy of over 90%. Students’ quality of writing is essentially improved with the help of the system in this paper. The actual case study shows that studying under the intelligent assistance system, the post-test scores of English business composition of the students in the experimental class increased significantly by 9.9393 points (P < 0.05) compared with the average scores of the control class, and it is obvious that applying the model of this paper to the classroom teaching can lead to a significant improvement in the performance of the students, which demonstrates the good prospect of its application.
This study focuses on the innovation of teaching methods for Civic Education in colleges and universities, and provides a structured knowledge framework for teaching by constructing a Civic Knowledge Mapping and integrating course knowledge points. On this basis, a new classroom teaching mode is designed to integrate online and offline teaching resources to enhance student interaction and participation. A knowledge tracking model of key-value memory network (MKVMN) based on multifeature fusion is proposed to accurately track students’ mastery of Civics and Politics knowledge by capturing students’ multi-dimensional learning behavior characteristics. To optimize the recommended path for students’ personalized learning, an improved ant colony algorithm is introduced to generate personalized learning paths based on students’ individual differences. The experimental results show that when the number of learning units is 0-10 (pre-study period), the improved ACO algorithm model does not have obvious advantages for students’ learning, but when the number of learning units reaches 11-50, the difference between the experimental group students’ learning performance and the control group becomes more and more obvious, so it can be seen that the improved ACO algorithm can obviously improve the students’ Civic and Political Science learning performance. In addition, the IACS-PRA algorithm is especially effective in long path recommendation, which finds the optimal personalized recommendation path through a gradual approach to help students learn Civics and Political Science more efficiently, and provides a practical demonstration for the digital transformation of Civics and Political Science education in the new era.
The effective integration of intelligent interaction design and visual communication design education is an important attempt to improve the educational effect. This paper firstly constructs the evaluation system for the teaching effect of intelligent interaction design and visual communication design courses, and then establishes a set of evaluation models based on fuzzy logic inference algorithm. In the example application part, the G1-entropy weighting method designed in this paper is used to measure the weights of each influence index, followed by an empirical study using School A as an example, and finally the multiple linear regression analysis is used to make further exploration on the influencing factors of the teaching effect of the course. The study concludes that in the subjective weight calculation experiment, it is found that the weight of external influences accounts for the highest proportion of 0.277, that is, experts believe that the overall planning has a strong influence on the course effect. Further, the regression modeling yields that learning interest, curriculum, faculty, teaching content, and practical activities have significant positive correlation with teaching effectiveness.
Influenced by the backward management methods and other factors, the integration and sharing of digital educational resources in colleges and universities have certain problems, and cannot give full play to the advantages of digital educational resources in colleges and universities. Based on this, this study proposes a targeted digital education resources integration strategy, using particle swarm algorithm to optimize the sorting of digital education resources, to obtain high-quality digital education resources, introducing fuzzy clustering algorithm and combining with the principle of decision tree, to accurately classify and integrate digital education resources. On the basis of realizing the integration and classification of digital educational resources, a digital educational resource sharing model is formed to promote the effective use of digital educational resources. The digital educational resources integration strategy proposed in this paper is adopted to carry out the application practice of digital educational resources integration and sharing in S colleges and universities. The mean values of the three dimensions of students’ learning attitude, teachers’ teaching, and teaching effect in S colleges and universities reached 3.48, 3.97, and 3.74, respectively, and this paper’s digital educational resources integration strategy method has a positive positive impact on the dimensions of students’ learning attitudes, teachers’ teaching, and teaching effect in Civic and Political Education in S colleges and universities.
With the development of globalization, the cross-cultural market is facing needs such as diversification and personalization of consumer demand. Based on the theory of market segmentation, the study proposes an ant colony algorithm to improve the market segmentation model of K-means clustering, and examines its effectiveness. Further, a personalized recommendation algorithm based on multivariate dynamic user profiles is proposed to recommend products to target users more accurately. A reliable simulation environment is constructed based on the KuaiRec dataset and the classical LastFM dataset to properly evaluate the performance and effectiveness of the model on the recommendation platform. Through the K-means ant colony clustering algorithm proposed in this paper to divide the interest information and attribute information of users, the users as a whole are classified into specific categories, and the online_reward value of the personalized recommendation algorithm based on multivariate dynamic user profiles proposed in this paper fluctuates from 50.05 to 50.49, which is a significantly superior performance. As a result, this paper concludes that crosscultural marketing strategies should be marketed at four levels: product, price, channel, and promotion, in order to adapt to regional cultures, attract consumers, and build consumer loyalty and satisfaction.
Consolidating and improving supply chain resilience and maintaining supply chain stability and security is an important foundation for promoting the realization of high-quality development. After initially selecting supply chain resilience related indicators, the research is screened and downgraded through factor analysis to establish a supply chain resilience evaluation index system. Subsequently, based on the model integration framework, the supply chain toughness evaluation model with improved Stacking integration model is constructed on the basis of a single machine learning algorithm and an integrated learning algorithm, and the model parameters are adjusted and optimized through the learning curve to achieve the optimal evaluation effect and compared with the existing model. The results show that the Stacking supply chain toughness evaluation model constructed in this paper has a relative error of 23% or less in 3685 enterprise samples and accounts for 98.78%. It shows that the Stacking integrated model established in this paper has good prediction effect and high accuracy, which has certain value and significance to the research of supply chain toughness prediction, and can provide scientific reference basis for enterprises.
Taxation, as one of the rigid expenditures of enterprises, directly affects the production and manufacturing costs of enterprises, and influences their disposable income as well as the improvement of economic level. Based on the data of Shanghai and Shenzhen A-share listed enterprises in China from 2013 to 2023, the study explores the impact of corporate tax burden on economic development based on multiple regression models, and conducts robustness tests by double-difference and reduced-sample methods. The study finds that corporate tax burden has an inverted “U”-shaped impact on economic development, which is positively correlated with economic development when the corporate tax burden is at a low level, and negatively correlated with economic development when the corporate tax burden crosses the most critical point. In addition, in the heterogeneity test of emerging and non-emerging advantageous industries, the tax burden and the high-quality development of enterprises show an inverted “U”-shaped relationship, but the inflection point of the emerging advantageous industries will appear earlier.
Asphalt as a common material for urban road construction, asphalt pavement has been favored by the municipal industry because of its good wear resistance and stability. This paper identifies the raw materials for this study, and obtains the samples for this study under the technical guidance of the preparation process. In order to more intuitively observe the role of the two, the construction of two-dimensional numerical model, and with the help of water-immersion Marshall test, freeze-thaw splitting test, rutting test, trabecular bending test, together to explore the effect of compounded fibers on the performance of modified asphalt mixtures. When 6% SBR was added on 2% ZnO+4% DTDM, the flexural strength modulus increased from the initial 5482.76 to 6217.54 MPa, and its increase was 18.53%, which indicates that the addition of fiber has a promoting effect on the flexural properties of modified asphalt mixtures.
This study takes the intelligent transportation information management system as the basic framework and focuses on the technical scheme of the traffic flow regulation module in the system. Taking the intersection in urban traffic as the research scenario, we extract the characteristics of urban traffic time and regulation objective function to construct the traffic flow scheduling model. The particle swarm algorithm (PSO) is used to optimize the traffic flow control model, and the inertia weights and the four degree and position update mechanism are improved for the problems of PSO algorithm, such as easy to fall into local optimization. The improved particle swarm algorithm (MPSO) in this paper is utilized to solve the traffic flow scheduling problem, and compared with the PSO algorithm to highlight the effectiveness of the improved operation in this paper. The results show that the optimized traffic flow regulation model based on MPSO algorithm has significant performance advantages in indicators such as average parking delay. Compared with the PSO algorithm, the MPSO algorithm in this paper obviously has higher convergence accuracy and can achieve more excellent regulation solution set in the intersection traffic scheduling scenario. The application of the method in this paper can effectively solve the problems of vehicle congestion and frequent traffic accidents in urban intersections.
In recent years, China’s health insurance drug negotiation has become increasingly improved, and the speed of access to the health insurance catalog has increased dramatically. Under the implementation of health insurance negotiated drugs and dual-channel policy, this study investigates the application of negotiated drugs in a certain region to explore their accessibility and affordability. On this basis, it links the health insurance department, designated pharmacies and medical institutions to explore the precise regulatory mechanism of dual-channel drugs in health insurance. For the drug safety supervision therein, collaborative filtering algorithms, attention mechanisms and multi-task learning are utilized to construct an adverse drug reaction prediction model. It is found that under the influence of the health insurance dual-channel policy, the accessibility and affordability models of medicines are enhanced, the types of negotiated medicines, the number and total amount of purchases are increased year by year, and the total amount of purchases by medical institutions and retail pharmacies are enhanced by 3.42 times and 2.36 times, respectively. The proposed prediction model has good accuracy and applicability in predicting adverse drug reactions, with AUC and AUPR values of 0.93 and 0.83 on different datasets, which are better than the comparison methods. It is recommended to continuously promote the construction of the “dual-channel” management mechanism of designated medical institutions and retail pharmacies to enhance the convenience and sense of access to medical care of the insured. x
The enterprise financial risk under the new era economy exists in all aspects of enterprise operation, thus this paper screens the enterprise financial risk early warning indicators from the four aspects of enterprise profitability, operation ability, development ability, and debt repayment ability. The logistic Steele model is introduced to optimize the population size function of differential evolution algorithm to achieve the dynamic adaptive population size. Then the adaptive differential evolution algorithm is used to optimize the threshold value of BP neural network, and the neural network prediction model based on the improved differential evolution algorithm is derived. Analyze the operation steps of the improved differential neural network algorithm model in enterprise financial security detection to realize the optimal solution of the enterprise financial risk warning model. Compare and analyze the predicted value of the improved differential neural network algorithm model with the real value of the enterprise financial development, and analyze the use of differential evolutionary algorithm in the prediction of enterprise financial risk.The prediction error of the net asset growth rate of enterprise Q in the 1st quarter and the 3rd quarter of the year 2024 is 0.0119 and -0.05309, respectively, with a smaller absolute value of the error, and the improved differential neural network algorithm is able to effectively predict the corporate financial risk.
In recent years, foreign research on the optimal allocation of supply chain resources and operational coordination in the digital economy environment has also made more and more progress, and the current status of domestic research is in the development stage, and supply chain management in the context of the digital economy has become another research hotspot. In this regard, this paper studies the application of Hamiltonian circuit in supply chain resource allocation from three aspects, such as product pricing strategy, supply chain management optimization and consumer behavior, and constructs the optimal resource allocation model according to the steps of resource allocation establishment. Through the Hamiltonian loop algorithm, the revenue function of cross-border ecommerce supply chain services is obtained. Using the Hamiltonian loop algorithm, the optimal price model of supply chain logistics is simulated and simulated experiments are carried out, and the optimal logistics and distribution services will be priced at about 225 under the high competition situation with competition coefficient k 0.6 . Under the optimal allocation of supply chain resources, analyzing the user’s satisfaction, retail customers’ satisfaction with the supply of goods in 2021 is 81.806 points, higher than the province’s 0.913 points, and the experimental results show that the resource allocation model can meet the needs of the customers, making the supply and demand of the product resources more balanced, which argues the scientific nature and reliability of this study.
The accelerated pace of life and social competition become more and more intense, and the problem of psychological pressure faced by people in their study, work and life becomes more and more serious and common, this paper proposes a multi-channel physiological feature fusion method of psychological state assessment for the identification of students’ psychological state in the ideological and political education of college students. The collected multidimensional physiological signal data, such as pulse and picoelectricity, are feature extracted, and the wavelet transform is used to reduce the noise of the physiological signals and realize the waveform filtering, and then the DS evidence theory is combined with the SVM, and the extracted physiological parameters of pulse and picoelectricity are used to realize the effective assessment of psychological stress. Experiments show that the method proposed in this paper of using wavelet decomposition coefficients instead of the original physiological signals as model input can improve the accuracy of psychological stress detection, and the MAPE value of psychological state assessment using the SVM-DS algorithm is 12.28%, which can realize the assessment of students’ psychological state in ideological and political education of college students.
In recent years, art education in colleges and universities has been more and more emphasized by the state and education departments, and has been comprehensively promoted and developed. The study builds the evaluation index system of art education development and assesses the development of art education in a university in order to identify its realistic dilemma. On this basis, the dung beetle algorithm is used to optimize the random forest algorithm to construct a decision tree assessment model of art education development. Through comparison experiments, the prediction accuracy and stability of the DBO-RF model are confirmed, and the deviation of its assessment results from the real value is below 4%, and the RMSE (12.247), MAE (9.133), and MSE (178.829) are lower than that of the comparison method, and the EV (0.721) and R² (0.719) are higher than that of the comparison method, which is applicable to a certain extent. The long-term and overall development of art education in colleges and universities can be promoted by establishing art education mechanisms, strengthening art practice activities, establishing resource sharing channels and developing scientific systems.
In order to improve the design of intelligent products, user cognition and perceptual engineering are integrated into intelligent product design. And through subjective survey and physiological measurements and other techniques to measure the user’s emotional experience of the product, to construct the user’s emotional evaluation model based on BP neural network. Multimodal interaction technology is used to optimize the product design method, and the implicit needs of users for intelligent products are obtained through the method of multimodal perception, which is matched with the product interaction, so as to propose the intelligent product design strategy based on multimodal interaction. In order to verify the effect of the strategy, physiological indicators and perceptual imagery are obtained to evaluate the products. Finally, the user satisfaction of intelligent products under this strategy is studied. The benefit ratio of the smartwatch designed based on the design strategy of this paper (0.438811) is better than other market competitors. The user satisfaction of the 10 experience dimensions of this smartwatch is distributed in the range of [80%, 93%], the dimension with the highest satisfaction is functionality, the lowest is attractiveness, and the overall satisfaction is 86.6%, and the smartwatch designed by this paper’s design strategy obtains a high level of user satisfaction.
As an important clean energy project, the optimization of the construction and operation of photovoltaic (PV) power plants is crucial in the context of the global active promotion of low-carbon development. This paper focuses on the optimization of cable wiring scheme for PV power plants based on the taboo search (TS) algorithm. A mathematical model is established by comprehensively considering the constraints such as power loss objective and tidal current calculation in the wiring optimization process. The wild dog optimization algorithm is improved using the Lévy ϐlight algorithm, and the initialization phase of the taboo search algorithm is improved by the improved wild dog optimization algorithm, and the established cabling optimization model is solved using the improved taboo search algorithm (LDOA-TS). The experimental results show that the LDOA-TS algorithm has a signiϐicant performance advantage over other algorithms in the model solving process. At the same time, the simulation results obtained from the optimization model in this paper are basically consistent with the actual wiring pattern under different working conditions. And through the model of this paper for cable optimization wiring compared to the original wiring scheme in the point cable length and power loss were reduced by 30.30% and 49.95%, to meet the constraints at the same time to effectively achieve the model objectives, and has obvious economic beneϐits, in line with the needs of the low-carbon era of photovoltaic power plant construction and operation.
With the gradual depletion of fossil energy resources and the increasingly severe environmental problems, photovoltaic power generation as a typical new energy industry has been highly favored in recent years. In the face of the low efficiency of components often faced by photovoltaic power plants in actual operation, this paper proposes a maximum power point tracking algorithm (IGWO) based on the Gray Wolf optimization algorithm, which optimizes and joins the dynamic weights to expand the search range of the algorithm, and improves the efficiency of solar energy utilization. The gray wolf algorithm is further applied to the optimization of photovoltaic (PV) arrays in power stations, and a PV array reconfiguration algorithm based on the gray wolf optimization algorithm is proposed to randomly generate a radial structure by the broken circle method, and the best reconfiguration scheme is obtained through iterative optimization search. The optimization experiment of photovoltaic power station was carried out, and the photovoltaic array reconstruction algorithm in this paper was used to reconstruct in the static shadow occlusion mode, and the GMPP after reconstruction was significantly improved, and the shadow occlusion mode was increased to 14515.565W, 10626.844W, and 10636.467W, respectively, and the tracking accuracy of the IGWO algorithm in this paper also reached 99.9%, 99.5%, and 99.6%, respectively. The tracking accuracy of the IGWO algorithm in this paper for MPPT tracking control is consistently above 99% level under dynamic shadow shading mode.
Fe-based soft magnetic composites have important applications in reactor core manufacturing due to their superior magnetic properties. In this paper, the vibration noise characteristics of the reactor with core made of this material are investigated and simulated and optimized by finite element method. First, a three-dimensional finite element model of the reactor is established to analyze the electromagnetic force distribution and vibration displacement velocity, and then the accuracy of the finite element analysis model is verified by combining the simulation experimental data. On this basis, the oxidation time parameters of the Fe-based soft magnetic composite material are adjusted, and the optimal parameters are selected to improve the vibration of the reactor, so as to achieve the purpose of improving the working condition of the reactor. The results show that the magnetic loss and other properties of the material have an important influence on the core vibration, and the reasonable optimization of the composite material structure parameters can reduce the vibration amplitude of the reactor and effectively suppress the noise. This study provides a theoretical basis for the design and optimization of this type of reactor.
Fe-based soft magnetic composites are widely used in power electronics and power system equipment due to their excellent magnetic properties and low iron loss. As a key component, the performance of the core reactor directly affects the operation efficiency and stability of the power system, and the traditional design method is difficult to take into account the electromagnetic performance and noise control at the same time. In this study, genetic algorithm is used to co-optimize the core structure, electromagnetic parameters and noise characteristics to reduce losses, improve electromagnetic compatibility, and reduce the noise generated during operation. In terms of methodology, a multiphysical field calculation model is constructed based on finite element analysis, electromagnetic performance and noise source characteristics are simulated, and genetic algorithm is used to optimize the parameter combinations under the constraints to form an optimized design scheme. During the optimization process, a suitable objective function is selected and combined with a multi-objective optimization strategy to balance the electromagnetic performance and noise suppression effect. The results show that the optimized core reactor is better than the traditional design in terms of loss, magnetic field distribution and noise level. The optimization scheme derived from the study can effectively improve the electromagnetic characteristics of the equipment and significantly reduce the noise level, providing strong support for the design and improvement of related equipment.
The level of informationization infrastructure of the power system is constantly improving, and it is of great practical significance to carry out real-time perception and early warning of environmental risks during the construction period of the project based on image processing algorithms. This paper proposes a multi-scale parallel real-time detection algorithm based on SSD, which optimizes the network structure of SSD algorithm, combines and splices different sizes of inverted residual blocks and different types of activation functions with each other, and designs a kind of lightweight feature extraction network EPNets. Then, it proposes a lightweight parallel fusion structure, which is applied to the multi-scale prediction of the lightweight feature extraction network, and optimizes the environmental risk real-time detection speed of the algorithm. The algorithm is optimized for realtime environmental risk detection speed. A Bayesian network-based environmental risk behavior warning model is constructed to provide real-time warning for the detected risk behaviors. By comparing with the original algorithm and existing target detection algorithms, the multi-scale parallel fusion detection algorithm based on SSD proposed in this paper can maintain good detection speed with low loss degree, and its environmental protection risk identification time is only 9ms.Meanwhile, the early warning algorithm in this paper realizes the accurate early warning of the soil erosion risk in the study area through the soil erosion environmental protection risk during the construction period of transmission and transformation projects detected. It provides an objective guideline for the control of environmental protection risks and work priorities.
The regulatory capacity sufficiency of the grid is not only a technical indicator for dispatchers to measure the safe and stable operation of the grid, but also an important indicator for assessing the reliability of the grid, and an important basis for the planning and transformation of the grid. This paper combines the objective function and constraints of time and space scale optimal scheduling of provincial power grids with a high proportion of new energy, and establishes a model for optimal scheduling of power grids. Improved DE-ICA stochastic optimization search algorithm is used to seek the optimal value of the model, to obtain the optimal regulation method of the power grid driven by the multilevel spatio-temporal combinatorial optimization algorithm, and to put forward the quantitative assessment method of the adequacy of the power grid regulation capacity. Simulations and empirical case studies show that the regulation cost of the provincial grid is reduced after the application of the optimization algorithm, and the power balance effect and the regulation capacity adequacy are improved compared with the traditional scheme. The quantitative evaluation method of grid regulation capacity adequacy proposed in this study can comprehensively and accurately describe the transmission capacity of the grid under long-term supply and demand, which can provide more accurate reference information for power system security and planning.
Maximum supply capacity calculation is an important issue in grid planning, and with the large amount of renewable energy sources connected to the grid, the voltage instability problem becomes more and more prominent. In this paper, the maximum power supply capacity (TSC) of partitioned flexible interconnected grids under multi-temporal and spatial scales is dynamically modeled, and the TSC model is solved by using the deep deterministic policy gradient (DDPG) method to achieve quantitative assessment of the TSC of the grid. Meanwhile, the effectiveness of the model and algorithm is verified through simulation experiments. The stepwise approximation method and DDPG algorithm without considering the transient voltage stabilization constraints and the obtained TSC in which the node voltages are less than 0.80 p.u. are all greater than 1.0 s, and the transient voltages are destabilized. While the DDPG algorithm considering transient voltage stabilization, the obtained node voltage is greater than 0.80p.u., and the transient voltage is in a stable state, which indicates that the algorithm can effectively reduce the risk of transient voltage instability in the power grid. The sum of the TSCs of A and B divisions after the zonal flexible interconnection is 9348 MW, which is higher than the sum of the TSCs of 8696 MW during the zonal open-loop operation, indicating that the zonal flexible interconnection can improve the overall TSC level of the power grid. In addition, compared with the traditional algorithm and other reinforcement learning algorithms, the TSC calculation based on the DDPG algorithm is more efficient and accurate. This paper provides methodological guidance for evaluating the power supply capacity of power grids at multi-temporal and spatial scales.
Liaoning region is selected as the study area, and its meteorological data from 1974 to 2024 are used as the study samples. Based on the four indicators, SPI, SPEI, EDDI and CJDI, the normalized composite drought characteristic indicators were constructed by using the CVine joint function and entropy weighted TOPSIS, so as to explore the drought calendar and drought intensity in the Liaoning region, and to analyze the spatial and temporal evolution of the drought cycle. The results showed that the Kendall and Spearman rank correlation coefficients reached above 0.60 and 0.73, respectively, and therefore, the drought duration and drought intensity were strongly correlated. The normalized composite drought characteristics index had a significant negative correlation association with SPI (P<0.01). The normalized composite drought characteristic index has a significant positive correlation association with SPIE (P<0.05). SPI and SPEI are one of the important reasons to study the spatial distribution and temporal pattern of regional drought.
This study focuses on the construction of Spring Festival Gala mascot culture using intelligent computational modeling, so as to explore the brand innovation and communication path of Chinese intangible cultural heritage. The Apriori algorithm is utilized to extract the features of intangible cultural heritage in the mascot design, and at the same time, the association rules between different intangible cultural heritage features are mined and integrated into the design. The traditional Apriori algorithm is improved based on Boolean matrix and adaptive updating support calculation strategy to ensure its effectiveness and innovativeness for mascot design. Combined with the theory of propagation dynamics, the propagation model of this paper is constructed by adding the node of latent propagator on the basis of the traditional model of infectious disease (SIR). And in order to enhance the influence of the mascot in the communication network, this paper proposes a mascot accurate recommendation model for its further dissemination. The research results show that the method of this paper can effectively extract the non-heritage cultural features and association rules in the Spring Festival Gala mascot, and the Spring Festival Gala mascot designed by the method of this paper can ensure high economic benefits under the premise of high quality. In addition, the communication model and precise recommendation method constructed in this paper can also give full play to the communication role and effectively communicate the Spring Festival Gala mascot and the non-heritage cultural elements it carries.
Under the background of the digital era, the self-media platform breaks the information barriers between the communicators and the receivers, effectively alleviating the information asymmetry problem between the two. Through observation and research, this paper finds that the current channels for receivers to obtain digital information can be divided into user-generated content (UGC), professional-generated content (PGC), and brand-generated content (BGC) according to the classification of the main body, but most of the managers are negligent in the management of these digital contents, and do not really utilize the value of their dissemination. Digital content generation and dissemination based on natural language processing (NLP) technology has become an important way to solve this problem. The method is based on the unified processing of a large amount of corpus, input Word2vec model and Skip-gram model two types of language models for training, with the obtained language model for the required text can be obtained word vectors, the different lengths of the text will be unified vectorization. By introducing evaluation indexes such as dissemination efficiency, content quality and coverage, the effect of generated content can be measured objectively. The value of generating digital content to improve the dissemination efficiency is verified through the evaluation of the actual effect.
The computer programs designed for music creation are analyzed to integrate computer software into music creation. Then a computer program utilizing JavaScript is introduced, including the theoretical basis of JavaScript, the identifiers, and the basic syntax. Finally, the characteristics of JavaScripts in Max software are studied, and JazzB JavaScript objects are used to program and analyze the morphological changes of the rhythm structure of jazz. The results show that the designed jazzy structure includes many jazz instruments such as the piano, the guitar, the bass, and the saxophone; the rhythm of the melody varies five times, from 128 beats per minute in the beginning to 132 beats per minute in the end. In the whole melody, electro-acoustic instruments account for 62.5%, acoustic instruments account for 25%, and synthetic instruments account for 12.5%. Hence, the contents of the music is enriched and condensed, while its texture is also ensured. The accuracy of JavaScript has reached the last 5 decimal places and the running speed is Millisecond (105ms), which is more powerful than other programming languages. When using it for the design of music structure, it can have a better use experience, which is of great significance in promoting music design.
The computer programs designed for music creation are analyzed to integrate computer software into music creation. Then a computer program utilizing JavaScript is introduced, including the theoretical basis of JavaScript, the identifiers, and the basic syntax. Finally, the characteristics of JavaScripts in Max software are studied, and JazzB JavaScript objects are used to program and analyze the morphological changes of the rhythm structure of jazz. The results show that the designed jazzy structure includes many jazz instruments such as the piano, the guitar, the bass, and the saxophone; the rhythm of the melody varies five times, from 128 beats per minute in the beginning to 132 beats per minute in the end. In the whole melody, electro-acoustic instruments account for 62.5%, acoustic instruments account for 25%, and synthetic instruments account for 12.5%. Hence, the contents of the music is enriched and condensed, while its texture is also ensured. The accuracy of JavaScript has reached the last 5 decimal places and the running speed is Millisecond (105ms), which is more powerful than other programming languages. When using it for the design of music structure, it can have a better use experience, which is of great significance in promoting music design.
The computer programs designed for music creation are analyzed to integrate computer software into music creation. Then a computer program utilizing JavaScript is introduced, including the theoretical basis of JavaScript, the identifiers, and the basic syntax. Finally, the characteristics of JavaScripts in Max software are studied, and JazzB JavaScript objects are used to program and analyze the morphological changes of the rhythm structure of jazz. The results show that the designed jazzy structure includes many jazz instruments such as the piano, the guitar, the bass, and the saxophone; the rhythm of the melody varies five times, from 128 beats per minute in the beginning to 132 beats per minute in the end. In the whole melody, electro-acoustic instruments account for 62.5%, acoustic instruments account for 25%, and synthetic instruments account for 12.5%. Hence, the contents of the music is enriched and condensed, while its texture is also ensured. The accuracy of JavaScript has reached the last 5 decimal places and the running speed is Millisecond (105ms), which is more powerful than other programming languages. When using it for the design of music structure, it can have a better use experience, which is of great significance in promoting music design.
The computer programs designed for music creation are analyzed to integrate computer software into music creation. Then a computer program utilizing JavaScript is introduced, including the theoretical basis of JavaScript, the identifiers, and the basic syntax. Finally, the characteristics of JavaScripts in Max software are studied, and JazzB JavaScript objects are used to program and analyze the morphological changes of the rhythm structure of jazz. The results show that the designed jazzy structure includes many jazz instruments such as the piano, the guitar, the bass, and the saxophone; the rhythm of the melody varies five times, from 128 beats per minute in the beginning to 132 beats per minute in the end. In the whole melody, electro-acoustic instruments account for 62.5%, acoustic instruments account for 25%, and synthetic instruments account for 12.5%. Hence, the contents of the music is enriched and condensed, while its texture is also ensured. The accuracy of JavaScript has reached the last 5 decimal places and the running speed is Millisecond (105ms), which is more powerful than other programming languages. When using it for the design of music structure, it can have a better use experience, which is of great significance in promoting music design.
In order to realize the quantitative management of the quality of higher education, this paper puts forward an evaluation model of auxiliary employability of special people under the concept of public employment service based on AHP-FUZZY algorithm. The phase space distribution structure model of special people’s auxiliary employability under the concept of public employment service is constructed, the index parameter set of special people’s auxiliary employability under the concept of public employment service is established, the fuzzy association rule distribution set is constructed by principal component analysis and fuzzy parameter estimation, and the association rule characteristic quantity of special people’s auxiliary employability under the concept of public employment service is extracted. Advanced statistical analysis methods, such as principal component analysis, big data fusion analysis and fuzzy detection model, are adopted to classify the multi-dimensional attribute features of special people’s auxiliary employability under the concept of public employment service, and the data is partitioned and scheduled in the fuzzy clustering center according to the differences of statistical feature parameters of employability analysis reports, and the feature decomposition model of special people’s auxiliary employability under the concept of public employment service is constructed. The auxiliary employability of special people under the concept of public employment service is fused by blocks and the regional structural parameters are reorganized. The binary structural characteristics of auxiliary employability analysis of special people under the concept of public employment service are reconstructed in the subspace fusion database. According to the reconstruction results, fuzzy clustering is carried out under principal component analysis and fuzzy parameter estimation, and the optimal evaluation of auxiliary employability of special people under the concept of public employment service is realized. Based on SPSS statistical analysis software and Matlab simulation tool, the empirical simulation analysis of the evaluation shows that the characteristic clustering of the evaluation of the auxiliary employability of special population under the concept of public employment service is good, the reliability of the evaluation is high, and the parameter analysis of the evaluation index of the auxiliary employability of special population is accurate and reliable.
cIn order to realize the optimal distribution of university management power under the overall planning and scheduling mode, an evaluation model of university management power distribution balance based on analytic hierarchy process (AHP) is put forward, and a method based on the integration of Internet thinking and technology, a sampling model of characteristic information of university management power distribution balance under the overall planning and scheduling mode, According to the quantitative recursive analysis results of the sample data of university management power distribution balance under the overall planning and scheduling mode, the information fusion of university management power distribution under the overall planning and scheduling mode is carried out, the association rule fusion characteristic distribution parameter set of the panel data of university management power distribution balance under the overall planning and scheduling mode is extracted, and the data fusion of university management power distribution balance under the overall planning and scheduling mode is carried out by combining the joint statistical probability density distribution detection method. Combined with the piecewise linear estimation method, this paper constructs the statistical characteristics of the equilibrium evaluation of university management power distribution under the overall planning and scheduling mode, analyzes the equilibrium characteristics of university management power distribution under the overall planning and scheduling mode according to the quantitative recursive analysis results of prior samples, extracts the equilibrium characteristics of university management power distribution under the overall planning and scheduling mode, and clusters the information of the equilibrium characteristics of university management power distribution under the overall planning and scheduling mode by structural characteristics extraction and fusion clustering methods. According to the results of distributed integration of features, it evaluates the balance of management power distribution in colleges and universities under the overall planning and scheduling mode. The simulation results show that this method has high accuracy and good confidence in evaluating the balance of management power distribution in colleges and universities under the overall planning and scheduling mode, and improves the ability of managing and controlling the balance of management power distribution in colleges and universities under the overall planning and scheduling mode.
cIn order to realize the optimal distribution of university management power under the overall planning and scheduling mode, an evaluation model of university management power distribution balance based on analytic hierarchy process (AHP) is put forward, and a method based on the integration of Internet thinking and technology, a sampling model of characteristic information of university management power distribution balance under the overall planning and scheduling mode, According to the quantitative recursive analysis results of the sample data of university management power distribution balance under the overall planning and scheduling mode, the information fusion of university management power distribution under the overall planning and scheduling mode is carried out, the association rule fusion characteristic distribution parameter set of the panel data of university management power distribution balance under the overall planning and scheduling mode is extracted, and the data fusion of university management power distribution balance under the overall planning and scheduling mode is carried out by combining the joint statistical probability density distribution detection method. Combined with the piecewise linear estimation method, this paper constructs the statistical characteristics of the equilibrium evaluation of university management power distribution under the overall planning and scheduling mode, analyzes the equilibrium characteristics of university management power distribution under the overall planning and scheduling mode according to the quantitative recursive analysis results of prior samples, extracts the equilibrium characteristics of university management power distribution under the overall planning and scheduling mode, and clusters the information of the equilibrium characteristics of university management power distribution under the overall planning and scheduling mode by structural characteristics extraction and fusion clustering methods. According to the results of distributed integration of features, it evaluates the balance of management power distribution in colleges and universities under the overall planning and scheduling mode. The simulation results show that this method has high accuracy and good confidence in evaluating the balance of management power distribution in colleges and universities under the overall planning and scheduling mode, and improves the ability of managing and controlling the balance of management power distribution in colleges and universities under the overall planning and scheduling mode.
To enhance the effectiveness of network-based multimedia English education, this study proposes a new teaching model integrating learning motivation theory and constructivism. The model’s design is based on the analysis of statistical features from the parameters of network multimedia English teaching, utilizing learning motivation and constructivism as foundational theories. Additionally, the model incorporates deep learning techniques and intelligent datasets rooted in mathematical logic. By constructing both a network multimedia English teaching dataset and a multimedia teaching resource access dataset, the model applies structural similarity analysis to evaluate these datasets under the influence of learning motivation and constructivism. This paper further investigates the various constraints and objectives faced by students and courses in the context of network-based multimedia English education. The optimization of the teaching algorithm is realized within this framework, aiming to improve efficiency and learner engagement. Simulation results demonstrate that the proposed method offers robust resource matching capabilities with minimal deviation, contributing to enhanced student satisfaction. Moreover, it supports users in the excavation and strategic planning of network-based multimedia English education.
To enhance the effectiveness of network-based multimedia English education, this study proposes a new teaching model integrating learning motivation theory and constructivism. The model’s design is based on the analysis of statistical features from the parameters of network multimedia English teaching, utilizing learning motivation and constructivism as foundational theories. Additionally, the model incorporates deep learning techniques and intelligent datasets rooted in mathematical logic. By constructing both a network multimedia English teaching dataset and a multimedia teaching resource access dataset, the model applies structural similarity analysis to evaluate these datasets under the influence of learning motivation and constructivism. This paper further investigates the various constraints and objectives faced by students and courses in the context of network-based multimedia English education. The optimization of the teaching algorithm is realized within this framework, aiming to improve efficiency and learner engagement. Simulation results demonstrate that the proposed method offers robust resource matching capabilities with minimal deviation, contributing to enhanced student satisfaction. Moreover, it supports users in the excavation and strategic planning of network-based multimedia English education.
To enhance the effectiveness of network-based multimedia English education, this study proposes a new teaching model integrating learning motivation theory and constructivism. The model’s design is based on the analysis of statistical features from the parameters of network multimedia English teaching, utilizing learning motivation and constructivism as foundational theories. Additionally, the model incorporates deep learning techniques and intelligent datasets rooted in mathematical logic. By constructing both a network multimedia English teaching dataset and a multimedia teaching resource access dataset, the model applies structural similarity analysis to evaluate these datasets under the influence of learning motivation and constructivism. This paper further investigates the various constraints and objectives faced by students and courses in the context of network-based multimedia English education. The optimization of the teaching algorithm is realized within this framework, aiming to improve efficiency and learner engagement. Simulation results demonstrate that the proposed method offers robust resource matching capabilities with minimal deviation, contributing to enhanced student satisfaction. Moreover, it supports users in the excavation and strategic planning of network-based multimedia English education.
Within the style diagram landscape, the incorporation of artistic styles into garb indicates a mix of creative flair and realistic software. But, conventional methodologies regularly contain hard work-intensive tactics and provide restricted range in fashion. driven by using the ambition to convert this subject, our look at offers an modern approach that mixes Adaptive example Normalization (AdaIN) with conventional Convolutional Neural Networks (CNNs) to enable swift and high-fidelity fashion transfers for the introduction of creative patterns in clothing format. Capitalizing on AdaIN’s ability for efficient style adaptation and CNNs’ prowess in deep characteristic extraction, our version adeptly keeps content fidelity whilst introducing a variety of creative patterns. Via training our model with a carefully selected dataset that encompasses a dissimilation of art patterns and garb patterns, and using a custom loss function tailored to decorate the synergy among style transfer efficacy and the sensible splendor of the very last designs, we release new vistas of creativity in fashion layout. The experimental effects attest to the model’s prowess in generating visually compelling and revolutionary styles, markedly expanding the innovative horizons available to designers. Furthermore, our approach highlights the version’s versatility in assimilating diverse artistic expressions and its capability to democratize layout innovation, empowering designers to venture into uncharted stylistic domain names with extraordinary ease and performance. This examine not only charts a direction for the advanced application of machine getting to know in the creative sector but also deepens the confluence of generation and art in the realm of fashion.
Within the style diagram landscape, the incorporation of artistic styles into garb indicates a mix of creative flair and realistic software. But, conventional methodologies regularly contain hard work-intensive tactics and provide restricted range in fashion. driven by using the ambition to convert this subject, our look at offers an modern approach that mixes Adaptive example Normalization (AdaIN) with conventional Convolutional Neural Networks (CNNs) to enable swift and high-fidelity fashion transfers for the introduction of creative patterns in clothing format. Capitalizing on AdaIN’s ability for efficient style adaptation and CNNs’ prowess in deep characteristic extraction, our version adeptly keeps content fidelity whilst introducing a variety of creative patterns. Via training our model with a carefully selected dataset that encompasses a dissimilation of art patterns and garb patterns, and using a custom loss function tailored to decorate the synergy among style transfer efficacy and the sensible splendor of the very last designs, we release new vistas of creativity in fashion layout. The experimental effects attest to the model’s prowess in generating visually compelling and revolutionary styles, markedly expanding the innovative horizons available to designers. Furthermore, our approach highlights the version’s versatility in assimilating diverse artistic expressions and its capability to democratize layout innovation, empowering designers to venture into uncharted stylistic domain names with extraordinary ease and performance. This examine not only charts a direction for the advanced application of machine getting to know in the creative sector but also deepens the confluence of generation and art in the realm of fashion.
Within the style diagram landscape, the incorporation of artistic styles into garb indicates a mix of creative flair and realistic software. But, conventional methodologies regularly contain hard work-intensive tactics and provide restricted range in fashion. driven by using the ambition to convert this subject, our look at offers an modern approach that mixes Adaptive example Normalization (AdaIN) with conventional Convolutional Neural Networks (CNNs) to enable swift and high-fidelity fashion transfers for the introduction of creative patterns in clothing format. Capitalizing on AdaIN’s ability for efficient style adaptation and CNNs’ prowess in deep characteristic extraction, our version adeptly keeps content fidelity whilst introducing a variety of creative patterns. Via training our model with a carefully selected dataset that encompasses a dissimilation of art patterns and garb patterns, and using a custom loss function tailored to decorate the synergy among style transfer efficacy and the sensible splendor of the very last designs, we release new vistas of creativity in fashion layout. The experimental effects attest to the model’s prowess in generating visually compelling and revolutionary styles, markedly expanding the innovative horizons available to designers. Furthermore, our approach highlights the version’s versatility in assimilating diverse artistic expressions and its capability to democratize layout innovation, empowering designers to venture into uncharted stylistic domain names with extraordinary ease and performance. This examine not only charts a direction for the advanced application of machine getting to know in the creative sector but also deepens the confluence of generation and art in the realm of fashion.
Within the style diagram landscape, the incorporation of artistic styles into garb indicates a mix of creative flair and realistic software. But, conventional methodologies regularly contain hard work-intensive tactics and provide restricted range in fashion. driven by using the ambition to convert this subject, our look at offers an modern approach that mixes Adaptive example Normalization (AdaIN) with conventional Convolutional Neural Networks (CNNs) to enable swift and high-fidelity fashion transfers for the introduction of creative patterns in clothing format. Capitalizing on AdaIN’s ability for efficient style adaptation and CNNs’ prowess in deep characteristic extraction, our version adeptly keeps content fidelity whilst introducing a variety of creative patterns. Via training our model with a carefully selected dataset that encompasses a dissimilation of art patterns and garb patterns, and using a custom loss function tailored to decorate the synergy among style transfer efficacy and the sensible splendor of the very last designs, we release new vistas of creativity in fashion layout. The experimental effects attest to the model’s prowess in generating visually compelling and revolutionary styles, markedly expanding the innovative horizons available to designers. Furthermore, our approach highlights the version’s versatility in assimilating diverse artistic expressions and its capability to democratize layout innovation, empowering designers to venture into uncharted stylistic domain names with extraordinary ease and performance. This examine not only charts a direction for the advanced application of machine getting to know in the creative sector but also deepens the confluence of generation and art in the realm of fashion.
