This paper combines the development situation of blue carbon industry to formulate the multi-dimensional optimization model construction of blue carbon industry cluster path. First set the model decision variables and objective function, and divide the constraints. Select the genetic algorithm to solve the optimization model. Determine the research data sources and genetic algorithm parameters, and analyze the multidimensional optimization model. The sensitivity coefficients of each decision variable to the optimization model are 0.2~0.1, and its sensitivity level is III, which means that the selected decision variables meet the research requirements. Compared with the other three algorithms, this paper’s genetic algorithm has superiority in four performance indicators, indicating that the genetic algorithm is more suitable for optimization model solving, and finally, the optimization model of this paper is put into the actual blue carbon industry, and it is found that there is a significant difference in the effect of carbon reduction, economic gain, green environmental protection, and satisfaction before and after the optimization (P<0.05), which verifies the effectiveness of this paper's optimization for practical application, and finally, according to the optimization results, the Finally, according to the optimization results, the corresponding optimization path is proposed.
Although China’s research on English is not as early as that of the western countries, researchers, combining the basic national conditions of China and the actual situation of the nationals’ learning of English, have been making continuous efforts in the research on the construction and application of English corpus, and have already achieved satisfactory results. In this paper, we first analyze the related contents of English corpus, and construct English corpus corpus from phonological and semantic aspects by analyzing the correlation characteristics between English corpus and semantics, according to the basic principles of corpus selection. Combining two word vector similarity measures, Jaccard similarity and edit distance, finally constitutes the final similarity calculation algorithm for English sentences. The MECNC model is constructed by integrating the joint representation and co-representation learning methods, and using edge probability to abstract the connection between two nodes. Experimentally analyze the word vector similarity of English corpus with the results of English corpus recommendation based on multilayer network representation. The correlation scores of Jaccard similarity metric in WS-SIM, WS-REL, MEN, Mtruk-771, and Simverb-3500 are 0.8069, 0.6668, 0.7389, 0.7125, respectively, 0.2769, which achieves the best results, so Jaccard captures more of the correlation between words. Experiments on link prediction task were conducted on five corpora using 3, 5, 8, and 10-fold cross-validation methods, and on the corpus CKM [245,1550], MECNC model OM3 has a maximum AUC value close to 0.94 at a cross-validation number of 8, which shows that MECNC, which is used as a guiding information for intra-layer wandering, shows a better performance.
The international development of the railroad industry puts forward higher requirements for the English application ability of senior railroad students, and reinforcement learning provides new ideas for the optimization of their teaching strategies. Based on reinforcement learning, the article constructs an adaptive learning path recommendation model (RL4ALPR). The model achieves application learning of multi-scenario knowledge of English in the railroad industry through railroad English knowledge level modeling, candidate learning item screening, recommender modeling, and reward calculation. The recommended effective value of the model in this paper is 0.581 at a learning path length of 60, which is 7.79% to 13.70% higher than the control model. The model realizes accurate recommendation of English exercises for the railroad industry based on the answers to the exercises. The evaluation scores of the students in the experimental class under the intervention of the model in this paper are improved to 24.26, 17.50, and 19.64 for speaking, reading comprehension, and translation of English in the railroad, respectively. Under the model of this paper, English teaching in the higher vocational railroad industry is highly recognized by students in terms of “content setting”, “teaching quality” and “teaching effect”. And the experimental class is better than the control class in terms of the level of knowledge about English for the railroad industry, the application of English for the railroad industry in multiple scenarios, and the comprehensive ability evaluation scores of 4-5 points more than the control class.
Based on the scheme of multi-objective planning, this paper conducts an in-depth investigation on the design path of interdisciplinary teaching aids for STEAM project-based learning in the context of science education. A multi-objective planning model is constructed, which includes the integration of subject knowledge, the cultivation of students’ ability and cost control, and a multi-objective genetic algorithm is introduced to solve the model. The feasibility of the design path of this paper and the enhancement of students in project-based learning are verified through real cases. Compared with the other three schemes, the interdisciplinary teaching aids production using the mathematics and electricity fusion scheme can maximize the Pareto optimality, i.e., the integration of disciplinary knowledge and the cultivation of students’ abilities are maximized, as well as the goal of minimizing the production cost. The use of this paper’s scheme to produce teaching aids and apply them in course practice can effectively enhance students’ interest in learning and course performance.
With the deterioration of the global economic situation and the stagnation or regression of the development of enterprises, the problem of college students’ employment and entrepreneurship has been particularly prominent in recent years, and it is also one of the key points that can not be ignored in carrying out economic construction. The article realizes the prediction of college students’ entrepreneurship and employment market trends based on ARIMA-LSTM by designing the ARIMA algorithm model and combining it with the LSTM model architecture, taking the college students’ entrepreneurship and employment data from 2010 to 2022 as the research data, and using two evaluation indexes, namely, the mean absolute percentage error (MAPE) and the root mean square error (RMSE), to predict the results. Evaluation. From the analysis results ARIMA model prediction fit is high. Comparing the prediction results of the combined model with those of the LSTM model and the ARIMA model, the comparison results show that the combined model constructed in this paper can effectively fit the linear and nonlinear intertwined and superimposed trends of the time series compared with a single model, and the relative error of prediction is smaller at 33.78, which makes the results more accurate. The combined model can help the management department related to college students’ employment and entrepreneurship make reasonable decisions and improve efficiency.
This paper attempts to conduct a systematic study on the constructions of quantity phrases in modern Chinese on the basis of relevant research results, drawing on the theory of constructive grammar, in order to demonstrate the mechanism of constructions of quantity phrases in modern Chinese. The study firstly researches and analyzes the matching and distribution of quantity phrases as well as the Chinese construct grammar. Then, the study is based on random forests to investigate the constructions of quantifiers. By extracting and labeling six modern Chinese corpora, the analysis is carried out using random forests. On this basis, in order to further analyze the role of the relationship between the constructions of quantifiers, this paper also invokes a multinomial logit regression model for the study. It is found that the construct variant, regional variant, verb immediately following at the end of the sentence, structure initiation, and verb prototypicality are important factors affecting the number word constructions. In addition, the probability of quantifiers was higher when the construction variants were A and D, and sentence initiation while more inclined to co-occur with quantifiers. These findings reveal constraints on quantifier constructions and demonstrate the advantages of combining machine learning methods to analyze Chinese constructions.
Under the support of education digitalization strategy, in order to adapt to the development needs of education modernization, it is necessary to strengthen the research on the application of artificial intelligence technology in the main education of Marxism. Based on this, this paper closely follows the background of the artificial intelligence era, takes Marxist theory as a guide, and builds an intelligent communication platform for Marxist education based on the deep reinforcement learning model and the new media platform, which serves as a key link in the precise communication path of Marxist education. Relying on the state representation model and decision-making model in the deep reinforcement learning algorithm, the platform realizes the intelligent recommendation and dissemination of Marxist education content. The results of the precise communication path show that the intelligent communication platform has good application recognition and perceived satisfaction, and the audience students have a strong sense of belonging and responsibility for Marxist education in the communication, and the average score of the survey on the cultivation of values such as life ideals and political attitudes is above 4.50 points. The precise communication path of Marxist education proposed in this study, as an implementable countermeasure in the new media environment, can help the audience students to establish a correct worldview, life view and values.
The rapid development of the information age has prompted the exchange and sharing of information resources more and more frequently. Aiming at the problem of propagating information data in the center of data network, which is easy to cause congestion and delay, this paper uses deep neural network to research on the optimal path selection method for propagating information. A network traffic prediction model is designed based on multi-task learning and LSTM, and a dynamic multipath load balancing algorithm (FNN-LB) based on feed-forward neural network is proposed to solve the problem of scheduling and allocation of network traffic. The traffic prediction accuracy and generalization ability of the MT-LSTM model are verified, and the prediction mean square error is only 0.573%. Analyzed from several performance metrics, the FNN-LB algorithm improves the network throughput by 2.34% to 10.35% relative to other algorithms, effectively reduces the number of idle and overloaded links, as well as the average network delay and packet loss rate of the rat flow, while the first packet round-trip delay of the rat flow is reduced by more than 12.58%. Therefore, the proposed method in this paper can ensure the transmission quality of communication information data and improve the efficiency of data flow of communication information.
The integration of artificial intelligence and tourism culture industry requires that it is consumer-centered, and everything is based on the fundamental starting point of improving service quality and providing better tourism products. The article explores the impact of AI application on the cultural cognition level of tourists based on the role mechanism of AI and innovative inheritance methods in tourism culture inheritance. The level of tourists’ cultural cognition is quantified through the degree of understanding of tourism culture, the willingness to accept and disseminate tourism culture, the degree of preference and internalization of tourism culture, and the willingness to practice tourism culture, and the relevant research data are obtained through questionnaires. Then the benchmark regression model was constructed by combining the multiple linear regression model with the level of cultural cognition of tourists and the level of AI application as the explanatory variables and core explanatory variables. For every 1 percentage point increase in the level of AI application in tourism cultural heritage, the level of cultural cognition of tourists will increase by 0.419 percentage points. The application of artificial intelligence in tourism culture inheritance can expand the way of tourism culture inheritance and enhance the cultural cognition level of tourists through intelligent transmittable knowledge base.
Entity-relationship extraction task is one of the very important research directions in the field of natural language processing, aiming at identifying and determining the existence of specific relationships between entity pairs from unstructured text. The study firstly introduces the related theories of graph neural networks in terms of graph representation learning and graph neural networks, and then makes full use of the information of dependent syntactic trees to propose a relationship extraction model based on dependency graph convolution (DGGCN). The validity of the model and the entity extraction effect are verified through relevant experiments.The DGGCN model is fully experimented on the public datasets NYT and WebNLG, and the F1 value is effectively improved.According to the results of the ablation experiments, it is shown that the DGGCN model improves the entity and ternary extraction results by 0.5% and 4.3%, respectively. In the long and short distance entity extraction results, the DGGCN model outperforms the benchmark model in both long and short distance entity relations, but the extraction performance gap between short and long distance entity relations is still large and needs further improvement.
Urban safety development is one of the guarantees for the overall development of the city, and the study uses Delphi method, entropy weight method and TOPSIS method in the assessment of urban safety development. An improved Delphi-entropy weight-TOPSIS combination assessment model is constructed to evaluate the urban safety development. The evaluation index system of urban safety development is constructed, and the evaluation indexes of urban safety development are calculated by Delphi method and entropy weight method respectively, and the subjective and objective weights of the evaluation indexes of urban safety development are derived, and finally, the comprehensive weights are calculated by the method of combined weight assignment. The comprehensive weights of the guideline layer of the urban safety development evaluation index system are 0.1874, 0.2080, 0.2005, 0.2187, and 0.1854, respectively.The evaluation index system is used for empirical research, and City A is taken as the object of the research to assess its urban safety development status during the 10-year period from 2014 to 2023. From the evaluation results, it is known that the overall urban safety development of City A during the 10-year period shows an upward trend, with slight fluctuations in the process, but the overall development is good, and the evaluation score of urban safety development improves from 0.4657 points in 2014 to 0.6479 points in 2023.
As an important part of economic activities, logistics industry ushers in new development opportunities and challenges in the wave of digital transformation. The study explores the path of integration and development of digital economy industry and logistics industry, designs the path of building intelligent logistics ecosystem, and constructs the logistics distribution path optimization model based on time window. When analyzing and solving the logistics distribution path optimization problem, the ant colony algorithm (ACO) is improved by introducing the hierarchical idea of the artificial bee colony algorithm (ABC) and limiting the pheromone concentration on each path, controlling it within a known range, to make up for the shortcomings of the ant colony algorithm of precocious maturity and search stagnation. Using MATLAB software to simulate the logistics and distribution of M fresh food e-commerce enterprises, the comprehensive cost solved based on ABC-ACO algorithm is 75.64 yuan and 33.45 yuan less than the results of ACO and GA solving, respectively, and the optimal route traveling mileage is 21.35 km and 6.03 km shorter than the mileage solved by ACO and GA solving, respectively. It shows that the performance of the improved ant colony algorithm is better than that of the basic ant colony algorithm and the genetic algorithm, and it points out the direction for the future logistics and distribution of the distribution center. The empirical analysis found that the digital economy industry and logistics industry show a synergistic trend, and there is a large space for integration and development.
The concept of “Internet+Sports” has promoted the application of artificial intelligence and other emerging technologies in the field of sports. This paper mainly focuses on the special physical training, and explores the application and realization path of artificial intelligence technology in physical training test. In this paper, PSO-BP model is constructed based on BP neural network optimized by PSO intelligent algorithm and applied in physical training test. In addition, for the classification of physical training, this paper follows the basic principles of physical training system construction, establishes the physical training measurement index system through the results of expert solicitation, and determines the weights of each index by using the hierarchical analysis method. Through the empirical analysis of the PSO-BP model in this paper, it can be seen that the fitting results of the training samples of male and female students show that the corresponding correlation coefficients of male and female students are 0.99908 and 0.99898, respectively.The errors of the evaluation output values of the physical training measurements and the expected values are within ±3.5, and the prediction error of the BP neural network model optimized by the PSO algorithm is significantly reduced, and the relative errors of the evaluation of male and female students are reduced by 0.988% and 0.833%, respectively. The results show that the results of physical training measurement and evaluation using PSO-BP neural network model are more accurate, which proves that the performance of PSO-BP neural network in this paper has been effectively improved and optimized, and at the same time, it can meet the application requirements of physical training measurement and evaluation.
The extreme high temperature and erosive environment service environment in bridge construction puts forward higher requirements for high performance concrete and other aspects of performance. In this paper, compound mineral admixture is selected as a research breakthrough, and X-ray diffraction analysis (XRD) and Raman spectroscopy are used to explore the micromechanical behavior of compound mineral admixture in high-performance concrete. In the Raman spectral analysis, the stress distribution of the fitted curve of the compound mineral admixture is more flat and uniform, and the offset of the G’ peak position is higher than that of the reference concrete and the single-mineral-admixture concrete, and the stress can reach 2.5 MPa under 1% strain, showing good interfacial bond, stress transfer efficiency, etc. The physical phase data of the XRD also shows the frost resistance of compound mineral admixture, with the ability to mitigate carbon dioxide, and the ability to reduce the carbon footprint of the concrete, with the ability to reduce the carbon dioxide. The XRD data also show the frost resistance of the compound mineral admixture, which has the performance of slowing down carbonization. The NSGA-II algorithm is introduced and improved to propose a concrete proportion optimization model. The final evaluation function converges from 35 generations and the final value is 0.4558, which achieves the adaptive optimization of compound mineral admixture.
Semantics in public English texts are more challenging to understand accurately because they are influenced by specific contextual contexts. Traditional English text semantic understanding methods do not design their semantic understanding methods based on the conceptual semantic features of the text, and they have the problem of poor accuracy in understanding the deep semantics of English texts. For this reason, the article takes the public English text semantic algorithm as the research perspective, firstly conducts relevant theoretical research on English text semantic feature representation, then explores the text semantic extraction method based on the Dependency Tree-CRF, and deepens the understanding of English text semantics through the conceptualization and attention embedding methods. In the experiment of comparing the semantic coherence model with manual scoring, the experiment shows that by applying the semantic analysis model designed in this paper to the task of correcting the English writing of domestic college students and comparing it with the experimental results of manual scoring, it is found that the average absolute error between the scoring of the English compositions by this paper’s model and the scores of the compositions corrected by the teachers is 3.2051, i.e., the difference between the results of the manual correcting and the results of the correction by this paper’s model is It is not big, from which we can get that the model of this paper has good practical value.
In order to solve the shortcomings of the sound source separation method, this paper proposes a melody extraction method based on saliency and improved joint neural network, constructs the pitch saliency feature function according to the idea of harmonic energy superposition, pre-processes the audio, and then builds the joint neural network based on Res-CBAM according to the idea of joint neural network of music detection and pitch estimation classification to realize the melody pitch contour tracking. In addition, the calculation of the significance function is introduced to highlight the pitch significance features, so that the graphs input to the neural network have clearer melodic features. The results show that before and after the suppression of the accompaniment, the difference in the time-domain waveforms is not significant in the treble range, but there is a significant difference in the low-frequency range. In addition, the OA accuracy of the Res-CBAM algorithm proposed in this paper is up to 41.14% higher than other algorithms (P < 0.05), and the accuracy of the model is good. Applying this recognition model to teaching found that teaching with this model can significantly improve the subjects' perception of music (t=.197, p=0.002<0.05). It can be seen that the application of the Res-CBAM algorithm to actual music teaching is of great practical importance.
Based on the full active suspension and road input model, this paper introduces the fuzzy control theory and genetic algorithm design theory, adopts the fuzzy control method to control the actuator’s actuation force, creates the fuzzy control system of the automobile active suspension system, and optimizes the fuzzy control rules by using the improved genetic algorithm to ultimately realize the vibration damping effect enhancement in the process of driving the automobile vehicle. Simulation experiments and sample vehicle road experiments are used to verify the performance and utility of the fuzzy controller based on the improved genetic algorithm proposed in this paper. In the simulation experiments carried out with the help of Matlab/Simulink software, the control active suspension body controlled by the fuzzy controller based on the improved genetic algorithm reduces the root mean square value of angular acceleration of pendulum vibration, pitching rotation and lateral tilting rotation by 58.93%, 52.31% and 57.74%, respectively, compared with that of the conventional controller, the root mean square value of the dynamic deflection of the suspension is reduced, and the vehicle driving performance shows good stability and stability. The vehicle traveling shows good smoothness and stability. In the prototype road test, the root mean square value of the corresponding acceleration of the fuzzy-controlled active suspension optimized based on the improved genetic algorithm in this paper is reduced by 42.67%, 39.45% and 37.23%, respectively, compared with that of the passive suspension. Overall, the optimized design of fuzzy controller based on genetic algorithm proposed in this paper greatly improves the vibration damping effect of the active suspension system.
Semantic accuracy plays an important role in improving the quality of English translation teaching. This paper proposes a semantic translation model based on convolutional neural network. It is based on the semantic correlation expression and the statistical machine translation model of hierarchical phrases, and combines the convolutional neural network to propose a translation model optimization method that integrates sentence and document information. The method evaluates the semantic match between source language phrases and candidate target phrases by utilizing the sentence context of the source language phrases and the topic information of the documents in which they are located. The optimization method for evaluating the accuracy of English semantic translation is also given. In the simulated translation experiments, the accuracy of the translation correctness evaluation of this method is maintained at 92.5% and above, with high semantic accuracy. The research constructs a high and stable English semantic translation model, which provides informative aids for English translation teaching.
Multidimensional vector space is the basis of lexical semantic correlation computation, which is able to assess the similarity between lexical semantics. In this paper, we implement a Japanese lexical named entity recognition and semantic relation calculation method based on this method. Dependency relations are fitted using N-Gram and knowledge expansion, contextual relations are corrected using collocation frequency, and semantic interactions are determined by semantic linking methods. The accuracy and recall of the identification of this method are higher than that of the spatial semantic role method by 0.78% and 4.93%, respectively, and the quantized values of the calculated correlations accurately reflect the strong and weak lexical semantic relationships. The results of the disambiguation experiments show that the maximum correlations computed using the method of this paper are consistent with the corresponding semantic items. Therefore, the method designed in this paper for recognizing named entities and calculating semantic relations of Japanese words has a relatively accurate recognition rate of semantic relations and has the ability of disambiguation.
Visual communication design, as an indispensable part of product design, plays an important role in enhancing the cultural connotation and aesthetic value of products. Based on fractal theory and supported by Iterative Function System (IFS), this paper studies the visual communication style design of patterns. Taking the flower pattern as an example, a method of automatic generation of flower pattern based on fractal geometry is proposed, and the effective value ranges of each parameter are derived through experiments and analyses to realize the digital visual communication design of the traditional handmade pattern. Then the generated fractal graphic is used as the content graphic, the style graphic is determined, the style migration technology is introduced, and the convolutional neural network model is constructed to build the style migration model of the product graphic, and experimental analyses are carried out to further improve the visual communication design of the product graphic. The average scores of this paper’s product graphic style migration method on aesthetics and style similarity are 3.95 and 3.81, respectively, and the p-values of the Mann-Whitney U-test are all less than 0.0001, which are significantly better than the baseline method. The average overall style similarity of this method on the real dataset is 86.27%, and the accuracy and mean square error on local style features are better than the VividGraph method, which has higher efficacy in performing product pattern style migration to realize visual communication style design.
Aiming at some configuration and scheduling problems of automated guided vehicles (AGV), shore bridges and yard bridges in the loading and unloading operation process of container terminals in the port logistics system, the flow characteristics of containers between ships and yards are analyzed in detail in the light of operational characteristics. Considering the intersection of AGVs with shore bridges at the quay front and the intersection of AGVs with yard bridges in the yard area, a container truck scheduling optimization model based on the objective of minimizing the operation cost is designed. And adaptive particle swarm algorithm (APSO-C) is used to solve the three-dimensional scheduling model of container in port logistics system. The results show that the fastest arrival scheduling rule is basically better than the shortest distance scheduling rule, and with the increase of the container task volume, the gap between the two scheduling rule optimization objectives in the same situation is getting bigger and bigger. Compared with the shortest distance, the fastest arrival has a shorter total completion time, which is more in line with the actual terminal operation scheduling. In addition, as the number of shore bridges increases, the operation time gap between single-load AGV mode and multi-load AGV mode is proportional to the number of shore bridges. Obviously the APSO-C algorithm has better performance in the container scheduling optimization process, which is more in line with the actual operation requirements of the terminal.
Mural paintings in tombs are always facing protection problems in the process of display. In order to achieve the necessary balance between fresco protection and display, this paper discusses the application scenario and implementation steps of utilizing digital twin technology to protect frescoes, and builds a fresco protection display system. Focusing on gesture interaction, this paper uses Kinect interactive device to realize the recognition of human gestures. The average recognition speed of this paper’s method is about 0.02s, and it has high recognition accuracy under different angles and depths, and different gesture movement trajectories. The designed gesture virtual interaction system can improve the satisfaction of visiting the tomb murals and realize the balance between the protection and display of murals.
As an important part of Chinese traditional culture and art, how to efficiently realize the recognition, retrieval and style appreciation of calligraphy is of great significance. Aiming at the shortcomings of the traditional geometric feature recognition model with low recognition efficiency, this paper applies morphological neural network to the geometric feature recognition of calligraphy to design a geometric feature recognition model for calligraphy. Image enhancement is performed on the calligraphic graphics, the expansion pooling subnet is designed to replace the maximum pooling layer, and the calligraphic geometric feature recognition network is constructed by combining the residual block structure. The average recognition accuracy of this model in the geometric feature refinement recognition task is as high as 97.23%, which is higher than that of the comparative models such as CNN, LeNet-5, and the recognition accuracies are not less than 96% for the Euclidean, Liu, Zhao, and Yan styles. Using the model of this paper to explore the influence of calligraphic line fluidity and structural changes on the geometric features, it is analyzed that the “line” has a more significant influence on the geometric features of calligraphy than the “structure”. In the six types of traditional calligraphy, such as large seal, small seal, official script, regular script, line script, and cursive script, cursive script is only similar to the geometric characteristics of line script, and the geometric characteristics are very unique.
Cross-language text categorization techniques can achieve more efficient localization and use of text data in multilingual languages by overcoming the differences between different languages. In this paper, firstly, by combining cross-language word vectors and adversarial training, support vector machines are utilized to improve the alignment effect of English-Chinese cross-language words and sentences in the feature space, and to achieve higher quality English-Chinese cross-language text classification. Then the variational mechanism is combined with multi-task learning to align the potential semantic space of multimodal data, maintain the domain invariance of different modal data representations, improve the generalization ability of the model, and ensure the consistency of the variational machine translation training process and the prediction process. The two are combined to construct a hybrid variational multimodal machine translation model based on semantic alignment, experimentally validate the effect of the text categorization algorithm on datasets such as Multi30k, and examine the quality of English-Chinese and Chinese-English translations. In the experiments, it is found that on the MSCOCO dataset, the BLEU of English to Chinese and Chinese to English of this paper’s model is 61.26 and 60.15 respectively, and the translation quality is significantly better than the baseline model. The model achieved the best results in all 3 actual translation tasks. And compared with the complete model, the translation performance of different removal cases in the ablation experiments are decreased, which verifies the effectiveness of the model of this paper as a whole and different components. The method in this paper can effectively reduce the feature differences between different languages, and has important practical application value for solving cross-language text categorization and machine translation problems.
As a small-scale power generation and distribution system, microgrid, by virtue of its high efficiency and clean power generation, has been taken by scholars around the world as a key research object for the sustainable development of national energy. Taking microgrid as the main research object, this paper explores the construction of power load identification model and optimization of scheduling capacity of microgrid. The improved Least Squares Support Vector Machine (LS-SVM) algorithm is used to construct the power load identification model, which realizes the accurate prediction of power load data. The optimal scheduling model of the microgrid is constructed based on the nonlinear planning method, and the co-evolutionary genetic algorithm (DCGA) with the improved difference strategy is used to solve and find the optimal model.The curve of the predicted value of the power load of the LS-SVM is basically fitted to the curve of the real value, and its prediction of the power load is more accurate than that of the BP neural network model. The daily running costs of the genetic algorithm, CCGA algorithm and DCGA algorithm are 1750.34 yuan, 1730.59 yuan and 1709.83 yuan, respectively. The daily running cost of the improved DCGA algorithm in this paper is 1763.59 yuan, which is reduced by 2.31% and 1.20% compared with the genetic algorithm and co-evolutionary genetic algorithm, respectively, and the DCGA algorithm has the fastest convergence speed, which indicates that it has the strongest ability to search for optimization, and it can effectively reduce the operating cost of microgrids, and it has a high practical value.
In this paper, the embedding vectors are obtained by Bert coding, and then the obtained embedding vectors are adaptively fused with features to realize legal text classification by a classifier, on the basis of which a multi-label text classification model (AFDAM) is proposed to capture the target words in a sentence. At the same time, the pre-trained continuous bag-of-words representation (CBOW) is used to initialize the vector representation of the label information, and then these label information is adaptively fused with the feature information of the text, which effectively promotes the multi-label legal text classification, and accelerates the development of informationization and intelligence in the legal field. The results show that the text feature enhancement module has the most prominent impact on the text classification effect, and its accuracy on the three datasets is improved by 0.46%-1.19%. In addition, the introduction of target vectors and text expansion also gained 0.54%-1.7% and 0.59%-1.53% and 1.08% increases in model accuracy, respectively. In addition, the addition of offense and statute information can significantly improve the prediction of sentence length, and the statute information improves the results more significantly than the offense information. And the classification effect of the AFDAM model proposed in this paper increased by 0.1453-0.257 than the other five models.
Quality management is one of the factors determining the running level of a university, so it is necessary to evaluate university management scientifically and comprehensively. In this paper, a university management evaluation index system is constructed and optimized with reference to the multi-factor decision tree technology, and the cause degree and centrality degree are calculated through the DEMATEL method model, the causality diagram is established, and the ISM hierarchical structure analysis is subsequently carried out. The management influences with high centrality degree are service ability, student quality, employment, school size and presidential leadership. Through the results of the reachable matrix, four levels of college management influencing factors are divided, and it is found that the fundamental factors affecting the management level of colleges and universities are concentrated in the socio-economic and cultural level, the deeper factors are mainly the school’s own scale, funds and teachers and students, and the leadership quality and service ability are the superficial factors. Therefore, the improvement of university management evaluation system can be carried out with reference to the above levels and indicators.
With the rapid development of social networks, the powerful interactive function of social networks and the high degree of user participation make the information generated in large quantities and spread rapidly, which also provides a dissemination path for the mass dissemination of Marxism. The article analyzes the overall structure of complex social networks and node centrality indexes for the overall topological characteristics of the networks, in order to further analyze the information dissemination characteristics of social networks for the mass dissemination of Marxism. Subsequently, a social network information dissemination model based on SIR is established according to the specific structural characteristics and dissemination modes of the social network, and the experimental test of the information dissemination effect is carried out. Finally, a Marxist mass communication strategy is proposed based on the experimental results. In the experiments on the effect spreading of node information, the nodes numbered 4, 12, and 20 have the strongest information spreading effect, with the number of nodes infected exceeding 30, and the corresponding number of interaction activities are 575, 511, and 663.This suggests that individuals within the aggregated group can build trust with the group members by participating in frequent interactions to improve the effect of information spreading. The development and dissemination of Marxist mass social networks cannot be separated from a series of measures such as a sound social network regulatory system.
This paper introduces a multilayer Bayesian model based on probabilistic and Bayesian inference models to infer discourse hierarchical features in the English corpus at both intra-individual and inter-individual behavior levels. Based on the existing English corpus observation data, the Bayesian method is used to organically combine the prior knowledge and the observed English corpus discourse hierarchical features data, derive and incorporate the posterior probability distribution of many uncertain information target variables, and obtain the discourse hierarchical features of excellent English teachers in the English corpus, which provides more valuable information for educational management. For the emotional features of different teachers’ classroom discourse, the level of “emotionally full” is higher than that of “emotionally depressed”, which indicates that the classroom emotions of excellent teachers are more full, demonstrating the discourse hierarchy features of excellent English teachers. The Bayesian estimation has excellent estimation accuracy and explains the discourse hierarchy of teachers in the English corpus well.
Apriori algorithm is a classic frequent itemset mining algorithm, but it has the problems of more time consumed by the self-connection process and high overhead of conversion between memories. In order to improve the frequent itemset mining effect of Apriori algorithm, this paper improves the existing adaptive genetic algorithm by using the average population fitness and fitness value discretization, and improves the Apriori algorithm by using the optimized genetic algorithm, so as to solve the strong association rules. Compared with the traditional Apriori algorithm, the algorithm in this paper has less time overhead and improves 2.4%, 2.4%, and 2.7% on average in recall, accuracy, and F1 value. On the Accidents and Retail datasets, the improved Apriori algorithm is faster than the NSFI algorithm by 6.12% and 13.52% on average, reducing the computational complexity. Using the improved algorithm to analyze the characteristics of cross-provincial migrants, it is found that the migrant population is younger in age, with lower education level, mostly of agricultural household registration, and mostly of Han nationality, which verifies the practical application value of the improved algorithm.
Teachers’ educational decision-making behavior is a deep factor affecting the quality of teaching and has a guiding role in the whole process of teaching activities. In this paper, lagged sequence analysis is used to focus on comparing the differences in multi-objective educational decision-making behaviors between backbone teachers and novice teachers. At the same time, a collaborative filtering recommendation algorithm based on improved cosine similarity combining teacher users and teaching resources is designed to achieve personalized teaching resources recommendation for teachers. And the personalized teaching path for teachers was designed by combining the characteristics of teachers’ educational decision-making behaviors. In terms of static decision-making behavior, backbone teachers pay more attention to cognitive decision-making, while novice teachers pay more attention to procedural decision-making. In terms of dynamic decision-making behavior, backbone teachers’ decision-making strategies are more balanced and diverse and goal-focused than novice teachers. The personalized teaching path of this paper is much better than traditional teaching methods in actual teaching experiments, and there is a highly significant difference between the pre and post-test scores of students in the experimental group using the path (p=0.000<0.01), and teachers are more satisfied with the accuracy of the resource recommendation and the teaching effect of the path. The personalized teaching path designed in this paper helps teachers' educational decision-making in teaching and provides a feasible implementation path for personalized teaching.
The physical and mechanical properties of the rock body at the foot of the slope are prone to deterioration under water-rock action, which affects the stability of the slope body. Accurate understanding of the damage mechanism of anticlinal rocky slopes in reservoir area under the condition of deterioration of the rock body at the foot of the slope is the key to the reasonable evaluation of stability. In this paper, the main lithological characteristics of the anti-dipping rocky slopes in the reservoir area and the distribution characteristics of slope height, slope angle and inclination angle of the rock layer are investigated as the research object, and the deformation and damage characteristics and laws of the rock body are obtained. Numerical simulation of anticline slopes was carried out using GDEM mechanical analysis software based on the discrete element method of continuous medium mechanics. It is found that the upper and middle parts of the slope where the invert body is located in the studied engineering example have deep tensile cracks and shallow surface block tipping damage, while the middle and lower parts show deep bending deformation, and there is a gradual transition zone in the contact between the deformed rock layer and the bedrock. As the distance between the cave and the basement increases, the rock layers gradually tilt towards the Yellow River from the north-east to the north-west, with the rock layers at the base of the cave tilting between 340° and 350°. The inclination of the rock layer in the example slope is 76°, and the main rupture surface of the slope, i.e. the location of the largest bending moment of the rock layer, has a small inclination angle with the horizontal plane. The slope angle is 48°, and the sum of the angles of the slope angle and the inclination angle of the rock layer is obviously larger than 117°, and the slope will be deformed and damaged, which is consistent with the value of the conditions for the slope to be deformed and damaged.
In recent years, with the increasing psychological pressure on students, psycho-pedagogical methods have been highly emphasized. This article takes students’ multimodal emotion recognition as a research perspective. The article firstly studies the unimodal emotion recognition methods of expression, text and speech respectively. Then it proposes a multimodal emotion recognition algorithm based on dual-attention mechanism and gated memory network, and then conducts emotion recognition experiments to validate this paper’s method. The article further proposes an intervention pathway to further assist in solving students’ mental health problems by designing a virtual reality mental health intervention system. Using the method of this paper in Multimodal database unimodal emotion recognition experiments, found that the network of the model used in this paper has better experimental results, which verifies the effectiveness of the method of this paper, and the accuracy rate of emotion recognition is 60.65%. After testing the mental health level of 8000 students in a school, it was found that the number of hypermodality and the screening rate were low except for the high score of compulsion, from which it can be concluded that the students in our school are in good mental health as a whole after applying the method of this paper.
Knowledge mapping, as an emerging knowledge management tool, provides a new perspective of knowledge learning for physical education teaching. In this study, knowledge mapping is introduced into physical education teaching, and a comprehensive physical education knowledge map is constructed by integrating the teaching resources and contents of physical education teaching and utilizing related techniques such as knowledge extraction and knowledge fusion. The method of fusion of sports knowledge graph is also proposed, including three parts: graph approximation, similarity calculation, and subgraph fusion. Finally, the constructed knowledge graph is practically applied, and a recommendation model based on sports knowledge graph and neural network is constructed to realize the sports teaching application of intelligent educational knowledge graph. The entity recognition module optimized the recognition accuracy rate on the objective existence entities of sports by 1.45%, and the relationship extraction module outperformed AGGCN in all three indicators. The training method of this paper is better than the MICT sports training method in improving students’ cardiorespiratory capacity and flexibility quality. The improvement of students’ 800m running performance under this paper’s training program is 0.13min more than that of MICT.It is proved that the sports course recommendation model based on knowledge graph and neural network provides a reference for the management and application of knowledge data in physical education, with a view to promoting the progress in the field of intelligent education.
This paper follows the principle of construction of evaluation index system to formulate the evaluation index system of teaching quality of college courses, which is mainly composed of 5 first-level indexes and 25 second-level indexes, and in addition, the real assessment data of ten students of a 985, 211 college on teachers’ teaching quality assessment is taken as the main source of data for this study. The combined algorithm of hierarchical analysis and fuzzy comprehensive evaluation is used to construct a university course teaching quality assessment model, and the model is analyzed by example verification. The comprehensive evaluation scores of the secondary indicators of the university’s course teaching quality are (2.1781, 2.879, 2.1934, 1.7756, 0.9739), and based on the principle of maximum affiliation degree, it is concluded that the students’ grade of the university’s course teaching quality is good (2.879), and the results are in line with the university’s actual course teaching, and at the same time, it is proved that the model of this paper has an excellent application effect.
From the perspective of artificial intelligence (AI), this paper explores the application and impact of cluster analysis in the criticism of narrative ethics in Chinese new century literature. Utilizing AI paper processing technology, a large amount of literary text data is quickly obtained and processed, and a knowledge map of narrative literary works is constructed. Meanwhile, a clustering algorithm is used to divide the keywords of literary works into cluster classes to improve the efficiency of rapid literary analysis. The regression model is used to evaluate the effect of the cluster analysis method in the AI perspective on the ethical criticism of literary narratives. The accuracy, recall, and F1 value of the two AI techniques selected in this paper in the classification of literary text themes, keywords, and emotions are 85% to 90%, which is higher than the comparison methods, and combined with the clustering algorithm, the keyword categories of the literary text can be obtained quickly and precisely. In addition, by constructing a knowledge graph, this paper can help users grasp the character relationships in literary texts more clearly and assist in ethical criticism. The investigators are highly satisfied with the method of this paper, the average rating of each dimension is between 4.09 and 4.7, and the method has a significant contribution to the effect of ethical criticism of literary narratives.
Generative artificial intelligence, as a new technology paradigm, has received more and more attention for its powerful generative ability and wide application prospects. Especially in automated control systems, the application of technology based on generative artificial intelligence is gradually becoming a hot spot of research. In this paper, the generative AI automation control system is divided into four levels: input layer, processing layer, instruction generation and control execution layer, and combined with dual encoders, the attention model of multilingual to semantic expression is constructed. Two-dimensional variables are selected to construct a fuzzy PID control system to realize automation control for generative AI system. Comparing the control effects of fuzzy control PID and classical PID, the average errors of the two systems are 1= , 2= respectively. The maximum overshoot and rise time are 9% and 0.08 s, 5% and 0.04 s. The fuzzy PID control effect is more accurate, and at the same time improves the dynamic performance of the system. Analyze the implementation effect on the innovative service application of generative artificial intelligence. Comparing the overall recognition effect of the control system B proposed in this paper, and the two systems with reference to A, their overall recognition effect indexes are 0.94755 and 0.87211, respectively, and the fuzzy PID control system plays an auxiliary enhancement role in the contextual feature recognition of translation services in the intelligent library.
The assessment of economic quality is of great significance in grasping the state of national economic development at a macro level. This paper focuses on exploring the assessment methods of economic quality and introducing deep learning models to improve the shortcomings of the traditional economic quality assessment in the assessment process. The economic quality assessment system is constructed from five dimensions, including economic vitality, and the MIV indicator values are improved by combining set-pair analysis and generalized regression neural network, so as to realize the automatic screening of economic quality evaluation indicators. According to the screening results of the indicators, the hierarchical analysis method is used to assign weights to the indicators, and the comprehensive index of economic quality is measured based on the results of the assignment.From 2012 to 2022, the economic quality of the 30 provinces in China shows an upward trend as a whole, and the comprehensive index of economic quality in 2022 is 0.90, which is an increase of 52.54% compared with that in 2012. The assessment results are consistent with the actual results, indicating that the method of this paper can effectively complete the measurement and assessment of the economic quality index, which is important for the study of economic quality.
The article builds a simulation system based on human physiological parameters, collects human physiological data through the human physiological model, and simulates human physiological signals. The load adaptability of trainers to aerobics training was explored by studying the changes in the SI values of T-lymphocytes of the subjects’ bodies during aerobics training. SPSS and independent samples t-test were used to analyze the exercise data of the experimental group and the control group, so as to verify whether the aerobics training has a good exercise effect. At Week 0, the SI values of T lymphocytes in the immediate post-exercise group and the 3-hour recovery group after exercise were 0.88, 0.61 and 0.70, respectively. In Week 2, it dropped to 0.34 and 0.49, respectively. At Week 6, the SI values of lymphocytes in the two groups were 0.60 and 0.30, respectively. The SI values of T lymphocytes in Week 0, Week 2, Week 4 and Week 6 in the quiet group were 0.88, 0.48, 0.80 and 0.50, respectively. Before the experiment, there was no significant difference between the experimental group and the control group in terms of exercise effect, and after the experiment, a significant difference was produced, and the exercise effect of the experimental group far exceeded that of the control group. The experimental group’s exercise effect improved by 6.43, 5.13, 6.91, 6.38, and 5.80 points on each of the five dimensions, a significant difference. The control group, on the other hand, remained essentially unchanged.
In today’s rapid development of information technology and big data technology, consumer behavior is undergoing a profound transformation. This study focuses on the decision-making stage of consumer journey, selects indicators based on webpage click stream data, improves the K-means algorithm, and realizes the identification of consumer journey nodes using the binary K-means algorithm. Based on the review recommendation scenario, from the perspective of consumer decision-making journey, we introduce the “attention-attitude-understanding-purchase intention” stage-based decision-making model, apply it to the model design of deep learning, and combine the attention mechanism and co-attention mechanism to propose a product recommendation method based on online reviews. The results show that consumers in clusters 1-4 are in the consumer journey nodes of attention, understanding, attitude, and purchase intention, respectively. The product recommendation model exhibits better recommendation accuracy and time efficiency, with accuracy improved by 18.72%~67.12% and time reduced by 8.39%~62.03% over the comparison method. This paper realizes the innovation of deep learning method with the support of consumer behavior theory, and improves the methodological technical support for accurate online marketing strategy.
The pattern design of clothing appearance is one of the important links in clothing design, which makes an important contribution to the overall aesthetics and sales of clothing. As a product of computer technology, the development and application of graphic processing technology has been extended to various industries and fields of society, especially in the field of design with more extensive use. However, the current clothing pattern design is still too dependent on the designer, so this paper is based on pattern processing, combined with fractal algorithm and genetic algorithm to build a pattern generation algorithm for clothing pattern. And the quality of the generated pattern is optimized based on the anti-alignment algorithm, so as to improve the overall quality of the generated pattern. After testing, the real-time generation speed of the pattern generation algorithms for clothing patterns in this paper is greater than 15FPS, and from the subjective and objective points of view, the generated patterns have good quality to meet the needs of use. After the anti-alignment optimization of this paper’s algorithm in different error intervals in the number of pixels accounted for the percentage of screen pixels are the highest, are more than 99%, to further validate the optimization effect of this paper’s method. Finally, in the evaluation of the use of the algorithm, the testers have a high degree of satisfaction with the dimensions of this paper’s algorithm, respectively, 4.04, 3.98, 4.21 and 4.11, which shows that this paper’s algorithm can satisfy the practical needs and can realize the intelligent generation of clothing pattern design.
The legal positioning of blockchain technology applied to evidence and its attributes are the basis for its evidence review and rule design. This paper starts from analyzing the evidence attributes of blockchain electronic data, combines relevant regulations and judicial interpretations, and clarifies the legal effect of blockchain electronic data. Combined with the judicial application of blockchain evidence at home and abroad, it points out the specialized review rules of blockchain evidence. Obtain the blockchain access evidence process, and propose the block file storage method based on RS code as well as the decryption outsourcing attribute-based encryption scheme with the same sub-policy to improve the CP-ABE encryption scheme. Explore the rules for blockchain deposits and clarify the rules and institutional value of blockchain deposits for admissibility. Analyze the theoretical and practical operational performance of the improved attribute-based encryption algorithm. Optimize the evidence storage capacity of blockchain, and analyze the performance of the blockchain technology scheme designed in this paper in the intelligent review of access evidence. In the forensic scenario run by the algorithm in this paper, the stored evidence data is reduced by 1417 characters, the transaction response time is shortened by 175.361ms on average, and the block size is reduced by about 4 times. It proves that the blockchain algorithm scheme proposed in this paper can shrink the cost of depositing evidence, reduce the time of depositing evidence, and improve the efficiency of depositing evidence in the public security forensic system.
As the material foundation of language, speech is the basis for mastering language skills and capturing language information, and English learning must begin with the correct mastery of spoken language. Therefore, spoken language teaching occupies a rather important position in English teaching. In this study, we extract various features such as time-domain features and frequency-domain features from English spoken audio signals, use fuzzy logic inference model to represent each audio feature mapping as an affiliation function, and then optimize the parameters of the affiliation function by using adaptive neuro-fuzzy inference system, and solve the affiliation function to get the result of speech matching by the center of gravity method. Subsequently, a speech evaluation system is designed based on the speech matching model to assist intelligent spoken language teaching. The results of teaching practice show that students in the experimental class using the voice assessment system as a learning aid are significantly better than the control class in terms of speaking skills and learning attitudes (P<0.05). Through real-time feedback and personalized practice, the voice assessment system enables students to correct pronunciation errors immediately and gradually improve their speaking fluency and accuracy. It can also improve students' self-efficacy and learning motivation. This study confirms the effectiveness of the fuzzy logic-based audio classification and speech matching model in improving students' spoken English proficiency and reveals its potential for wide application in future spoken English education.
Existing natural language generation models often face the problems of context loss and incoherent responses when dealing with multi-round dialogs. In this paper, a multi-round dialog system based on Transformer architecture is constructed, and an intention recognition algorithm is used to form a technical support for the construction of multi-round dialog system. And the attention adapter is introduced into the natural language generation module in the system, which utilizes contextual features to improve the performance of the natural language generation model. Semantic slot extraction experiments are carried out on the ATIS dataset, and the F1 values of the semantic slot extraction task and intention recognition task of the BERT multi-task natural language generation model with the addition of the attention mechanism are improved by 0.64% and 0.15%, respectively. The multi-round dialog system designed in this paper has a perplexity of 18.33, and the BLEU metrics are higher on orders 1-4 compared to other models. Manually evaluated in terms of syntactic semantic coherence, relevance, and information content, the system performs better. It shows that the natural language generation model incorporating the attention mechanism can effectively improve the application effect of the multi-round dialog system.
The continuous development of data analysis technology in the era of big data provides new methods for the analysis of college students’ ideological dynamic data, and also provides new ideas for the scientific construction of ideological and political education disciplines in colleges and universities. In this paper, based on the word frequency analysis, set up the keyword context, collect the keywords of the ideological dynamics of party members and students, extract and examine the feature vectors in them, put all the keyword feature vectors, form the keyword feature vector set, use the method of keyword vector research, carry out the descriptive and differential analysis of the survey data of the ideological dynamics of the party members’ development in the management of colleges and universities, and construct the party members’ ideological dynamics management mechanism based on the existing problems , analyzing the effect of management decisions in colleges and universities. The kurtosis values of the four indicators of party members’ political thoughts, learning thoughts, innovative thoughts and consumption thoughts are -1.4685, -0.4496, -0.9871 and -1.5614 respectively, which are on the low side, indicating that the performance of the respondents is more unified and concentrated in these four indicators, and laying a feasible foundation for the subsequent relevant analysis. In campus adaptation, self-efficacy, and life satisfaction, the number of party students who performed agreeably, correctly, and positively were 116, 202, and 142, respectively, and after the reform of college management decisions, the students’ performance in these three aspects changed.
Driven by big data, e-commerce platforms have accumulated massive user behavior data, which can be transformed into valuable information after cleaning and feature selection. This study analyzes users’ historical behavioral data on e-commerce platforms, constructs a gradient boosting decision tree prediction model based on user, product, category, and two-by-two interaction behavioral features, and extracts designed feature data from raw CSV data based on Hiv as the prediction basis of the model. At the same time, clustering analysis is performed based on the user’s purchasing behavior (dwell time, browsing frequency) to generate user profiles. The experimental results show that after 7 days, the purchase conversion rate of browsing, collecting, adding to cart and purchasing tends to 0. Therefore, the time window for purchase behavior prediction is chosen to be 7 days. In this paper, the prediction model is only trained to 20 epochs, and the Loss value converges to about 0.14, which shows a good training effect. The model has the best classification performance for user purchase behavior prediction, with precision, recall, and F1 values between 0.91 and 0.97. The clustering algorithm divides the user purchase behavior into four clusters, where cluster class 4 has the best user value. In summary, using the gradient boosting decision tree model, e-commerce platforms can more accurately predict user purchasing behavior, thus improving user experience and platform economic benefits.
The convergence of constitutional fundamental rights and administrative enforcement power should pursue multiple legal values, which requires that the operation of the power therein should be more division of labor than cooperation, and that constraints and synergies should be given equal importance. The article will construct the basic constitutional rights and administrative power into the constitutional construction of the subject and the subject of the executive branch, the introduction of the evolution of the game theory to construct the constitutional construction of the subject and the subject of the executive branch of the evolution of the game model, and the design of the game model of the gain function, the replication of dynamic equations and ESS equilibrium point. The initial value of each parameter in the evolutionary game model is set, and the evolutionary stable point of administrative power is simulated by MATLAB software, and the influence of the reward and punishment allocation coefficients on the evolutionary results of the system is explored. When the system evolution stable point strategy is (0,0) and (1,1), the two sides of the game tend to the stable equilibrium state of active cooperation, strengthened regulation and strict supervision. When the reward distribution coefficient and the punishment distribution coefficient gradually increase, the two sides in the evolutionary game system tends to stabilize the point (1,1) the faster the rate will be. In the process of constructing the fundamental rights of the constitution, combining internal and external with the administrative rights list monitoring mechanism can realize the optimal restriction on the application of administrative rights and promote the orderly and stable operation of administrative power.
This topic is based on the perspective of diagnostic evaluation, formative new evaluation, summative evaluation, in-depth analysis of deep learning to help the intelligent development of ideological and political education. The initially formulated questionnaire was modified several times, and the questionnaire design task was finally completed, and the formal distribution of questionnaires began to obtain the data for this study. At this level, the empirical research method combining quantitative research and qualitative research is used to deeply analyze the current situation of the intelligent development of ideological and political education in colleges and universities under the perspective of deep learning, with a view to contributing to the intelligent development of ideological and political education in colleges and universities. The mean value of the five dimensions of ideological and political education to stimulate the effectiveness of active learning (A1), promote the degree of in-depth understanding (A2), deepen the effect of interactive participation (A3), enhance the ability of higher-order thinking (A4), and expand the quality of the transfer of the use of the quality of the five dimensions (A5) is higher than 3.55 points, in addition to the significant difference in the characteristics of the samples (gender, academic qualifications, major, political profile), and puts forward five paths for the development of the five development paths, which are aimed at helping to promote the new era of the intelligent development of Civic and Political Education.
Intangible cultural heritage is an important part of national culture, carrying rich historical information and cultural value. This paper mainly generates digital media art based on the characteristics of intangible cultural symbols through fractal geometry, and builds a digital media communication mode of intangible cultural art on the basis of dynamic texture, so as to realize the digital protection and inheritance of intangible cultural heritage. On the basis of Transformer network model, further combined with fractal geometry technology, inductive bias lacking in Transformer is introduced from the perspective of translation invariance and locality, and the dynamic texture generation method of digital media art combined with fractal geometry and Transformer model is formed in this paper. Experimental results show that the application of translation invariance and locality can increase the ODS, OIS and AP indexes by 17.17%, 27.72% and 25.38%, respectively. In the self-built Miao pattern data set, this method can generate dynamic texture features of Miao culture and art more completely and clearly. At the same time, this paper can create digital media art for Miao embroidery patterns through the generated results of this method, and improve the audience influence and satisfaction of intangible cultural heritage.
Optimization problems usually involve multiple objectives, while fuzzy cognitive maps can effectively show the causal relationship between concepts, and the combination of the two can greatly advance the development of the education field. In this paper, we design a fuzzy cognition-based knowledge map for labor education courses and a multi-objective optimization model for labor education courses to optimize learners’ learning paths and recommend personalized exercises from multiple stages. Through teaching experiments and regression analysis, the teaching effect of the multi-objective optimization algorithm in labor education courses is evaluated. This paper borrows the k-means algorithm to classify learners into four clusters, and the algorithm provides learning path optimization for different clusters of learners in labor education courses. The exercise recommendation accuracy of this paper’s algorithm ranges from 0.91 to 0.97 and has better novelty and diversity recommendation performance. In the experimental class in the fuzzy cognitively oriented multi-objective optimization labor course, the learners’ labor scores improve faster and are about 3.8 points higher than those of the traditional teaching, and the regression results show that this paper’s model has a positive and positive effect on the teaching effect. The average satisfaction scores of this paper’s model in labor education courses for the friendliness of teaching aid, effectiveness of cognitive diagnosis method, usefulness of path optimization, and reasonableness of personalized recommendation of exercises are above 4.3, indicating that the model has practical application value in labor education courses.
As people’s demand for high-quality development of education becomes stronger and stronger, the field of education is paying more and more attention to the fundamental task of education by establishing moral values. In this paper, the improved genetic algorithm based on predation strategy is applied to the learning path recommendation system to realize the auxiliary teaching of Civics education. The study first proposes a Bayesian knowledge tracking model based on multiple interactions for knowledge tracking of students’ Civics and Political Science competence, and carries out model comparison and knowledge tracking visualization and analysis on three datasets and the real dataset of practice questions. Then according to the constructed learner model and knowledge connectivity state model, the personalized learning path construction model is designed by using learner features, knowledge point features, and generic learning paths as inputs, combined with the improved genetic algorithm based on predation strategy. The intelligent assisted teaching system designed in this paper is put into practice for Civics teaching and scored by questionnaire and paired t-test method. The results of the study said that the knowledge tracking model proposed in this paper compared with other models, the model in this paper improves the accuracy rate by 1%~2%. Using the non-elite individual set to enrich the population diversity to participate in genetic operation and iteration, the experiment shows that PSGA performs well in multiple comparisons with PSO and SGA methods, and can construct personalized learning paths more accurately, stably and effectively. The results of teaching practice show that the teaching system proposed in this paper can effectively improve students’ learning ability in Civics.
The further deepening of education informatization has led to a significant shift in teaching methods as well as learning tools, and it is of research significance to explore how to use online learning platforms more effectively in non-traditional teaching environments. In this study, after pre-processing the online teaching data of Marxist theory in the Civics course, the Squeeze method is used to extract the relevant features of teaching interaction behavior in the data. Convolutional neural network is used to realize the prediction of teaching interaction behavior based on the input features, so as to realize the real-time intervention and effect enhancement strategy of teaching interaction. It is verified that the ICAM-ResNet neural network prediction model proposed in this paper has a good effect in making online teaching interactive behavior prediction, and the prediction accuracy can reach 0.816. After implementing the intervention strategy according to the prediction results, the average online learning time of students increased from 30.61 min (1 class period) to 44.54 min (16 class periods), and most of the students would actively answer the questions in the classroom, and the rate of answering correctly increased, so that the effect of teacher-student interactions was substantially improved. On the one hand, this study provides a new way of thinking for the teaching research of Marxist theory course, on the other hand, the results of the study are conducive to optimizing the teaching practice of the course and promoting the teaching interaction, so as to promote the development of the teaching of the course.
The concept of urban green development promotes the development of intelligent technology as a new energy power, so that digital intelligent technology continues to enter into people’s vision, but also gradually accepted by the people. However, there is a lack of research on intelligent perception that focuses on residents’ attitudes, so this paper takes the theory of perceived value as the basis to analyze the influence path of intelligent perception of urban green space. Based on structural equation modeling, this paper explores the relationship between intelligent perception and residents’ attitudes in terms of perceived functional value, perceived emotional value, perceived social value, cognitive value and perceived risk. Then the intelligent perception prediction model for urban green space is constructed by using variational modal decomposition (VMD) combined with support vector regression (SVR), and the actual performance of this paper’s model is examined through experiments. This paper takes City Y as an example for prediction, and the results show that the intelligent perception of green space in City Y from 2023 to 2026 continues to show an upward trend. In addition, in order to prove the superiority of this paper’s model, its MAE, MAPE, RMSE and IA are compared with the prediction models of ARMA, BP, SVR and RF, respectively, and this paper’s model achieves the best results with the values of 4.2495, 15.8082, 3.5247 and 0.5225 for each index. In conclusion, the prediction model proposed in this paper has high accuracy in intelligent perception prediction.
The big data environment is dynamically changing, so the multi-objective optimization algorithm for the integration of English translation information technology needs to have dynamic adaptability. In this paper, we first construct a multi-objective learning parameter model for English translation information technology. Then a reference point-based environment unpredictable dynamic multi-objective optimization algorithm (UDERP) is proposed to realize the dynamic adaptability of the multi-objective optimization algorithm. Finally, the designed English translation information technology incorporating the UDERP algorithm is simulated and tested. The performance of UDERP algorithm, DNSGA-II algorithm and DSS algorithm are compared with each other using three test functions of FDA series. When the environment changes the optimal solution derived from the algorithm proposed in this paper is closer to the real Pareto solution. Comparing the neural machine translation based on cross-language pre-trained language model and the neural machine translation based on multi-coverage model, the English translation information technology designed in this paper has a better convergence effect and can realize more accurate parameter estimation.
Fintech has not only greatly improved the operational efficiency of banks by introducing cutting-edge technologies such as big data, artificial intelligence, and blockchain, but also posed new challenges to banks’ risk management. This paper uses Monte Carlo simulation to explore the impact of fintech on banks’ operational efficiency and risk management. A VaR data model is used to analyze the impact of fintech on the operational efficiency of three types of commercial banks: big five banks, joint-stock banks, and city commercial banks. The non-performing loan ratio of China MS Bank is used as the empirical object for quantitative analysis of bank risk management. Monte Carlo simulation is used to realize the VaR calculation of banks’ NPL ratio. The empirical analysis finds that the impact of fintech on the operational efficiency of all three types of commercial banks is relatively significant, but there are differences in direction and lag period. Meanwhile, FinTech increases banks’ NPL ratio. It shows that fintech has a negative impact on bank risk management, for this reason, this paper develops relevant strategies to deal with the risk challenges brought by fintech according to bank types.
Deep reinforcement learning, as an advanced machine learning method, is capable of automatically learning optimal decision-making strategies in complex environments. The core objective of this paper is to apply deep reinforcement learning algorithms to SolidLab’s microcontroller programming in order to realize the intelligent control of the linear one-stage inverted pendulum system. The study takes the linear one-stage inverted pendulum produced by A Technology Company as the control object, and adopts the model-free control structure of the deep reinforcement learning algorithm to build the controller and conduct virtual simulation experiments. Comparing the experimental effects of LQR and DQN algorithms, the LQR algorithm is better than the DQN algorithm in stabilizing pendulum control of inverted pendulum. Accordingly, a balance controller based on the offline Q learning algorithm is further designed to realize the inverted pendulum stabilization in kind. After optimizing the design strategy, the inverted pendulum system can be rapidly stabilized within 0.9s when it is perturbed by a small angle of about 12°. It shows that the method in this paper can realize the intelligent control of the inverted pendulum system at the linear level.
The steady development of economy makes the number of high-rise buildings increasing, and the water supply and drainage system becomes an important part of high-rise building construction. The purpose of this paper is to explore the drainage efficiency and fire fighting efficiency of BIM and green new energy in water supply and drainage system. Revit Mep software is mainly used to design the fire fighting module in the drainage system of high-rise buildings by combining BIM technology and new energy. A simulation mathematical model of water supply and drainage efficiency (including fluid control equations, etc.) was designed, and simulation experiments were conducted on the fire treatment efficiency of green new energy fire protection technology. The investigators’ ratings of the evaluation indexes of the water supply and drainage system design of BIM synergistic green new energy fire protection technology ranged from 4.06 to 4.54, indicating the feasibility of the approach. When the building floor is 25 floors, the water supply and drainage efficiency of the system in this paper is 80.45% and 84.52%, respectively. The fire fighting simulation experiment shows that the green new energy fire fighting technology integrating BIM can reduce the temperature of the room to 200~500℃ in 5 minutes. In the experiment, the method can extinguish the fire quickly and the residual concentration of smoke after extinguishing can be reduced to the normal range.
The purpose of this paper is to explore the effectiveness of integrated energy electric energy substitution in agriculture in the environment of energy saving and emission reduction. The fuzzy clustering algorithm is used to divide different family clusters according to the energy saving and emission reduction ability based on the use of comprehensive energy in agricultural enterprises. Based on the exponential smoothing method and equivalent calorific value method, a prediction model of electric energy substitution efficiency was constructed. Combined with the ANP method, the evaluation indexes of agricultural electric energy substitution efficiency were weighted and graded. In this paper, 100 agricultural enterprises are divided into 5 clusters according to their energy saving and emission reduction ability, which are optimization level, managed level, development level, pressure-based level, and initial level. The model of this paper predicts that the consumption of terminal coal, oil, and electric energy of agricultural projects in 2027 is between 31663.68 and 7447.991.67 million tons of standard coal, and that the electric energy substitution in the same year can be up to 693,755.69 million tons of standard coal. The comprehensive scores of the first-level indicators of economic benefit, environmental benefit, and social benefit are 91.06, 91.26, and 92.01 in order, and the comprehensive efficiency grade is “Excellence”. To summarize the results, this study suggests increasing the investment of electric energy substitution equipment in agricultural production to promote the synergistic development of integrated energy system and electric energy substitution strategy.
This paper describes the teaching problems and methods that need to be solved for the innovation of education mode from the needs of teaching conditions, teacher strength, school-enterprise cooperation, etc., and puts forward the “three lines and four passes” education mode based on situational teaching workshop for geology majors. The evaluation index system is constructed for the quality of “three lines and four passes” education model, and various colleges and universities in a certain province are selected for empirical analysis to analyze the quality of education for students in the province from three aspects. In order to determine the contribution size of the 10 secondary indicators in the education quality evaluation index system, the method of determining the weights of the indicators by using principal component analysis was used to calculate the education quality indexes of the public higher vocational colleges and universities and private higher vocational colleges and universities. The K-means cluster analysis method was performed on the basis of the hierarchical cluster analysis method, and seven tiers were divided. The analysis results show that professional education, facilities and equipment, teacher-student cooperation, group competitions, practical exercises, talent cultivation and vocational training have a greater weight of 5% in the evaluation index system of public and private institutions. In conclusion, it is concluded that the “three lines and four passes” education model based on master craftsmen workshop proposed in this paper has a better teaching effect on the quality of education.
Robotic process automation (RPA) technology along with the rapid development of information technology is increasingly widely used in various industries. This paper mainly explores its application in the field of electric power, and utilizes RPA technology to improve the quality and efficiency of power marketing audit. In order to solve the data anomaly problem in the process of power marketing audit, this paper adopts K-means algorithm to cluster the anomalous data, and combines with the correlation calculation to realize the identification and monitoring of the anomalous data of power marketing audit. Applying RPA technology to intelligent power marketing audit, we learn the normal pattern of data by training the self-encoder network, and correct or reject the abnormal data monitored. Reinforcement learning is used to optimize the audit strategy of RPA technology, and the efficiency of the audit is improved by maximizing the cumulative rewards. The application of RPA technology significantly improves the efficiency and accuracy of the overdue prediction and the work order generation and dispatching in the electric power marketing audit, in which the average working time of the overdue prediction work is reduced by 94.92% after the application of RPA technology, the average accuracy is improved by 21.80%, and the average working time of the work order generation and dispatching process is reduced by 21.80%, and the average working time of the work order generation and dispatching process is improved by 21.80%. The average working time of work order generation and dispatching process is reduced by 97.99% and the average accuracy rate is increased by 14.54%. The application of RPA technology effectively improves the efficiency and quality of power marketing audit.
In this paper, the entire chord progression is added to the generation process through a bidirectional LSTM model, and the Skip-connection method is used to accelerate the convergence speed in all recursive layers except the first one. Different musical emotions are classified based on the Hevner emotion model, and features such as pitch, duration, and tempo of musical emotions are parameterized. The forward neural network is used to construct the music emotion classification model, and the gradient descent learning algorithm is used to algorithmically control the forward neural network model. At the same time, this paper explains the significant enhancement of college students’ self-identity by music aesthetic education based on neural network model from two perspectives: theoretical research and empirical analysis. The results show that the music generation and music emotion classification models constructed based on the neural network algorithm in this paper show good performance in the experiments. After applying the neural network model containing music generation and music emotion classification to music aesthetic education and counseling college students on self-identity, the mean score of self-identity scale of students in the experimental group increased from 50.83 to 88.56, with an improvement of 75.78%, and the results were significant at the 1% level. The effectiveness of this paper’s method in enhancing college students’ self-identity is fully demonstrated.
VTOL uavs combine the advantages of VTOL capability of multi-rotor uavs and efficient fixed-wing cruise, but they also face challenges in performance, including weak wind resistance when hovering, low flight mode conversion efficiency and high-altitude fluctuation during conversion. In view of this, this paper introduces a new type of composite wing UAV, namely lifting wing quadrotor. Compared with quadrotor UAV, it is unique in that it is equipped with a lifting wing installed at a special Angle, which effectively improves the range and load, and solves the problem of weak wind resistance in the hovering stage of tail-seat UAV, and can realize efficient transition flight. A longitudinal position controller based on TECS total energy control algorithm is designed according to the flight characteristics of the transition stage. The effectiveness of the dynamic model and controller design is verified by experiments. The results show that the control algorithm can effectively improve the flight stability of the lift-wing quadrotor during the transition stage.
The article evaluates and predicts the effectiveness of medical English teaching through stepwise regression analysis in multivariate analysis method. It constructs an analytical prediction model of medical English teaching evaluation based on multivariate regression analysis. After initially establishing the evaluation index system of medical English teaching, effective evaluation indexes are screened out through stepwise regression, an effective evaluation index system of medical English teaching is constructed, and multiple regression equations for the quality of medical English teaching (students’ performance in medical English) are established. The prediction model of medical English teaching quality is constructed by eliminating the influential factors and abnormal data that do not have significance through multiple linear regression analysis. Teaching quality prediction equations were constructed by choosing teaching content, teaching method, teaching organization, teaching expression, teaching attitude, and overall effect of teaching. Among them, teaching content and teaching expression were significant, and the final prediction model of medical English teaching quality was Y=0.1958+0.1142*teaching content+0.7232* teaching expression. The 79.26% of students’ medical English performance can be explained by the multivariate linear regression analysis model.
Dance is interpreted through human body movements, dance movements can express the thoughts and emotions of dancers, and whether the dance movements are standardized or not in the creation of dance drama determines the quality of the creation of dance drama. In this paper, with the support of artificial intelligence and information technology, based on image recognition technology, we carry out the optimization research of dance movement recognition for dance drama creation. In this study, the principle and process of image recognition technology are first studied in depth, and then the motion detection method for dance movements is analyzed considering the static state of the background of the dance drama. On this basis, the recognition optimization of dance movements is completed based on the deep convolutional embedding attention mechanism, and the evaluation method based on recognition optimization is proposed for the creation of dance drama. The embedding method in this paper improves 12% over the baseline method, with an OA of 98.65%, while the amount of participation and FLOPs increase slightly. And the score1 and score2 of this paper’s method are the highest, which indicates that this method obtains a high model accuracy while sacrificing less number of model parameters and computational complexity. In addition, the network model structure of this paper is more efficient compared to other network model results. In the recognition effect analysis, the correct recognition rate of six standard dance movements such as center of gravity transition, time step, square step, lock step, fixed step and others are above 80%, with high recognition accuracy and excellent model performance.
Dance drama performance is an important part of human civilization, which runs through the long history of human development. With the continuous improvement of people’s spiritual and cultural needs, the pursuit of dance theater performance has become more and more intense. In this paper, we start from the basic knowledge of fractal geometry, such as the theory, concept, dimension, and generation mechanism of fractal, to launch the research on the optimization of the spatial layout of dance drama performance. The main work of this paper involves the following aspects: (1) It focuses on the definition and characteristics of fractal geometry, and deeply studies the application of fractal geometry in the optimization of the spatial layout of dance drama performances. (2) The demand for dance drama performance space optimization is analyzed in terms of developmental changes and audience demand. It can be seen that the footprint of the dance performance continues to increase, but the use of space is gradually decreasing, and the audience has higher expectations for the stage performance, the demand for the development of the dance performance and the audience demand to promote the process of optimization of the space layout of the dance performance. (3) This paper adopts the left-right symmetrical presentation, fully considers the visual effect of the viewers, and constructs the optimized layout of dance drama performance space based on fractal geometry. (4) The optimized layout method of this paper is applied to the actual evaluation of the effect, in which the two groups A and B, located in the main audience area, have higher overall ratings, respectively 5.78 and 6.04, and the main audience area has a better perspective experience, so the overall experience is relatively better. The score of the viewer experience after optimization of the dance drama performance space layout in this paper is 5.715 points, which indicates that the viewers are satisfied with the effect of optimization of the layout in this paper, and the better results of the optimization of the dance drama performance space layout in this paper provide some reference for the dance drama performance space layout.
The stability of slopes is related to many factors, among which rainfall and water table fluctuation are the most common natural factors leading to slope damage. Based on a non-homogeneous slope, numerical simulation analysis using FLAC3D software and intensity discounting method was conducted in the article to discuss the stability of the slope under different morphologies, to explore the influence of diving surface height and pit water level line on the slope stability, to put forward the support scheme and to carry out the effect test. The analysis shows that the increase of slope gradient, step length and slope height negatively affects the slope stability, among which the effect of slope height is the most significant, and the stability coefficient decreases by 49.61% when the slope height increases by 60m under the action of groundwater. The height of diving surface and pit water level line are both inversely proportional to the slope stability, and the decrease of slope stability produced by the increase of both is 10.53% and 42.06%, respectively, and the latter’s influence on the slope safety coefficient is much larger than the former. In addition, the comprehensive landslide prevention and control program, which adopts the construction of drainage facilities, the selection of drainage scheme and the strengthening of support at the rock stratum interface area, effectively improves the safety coefficient of the slope.
Accelerating the formation of new-quality productivity is a must for transforming the mode of economic growth and realizing high-quality development. Taking the new quality productivity as the research perspective, this study selects the panel data of 30 provincial-level administrative regions in China from 2013 to 2023 as the research object, combines the regression analysis method to empirically analyze the influence paths between high-performance computing, the new quality productivity, and the economic policy innovation, and explores the role mechanism of the new quality productivity through the mediation effect analysis. The results show that the regression coefficients of high-performance computing on new quality productivity and economic policy innovation, as well as on new quality productivity and economic policy innovation, are all greater than 0 (p < 0.01), i.e., high-performance computing accelerates the formation of new quality productivity and further promotes the development of economic policy innovation. In the economically developed eastern region, this path of action is significant at the 1% level, and its driving effect of HPC accelerating new quality productivity on economic policy innovation is stronger, compared to the central, western and northeastern regions, which are significant at the 5% level. The article's analysis advances the understanding of the drivers of new quality productivity development and the effects, mechanisms, and regional differences of high-performance computing-enabled new quality productivity and economic policy innovation.
Under the rapid development of China’s infrastructure, concrete materials are widely used. Traditional concrete materials have defects such as poor compressive performance, and steel fibre concrete has a broad engineering application prospect. In this study, the compressive performance of steel fibre recycled concrete was analysed and tested using experiments such as cubic compression test and split tensile compression test. Subsequently, the compressive strength prediction model of steel fibre recycled concrete is constructed by combining the experimental test results and the uniaxial compression constitutive model, and the prediction effect of the model is analysed. The results show that when the volume rate of steel fibre admixture in recycled concrete is 1.2%, the compressive strength of recycled concrete is the highest under different loading conditions, indicating that the admixture of steel fibre can improve the compressive performance of recycled concrete. It was also found that the prediction error of the prediction model for the compressive strength of concrete under standard curing conditions and low-pressure conditions averaged 1.49% and 1.19%, which has good prediction effect. The compressive strength prediction model proposed in this paper can achieve reliable prediction of the compressive properties of steel fibre recycled concrete, which lays a foundation for the reasonable use of recycled concrete materials under different conditions in infrastructure projects.
Starting from the concept of digital media art, virtual reality technology is used to complete the design of the virtual simulation environment, and the interaction function of the virtual simulation environment is perfected through relevant development software. With the support of artificial intelligence technology, we propose a strategy to improve the visual expression of digital media art based on support vector machines, and we also design a detailed implementation process. The subjects of this study were selected to evaluate the design of the virtual simulation environment and the visual expressiveness enhancement strategy using the scale test method. The experimental group was significant in the dimensions of integration (P=0.005, T=1.553), immersion (P=0.007, T=2.693), interactivity (P=0.001, T=0.867), and virtuality (P=0.002, T=3.581) before and after the intervention, and it was concluded that Support Vector Machines have an enhancement effect on the visual expressiveness of the creation of digital media art.
Cross-border e-commerce refers to a kind of international commercial activity in which trading entities belonging to different customs borders reach transactions, make payments and settlements through e-commerce platforms, and deliver commodities through cross-border logistics to complete the transactions. Based on the relevant components of cross-border logistics cost and risk, this paper applies Markowitz, Capital Asset Pricing Model (CAPM), Value at Risk Model (VaRM), and Creditmetrics model to measure the risk of cross-border logistics, respectively. Through the cost measurement of cross-border logistics losses, a simplified logistics risk cost minimization model is derived. The model is applied to Guangxi’s cross-border logistics company M. Monte Carlo simulation is used to estimate the risk and cost of its cross-border logistics, respectively, and the probability of IRR>13.246% is simulated to be 32.963%, which indicates that the probability of cross-border logistics results exceeding the IRR of 13.246% given in the economic analysis is 32.963%. It can be seen in the logistics cost estimation that the mean value of the monthly logistics cost estimation of Company M is 2764000.564 yuan, and the standard deviation is 15126.36321 yuan, and after 3000 simulation operations, the logistics cost estimation has a 95% probability of falling on the interval [2572000 yuan, 2964000 yuan]. In response to the results of the simulation operations, a logistics risk and cost control strategy is proposed that is consistent with the long-term development of M Company.
In the process of airborne LiDAR point cloud cable line extraction, there are problems such as complex shape of the pole tower and high noise influence, which lead to low accuracy of cable line point cloud extraction. This paper proposes a cable line point cloud extraction and reconstruction method based on point cloud chunking processing, improved multidimensional filtering, and density clustering algorithm. Firstly, the point cloud filtering data processing technology, and its three key techniques of streamlining, filtering, and alignment in point cloud data preprocessing are introduced. Secondly, the overall point cloud is processed in chunks according to the direction of power lines. Then, on the basis of surface fitting algorithm, the idea of grid division is introduced to propose an improved multidimensional filtering algorithm with point cloud filtering. Finally, the cable line point cloud is accurately extracted by the given adaptive density clustering solution, and the method of this paper is tested and evaluated for accuracy based on the measured point cloud data. The results show that: using the algorithm to extract the cable line points of the integrated integrity rate of 95.9796% or more, a time can be realized in the successful extraction of the power line, in order to ensure the accuracy of the extraction at the same time to improve the extraction efficiency, the research in this paper can be for the intelligent inspection of the cable line to provide a good value of engineering applications.
Cables are widely used in power transmission, and the measurement of key dimensions of cables is an indispensable part of the cable preparation process to help ensure their quality. In this article, a handheld laser 3D scanner is used to collect 3D point cloud data of cable dimensions, and the point cloud is denoised by bilateral filtering algorithm and combined with the direct method of coarse alignment and the ICP method of fine alignment to realize the alignment of 3D point cloud data of cables. Then, the cable diameter coordinates are obtained by fitting the cylindrical surface of the cable size to realize the calculation of the cable diameter, and a residual network-based edge detection model of the cable insulation layer is proposed to improve the feature extraction capability of the cable 3D point cloud data through the hollow convolutional residuals and the spatial attention mechanism. For the effectiveness of the above method, the cable 3D point cloud data is quantitatively verified. The average accuracy of the cable diameter calculation based on cylindrical surface fitting is 0.025 m. The AP value of the cable insulation layer edge detection model constructed based on residual network is 0.816, and the error range of the calculation results is between -1.46% and 1.44% when the cable insulation layer thickness is calculated based on the cable insulation layer edge detection results. Learning and analyzing dimensional features of cable 3D point cloud data by deep learning training model can significantly improve the measurement accuracy and measurement efficiency of cable dimensional features, which can provide a guarantee for improving the safety of cable operation.
Higher vocational colleges and universities should realize the optimal allocation of teaching resources to provide the necessary guarantee for the improvement of talent cultivation quality. The study puts forward the evaluation index system of teaching resource allocation for teaching resource allocation in higher vocational education, constructs the multi-objective allocation optimization model of teaching resources on this basis, determines the index weights by using the objective combination assignment method combining the principal component analysis method and entropy weight method, and applies NSGA-II algorithm to solve the model. Simulation analysis is carried out with several higher vocational colleges and universities in a city as an example, and the allocation optimization results of multiple teaching resources in higher vocational colleges and universities are obtained. After the optimization of resource allocation, the utilization efficiency and allocation efficiency of teaching resources in each college and university as a whole have been improved by 16.6% and 3.4%, respectively, and all of them tend to be in the state of equilibrium of allocation. The constructed teaching resource allocation optimization model can realize the optimization of teaching resource allocation and promote the reasonable allocation and utilization efficiency of teaching resources in higher vocational education.
Common wealth is the essential requirement of socialism, and it is also the goal that countless people have been relentlessly pursuing for thousands of years, and people have never ceased to earnestly aspire to and relentlessly pursue for equal enjoyment and common wealth. This paper studies the impact of coastal wetland tourism resource protection on the realization of common wealth. It analyzes the tourism resources of coastal wetland from the aspects of economic value and resource protection and utilization strategy. On this basis, a multilevel regression model is used to analyze the impact between the two.The tourism economy of each coastal wetland developed rapidly in 2023, which increased by 65%~97% compared with 2015, implying that the conservation and utilization of tourism resources can lead to economic growth and promote economic development. In the multilevel model, resource protection strategy expenditure (0.070), ecological condition (0.265), strategy realization channel (0.053) and institutional trust (0.166) all show significant effects on the level of common wealth. While the regional level variables GDP per capita, provincial ecological level, and tourism resource utilization have unstable effects on the common wealth of urban residents. Based on the multilevel regression model, this study investigates the influence mechanism of tourism resources protection and utilization strategy on common wealth, which provides a basis for the full development of the positive effect of coastal wetland tourism resources protection and utilization strategy on the realization of common wealth.
In the era of digital imaging, the art of photography has undergone profound changes, in which the calculation method of temporal art and symbolic space has become the key to understanding this art form. This paper analyzes the temporal form of photographic art and designs the symbolic space in photographic art, using digital photography technology based on drone remote sensing combined with collage photography technology. And through a variety of calculation methods, the time consciousness and symbolic space of the creative works are quantitatively embodied. The resolution of the photographic works obtained by scanning drone photography and surround photography is 9.448 and 9.966mm respectively, and the error in plane and height is low. The use of collage technology to express different emotions is demonstrated by the audience’s recognition score of more than 4. Digital technology embodies the time consciousness and symbolic space of photography, “storytelling”, “composition and perspective”, “light and color”, with an average increase of 16.9%, 20.36%, and 13.06% respectively. The regression results show that “image capture and processing”, “post-processing”, “high resolution and color reproduction”, “autofocus”, “Digital Signal Processing” can all contribute to the time-conscious and symbolic spatial embodiment of photographic art at the 0.001 level.
Tang Dynasty costumes are regarded as a brilliant brushstroke in the history and culture of Chinese costumes, and the fate of the whole Tang Dynasty can be analyzed through the evolution of Tang Dynasty costumes. In this paper, we have constructed a dress semiotics system from the social level, psychological level and cultural trait level, through the transfer of the imagery dress structure to the real dress, to express its symbolic meaning, and applied the constructed system to the Tang dress symbols, to interpret the meaning of the Tang dress symbols from the two levels of society and culture. Using CiteSpace information visualization software, combined with the literature of “Tang Dynasty Costume”, the study explores the dynamic evolution law of Tang Dynasty costume culture. The results of the study show that the earliest year for the keyword “Tang Dynasty costumes” is 1985, and the frequency is as high as 68 times. The keywords with the highest degree of centrality are dress and Tang Dynasty culture, both of which are 0.38. A number of new keywords with strong salience emerged in 2013-2023, among which the ones with a sudden increase of intensity greater than 5 are artistic features, clothing styles, clothing colors, clothing shapes, sweater, structural design, cheongsam, and knitted fabrics, and therefore the future hotspot of the Tang Dynasty dress research shifts to these keywords.
The combination of deep learning and digital media technology provides great scope for content creation. The article uses Generative Adversarial Network (GAN) in deep learning for content generation. Based on the three major forms of digital media content (image, audio, and video), image, audio, and video are generated by U-Net_GAN model, MAS-GAN model, and SSFLVGAN model, respectively, to construct a digital media content generation model based on generative adversarial networks. Subsequently, the model is validated for performance and the generated images, audio and video are evaluated for effectiveness. By studying the shortcomings of digital media content generation, we propose suggestions to improve its dissemination effect. The U-Net_GAN model outperforms other image generation models in all the indexes of generating images. The performance of speech generation and enhancement of MAS-GAN is much better than other audio generation and enhancement models. The average score of HDR video generated by SSFLVGAN is 4.20, and the average DMOS score is 5.97. The average DMOS score of SSFLVGAN is 5.97. DMOS score is 5.97, which are both 0.16 points higher than the traditional scheme. SSFLVGAN and the traditional scheme are comparable in terms of the picture impact of the generated video. The picture detail effect of the SSFLVGAN generated video is much better than the traditional scheme.
Based on the realization path of new quality productivity on industrial transformation and upgrading as well as talent supply, this paper carries out theoretical analysis in three directions, namely, direct effect, indirect effect and non-linear characteristics, and puts forward relevant research hypotheses. The panel data benchmark regression model, mediation effect model and threshold regression model are constructed respectively to verify the proposed hypotheses. The panel entropy method is used to measure the level of new quality productivity and carry out related research on the driving of new quality productivity. The partial differential decomposition of the spatial Durbin model shows that the direct and indirect effects of the new quality productivity are significantly positive at the 5% and 1% levels, respectively, indicating that the new quality productivity can promote the upgrading of industrial structure in the theoretical provinces. Introducing the variable of technological innovation for the mediation effect test, by observing columns (1) and (2), the regression coefficients of new quality productivity on technological innovation and new quality productivity on industrial structure upgrading are both positive at 0.2484 and 0.2048, respectively, which indicates that regional technology plays a mediating effect in the influence of new quality productivity on industrial structure upgrading.
With the wide application of deep learning in the field of education, student emotion perception has become one of the research hotspots. The study recognizes learners’ facial expressions by face detection algorithm after collecting learners’ data and preprocessing. Algorithms such as convolutional neural network and ConvLSTM are used to recognize learners’ emotions, and learners’ emotions are constructed to be modeled. Evaluate the learner emotion performance of this paper’s model and compare it with other emotion recognition models. The model of this paper is used for practical research to collect students’ emotions in six classes, and statistics and analysis are performed. Finally, by studying the relationship between students’ emotions and behaviors, targeted suggestions for improving students’ behaviors are proposed. The accuracy of this paper’s model in recognizing student emotions on the RAF-DB dataset and classroom dataset is 90.32% and 97.65%, respectively, which is much higher than that of other pre-trained models. The recognition accuracy of this paper’s model for eight types of student emotions is between [0.93, 0.98]. In the statistics of classroom students’ emotions, the main emotions of students in session 1 were concentration, in session 2 were surprise and concentration, in sessions 3, 4, and 6 were surprise and delight, and in session 5 were concentration and disappointment. Focus was significantly positively correlated with “serious attendance”, “thinking”, “answering questions”, “discussing” and “doing tests”, tiredness was significantly positively correlated with “answering questions”, “reviewing” and “deserting”, boredom was significantly positively correlated with “answering questions”, “doing quizzes”, “reviewing” and “desertion”, doubts were significantly positively correlated with “discussing”, “doing quizzes” and “reviewing”, distraction was significantly positively correlated with “reviewing” and “desertion”, happiness was significantly positively correlated with “discussion”, and disappointment was significantly positively correlated with “desertion”.
Under the background of mediatized society, the fusion of reality and reality between the real world and cyberspace has made the role of social network public opinion more and more significant, and the occurrence of any major emergencies will trigger network public opinion. In this paper, the TF-IDF algorithm is used to extract the feature items of social media opinion data, synthesize them into text vectors and input them into the LDA topic model to mine the opinion topic words, and then combine the co-occurrence of the key topic words to draw the semantic maps of the opinion topic words on the web, so as to explore the dynamic evolution of the opinion topic words. The opinion text vectors are then used as inputs to extract the local features of the opinion text through CNN model, combine with BiLSTM model to obtain the global features and temporal information of the opinion text, and realize the dynamic prediction of opinion sentiment through SoftMax classifier. Taking the Xin Guan epidemic event as an example, and divided into three phases: latent period, outbreak period and recession period, the number of public opinion comments on microblog platform during the outbreak period can reach 1942.59 comments/day, and the evolution of public opinion topic words in different public opinion phases are dominated by themes such as “epidemic”, “pneumonia” and so on. When the CNN-BiLSTM model is used to predict the public opinion sentiment dynamics, the prediction accuracy is between 95.84% and 97.56%. Through the effective use of deep learning technology, it can clarify the orientation of public opinion development driven by social media data and provide reliable data support for social media public opinion guidance.
In order to realize the strategic goal of environmental protection and low carbon, designing a set of resource clustering and regulation strategies that take into account energy saving and operating costs has become a research challenge for virtual power plants. In this study, the ICEEMDAN-CNN-SSDAE hybrid model is used to realize high-precision prediction of electricity price and load data in virtual power plants. The objective function and constraints of resource clustering and cooperative regulation of virtual power plants are established under the condition of demand response, and solved by Markov process. Finally, the virtual power plant resource clustering and co-regulation model is constructed on the basis of the deep reinforcement learning model framework by combining the prediction model and objective function. The results show that the ICEEMDAN-CNN-SSDAE model proposed in this paper can guarantee high prediction speed (0.062s and 0.059s) while having high prediction accuracy. It is also found that the average capacity of the output power of each component in the virtual power plant system after the model clusters and optimizes the regulation of a virtual power plant resources increases by 0.535-0.686 MW/h compared with the pre-optimization period, and the economic efficiency and energy utilization are also improved to different degrees. The research in this paper verifies the rationality and effectiveness of the proposed model, and provides certain theoretical basis and guidance method for virtual power plant resource clustering and cooperative scheduling.
Based on the overall demand analysis of intelligent class scheduling system, this paper determines the overall structural design scheme of intelligent class scheduling system, and realizes the intelligent class scheduling system using software development language. Aiming at the problems of overfitting and easy to fall into the local optimum of the benchmark genetic algorithm, the adaptive genetic algorithm optimization in the intelligent scheduling system is realized through the nonlinearization of the fitness function, the crossover operator, and the variational operator. Determine the experimental environment and set up groups (experimental group and control group) to evaluate the optimization performance of the algorithm and the application effect of the system. The program based on Improved Adaptive Genetic Algorithm (IAGA) (class time distribution balance: 0.79) is 0.23 higher than the program based on Adaptive Genetic Algorithm (AGA) (class time distribution balance: 0.56) in terms of class time distribution balance, and IAGA algorithm is more effective and superior in solving the problem of class scheduling in colleges and universities as compared to AGA algorithm. This system can reduce the heavy workload of teaching affairs, and also solve the scheduling difficulties of colleges and universities in the case of teacher shortage.
In this paper, the CD4511 chip is selected as the focus of this research to build the LK8820 platform, which mainly consists of the power supply, interface and reference voltage board (IV), power supply and measurement board (PM), digital function pin board (PE), and analog function board (WM). The input and output pins of the CD4511 IC chip are connected to the PIN pins on the PE board of the LK8820 test platform for testing, and the test functions are written in the C language environment. After the test program is written, the LK8820 test platform is used to test the CD4511 integrated circuit chip for the environmental adaptability of electrical parameters. The use of highly integrated chip CD4511 makes the small range of measurement accuracy is very high, but the large current range error is relatively large, due to the external large current range using precision resistors with an accuracy of 0.5% in parallel, after calibration, the error is controlled within the allowable range. 6 input pins of the input high level test results and the input low level test results are in the range of RMS, the number of anomalies is 0, which meets the IC electrical parameters environmental adaptability test. The test results of input high level and input low level of 6 input pins are all within the RMS value range, and the number of abnormalities is 0, which meets the requirement of environmental adaptability test of integrated circuits.
This paper proposes an accounting statement evaluation model based on hierarchical analysis algorithm (AHP)-fuzzy comprehensive evaluation (FCE) under the theory of combinatorial mathematics. The initial evaluation index system is determined based on the principles of evaluation index system construction, and after the Delphi method screening, the final accounting statement evaluation index system is composed of 14 secondary indicators and 5 primary indicators. Using hierarchical analysis algorithm to calculate the weights of the indicators, and substituting the calculated weights into the comprehensive fuzzy matrix to finalize the task of evaluation and analysis of accounting statements. The first-level indicators are Solvency A2 (0.1680) < Profitability A1 (0.1797) < Operating Capacity A4 (0.1971) < Cash Capacity A5 (0.2093) < Development Capacity A3 (0.2459), while the weights of the second-level indicators are distributed in the range of [0.0174, 0.2079]. The comprehensive evaluation score of the accounting statement of X Breweries Group Company is 73.31, indicating that the overall condition of the company's accounting statements is good.
In the field of modern architectural design, the application of artificial intelligence technology and pictorial interaction design is gradually causing revolutionary changes. This paper explores how to integrate these advanced technologies into architectural space design with a view to improving design efficiency and enhancing user experience. Artificial Intelligence Generated Content (AIGC) technology and BIM+VR technology are applied to architectural space design in general, where BIM+VR technology is used for visual modeling of architectural space design solutions and realizing 3D image interaction with users. Specifically, this paper proposes an intelligent assisted design method for architectural space based on Pointnet++ deep learning neural network and a simulation design method for 3D virtual architectural space to realize intelligent and personalized design of architectural space.The average class accuracy and overall assessment accuracy of Pointnet++ trained assessment points reached 83.47% and 76.63% respectively The design scheme given by this model has intelligence, objectivity and authenticity, which can better realize the intelligent assistance for architectural space design. In addition, the 3D virtual architectural space experience system constructed in this paper scores more than 90 points in all experience indicators, with good user experience performance, to meet the user’s image interaction needs, so as to provide a basis for the optimization of architectural space design.
In the context of continuous innovation in science and technology, consumer demand is becoming increasingly diversified, especially in product packaging design, personalization and uniqueness have become a new pursuit. The article proposes a computer image fusion DPformer-GAN model based on Transformer model and GAN, which is used to realize personalized packaging image fusion and generation. The digital image is then converted into a personalized packaging object through digital printing technology, which then realizes the innovative practice of personalized packaging.The DPformer-GAN model reduces the overlap and occlusion by 12.81% and 2.98%, respectively, compared with the better-performing CGL-GAN model when fusing and generating personalized packaging images. When personalized packaging images fused with computer images are used for digital printing proofing, keeping the percentage of dots lower than 50% can achieve the maximum degree of color reproduction and better retain the visual effect of digital images. Consumers are more favorable to the aesthetics and user experience of personalized packaging, with a favorability rating of 9.16 points and 9.29 points, respectively. It is feasible to use digital printing technology to print the packaging images generated by computer image fusion into personalized packaging, which can also further enrich the image practice options of personalized packaging.
The current gemstone jewelry design is not specialized and systematic enough, and most of the gemstone jewelry styles are common and single, which makes the value of gemstone not reasonably reflected. Starting from the evolution of gemstone facets, facet texture and cut classification, the article takes the three-dimensional features of gemstone facets as the basis and optimizes the cut parameters of gemstone facets by combining with geometric transformation theory. The optimized cutting parameters were used as the basis for 3D modeling, and the 3D model of the round faceted gemstone facets was established with the simulation software, and the quantitative analysis of the data was carried out through the brightness, uniformity, and scintillation values. When the table width ratio is 50-60%, the difference between the crown angle and the inclination angle of the star facet corresponding to the circular faceted gemstone facets varies from 9.172° to 20.673°. When the length ratio of the lower girdle facets is 65% to 95%, respectively, the range of the difference between the lower girdle facet inclination and the pavilion angle is between 0.781° and 1.967°. The scintillation value obtained after designing the gem facets using the method of this paper is 4.67 times higher than that of the traditional method. The optimized design model of round faceted faceted gemstones constructed on the basis of geometric transformation theory can provide new ideas for the jewelry design of round faceted faceted gemstones.
Local colleges and universities are an important part of China’s higher education reform and development, and the quality of cultivation of top-notch innovative talents has a direct impact on the development speed and level of local economy. In this study, the decision tree algorithm is used to establish a prediction and early warning model for students’ performance in the process of cultivating new engineering top-notch innovative talents in colleges and universities, and the K cross-validation method is used to optimize the model parameters and improve the prediction accuracy. Then, based on the intelligent prediction model and the cyclic structure intervention theory, we constructed a dynamic adjustment model for the cultivation system of new engineering top-notch innovative talents. The results of the empirical application of the model show that the hardware and facility conditions of talent cultivation in college D have significant improvement under the application of the dynamic adjustment model. In addition, both graduates (>4.00 points) and employers (>3.67 points) gave a high degree of achievement to the training quality of the university’s top innovative talents in new engineering disciplines. This study helps to meet the demand for high-quality engineering talents for regional economic and social development, enhance the adaptability of higher education and improve the quality of talent cultivation.
The nation-state is regarded as the basic form of the modern state, but whether the modern state is really a “one nation, one country” type of political community as depicted by the nation-state narrative. This paper explores the influence of national narratives on national identity by constructing an evaluation index system, using a questionnaire survey method, and taking adolescents as the research object. The independent variables of this study are national narrative, including national language, national spirit and national memory, and the dependent variable is national identity, including cognitive tendency, emotional tendency and behavioral tendency. The weights of the indicators and regression results were calculated by AHP-entropy weighting method. The analysis results show that national vocabulary has a great influence on national language, while sense of belonging is the biggest factor affecting emotional tendency, and most of the dimensions of national narrative are positively correlated to national identity with a significant effect. The correlation coefficient between national etiquette and national identity is 0.203, and the correlation coefficient between national history and national identity is 0.254. National history and national etiquette have a significant effect on national identity.
The countries in East Asia are neighbors in one country, and their cultures are cross-fertilized with each other, so it is of practical significance to enhance the sense of regional community on this basis. In order to explore the effect of cultural exchange and cooperation on the enhancement of regional community consciousness, this paper constructs a semantic graph for the relevant comments on social platforms, combines GNN and LSTM, and constructs a GNN-LSTM sentiment recognition model to identify and quantitatively represent regional community consciousness. Regression analysis is used to test the enhancement effect of cultural exchange and cooperation on regional community consciousness. The experimental results show that the GNN-LSTM model has a better emotion recognition effect and can provide help for the extraction and quantitative representation of regional community consciousness. The regression coefficients of cultural exchange status on the two models are 0.423 and 0.439 (p<0.01), indicating that cultural exchange has an enhancing effect on regional community consciousness. Cultural distance acts as a mediating variable, the more frequent the cultural exchange and cooperation, the smaller the cultural distance, the more the regional community consciousness can be enhanced.
China’s tourism industry has become a strategic pillar industry in China, playing an important role in developing the economy and providing employment. Therefore, how can we avoid or reduce the hazards of tourism emergencies and give full play to the development advantages that tourism brings to the city has become the focus of this paper. In this paper, the objective function is used to construct a two-stage stochastic optimization model without opportunity constraints to minimize the partial cost of the first stage and the expected total cost of the second stage. Considering the problem of maximizing the utilization rate of emergency shelters in tourist attractions, the opportunity constraint model is introduced to help decision makers allocate resources reasonably. Based on the center siting cost and vehicle distribution cost, a mixed integer nonlinear objective function model is constructed and the model is solved using the improved ant colony algorithm. Seven emergency management simulation scenarios are set up to analyze the effect of emergency management by combining simulation and empirical research. The experimental results show that among the emergencies at all levels of the sites in Y scenic area in the past 5 years, the number of level 2 emergencies is the highest, and the average number of emergencies occurred in each site in the past 5 years is 7.48. According to the model’s solution of the site selection results, the emergency center A covers 5 distribution warehouses, and the emergency center B covers 10 distribution warehouses.
The numerical simulation of the velocity decay characteristics of multilayer spherical fragments under bombardment loading is carried out by using LS-DYNA, and the distribution law of the velocity decay characteristics of multilayer spherical fragments is obtained. The ballistic limit (V50) of the multilayer spherical fragment on a 4mm 2024 aluminum target at 90° angle of attack is also obtained by ballistic test. Based on the consistency between the numerical simulation and the test results, the influence of the quality of the multilayer spherical fragment on V50 is analyzed. The air resistance coefficient is calculated with the numerical simulation results by constructing a rag flight distance calculation model. The maximum error between the calculated results and the test results is about 2%, and the theoretical calculated values are in good agreement with the numerical simulation and test results. Under the condition of the same initial velocity, the attenuation coefficient of the spherical fragment in long-distance flight is constant. The aerodynamic drag coefficient is related to the initial velocity of the fragment, which is linearly related to the initial velocity in the range of the design concern of the combat unit (1.2-2.2km/s).
MOOC as a new teaching mode is developing in full swing, however, MOOC courses face the thorny problems of high dropout rate and low completion rate. Therefore, this paper selects 12 learning behaviors and uses logistic regression model, decision tree and other methods to predict the withdrawal behavior according to the MOOC data on 365 University platform. The logistic regression prediction is analyzed for prediction accuracy, and its AUC value is 0.83 and 0.75, which proves that the logistic regression analysis can achieve the prediction of MOOC withdrawal behavior more stably and accurately, and helps to provide scientific guidelines for improving MOOC learning mode and learning efficiency. From the case study, it is obtained that among all the learning behaviors, the weight of online rate is 0.7582, which has the highest weight, indicating that the online rate of college students is an important index for judging whether they will produce withdrawal behaviors, which deserves the attention of MOOC platforms and educators.
By optimizing the automation configuration of medium-voltage distribution lines, capturing the initial signals of cable insulation hidden danger, combining the real case data of 6 years of distribution network insulation faults and hidden danger in a city of Zhejiang, summarizing the waveform law and progressive signal characteristics in the process of insulation hidden danger deterioration, a set of real-time monitoring method based on the analysis of big data of the medium-voltage distribution line cable insulation deterioration of the corona hidden danger has been developed. The method is based on the master station to realize localization, instead of periodic on-site equipment charged detection, has been verified on-site and found discharge traces cable head in advance. This method utilizes distribution automation and dispatch automation configurations to capture the instantaneous zero-sequence overcurrent signals corresponding to insulation degradation discharges, waveform characteristics, acoustic mutations, and environmental information as input. A quantitative risk algorithm consisting of eight analysis dimensions such as zero-sequence spike characteristics, number of spikes, and synchronization of acoustic ripple and spike timing is used. Three optional computational media, including master station, enhanced DTU, and DTU external component, are used to give hidden risk localization. The two methods, local discharge detection robot and manual detection, are used to confirm the site and then carry out out outage maintenance to prevent the further expansion of hidden dangers. The method relies on the distribution automation of existing protection devices and master station configuration to assist a small number of sensors and edge computing devices to realize, through the protection device uninterrupted monitoring instead of manual periodic local discharge detection. It solves the problems of high cost of periodic testing, unavoidable accidents caused by continuous insulation degradation in the interval of testing cycle, hidden location of some cables and blind area of testing, and effectively improves the reliability of power supply.
Deep learning-based methods can be combined with skeleton data, but they only consider the feature vectors formed by joint coordinates and do not extract the spatio-temporal dependencies between skeletons. In order to provide a more comprehensive detection and recognition of spatio-temporal relationships in human action sequences, this paper proposes a graph neural network-based human action detection and recognition method by combining YOLOv5, AlphaPose, and spatio-temporal graph convolutional network (ST-GCN) algorithms under the interpretable artificial intelligence (XAI) perspective. Firstly, the improved YOLOv5s target detection algorithm is used to get the human body detection frame and obtain the human body position information, then the AlphaPose pose estimation algorithm is used to obtain the coordinate information of the joint points of the human skeleton, and finally the improved ST-GCN algorithm is used to construct the spatio-temporal graph and extract the spatio-temporal dependencies between the joints to complete the human body action recognition. Through experimental verification, the method can accurately recognize human fall, running, kicking, and squatting actions on the dataset, with a recognition accuracy of 92.04%, and compared with the five baseline models, the method has higher recognition accuracy, with the values of each index greater than 91%, which can provide technical support for human behavior recognition.
The development of society and the change of the times have brought some degree of change to the development of preschool music classroom. This paper introduces the OBE concept into the education of preschool music course, designs the teaching objectives of the music course according to the guidance of the concept in order to realize systematic teaching, and analyzes the students’ cognition of various dimensions in the preschool music course by using the cognitive level diagnosis method. Based on this method and the Hadoop system, a big data platform for integrated teaching of preschool music course is constructed, and teachers are assisted to intervene in teaching through the platform’s teaching data query, statistics and analysis functions, so as to realize the integrated teaching mode of preschool music course and mathematical statistical analysis. The results of the teaching practice show that after the implementation of the integrated teaching mode, students’ learning attitude towards the preschool music course and their knowledge of music-related knowledge increased significantly (P<0.05), and the level of independent and inquiry learning was also improved. This study can make the teaching of preschool music course more meaningful, more adaptable to the needs of talent training in today's society, and create an integrated teaching curriculum that is more conducive to the cultivation of students' musical literacy and interest.
ETC gantry data and other monitoring data provide data support for highway traffic flow prediction, for this reason, this paper proposes an attention mechanism-driven traffic flow prediction model to scientifically coordinate and schedule highway traffic conditions. Based on the fusion of multivariate monitoring data, the model utilizes ConvLSTM to generate global location coding, learns the data characteristics through the jump expansion attention structure, and completes the traffic flow prediction using the mask attention structure. The example analysis verifies that the predicted values of traffic flow and speed of this paper’s model are closer to the real values, and compared with the models such as ARIMA, LSTM and BiLSTM, this paper’s model has lower values of RMSE and MAE indexes in the prediction of traffic flow and speed, and the prediction error is smaller. The article also validates the model’s prediction under 5min, 15min and 30min prediction lengths, showing that the model has excellent performance and good prediction stability.
With the continuous development of high-power laser equipment and the continuous expansion of the scope of the application platform, the demand and application of high-power laser equipment in various fields are becoming more and more extensive, and its output power has also put forward higher requirements. In order to promote the development of high power laser equipment toward higher energy conversion efficiency, research and design temperature control device to manage the waste heat generated in the energy conversion process of high power laser equipment. On the basis of PID control algorithm using LADRC algorithm, rapid realization of temperature precision control, so as to enhance the energy conversion efficiency of high-power laser equipment. When the temperature control device in the temperature control range of 10 ℃ ~ 40 ℃, the temperature control accuracy is better than ± 0.03 ℃, and in 144s to reach the set temperature, the temperature control overshoot is less than 2.33%, to meet the requirements of the laser working temperature control in the working process of high-power laser equipment, and to lay the foundation for the realization of high energy conversion efficiency. Compared with the modified PID controller, the energy conversion efficiency is relatively improved by 1.57%. The temperature control device designed based on the improved PID control algorithm in this paper can significantly improve the energy conversion efficiency.
In today’s big data environment, the demand for digital transformation of traditional libraries is becoming more and more urgent. The article adopts BERT-BiLSTM-CRF model to extract digital library resources and retrograde entities, and constructs digital library resources knowledge graph. On the basis of digital library resources integration, it combines the collaborative filtering algorithm based on users and items to construct and improve the intelligent recommendation mechanism of digital book resources. The integration results of digital library resources and intelligent recommendation results are analyzed separately, and a survey on reader satisfaction is conducted. The recognition accuracy of this paper’s method is significantly higher than that of the traditional text-like processing data model. The collaborative filtering algorithm in this paper provides statistical analysis of the types of book resources read by each reader, and recommends the top 5 book types in terms of similarity to him/her. This paper’s method has better results in book resource division and book resource recommendation accuracy compared to other recommendation methods. The average value of readers’ satisfaction with the resource recommendation mechanism of the digital library in S city for each dimension and each index is more than 4 points.
Planted roofs have good heat preservation and insulation properties, which can effectively alleviate the urban heat island effect and reduce the energy consumption of buildings and the carbon dioxide content in the atmosphere. The study describes the heat transfer process of planted roofs into three parts, derives the heat transfer equations of the leaf layer, soil layer, and roof layer of planted roofs, and clarifies the calculation of relevant parameters in the model of planted roofs. Taking integrated design as the technical standard, the stereotypical design of planted roof buildings and their building parts, components, fittings, engineering equipment, etc. The insulation exterior wall panel enclosure system is standardized to realize industrialized production of wall panel components, integrated design of connection nodes, and assembly construction. The analysis results show that during the test time, the average convective heat transfer heat flow of Module H containing vegetation is a maximum of 119.21W/m2, and the total convective heat transfer heat flow of the whole day is 2835.99w/m2, which has the best thermal insulation performance. Among all the roof modules, only Module H has the heat transfer direction from outdoor to indoor throughout the day. Finally, based on the above conclusions, the self-insulated exterior wall system’s specific construction method and technology are given to provide the basis and reference for the specific construction in practice.
This paper first introduces the regional power marketing management platform, after which the 3 major functional modules of this power marketing management platform are designed. Then MobileFaceNet is used as the basic network for face recognition feature extraction in the context of deep learning, and the SE module is used to optimize the network performance and network expressiveness. Afterwards, the Taylor expansion of the negative log-likelihood function is used as an optimization criterion to optimize the face detection model (MTCNN) and the face recognition model (SE-MobileFaceNet). Finally, the running effect and performance of SE-MobileFaceNet model are measured. The main conclusions are as follows: in 1:1 mode, the accuracy of SE-MobileFaceNet model for the three datasets DRDS, DE and DPDS is 95.99%, 96.98% and 98.83%, respectively. In addition, the SE-MobileFaceNet model can avoid excessive redundant calculations, so that its recognition rate reaches 95%.The accuracy of the SE-MobileFaceNet model for monitoring and recognizing the information of the management platform ranges from 97.43% to 100%, and it has a good operating effect in the identification of the regional electric power marketing management information platform, and the overall satisfaction rate of the testers for the model is also >85%. The overall satisfaction of the testers to the model is also >85%. Obviously, the SE-MobileFaceNet model proposed in this paper has a very broad application in regional power marketing management information platform identity recognition.
The development of urbanization is rapidly changing, and various undertakings are flourishing, while the sports industry, as an important segment of urban regional economic development, plays an inestimable role in the development of the entire city construction. The study takes the sports industry and economic development of 27 provincial capital cities in China from 2018 to 2022 as the research object, establishes the evaluation indexes for the high-quality development of the sports industry based on the principle of index construction, and establishes the weights of the indexes. Taking Harbin as a case study, the effect between urban sports industry and economic growth is analyzed with the help of impulse response analysis, Granger causality test, and variance decomposition of VAR model. The results show that the development of urban sports industry and economic growth can promote each other, with a long-term cointegration relationship, and the positive effects between the two are slowly reduced over time when they are impacted in the long term. Granger test shows. It indicates that there is a unidirectional causal relationship between urban sports industry and economic growth.
Flexibility control and vision of robots are important acquisition and feedback links in robot control, and the study of multi-sensor data fusion is becoming more and more important as the complexity of robot tasks increases. This paper describes the robot kinematics and inverse kinematics process by studying the knowledge of D-H model theory and parameter definitions in the machine kinematics model, reveals the changing relationship between the robot joint control and end pose, and establishes a kinematics-based vision servo control model. On this basis, the coupling error compensation algorithm is used to combine the visual position control quantity as well as the force sensing position correction quantity to form the final visual and force sensing supple control strategy. Meanwhile, for the lack of adaptability of classical impedance force control on unknown constraint environments, a two-fuzzy adaptive sliding mode controller is designed according to the Lyapunov stability theorem to drive the robot end in order to achieve the actual position tracking expectation. The results of simulation experiments and motion contour tracking experiments show that the control algorithm proposed in this paper has better control accuracy and is more robust to noise and uncertainty, and the controller is also able to reduce the effect of torque saturation on the robot system.
Garden is an important support for regional economic development, but also an important support for regional ecological environmental protection, the rational allocation of water resources in the garden is one of the effective ways to solve the problem of water shortage. This paper takes the Internet of things, digital twin as the technical basis, uses the multi-objective optimisation algorithm to construct the water resource management model of the garden area, and uses the artificial fish swarm algorithm to solve the model. By constructing a digital twin irrigation district water resources scheduling management platform, the water resources elements of the garden area are comprehensively monitored and sensed, and the intelligent simulation of the water resources allocation management process and decision-making scheme evaluation and optimisation are achieved, so as to enhance the intelligent and refined management level of water resources scheduling of the garden area, and comprehensively realise the saving and intensive use of water resources. Taking X garden area as a research case, the water resources management model finally derives the optimal water resources allocation scheme under 50%, 75% and 90% in each planning year, which provides support for the efficient use of water resources in X garden area.
This paper evaluates the quality of university English teaching based on the hierarchical analysis algorithm (AHP) and fuzzy comprehensive evaluation algorithm (FCEA) in order to grasp the teaching situation more objectively. Based on the principle of evaluation index system construction, the evaluation indexes of university English teaching quality are determined. Using hierarchical analysis algorithm to calculate the weights of its indicators, and constructing a fuzzy comprehensive evaluation matrix based on expert ratings to finalize the assessment of university English teaching quality. Taking a university as the research subject, the English teaching quality assessment result of the university is 3.7351, and its corresponding fuzzy comprehensive evaluation A=(0.2893,0.3981,0.1359,0.1120,0.0648), which summarizes the teaching quality of university English as good according to the principle of maximum affiliation degree. In order to improve the teaching of college English in this university, corresponding teaching strategies of college English are proposed.
Deep learning, as a multilayer neural network structure for deep learning of data features, can describe the nonlinear mapping relationship for the assessment of college civic education. Aiming at the current education quality assessment model based on deep learning, this paper proposes an optimized convolutional neural network (HOA-CNN) based on Hummingbird Optimization Algorithm to assess the quality of Civic and Political Education in colleges and universities. According to the correlation coefficient between the objective assessment results and the subjective assessment results, the objective assessment results of the quality of Civic and Political Education in colleges and universities are obtained. The test results show that the linear correlation coefficient and the rank correlation coefficient between the assessment results of this method and the subjective assessment results are closer to 1. The goodness-of-fit of the assessment of the quality of college civic education under the model of this paper is significantly higher than that of the two control models. The simulation test results show that the assessment results of the university civic education quality assessment model constructed by the optimized convolutional neural network based on the hummingbird optimization algorithm are more accurate.
When a steam turbine blade has cracks, fractures, or other flaws, the steam turbine’s operating circumstances will change the vibration characteristics of the blades, complicating the problem identification process. The important defect features are difficult to automatically and effectively extract from the recorded vibration signals. In this study, the input signal characteristics for a particular operating situation are used as labels to reconstruct a trained autoencoder utilizing a reverse error. The supervised autoencoder receives the fault features for various speed circumstances, which it then protectively maps to a series of reference condition features. The goal is to eliminate the disruption brought on by variations in fault feature values brought on by alterations in operating circumstances. The experimental findings demonstrate that this approach can more effectively convert feature sequences under various working situations and address the issue of fault feature distortion brought on by changes in working conditions. In addition, comparison of clustering visualization and accuracy of classification methods on data before and after commutation demonstrates that the proposed supervised autoencoder model can extract accurate classifiable features for fault classification.
With the deepening of the exploration of informationization in the construction industry, the smart construction site comes into being under the support of technological development and policy. The article combines artificial intelligence technology with electric power smart site, and deeply researches the application of artificial intelligence technology in electric power smart site. For the security monitoring in the smart construction site, a SSD7-FFAM lightweight target detection method is proposed based on the SSD7 algorithm, using feature fusion and attention mechanism methods. Then, based on the fast acquisition of temporal information of surveillance video scenes, an adaptive compression technique with wavelet sparse measurement is designed. Through the model comparison analysis, the SSD7-FFAM algorithm achieves better detection effect and detection speed of 84.97% and 83.45FPS in real application scenarios, and has a smaller number of parameters and computation.The AVCS method can be effectively adaptively adopted, and most of the reconstructed image PSNR values of this method are greater than 40dB under different sampling rates, and the quality of the reconstructed image is better than the Contrast compression technique, which can be used for the high rate compression of intelligent construction site monitoring video. The research results will provide informative ideas for construction companies to introduce AI technology in power smart construction sites.
As the trend of economic globalization continues to develop, air transport, as a fast and convenient mode of transportation, is playing an increasingly important role in economic development. This study analyzes the driving force of airside economic construction from four levels: primary influence, secondary influence, derivative influence and permanent influence. It also analyz es the dynamic relationship between the aviation industry and the construction of airside economy. In order to further research on the development of airside economic construction, this paper uses the entropy weight method to optimize the gray situation de cision making theory, and conducts research on the development and countermeasures of airside economic construction in Henan Province. According to the gray decision making effect measurement calculation, it is known that the key construction area of airsi de economy in Henan Province should be selected as H2 area, with the effect measurement score of 0.9789, the highest value. The economic effect achieved by prioritizing the development of tertiary industry or the joint development of secondary and tertiary industries in the construction of airside economy in the H2 area is the highest, with the effect measurement scores of 0.755 and 0.749, respectively.
The research combines PBL teaching method, CDIO theory and school-enterprise collaborative education mechanism to construct a school-enterprise collaborative teaching model based on PBL-CDIO. And then, the empirical research of PBL-CDIO school-enterprise collaborative teaching mode is realized through the teaching experiment method. The independent sample t-test is used to test the changes in the professional knowledge level and basic working ability of the experimental group and the control group before and after the experiment, and to judge the teaching effect of the school-enterprise collaborative teaching mode based on PBL-CDIO in this paper. The pre-test sig values of professional knowledge and basic work ability of the experimental and control groups are greater than 0.05, and there is no significant difference between the two groups. The posttest sig values of the dimensions of professional knowledge in the experimental group increased by 10.20, 10.46, 10.49 and 9.47 respectively, and the sig values of the dimensions of basic work competence increased by 9.89, 9.72, 8.66 and 10.10 respectively. The overall change in the level of professional knowledge and basic work competence in the control group was less than 1 point. The posttest expertise and basic work ability sig of both groups were less than 0.05. After the experiment, the expertise and basic work ability of the experimental group were much better than that of the control group. The school-enterprise cooperative teaching mode based on PBL-CDIO proposed in this paper has good teaching effect.
This paper constructs a two-party evolutionary game model based on the perspectives of sharing platforms and consumers, exploring the dynamics of platforms’ decisions to actively operate with blockchain technology and the evolution of consumer rights protection behaviors. It is discovered from analysis that certain variables exert considerable influence on the stability of the strategies of both parties. From the consumer perspective, the improvements in the performance of the blockchain technology significantly increase the consumers’ willingness to protect their rights: the consumers with initially high levels of rights protection activation intensified their actions when their rights were violated. Thus, with the effective reduction of the cost of safeguarding rights, this trend has been additionally strengthened. As for the platform side, the performance of the blockchain technology exerts positive incentives on the operation of the platforms, although the marginal impact gradually declines with the developing blockchain technology, which in return reveals that platforms need to pay attention to a range of aspects including technology maturity. Measures of dual constraints including heavy fines from government and negative impacts of passive operations help to rein in passive operation among the platforms. Significantly, higher values of fines or negative effects lead to higher tendencies of having proactive operation strategy among the platforms.
Objective, to investigate the correlation between abdominal aortic calcification and paravertebral muscle degeneration, and to explore possible common risk factors for both. Methods, all patients with lumbar spinal stenosis admitted to Hospital X for MC and CT examination from 2016 to 2024 were selected, and through screening and exclusion, a total of 352 patients with LSS were included in the study, which consisted of 202 males and 150 females aged 40-80 years, with a mean of 63.24 years. The degree of paraspinal muscle degeneration in lumbar MRI, the degree of abdominal aortic calcification in lumbar CT scanning, as well as the patient’s age, duration of LSS, glomerular filtration rate and other indicators were counted, and the distribution characteristics of abdominal aortic calcification and its correlation with paraspinal muscle degeneration were analyzed by the method of multiple regression. Results, of the 352 patients with LSS who were included to meet the criteria, the calcification group (151, 42.90%) and the non-calcification group (201, 57.10%). Mild, moderate and severe paravertebral muscle degeneration accounted for 56.53%, 28.69% and 14.77%, respectively. The AACS in patients with mild PD degeneration stage, moderate PD degeneration stage and severe PD degeneration, all showed a gradual increasing trend with age (P<0.001). Regression results showed that age, paravertebral muscle degeneration and eGFR were risk factors for AAC in patients with LSS. Conclusion, there was a significant correlation between abdominal aortic atherosclerotic calcification and paravertebral muscle degeneration (P<0.001), and the degree of PD degeneration can be used as an effective indicator for early warning of the occurrence of AAC in patients with LSS.
In today’s society, ancient cities, as important components of historical and cultural heritage and urban development, are receiving increasing attention for their protection, utilization, and management. This research mainly focuses on the construction of an evaluation system for the spatial historical evolution of ancient city streets and the corresponding management strategies. Through a comprehensive evaluation of the spatial issues and characteristics of the ancient city streets, a multi-dimensional evaluation system for the historical evolution of the ancient city space with a total of 13 indicator factors, including historicity, is constructed. Taking Suzhou Ancient City as an example for empirical analysis, five typical types of ancient city streets are identified. Finally, corresponding update strategies are proposed for different types, especially the utilization of biomaterials and the design of plant landscapes, providing more innovative and sustainable management suggestions for the revitalization planning of the ancient city.
In this paper, the structures of three phosphorus-containing organosilicon compounds, including N,N-di-methylenephosphoric acid n-propylamine (DPPA), N,N-di-methylenephosphoric acid aminopropyldimethylsilanol (DPDS), and N, N-di-methylenephosphoric acid aminopropyldimethylsilylene glycol (DPMS), have been designed by using a molecular dynamics simulation method. And the preparation of three phosphorus-containing organosilicon compounds was accomplished experimentally by using raw materials such as bisphenol A-type epoxy organosilicon, n-propylamine and phosphite. The structures of the above several substances were proved by means of characterization such as Fourier infrared spectroscopy, hydrogen NMR , and epoxy value. Molecular dynamics simulation analysis revealed that the bond lengths of N atoms to Si atoms, N atoms to O atoms, and N atoms to were 3.03 Å, 3.05 Å, and 2.85 Å, respectively. Si did not participate in the addition reaction, but the intermolecular interactions caused a change in the chemical environment of Si, which reduced intermolecular distances and made it easier for the phosphorus groups to aggregate. This study is very important for the development of new preparation strategies of phosphorus-containing organosilicon and the promotion of phosphorus-containing organosilicon industry.
Social network structural characteristics of top management (TMT) are important variables that affect the outcome of team functioning, and variability in network structural characteristics leads to variability in TMT performance. This paper analyzes TMT social network structure characteristics based on TMT’s social relationship network using machine learning techniques. The top management interlocking network and technological innovation (machine learning technology) are divided into dimensions respectively, and the machine learning technology is used as a mediating variable to establish a model of the mediating effect of machine learning technology between top management interlocking network and green innovation. Statistical analysis of sample data and structural characterization of TMT social relationship networks by machine learning technology are conducted, and regression equations are used to verify the research hypotheses. The test results of the mediating effect of utilized innovation and exploratory innovation covered by the machine learning technology show that the overall regression effect of the model is good ( =0.537, =0.579, F-statistical test is significant), i.e., the mediating variables, utilized innovation and exploratory innovation, positively affect the green innovation performance and are significant. Meanwhile, the heterogeneity and size of TMT’s social relationship network, as well as relationship strength and relationship quality all have a significant and positive effect on green innovation.
Industrial processes are constantly developing towards large-scale and automation, and the smooth, safe, high-quality, and efficient operation of industrial processes has become a hot spot of concern, and higher requirements have been put forward for the control of production processes. This study analyzes the high-dimensional data in the closed-loop system of industrial network based on the HOPLS-SVM algorithm with higher-order singular value decomposition method. A BP neural network model is constructed with the processed data as the input set to realize the real-time prediction of the main data in the project, and the error of the prediction model is corrected by using linear regression method, and then the project prediction control system is constructed by piggybacking on the model. The results show that the prediction performance of the model in this paper is better than that of the comparison model, and the average absolute error is only 0.0347. At the same time, it is found that the control system in the decomposition project of CHP is able to regulate and optimize the temperature and flow rate of the crude product, which ensures the balance between the product temperature and yield, and the safety of the project operation. The engineering control method designed in this study has strong adaptability and effectiveness, and can provide solutions to engineering control problems in complex industrial processes.
This paper designs a military intelligent wearable device, aiming at realizing human-computer interaction and monitoring military status signs and data characteristics through this device. First of all, the overall system of the product contains temperature and humidity module, blood pressure detection module, heart rate measurement module and display module, and the extracted feature data are subjected to data intelligence preprocessing. Then the pre-processed feature data is functionally compressed and an artificial intelligence feature classification model is constructed, through which the compressed feature data is analyzed and displayed. Finally, the interactive performance of the wearable device is completed through data intelligent processing and device relevance calculation. After application analysis, it is found that the actual monitoring error is below 0.1 under different fatigue levels, and the specificity and positive prediction value of the wearable system can reach up to 100%. The highest accuracy of monitoring the physical state of military personnel is 99.31%, in addition to monitoring the heart rate in sedentary state and exercise state, with an average error value of 1.24 and 1.29. Therefore, the smart wearable device designed in this paper can well realize human-computer interaction, and the performance of product design is superior.
This study focuses on the Dingjiafen slope in Chuxiong City, China, with the aim of improving the accuracy of slope landslide risk prediction. Formulas for calculating the critical soil layer thickness at the onset of slope instability are derived based on the physical model of the slope. Using the Digital Elevation Model (DEM) and ArcGIS, the critical and maximum soil layer thickness of each slope unit are calculated to predict potential landslide areas. FLAC-3D is employed to simulate and analyze the slope’s stability under natural conditions, and the numerical simulation results are compared with the predictions in ArcGIS. The findings reveal variations in the critical and maximum soil layer thickness among different slope units due to diverse topography. The slope units on both sides of the Chumeng Highway slopes, with a critical soil layer thickness ( ) between 1 and 3 meters, are connected, aligning with the results of FLAC-3D three-dimensional numerical simulation and the actual sliding positions on-site. Applying this method to simulate the soil layer thickness at the critical state for each slope unit enables slope stability prediction, offering a new perspective for the analysis and prediction of slope stability.
Food security is an important foundation of national security, and the fundamental of guaranteeing national food security lies in arable land protection. In order to realize the protection of arable land in the context of ecological civilization, this paper designs a governance framework for arable land protection based on supply, empowerment, and control on the basis of the trinity of arable land protection policy of quantity, quality, and ecology, and constructs a multi-objective land use structure optimization model, and obtains a scenario prognosis for the optimal allocation of the land use structure by using a hybrid genetic algorithm. Taking County A as the specific research object, it can be seen by predicting the land use structure under the natural development scenario of County A that, relative to the status quo in 2023, the predicted share of arable land in 2050 has the largest decrease of 0.49%, and the shares of garden land, forest land, grassland and water area have all decreased, which is mainly converted into construction land (increased by 0.78%). From the Pareto frontier solution of land ecological benefit objective and economic benefit objective, three typical schemes of land use structure optimization were obtained, among which, the optimization scheme of balanced development of economy and ecology balanced economic and ecological development balanced economic development and ecological protection, and was selected as the optimization scheme of this paper. The increase of arable land area in this scheme is 0.38%, much higher than the -2.46% in the unoptimized case, which is in line with the requirement of arable land retention and can be used as a reference for further optimization of arable land protection framework.
The popularity of network video service, which leads to the specification of the network video service quality is becoming more and more urgent, and human is the ultimate watch users of network video, Evaluation of human observers on the video’s perception of the situation is becoming more important, depending on the source video network video know nothing, you will need to refer to the participation of video quality evaluation algorithm. However, when the quality evaluation is done by human rating, it is time-consuming and laborious, so the computer is required to make an objective evaluation of the video. In the objective evaluation, the excellent performance of convolutional neural network based on deep learning in feature extraction contributes to the rapid development of the research field of video quality evaluation. However, the development of deep learning algorithms requires appropriate data sets for training and testing. The existing data sets are relatively small in scale and not comprehensive in terms of video content types and distortion types. Therefore, it is necessary to provide a new data set to evaluate the quality of video without reference, expand the scale of the data set, expand the content and distortion type of the video. At the same time, considering the new development of network video services, the video resolution is positioned as high definition, and the original video sampling ratio is 4:2:2. The dataset is freely available to relevant researchers for scientific research.
Graph neural networks are an effective method for action recognition using human skeletal data, but previous recognition methods lack attention to spatial features. In order to improve this research deficiency, this paper conducts action recognition research based on ST-GCN. An action recognition network based on two-way skeletal joint information is proposed, where the human body is divided into various parts to calculate the representation vectors, and a graph convolutional neural network is trained to obtain the classification results. Attention mechanism is designed to minimize the effect of background noise, and data enhancement by means of flipping and shifting is performed to improve the model performance. Ablation experiments verify high accuracy when using both the attention mask matrix and the global self-attention mechanism, as well as when using both the joints network branching and the parts network branching. The model in this paper recognizes all 12 actions of the NW-UCLA dataset with accuracies higher than 92%, and the data enhancement effect is also verified.
Based on the research of digital economy and digital transformation of manufacturing industry, the article constructs the evaluation index system of digital economy development level and digital transformation of manufacturing industry in Yangtze River Delta respectively. The entropy weight TOPSIS method is used to measure and analyze the level of digital economy development and the level of digital transformation of manufacturing industry in the Yangtze River Delta respectively. The coupling coordination degree of digital economy and manufacturing digital transformation in the Yangtze River Delta in general and in each province and city is calculated and analyzed. On the basis of the relevant conclusions, recommendations for the digital transformation of the manufacturing industry and the elimination of the “digital divide” in the Yangtze River Delta are proposed. The overall trend of digital economy in the Yangtze River Delta (YRD) is on the rise, but there is a significant “digital divide”. The increase in the level of digital economy development in the Yangtze River Delta over the past 10 years is 10.76%. The average value of digital economy development water in Shanghai, Jiangsu, Zhejiang and Anhui is 0.358, 0.549, 0.491, 0.185 respectively, and the development of digital economy in Anhui is insufficient. Shanghai’s manufacturing digitalization water average is 0.750, the highest level in the Yangtze River Delta. Zhejiang and Anhui are slightly behind in manufacturing digital transformation. The coupling coordination degree of digital economy and manufacturing digital transformation in the Yangtze River Delta grew from 0.592 to 0.879, and the type of coupling coordination development shifted from barely coordinated to well coordinated. Shanghai and Jiangsu have reached the good coordination level. Zhejiang is at the intermediate coordination level. And Anhui is at the barely coordinated stage, which is the lowest level in the Yangtze River Delta.
Artificial Intelligence has been applied in many aspects of life, however, AI algorithms have been less used in the field of music. In this paper, a multi-track based pop music generation model MuseGAN is proposed, due to its poor contextualization and excessive tempo jumps in generating pop music samples. In this paper, a new multi-track pop music generation model-Recurrent Feature Generation Adversarial Network RFGAN is proposed. the model addresses the temporal relevance of the music structure and the repetitive nature of the musical section, and proposes a temporal model that enhances the contextual relevance of the music samples in terms of the time series, and improves the generative model according to this temporal model by converting the unidirectional structure in the original model to a recurrent structure, adding the feature extractor to the previous level of training information, which is combined with arbitrary noise and passed to the next training. An average pooling layer is added at the end of the generative model as a solution to the situation where the model generates too much noise for pop music samples. The improved model is superior to the pre-improvement model in terms of stability, convergence speed, and overfitting in pop music generation. In the audience scoring experiment, 60% of the top 5 pop music scores were generated using the RFGAN model proposed in this paper, indicating that the pop music generated using the RFGAN model has reached a high level comparable to the level of artificial pop music composition.
Groundwater seepage has a greater impact on the stability of foundation engineering, and it is also an important factor that restricts the development of geotechnical engineering projects and the quality of engineering surveys. In this paper, tunnel engineering is selected as the foundation engineering project under study to investigate the specific influence of groundwater seepage in the process of tunnel excavation. The flow-solid coupling model is constructed, and the safety coefficients of the tunnel project in different situations are calculated based on the strength discount method and the ultimate strain method. Numerical analysis software is used to establish the calculation model of the influence of groundwater seepage on the stability of tunnel excavation. The displacement of surrounding rock around the hole is selected as the evaluation index of tunnel stability, and the effect of groundwater seepage on each index is calculated by the analysis software. The study shows that groundwater seepage will make the rock body around the palm face after tunnel excavation change significantly from the pre-excavation bending. The seepage increases the displacement of the surrounding rock, and the coefficient of increase of vertical displacement is larger than the coefficient of increase of horizontal displacement. At the same time, the flow-solid coupling effect of groundwater seepage increases the surrounding rock stress, and the increase coefficients of each key point of the tunnel excavation are between 1.11-1.50, resulting in significant deformation of the bottom of the arch, the top of the arch, and the arch girdle. In addition, the groundwater seepage makes the tunnel safety coefficient decrease from 9.03 to 5.18, which significantly causes the decrease of tunnel stability.
In the current business environment, artificial intelligence is becoming a key force driving performance management and organizational change. In this paper, finance, customer, internal process, learning and growth are selected as the indicators of performance evaluation of Enterprise A through the balanced scorecard model, and the fuzzy algorithm is used to provide comprehensive scores and grades for the above indicators. In addition, this paper sets up an organizational structure change evaluation model and analyzes the effect of the system on the organizational change of Enterprise A through the measurement of key indicators. The recognition rating of the questionnaire set up in this paper by the employees of Enterprise A is 4.15, and the recognition degree of the Intelligent Performance Management System is “basically recognized”. The intelligent performance management system improves the design and execution of internal processes in Enterprise A, and promotes the organizational change of Enterprise A in terms of total resource utilization. In conclusion, this study provides reliable technical support for enterprise performance management and organizational change.
Colleges and universities are an important part of higher education, providing a large number of talents for social development. The study optimizes the way of student management in colleges and universities based on artificial intelligence technology. Firstly, the K-means algorithm in cluster analysis is used to classify students’ campus behavioral characteristics. Then use Apriori algorithm to correlate students’ behavioral characteristics with academic performance. Finally, colleges and universities can take differentiated management measures for different categories of students. The clustering analysis of 12,885 students’ consumption behavior, work and rest behavior, and study behavior in college Z, followed by the correlation analysis between the clustering results and academic performance, and a total of 10 correlation rules were found. Colleges and universities can formulate management rules based on the analysis results to improve management efficiency. In addition, the student management work of colleges and universities can be optimized and upgraded in several directions, including the awareness of student management work in colleges and universities, the information platform, the archive management work, the student management team, and the information security work.
Under the background of the current information age, the electronic and intelligent transformation of the bidding industry has become an inevitable trend, and the e-bidding model stands out and greatly improves people’s understanding of bidding. Aiming at the traditional e-bidding system, in order to solve the problem of the lack of the traditional e-bidding system that provides the bidding body with referable opinions, this study firstly constructs the e-bidding risk assessment indexes and realizes the optimization of the evaluation module of the system. Then the recommendation algorithm based on deep learning implements the optimization design of the e-header bidding system. This study constructs an optimized recommendation model by fusing knowledge graphs on the basis of deep learning. Then the e-tendering optimization system is designed according to the actual needs of e-tendering, combined with the recommendation model of this paper. The accuracy index ACC of this paper’s recommendation model is improved by about 3% on average compared with other best-performing recommendation models on each dataset, which verifies the excellent performance of this paper’s recommendation algorithm. This study constructs an optimized e-tendering system and proposes suggestions for the development and operation strategy of corporate e-tendering, contributing to the development of e-tendering transactions and the participation of social capital.
The flipped classroom relies on a smart platform to assist the implementation of English translation teaching, combining the smart platform with the students as the core to realize the efficient interaction of English translation teaching and enhance the students’ interest in English translation learning. This paper develops an easy-to-use interactive system for English translation teaching in flipped classroom based on Fine Report, and utilizes MySQL database to store the relevant data generated in the process of use. In this system, the BERT model trained by matrix masking strategy is used as the basis, and the neural machine translation model that assists teachers in English translation homework correction is established by combining the NMT model. Then the K-Means clustering algorithm is optimized by the adaptive K-value selection method, and the students’ learning data on the system is clustered by using the improved K-Means, and the student performance evaluation model is established by combining the CART decision tree. A pedagogical comparison experiment was carried out for the feasibility of the interactive system for teaching English translation in the flipped classroom. The BLUE value of machine translation using the BERT-NMT model was always above 30, and the average accuracy of student performance prediction of the K-Means-CART model could reach 84.85%. The English translation performance of the students in the experimental class was significantly improved after the teaching experiment, and the overall satisfaction of the students with the interactive system for teaching English translation was 4.038 points, which was between the satisfied~very satisfied level. Fully combining intelligent technology to assist teachers in teaching English translation under the flipped classroom can help to enhance the quality of cultivating English translation talents in colleges and universities.
When a laser beam passes through a solid physical material with a nonlinear refractive index, it can produce an optical nonlinear effect, which depends on the refractive index that changes with the light intensity. Based on an analysis of the linear principle of nonlinear optics, the article describes the coupled wave equations under the nonlinear optical phenomenon. It introduces the phase-matching method of frequency conversion and the theoretical basis of optical waveguide. Starting from the classical Maxwell’s equations, the nonlinear optical transmission equations and the optical effect model are established, and then the finite element method (FEM) simulation model is constructed based on the FEM model to analyze the nonlinear optical phenomena of solid-state physical silicon materials. To verify the validity of the FEM model, the optical bistability effect and four-wave mixing spectrum of the nonlinear optical phenomena are simulated and analyzed, and the homochiral spinning effect and transmission spectrum are investigated. When the solid-state physical silicon material is rotating, the laser power required to observe the optical bistability is up to 9.51 W when the rotation rate is increased from 12 kHz to 24 kHz, and the four-wave mixing intensity decreases from 0.115 to about 0.028 when the oscillator frequency of the solid-state physical silicon material is increased from 15 MHz to 30 MHz. The plasma resonance absorption wavelength of the solid physical silicon material is at 791 nm, and the effective refractive index obtained from the simulation is 0.61 in the real part, which is only 1.64% lower than the actual refractive index. The trend of nonlinear optical phenomena in solid-state physics can be effectively obtained by using the FEM model, which provides a new idea for the application expansion of the optical force system.
With the development of the informationization era, it has become the norm for teachers of Civics and Political Science courses in colleges and universities to assist classroom teaching through network resources. In order to further utilize network resources to make them better serve the classroom teaching of Civics and Politics courses in colleges and universities, this paper optimizes the teaching resources recommendation technology based on deep neural network. Defining the network teaching resources data as a ternary group , we put forward the research hypothesis and LSTM model, and establish the G-LSTM recommendation model for recommending the teaching resources of ideological network. The overall framework of G-LSTM model is described, and the recommendation based on G-LSTM is applied to the ideological network teaching resources recommendation. Adopt AUC, MRR and NDCG as evaluation indexes to check the performance indexes of G-LSTM model. Combined with the actual teaching of ideologic theory class, the practical effect of G-LSTM recommendation model is analyzed. 67.81% of students and 39.71% of teachers recognize each recommended online teaching resources. It shows that the improved LSTM model in this paper can further screen the ideological and political network teaching resources, and the teaching resources recommended by the model are more suitable for the teaching of ideological and political theory.
Machine learning-based learning analytics can fully use the learner learning behavior interaction data recorded by online English teaching systems, providing support for observing students’ learning process from the perspective of learning behavior. In this paper, we construct a framework for recognizing college students’ English learning behavior patterns, propose an SGT-based feature extraction algorithm for learning sequences, and use Gaussian mixture models to identify the extracted learning characteristic sequences. Subsequently, a K-means clustering algorithm is used for sequence clustering and lag sequence analysis. At the same time, the English personalized teaching method is designed by combining the proposed personalized knowledge point recommendation method of multi-knowledge fusion in-depth knowledge tracking and group feature collaborative filtering. The results show that college students’ English learning behaviors are classified as active, passive, and passive, and the behavioral sequences of students in different modes are differentiated, in which the sequence residual value of active learners is greater than 1.96. There is a significant difference between the personalized teaching mode and the ordinary teaching mode in terms of the learning mode and the learning effect (P<0.05), and it can achieve a better English teaching effect.
The development of media technology profoundly affects the presentation mode, dissemination rate and scale of news information, which in turn reshapes the business chain and business landscape of the entire news media industry. Based on the analysis of the shortcomings of the LDA model, this paper proposes an improved LDA model with binomial distribution, and applies it to the analysis of the evolution of news topics. The model introduces binomial distribution to enhance the discriminative ability of lexical items, and parallelises it to improve the classification effect of news topics. In order to effectively obtain the relevant features of cultural communication in news documents, this paper introduces BERT to obtain word embedding and word vector matrix, and then realises the generation of theme word structure and theme words. The performance of the improved LDA model is verified through the THUCNews dataset, and the news topic morphology is visualised and analysed with the example data, and its morphological evolution, as well as the degree of contribution to cultural communication, is studied. The theme consistency score of the improved LDA model is -13.39 when the word generation probability is 1, which is 19.14% higher than that of the traditional LDA model. The intensity of the ‘cultural policy’ news format theme increases 14.44 times from 2010 to 2023, and the mean value of the ‘cultural governance’ news format theme’s contribution to cultural dissemination reaches 0.091. Based on the innovation and evolution of news forms, we can empower more communication channels for culture and spirit, so as to enhance people’s cultural self-confidence and national cultural soft power.
Dance drama is a comprehensive art with dramatic conflicts and plots based on the use of dance’s own language system, which plays an important role in cultural dissemination and aesthetic experience. The article designs a resource library of classic dance drama works in the way of WEB site, establishes a data dynamic distribution strategy to deal with structured data, and combines the consistent hash algorithm to optimize the load balancing of structured data in the resource library. Then, a graph convolutional neural network model and a sample-weighted aesthetic classification model are combined to establish an aesthetic assessment model for images of classical dance drama works, and a regularized matching module is designed. For the application effectiveness of the structured data processing strategy, the structured data processing of the classic dance and drama works resource library is verified, and the hyperparameters of the model, evaluation results and ablation experiments are also analyzed. Combined with the data in the resource library of classic dance drama works, the aesthetic experience of the audience was analyzed using a questionnaire. After using the dynamic distribution strategy to process the structured data, its write and query times were shortened by 40.05% and 17.89% compared to before use, and the response time under different index query load balance degrees did not exceed 55ms.The accuracy of the aesthetic assessment model for classical dance and drama works was 48.85%, and the accuracy improvement of the G-AANet model compared to BoTNet ranged from 0.93% ~ between 6.12%. The resource base of classical dance drama works established through structured data processing helps to enhance the audience’s aesthetic experience of dance drama works and helps them to appreciate the spiritual connotation of dance drama works.
This paper is based on the digital image processing technology, using the undamaged image information to restore and protect the frescoes. The discrete binary wavelet change is used to decompose and denoise the image signal. And decompose and filter the high-frequency component and low-frequency component of the image, choose different components, respectively, carry out coefficient transformation, and solve the OMP least-paradigm for different random matrices. The color space is selected, and the mural color space is channel decomposed according to the grayscale mode and restored separately. Establish an assumed datum for each independent face of the mural, establish a spatial coordinate system for it, realize the transformation of spatial coordinates, and realize the super-resolution three-dimensional reconstruction of the mural based on the generative adversarial network and the self-attention mechanism. Objective evaluation indexes and subjective evaluation indexes are established to compare the protection effect of different algorithms on murals. Compared with the traditional algorithm CDD, this paper’s algorithm improves the restoration time by 9.545~15.625 s, and the peak signal-to-noise ratio index improves by 1.35~4.769 db. In the results of the image extraction and processing, the calculated values of discrete curvature of the mural segments AB, CD, and EF ranges from -0.00945 to -0.00478, and the difference of standard deviation of the curvature from the target curvature is 6.477%. The approximate target curvature is obtained, and the algorithm has strong adaptive ability.
Electricity theft management is closely related to the economy of electric power enterprises. This paper proposes a power theft estimation method based on semi-supervised learning and time series analysis prediction. The electricity consumption data of power theft users are extracted as time series data, and in order to achieve multi-step prediction, MMD is utilized to improve the LSSVR semi-supervised learning algorithm. In addition, a perturbation term is introduced to optimize the convergence effect of the artificial bee colony algorithm, and a time series prediction algorithm based on improved artificial bee colony is established. Bringing in the power theft monitoring process to identify whether the user has power theft behavior, using the real power consumption dataset as the experimental validation data, comparing the identification accuracy of the prediction model. Predict the potential power theft of each user, solve the optimization model with the goal of optimal economic efficiency, and determine the actual ranking order of power theft users. The improved time series prediction algorithm proposed in this paper has a global error of 0.0003 and 0.0027 in dataset 1 and dataset 2, respectively, with the lowest global error and the highest overall accuracy of PSE prediction. And the algorithm predicts the list of users to be scheduled is basically the same as the list of users determined by the real PSE, which can achieve the maximum economic benefits.
Teaching evaluation is the feedback on the teaching effect of teachers and the learning effect of students. It has become a critical link in colleges and universities teaching management and teaching inspection. This paper proposes and applies an improved BT-SVM multi-classification algorithm to the education evaluation model. By calculating the relative distance between classes, the error accumulation phenomenon existing in the traditional SVM when dealing with multi-classification problems is solved. A classifier structure based on an incomplete binary tree is constructed to automatically classify teaching data by gradually dividing the data set and training the SVM classifier. By calculating the decision function value of the test sample in the binary tree, the category to which it belongs can be quickly determined. The education evaluation model follows the principle of legal compliance to improve the quality and efficiency of model evaluation and ensure the rule of law construction in colleges and universities. The research results show that the error rate of the BT-SVM algorithm in machine learning is below 0.1%, the fairness index is between 0.1-2, and the prediction accuracy is 96%. It shows that the machine learning algorithm can effectively improve the efficiency of education evaluation work and has the principle of fair legal compliance.
More and more problems are revealed in the process of popularization of higher education, especially the imperfection of the quality assurance system of higher education, which restricts and hinders the development of colleges and universities to a certain extent. This paper uses structural equation modeling to analyze the influencing factors of higher education quality. And consequently, it combines digital technology to build a higher education quality assurance system. Take a university as an example to practice, through the higher education quality assurance system evaluation index selection and empowerment, combined with the fuzzy comprehensive evaluation method to establish an evaluation model, to assess the effectiveness of the practice of the educational quality assurance system of the sample university. The management level (0.4380) and faculty (0.1472) of the university have the most significant influence on the quality of higher education. Under the constructed higher education quality assurance system, the comprehensive scoring result of the sample colleges and universities is at a good level (8.227), with the highest quality level in the dimensions of teaching effectiveness (8.7341) and student development (8.7000), which indicates that the digitization-based higher education assurance system is able to effectively ensure and promote the quality of education in colleges and universities.
Accompanied by the increasing consumer quality and the exploration of enterprises centered on user experience, the new retail model has emerged, and the emerging retail model also plays an important role in enhancing customer loyalty. This paper establishes an experiential sensory marketing model by combining perceptual theory and emerging technology from the basic features of the new retail model. Multiple linear regression model is used to study the influence of experiential sensory marketing mode on customer loyalty, and the correlation coefficient is used to analyze the correlation between the two. The correlation coefficients between the experiential sensory marketing model and customer loyalty range from 0.457 to 0.669, which is a moderate correlation. For every 1 percentage point increase in experiential sensory marketing mode, there is a significant increase of 0.647 percentage points in customer loyalty, and the average score value of customer attention under experiential sensory marketing strategy is 4.31 points. The sensory marketing strategy in experiential retail environment needs to improve the marketing standardization system, relying on professional service platform to improve the customer’s emotional experience, and then enhance customer loyalty.
Most areas in Hunan Province are rich in shale gas blocks, and their shale gas reservoir physical properties, geological characteristics, and enrichment rules need to be further studied. The article chooses the five # logging data of the Xiaoyanxi Formation in Anhua, Hunan Province, as the research object, preprocesses the logging data by curve environmental influence correction, curve reconstruction, and normalization, calculates the total organic carbon content and mineral composition change of shale gas by multiple linear regression, and uses the multi-mineral content calculated by optimization algorithm combined with the volumetric model to realize the matrix porosity of the variable skeleton. Then, the differential equivalent medium, self-compatible approximation, and K-T models were used to calculate the shale rock skeleton modulus. Then the shale gas reservoir petrophysical model was constructed. The adsorbed gas and free gas of the shale gas reservoir were solved separately to obtain the total gas content of the shale gas reservoir. The average TOC content solved by the model is 1.79%, which is only 2.23% higher than the absolute error of the actual data. When the volume fraction of the organic matter mixture increased from 0 to 0.25, the relative change of the longitudinal and transverse wave velocity ratio was only 0.87%. The shale gas content in Anhua Xiaoyanxi Formation 5# in Hunan Province ranges from 0.87 to 8.41 cm³/g, significantly higher than the lower limit value for shale gas industrial development. Recorded well data can clarify the reservoir’s physical characteristics of shale gas in Hunan Province and provide data support for exploring shale gas.
Supply chain optimization configuration contributes to the improvement and development of enterprise business application system. This paper takes the supply chain of manufacturing enterprises as the research object and analyzes the economic benefits of supply chain optimization of manufacturing enterprises. Aiming at the current development environment of enterprises, it puts forward the necessity of the development of enterprise supply chain flexibility, and establishes the overall supply chain flexibility model that contains the supply flexibility of the supply chain, the manufacturing flexibility and the distribution flexibility of the distributors. Simplify the total cost model of supply chain and establish the demand-driven supply chain optimization model. Analyze and validate the parameter settings of the improved particle swarm algorithm, and obtain the operating efficiency of the improved particle swarm algorithm with the changes of ordering cycle and inventory capacity. Combined with the sample enterprises, analyze the financial savings of each link after supply chain optimization. Further measurements show that after supply chain optimization of Company R, the saving percentage is 10.24%, and the annual saving amount is 562,807 yuan, with obvious economic benefits.
Whether tourism culture and economy develop in a coordinated manner is the key to realize the transformation and interaction of industrial structure. This paper takes the related data of 11 prefecture-level cities in Shanxi Province from 2013 to 2022 as the research object, and after demonstrating the intrinsic mechanism (the relationship of mutual influence) of the development of tourism culture and local economy, it applies the econometric panel Granger causality test to quantitatively test the interactive relationship between the development of tourism culture and local economy. After that, we constructed the index system of tourism culture and local economy, used entropy value method and coupling coordination model to analyze the comprehensive development level and coupling coordination degree of tourism culture system and regional economic system, and used Robust regression analysis to study the influencing factors of coupling coordination degree. The results of the study show that at the 5% significance level, with a lag of 5 and 6 periods, the local economic development is the Granger cause of tourism culture, and the local economic development has an obvious driving effect on tourism culture. In the 10 years of the examination period, the coupling coordination between tourism culture and local economy keeps growing, and the coupling coordination is improved, but there is still a certain gap with the high-quality coordination, meanwhile, the regression results show that focusing on the holistic and balanced development of the influencing factors is conducive to further coordination and interaction between the two systems.
The development of time and technology has made the traditional basic computer teaching unable to meet the needs of current students, and the cultivation of computer thinking ability has become a hotspot of computer education concern. The article combines the flipped classroom model with the MOOC platform of network education, and establishes a MOOC flipped classroom teaching model applicable to computer basic education courses in art colleges. In this model, students’ assessment results data are collected, and the Apriori algorithm optimised by matrix optimisation and prior pruning strategy is used to mine the association relationship of the assessment results, which helps teachers to understand students’ computer knowledge mastery. T College of Fine Arts is used as a research example to illustrate the effectiveness of MOOC flipped classroom through the changes in students’ performance, competence, and satisfaction. The improved Apriori algorithm has an execution time of only 2.93s when its minimum support is 100%, which can be used to understand students’ computer application ability for different question types, majors and skill performances. The mean score of the final exam of the students in the experimental class was 82.79, which reached 111.79% of the score of the control class, and more than 80% of them were satisfied with the MOOC flipped classroom. The use of flipped classroom and network education model can achieve the innovative development of computer basic education courses in art colleges and help to enhance the teaching quality of computer basic education courses in art colleges.
In an energy plan with a high rate of renewable energy acquisition, the comprehensive development of wind and solar energy and flexible power sources such as energy storage will play a key role in this process. The power supply structure in some areas is dominated by coal power, and there is a serious shortage of flexible power supply, which hinders the development of renewable energy. Therefore, this paper proposed a new energy operation and consumption planning method considering the flexible adjustment of the power supply ratio. This paper established a two-layer power planning model with the lowest cost to the whole society and the largest consumption of renewable energy. Then, based on the copula theory, a wind-solar combined consumption probability model is established, and the new energy output curve of the planning year is predicted. Finally, the power supply optimization was solved by the Hooke-Jeeves iterative method. The experimental part took a certain region as the research object, setting the proportion of flexible power supply to 24%. It found out the newly installed capacity of various power supplies, and compared the actual data in the region. The research results have shown that increasing the proportion of flexibly regulated power supply can effectively improve the operation and absorption capacity of new energy, the wind abandonment rate is reduced by 6.21%, and the light abandonment rate is reduced by 5.38%.
In the current period, green finance has become an inevitable trend in the development of the financial industry. The study collects the audience demand of green financial products through Octopus collector, uses micro-word cloud analysis system for data de-weighting and Chinese word segmentation, and calculates the keyword weights in the words using TF-IDF algorithm, and realizes the identification of green financial product innovation and economic benefits by combining with multi-dimensional innovation map. Subsequently, the indicators on green financial product innovation and environmental economy from 2007 to 2022 are combed, and a VAR-based econometric model is established to analyze the impact relationship between green financial product innovation and environmental economic benefits. The results show that when the lag period is 10 periods, the contribution of environmental economic benefit itself to the change of environmental economic benefit tends to be 23.79%, while the contribution of green investment products, green bond products and green insurance products to environmental economic benefit tends to be 9.72%, 20.23% and 21.83%, respectively. Green product innovation has a certain influence on the fluctuation of environmental economic benefits, and green bond products and green insurance products have a greater impact on environmental economic benefits.
This paper used wireless network selection algorithm to apply it to preschool education collaborative management. Through the analysis of its necessity, the final conclusion was drawn. In the survey of teaching resources, it was found that after the cooperative management of preschool education using wireless network, the number of teachers has increased more than three times, and the number of textbooks has also increased significantly. In the survey of teaching courses, it was found that the cooperative management of preschool education by using wireless network can promote the rationalization of courses offered by schools, thus promoting the improvement of students’ learning ability. In the survey of children’s learning ability, it was found that after the cooperative management of preschool education through wireless network, the average learning ability of large class students was 88 points, which was increased by 27 points, and the speed of improvement was the fastest. In the survey of children’s living ability, it was found that children’s living ability has been greatly improved after the cooperative management of preschool education by wireless network. In the investigation of the teaching environment, it was found that the teaching environment of the school has been greatly improved after the cooperative management of preschool education with wireless network. By applying it to the collaborative management of preschool education, it brings convenience and advantages to the collaborative management of preschool education, which promotes the development of preschool education in the direction expected by people, and the development of children is more healthy and lively.
This paper analyzes the development trend of Adobe After Effects-based movie special effects technology, including accelerated GPU research, integration and enhancement of 3D special effects production functions, cross-platform and cross-software collaboration, as well as the impact on the way special effects artists create. In terms of GPU research, the acceleration performance of different GPUs in movie simulation in China and abroad is compared, and the 3D effects production function involves the creation of 3D models, movie settings, rendering standardization, as well as the rendering output and the addition of special effects. Cross-platform and cross-software collaboration focuses on the cross-platform nature of AE, designing a tagged text file format and a movie playback engine based on the Cocos2d-JS game engine, and dividing the file system module. The analysis shows that the waiting time for movie rendering under this paper’s model is 1s and 2s, and the end-of-task ratio is 0.02, which are the lowest in both sets of experiments. The highest mean values for the 10 simulations of GPU utilization are 72.42% and 72.83%, respectively. It can be seen that the CPU acceleration model based on Adobe After Effect in this paper can effectively reduce the waiting time for movie rendering and improve the processing speed and stability of movie special effects.
Sustainable agricultural development is a key component of the rural revitalization strategy, and strengthening the guidance and support for sustainable agricultural development is an inevitable choice for improving agricultural production capacity and realizing rural revitalization. The study constructs an evaluation system of sustainable agricultural development based on four dimensions: economic opportunity, social well-being, environmental quality, and climate action, selects relevant index data of each province from 2004 to 2022, and adopts a multi-level factor analysis method to comprehensively evaluate the sustainable agricultural development as well as the dynamic distribution of 31 provinces in China. The results show that Henan leads other provinces in economic opportunities with a score of 1.21, and Hebei ranks first in social well-being with abundant human resources and policy support. In the level of regional sustainable agricultural development, there is an uneven distribution pattern of “North > Central > South”. From the dynamic distribution of agricultural sustainable development in 31 provinces from 2004 to 2022, it is concluded that the development trend of agricultural sustainable development in China is better, and the gap between provinces has been narrowed. Finally, policy recommendations are put forward based on the situation of agricultural sustainable development to provide reference for the subsequent work on agricultural sustainable development.
The family, as a socially balanced system, possesses the functions of social information communication and social resource conversion. From the perspective of the family system as a provider of educational resources, the input of various forms of resources into the family environment either helps parents to teach their children, helps them to learn, or is detrimental to the healthy growth of their children. In this paper, based on the nonlinear model of the static resource-opportunity allocation problem study, the objective constraints are added to establish a linear programming model. The column enumeration method is used to solve the linear programming, while the sensitivity of the linear programming is analyzed by pairwise test. On the basis of the random initial solution, a multilayer transportation algorithm is designed as the initial solution to further reduce the time of enumerating columns and complete the construction of the solution framework for the resource-opportunity allocation problem. The model is used to solve the problem of the distribution of socio-economic resources to educational opportunities between “two-child” and “one-child” families. The results show that the socio-economic resources of different families have different opportunities for children’s education, and there are significant differences between different types of “two-child” families in the three aspects of parent-child relationship satisfaction, feelings of parenting, and interpersonal evaluation of the child, with the F-values of 5.265, 4.859, and 5.136, respectively, with a p<0.01. The “last-child advantage” in children's education is related to the number of years of education of the fathers. In the 1949-1969 generation, the average number of years of education of the fathers was only 6.763, while in the 1970-1990 generation, the average number of years of education of the fathers increased to 8.685, and the cultural level of the family as a whole improved significantly, and the mechanism of resource constraints on the cultural level of the family began to take effect. The resource constraint mechanism at the cultural level is beginning to take effect.
The temperature gradient formed by cooling and heat dissipation after shutdown of aero-engine will lead to thermal bending of rotor, which is also the main reason for bending vibration of rotor after engine secondary start-up. In this paper, the rotor system model of a GTF motor considering the gearbox structure is established, and the thermal bending deformation of the fan-gearbox-low pressure rotor caused by temperature gradient is analyzed. The critical speed of the rotor system considering the temperature field is calculated and the vibration characteristics of the engine after the second start are analyzed, which provides a reference for the design of the rotor system of GTF engine. The results show that the rotor mainly appears hot bending deformation in the direction of vertical axis, especially in the joint of disc axis. The large bearing stiffness of the gearbox has obvious inhibition on the hot bending deformation of the rotor, and the effect is obvious when the bearing stiffness is above 1E6. The vibration characteristics of rotor are greatly affected by temperature field. The amplitude of rotor system is larger and the sensitivity of gearbox structure is higher under the influence of temperature field. The amplitude is also the largest when the thermal bending amount is maximum about 10min, and the amplitude decreases by 50% after 40min. The bearing stiffness of the gearbox has a great influence on the vibration characteristics of the rotor system with hot bending deformation, and the vibration suppression effect is best when the bearing stiffness is between 1E6 and 1E8, and the peak point above 1E8 is close to the operating speed of the fan, which is bad for the safe operation of the engine.
Wind energy is a widespread natural phenomenon, which receives more and more attention because of its renewable and non-polluting nature, but the unpredictable and unstable wind speed makes the wind power control technology become a hot spot of concern. Firstly, the working principle of the wind turbine system is introduced, and the wind turbine speed model of the wind turbine drive system is established according to the system stability characteristics. Then on the basis of the traditional PID control algorithm, a wind turbine rotation speed regulation optimization algorithm based on PID optimization control is proposed-PID neural network control. The algorithm designs a three-layer forward PID neural network, and through the PID variable structure control, the low-speed axis of the fan connects the rotor axis with the gear box, which excites the operation of the aerodynamic gate for speed regulation, and compared with the traditional PID control, the method can regulate the airflow of the coal mine fan more quickly, and the overshooting amount is reduced by about 22%. Then, the BP neural network control is used to predict the air demand, and the deviation of the predicted air demand from the current air demand and its chemical rate are input into the controller. Finally, through the comparison of the control system and simulation experiments, it is proved that the BP neural network control has stronger robustness and adaptability, and can achieve better control effect.
The study uses a multilevel nonlinear optimization algorithm to optimize the low-carbon development path of agriculture with the dual constraints of government regulation and agricultural insurance. The algorithm solves the development path optimally through convergence analysis, parameter setting and constraint problem modeling. In addition, the study establishes an index system for evaluating agricultural low-carbon development, and assesses the effectiveness of low-carbon development through field application. The algorithmic path optimization in this paper has better performance in terms of solution quality, iteration number and solution time. At iterations 17, 43, 62 and 82, the algorithm of this paper found feasible solutions for path optimization. By 2023, the annual increase in pollutant emissions from agricultural production, total carbon emissions, carbon emission intensity of 10,000 yuan output value, and comprehensive energy consumption of 10,000 yuan output value are projected to be reduced to 42696.39(tons), 21141.5(10,000 tons), 1017.9(tons), and 6422.6(tons), respectively. The evaluation indicators Agricultural activity average carbon intensity, Reduction of carbon intensity and other indicators have relatively high weights, which is the main reason for the differences in low carbon development.The correlation between the effectiveness of agricultural low carbon development and the optimal sequence in 2023 is 0.9981, which demonstrates that the role of government regulation and agricultural insurance in promoting agricultural low carbon development.
In order to improve the efficiency of rail bolt automation operation, this study proposes a non-dominated sorting genetic algorithm II (NSGA-II) based on the improvement of elite strategy for the multi-robot task allocation problem. First, a multi-objective optimization model is established by combining the actual demands of rail bolt operations. Then, the classical NSGA-II algorithm is improved by introducing an elite strategy to enhance its global search capability and convergence performance. Finally, the effectiveness and superiority of the improved algorithm in task assignment are verified by simulation experiments. The experimental results show that the improved NSGA-II algorithm has significant advantages in optimizing the efficiency of rail bolting operations and task balancing, which provides a strong support for task allocation in multi-robot systems.
The integration of Civics elements into the EFL classroom is an organic supplement and deepening of the teaching content and materials, while EFL Civics classroom teaching is a powerful means to strengthen the deep and longitudinal development of students’ critical thinking. This paper discusses the relationship between EFL Civics classroom teaching and the development of critical thinking ability from the theoretical and practical levels respectively. On the basis of existing research, the evaluation index of students’ critical thinking ability is proposed. The CVM coefficient of variation method is improved, and the ICVM and BP neural network algorithm are combined to constitute the evaluation model of students’ critical thinking ability based on ICVM and BP neural network. According to the evaluation process, the level of students’ critical thinking ability after EFL-based Civics classroom teaching is derived. It also integrates teachers’ and students’ evaluation of the effect of English Civics elements integrated into the EFL classroom, and finally obtains the practical teaching effect of the EFL Civics classroom. The overall mean value in the teacher’s side is greater than 3.5 points, which indicates that teachers are basically positive about the effect of integrating Civics in EFL courses, and basically agree with the positive impact of English Civics elements on EFL classroom teaching. Based on the evaluation results of ICVM and BP model, the evaluation scores of students’ critical thinking skills and critical thinking monitoring are higher than the evaluation scores of critical thinking tendency, i.e., the elements of English Thinking can be effectively integrated into the EFL classroom and promote the development of students’ critical thinking skills.
Digital teaching strategies can significantly stimulate students’ interest in learning and provide personalized learning pathways. This paper proposes a multimodal action recognition method that integrates the word vector method, and designs a teaching decision optimization strategy based on this idea. Firstly, we compare the information of different modalities, complete the construction of multimodal action recognition network through the processing of image information and optical flow information, and combine the word vector method to guide the semantic learning of students’ actions. Then the design and realization process of the teaching decision aid system is introduced. Based on the above proposed action recognition method to collect students’ classroom behavior data for model training to be used in the system, the system consists of four modules: model training, classroom data collection, behavior recognition and data presentation. After the data collection, the action recognition of student behavior is carried out to provide teachers with feedback on student behavior information and assist them in making teaching decisions. In this paper, the above algorithms and systems have been verified by relevant experiments. After comparison with other algorithms, it is verified that the multimodal action recognition method designed in this paper, which incorporates the word vector method, has a high accuracy rate. In the comparison of the overall quality of instructional design decisions, the average value of the instructional decision aid system in this paper is 17.35, which is higher than the average score of excellent human teachers in the overall quality of instructional design decisions, indicating that the instructional decision aid system designed in this paper achieves the optimization of instructional decisions and reaches the level of excellent decisions.
This paper proposes a user electricity data mining method based on deep learning and improved locust optimization algorithm, and at the same time adopts the Pearson correlation coefficient method to reduce its dimension to improve the data mining effect of linear weighted KFCM algorithm. In order to deal with the electricity demand of massive electricity customers, the user electricity demand forecasting model is constructed based on the Extreme Learning Machine ELM algorithm by combining the relationship between short-term loads and factors of electricity customers. Construct the service optimization model with the maximization of benefit index as the objective function, and use the BAS algorithm to solve the optimal solution in order to achieve the effect of user service optimization. Determine the experimental platform and model parameters, and carry out an example analysis of demand forecasting and service optimization for electricity users.C class users have a small electricity load except for breakfast and dinner, and the maximum time period of the electricity load is from 18:00 to 20:00 hours. Combined with MAPE, the ELM model improves 4.57% than SVR, 21.9% than LSTM, and 34.37% than ARIMA, which indicates that the ELM model is more effective and higher in demand forecasting for electricity users. In addition, the optimal solution of the effect of the BAS algorithm is 69 yuan, 102 yuan and 49 yuan higher than that of the GA algorithm in terms of dividend transmission benefit, energy saving and emission reduction benefit, and electricity right trading benefit, respectively, and the optimal solution based on the BAS algorithm is closer to the actual benefit value, which fully proves the effectiveness of the service optimization model based on the BAS algorithm.
Mobile communication technology is a universal technology. It is one of the most advanced technologies in human history due to its rapid development speed, strong penetration and wide application. The rapid development and wide application of information and communication technology has brought profound impact on economic growth and social transformation. In order to test the relationship between population change and economic development in the process of urban development by mobile communication technology, cluster analysis and similarity coefficient are used to analyze the indicators of economic development and to predict the law of economic development. In order to understand the changes brought by mobile communication technology to economic development in detail, through the analysis of the usage of mobile communication users in China and the economic development of the region from 2017 to 2019, the results showed that Beijing was in the best development situation. Compared with other regions, Tibet was the lowest. It grew over time, with a 5% increase from 2017 to 2019. It could be seen from this point that vigorous development in the field of mobile communications would provide new opportunities for major cities to break through development bottlenecks and solve development dilemmas as well as promote urban transformation and innovative development.
Smart contract technology based on artificial intelligence background is gradually becoming a brand-new path to improve the efficiency of economic transactions due to its unique advantages. This paper initially explores the impact of smart contract technology on economic transaction efficiency through empirical analysis of models and data. The credit mechanism is introduced as an intermediate variable to analyze its mediating effect in the process of improving economic transaction efficiency by smart contract technology. The optimization of Fabric transaction mechanism is realized by using the improved credit model, which further exerts the role of smart contract technology in enhancing economic transaction efficiency. The principal component analysis is used to calculate the comprehensive score of economic transaction efficiency before and after the optimization of smart contract trading mechanism to show the effect of the development of smart contract technology on the improvement of economic transaction efficiency. This paper concludes that the development of smart contract technology will significantly and positively promote the improvement of economic transaction efficiency through benchmark regression analysis, mediation effect test and other methods. After the optimization of smart contract transaction mechanism, the comprehensive score of economic transaction efficiency produces significant improvement compared with the pre-optimization period, in which the average value of the comprehensive score of transaction efficiency in Guangdong, Jiangsu, Shanghai, and Beijing is improved by 20.18%, 24.52%, 33.77%, and 35.54%, respectively. It further indicates that smart contract technology is an effective path to improve economic transaction efficiency.
Elevator is a convenient building transportation for people to travel, and more and more elevators are being registered and put into use, and the ACCOMPANYING problems of elevator failure and maintenance are becoming more and more prominent. In this study, the Kalman filter algorithm is used to optimize the feature extraction performance and prediction accuracy of the deformable convolutional TimesNet model for elevator operation time series data, and the improved TimesNet model is fused with the DLinear model to construct the TimesNet DLinear model for predicting elevator operation accidents. Finally, the TimesNet DLinear model is used as the main analysis modu le to design the elevator operation accident prediction system. After testing, it is found that the TimesNet DLinear model can maintain a low error in the prediction of elevator operation data, with an average absolute error of 0 167 , and the prediction ac curacy is better than other prediction models. It is also found that the elevator operation accident prediction system is able to predict the accidents in the elevator operation in a certain district and make a warning according to the current error thresh old situation. The elevator operation accident prediction system proposed in this study is able to realize real time monitoring and early warning of elevator failures, providing an effective solution for real time decision making and scheduling of elevator maintenance.
With the development of renewable energy technology and the pursuit of sustainable development in the construction industry, the design of direct-soft photovoltaic systems integrated with buildings has become an important research direction. In this paper, a variety of photovoltaic power generation modules are selected and combined with building roof functions to design a solar photovoltaic building integration system. In addition, this paper constructs a multi-objective optimization configuration model, improves the multi-objective particle swarm algorithm, and analyzes the optimization effect of the improved particle swarm algorithm on the photovoltaic building integration system by using multiple sets of test functions and evaluation indexes, combined with a number of experiments. The improved particle swarm algorithm in this paper converges to the optimal value of 0.21 when iterating to 25 rounds. And with the increase of the number of nodes, the optimized particle swarm algorithm, the distribution of node voltages in the vicinity of the standard voltage. The PV building integrated system designed in this paper still has a generation output efficiency higher than 85% after 20 years, which shows good stability of power generation. And the power generation in its whole life cycle is about 1645710kwh, which greatly reduces the consumption of conventional energy. In conclusion, the PV building integrated system in this paper not only has significant advantages in terms of capacity efficiency, but also shows strong potential for environmental protection.
As the birthplace of national culture, traditional villages can convey cultural and social natures through spatial configuration. Based on the theory of spatial syntax, this paper combines the genetic algorithm to design the fitness function for optimization, and selects the streets and lanes of Wengji Village as the research sample, focusing on the analysis of its morphological evolution mechanism from 1975 to 2020. Through quantitative analysis, it is found that although the streets and alleys of Wengji Village show spatial scale expansion due to social and economic development, the village streets and alleys can still maintain the original spatial texture and style. The integration degree, selectability, synergy (0.4273~0.6395) and comprehensibility (0.3744~0.5761) of the streets and alleys in Wengji Village are all characterized by increasing, indicating that the spatial accessibility, spatial openness and spatial wholeness of the streets and alleys in Wengji Village have been improved. However, the degree of synergy and comprehensibility are still lower than 0.7, and there is some room for optimization of the wholeness and cognizability of the streets and lanes of Wengji Village. It is necessary to protect and continue the overall structure of the village, optimize and integrate the key spaces of the village, and rationally control the development process of the village, so as to promote the protection of the spatial form of the streets and lanes of Wengji Village and the continuation of the cultural lineage.
Green construction is becoming a mainstream model of the transformation and upgrading of the construction industry, which has the advantages of energy saving, environmental protection and ecology, which can effectively reduce energy deficiency and improve environmental quality, which is the need for high quality sustainable development. This study is based on BIM software and the intelligent construction technology to propose the green architectural design party case. Building energy-saving efficiency evaluation system, using fuzzy Borda method and the CRITIC method of evaluation, the objective of the index, and the example of a community, the use of the object meta-effect model. The evaluation scores of the energy saving efficiency of the building of green energy saving and renovation are in the 90.11-99.28 points, and the high energy demand in the process of running the use of the building is excellent in the heating, refrigeration and other aspects of the building. This paper shows that the goal of the green transformation project is basic, which is effective and the efficiency of energy efficiency is generated. This study can provide guidance for the work of the green building energy saving and renovation work, and further promote the energy saving and transformation of China.
The supply chain applies large number according to the technology, can reduce the cost of each link, optimize the resource allocation, increase the enterprise benefit. In this paper, the supply chain cost control program based on large number according to the previous forecast, the control of the event and the analysis of the three levels of the analysis of the supply chain. The combination time series model and the multivariate regression model, the joint CPFR concept, the establishment of the CPFR sales combination demand prediction model, the design form according to the sales prediction system, the resource optimization plan of the supply chain inventory in real time. Analyze the prediction effect of the combination prediction model, predict the product sales in the week, calculate the product safety inventory and the remaining inventory. The analysis is based on the cost control effect of the enterprise supply chain according to the sales forecast. The cost of purchasing the supply chain costs less than the operating income, which fell to 0.5107in 2023. The gross margin of gross profit was 0.53666 in 2023, which was controlled by the gross margin, and the gross profit was improved. It is said that the enterprise is using large number according to the technology to the supply chain resource optimization in the supply chain cost control, the cost control effect is better.
The research of modular and personalized balance strategy in assembly building design can improve the efficiency of construction and meet the demand of design diversification. Based on bim technology, an assembly building modular design method is proposed to determine the required space module, to determine the required space module, to strengthen the module structure, to set up the layout of the building, to formulate the modular panel and the assembly frame platform, and through the revit implementation of the three-dimensional visual design of modularity and personalization. The design of the 9 building of this article, in the collaborative function, spatial adaptability and the design diversity score in turn for 10th 10 “10” 9 points (full score 10). This article is designed to meet the demand of the building in daylighting and ventilation, the average daylighting coefficient is 6.440%, and the minimum value of the floor area of the building room is 18.25cent. Modular and personalized assembly frame structures have a better seismic resistance, and their limit cumulative energy consumption is 2.38 times the traditional way. Experts have the highest social benefit satisfaction in this article strategy, and the satisfaction score is 92.05.
Based on the definition of volatility and conditional value risk (CVaR), this paper introduces the implied volatility into CVaR model, and further analyzes the partial differential equation of stock portfolio optimization in the form of BS model. In the process of multi-stage investment, in order to reasonably control the investment risk of each stage, the CvaR model based on implied volatility is constructed by using the scenario tree method. With the data of 1166 trading days as the data, 4 stock assets as the data set of this study, the optimization model is applied to the calculation and analysis. The numerical simulation shows that the stock price fluctuation of the four multi-cycle stocks ranges from -23.45% to 41.97%, showing a clustering phenomenon. Among them, the volatility of stocks A and C is more obvious than that of stocks B and D, and the probability density tails of stocks are longer in the cycle, and they all show thick tail characteristics, indicating that the introduction of implied volatility of CVaR model makes the risk control of actual equity asset investment more reasonable.
In this paper, the characteristics and distribution of the spatial clustering diffusion characteristics and distribution of the spatial accumulation of rural areas are quantified by using the GIS space analysis method, the analysis method of the nuclear density estimation, the hotspot analysis, the spatial self-correlation, and the large number of the rural areas of Chongqing. Compared with the difference of the amount of the education facility in Chongqing, the difference between the amount of the education facility was compared, and the development gap of the education facility was assessed. The study showed that in 2023, the imbalance coefficient of the school of compulsory education in Chongqing was reduced from 0.3637 in 2013 to 0.02433 in 2023, and the primary school stage was reduced from 0.3582 to 0.1952. This paper shows that the imbalance coefficient of education resource layout in Chongqing is decreasing year by year, and the spatial equilibrium of resource space increases. This study provides the effective thinking and method for the adjustment of the education resource space layout structure in Chongqing, and provides the scientific decision basis for the calibration of the existing planning and the formulation of future planning.
This paper discusses the appreciation of the elderly to influence the actual exchange rate by using the requirements structure and the current account mechanism. Using the internal actual exchange rate formula and the Balassa-Samuelson effect, the propagation mechanism of the aging of the population was established. This paper discusses the influence of aging on trade balance, and sets up the panel model of countries of different age categories. Through heterogeneity analysis and multivariate regression test assessment. The study of mathematical methods found that the rate of pension care significantly affected the actual effective exchange rate. In countries where aging and moderate aging lead to depreciation, aging and non-ageing countries can rise.
In this paper, the basic Wiener filter structure and adaptive algorithm module are used to optimize the parameter adjustment and data noise processing in the adaptive filter algorithm. Based on the LMS criterion, the algorithm is further refined by quantization error and affine projection optimization, which improves the accuracy and speed of vortex and circulation data analysis. The optimized algorithm reduces noise and covariance error, and achieves excellent performance in filtering evaluation (SRTAE: \(1.623\times10^{-2}\,\text{m}\) and \(1.162\times10^{-4}\,\text{m/s}\)). The results show that the spatio-temporal coupling effect between vortex and circulation can be found through numerical modeling and spatio-temporal analysis. This study provides a valuable reference for promoting the application of computational mathematics in the field of climate monitoring.
The stretched Littlewood-Richardson coefficient \(c^{t\nu}_{t\lambda,t\mu}\) was conjectured by King, Tollu, and Toumazet to be a polynomial function in \(t\). It was shown to be true by Derksen and Weyman using semi-invariants of quivers. Later, Rassart used Steinberg’s formula, the hive conditions, and the Kostant partition function to show a stronger result that \(c^{\nu}_{\lambda,\mu}\) is indeed a polynomial in variables \(\nu, \lambda, \mu\) provided they lie in certain polyhedral cones. Motivated by Rassart’s approach, we give a short alternative proof of the polynomiality of \(c^{t\nu}_{t\lambda,t\mu}\) using Steinberg’s formula and a simple argument about the chamber complex of the Kostant partition function.
In this work, we study type B set partitions for a given specific positive integer \(k\) defined over \(\langle n \rangle = \{-n, -(n-1), \cdots, -1, 0, 1, \cdots, n-1, n\}\). We found a few generating functions of type B analogues for some of the set partition statistics defined by Wachs, White and Steingrímsson for partitions over positive integers \([n] = \{1, 2, \cdots, n\}\), both for standard and ordered set partitions respectively. We extended the idea of restricted growth functions utilized by Wachs and White for set partitions over \([n]\), in the scenario of \(\langle n \rangle\) and called the analogue as Signed Restricted Growth Function (SRGF). We discussed analogues of major index for type B partitions in terms of SRGF. We found an analogue of Foata bijection and reduced matrix for type B set partitions as done by Sagan for set partitions of \([n]\) with specific number of blocks \(k\). We conclude with some open questions regarding the type B analogue of some well known results already done in case of set partitions of \([n]\).
Suppose that \(\phi\) is a proper edge-\(k\)-coloring of the graph \(G\). For a vertex \(v \in V(G)\), let \(C_\phi(v)\) denote the set of colors assigned to the edges incident with \(v\). The proper edge-\(k\)-coloring \(\phi\) of \(G\) is strict neighbor-distinguishing if for any adjacent vertices \(u\) and \(v\), \(C_\phi(u) \varsubsetneq C_\phi(v)\) and \(C_\phi(v) \varsubsetneq C_\phi(u)\). The strict neighbor-distinguishing index, denoted \(\chi’_{snd}(G)\), is the minimum integer \(k\) such that \(G\) has a strict neighbor-distinguishing edge-\(k\)-coloring. In this paper we prove that if \(G\) is a simple graph with maximum degree five, then \(\chi’_{snd}(G) \leq 12\).
Let \(2 \le k \in \mathbb{Z}\). 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. In this work, focus is set upon graphs of girth \(k+1\). Efficient total colorings of finite connected simple cubic graphs of girth 4 are constructed starting at the 3-cube. It is conjectured that all of them are obtained by means of four basic operations. In contrast, the Robertson 19-vertex \((4,5)\)-cage, the alternate union \(Pet^k\) of a (Hamilton) \(10k\)-cycle with \(k\) pentagon and \(k\)-pentagram 5-cycles, for \(k > 1\) not divisible by 5, and its double cover \(Dod^k\), contain TCs that are nonefficient. Applications to partitions into 3-paths and 3-stars are given.
Using generating functions, we are proposing a unified approach to produce explicit formulas, which count the number of nodes in Smolyak grids based on various univariate quadrature or interpolation rules. Our approach yields, for instance, a new formula for the cardinality of a Smolyak grid, which is based on Chebyshev nodes of the first kind and it allows to recover certain counting-formulas previously found by Bungartz-Griebel, Kaarnioja, Müller-Gronbach, Novak-Ritter and Ullrich.
Topological indices have become an essential tool to investigate theoretical and practical problems in various scientific areas. In chemical graph theory, a significant research work, which is associated with the topological indices, is to deduce the ideal bounds and relationships between known topological indices. Mathematical development of the novel topological index is valid only if the topological index shows a good correlation with the physico-chemical properties of chemical compounds. In this article, the chemical applicability of the novel GQ and QG indices is calibrated over physico-chemical properties of 22 benzenoid hydrocarbons. The GQ and QG indices predict the physico-chemical properties of benzenoid hydrocarbons, significantly. Additionally, this work establishes some mathematical relationships between each of the GQ and QG indices and each of the graph invariants: size, degree sequences, maximum and minimum degrees, and some well-known degree-based topological indices of the graph.
Some methods of decomposing \(v(=mn)\times b\) incidence matrix of regular group divisible (RGD) designs into square submatrices of order \(m\) are described. Such designs are known as tactical decomposable designs. As a by–product, resolvable solutions of some RGD designs are obtained. A relationship between tactical decomposable designs and \(\left(2,\ n\right)-\)threshold schemes is also given.
Cultural heritage represents the historical and cultural achievements of a nation, playing a vital role in studying human civilization and preserving national languages and scripts. This study utilizes virtual simulation technology to design a virtual pavilion for Chinese language and writing, employing image and text feature extraction algorithms for feature fusion and 3D modeling. The effectiveness of Chinese character extraction is validated through feature point matching, while the virtual exhibition’s impact is assessed via user experience scores. Results indicate that the proposed algorithm achieves accurate extraction with no misrecognition. User interest rankings highlight text images as the most influential factor, followed by visual imagery, pavilion experience, scene art, and language culture. Analysis of user feedback shows an average experience score exceeding 60 points, confirming the pavilion’s effectiveness in preserving and promoting Chinese language and writing culture.
In recent years, due to the adjustment of economic structure, the people’s living standard and the increase of leisure time, the sports industry has become a new economic growth point. This paper studies and analyzes the characteristics of the industry background and business background of the sports industry, explores the factors and internal driving force affecting the design of its business model, and fully analyzes the mechanism, functional role, and logical relationship of the elements for constructing the business model of the sports industry, and then explores the characteristics of the business style of the sports industry. From the perspective of knowledge state, using the reinforcement learning mechanism, the evolution process of the sports industry business model from the first stage to the fourth stage is described. Taking Company H as a research case, the process and economic effect of the transformation and upgrading of its business model through the reinforcement learning mechanism is analyzed and it is found that as of 2023 the company’s operating income has increased by 2.4 times through transformation and upgrading, and its net profit has increased by 125.57 percentage points compared to 2016. It further understands the role that the enhanced learning mechanism brings to the development of the sports industry, and expects to be able to provide a reference for the sports industry to carry out business model transformation in the future.
We initiate a study of the toughness of directed graphs by considering the natural generalization of that for ordinary graphs. After providing some general results, computations are completed for a few natural examples. Maximum possible toughness is also considered. Some open problems are posed.
Let \(G\) and \(H\) be graphs and \(1\) be a positive number. An \(H\)-irregular labeling of \(G\) is an assignment of integers from \(1\) up to \(k\) to either vertices, edges, or both in \(G\) such that each sum of labels in a subgraph isomorphic to \(H\) are pairwise distinct. Moreover, a comb product of \(G\) and \(H\) is a construction of graph obtained by attaching several copies of \(H\) to each vertices of \(G\). Meanwhile, an edge comb product of \(G\) and \(H\) is an alternate construction where the copies of \(H\) is attached on edges of \(G\) instead. In this paper, we investigate the vertex, edge, and total \(H\)-irregular labeling of \(G\) where both \(G\) and \(H\) is either a comb product or an edge comb product of graphs.
This study applies Support Vector Machine (SVM) and K-Nearest Neighbors (KNN) algorithms to classify five types of basketball footwork. SVM maps the training data into a high-dimensional space using nonlinear transformation and classifies it with support vectors and a hyperplane. Experimental analysis showed minimal differences in peak and trough values of footwork movements; therefore, only mean and standard deviation features were retained, resulting in 12 effective features. KNN experiments demonstrated that recognition accuracy varies with different K values. The highest accuracy (80.7%) was achieved when K = 5 with the selected features. The study also examined the physical characteristics of basketball players, analyzing height, weight, and other indicators. Statistical results showed no significant body shape differences between experimental and control groups (P > 0.05). A T-test on dribbling, shooting, and layup performance also revealed no significant differences between the groups (P > 0.05).
This study explores the employment competitiveness of computer science majors by integrating combinatorial mathematics into the evaluation process. Utilizing the Analytic Hierarchy Process (AHP) and the improved FKCM clustering algorithm, we construct a hierarchical model to assess the impact of entrepreneurial education, learning motivation, and investment on job competitiveness. Data from 314 participants were analyzed using combinatorial techniques to derive optimal weightings for each factor, ensuring the evaluation model’s robustness. The results highlight significant gender differences in practical and feedback-based entrepreneurship education, with males outperforming females. However, no notable differences were observed in job interest, learning motivation, or overall employment competitiveness.
An (unrooted) binary tree is a tree in which every internal vertex has degree \(3\). In this paper, we determine the minimum and maximum number of total dominating sets in binary trees of a given order. The corresponding extremal binary trees are characterized as well. The minimum is always attained by the binary caterpillar, while the binary trees that attain the maximum are only unique when the number of vertices is not divisible by~\(4\). Moreover, we obtain a lower bound on the number of total dominating sets for \(d\)-ary trees and characterize the extremal trees as well.
This paper proposes an optimized Backpropagation (BP) neural network for improving intelligent elderly care talent training. To address BP’s limitations, including noise sensitivity and slow convergence, we introduce Particle Swarm Optimization (PSO) to refine network weights and thresholds. The model integrates course quality, teacher effectiveness, platform support, and market demand, aiming to optimize elderly care service talent cultivation. Experimental results demonstrate a significant improvement in prediction accuracy, with average error reduced from 9.94% to 6.3%. This enhanced model offers a more efficient and accurate solution for aligning educational outcomes with industry needs.
Amnesty international is recognized as a key force in promoting social development, with higher education also facing the need for innovation. This paper explores new opportunities in educational theory and policy proposed in a recent initiative. The proposal emphasizes filtering ideology, political education, and public opinion to enhance the accuracy of ideological and political teaching. By incorporating personal suggestions through interviews, the model recommends learning materials tailored to student characteristics. System implementation and testing demonstrate its potential as a core tool for ideological education in colleges, supporting the integration of knowledge, politics, and technology to meet students’ educational needs.
Networks with smaller strong diameters generally have better fault tolerance because they enable closer connections between vertices, leading to shorter information paths. This allows the network to maintain communication and functionality more effectively during attacks or failures. In contrast, larger strong diameters mean vertices are connected over longer distances, increasing vulnerability to disruptions. Thus, the strong diameter is a key metric for assessing and optimizing network fault tolerance. This paper determines the optimal orientations for the Cartesian and strong products of even cycles, provides the minimum strong diameters and their bounds under specific conditions, and establishes a lower bound for the maximum strong diameter. A conjecture about the exact value of the maximum strong diameter is also proposed.
For a graph \(F\) and a positive integer \(t\), the edge-disjoint Ramsey number \(ER_t(F)\) 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 edge-disjoint monochromatic copies of a subgraph isomorphic to \(F\). Since \(ER_1(F)\) is in fact the Ramsey number of \(F\), this concept extends the standard concept of Ramsey number. We investigate the edge-disjoint Ramsey numbers \(ER_t(K_{1, n})\) of the stars \(K_{1, n}\) of size \(n\). Formulas are established for \(ER_t(K_{1, n})\) for all positive integers \(n\) and \(t = 2, 3, 4\) and bounds are presented for \(ER_t(K_{1, n})\) for all positive integers \(n\) and \(t \ge 5\). Furthermore, exact values of \(ER_t(K_{1, n})\) are determined for \(n = 3, 4\) and several integers \(t \ge 5\).
The development of artificial intelligence enables computers to not only simulate human artistic creations, but also synthesize fine art works with deeper meanings based on natural images. This study digitally parses the fusion of fine art and philosophy visual expressions, and develops a visual expression system based on the fusion of fine art and philosophy by utilizing a variety of key big data algorithms for visual expressions such as adversarial networks. Research on pattern recognition of this system in art creation is carried out through model training, recommendation performance evaluation, pattern recognition strategy application and regression analysis. The model in this paper works best when the number of nearest neighbors k=15, and the recommendation model in this paper can provide a personalized list of artwork recommendations for different people. The recognition of the system in this paper in the five dimensions of “spiritual level”, “value level”, “philosophical level”, “aesthetic level” and “technical level” is distributed between 4.24\(\mathrm{\sim}\)4.55. The results of regression analysis indicated that the system in this paper can improve the artistic creation as well as pattern recognition.
With development of Internet of Things, big data and artificial intelligence, cell phone signaling data, point-of-interest data and machine learning methods have been widely used in research of various fields of transportation. The use of big data processing techniques and machine learning methods to mine intercity travel data collected by various types of traffic detectors provides a new way of thinking to study travel mode selection behavior. In this paper, we pre-processed cell phone signaling data, geospatial data and interest point data around three aspects: personal attributes, travel attributes and travel mode attributes, and designed intercity travel target group extraction, travel chain extraction, travel mode extraction and travel purpose extraction algorithms, which provide basis for travel feature analysis and travel mode choice behavior prediction modeling.
Conventional techniques to electric power network (EPN) design and management are insufficient to handle extreme weather events like hurricanes due to the growing complexity and fragility of power systems. As a sophisticated simulation and optimization tool, digital twin (DT) technology may offer real-time power infrastructure monitoring and prediction. This study aims to investigate the possible application of digital twin technology in enhancing power system resilience and streamlining the design process, as well as to use it for the 3D design of the full substation engineering infrastructure process. A digital twin-based EPN model that incorporates all of the main components of the power system—power plants, substations, transmission and distribution networks, and customers—is proposed in this paper. Every component of the power system undergoes vulnerability analysis, and the chance of the system failing is calculated using a Bayesian network (BN) model and a parametric vulnerability function. According to modeling projections, Hurricane Ike will cause the majority of consumers’ power supplies to be interrupted. The model predicts that power consumption for residential, commercial, and industrial buildings will be 96.4%, 96.0%, and 94.2%, respectively, depending on the kind of building.
Radar ranging and speed measurement are common applications in daily life, with performance largely dependent on the radar signal processor. However, existing civilian radar signal processors struggle with weak signal reception and low analysis efficiency. This study designs a high-speed radar signal processor based on FPGA architecture, incorporating a fusion processing algorithm to integrate different radar signal bands, enhancing processing efficiency and accuracy. The design includes data feature analysis, storage, and fusion modules. Tests showed that the processor achieved real-time performance with a processing time under 1ms, a ranging error below 1m, and speed measurement accuracy within 5m/s, meeting practical requirements.
Intriguing symmetries are uncovered regarding all magic squares of orders 3, 4, and 5, with 1, 880, and 275,305,224 distinct configurations, respectively. In analogy with the travelling salesman problem, the distributions of the total topological distances of the paths travelled by passing through all the vertices (matrix elements) only once and spanning all elements of the matrix are analyzed. Symmetries are found to characterize the distributions of the total topological distances in these instances. These results raise open questions about the symmetries found in higher-order magic squares and the formulation of their minimum and maximum total path lengths.
In this paper, we introduce the concept of vertex-edge locating Roman dominating functions in graphs. A vertex-edge locating Roman dominating \({(ve-LRD)}\) function of a graph \(G=(V,E)\) is a function \(f:V(G)\rightarrow\{0,1,2\}\) such that the following conditions are satisfied: (i) for every adjacent vertices \(u,v\) with \(f(u)=0\) or \(f(v)=0\), there exists a vertex \(w\) at distance \(1\) or \(2\) from \(u\) or \(v\) with \(f(w)=2\), (ii) for every edge \(uv\in E\), \(\max[f(u),f(v)]\neq 0\), and (iii) any pair of distinct vertices \(u,v\) with \(f(u)=f(v)=0\) does not have a common neighbour \(w\) with \(f(w)=2\). The weight of ve-LRD function is the sum of its function values over all the vertices. The vertex-edge locating Roman domination number of \(G\), denoted by \(\gamma_{veLR}(G)\), is the minimum weight of a {ve-LRD} function in \(G\). We proved that the vertex-edge locating Roman domination problem is NP-complete for bipartite graphs. Also, we present the upper and lower bounds of \({ve-LRD}\) function for trees. Lastly, we give the upper bounds of \({ve-LRD}\) function for some connected graphs.
Traditional personnel recruitment methods are often inefficient and struggle to find candidates who meet job requirements. In this paper, we first develop a comprehensive personnel management system for colleges and universities that streamlines the recruitment process and information management. Next, recruitment data from the system is analyzed using the fuzzy C-means algorithm to cluster applicant profiles and extract position-specific user characteristics. Finally, a joint embedded neural network is employed to match applicant profiles with job positions by optimizing an objective function. Experimental results demonstrate a high job matching rate (up to 98.1%), a significantly reduced recruitment cycle (from job posting to candidate onboarding in 25 days), and a system response time as low as 0.5 seconds. These findings highlight the effectiveness of big data technology in providing timely feedback, reducing recruitment costs and staff workload, and promoting the intelligent development of talent recruitment.
The rapid development of information technology makes intelligent decision support system play an increasingly important role in economic standardized management. The Intelligent Decision Support System (IDSS) constructed in this paper includes interaction layer, analysis layer and data layer. The system standardizes the management of enterprise economy through strategic forecasting and decision analysis, economic planning and control, and economic analysis. The study combines the fuzzy hierarchical analysis method (FAHP) and the fuzzy comprehensive evaluation method (FCE) to evaluate the standardized level of economic management of enterprise A. The evaluation score of the standardized level of enterprise A’s economic management is \(F=80.955\), which is greater than 80, and it belongs to the grade of “good”. It shows that the intelligent decision support system constructed based on this paper can effectively help standardize the management of enterprise economy.
In order to solve the multi-objective optimization problem of resource allocation in enterprise strategic management, the article firstly establishes a multi-objective resource allocation model for maximizing the benefits of enterprises in enterprise strategic management. Then, it optimizes and improves the initial population, convergence factor and dynamic weights of the gray wolf algorithm, increases the population diversity by using the population strategy of reverse learning, improves the convergence factor into a nonlinear factor, and finally changes the decision-making weights of the gray wolf leadership and applies the dynamic weights to improve the accuracy of the algorithm. Subsequently, the improved gray wolf algorithm is utilized for model decoupling. By applying this paper’s algorithm and the other two algorithms to solve the six algorithms 30*6, 60*6, 90*2, 90*4, 150*4 and 150*6 for 9 times, it is found that in the analysis of the 30*6 algorithm, the enterprise’s resource allocation reaches 5,000 when the time is 110 s. At the same time, this paper’s algorithm obtains a better non-dominated solution than the other two algorithms, which proves that this paper’s algorithm solves the multi-objective resource allocation problem of enterprise law industry is proved to be effective.
In food processing, foreign matter inevitably contaminates packaged food. To ensure food safety, ray-based detection is used; however, the original images suffer from aberrations and noise that degrade quality and hinder further processing. Thus, images are preprocessed to enhance quality by highlighting key features and suppressing irrelevant ones before abnormal pattern recognition. Following image segmentation, a BP neural network algorithm is applied for foreign object detection. In tests with contaminants such as metal wires, stones, and glass, the algorithm identified distinct abnormal fluctuations at gray levels of 132, 108, and 34, respectively, allowing it to reliably detect foreign objects. Although the practical detection rate reached 100%, occasional misjudgments suggest that further optimization is needed. Overall, this method introduces a novel approach to detecting foreign objects in food and offers promising new strategies for improving food safety monitoring.
Let \(G=(V,E,F)\) be a planar graph with vertex set \(V\), edge set \(E\), and set of faces \(F.\) For nonnegative integers \(a,b,\) and \(c\), a type \((a,b,c)\) face-magic labeling of \(G\) is an assignment of \(a\) labels to each vertex, \(b\) labels to each edge, and \(c\) labels to each face from the set of integer labels \(\{1,2,\dots a|V|+b|E|+c|F|\}\) such that each label is used exactly once, and for each \(s\)-sided face \(f \in F,\) the sum of the label of \(f\) with the labels of the vertices and edges incident with \(f\) is equal to some fixed constant \(\mu_s\) for every \(s.\) We find necessary and sufficient conditions for every quadruple \((a,b,c,n)\) such that the \(n\)-prism graph \(Y_n \cong K_2 \square C_n\) admits a face-magic labeling of type \((a,b,c)\).
A special type of algebraic intersection graph called the \(n\)-inordinate invariant intersection graph has been constructed based on the symmetric group, and its structural properties are studied in the literature. In this article, we discuss the different types of dominator coloring schemes of the \(n\)-inordinate invariant intersection graphs and their complements, \(n\)-inordinate invariant non-intersection graphs, by obtaining the required coloring pattern and determining the graph invariant associated with the coloring.
Let \(G\) be a connected graph and let \(d_G\) be the geodesic distance on \(V(G)\). The metric spaces \((V(G), d_{G})\) were characterized up to isometry for all finite connected \(G\) by David C. Kay and Gary Chartrand in 1965. The main result of this paper expands this characterization on infinite connected graphs. We also prove that every metric space with integer distances between its points admits an isometric embedding in \((V(G), d_G)\) for suitable \(G\).
MacMahon extensively studied integer compositions, including the notion of conjugation. More recently, Agarwal introduced \(n\)-color compositions and their cyclic versions were considered by Gibson, Gray, and Wang. In this paper, we develop and study a conjugation rule for cyclic \(n\)-color compositions. Also, for fixed \(\ell\), we identify and enumerate the subset of self-conjugate compositions of \(\ell\), as well as establish a bijection between these and the set of cyclic regular compositions of \(\ell\) with only odd parts.
The covering cover pebbling number, \(\sigma(G)\), of a graph \(G\), is the smallest number such that some distribution \(D \in \mathscr{K}\) is reachable from every distribution starting with \(\sigma(G)\) (or more) pebbles on \(G\), where \(\mathscr{K}\) is a set of covering distributions. In this paper, we determine the covering cover pebbling number for two families of graphs those do not contain any cycles.
Jeff Remmel introduced the concept of a \(\mathit{k}\)-11-representable graph in 2017. This concept was first explored by Cheon et al. in 2019, who considered it as a natural extension of word-representable graphs, which are exactly 0-11-representable graphs. A graph \(G\) is \(k\)-11-representable if it can be represented by a word \(w\) such that for any edge (resp., non-edge) \(xy\) in \(G\) the subsequence of \(w\) formed by \(x\) and \(y\) contains at most \(k\) (resp., at least \(k+1\)) pairs of consecutive equal letters. A remarkable result of Cheon et al. is that any graph is 2-11-representable, while it is still unknown whether every graph is 1-11-representable. Cheon et al. showed that the class of 1-11-representable graphs is strictly larger than that of word-representable graphs, and they introduced a useful toolbox to study 1-11-representable graphs, which was extended by additional powerful tools suggested by Futorny et al. in 2024. In this paper, we prove that all graphs on at most 8 vertices are 1-11-representable hence extending the known fact that all graphs on at most 7 vertices are 1-11-representable. Also, we discuss applications of our main result in the study of multi-1-11-representation of graphs we introduce in this paper analogously to the notion of multi-word-representation of graphs suggested by Kenkireth and Malhotra in 2023.
Topological Indices (TIs) are quantitative measures derived from molecular geometry and are utilized to predict physicochemical properties. Although more than 3000 TIs have been documented in the published literature, only a limited number of TIs have been effectively employed owing to certain limitations. A significant drawback is the higher degeneracy resulting from the lower discriminative power. TIs utilize simple graphs in which atoms and bonds are conceptualized as the vertices and edges of mathematical graphs. As multiple edges are not supported in these graphs, double and triple bonds are considered single. Consequently, the molecular structure undergoes alterations during the conversion process, which ultimately affects the discriminative power. In this investigation, indices for double-bond incorporation were formulated to preserve structural integrity. This study addresses, demonstrates, and verifies a set of double-bonded indices. The indices demonstrated promising results, exhibiting enhanced discriminative power when validated for polycyclic aromatic hydrocarbons using regression analysis. These indices and their potential applications will significantly contribute to QSAR/QSPR studies.
In 2003, the frequency assignment problem in a cellular network motivated Even et al. to introduce a new coloring problem: Conflict-Free coloring. Inspired by this problem and by the Gardner-Bodlaender’s coloring game, in 2020, Chimelli and Dantas introduced the Conflict-Free Closed Neighborhood \(k\)-coloring game (CFCN \(k\)-coloring game). The game starts with an uncolored graph \(G\), \(k\geq 2\) different colors, and two players, Alice and Bob, who alternately color the vertices of \(G\). Both players can start the game and respect the following legal coloring rule: for every vertex \(v\), if the closed neighborhood \(N[v]\) of \(v\) is fully colored then there exists a color that was used only once in \(N[v]\). Alice wins if she ends up with a Conflict-Free Closed Neighborhood \(k\)-coloring of \(G\), otherwise, Bob wins if he prevents it from happening. In this paper, we introduce the game for open neighborhoods, the Conflict-Free Open Neighborhood \(k\)-coloring game (CFON \(k\)-coloring game), and study both games on graph classes determining the least number of colors needed for Alice to win the game.
This paper investigates the number of rooted biloopless nonseparable planar near-triangulations and presents some formulae for such maps with three parameters: the valency of root-face, the number of edges and the number of inner faces. All of them are almost summation-free.
A graph is 1-planar if it can be drawn on the plane so that each edge is crossed by at most one other edge. In this paper, we confirm the total-coloring conjecture for 1-planar graphs without 4-cycles with maximum degree \(\Delta\geq10\).
For a graph \(G=(V,E)\) of size \(q\), a bijection \(f : E \to \{1,2,\ldots,q\}\) is a local antimagic labeling if it induces a vertex labeling \(f^+ : V \to \mathbb{N}\) such that \(f^+(u) \ne f^+(v)\), where \(f^+(u)\) is the sum of all the incident edge label(s) of \(u\), for every edge \(uv \in E(G)\). In this paper, we make use of matrices of fixed sizes to construct several families of infinitely many tripartite graphs with local antimagic chromatic number 3.
An outer independent double Roman dominating function (OIDRDF) of a graph \( G \) is a function \( f:V(G)\rightarrow\{0,1,2,3\} \) satisfying the following conditions:
(i) every vertex \( v \) with \( f(v)=0 \) is adjacent to a vertex assigned 3 or at least two vertices assigned 2;
(ii) every vertex \( v \) with \( f(v)=1 \) has a neighbor assigned 2 or 3;
(iii) no two vertices assigned 0 are adjacent.
The weight of an OIDRDF is the sum of its function values over all vertices, and the outer independent double Roman domination number \( \gamma_{oidR}(G) \) is the minimum weight of an OIDRDF on \( G \). Ahangar et al. [Appl. Math. Comput. 364 (2020) 124617] established that for every tree \( T \) of order \( n \geq 4 \), \( \gamma_{oidR}(T)\leq\frac{5}{4}n \) and posed the question of whether this bound holds for all connected graphs. In this paper, we show that for a unicyclic graph \( G \) of order \( n \), \( \gamma_{oidR}(G) \leq \frac{5n+2}{4} \), and for a bicyclic graph, \( \gamma_{oidR}(G) \leq \frac{5n+4}{4} \). We further characterize the graphs attaining these bounds, providing a negative answer to the question posed by Ahangar et al.
Let \(G\) be a \((p,q)\) graph. Let \(f\) be a function from \(V(G)\) to the set \(\{1,2,\ldots, k\}\) where \(k\) is an integer \(2< k\leq \left|V(G)\right|\). For each edge \(uv\) assign the label \(r\) where \(r\) is the remainder when \(f(u)\) is divided by \(f(v)\) (or) \(f(v)\) is divided by \(f(u)\) according as \(f(u)\geq f(v)\) or \(f(v)\geq f(u)\). \(f\) is called a \(k\)-remainder cordial labeling of \(G\) if \(\left|v_{f}(i)-v_{f}(j)\right|\leq 1\), \(i,j\in \{1,\ldots , k\}\) where \(v_{f}(x)\) denote the number of vertices labeled with \(x\) and \(\left|\eta_{e}(0)-\eta_{o}(1)\right|\leq 1\) where \(\eta_{e}(0)\) and \(\eta_{o}(1)\) respectively denote the number of edges labeled with even integers and number of edges labeled with odd integers. A graph with admits a \(k\)-remainder cordial labeling is called a \(k\)-remainder cordial graph. In this paper we investigate the \(4\)-remainder cordial labeling behavior of Prism, Crossed prism graph, Web graph, Triangular snake, \(L_{n} \odot mK_{1}\), Durer graph, Dragon graph.
Given a connected graph \(H\), its first Zagreb index \(M_{1}(H)\) is equal to the sum of squares of the degrees of all vertices in \(H\). In this paper, we give a best possible lower bound on \(M_{1}(H)\) that guarantees \(H\) is \(\tau\)-path-coverable and \(\tau\)-edge-Hamiltonian, respectively. Our research supplies a continuation of the results presented by Feng et al. (2017).
The degree of an edge \(uv\) of a graph \(G\) is \(d_G(u)+d_G(v)-2.\) The degree associated edge reconstruction number of a graph \(G\) (or dern(G)) is the minimum number of degree associated edge-deleted subgraphs that uniquely determines \(G.\) Graphs whose vertices all have one of two possible degrees \(d\) and \(d+1\) are called \((d,d+1)\)-bidegreed graphs. It was proved, in a sequence of two papers [1,17], that \(dern(mK_{1,3})=4\) for \(m>1,\) \(dern(mK_{2,3})=dern(rP_3)=3\) for \(m>0, ~r>1\) and \(dern(G)=1\) or \(2\) for all other bidegreed graphs \(G\) except the \((d,d+1)\)-bidegreed graphs in which a vertex of degree \(d+1\) is adjacent to at least two vertices of degree \(d.\) In this paper, we prove that \(dern(G)= 1\) or \(2\) for this exceptional bidegreed graphs \(G.\) Thus, \(dern(G)\leq 4\) for all bidegreed graphs \(G.\)
A proper total coloring of a graph \( G \) such that there are at least 4 colors on those vertices and edges incident with a cycle of \( G \), is called an acyclic total coloring. The acyclic total chromatic number of \( G \), denoted by \( \chi^{”}_{a}(G) \), is the smallest number of colors such that \( G \) has an acyclic total coloring. In this article, we prove that for any graph \( G \) with \( \Delta(G)=\Delta \) which satisfies \( \chi^{”}(G)\leq A \) for some constant \( A \), and for any integer \( r \), \( 1\leq r \leq 2\Delta \), there exists a constant \( c>0 \) such that if \( g(G)\geq\frac{c\Delta}{r}\log\frac{\Delta^{2}}{r} \), then \( \chi^{”}_{a}(G)\leq A+r \).
An \( (n,r) \)-arc in \( \operatorname{PG}(2,q) \) is a set \( \mathcal{B} \) of points in \( \operatorname{PG}(2,q) \) such that each line in \( \operatorname{PG}(2,q) \) contains at most \( r \) elements of \( \mathcal{B} \) and such that there is at least one line containing exactly \( r \) elements of \( \mathcal{B} \). The value \( m_r(2,q) \) denotes the maximal number \( n \) of points in the projective geometry \( \operatorname{PG}(2,q) \) for which an \( (n,r) \)-arc exists. We show by systematically excluding possible automorphisms that putative \( (44,5) \)-arcs, \( (90,9) \)-arcs in \( \operatorname{PG}(2,11) \), and \( (39,4) \)-arcs in \( \operatorname{PG}(2,13) \)—in case of their existence—are rigid, i.e. they all would only admit the trivial automorphism group of order \( 1 \). In addition, putative \( (50,5) \)-arcs, \( (65,6) \)-arcs, \( (119,10) \)-arcs, \( (133,11) \)-arcs, and \( (146,12) \)-arcs in \( \operatorname{PG}(2,13) \) would be rigid or would admit a unique automorphism group (up to conjugation) of order \( 2 \).
Let \( S \) be a connected union of finitely many \( d \)-dimensional boxes in \( \mathbb{R}^d \) and let \( \mathcal{B} \) represent the family of boxes determined by facet hyperplanes for \( S \), with \( \mathcal{F} \) the associated family of faces (including members of \( \mathcal{B} \)). For set \( F \) in \( \mathcal{F} \), point \( x \) relatively interior to \( F \), and point \( y \) in \( S \), \( x \) sees \( y \) via staircase paths in \( S \) if and only if every point of \( F \) sees \( y \) via such paths. Thus the visibility set of \( x \) is a union of members of \( \mathcal{F} \), as is the staircase kernel of \( S \). A similar result holds for \( k \)-staircase paths in \( S \) and the \( k \)-staircase kernel of \( S \).
The minimum dominating set problem asks for a dominating set with minimum size. First, we determine some vertices contained in the minimum dominating set of a graph. By applying a particular scheme, we ensure that the resulting graph is 2-connected and the length of each formed induced cycle is 0 mod 3. We label every three vertices in the induced cycles of length 0 mod 3. Then there is a way of labeling in which the set of all labeled vertices is the minimum dominating set of the resulting graph, and is contained in the minimum dominating set of the original graph. We also consider the remaining vertices of the minimum dominating set of the original graph and determine all vertices contained in the minimum dominating set of a graph with maximum degree 3. The complexity of the minimum dominating set problem for cubic graphs was shown to be APX-complete in 2000 and this problem is solved by our arguments in polynomial time.
In this paper we study a new graph parameter, the stacking number. Defined in relation to the eternal domination game, we show that there are highly connected graphs for which it is beneficial to allow multiple guards to occupy a vertex, answering an open question of Finbow et al. In fact, we show that for any sequence \( (s_i) \), allowing \( s_j \) guards to occupy a vertex can save arbitrarily many guards in comparison to allowing fewer than this on a vertex. We also show that the stacking number is \( 1 \) for all trees.
The body language of dancers is vital for conveying emotion. In this study, Kinect is used to detect and track dancers’ movements, and we develop two models: a dance action recognition model based on skeleton data and a dance emotion recognition model using an Attention-ConvLSTM. The action recognition model achieves 88.34% accuracy—reaching its best performance after just 40 iterations—while the emotion recognition model reaches an accuracy of 98.95%. Our analysis shows that features such as eigenvalue speed, skeleton pair distance, and inclination effectively differentiate emotions, although certain emotions (e.g., Excited vs. Pleased and Relaxed vs. Sad) can be confused. Notably, the leg’s skeletal points significantly influence emotion expression. Ultimately, the study establishes a dance emotion expression mechanism through coordinated movement changes of the head, hands, legs, waist, and torso.
Deep learning-based target detection algorithms outperform traditional methods by eliminating the need for manual feature design and improving accuracy and efficiency. This paper constructs a YOLOv5 target detection model using a deep convolutional neural network. To enhance accuracy, generalization, and detection speed, three data augmentation techniques—mosaic data enhancement, adaptive anchor frame, and adaptive image scaling—are applied. The model is further optimized with an attention mechanism and a modified YOLOv5 framework. A loss function and global average pooling enhance feature mapping for a fully convolutional network. Experimental results show that the improved YOLOv5n model achieves a 2.9979 percentage point increase in MAP, a 31% improvement in FPS, and a training time reduction of 10 minutes, completing 100 rounds in 20 minutes.
Metacognition, as a fundamental ability for learners to adapt to complex environments, is equally adapted to constructivist teaching and learning activities. In this paper, we propose a model of learning environment characteristics for metacognitive regulation under constructivist learning theory, and utilize Item2Vec algorithm, Self-Attention mechanism, and BiGRU model to construct a model of metacognitive ability. The model presents a kind of multi-channel network characteristic composed of Self-Attention mechanism and BiGRU model. Design a theoretical model of the learning environment oriented to improving students’ metacognitive ability, and analyze the functional modules of the overall system of the learning environment. Propose a learning activity aiming at the improvement of metacognitive ability and incorporating constructivist theory as the guiding concept to allocate the various aspects of the whole constructivist teaching activity. Analyze the implementation effect of constructivist teaching activities based on metacognitive strategies and organize the influencing factors of metacognitive strategies. The bivariate correlation analysis of students’ total test scores and usual grades are closely related to planning strategies, monitoring strategies, and regulating strategies, and the significance (two-tailed) is less than 0.01. This indicates that the higher the students’ scores, the higher the corresponding level of metacognitive strategies.
Civil engineering crack detection faces challenges due to complex environments and external interferences. This paper proposes an improved YOLO v8s-WOMA network, integrating ODConv, C2f-MA modules, and WIoU loss function to enhance crack identification accuracy. A BP neural network is also trained to assess crack damage. Experiments on the CBP dataset compare this method with existing detection algorithms. Results show that the proposed model achieves the highest mAP (90.5%), F1-score (90.3%), and accuracy (89.6%). Bridge crack detection errors remain within 0.1mm (width) and 20mm (length), ensuring precise damage assessment. The model effectively handles complex backgrounds, accurately detects cracks, and meets practical engineering needs.
The rapid growth of multilingual information online has made traditional translation insufficient, highlighting the need for intelligent language translation. This study employs a convolutional neural network to extract visual features from translated images and uses region-selective attention to align text and image features. The fused information is then processed through a sequence model to develop a computer vision-based translation algorithm. Results show that the proposed algorithm excels in key evaluation metrics, improving translation quality. It maintains a low leakage rate (1.30%), a mistranslation rate of 2.64%, and an average response time of 67.28ms. With strong generalization and applicability in multilingual translation, the algorithm demonstrates high performance and promising real-world applications.
This paper addresses the limitations of the traditional portfolio theory centered on the mean-variance model and expected utility theory, and proposes the establishment of a portfolio model that takes into account the subjective psychological factors of investors, taking into account the fact that investors are susceptible to the influence of various psychological biases, affective biases, and cognitive biases in the actual decision-making process, with respect to the theory of consistency of the assumptions of the investor’s risk attitude. The portfolio model based on fuzzy decision-making is proposed, combined with the development and application of linear programming in portfolio optimization, the return of assets is regarded as a random fuzzy variable, and the stochastic fuzzy portfolio model is constructed to consider the risk characteristics of investors. The portfolio returns under different emotions or different risk preferences are explored separately. Combined with the fund categorization allocation of the sample firms, the fund portfolio C based on the fuzzy portfolio model is proposed and compared with the equal weight allocation fund (fund portfolio A) and the risk coefficient weighted allocation fund (fund portfolio B) based on the risk level of return, respectively. Fund Portfolio C has the highest average return.
Given a prime \( p \), a \( p \)-smooth integer is an integer whose prime factors are all at most \( p \). Let \( S_p \) be the multiplicative subgroup of \( \mathbb{Q} \) generated by \(-1\) and the \( p \)-smooth integers. Define the \( p \)-smooth partial field as \( \mathbb{S}_p = (\mathbb{Q}, S_p) \). Let \( g \) be the golden ratio \( (1+\sqrt{5})/2 \). Let \( G_p \) to be the multiplicative subgroup of \( \mathbb{R} \) generated by \( g \), \(-1\), and the \( p \)-smooth integers. Define the \( p \)-golden partial field as \( \mathbb{G}_p = (\mathbb{R}, G_p) \). The partial field \( \mathbb{S}_2 \) is actually the well-known dyadic partial field and \( \mathbb{S}_3 \) has sometimes been called the Gersonides partial field. We calculate the fundamental elements of \( \mathbb{S}_5 \), \( \mathbb{G}_2 \), \( \mathbb{G}_3 \), and \( \mathbb{G}_5 \).
Our proofs make use of the SageMath computational package.
Let \(P_k\) and \(C_k\) respectively denote a path and a cycle on \(k\) vertices. In this paper, we give necessary and sufficient conditions for the existence of a complete \(\left\{P_7,C_6\right\}\)-decomposition of the cartesian product of complete graphs.
Rural ecological protection and restoration projects are actively underway worldwide, yet in‐depth research on the evolution of rural ecosystems and their underlying mechanisms remains limited. This study investigates the distribution characteristics of rural ecosystems in Ganzhou District by analyzing their number, spatial type, and density. Geodetectors are employed to examine the spatial heterogeneity and key driving factors of these ecosystems. In addition, we assess how the integrated ecosystem service index responds to land use changes, revealing that the proportion of ecological land—contributing up to 50%—is the most significant factor, with grassland showing a strong positive effect (average coefficient 7.99) and construction land exhibiting a negative correlation with the CES index. These findings offer scientific guidance for enhancing rural ecological protection through improved legislation, ecological compensation, and legal aid.
The internal defects and concrete strength detection of concealed mass concrete structures (dams, fan foundations, tunnel arches, etc.) has been a difficult problem in the industry, and there is a lack of effective nondestructive testing technology, conventional single-sided nondestructive testing technology (ground-penetrating radar, ultrasonic array, impact echo method, etc.) in reinforced concrete structures can not be more than 3m in depth, and the practical application is limited. For this reason, we have developed a new face wave CT inspection technique based on elastic wave face wave, combining the excellent wavelength method and multiple filtering method to solve the problem of difficult extraction of frequency dispersion curves of the face wave in concrete, and through finite element simulation and example verification, it is confirmed that the method can detect the defects and strength of the concrete structure on a single side, and the effective detection depth is more than 4m, which has a strong practical application value.
With the advancement of information technology, universities accumulate vast amounts of data, but effectively extracting and utilizing this information remains a challenge. Existing studies on university management data often rely on shallow analysis with basic models and tools, offering limited efficiency improvements. This paper explores an optimized higher education management data analysis algorithm, leveraging artificial intelligence and multimedia technology to enhance efficiency. A comparative study with traditional methods shows that the proposed algorithm improves university management data analysis efficiency by 11.4%.
Three remarkable determinant identities of skew–symmetric matrices are reviewed in a more transparent manner.
In graph theory, the center function identifies a set of vertices in a connected graph G that minimizes the maximum distance from any other vertex. We examine the behavior of the center function on connected graphs through a set of axioms. While universal axioms apply to all connected graphs, they cannot fully characterize certain graphs. To address this limitation, non-universal axioms for specific graph classes were introduced. This study is focused on establishing an axiomatic characterization of the center function on fan graphs by utilizing a combination of universal and non-universal axioms.
To counter threats to low-orbit communication satellites from hacker attacks and spectrum interference, this study develops an adversarial sample detection model using a variational self-encoder and a fast region-based convolutional network for spectrum interference detection. The proposed model achieves 97.68% accuracy and an F1 score of 96.86% in intrusion traffic detection, with AUC values above 95% for various network attacks. For single-tone interference, it attains 98.65% accuracy, 96.21% recall, and 93.14% precision, converging within 200 iterations with an average recognition accuracy of 95.47%. These results confirm the model’s ability to detect adversarial threats and interference, enhancing satellite communication security.
The wires and ground wires on transmission towers cannot be straight lines, but present different sizes of arcs, which directly affect the safety and transmission quality of the line. In response to this, a research proposes an online monitoring system for transmission towers based on computer video algorithms. The system collects environment and mechanism parameters of transmission lines by installing sensors on transmission towers, monitors them through computer video algorithms, and combines grey wolf algorithm and deep learning models to predict sag, thereby achieving crisis warning of the power grid around transmission towers. The outcomes denoted that during the field testing process, the warning accuracy of the system reaches over 98.57%, and the response time is only 0.5 seconds. The false negative rate and false positive rates are 2% and 0.5%, respectively. Based on the above content, it can be concluded that the proposed online monitoring system for transmission towers can effectively achieve line anomaly warning and maintain stable line operation.
The study analyzes the stylistic evolution of contemporary Chinese literary works using the MONK project. Text mining tools in the project are used to analyze the thematic classification, emotional tendency and stylistic type changes of the works. Among them, LDA model and GBDT algorithm are used to identify the thematic classification of Chinese modern and contemporary literary works, SO-PMI algorithm is used to identify the emotional tendency in the works, and the vector space model can classify the style of the works. Based on the above methods, the theme and emotional changes of modern and contemporary Chinese literary works can be categorized into 3 stages: the awakening of Enlightenmentism at the beginning of the 20th century, the diversified presentation during the revolutionary period, and the diversified development after the reform and opening up. The styles of modern and contemporary Chinese literary works can be divided into epic style, lyrical style, rural theme style and intellectual theme style.
Forecasting the volatility of the stock market price is indispensable for managing the risks associated with market dynamics and provides valuable insights for financial decision in trading strategies. This study aims to enhance the accuracy of volatility prediction for stock market price using hybrid models combining econometric and deep learning approaches. Specifically, it introduces a novel GARCH-CNN-LSTM hybrid model for more precise volatility forecasting of stock market price. The GARCH model is efficient at capturing volatility clusters and kurtosis features, while the CNN excels in extracting spatial patterns from time series data, and LSTM effectively preserves essential information over extended periods. GARCH(1,1) model is selected based on AIC, maximum log-likelihood, and parameter significance. Subsequently, CNN and LSTM models are chosen for their complementary capabilities in volatility prediction. We evaluated the forecasting performance of the hybrid models from out-sample test data, employing Mean Square Error, Root Mean Square Error and Mean Absolute Error. The result indicates that the new model outperforms the existing models with an improvement of 8% to 13% accuracy. Furthermore, we conduct the Diebold-Mariano test to confirm significant differences in performance.
The study constructs a solar cell simulation model and tracks the maximum power output from the solar cell using the MPPT algorithm. Simulation simulation experiments are conducted to analyze the effects of changes in environmental factors such as season, weather, light, temperature, wind speed, etc. on the current and power output of solar cells. The total output power and the peak output power of the solar cell are the largest in summer, which are 7407.69kW and 114.93kW, respectively, and the total output power and the peak output power of the solar cell are the smallest in fall, which are 1748.96kW and 31.58kW, respectively. The peak power output of the solar cell is the largest in sunny days, which is 107.56kW, and the smallest in rainy days, which is 37.06kW. The total solar cell power output is maximum (7896.93kW) on clear to cloudy days and minimum (1955.27kW) on rainy days. The solar cell output current and maximum power values decreased with decreasing light intensity. The ambient temperature has little effect on the short circuit current, the output current increases slightly with increasing temperature, the open circuit voltage decreases drastically with increasing temperature and the maximum output power decreases with increasing temperature. The maximum output power of the solar cell increases with increasing wind speed.
The rise of digital humanities reflects a paradigm shift in literary research. This project applies natural language processing to ancient Chinese literature, embedding an attention mechanism into an iterative null convolutional network for named entity recognition. It also integrates the MacBERT pre-training model with a dual-channel structure of aspectual word and semantic features, designing a hierarchical attention mechanism for aspect-level sentiment analysis. Experimental results show improved recognition and sentiment analysis performance, with evaluation scores exceeding 83%. In Ming Dynasty fiction, craftsmen (44.7%) and merchants (22.4%) were the most frequent characters, highlighting the rise of a commercial economy and civic class. In Tang Dynasty poetry, 67.9% of sentiments were positive, with themes of national honor (0.334) and send-off emotions (0.226) commonly linked, reflecting the era’s prosperity and literary aspirations.
The family of graphs of reduced words of a certain sub-collection of permutations in the union \(\cup_{n\geq 4}\frak{S}_{n}\) of symmetric groups is investigated. The sub-collection is characterised by the hook cycle type \((n-2,1,1)\) with consecutive fixed points. A closed formula for counting the vertices of each member of the family is given and the vertex-degree polynomials for the graphs with their generating series is realised. Some isomorphisms of these graphs with various combinatorial objects are established. Lastly, a link with the Poincar\’e polynomial of the integral cohomology ring of the Grassmannian \({\rm Gr}(2,n)\) is also given.
In this paper, we introduce the concept of the Over-inversion number, which counts the overlined permutations of length \(n\) with \(k\) inversions, allowing the first elements associated with the inversions to be independently overlined or not. We explore its properties and combinatorial interpretations through lattice paths, overpartitions, and tilings, and provide a combinatorial proof demonstrating that these numbers form a log-concave and unimodal sequence.
This paper aims at resolving the issue that the conventional literature study can’t deal with the large amount of data, the author proposes a research method for theme clustering and text mining of Chinese modern and contemporary literary texts in the network era. The author studied how to effectively improve the thematic clustering performance of literary texts based on keyword clustering ensemble method. Comparing two clustering ensemble methods (K-means based data ensemble and incremental clustering based algorithm ensemble) and four keyword extraction methods (TF-ISF CSI, ECC, TextRank), the effects of various keywords on the results of thematic clustering were analysed. Experiments indicate that the clustering algorithm can greatly increase the topic clustering efficiency, and it is more stable when the key words are less. The author’s research provides new technological means for text mining and thematic clustering in contemporary Chinese literature, which helps to promote the development of digital humanities research.
The undifferentiated recommendations in current library management systems fail to meet the diverse and personalized needs of users, and the vast amounts of user data accumulated over the years remain largely untapped. This paper integrates personalized recommendation requirements in self-service libraries with K-means clustering to design a labeling system and set user profile weights. Building on traditional reinforcement learning, we propose an Actor–Critic based recommendation algorithm that models the library recommendation task as a Markov decision process to automatically learn an optimal strategy by maximizing expected long-term rewards. The DDPG algorithm is employed to train the parameters of this framework, achieving improved personalized performance. Comparative experiments on datasets (ML-100k, Yahoo! Music, ML-1M, and Jester) demonstrate that our model outperforms traditional methods and DeepFM, with scores of 0.7708, 0.1918, 0.7155, and 0.3936, respectively. This study provides innovative insights for accurate recommendations and enhanced user experience in libraries.
The application of virtual reality (VR) technology in teaching is increasingly widespread. This study leverages VR to create cross-cultural teaching contexts and develop speech recognition models for language learning. An ecological model of language learning based on VR is constructed, and a cross-cultural contextual VR system is implemented and introduced into language education. Testing reveals that the system achieves a speech recognition efficiency of 99.7% and a correctness rate of 99.5%. Moreover, a comparison of pre- and post-test data between experimental and control groups shows that the experimental group significantly outperformed the control group in English proficiency (p < 0.05). Overall, the cross-cultural contextual VR system demonstrates a significant positive impact on language learning outcomes.
New media advertising boosts platform revenue, and intelligent content optimization enhances its effectiveness. This paper applies a multi-task deep learning neural network to optimize advertisement content, leveraging attention mechanisms and loss functions to improve performance. Blockchain technology is integrated to create a personalized and accurate recommendation system. Experimental results show that the proposed model effectively optimizes ad content, meeting functional and performance requirements. Most users’ ad browsing duration exceeds 50 seconds, outperforming traditional recommendation systems. The proposed system offers strong targeting, fast results, and cost efficiency, significantly enhancing user engagement with ad content.
With the frequent occurrence of global climate change and extreme weather events, meteorological forecasting technology has gradually become an auxiliary technology for production activities. In order to improve the quality of meteorological analysis results, a technology utilizing cloud radar data as the core is proposed. The vertical distribution of water vapor and liquid water in the atmosphere is detected by a ground-based microwave radiometer. The median filtering method is used to further smooth the classified and preliminarily removed reflectance factor data, and computer information processing technology is used for data analysis. The experimental results of Taiyuan ground based remote sensing high altitude detection experiment showed that in the data availability test, the research method had a data availability rate of 97.3% when the height was 2km in humidity data. When conducting accuracy analysis of the results, the root mean square error of the relative humidity profile was only 22.0% when the height increased to 12km. This indicates that the research method can conduct high-quality meteorological analysis and provide assistance for meteorological forecasting.
A \((d, 1)\)-total labelling of a graph \(G\) is an assignment of integers \(\{0,1,\cdots,l\}\) to the vertices and edges of the graph such that adjacent vertices receive distinct integers, adjacent edges receive distinct integers, and the integer received by a vertex differs at least \(d\) from those received by its incident edges. The minimum number \(l\) required for such an assignment is called the \((d, 1)\)-total number of the graph \(G\). This paper contributes to \((d,1)\)-total labelling of two infinite families of snarks, the Goldberg family and the Loupekhine family. We completely determine the \((d,1)\)-total numbers of these two families of snarks for all \(d\geq2\).
A prism fuzzy number is the integration of triangular and trapezoidal fuzzy numbers. In this artifact, the balancing point and the grading value of the prism fuzzy number is defined. By using prism fuzzy number, we were able to infer the Trapezoidal and Triangular fuzzy numbers. A comparative study with the current model is done to corroborate our findings. An enhanced grading technique for evaluating the prism fuzzy numbers is defined. Finally, the application of prism fuzzy numbers to assess student’s interest in higher studies and employment is illustrated using the MATLAB simulation. A statistical analysis is demonstrated using the Python programme with real-life data.
Achieving accurate prediction of financial market fluctuations is beneficial for investors to make decisions, while machine learning algorithms can utilize a large amount of data for training and learning, which has good effect on predicting financial market fluctuations. The article first analyzes the financial dataset, and then constructs a feature selection model by combining Boruta and SHAP to screen the financial data features. Based on the LSTM model, a new Dropout layer and fully connected layer are designed to construct the AMP-LSTM model to realize the prediction of financial market fluctuations. The Boruta SHAP algorithm has a RMSPE of 0.242, which is good for screening. The prediction performance of the AMP-LSTM model is significantly better than that of the traditional LSTM (p<0.01), and the predicted values are closer to the actual values. The method in this paper performs better than MLP, RNN and other methods in general in terms of error performance when predicting indicators such as WTI, Brent, LGO, etc., and is able to realize the prediction of financial market volatility in the digital economy environment.
Aiming to address shortcomings in existing time series prediction models, this paper proposes an LSTM model enhanced by fused multi-scale convolutional attention (MCA-LSTM). We design the experimental parameters, construct a stock price dataset, and model the improved LSTM using individual stock closing prices, with prediction accuracy evaluated via RMSE, MAPE, and MAD. To assess the arbitrage and generalization performance of the MCA-LSTM portfolio model, we compare the application of the MCA-LSTM-BL model. Furthermore, within the framework of a mean semi-absolute deviation (MSAD) portfolio optimization model, we develop a new portfolio optimization approach based on return forecasting (MCA-LSTM+MSAD). The asset values and return predictions of various portfolio models are analyzed under transaction cost considerations, and the proposed MCA-LSTM+MSAD model achieves an excess return of 56.98%, consistently maintaining the highest portfolio value throughout the trading period. Overall, our findings indicate that the MCA-LSTM+MSAD model is a promising tool for portfolio optimization and warrants further development for real investment applications.
In this paper, we prove that, the Wiener index of a connected tripartite graph is any natural number except 1, 2, 5, 6 and 11.
For every connected graph \(F\) with \(n\) vertices and every graph \(G\) with chromatic surplus \(s(G)\leq n\), the Ramsey number \(r(F,G)\) satisfies \(
r(F,G) \geq (n-1)(\chi(G)-1) + s(G), \) where \(\chi(G)\) denotes the chromatic number of \(G\). If this lower bound is attained, then \(F\) is called \(G\)-good. For all connected graphs \(G\) with at most six vertices and \(\chi(G) \geq 4\), every tree \(T_n\) of order \(n\geq 5\) is \(G\)-good. In case of \(\chi(G) = 3\) and \(G \neq K_6-3K_2\), every non-star tree \(T_n\) is \(G\)-good except for some small \(n\), whereas \(r(S_n,G)\) for the star \(S_n = K_{1,n-1}\) in a few cases differs by at most 2 from the lower bound. In this note, we prove that the values of \(r(S_n,K_6-3K_2)\) are considerably larger for sufficiently large \(n\). Furthermore, exact values of \(r(S_n,K_6-3K_2)\) are obtained for small \(n\).
Let \(A\) be a real algebra. It is called locally complex algebra if every non-zero element generates a subalgebra isomorphic to either \(\mathbb{R}\) or \(\mathbb{C}.\) It is said to satisfy the uniqueness of the square root except the sign if the equation \(x^2=y^2\) implies \(y=\pm x.\) We show the following:
1. Every locally complex algebra is a quadratic algebra.
2. Every alternative locally complex algebra is isomorphic to either \(\mathbb{R},\) \(\mathbb{C},\) \(\mathbb{H}\) or \(\mathbb{O}.\)
3. Every commutative locally complex algebra without divisors of zero is isomorphic to \(\mathbb{R}\) or \(\mathbb{C}.\)
4. Every finite-dimensional algebra satisfying the uniqueness of the square root except the sign has dimension \(\leq 2\) and contains non-zero idempotents.
To solve the problem of identifying intrinsic relationships between objects and mirror segmentation in semantic segmentation of urban scenes using current multi-modal data, this study innovatively integrates color images, depth information, and thermal images to propose a network model that integrates modal memory sharing and form complementarity, and a hierarchical assisted fusion network model. Compared with existing advanced urban scene semantic segmentation methods, the proposed method performed excellently in terms of performance, with an average pixel accuracy and mean intersection over union of over 80% for different objects. In addition, the research method achieved clearer and more complete segmentation results by strengthening contextual associations, and edge processing is also smoother. Even in object segmentation with similarities in distance, shape, and brightness such as “vegetation” and “sidewalk”, the research method still maintained high accuracy. The research method can effectively handle the complexity of urban scenes, providing a new solution for semantic segmentation of multi-modal data in urban scenes.
Blockchain technology has the characteristics of data anti-tampering and anti-forgery, which can provide solution ideas for the secure storage and transmission of data in distributed networks. The study applies blockchain technology to data auditing, constructs an aggregated signature based on conditional identity anonymization to protect user privacy, simplifies the auditing computation by using homomorphic hash function, and deploys three kinds of smart contracts on the blockchain to design a blockchain-based data integrity auditing scheme. For the privacy protection problem, a blockchain privacy protection model based on differential privacy is constructed by integrating the differential privacy policy into the blockchain smart contract layer. The experimental results show that the data integrity auditing scheme has superior blockchain storage cost and time overhead, and the average time overhead under different dynamic operations is below 30ms. The privacy protection model also exhibits high efficiency, with encryption and decryption times of 0.075s and 0.063s, respectively, under the largest data file, and a significant speed advantage in all phases of operation. The proposed scheme in this paper meets the needs of data integrity and privacy protection, and can provide efficient services for users.
Mango weaving, a traditional handicraft in Guangxi, is facing decline. This study explores AI technology’s role in its protection and innovation by analyzing consumer reviews using perceived value theory and the LDA topic model to identify preferences for improving production. A lightweight generative adversarial network with a non-local attention mechanism is proposed for text-to-multi-objective image generation, aiding innovative design. Consumers expressed 82.6% satisfaction with mango weaving. Reviews were categorized into five themes, highlighting the need for improvements in emotion, quality, and price. The AI-generated image model outperformed others, with IS and FID scores improving by 21.85% and 16.46%, respectively. AI enhances mango weaving by refining design, improving product quality, and expanding its preservation and development.
The ocean is vital for human survival and development, serving as the birthplace of life and a source of food, minerals, and scientific research materials. It plays a crucial role in global trade, economic growth, climate regulation, and ecological balance. Underwater positioning technology is fundamental to marine engineering, with underwater acoustic passive positioning being essential for sonar source localization. Active and passive acoustic systems help measure underwater noise and determine target locations. Passive systems rely on signals emitted by targets, while active systems use interaction signals for positioning. This study applies machine learning to improve acoustic beacon signal recognition in underwater positioning. Results show that machine learning enhances recognition speed by 8% and detection accuracy by 9% compared to traditional methods. By optimizing underwater acoustic signal recognition, this approach enhances positioning accuracy, reduces costs, and advances intelligent marine technology, providing innovative solutions for complex marine environments.
Nowadays, “Artificial Intelligence + Education” is transforming teaching and learning. In this study, we employ AI-based data mining to innovate educational management by designing an academic monitoring system using K-means clustering and developing an early warning model through stacking multi-model superposition. Targeted management measures, including personalized video recommendations, are implemented based on the model’s predictions to promote individualized student development. By analyzing daily behavior data from 500 college students, the K-means algorithm effectively classified them into four groups, and the academic alert model achieved a prediction accuracy of 84.19%, outperforming single base models. The implementation of this personalized management method significantly improved student performance compared to traditional approaches, demonstrating its potential to enhance educational outcomes.
Let \( p \) be a prime number, and let \( k \) and \( m \) be positive integers with \( k \geq 2 \). This paper studies the algebraic structure of \(\lambda\)-constacyclic codes of arbitrary length over the finite commutative ring \( R = \frac{\mathbb{F}_{p^m}[u, v]}{ \langle u^k, v^2, uv – vu \rangle } \), where \(\lambda\) is a unit in \( R \) given by \( \lambda = \sum\limits_{i=0}^{k-1} \lambda_i u^i + v\sum\limits_{i=0}^{k-1} \lambda_i’ u^i \), with \(\lambda_i, \lambda_i’ \in \mathbb{F}_{p^m}\) and \(\lambda_0, \lambda_1 \neq 0\). We provide a complete classification of these constacyclic codes, determine their dual structures, and compute their Hamming distances when the code length is \( p^s \).
In this paper, the hyperoctahedral group algebra \(\mathscr{F}[\overrightarrow{S_{n}}]\) over a splitting field \(\mathscr{F}\) of wreath product \(\overrightarrow{S_{n}}\) with \(\text{char}(\mathscr{F})\nmid|\overrightarrow{S_{n}}|\), is considered and the unique idempotents corresponding to the four linear characters of the group \(\overrightarrow{S_{n}}\) are explored. Also, by establishing the minimum weights and dimensions, all group codes generated by the linear idempotents in the aforementioned group algebra are completely characterized for every \(n\). The nonlinear idempotents corresponding to nonlinear characters of \(\overrightarrow{S_{3}}\) are also obtained and various group codes in \(\mathscr{F}[\overrightarrow{S_{3}}]\) generated by linear and nonlinear idempotents are examined.
Let \( G = (V, E) \) be a graph with minimum degree at least one. The general inverse degree of \( G \) is defined as \(\sum\limits_{v \in V} \frac{1}{d^{\alpha}(v)}\), where \( \alpha \) is a real number with \( \alpha > 0 \). In this paper, we present sufficient conditions involving the general inverse degree with \( \alpha \geq 1 \) for some Hamiltonian properties of graphs and upper bounds for the general inverse degree with \( \alpha \geq 1 \).
In today’s era, the rapid development of artificial intelligence is transforming warehousing and logistics by enhancing efficiency and reducing labor costs. In this paper, we first employ a least squares support vector machine to develop an inventory prediction model for warehousing logistics, accurately forecasting inventory values. Next, we design an automated logistics and warehousing architecture that facilitates seamless data transfer and information feedback. Finally, this architecture is used to build a comprehensive inventory management model. Our analysis shows that the AI-based prediction nearly matches the actual inventory value (229 vs. 230) and achieves an inventory turnover rate of 5 times per month, which significantly reduces backlog and improves overall management efficiency and user satisfaction.
The Cascaded Integrator Comb (CIC) decimation filter is a pivotal technology extensively employed in digital signal processing (DSP). This paper delves into a comprehensive examination of the CIC algorithm within software-defined radio (SDR) systems from the perspective of parallel computing and introduces a novel Non-Recursive Implementation (NR-I) on an NVIDIA GPU using CUDA. The NR-I approach significantly reduces computational load by unfolding the recursive CIC structure with pre-derived Unfold Factors. Further optimization was achieved through data-transfer enhancements using PM Implementation (PM-I) and ODT Implementation (ODT-I). Experimental results demonstrate that NR-I achieves a speedup of over 449.48. Additionally, the data-transfer optimizations resulted in substantial performance improvements, with PM-I and ODT-I reducing execution time by 43.24% and 64.22%, respectively. The GPU implementation’s speedup is significantly greater than that of OpenMP, ranging from 3.34 to 10.22 times. These results underscore the effectiveness of the proposed Non-Recursive Implementation in accelerating time-intensive and data-intensive computations.
This paper presents a new sequence called the \(k-\)division sequence. The Pell and Lehmer sequences are then used to define new sequences called the \(k-\)division \(L-\)Lehmer-Pell sequences and some properties of these sequences are determined. Then the \(k-\)division \(L-\)Lehmer-Pell sequences and corresponding self-invertible matrices are used in a new Affine-Hill cipher algorithm. The security of this cipher is examined.
In the era of globalization and intense market competition, strategic human resource management (SHRM) is critical for boosting corporate competitiveness. This study employs structural equation modeling (SEM) and multiple linear regression to uncover the complex influence of SHRM perceptions on employee proactive behaviors, and uses a convolutional neural network (CNN) to explore nonlinear relationships and validate the SEM findings. Results reveal that SHRM perception has a significant positive effect on employee proactive behavior (\(\beta = 0.254\), \(p<0.001\)). Mediators such as job self-efficacy and conceptual psychological contract play a positive role, with indirect effects of 0.1043 and 0.1726, respectively, while insider identity perception significantly moderates the relationship (\(\beta = 0.09\), \(p<0.01\)). The CNN model ranks the importance of variables in descending order as: conceptual psychological contract, job self-efficacy, SHRM perception, job category, and insider identity perception, consistent with the SEM results. These findings highlight the potential of CNNs to optimize HR strategies and enhance employee motivation.
One of the urgent challenges in auditing today is preventing accounting management risk. This study integrates big data auditing technology to enhance audit quality by developing an audit risk assessment index system based on material misstatement risk and inspection risk. By combining the hierarchical analysis and entropy weighting methods to assign risk indicators, the accounting audit risk index for Company Z was calculated using a multi-level fuzzy comprehensive evaluation method and regression analysis to examine impact factors. Empirical evidence shows that the overall expected audit risk is 0.412—indicating a low to average risk level—with significant correlations between the previous year’s audit opinion, audit fee, and other factors such as the largest shareholder’s holding, board size, percentage of independent directors, operating income growth, net profit, and the audit environment. The study focuses on developing effective prevention and response strategies in the era of big data and offers recommendations to reduce potential auditing risks.
A radio labeling of a graph \( G \) is a mapping \( f : V(G) \to \{0, 1, 2, \dots\} \) such that \( |f(u)-f(v)| \geq \text{diam}(G) + 1 – d(u,v) \) for every pair of distinct vertices \( u,v \) of \( G \), where \( \text{diam}(G) \) is the diameter of \( G \) and \( d(u,v) \) is the distance between \( u \) and \( v \) in \( G \). The radio number \( \text{rn}(G) \) of \( G \) is the smallest integer \( k \) such that \( G \) admits a radio labeling \( f \) with \( \max\{f(v) : v \in V(G)\} = k \). In this paper, we give a lower bound for the radio number of the Cartesian product of a tree and a complete graph and give two necessary and sufficient conditions for the sharpness of the lower bound. We also give three sufficient conditions for the sharpness of the lower bound. We determine the radio number of the Cartesian product of a level-wise regular tree and a complete graph which attains the lower bound. The radio number of the Cartesian product of a path and a complete graph derived in [B. M. Kim, W. Hwang, and B. C. Song, Radio number for the product of a path and a complete graph, J. Comb. Optim., 30 (2015), 139–149] can be obtained using our results in a short way.
Let \( G \) be a connected graph with \( m \) edges. The density of a nontrivial subgraph \( H \) with \( \omega(H) \) components is \( d(H) = \frac{|E(H)|}{|V(H)| – \omega(H)} \). A graph \( G \) is uniformly dense if for any nontrivial subgraph \( H \) of \( G \), \( d(H) \leq d(G) \). For each cyclic ordering \( o=(e_1, e_2, \dots, e_m) \) of \( E(G) \), let \( h(o) \) be the largest integer \( k \) such that every \( k \) cyclically consecutive elements in \( o \) induce a forest in \( G \); and the largest \( h(o) \), taken among all cyclic orderings of \( G \), is denoted by \( h(G) \). A cyclic ordering \( o \) of \( G \) is a cyclic base ordering if \( h(o) = |V(G)| – \omega(G) \). In [15], Kajitani et al. proved that every connected nontrivial graph with a cyclic base ordering is uniformly dense, and conjectured that every uniformly dense graph has a cyclic base ordering. This motivates the study of \( h(G) \). In this paper, we investigate the value of \( h \) for some families of graphs and determine all connected graphs \( G \) with \( h(G) \leq 2 \).
An open-locating-dominating set of a graph models a detection system for a facility with a possible “intruder” or a multiprocessor network with a possible malfunctioning processor. A “sensor” or “detector” is assumed to be installed at a subset of vertices where each can detect an intruder or a malfunctioning processor in its neighborhood, but not at its own location. We consider a fault-tolerant variant of an open-locating-dominating set called an error-correcting open-locating-dominating set, which can correct a false-positive or a false-negative signal from a detector. In particular, we prove the problem of finding a minimum error-correcting open-locating-dominating set in an arbitrary graph is NP-complete. Additionally, we characterize the existence criteria for an error-correcting open-locating-dominating set in an arbitrary graph. We also consider extremal graphs that require every vertex to be a detector and minimum error-correcting open-locating-dominating sets in infinite grids.
Let \( G = (V, E) \) be a graph with vertex set \( V \) and edge set \( E \). A set \( S \subset V \) is a dominating set if every vertex in \( V – S \) is adjacent to at least one vertex in \( S \), an independent set if no two vertices in \( S \) are adjacent, and a total dominating set if every vertex in \( V \) is adjacent to at least one vertex in \( S \). The domatic number \( \text{dom}(G) \), idomatic number \( \text{idom}(G) \), and total domatic number \( \text{tdom}(G) \), of a graph \( G \) equal the maximum order \( k \) of a partition \( \pi = \{V_1, V_2, \ldots, V_k\} \) of \( V \) into {dominating sets, independent dominating sets, total dominating sets}, respectively. A queens graph \( Q_n \) is a graph defined on the \( n^2 \) squares of an \( n \)-by-\( n \) chessboard, such that two squares are adjacent if and only if a queen on one square can move to the other square in one move, that is, the two squares lie on a common row, column, or diagonal. In this note, we determine the value of these three numbers for \( Q_n \) for the first several values of \( n \). In addition, we introduce the concepts of graphs being \( \gamma \)-domatic, \( i \)-domatic, \( \alpha \)-domatic, \( \Gamma \)-domatic, \( \gamma_t \)-domatic, and \( \Gamma_t \)-domatic.
Let \(\mathcal{F}\) be a family of graphs, and \(H\) a “host” graph. A spanning subgraph \(G\) of \(H\) is called \(\mathcal{F}\)- saturated in \(H\) if \(G\) contains no member of \(\mathcal{F}\) as a subgraph, but \(G+e\) contains a member of \(\mathcal{F}\) for any edge \(e\in E(H) – E(G)\). We let \(Sat(H,\mathcal{F})\) be the minimum number of edges in any graph \(G\) which is \(\mathcal{F}\)-saturated in \(H\), where \(Sat(H,\mathcal{F}) = |E(H)|\) if \(H\) contains no member of \(\mathcal{F}\) as a subgraph. Let \(P_{m}^{r}\) be the \(r\)-dimensional grid, with entries in each coordinate taken from \(\{1,2,\cdots , m\}\), and \(K_{t}\) the complete graph on \(t\) vertices. Also let \(S(F)\) be the family of all subdivisions of a graph \(F\). There has been substantial previous work on extremal questions involving subdivisions of graphs, involving both \(Sat(K_{n},S(F))\) and the Turan function \(ex(K_{n},S(F))\), for \(F = K_{t}\) or \(F\) a complete bipartite graph. In this paper we study \(Sat(H, S(F))\) for the host graph \(H = P_{m}^{r}\), and \(F = K_{4}\), motivated by previous work on \(Sat(K_{n}, S(K_{t}))\). Our main results are the following; 1) If at least one of \(m\) or \(n\) is odd with \(m\geq 5\) and \(n\geq 5\), then \(Sat(P_{m}\times P_{n}, S(K_{4})) = mn + 1.\) 2) For \(m\) even and \(m\geq 4\), we have \(m^{3} + 1 \le Sat(P_{m}^{3}, S(K_{4}))\le m^{3} + 2.\) 3) For \( r\geq 3\) with \(m\) even and \(m\geq 4\), we have \(Sat(P_{m}^{r}, S(K_{4})) \le m^{r} + 2^{r-1} – 2\).
An undirected graph is said to be cordial if there is a friendly (0,1)-labeling of the vertices that induces a friendly (0,1)-labeling of the edges. An undirected graph \(G\) is said to be \((2,3)\)-orientable if there exists a friendly (0,1)-labeling of the vertices of \(G\) such that about one-third of the edges are incident to vertices labeled the same. That is, there is some digraph that is an orientation of \(G\) that is \((2,3)\)-cordial. Examples of the smallest noncordial/non-\((2,3)\)-orientable graphs are given, and upper bounds on the possible number of edges in a cordial/\((2,3)\)-orientable graph are presented. It is also shown that if \(T\) is a linear operator on the set of all undirected graphs on \(n\) vertices that strongly preserves the set of cordial graphs or the set of \((2,3)\)-orientable graphs, then \(T\) is a vertex permutation.
With the social progress and technological development, China’s criminal activities gradually show the characteristics of specialization, networking, and hotspotting, which leads to the phenomenon of high incidence but low detection rate, and the prediction of the criminal phenomenon is particularly important. In this paper, we construct a graph self-encoder, and derive the formula of the GAE loss function from the corresponding reconstructed neighbor matrix and node feature loss function of GAE. The spatial channel attention mechanism is introduced to improve the performance of the model, and the time window dimension is mapped to the perceptual self-attention module, and the objective function is constructed by generating a collection of crime matrices for future time windows. A multi-raster layer analysis model is added to optimize the model, generate a risk map of criminal activities, quantify the risk value of each element, and form a spatio-temporal prediction effect. Comparison experiments are used to analyze the optimization effect of the model, and the absolute error of the optimized model is no more than 0.05 for four types of cases. The prediction results of the cases of property invasion in different time periods show that the number of cases occurring in the early hours of the morning is 508, and the average PEI index is 0.19, which is smaller compared with other time periods.
With the rapid urbanization and expansion of subway rail transit, the subway has become an essential mode of public transportation. This study explores the impact of subway car color design on passengers’ psychological responses. Utilizing computer vision technology and a pruning algorithm, a target detection model for passenger expression recognition was developed, serving as an intuitive measure of psychological reactions. An optimized expression feature extraction network was constructed for facial expression recognition, while a multidimensional data analysis model, based on data mining, provided comprehensive insights. The study reveals that green, red, and yellow lighting evoke positive psychological responses, whereas blue and purple induce calmer or more somber reactions. These findings offer valuable guidance for urban subway carriage color lighting design, enhancing passenger experience.
As economic globalization progresses, air transport has become increasingly vital to economic development due to its speed and convenience. This study examines the driving forces of airside economic construction across four levels: primary, secondary, derivative, and permanent influences. It explores the dynamic interplay between the aviation industry and airside economic construction. Using the entropy weight method to optimize the grey situation decision-making theory, the paper investigates the development strategies for Henan Province’s airside economy. Results indicate that the H2 area should be prioritized as the key construction zone, achieving the highest effect measurement score of 0.9789. Furthermore, focusing on the development of the tertiary industry or the joint advancement of secondary and tertiary industries in the H2 area yields the most significant economic impact, with effect measurement scores of 0.755 and 0.749, respectively.
This paper explores the integration of blockchain technology into the teaching quality evaluation system of universities. A practical teaching quality evaluation index system for applied technology universities is developed, ensuring data authenticity through blockchain’s de-trusting mechanism. To enhance data storage efficiency, the PBFT consensus algorithm is improved and incorporated into a technical architecture adopting an “off-chain storage + on-chain sharing” model. The algorithm scoring formula and improved PBFT consensus algorithm are analyzed to demonstrate their effectiveness. Practical applications in applied technology universities highlight the benefits of blockchain in higher education evaluation. The CBFT-based consensus algorithm achieves average CPU utilization of 13.4% compared to 18.5% in traditional algorithms, while ensuring data transparency and tamper-proofing. Additionally, the algorithm improves transaction throughput and reduces resource consumption, enabling efficient operation of the teaching evaluation system in applied sciences universities.
Translation as a cross-cultural information exchange and exchange activity has the nature of dissemination. Combining communication and translation helps make translation an open, dynamic, and comprehensive discipline. Translators play the role of gatekeepers in communication studies. The choice of a translator is affected by any change in the translator himself, such as his personal preference, motivation, life experience, aesthetic orientation, psychological factors and values, which can call for different translations to be produced. The translation of classics is not like the translation of ordinary works. It puts forward higher requirements for the translator. The beauty and subtlety of its words and characters require the translator to have a profound knowledge of the target language; its connotation and thought are broad and profound, and the translator needs to understand the source language. Transparency of this understanding. And such a master is really rare, and it is difficult to cultivate, so excellent translation works of classics are not common. In addition, translations are becoming more and more diverse, and there is inevitably a mix of people and irregularities in the intermediate translations. This paper explores the translation of classics that combines machine learning technology with the perspective of communication, and proposes an efficient translation model. The experimental results show that the model can effectively improve translation efficiency and accuracy.
We consider the generating function for increasingly labelled trees. By generalizing the proof through symbolic method, we are able to study various statistics regarding binary increasing trees with respect to height restrictions. We then apply our approach to special colorings of increasing trees in order to obtain their generating functions and, from there, derive the counting sequence for \((ak+a)\)-colored recursive trees. We also present some interesting bijections between colored and non-colored increasing trees.
This paper aims to enhance the moral and vocational qualities of college students by integrating moral education elements into career planning education. The BOPPPS teaching model is constructed, comprising six modules: introduction, objectives, pre-test, participatory learning, post-test, and summary, to effectively stimulate students’ interest and initiative. Moral education elements are integrated into career planning education through an intelligent teaching platform, incorporation into teaching processes, and the use of the second classroom to promote in-class and out-of-class linkages. Additionally, a fuzzy classroom teaching evaluation system is developed to assess the effectiveness of career planning education. The results indicate high reliability and validity of the evaluation system, with an alpha coefficient exceeding 0.8, a KMO value of 0.938, and a Bartlett’s test P-value of 0.000. Students’ positive classroom mood improved significantly from 35.79% to 68.42%, alongside an enhanced evaluation of classroom learning. The findings demonstrate the practical value of this approach in advancing education reform.
The combination of thermal power units’ stability and energy storage systems’ rapid response time enhances power system frequency control. However, high costs and battery life impacts from charging/discharging strategies limit energy storage adoption. This study proposes an adaptive weight-based particle swarm optimization algorithm (APSO) to optimize energy storage control for joint thermal-storage frequency modulation (FM). By analyzing the coupling between state of charge (SOC) and charging/discharging power, the study implements “shallow charging and discharging” with dynamic SOC constraints. The improved PSO algorithm integrates adaptive weighting to overcome local optimal convergence, enhancing global search capabilities and particle migration. Simulation results, based on real-world power plant data, show improved FM accuracy, faster regulation, and reduced energy storage system loss, significantly boosting economic efficiency.
With the increasing penetration of distributed intermittent energy into distribution networks, the self-healing problem of distribution networks faces significant challenges. The load level and demand response must be considered as critical factors affecting fault recovery. This paper proposes a fault recovery strategy that combines islanding division and network reconstruction. First, a distribution network model with a distributed energy storage system is established. To optimize the use of distributed energy resources, controllable loads that can respond to demand are prioritized, and high-priority loads are included in the islanded network after a fault. Based on the islanding division results, the remaining non-faulty power loss areas are restored through main network reconstruction. The improved whale optimization algorithm is employed to solve the problem. Simulation results demonstrate that load demand response is closely linked to the islanding process, and an optimal fault recovery strategy can be achieved by utilizing the distributed energy storage system and the main network.
With the rise of digital technology, global cross-border information flows are driving significant growth in international digital commerce. This paper employs Meta-analysis to examine the impact of cross-border information flows on global trade competitiveness. It outlines the Meta-analysis paradigm, explores the relationship between data element valorization and trade competitiveness, and highlights the varying effects across different stages of the trade process. Using correlation coefficients as effect values, the study transforms and calculates data with the help of formulas and software to comprehensively analyze and test the relationship. The findings reveal rapid growth in China’s digital economy, expanding from 22.6 trillion yuan in 2016 to 51.9 trillion yuan in 2022, deeply influencing industrial structures. In global cross-border data flows, China and Russia exhibit tighter regulations, with China’s DSTRI value rising from 0.325 to 0.347 million USD, demonstrating that cross-border data flows significantly impact global trade competitiveness.
In the era of intelligent education, technology is reshaping traditional music education by enhancing teaching quality, optimizing curriculum design, and improving teacher resources. However, its redistributive effects remain underexplored. This study examines how intelligent education technology impacts resource distribution in music education, focusing on the context of music teacher certification. The research highlights the reform needs of music teacher education, including student-centered goals, improved teaching methods, and optimized curricula. It introduces a music intelligence system based on a radial basis function (RBF) neural network and evaluates its potential in promoting equitable resource distribution through interactive teaching. Findings reveal that intelligent education technology enhances student learning outcomes and music skills by enabling personalized learning paths and strengthening practical teaching. Experimental results confirm the system’s effectiveness in significantly improving students’ music grades, demonstrating its value in transforming music education.
In the modern era, the cultivation of foreign talents extends beyond the traditional enhancement of humanistic knowledge, with literature playing a pivotal role. Addressing the challenges posed by the “golden curriculum,” this study uses the “Selected British and American Stories” program as an example to explore a blended learning and sorting approach. Aligned with the Ministry of Education’s emphasis on “golden subjects,” the research formulates an implementation strategy for curriculum development. In the context of the Ministry’s promotion of the mixed funding program in 2019, the study highlights the necessity of guiding students to utilize the Internet for data-driven blended learning. By emphasizing active engagement, intrinsic motivation, and flexible learning approaches, the proposed strategy aims to enhance teaching quality and align with contemporary educational reform priorities. Furthermore, the paper underscores the significance of equitable teaching evaluation as a feedback mechanism, actively contributing to the overall improvement of teaching quality.
An injective coloring of a given graph \(G = (V, E)\) is a vertex coloring of \(G\) such that any two vertices with a common neighbor receive distinct colors. An \(e\)-injective coloring of a graph \(G\) is a vertex coloring of \(G\) in which any two vertices \(v, u\) with a common edge \(e\) (\(e \neq uv\)) receive distinct colors; in other words, any two end vertices of a path \(P_4\) in \(G\) achieve different colors. With this new definition, we want to take a review of injective coloring of a graph from the new point of view. For this purpose, we review the conjectures raised so far in the literature of injective coloring and \(2\)-distance coloring, from the new approach of \(e\)-injective coloring. Additionally, we prove that, for disjoint graphs \(G, H\), with \(E(G) \neq \emptyset\) and \(E(H) \neq \emptyset\), \(\chi_{ei}(G \cup H) = \max\{\chi_{ei}(G), \chi_{ei}(H)\}\) and \(\chi_{ei}(G \vee H) = |V(G)| + |V(H)|.\) The \(e\)-injective chromatic number of \(G\) versus the maximum degree and packing number of \(G\) is investigated, and we denote \(\max\{\chi_{ei}(G), \chi_{ei}(H)\} \leq \chi_{ei}(G \square H) \leq \chi_{2}(G)\chi_{2}(H).\) Finally, we prove that, for any tree \(T\) (\(T\) is not a star), \(\chi_{ei}(T) = \chi(T),\) and we obtain the exact value of the \(e\)-injective chromatic number for some specified graphs.
In the literature of algebraic graph theory, an algebraic intersection graph called the invariant intersection graph of a graph has been constructed from the automorphism group of a graph. A specific class of these invariant intersection graphs was identified as the \(n\)-inordinate invariant intersection graphs, and its structural properties has been studied. In this article, we study the different types of proper vertex coloring schemes of these \(n\)-inordinate invariant intersection graphs and their complements, by obtaining the coloring pattern and the chromatic number associated.
This paper examines how digital entertainment consumption drives China’s economic growth from multiple dimensions. Using panel data from 260 prefecture-level cities (2020–2022) and a multi-temporal double-difference method, the study finds that digital entertainment consumption significantly promotes economic growth, with a direct effect coefficient of 0.748. Robustness tests via the PSM-DID method confirm this effect, with a coefficient of 0.714, significant at the 5% level. In the low digital divide group, the regression coefficient is 6.325, while it is significantly lower in the high digital divide group, indicating that the digital divide weakens the effect. Heterogeneity analysis shows that enhancing consumer experience, generating new businesses, and boosting cultural influence positively impact growth. The findings provide insights for the sustainable development of the entertainment industry and the digital economy.
Financial frauds, often executed through asset transfers and profit inflation, aim to reduce taxes and secure credits. To enhance the accuracy and efficiency of accounting data auditing, this study proposes an anomaly detection scheme based on a deep autoencoder neural network. Financial statement entries are extracted from the accounting information system, and global and local anomaly features are defined based on the attribute values of normal and fraudulent accounts, corresponding to individual and combined anomaly attribute values. The AE network is trained to identify anomalies using account attribute scores. Results demonstrate classification accuracies of 91.7%, 90.3%, and 90.9% for sample ratios of 8:2, 7:3, and 6:4, respectively. The precision, recall, and F1 score reach 90.85%, 90.77%, and 90.81%, respectively. Training takes 95.81ms, with recognition classification requiring only 0.02ms. The proposed deep neural network achieves high recognition accuracy and speed, significantly improving the detection of financial statement anomalies and fraud.
The core of financial institutions’ big data lies in risk control, making network security threat identification essential for enhancing data processing and service levels. This study applies the principles of network information transmission security prevention, combining frequency domain analysis and distributed processing to extract threat characteristics. A financial network security threat identification model is developed using BiGRU and Transformer models, and a SQLIA defense system is constructed by integrating multi-variant execution and SQL injection attack prevention. Additionally, an intelligent network security defense strategy is formulated based on finite rationality theory. Simulation results show an F1 composite score of 90.78% for threat identification, and the STRIPS-BR defense strategy reduces relative risk by 74.81% during peak times compared to other strategies. Supported by big data, this system ensures secure data transmission and enhances the network service capabilities of financial institutions.
Fine chemical processes are integral to modern industries such as automotive, environmental protection, aviation, and new energy. However, these processes involve highly toxic substances and complex chemical interactions, making them vulnerable to uncontrollable circumstances and posing significant risks to human safety and the environment. This work proposes an enhanced GA-LVW algorithm for reliability assessment of fine chemical processes, focusing on essential operating units. The method utilizes global-local structure analysis to extract features from operating unit variables, reducing data noise, simplifying the construction of fuzzy rules, and improving model resilience. The extracted features are integrated into a fuzzy inference system. The proposed approach is validated using the Tennessee Eastman (TE) process model and the R-22 production process in a fluoride facility. Results demonstrate that the enhanced GA-LVW algorithm significantly improves the system’s efficiency and maintainability compared to conventional fuzzy inference systems.
Over the past two decades, with the support of the Party and the state, universities have established educational principles integrating curriculum reform, teaching beliefs, and political theories. Despite significant progress in ideological and political theory research, challenges remain that hinder sustainable development. This paper leverages a computerized algorithmic model of complex information networks to explore the intersection of scientific and humanistic approaches in education. By combining these methods, the study provides an optimized knowledge and political model for university education and analyzes its credibility. Empirical results indicate that the proposed model achieves a 91% accuracy rate. The improved model enhances the intellectual and political vitality of university theoretical courses, strengthens educational principles, and ensures the quality of university education.
A positive integer \(k\) is called a magic constant if there is a graph \(G\) along with a bijective function \(f\) from \(V(G)\) to the first \(|V(G)|\) natural numbers such that the weight of the vertex \(w(v) = \sum_{uv \in E} f(u) = k\) for all \(v \in V\). It is known that all odd positive integers greater than or equal to \(3\) and the integer powers of \(2\), \(2^{t}\), \(t \geq 6\), are magic constants. In this paper, we characterize all positive integers that are magic constants and generate all distance magic graphs, up to isomorphism, of order up to \(10\).
The Radenković and Gutman conjecture establishes a relationship between the Laplacian eigenvalues of any tree \(T_n\), the star graph \(S_n\), and the path graph \(P_n\), i.e., \({LE}(P_n) \leq {LE}(T_n) \leq {LE}(S_n).\) In this paper, we prove this conjecture for a class of trees with \(n\) vertices and having diameter \(16\) to \(30\).
To address large prediction errors in traditional risk assessment methods, the X-means clustering algorithm is utilized to segment financial product customers, combined with correlation strength analysis to understand customer behaviors and needs. Using the Hoteling model, a two-step pricing strategy is proposed, revealing that data product prices are inversely proportional to depreciation rate, timeliness, and customization degree, and deriving the platform’s optimal pricing strategy. A financial risk indicator system is developed using principal component analysis for systematic risk assessment. In call option pricing prediction, the model converges at Epoch=40, achieving a normalized predicted price of 0.154 (true value: 0.153). For put options, the model converges at Epoch=100, with a predicted normalized price of 0.146 (true value: 0.145). The results demonstrate the model’s accuracy in pricing prediction, providing effective support for real-time market risk monitoring and timely risk prevention.
This study develops a stereoscopic vision system using a two-camera calibration method and BP neural networks combined with genetic algorithms to measure precision component dimensions. Images are processed using edge detection and Hough transform algorithms, and a machine vision-based inspection model is constructed. Bearing components are used as the research object to detect dimensions, edges, geometric parameters, and loose components under six angles. Maximum measurement deviation is 0.04 mm, and edge detection results are clear and concise. Geometric parameter deviations remain within [-5%, 5%], achieving high recognition accuracy. The detection model’s classification accuracy is 97.49%, with verification accuracy at 98.01%. Comprehensive false detection and leakage rates are 1.03% and 0.46%, respectively. The model demonstrates superior detection performance across various angles for bearing components.
We study a discrete-time model for the spread of information in a graph, motivated by the idea that people believe a story when they learn of it from two different origins. Similar to the burning number, in this problem, information spreads in rounds and a new source can appear in each round. For a graph \(G\), we are interested in \(b_2(G)\), the minimum number of rounds until the information has spread to all vertices of graph \(G\). We are also interested in finding \(t_2(G)\), the minimum number of sources necessary so that the information spreads to all vertices of \(G\) in \(b_2(G)\) rounds. In addition to general results, we find \(b_2(G)\) and \(t_2(G)\) for the classes of spiders and wheels and show that their behavior differs with respect to these two parameters. We also provide examples and prove upper bounds for these parameters for Cartesian products of graphs.
This study explores how employee satisfaction moderates the relationship between corporate performance and innovative behavior using deep learning models: Autoencoder and restricted Boltzmann machines (RBM). The Autoencoder extracts key features for better analysis, while the RBM-based model analyzes the relationships among employee satisfaction, corporate performance, and innovative behavior. Results show a positive correlation between employee satisfaction and innovative behavior (0.460) and between innovative behavior and corporate performance (0.348). Regression analysis reveals that employee satisfaction indirectly impacts corporate performance through innovative behavior (impact: 0.10, t = 5.25). Differences in satisfaction, innovative behavior, and performance were observed across employee attributes. This study highlights the role of employee satisfaction in enhancing corporate performance and innovation, offering insights for human resource strategies.
An hourglass \(\Gamma_0\) is the graph with degree sequence \(\{4,2,2,2,2\}\). In this paper, for integers \(j\geq i\geq 1\), the bull \(B_{i,j}\) is the graph obtained by attaching endvertices of two disjoint paths of lengths \(i,j\) to two vertices of a triangle. We show that every 3-connected \(\{K_{1,3},\Gamma_0,X\}\)-free graph, where \(X\in \{ B_{2,12},\,B_{4,10},\,B_{6,8}\}\), is Hamilton-connected. Moreover, we give an example to show the sharpness of our result, and complete the characterization of forbidden induced bulls implying Hamilton-connectedness of a 3-connected {claw, hourglass, bull}-free graph.
Special attention has been given to China’s socio-economic development, the gradual improvement of living standards, and the increasing emphasis on preschool education by families and society. However, this process is influenced by various factors, such as school conditions, family dynamics, teacher performance, and social influences, which negatively affect the quality of kindergarten brand image and learning outcomes. These challenges hinder the effective empowerment of children across different fields. To achieve the goals of kindergarten education, teachers should leverage the comprehensive nurturing value of labor education to maximize and optimize its educational impact. Kindergarten brand image evaluation is a critical component of early childhood education, helping educators and researchers assess its effectiveness and identify areas for development. This paper addresses the issues in China’s current kindergarten brand image evaluation practices and proposes an evaluation method based on the support vector mechanism (SVM) and component analysis to enhance evaluation quality. The proposed approach aims to improve the accuracy and reliability of kindergarten brand image assessments, contributing to the advancement of early childhood education.
This research delves into the pathway energy framework for flower families, a class of simple connected graphs, whose path matrix \( P \) is constructed such that each entry \( P_{ij} \) quantifies the maximum number of vertex-disjoint paths. By analyzing the characteristic values of this matrix, we establish the pathway energy bounds specific to these flower graph families. Additionally, a comprehensive algorithm is developed to evaluate the time complexity across different flower family configurations, utilizing numerous trials to capture their average, maximum, and minimum computational behaviors. This analysis offers a comparative study of the structural intricacies that lead to increased computational complexity, highlighting which graph topologies tend to impose higher algorithmic challenges. The proposed method introduces a refined and adaptable approach, deepening the exploration of characteristic graph properties and their computational impact, thereby expanding the practical applications of these findings in graph theory.
Let \(G=(V,E)\) be a simple connected graph with vertex set \(V\) and edge set \(E\). The Randić index of graph \(G\) is the value \(R(G)=\sum_{uv\in E(G)} \frac{1}{\sqrt{d(u)d(v)}}\), where \(d(u)\) and \(d(v)\) refer to the degree of the vertices \(u\) and \(v\). We obtain a lower bound for the Randić index of trees in terms of the order and the Roman domination number, and we characterize the extremal trees for this bound.
This study investigates the impact of gamification teaching on students’ motivation in physical education using questionnaires, teaching experiments, and mathematical statistics. A gamified sports teaching model, grounded in the self-determination motivation theory and analyzed through a multiple regression model, was designed to assess motivational stimulation. Results showed that gamified physical education significantly improved motivation in the experimental class compared to the control class (P < 0.05). The average physical education score in the experimental class was 77.67, 5.08 points higher than the control class. Internal motivation, identity regulation, intake regulation, and external regulation ratings were 4.132, 3.992, 4.172, and 4.156, respectively. Regression analysis confirmed that gamified teaching positively influenced motivation, with self-determination theory effectively mediating students’ physical education learning motivation.
In this paper, it is pointed out that the definition of `Fibonacci \((p,r)\)-cube’ in many papers (denoted by \(I\Gamma_{n}^{(p,r)}\)) is incorrect. The graph \(I\Gamma_{n}^{(p,r)}\) is not the same as the original one (denoted by \(O\Gamma_{n}^{(p,r)}\)) introduced by Egiazarian and Astola. First, it is shown that \(I\Gamma_{n}^{(p,r)}\) and \(O\Gamma_{n}^{(p,r)}\) have different recursive structure. Then, it is proven that all the graphs \(O\Gamma_{n}^{(p,r)}\) are partial cubes. However, only a small part of graphs \(I\Gamma_{n}^{(p,r)}\) are partial cubes. It is also shown that \(I\Gamma_{n}^{(p,r)}\) and \(O\Gamma_{n}^{(p,r)}\) have different medianicity. Finally, several questions are listed for further investigation.
A \(q\)-total coloring of \(G\) is an assignment of \(q\) colors to the vertices and edges of \(G\), so that adjacent or incident elements have different colors. The Total Coloring Conjecture (TCC) asserts that a total coloring of a graph \(G\) has at least \(\Delta+1\) and at most \(\Delta+2\) colors. In this paper, we determine that all members of new infinite families of snarks obtained by the Kochol superposition of Goldberg and Loupekine with Blowup and Semiblowup snarks are Type~1. These results contribute to a question posed by Brinkmann, Preissmann and D. Sasaki (2015) by presenting negative evidence about the existence of Type~2 cubic graphs with girth at least 5.
Generative adversarial network (GAN) technology has enabled the automatic synthesis of realistic face images from text. This paper proposes a model for generating face images from Chinese text by integrating a text mapping module with the StyleGAN generator. The text mapping module utilizes the CLIP model for pre-training Chinese text, employs a convolutional-inverse convolutional structure to enhance feature extraction, and incorporates a BiLSTM model to construct complete sentences as inputs for the StyleGAN generator. The generator interprets semantic features to generate face images. Validation on Face2Text and COCO datasets yields F1 values of 83.43% and 84.97%, respectively, while achieving the lowest FID and FSD scores of 103.25 and 1.26. The combination of CLIP pre-training and word-level semantic embedding improves image quality, offering a novel approach for face recognition applications in public safety.
In this note, we establish six Gallai theorems involving twelve minority and majority parameters. Accordingly, the complexity problems corresponding to some of these parameters are obtained.
The promotion of industrial digital transformation is a crucial breakthrough in the evolution of economic structures and the physical layout of spaces. It has the potential to elevate the entire industrial chain to a high-end value chain, creating more profit opportunities and enhancing the influence of domestic industries in the international cycle. This study uses the cities in the Yangtze River Delta Economic Belt as a case study to explore the spatial effects of digital transformation on the healthy transformation of traditional industrial structures. It constructs relevant spatial coupling models and empirically verifies them by testing specific assumptions. The experimental results indicate that the model is significant at a level greater than 5%, making it suitable for selecting spatial measurement models. The mean square error of its network simulation output is 0.1333, confirming the expected hypothesis and demonstrating that digital transformation has a significant spatial driving effect on industrial upgrading.
A \(k\)-tree is a graph that can be formed by starting with \(K_{k+1}\) and iterating the operation of making a new vertex adjacent to all the vertices of a \(k\)-clique of the existing graph. A structural characterization of 3-trees with diameter at most 2 is proven. This implies a corollary for planar 3-trees which leads to a description of their degree sequences.
Electric shock accidents remain a major safety concern for distribution workers. Recent advancements in video AI applications allow for detecting when workers cross safety lines, but determining their height and the spatial distance between them and live equipment is still a challenge. This article proposes a pre-control system using LiDAR, an edge processing module, and a warning module to ensure safe operations in power distribution scenarios. The system scans the area in real time, uses deep learning to identify objects like distribution stations, human bodies, high-voltage equipment, and transmission lines in point clouds, and calculates the distance between operators and high-voltage equipment. When this distance approaches or exceeds safety limits, the warning module issues voice alerts. Experimental results show that this system significantly reduces false alarms compared to video-based methods, accurately measures distances, and provides timely warnings, making it a practical solution for enhancing worker safety in power distribution operations.
In this paper, we present a new combinatorial characterization of Hermitian cones in \(\mathrm{PG}(3,q^2)\).
Let \(K_n\), \(P_n\), and \(Y_n\) respectively denote a complete graph, a path, and a \(Y\)-tree on \(n\) vertices, and let \(K_{m,n}\) denote a complete bipartite graph with \(m\) and \(n\) vertices in its parts. Graph decomposition is the process of breaking down a graph into a collection of edge-disjoint subgraphs. A graph \(G\) has a \((H_1, H_2)\)-multi-decomposition if it can be decomposed into \(\alpha \geq 0\) copies of \(H_1\) and \(\beta \geq 0\) copies of \(H_2\), where \(H_1\) and \(H_2\) are subgraphs of \(G\). In this paper, we derive the necessary and sufficient conditions for the \((P_5, Y_5)\)-multi-decomposition of \(K_n\) and \(K_{m,n}\).
With the rapid development of wireless communication networks, it brings more and more convenience to users. However, with the expansion of network size, the limitation of channel resources in network communication is becoming more obvious. Effective channel assignment has a great impact on the quality of communication networks. However, in real communication networks, underutilization of channels and excessive number of channels produce large interference, so it is necessary to find a reasonable channel assignment method. In this paper, we study the optimal channel assignment strategy for the Cartesian product of an \(m\)-vertex complete bipartite graph and an \(m\)-order cycle, where \(m\geq 5\) is odd. Determines the exact value and lower bound of its radio number.
This study introduces a novel approach to investigating Sombor indices and applying machine learning methods to assess the similarity of non-steroidal anti-inflammatory drugs (NSAIDs). The research aims to predict the structural similarities of nine commonly prescribed NSAIDs using a machine learning technique, specifically a linear regression model. Initially, Sombor indices are calculated for nine different NSAID drugs, providing numerical representations of their molecular structures. These indices are then used as features in a linear regression model trained to predict the similarity values of drug combinations. The model’s prediction performance is evaluated by comparing the predicted similarity values with the actual similarity values. Python programming is employed to verify accuracy and conduct error analysis.
Criminal evidence serves as the foundation for criminal proceedings, with evidence used to ascertain the facts of cases being critical to achieving fairness and justice. This study explores the application of digital information technology in building a data resource base for criminal cases, formulating standard evidence guideline rules, and optimizing evidence verification procedures. A named entity recognition model based on the SVM-BiLSTM-CRF framework is proposed, coupled with an evidence relationship extraction model using the Transformer framework to improve evidence information extraction through sequential features and global feature capturing. Results show that the F1 value for entity recognition in criminal cases reaches 94.19%, and the evidence extraction model achieves an F1 value of 81.83% on the CAIL-A dataset. These results are utilized to construct evidence guidelines, helping case handlers increase case resolution rates to approximately 99%. The application of digital technology enhances evidence collection efficiency, accelerates case closures, and offers a pathway to improving judicial credibility.
In this paper we consider some new weighted and alternating weighted generalized Fibonomial sums and the corresponding \(q-\)forms. A generalized form of weight sequences which contains squares in subscripts is discussed for the first time in the literature. The main key to get success in sums is an ability to change one sum into another that is simpler in some way. Thus, in order to prove these sums by doing some manipulations and tricks, our approach is to use classical \(q-\)analysis, in particular a formula of Rothe, a version of Cauchy binomial theorem and Gauss identity.
Total dominator total coloring of a graph is a total coloring of the graph such that each object of the graph is adjacent or incident to every object of some color class. The minimum namber of the color classes of a total dominator total coloring of a graph is called the total dominator total chromatic number of the graph. Here, we will find the total dominator chromatic numbers of wheels, complete bipartite graphs and complete graphs.
Meta-analysis was conducted to investigate the effects of static versus dynamic stretching on athlete agility. Keywords such as dynamic stretching, static stretching, athletes, and agility were searched through China Knowledge Network (CNKI), Wanfang, Pubmed, Web of Science, and EBSCO. Inclusion and exclusion criteria were established, and Endnote software was used to screen the literature, with statistical analysis performed using Stata and Revman. A total of 15 papers with 322 groups of experiments were included, with interventions typically performed three times a week. The quality of the included papers, assessed using Review Manager, showed all studies to be randomized controlled trials with low-risk indicators. Meta-analysis results indicated high heterogeneity with SMD=0.11 and significant differences (P<0.00001<0.05). The findings suggest that static and dynamic stretching, with an intervention period of about 15 weeks and a frequency of approximately three times per week, have a significant effect on athlete agility.
A new series of four-associate class partially balanced incomplete block designs in two replications has been proposed. The blocks of these designs are of two different sizes. The blocks can be divided into two groups such that every treatment appears in each group exactly once, and any two blocks belonging to two different groups have a constant number of treatments in common, i.e., these designs are affine resolvable.
We initiate to study a \(D\)-irregular labeling, which generalizes both non-inclusive and inclusive \(d\)-distance irregular labeling of graphs. Let \(G=(V(G),E(G))\) be a graph, \(D\) a set of distances, and \(k\) a positive integer. A mapping \(\varphi\) from \(V(G)\) to the set of positive integers \(\{1,2,\dots,k\}\) is called a \(D\)-irregular \(k\)-labeling of \(G\) if every two distinct vertices have distinct weights, where the weight of a vertex \(x\) is defined as the sum of labels of vertices whose distance from \(x\) belongs to \(D\). The least integer \(k\) for which \(G\) admits a \(D\)-irregular labeling is the \(D\)-irregularity strength of \(G\) and denoted by \(\mathrm{s}_D(G)\). In this paper, we establish several fundamental properties on \(D\)-irregularity strength for arbitrary graphs. We also determine this parameter exactly for families of graphs with small diameter or small maximum degree.
Let \( 0<k\in\mathbb{Z} \). Let the star 2-set transposition graph \( ST^2_k \) be the \( (2k-1) \)-regular graph whose vertices are the \( 2k \)-strings on \( k \) symbols, each symbol repeated twice, with its edges given each by the transposition of the initial entry of one such \( 2k \)-string with any entry that contains a different symbol than that of the initial entry. The pancake 2-set transposition graph \( PC^2_k \) has the same vertex set of \( ST^2_k \) and its edges involving each the maximal product of concentric disjoint transpositions in any prefix of an endvertex string, including the external transposition being that of an edge of \( ST^2_k \). For \( 1<k\in\mathbb{Z} \), we show that \( ST^2_k \) and \( PC^2_k \), among other intermediate transposition graphs, have total colorings via \( 2k-1 \) colors. They, in turn, yield efficient dominating sets, or E-sets, of the vertex sets of \( ST^2_k \) and \( PC^2_k \), and partitions into \( 2k-1 \) such E-sets, generalizing Dejter-Serra work on E-sets in such graphs.
The scientific knowledge graph is an emerging research method in this context. In the research of physical education teaching, the research and sorting out of the research results of physical education teaching in my country from the perspective of scientometrics and information visualization is still slightly insufficient. The similarity between the frontiers of physical education teaching research in China and the United States in the past five years is that both countries have paid more attention to research topics such as physical education teaching methods and physical education courses. This paper proposes a rough set knowledge reduction algorithm based on improved genetic algorithm. The support and importance of conditional attributes to decision attributes are introduced into the information system, which are added to the genetic algorithm as heuristic information, and the concepts of population dissimilarity and individual dissimilarity are proposed to improve the genetic algorithm. The research on school physical education in my country is biased towards problem research, while the research on physical education teaching methods in the United States is biased towards student health; In addition, starting from the national conditions, the hotspots in the field of physical education teaching in my country tend to be “Sports and Health Curriculum Standards”, physical education teachers, physical education ideas, educational theories and college sports, while the hotspots in the field of physical education teaching in the United States tend to be physical activity, children and adolescents , students, women, exercise education, physical education, self-determination theory, and the integration of psychological motivation and physical education. Experimental data analysis my country’s physical education curriculum research should appropriately increase the attention to the details of physical education curriculum, and my country’s physical education teaching practice research should appropriately increase the research on physical education from the perspective of public health.
In secret sharing, the relationships between participants and the information they hold can be modeled effectively using graph structures. Graphs allow us to visualize and analyze these relationships, making it easier to define access structures, optimize share distributions, and ensure security. This paper provides the first comprehensive review of existing research on the application of graph theory to secret sharing comparing different classic and modern approaches and analyzing the current litterature. Through this study we highlight the key advances and methodologies that have been developed, underscoring the pivotal role of graph theoretic approaches in enhancing the security and efficiency of secret sharing schemes. Furthermore, the review identifies open challenges and future research directions, providing insights into potential innovations that could further strengthen cryptographic practices. This work serves as a foundational resource for researchers and practitioners seeking to deepen their understanding of the intersection between graph theory and secret sharing, fostering the development of more robust and sophisticated cryptographic solutions.
A proper coloring assigns distinct colors to the adjacent vertices of a graph. An equitable near proper coloring of a graph \(G\) is an improper coloring in which neighbouring vertices are allowed to receive the same color such that the cardinalities of two distinct color classes differ by not more than one and the number of monochromatic edges is minimised by giving certain restrictions on the number of color classes that can have an edge between them. This paper discusses the equitable near proper coloring of line, middle, and total graphs of certain graph classes, such as paths, cycles, sunlet graphs, star graphs, and gear graphs.
This paper investigates the Turan-like problem for \(\mathcal{K}^-_{r + 1}\)-free \((r \geq 2)\) unbalanced signed graphs, where \(\mathcal{K}^-_{r + 1}\) is the set of unbalanced signed complete graphs with \(r+1\) vertices. The maximum number of edges and the maximum index for \(\mathcal{K}^-_{r + 1}\)-free unbalanced signed graphs are given. Moreover, the extremal \(\mathcal{K}^-_{r + 1}\)-free unbalanced signed graphs with the maximum index are characterized.
Directed hypergraphs represent a natural extension of directed graphs, while soft set theory provides a method for addressing vagueness and uncertainty. This paper introduces the notion of soft directed hypergraphs by integrating soft set principles into directed hypergraphs. Through parameterization, soft directed hypergraphs yield a sequence of relation descriptions derived from a directed hypergraph. Additionally, we present several operations for soft directed hypergraphs, including extended union, restricted union, extended intersection, and restricted intersection, and explore their characteristics.
In this paper, we give a classification of all Mengerian \(4\)-uniform hypergraphs derived from graphs.
Big data technology makes it possible to scientifically analyse a large amount of marketing data, which plays an important role in the development of marketing strategies for products and the improvement of marketing effects. In this paper, a marketing data stream analysis system is designed based on the stream analysis method. The system designs and optimises the marketing data storage and retrieval, data acquisition and streaming calculation engine to achieve real-time user behaviour data streaming analysis. The average response time accuracy of the system’s data can reach 96%, the throughput rate is 11.8% ahead of the maximum compared to the Word Count system, and the before-and-after ratios of the PUSH message click rate, the user registration success rate, the online shop attention rate, the returning customer rate, and the loyal customer rate are 1.03, 1.02, 1.27, 1.11, 1.27, and 1.78, respectively. It indicates that this paper’s design of the marketing data streaming analysis system has good performance and application effect.
With the construction of the national discourse power, the international communication of German language has also attracted the attention of the public, and its own communication attributes and characteristics have also become a hot topic around the world. A machine learning development process includes operations such as data preprocessing, feature engineering, model design, and super parameter optimization. Changes in the configuration of each operation may affect the final quality of the model. Nor is it mainly the problem of teachers’ teaching, but the communication barrier caused by cultural differences. We can see that there are still many obstacles and misunderstandings in language, thought, cross-cultural communication and knowledge in many communication occasions between China and Germany. Through reviewing and summarizing the previous studies on intercultural communication, this paper analyzes the current situation of intercultural communication studies, points out the problems existing in the current research, and tries to put forward the cultivation methods of intercultural communication.
The \( n \)-dimensional Möbius cube \( MQ_n \) is an important variant of the hypercube \( Q_n \), which possesses some properties superior to the hypercube. This paper investigates the fault-tolerant edge-pancyclicity of \( MQ_n \), and shows that if \( MQ_n \) (\( n \geq 5 \)) contains at most \( n-2 \) faulty vertices and/or edges then, for any fault-free edge \( uv \) in \( MQ_n^i (i=0,1) \) and any integer \( \ell \) with \( 7-i \leqslant \ell \leqslant 2^n – f_v \), there is a fault-free cycle of length \( \ell \) containing the edge \( uv \), where \( f_v \) is the number of faulty vertices. The result is optimal in some senses.
For a connected graph \(G\), the edge Mostar index \(Mo_e(G)\) is defined as \(Mo_e(G)=\sum\limits_{e=uv \in E(G)}|m_u(e|G) – m_v(e|G)|\), where \(m_u(e|G)\) and \(m_v(e|G)\) are respectively, the number of edges of \(G\) lying closer to vertex \(u\) than to vertex \(v\) and the number of edges of \(G\) lying closer to vertex \(v\) than to vertex \(u\). We determine a sharp upper bound for the edge Mostar index on bicyclic graphs and identify the graphs that achieve the bound, which disproves a conjecture proposed by Liu et al. [Iranian J. Math. Chem. 11(2) (2020) 95–106].
In a recent paper Cameron, Lakshmanan and Ajith [6] began an exploration of hypergraphs defined on algebraic structures, especially groups, to investigate whether this can add a new perspective. Following their suggestions, we consider suitable hypergraphs encoding the generating properties of a finite group. In particular, answering a question asked in their paper, we classified the finite solvable groups whose generating hypergraph is the basis hypergraph of a matroid.
Given a connected graph \(G\) and a configuration \(D\) of pebbles on the vertices of \(G\), a pebbling transformation involves removing two pebbles from one vertex and placing one pebble on its adjacent vertex. A monophonic path is defined as a chordless path between two non-adjacent vertices \(u\) and \(v\). The monophonic cover pebbling number, \(\gamma_{\mu}(G)\), is the minimum number of pebbles required to ensure that, after a series of pebbling transformations using monophonic paths, all vertices of \(G\) are covered with at least one pebble each. In this paper, we determine the monophonic cover pebbling number (\(MCPN\)) for the gear graph, sunflower planar graph, sun graph, closed sun graph, tadpole graph, lollipop graph, double star-path graph, and a class of fuses.
Chinese animation has long faced challenges, with foreign animation dominating the market and domestic animation struggling to compete. The rise of new media has driven the industrialization and branding of Chinese animation, linking it to complex social and cultural networks that shape its future competitiveness. Similarly, sports events, as cultural phenomena, hold both entertainment and cultural significance, reflecting societal modernization. This study categorizes mascot design features of sports events into appearance, color, and accessory characteristics, providing theoretical insights to enhance understanding of event culture. Experimental results show that an optimized cellular genetic algorithm improves mascot design, aligning with human aesthetics while promoting the spirit of sports globally.
By means of the generating function method, a linear recurrence relation is explicitly resolved. The solution is expressed in terms of the Stirling numbers of both the first and the second kind. Two remarkable pairs of combinatorial identities (Theorems 3.1 and 3.3) are established as applications, that contain some well–known convolution formulae on Stirling numbers as special cases.
A \(\mathcal{Y}\) tree on \(k\) vertices is denoted by \(\mathcal{Y}_k\). To decompose a graph into \(\mathcal{Y}_k\) trees, it is necessary to create a collection of subgraphs that are isomorphic to \(\mathcal{Y}_k\) tree and are all distinct. It is possible to acquire the necessary condition to decompose \(K_m(n)\) into \(\mathcal{Y}_k\) trees (\(k \geq 5\)), which has been obtained as \(n^2m(m-1) \equiv 0 \pmod{2(k-1)}\). It has been demonstrated in this document that, a gregarious \(\mathcal{Y}_5\) tree decomposition in \(K_m(n)\) is possible only if \(n^2m(m-1) \equiv 0 \pmod{8}\).
Let \( G \) be a graph, the zero forcing number \( Z(G) \) is the minimum of \( |Z| \) over all zero forcing sets \( Z \subseteq V(G) \). In this paper, we are interested in studying the zero forcing number of quartic circulant graphs \( C_{p}\left(s,t\right) \), where \( p \) is an odd prime. Based on the fact that \( C_{p}\left(s,t\right) \cong C_{p}\left(1,q\right) \), we give the exact values of the zero forcing number of some specific quartic circulant graphs.
Behera and Panda defined a balancing number as a number b for which the sum of the numbers from \(1\) to \(b – 1\) is equal to the sum of the numbers from \(b + 1\) to \(b + r\) for some r. They also classified all such numbers. We define two notions of balancing numbers for Farey fractions and enumerate all possible solutions. In the stricter definition, there is exactly one solution, whereas in the weaker one all sufficiently large numbers work. We also define notions of balancing numbers for levers and mobiles, then show that these variants have many acceptable arrangements. For an arbitrary mobile, we prove that we can place disjoint consecutive sequences at each of the leaves and still have the mobile balance. However, if we impose certain additional restrictions, then it is impossible to balance a mobile.
For a graph G and for non-negative integers p, q, and r, the triplet \((p, q, r)\) is said to be an admissible triplet if \(3p + 4q + 6r = |E(G)|\). If G admits a decomposition into p cycles of length 3, q cycles of length 4, and r cycles of length 6 for every admissible triplet \((p, q, r)\), then we say that G has a \(\{C_{3}^{p}, C_{4}^{q}, C_{6}^{r}\}\)-decomposition. In this paper, the necessary conditions for the existence of \(\{C_{3}^{p}, C_{4}^{q}, C_{6}^{r}\}\)-decomposition of \(K_{\ell, m, n} (\ell \leq m \leq n)\) are proved to be sufficient. This affirmatively answers the problem raised in Decomposing complete tripartite graphs into cycles of lengths 3 and 4, Discrete Math. 197/198 (1999), 123-135. As a corollary, we deduce the main results of Decomposing complete tripartite graphs into cycles of lengths 3 and 4, Discrete Math., 197/198, 123-135 (1999) and Decompositions of complete tripartite graphs into cycles of lengths 3 and 6, Austral. J. Combin., 73(1), 220-241 (2019).
A graph G(V, E) is Γ-harmonious when there is an injection f from V to an Abelian group Γ such that the induced edge labels defined as w(xy) = f(x) + f(y) form a bijection from E to Γ. We study Γ-harmonious labelings of several cycles-related classes of graphs, including Dutch windmills, generalized prisms, generalized closed and open webs, and superwheels.
If Γ is a finite group and G a graph such that Aut(G) ≡ Γ acts regularly on V(G), then we say that G is a graphical regular representation (GRR) of Γ. The question asking which finite groups have at least one GRR was an important question in algebraic graph theory and it was completely solved as a result of work done by several researchers. However, it remains a challenge to discern whether a group known to have GRRs has GRRs with specific properties, such as being trivalent. In this paper, we shall be deriving simple conditions on the parameters of a subset of a dihedral group for easily constructing trivalent graphical regular representations (GRR) of the group. Specifically, we shall prove the following:
Let n be an odd integer greater than 5 and let r, s, and t be integers less than n such that the difference of any two of them is relatively prime to n. If 3r – 2s = t (mod n), then Cay(Dn, {abr, abs, abt}) is a GRR of Dn.
We will also be looking at very convenient corollaries of this result. But another main aim of this paper is to show how a simple use of Schur rings can be used to derive such results. This paper therefore also serves as a review of some basic results about Schur rings which we feel should be among the standard armory of an algebraic graph theorist.
This paper presents an investigation of a modified Leslie-Gower predator-prey model that incorporates fractional discrete-time Michaelis-Menten type prey harvesting. The analysis focuses on the topology of nonnegative interior fixed points, including their existence and stability dynamics. We derive conditions for the occurrence of flip and Neimark-Sacker bifurcations using the center manifold theorem and bifurcation theory. Numerical simulations, conducted with a computer package, are presented to demonstrate the consistency of the theoretical findings. Overall, our study sheds light on the complex dynamics that arise in this model and highlights the importance of considering fractional calculus in predator-prey systems with harvesting.
For a set \( S \) of vertices in a connected graph \( G \), the multiplicative distance of a vertex \( v \) with respect to \( S \) is defined by \(d_{S}^{*}(v) = \prod\limits_{x \in S, x \neq v} d(v,x).\) If \( d_{S}^{*}(u) \neq d_{S}^{*}(v) \) for each pair \( u,v \) of distinct vertices of \( G \), then \( S \) is called a multiplicative distance-locating set of \( G \). The minimum cardinality of a multiplicative distance-locating set of \( G \) is called its multiplicative distance-location number \( loc_{d}^{*}(G) \). If \( d_{S}^{*}(u) \neq d_{S}^{*}(v) \) for each pair \( u,v \) of distinct vertices of \( G-S \), then \( S \) is called an external multiplicative distance-locating set of \( G \). The minimum cardinality of an external multiplicative distance-locating set of \( G \) is called its external multiplicative location number \( loc_{e}^{*}(G) \). We prove the existence or non-existence of multiplicative distance-locating sets in some well-known classes of connected graphs. Also, we introduce a family of connected graphs such that \( loc_{d}^{*}(G) \) exists. Moreover, there are infinite classes of connected graphs \( G \) for which \( loc_{d}^{*}(G) \) exists as well as infinite classes of connected graphs \( G \) for which \( loc_{d}^{*}(G) \) does not exist. A lower bound for the multiplicative distance-location number of a connected graph is established in terms of its order and diameter.
Earlier optimal key pre\(-\)distribution schemes (KPSs) for distributed sensor networks (DSNs) were proposed using combinatorial designs via transversal designs, affine, and partially affine resolvable designs. Here, nearly optimal KPSs are introduced and a class of such KPSs is obtained from resolvable group divisible designs. These KPSs are nearly optimal in the sense of local connectivity. A metric for the efficiency of KPSs is given. Further, an optimal KPS has also been proposed using affine resolvable \( L_{2} \)-type design.
We study the nonzero algebraic real algebras \( A \) with no nonzero joint divisor of zero. We prove that if \( Z(A) \neq 0 \) and \( A \) satisfies one of the Moufang identities, then \( A \) is isomorphic to \( \mathbb{R} \), \( \mathbb{C} \), \( \mathbb{H} \), or \( \mathbb{O} \). We show also that if \( A \) is power-associative, flexible, and satisfies the identity \( (a,a,[a,b])=0 \), then \( A \) is isomorphic to \( \mathbb{R} \), \( \mathbb{C} \), \( \mathbb{H} \), or \( \mathbb{O} \). Finally, we prove that \( \mathbb{R} \), \( \mathbb{C} \), \( \mathbb{H} \), and \( \mathbb{O} \) are the only algebraic real algebras with no nonzero divisor of zero satisfying the middle Moufang identity, or the right and left Moufang identities.
The metric dimension of a graph is the smallest number of vertices such that all vertices are uniquely determined by their distances to the chosen vertices. The corona product of graphs \( G \) and \( H \) is the graph \( G \odot H \) obtained by taking one copy of \( G \), called the center graph, \( |V(G)| \) copies of \( H \), called the outer graph, and making the \( j^{th} \) vertex of \( G \) adjacent to every vertex of the \( j^{th} \) copy of \( H \), where \( 1 \leqslant j \leqslant |V(G)| \). The Join graph \( G + H \) of two graphs \( G \) and \( H \) is the graph with vertex set \( V(G + H)=V(G) \cup V(H) \) and edge set \( E(G + H)=E(G) \cup E(H) \cup \{uv :u \in V(G),v \in V(H)\} \). In this paper, we determine the Metric dimension of Corona product and Join graph of zero divisor graphs of direct product of finite fields.
The secure edge dominating set of a graph \( G \) is an edge dominating set \( F \) with the property that for each edge \( e \in E-F \), there exists \( f \in F \) adjacent to \( e \) such that \( (F-\{f\})\cup \{e\} \) is an edge dominating set. In this paper, we obtained upper bounds for edge domination and secure edge domination number for Mycielski of a tree.
In this paper we contribute to the literature of computational chemistry by providing exact expressions for the detour index of joins of Hamilton-connected (\(HC\)) graphs. This improves upon existing results by loosening the requirement of a molecular graph being Hamilton-connected and only requirement certain subgraphs to be Hamilton-connected.
The geometrical properties of a plane determine the tilings that can be built on it. Because of the negative curvature of the hyperbolic plane, we may find several types of groups of symmetries in patterns built on such a surface, which implies the existence of an infinitude of possible tiling families. Using generating functions, we count the vertices of a uniform tiling from any fixed vertex. We count vertices for all families of valence \(5\) and for general vertices with valence \(6\), with even-sized faces. We also give some general results about the behavior of the vertices and edges of the tilings under consideration.
This study extends the concept of competition graphs to cubic fuzzy competition graphs by introducing additional variations including cubic fuzzy out-neighbourhoods, cubic fuzzy in-neighbourhoods, open neighbourhood cubic fuzzy graphs, closed neighbourhood cubic fuzzy graphs, cubic fuzzy (k) neighbourhood graphs and cubic fuzzy [k]-neighbourhood graphs. These variations provide further insights into the relationships and competition within the graph structure, each with its own defined characteristics and examples. These cubic fuzzy CMGs are further classified as cubic fuzzy k-competition graphs that show competition in the \(k\)th order between components, \(p\)-competition cubic fuzzy graphs that concentrate on competition in terms of membership degrees, and \(m\)-step cubic fuzzy competition graphs that analyze competition in terms of steps. Further, some related results about independent strong vertices and edges have been obtained for these cubic fuzzy competition graph classes. Finally, the proposed concept of cubic fuzzy competition graphs is supported by a numerical example. This example showcases how the framework of cubic fuzzy competition graphs can be practically applied to the predator-prey model to illustrate the representation and analysis of ambiguous information within the graph structures.
A graph \( X \) is \( k \)-spanning cyclable if for any subset \( S \) of \( k \) distinct vertices there is a 2-factor of \( X \) consisting of \( k \) cycles such that each vertex in \( S \) belongs to a distinct cycle. In this paper, we examine the \( k \)-spanning cyclability of 4-valent Cayley graphs on Abelian groups.
A path \(x_1, x_2, \dots, x_n\) in a connected graph \( G \) that has no edge \( x_i x_j \) \((j \geq i+3)\) is called a monophonic-triangular path or mt-path. A non-empty subset \( M \) of \( V(G) \) is a monophonic-triangular set or mt-set of \( G \) if every member in \( V(G) \) exists in a mt-path joining some pair of members in \( M \). The monophonic-triangular number or mt-number is the lowest cardinality of an mt-set of \( G \) and it is symbolized by \( mt(G) \). The general properties satisfied by mt-sets are discussed. Also, we establish \( mt \)-number boundaries and discover similar results for a few common graphs. Graphs \( G \) of order \( p \) with \( mt(G) = p \), \( p – 1 \), or \( p – 2 \) are characterized.
This note presents a counterexample to Propositions 7 and 8 in the paper [1], where the authors determine the values of \( V \) and \( W \). These values are crucial in determining the Hamming distance and MDS codes in the family of certain constacyclic codes over \(\mathbb{F}_{p^m}[u]/\langle u^3 \rangle\), which implies that the results found in [2] are incorrect. Furthermore, we provide corrections to the aforementioned results.
For a graph \( G \) and for non-negative integers \( p, q \) and \( r \), the triplet \( (p, q, r) \) is said to be an admissible triplet, if \( 3p + 4q + 6r = |E(G)| \). If \( G \) admits a decomposition into \( p \) cycles of length \( 3 \), \( q \) cycles of length \( 4 \), and \( r \) cycles of length \( 6 \) for every admissible triplet \( (p, q, r) \), then we say that \( G \) has a \( \{C_{3}^{p}, C_{4}^{q}, C_{6}^{r}\} \)-decomposition. In this paper, the necessary conditions for the existence of \( \{C_{3}^{p}, C_{4}^{q}, C_{6}^{r}\} \)-decomposition of \( K_{\ell, m, n}(\ell \leq m \leq n) \) are proved to be sufficient. This affirmatively answers the problem raised in \emph{Decomposing complete tripartite graphs into cycles of lengths \( 3 \) and \( 4 \), Discrete Math. 197/198 (1999), 123-135}. As a corollary, we deduce the main results of \emph{Decomposing complete tripartite graphs into cycles of lengths \( 3 \) and \( 4 \), Discrete Math., 197/198, 123-135 (1999)} and \emph{Decompositions of complete tripartite graphs into cycles of lengths \( 3 \) and \( 6 \), Austral. J. Combin., 73(1), 220-241 (2019)}.
The λ-fold complete symmetric directed graph of order v, denoted λKv*, is the directed graph on v vertices and λ directed edges in each direction between each pair of vertices. For a given directed graph D, the set of all v for which λKv* admits a D-decomposition is called the λ-fold spectrum of D. In this paper, we settle the λ-fold spectrum of each of the nine non-isomorphic orientations of a 6-cycle.
In this paper, we provide a correction regarding the structure of negacyclic codes of length \(8p^s\) over \(\mathcal{R} = \mathbb{F}_{p^m} + u \mathbb{F}_{p^m}\) when \(p^m \equiv 3 \pmod{8}\) as classified in [1]. Among other results, we determine the number of codewords and the dual of each negacyclic code.
The multiplicative sum Zagreb index is a modified version of the well-known Zagreb indices. The multiplicative sum Zagreb index of a graph \(G\) is the product of the sums of the degrees of pairs of adjacent vertices. The mathematical properties of the multiplicative sum Zagreb index of graphs with given graph parameters deserve further study, as they can be used to detect chemical compounds and study network structures in mathematical chemistry. Therefore, in this paper, the maximal and minimal values of the multiplicative sum Zagreb indices of graphs with a given clique number are presented. Furthermore, the corresponding extremal graphs are characterized.
Let \( G = (V, E) \) be a graph. A subset \( S \subseteq V \) of vertices is an \textit{efficient dominating set} if every vertex \( v \in V \) is adjacent to exactly one vertex in \( S \), where a vertex \( u \in S \) is considered to be adjacent to itself. Efficient domination is highly desirable in many real-world applications, and yet, in general, graphs are often not efficient. It is of value, therefore, to determine optimum ways in which inefficient graphs can be changed in order to make them efficient. It is well known, for example, that almost no \( m \times n \) grid graphs have efficient dominating sets. In this paper, we consider the minimum number of vertices that can be removed from an \( m \times n \) grid graph so that the remaining graph has an efficient dominating set.
Let \( G = (V, E) \) be any graph. If there exists an injection \( f : V \rightarrow \mathbb{Z} \), such that \( |f(u) – f(v)| \) is prime for every \( uv \in E \), then we say \( G \) is a prime distance graph (PDG). The problem of characterizing the family of all prime distance graphs (PDGs) with chromatic number 3 or 4 is challenging. In the fourth part of this series of articles, we determined which fans are PDGs and which wheels are PDGs. In addition, we showed: (1) a chain of \( n \) mutually isomorphic PDGs is a PDG, and (2) the Cartesian product of a PDG and a path is a PDG. In this part of the series, we improve (1) by showing that there exists a chain of \( n \) arbitrary PDGs which is a PDG. We also show that the following graphs are PDGs: (a) any graph with at most three cycles, (b) the one-point union of cycles, and (c) a family of graphs consisting of paths with common end vertices.
Let \(P_n\) and \(K_n\) respectively denote a path and complete graph on \(n\) vertices. By a \(\{pH_{1}, qH_{2}\}\)-decomposition of a graph \(G\), we mean a decomposition of \(G\) into \(p\) copies of \(H_{1}\) and \(q\) copies of \(H_{2}\) for any admissible pair of nonnegative integers \(p\) and \(q\), where \(H_{1}\) and \(H_{2}\) are subgraphs of \(G\). In this paper, we show that for any admissible pair of nonnegative integers \(p\) and \(q\), and positive integer \(n \geq 4\), there exists a \(\{pP_{4}, qS_{4}\}\)-decomposition of \(K_n\) if and only if \(3p+4q=\binom{n}{2}\), where \(S_4\) is a star with \(4\) edges.
Natural environment protection compensation refers to the legal system that protects the natural ecological environment, protects the natural environment and makes the beneficiaries of the natural environment get compensation by some means, so as to adjust the interests of the relevant subjects of natural ecological environment protection. This paper discusses the ecosystem service function and its type division of newly built areas in Ganjiang, and the emergy evaluation of ecosystem service function of newly built areas in Ganjiang, establishes the regular scheduling and joint optimal scheduling models of natural ecosystem service value single reservoir, intro-duces the corresponding model solving methods, and applies the ant colony algorithm to the optimal schedule is a lesson. According to the ant colony algorithm, the best way to study the region is to determine these algorithms. Combined with the kernel density analysis method, the spatial scope, potential corridors and key recovery points of ecological corridors are identified, and the optimization mode of natural ecological security pattern of Shule River is constructed. The experimental results show that the optimized ant colony algorithm proves that joint scheduling plays a more prominent role in ecological environment protection, mainly in ecological support and ecological regulation. At the same time, it verifies the applicability of ant colony algorithm in joint scheduling, and improves the average protection efficiency of natural ecology to 20.9%.
The semantic function of modern Chinese “negation + X” modal words based on communication technology and big data corpus has gained wide attention. As the basis of SOA architecture, Web services provide the key resources for worldwide information transfer and information sharing with their characteristics of loose coupling, platform independence, and data exchange without additional support from third-party hardware and software. However, along with the popularization and improvement of Web service technology, the number and types of Web services in the Internet are also increasing massively, and there are a large number of Web services with various functions, quality and granularity. Therefore, how to quickly and accurately discover Web services that satisfy users’ query requests from a large and complex set of services has become a critical problem to be solved in the current Web service discovery research. Based on the real corpus, this paper analyzes the similarities and differences in the semantic functions of modern Chinese “negation + X” modal words by combining lexicalization and grammatization, cognitive linguistics, systemic functional grammar and other related theories. The experimental results demonstrate that the model is designed for automatic annotation of semantic word classes, and the annotation algorithm based on the hidden horse model, combined with the Viterbi algorithm based on dynamic programming, achieves a correct rate of 94.3% in the closed test and 89.1% in the open test despite the small size of the training corpus and severe data sparsity, and the model fitting effect meets the dynamic expectations.
The relationship between competition state anxiety, motor motivation and coping styles of adolescent track and field athletes in China was investigated using interview and questionnaire research methods. The results showed that the mean scores of cognitive state anxiety and somatic state anxiety were lower in junior track and field athletes who had entered the echelon for a short period of time than in older athletes, and the opposite was true for state self-confidence; there were highly significant differences and significant differences in the identity regulation and introjection regulation dimensions of motor motivation; and there were significant differences in the focused problem-solving coping dimension of coping style. This paper proposes an algorithm for classifying athletic visual mirrors based on sequential model mining. This paper focuses on two issues – feature extraction and definition of semantic rules. In the feature extraction stage, the track and field video footage is automatically segmented into a series of identifiable sequences of athletic events, and then each type of behavioral event is identified using a mechanically learned algorithm. There were no significant differences between the three age groups in terms of race state anxiety, identity regulation and introjection regulation, and no significant differences in coping styles. There were no significant differences in the anxiety of competition status, motivation and coping styles among youth athletes of different sport levels. The results showed the effectiveness of the present algorithm for classifying track and field video cameras.
A graph labeling is an assignment of integers to the vertices or edges or both, which satisfies certain conditions. The domination cover pebbling number of a graph \( G \) is \( \psi(G) \), which is the minimum number of pebbles required such that any initial configuration of \( \psi(G) \) pebbles can be transformed through a number of pebbling moves so that the set of vertices with pebbles after the pebbling operation forms a dominating set of \( G \). In this paper, we explore the relationship between two graph parameters, namely graph labeling and domination cover pebbling.
In this paper, we study the \( A_\alpha \)-spectral radius of graphs in terms of given size \( m \) and minimum degree \( \delta \geq 2 \), and characterize the corresponding extremal graphs completely. Furthermore, we characterize extremal graphs having maximum \( A_\alpha \)-spectral radius among (minimally) \( 2 \)-edge-connected graphs with given size \( m \).
The metric dimension of a graph \(\Gamma = (V, E)\), denoted by \( \operatorname{dim}(\Gamma) \), is the least cardinality of a set of vertices in \(\Gamma\) such that each vertex in \(\Gamma\) is determined uniquely by its vector of distances to the vertices of the chosen set. The topological distance between an edge \(\varepsilon = yz \in E\) and a vertex \( k \in V \) is defined as \( d(\varepsilon, k) = \min\{d(z, k), d(y, k)\} \). A subset of vertices \( R_{\Gamma} \) in \( V \) is called an edge resolving set for \(\Gamma\) if for each pair of different edges \( e_{1} \) and \( e_{2} \) in \( E \), there is a vertex \( j \in R_{\Gamma} \) such that \( d(e_{1}, j) \neq d(e_{2}, j) \). An edge resolving set with minimum cardinality is called the edge metric basis for \(\Gamma\) and this cardinality is the edge metric dimension of \(\Gamma\), denoted by \( \operatorname{dim}_{E}(\Gamma) \). In this article, we show that the cardinality of the minimum edge resolving set is three or four for two classes of convex polytopes (\( S_{n} \) and \( T_{n} \)) that exist in the literature.
Language learning cannot be separated from the environment, and the environment for second language acquisition is becoming more and more perfect and ideal. It makes the traditional single and limited English learning environment gradually move towards a three-dimensional and diversified learning environment. On the premise of the great development of higher education in China, this study aims to conduct research and discussion on higher English teaching. In combination with other successful or well functioning higher English teaching reforms, it studies and discusses some problems faced in the construction and implementation of vocational English teaching application system in China, and finds solutions and methods. Therefore, this study has practical significance for the reform and development of higher English education in China. This manuscript is based on the design of the college English teaching system module of Web technology to realize the sharing of information resources. In addition, with the deepening understanding of the importance of English teaching in colleges and universities, improving English level and English teaching level is the goal of colleges and universities. English teachers urgently need to understand the factors that affect students’ English level in order to teach students in accordance with their aptitude and find the best teaching methods. The experimental results show that the system realizes the management, query and sharing functions of open level information, and has high security and interactivity. The development of the system conforms to the development trend of network information technology and promotes the informatization and standardization of college English teaching management.
At present, the social economy is entering the information age represented by computer, communication technology and network technology as the core, and the continuous development of modern information technology will certainly have a great impact on the teaching mode, content and methods of traditional accounting computerization. We aim to improve the existing higher vocational accounting teaching mode by building a multi-integrated teaching mode through federated learning based on 5G communication network as an environment for efficient information transfer. In addition, we develop a joint optimization strategy for priority-dependent task offloading, wireless bandwidth, and computational power in a distributed machine learning approach to ensure that more resources are allocated to users with higher priority while protecting user data privacy and reducing learning overhead. We have conducted extensive simulation experiments for both environments, and these simulation results demonstrate the effectiveness of our proposed solutions for different problems from different perspectives.
This study explores the integration of “Internet+” into university education to enhance students’ learning, innovation, and entrepreneurship (I&E) skills. By updating educational concepts and methods, we aim to establish a comprehensive I&E framework that includes mindset development, knowledge acquisition, skill enhancement, and team support. Practical training and network learning communities are emphasized to provide global platforms for skill improvement and project incubation. Through a case study, we analyze the development and effectiveness of an I&E education platform, highlighting the importance of targeted demand surveys and data analysis. Our findings demonstrate the significance of aligning talent training with industry needs, fostering creativity, and promoting entrepreneurial success through collaborative school-enterprise initiatives.
The concept of graph energy, first introduced in 1978, has been a focal point of extensive research within the field of graph theory, leading to the publication of numerous articles. Graph energy, originally associated with the eigenvalues of the adjacency matrix of a graph, has since been extended to various other matrices. These include the maximum degree matrix, Randić matrix, sum-connectivity matrix, and the first and second Zagreb matrices, among others. In this paper, we focus on calculating the energy of several such matrices for the join graph of complete graphs, denoted as \( J_{m}(K_{n}) \). Specifically, we compute the energies for the maximum degree matrix, Randić matrix, sum-connectivity matrix, first Zagreb matrix, second Zagreb matrix, reverse first Zagreb matrix, and reverse second Zagreb matrix for \( J_{m}(K_{n}) \). Our results provide new insights into the structural properties of the join graph and contribute to the broader understanding of the mathematical characteristics of graph energy for different matrix representations. This work extends the scope of graph energy research by considering these alternative matrix forms, offering a deeper exploration into the algebraic and spectral properties of graph energy in the context of join graphs.
Tunnels are essential infrastructure elements, and it is critical to maintain their stability for both operation and safety. Using engineering techniques, this study examines the correlation between rock mass motorized characteristics and tunnel surrounding rock stability. This study utilizes the multi-sensor monitoring data of the surrounding rock mechanical characteristics and tunnel support structure collected during tunnel boring machine construction as its research object. The integrated cuckoo search optimized Upgraded dynamic convolutional neural network (ICSO-UDCNN) has been utilized for predicting the tunnel parameters. In general, the surrounding rock’s hardness correlates with its level, which in turn determines how quickly tunnels are being excavated. There is a stronger correlation of 98\% between the field penetration index (FPI) variables of the rock’s characteristic slope along the conditions surrounding the tunnel. The most significant factor influencing its deformation is the surrounding rock’s mechanical characteristics. For engineers and other decision-makers engaged in tunnel design, building, and maintenance, the study’s findings add a greater understanding of the variables affecting tunnel stability. This research provides an establishment for enhancing security protocols, lowering hazards related to tunneling operation, and optimizing tunnel engineering techniques by quantitatively evaluating the influence of rock mass mechanical factors on solidity.
Let \( G \) be a graph and let \( 0 \leq p, q \) and \( p + q \leq 1 \). Suppose that each vertex of \( G \) gets a weight of \( 1 \) with probability \( p \), \( \frac{1}{2} \) with probability \( q \), and \( 0 \) with probability \( 1-p-q \), and vertex weight probabilities are independent. The \textit{fractional vertex cover reliability} of \( G \), denoted by \( \operatorname{FRel}(G;p,q) \), is the probability that the sum of weights at the end-vertices of every edge in \( G \) is at least \( 1 \). In this article, we first provide various computational formulas for \( \operatorname{FRel}(G;p,q) \) considering general graphs, basic graphs, and graph operations. Secondly, we determine the graphs which maximize \( \operatorname{FRel}(G;p,q) \) for all values of \( p \) and \( q \) in the classes of trees, connected unicyclic and bicyclic graphs with fixed order, and determine the graphs which minimize it in the classes of trees and connected unicyclic graphs with fixed order. Our results on optimal graphs extend some known results in the literature about independent sets, and the tools we developed in this paper have the potential to solve the optimality problem in other classes of graphs as well.
Let \( G \) be an undirected graph. A \textit{vertex tree cover} of \( G \) is a collection of trees such that every vertex of \( G \) is incident with at least one tree. Similarly, an edge tree cover is a collection of trees such that every edge of \( G \) is contained in at least one tree. The tree cover number is defined as the minimum number of trees required in such a cover. In this paper, we demonstrate that when considering specific types of tree covers, only vertex permutations act as linear operators that preserve the tree cover number of \( G \).
To fulfil the requirements of task scheduling for processing massive amounts of graph data in cloud computing environments, this thesis offers the LGPPSO method, which is based on Particle Swarm Optimisation. The LGPPSO algorithm considers the task’s overall structure when initialising numerous particles in order to broaden the search range and raise the likelihood of finding an approximation optimal solution. We evaluated it in large-scale simulation trials with 100 performance-heterogeneous virtual machines (VMs) using both randomly generated and real application graphs, and evaluated its effectiveness against the CCSH and HEFT algorithms. The experimental findings demonstrate that, in both randomly generated graphs and real graph structure applications, significantly reducing the scheduling duration of large-scale graph data is the LGPPSO algorithm. For randomly generated 200 and 400 node tasks, respectively, the scheduling length is shortened by approximately 8.3% and 9.7% on average when compared to the HEFT algorithm. The LGPPSO technique minimises the scheduling length for actual graphical structure applications by an average of 14.6% and 16.9% for the Gaussian 100 and 200 node examples, respectively.
The application of graph theory is instrumental in the construction of a practical teaching mode structure for an ideological and political theory (IPE) course rooted in fuzzy system theory. In recent years, IPE education has received more and more attention. System theory can well construct the IPE teaching mode, so we need to have a good grasp of system theory. This paper starts with the significance of system theory in the practical teaching of IPE, finds the IPE curriculum that conforms to system theory, and constructs the basic mode of practical teaching of IPE on this basis. Using the idea of a multi-level fuzzy comprehensive evaluation to quantify teachers’ teaching quality, this paper establishes an algorithm model to quantitatively measure teachers’ teaching quality fuzzy.
The new curriculum standards have put forward new requirements for high school English vocabulary teaching, and the English vocabulary eco-classroom under the guidance of ecological linguistics theory can precisely make up for the shortcomings in the traditional vocabulary classroom and meet the challenges of the times. However, most of the existing researches on ecological classroom are combined with macro English subjects, and few of them are about English vocabulary teaching. This study takes the principles of ecological linguistics as the theoretical basis to support the conceptual construction and morphological reliance of the vocabulary ecological classroom, supplemented by modal theory as the process orientation of the four major stages in the teaching process, constructs a new vocabulary ecological classroom model based on Markov chain model, and applies the ecological classroom model to high school English vocabulary teaching to verify its teaching effects. The experimental results show that the Markov chain-based high school English vocabulary teaching under the “ecological linguistics” model can help students’ interest in vocabulary learning and promote their vocabulary learning level, accounting for 15% improvement in the learning effect.
With the development of social economy, urban planning has been paid more and more attention. At present, China is implementing low-carbon environmental planning in stages, stages and stages with the development goal of building two types of society, namely, national master plan and strategic development. This paper analyzes low-carbon environmental planning based on blockchain, then expounds the specific content of whole process project management, and completes the design of green building index system based on low-carbon environmental planning. At the same time, the design method of new dynamic structure and index system of green building is expounded. The comparative analysis shows that the material loss cost of the new environmental protection building engineering and index system is low, and the material utilization rate is high, accounting for more than 95%.
The question on how to colour a graph \( G \) when the number of available colours to colour \( G \) is less than that of the chromatic number \(\chi(G)\), such that the resulting colouring gives a minimum number of edges whose end vertices have the same colour, has been a study of great interest. Such an edge whose end vertices receive the same colour is called a bad edge. In this paper, we use the concept of \(\delta^{(k)}\)-colouring, where \( 1 \leq k \leq \chi(G)-1 \), which is a near proper colouring that permits a single colour class to have adjacency between the vertices in it and restricts every other colour class to be an independent set, to find the minimum number of bad edges obtained from the same for some wheel-related graphs. The minimum number of bad edges obtained from \(\delta^{(k)}\)-colouring of any graph \( G \) is denoted by \( b_k(G) \).
Let \( G \) be a simple finite graph. A \( k \)-coloring of \( G \) is a partition \(\pi = \{ S_1, \cdots, S_k \}\) of \( V(G) \) so that each \( S_i \) is an independent set and any vertex in \( S_i \) takes color \( i \). A \( k \)-coloring \(\pi = \{ S_1, \cdots, S_k \}\) of \( V(G) \) is a neighbor locating coloring if for any two vertices \( u, v \in S_i \), there is a color class \( S_j \) for which one of them has a neighbor in \( S_j \) and the other does not. The minimum \( k \) with this property is said to be the neighbor locating chromatic number of \( G \), denoted by \(\chi_{NL}(G)\). We initiate the study of the neighbor locating coloring of graphs resulting from three types of product of two graphs. We investigate the neighbor locating chromatic number of Cartesian, lexicographic, and corona products of two graphs. Finally, we untangle the neighbor locating chromatic number of any of the aforementioned three products of cycles, paths, and complete graphs.
The significance of advancing socialist S&T legal system building with Chinese features is examined in this paper, along with the extensive effects it has on S&T innovation and economic growth. Against the backdrop of an increasingly competitive global scientific and technology landscape, China’s ability to innovate and achieve sustainable growth hinges on the establishment of an ideal science and technology legislative framework. This essay first examines the primary obstacles to China’s development of a science, technology, and innovation (S&T) legal system, including inadequate protection for intellectual property rights, a flawed process for transforming scientific and technological advancements, and an insufficient system for encouraging enterprise innovation. Then, this research presents a quantitative analysis model to optimize the path of science and technology legal building by applying the improved particle swarm optimization method (PSO). The model takes into account a wide range of variables, including the degree of intellectual property protection, the strength of legal backing, the pace at which scientific and technological advancements are transformed, etc. Through the analysis of simulation data, the model also confirms the promotion effect of the legal system construction on the quantity of patent applications, the success rate of innovation projects, the enterprise R&D expenditure, and the expansion of the local economy. The study’s findings demonstrate that bolstering the science and technology legal system can effectively encourage businesses to boost R&D investment and foster regional economic development in addition to greatly raising the quantity of patent applications and the success rate of innovation projects. The rigorous intellectual property protection laws and ideal legal framework for the conversion of accomplishments greatly boost the regional innovation vitality and economic efficiency, particularly in the case study of Zhongguancun in Beijing and East China. Moreover, adaptive weighting is used to enhance the PSO algorithm and optimize the development of science and technology legal system’s comprehensive performance index, thereby confirming the model’s viability and efficacy. The study’s findings offer theoretical justification and helpful advice for China’s development of a science and technology legal framework, which is crucial for fostering innovation in these fields and boosting the country’s competitiveness.
At present, there are few systematic researches on macro-scale heterogeneous modeling and numerical simulation of dynamic mechanical properties of 3-D braided composites. In this paper, the parametric virtual simulation model of 3D five-directional braided composites is realized in the way of “point-line-solid” based on the integrated design idea of process-structure-performance. And the impact compression numerical simulation of the material is carried out by using multi-scale analysis method. The effects of strain rate and braiding angle on transverse impact compression properties and fracture characteristics of composites is studies and verified by comparing the test results with the numerical simulation results systematically. The dynamic failure mechanism of the matrix and fiber bundles during the impact compression process is revealed. The results show that the macro-scale heterogeneous simulation model of 3D five-directional braided composites established is effective, and the numerical simulation results agree well with the test results. The matrix fracture and shear failure of fiber bundles are presented simultaneously under transverse impact compression. The failure of fiber bundles and matrix mainly concentrates on two main fracture shear planes. And the included angle between the fracture shear planes and the vertical direction is consistent with the corresponding internal braiding angle of specimens.
The paper extensively examined the intricate components underpinning innovation ability, culminating in the construction of a linear spatial model delineating innovation and entrepreneurship prowess. This paper analyzed the components of the connotation of innovation ability, then constructs a linear spatial model of innovation and entrepreneurship ability, proposes a multi-objective function model of the utilization efficiency and allocation efficiency of education resources, and uses the grey correlation algorithm The experimental simulation and model solution are carried out. The simulation results show that, through the optimization, the utilization efficiency and allocation efficiency of the educational resources for innovation and entrepreneurship for all are increased by 18.72% and 20.98% respectively, and tend to be in equilibrium, which can achieve the optimization of educational resources allocation. Among all people, the correlation value with ideal entrepreneurship is 0.3177, achieving the most excellent innovation and entrepreneurship education.
For a graph \(G\), two vertices \(x,y\in G\) are said to be resolved by a vertex \(s\in G\), if \(d(x|s)\neq d(y|s)\). The minimum cardinality of such a resolving set \(\textit{R}\) in \(G\) is called its metric dimension. A resolving set \(\textit{R}\) is said to be fault-tolerant, if for every \(p\in R\), \(R-p\) preserves the property of being a resolving set. A fault-tolerant metric dimension of \(G\) is a minimal possible order fault-tolerant resolving set. A wide variety of situations, in which connection, distance, and connectivity are important aspects, call for the utilisation of metric dimension. The structure and dynamics of complex networks, as well as difficulties connected to robotics network design, navigation, optimisation, and facility positioning, are easier to comprehend as a result of this. As a result of the relevant concept of metric dimension, robots are able to optimise their methods of localization and navigation by making use of a limited number of reference locations. As a consequence of this, numerous applications of robotics, including collaborative robotics, autonomous navigation, and environment mapping, have become more precise, efficient, and resilient. The arithmetic graph \(A_l\) is defined as the graph with its vertex set as the set of all divisors of \(l\), where \(l\) is a composite number and \(l = p^{\gamma_1}_{1} p^{\eta_2}_{2}, \dots, p^{n}_{n}\), where \(p_n \geq 2\) and the \(p_i\)’s are distinct primes. Two distinct divisors \(x, y\) of \(l\) are said to have the same parity if they have the same prime factors (i.e., \(x = p_{1}p_{2}\) and \(y = p^{2}_{1}p^{3}_{2}\) have the same parity). Further, two distinct vertices \(x, y \in A_l\) are adjacent if and only if they have different parity and \(\gcd(x, y) = p_i\) (greatest common divisor) for some \(i \in \{1, 2, \dots, t\}\). This article is dedicated to the investigation of the arithmetic graph of a composite number \(l\), which will be referred to throughout the text as \(A_{l}\). In this study, we compute the fault-tolerant resolving set and the fault-tolerant metric dimension of the arithmetic graph \(A_{l}\), where \(l\) is a composite number.
In this paper, we identify LWO graphs, f\-ind the minimum \(\lambda\) for decomposition of \(\lambda K_n\) into these graphs, and show that for all viable values of \(\lambda\), the necessary conditions are suf\-f\-icient for LWO–decompositions using cyclic decompositions from base graphs.
For a graph \( G \) and a subgraph \( H \) of a graph \( G \), an \( H \)-decomposition of the graph \( G \) is a partition of the edge set of \( G \) into subsets \( E_i \), \( 1 \leq i \leq k \), such that each \( E_i \) induces a graph isomorphic to \( H \). In this paper, it is proved that every simple connected unicyclic graph of order five decomposes the \( \lambda \)-fold complete equipartite graph whenever the necessary conditions are satisfied. This generalizes a result of Huang, *Utilitas Math.* 97 (2015), 109–117.
We classify the geometric hyperplanes of the Segre geometries, that is, direct products of two projective spaces. In order to do so, we use the concept of a generalised duality. We apply the classification to Segre varieties and determine precisely which geometric hyperplanes are induced by hyperplanes of the ambient projective space. As a consequence we find that all geometric hyperplanes are induced by hyperplanes of the ambient projective space if, and only if, the underlying field has order \(2\) or \(3\).
A modification of Merino-Mǐcka-Mütze’s solution to a combinatorial generation problem of Knuth is proposed in this survey. The resulting alternate form to such solution is compatible with a reinterpretation by the author of a proof of existence of Hamilton cycles in the middle-levels graphs. Such reinterpretation is given in terms of a dihedral quotient graph associated to each middle-levels graph. The vertices of such quotient graph represent Dyck words and their associated ordered trees. Those Dyck words are linearly ordered via a rooted tree that covers all their tight, or irreducible, forms, offering an universal reference point of view to express and integrate the periodic paths, or blocks, whose concatenation leads to Hamilton cycles resulting from the said solution.
The hub cover pebbling number, \(h^{*}(G)\), of a graph $G$, is the least non-negative integer such that from all distributions of \(h^{*}(G)\) pebbles over the vertices of \(G\), it is possible to place at least one pebble each on every vertex of a set of vertices of a hub set for \(G\) using a sequence of pebbling move operations, each pebbling move operation removes two pebbles from a vertex and places one pebble on an adjacent vertex. Here we compute the hub cover pebbling number for wheel related graphs.
An outer independent double Roman dominating function (OIDRDF) on a graph \( G \) is a function \( f : V(G) \to \{0, 1, 2, 3\} \) having the property that (i) if \( f(v) = 0 \), then the vertex \( v \) must have at least two neighbors assigned 2 under \( f \) or one neighbor \( w \) with \( f(w) = 3 \), and if \( f(v) = 1 \), then the vertex \( v \) must have at least one neighbor \( w \) with \( f(w) \ge 2 \) and (ii) the subgraph induced by the vertices assigned 0 under \( f \) is edgeless. The weight of an OIDRDF is the sum of its function values over all vertices, and the outer independent double Roman domination number \( \gamma_{oidR}(G) \) is the minimum weight of an OIDRDF on \( G \). The \( \gamma_{oidR} \)-stability (\( \gamma^-_{oidR} \)-stability, \( \gamma^+_{oidR} \)-stability) of \( G \), denoted by \( {\rm st}_{\gamma_{oidR}}(G) \) (\( {\rm st}^-_{\gamma_{oidR}}(G) \), \( {\rm st}^+_{\gamma_{oidR}}(G) \)), is defined as the minimum size of a set of vertices whose removal changes (decreases, increases) the outer independent double Roman domination number. In this paper, we determine the exact values on the \( \gamma_{oidR} \)-stability of some special classes of graphs, and present some bounds on \( {\rm st}_{\gamma_{oidR}}(G) \). In addition, for a tree \( T \) with maximum degree \( \Delta \), we show that \( {\rm st}_{\gamma_{oidR}}(T) = 1 \) and \( {\rm st}^-_{\gamma_{oidR}}(T) \le \Delta \), and characterize the trees that achieve the upper bound.
We introduce a two-player game where the goal is to illuminate all edges of a graph. At each step the first player, called Illuminator, taps a vertex. The second player, called Adversary, reveals the edges incident with that vertex (consistent with the edges incident with the already tapped vertices). Illuminator tries to minimize the taps needed, and the value of the game is the number of taps needed with optimal play. We provide bounds on the value in trees and general graphs. In particular, we show that the value for the path on \( n \) vertices is \( \frac{2}{3} n + O(1) \), and there is a constant \( \varepsilon > 0 \) such that for every caterpillar on \( n \) vertices, the value is at most \( (1 – \varepsilon) n + 1 \).
Let \(G\) be a group, and let \(c\in\mathbb{Z}^+\cup\{\infty\}\). We let \(\sigma_c(G)\) be the maximal size of a subset \(X\) of \(G\) such that, for any distinct \(x_1,x_2\in X\), the group \(\langle x_1,x_2\rangle\) is not \(c\)-nilpotent; similarly we let \(\Sigma_c(G)\) be the smallest number of \(c\)-nilpotent subgroups of \(G\) whose union is equal to \(G\). In this note we study \(D_{2k}\), the dihedral group of order \(2k\). We calculate \(\sigma_c(D_{2k})\) and \(\Sigma_c(D_{2k})\), and we show that these two numbers coincide for any given \(c\) and \(k\).
Let \(p > 2\) be prime and \(r \in \{1,2, \ldots, p-1\}\). Denote by \(c_{p}(n)\) the number of \(p\)-regular partitions of \(n\) in which parts can occur not more than three times. We prove the following: If \(8r + 1\) is a quadratic non-residue modulo \(p\), \(c_{p}(pn + r) \equiv 0 \pmod{2}\) for all nonnegative integers \(n\).
Let \( G=(V,E) \) be a simple connected graph with vertex set \( G \) and edge set \( E \). The harmonic index of graph \( G \) is the value \( H(G)=\sum_{uv\in E(G)} \frac{2}{d_u+d_v} \), where \( d_x \) refers to the degree of \( x \). We obtain an upper bound for the harmonic index of trees in terms of order and the total domination number, and we characterize the extremal trees for this bound.
A \((p, g)\)-graph \(G\) is Euclidean if there exists a bijection \(f: V \to \{1, 2, \ldots, p\}\) such that for any induced \(C_3\)-subgraph \(\{v_1, v_2, v_3\}\) in \(G\) with \(f(v_1) < f(v_2) < f(v_3)\), we have that \(f(v_1) + f(v_2) > f(v_3)\). The Euclidean Deficiency of a graph \(G\) is the smallest integer \(k\) such that \(G \cup N_k\) is Euclidean. We study the Euclidean Deficiency of one-point union and one-edge union of complete graphs.
The dominating set of a graph \(G\) is a set of vertices \(D\) such that for every \(v \in V(G)\) either \(v \in D\) or \(v\) is adjacent to a vertex in \(D\). The domination number, denoted \(\gamma(G)\), is the minimum number of vertices in a dominating set. In 1998, Haynes and Slater [1] introduced paired-domination. Building on paired-domination, we introduce 3-path domination. We define a 3-path dominating set of \(G\) to be \(D = \{ Q_1,Q_2,\dots , Q_k\, |\:Q_i \text{ is a 3-path}\}\) such that the vertex set \(V(D) = V(Q_1) \cup V(Q_2) \cup \dots \cup V(Q_k)\) is a dominating set. We define the 3-path domination number, denoted by \(\gamma_{P_3}(G)\), to be the minimum number of 3-paths needed to dominate \(G\). We show that the 3-path domination problem is NP-complete. We also prove bounds on \(\gamma_{P_3}(G)\) and improve those bounds for particular families of graphs such as Harary graphs, Hamiltonian graphs, and subclasses of trees. In general, we prove \(\gamma_{P_3}(G) \leq \frac{n}{3}\).
Two colorings have been introduced recently where an unrestricted coloring \(c\) assigns nonempty subsets of \([k]=\{1,\ldots,k\}\) to the edges of a (connected) graph \(G\) and gives rise to a vertex-distinguishing vertex coloring by means of set operations. If each vertex color is obtained from the union of the incident edge colors, then \(c\) is referred to as a strong royal coloring. If each vertex color is obtained from the intersection of the incident edge colors, then \(c\) is referred to as a strong regal coloring. The minimum values of \(k\) for which a graph \(G\) has such colorings are referred to as the strong royal index of \(G\) and the strong regal index of \(G\) respectively. If the induced vertex coloring is neighbor distinguishing, then we refer to such edge colorings as royal and regal colorings. The royal chromatic number of a graph involves minimizing the number of vertex colors in an induced vertex coloring obtained from a royal coloring. In this paper, we provide new results related to these two coloring concepts and establish a connection between the corresponding chromatic parameters. In addition, we establish the royal chromatic number for paths and cycles.
A ranking on a graph \(G\) is a function \(f: V(G) \rightarrow \left\{1, 2, \ldots, k \right\}\) with the following restriction: if \(f(u)=f(v)\) for any \(u, v \in V(G)\), then on every \(uv\) path in \(G\), there exists a vertex \(w\) with \(f(w) > f(u)\). The optimality of a ranking is conventionally measured in terms of the \(l_{\infty}\) norm of the sequence of labels produced by the ranking. In \cite{jacob2017lp} we compared this conventional notion of optimality with the \(l_p\) norm of the sequence of labels in the ranking for any \(p \in [0,\infty)\), showing that for any non-negative integer \(c\) and any non-negative real number \(p\), we can find a graph such that the sets of \(l_p\)-optimal and \(l_{\infty}\)-optimal rankings are disjoint. In this paper we identify some graphs whose set of \(l_p\)-optimal rankings and set of \(l_{\infty}\)-optimal rankings overlap. In particular, we establish that for paths and cycles, if \(p>0\) then \(l_p\) optimality implies \(l_{\infty}\) optimality but not the other way around, while for any complete multipartite graph, \(l_p\) optimality and \(l_{\infty}\) optimality are equivalent.
We use a representation for the spanning tree where a parent function maps non-root vertices to vertices. Two spanning trees are defined to be adjacent if their function representations differ at exactly one vertex. Given a graph \(G\), we show that the graph \(H\) with all spanning trees of \(G\) as vertices and any two vertices being adjacent if and only if their parent functions differ at exactly one vertex is connected.
A \((0,1)\)-labeling of a set is said to be friendly if the number of elements of the set labeled 0 and the number labeled 1 differ by at most 1. Let \(g\) be a labeling of the edge set of a graph that is induced by a labeling \(f\) of the vertex set. If both \(g\) and \(f\) are friendly then \(g\) is said to be a cordial labeling of the graph. We extend this concept to directed graphs and investigate the cordiality of directed graphs. We show that all directed paths and all directed cycles are cordial. We also discuss the cordiality of oriented trees and other digraphs.
We propose and study the problem of finding the smallest nonnegative integer \(s\) such that a GDD\((m, n, 3; 0, \lambda)\) can be embedded into a BIBD\((mn + s, 3, \lambda)\). We find the values of \(s\) for all cases except for the case where \(n \equiv 5 \pmod{6}\) and \(m \equiv 1, 3 \pmod{6}\) and \(m \ge 3\), which remains as an open problem.
A simple graph \(G\) with \(p\) vertices is said to be vertex-Euclidean if there exists a bijection \(f: V(G) \rightarrow \{1, 2, \ldots, p\}\) such that \(f(v_1) + f(v_2) > f(v_3)\) for each \(C_3\)-subgraph with vertex set \(\{v_1, v_2, v_3\}\), where \(f(v_1) < f(v_2) < f(v_3)\). More generally, the vertex-Euclidean deficiency of a graph \(G\) is the smallest integer \(k\) such that \(G \cup N_k\) is vertex-Euclidean. To illustrate the idea behind this new graph labeling problem, we study the vertex-Euclidean deficiency of two new families of graphs called the complete fan graphs and the complete wheel graphs. We also explore some related problems, and pose several research topics for further study.
A signed magic rectangle \(SMR(m, n; r, s)\) is an \(m \times n\) array with entries from \(X\), where \(X = \{0, \pm1, \pm2, \ldots, \pm(ms – 1)/2\}\) if \(mr\) is odd and \(X = \{\pm1, \pm2, \ldots, \pm mr/2\}\) if \(mr\) is even, such that precisely \(r\) cells in every row and \(s\) cells in every column are filled, every integer from set \(X\) appears exactly once in the array, and the sum of each row and of each column is zero. In this paper, we prove that a signed magic rectangle \(SMR(m, n; r, 2)\) exists if and only if \(m = 2\) and \(n = r \equiv 0, 3 \pmod{4}\) or \(m, r \geq 3\) and \(mr = 2n\).
A graph \(G\) is asymmetric if its automorphism group is trivial. Asymmetric graphs were introduced by Erdős and Rényi [1]. They suggested the problem of starting with an asymmetric graph and removing some number, \(r\), of edges and/or adding some number, \(s\), of edges so that the resulting graph is non-asymmetric. Erdős and Rényi defined the degree of asymmetry of a graph to be the minimum value of \(r+s\). In this paper, we consider another property that measures how close a given non-asymmetric graph is to being asymmetric. Brewer et al defined the asymmetric index of a graph \(G\), denoted \(ai(G)\) is the minimum of \(r+s\) so that the resulting graph \(G\) is asymmetric [2]. It is noted that \(ai(G)\) is only defined for graphs with at least six vertices. We investigate the asymmetric index of both connected and disconnected graphs including paths, cycles, and grids, with the addition of up to two isolated vertices. Furthermore for a graph in these families \(G\) we determine the number of labelled asymmetric graphs that can be obtained by adding or removing \(ai(G)\) edges. This leads to the related question: Given a graph \(G\) where \(ai(G)=1\), what is the probability that for a randomly chosen edge \(e\), that \(G-e\) will be asymmetric? A graph is called minimally non-asymmetric if this probability is \(1\). We give a construction of infinite families of minimally non-asymmetric graphs.
A graph \(G\) is said to arrow the graphs \(F\) and \(H\), written \(G \rightarrow (F, H)\), if every red-blue coloring of \(G\) results in a red \(F\) or a blue \(H\). The primary question has been determining graphs \(G\) for which \(G \rightarrow (F, H)\). If we consider the version for which \(F = H\), then we can ask a different question: Given a graph \(G\), can we determine all graphs \(F\) such that \(G \rightarrow (F, F)\), or simply \(G \rightarrow F\)? We call this set of graphs the down-arrow Ramsey set of \(G\), or \(\downarrow G\). The down-arrow Ramsey set of cycles, paths, and small complete graphs are determined. Graph ideals and graph intersections are introduced and a method for computing down-arrow Ramsey sets is described.
We use a dynamic programming algorithm to establish a new lower bound on the domination number of complete cylindrical grid graphs of the form \(C_n\square P_m\), that is, the Cartesian product of a path and a cycle, when \(n\equiv 2\pmod{5}\), and we establish a new upper bound equal to the lower bound, thus computing the exact domination number for these graphs.
In this paper, we address computational questions surrounding the enumeration of non-isomorphic André planes for any prime power order \(q\). We are particularly focused on providing a complete enumeration of all such planes for relatively small orders (up to 125), as well as developing computationally efficient ways to count the number of isomorphism classes for other orders where enumeration is infeasible. André planes of all dimensions over their kernel are considered.
We use a dynamic programming algorithm to establish a lower bound on the domination number of complete grid graphs of the form \(C_n\square P_m\), that is, the Cartesian product of a cycle \(C_n\) and a path \(P_m\), for \(m\) and \(n\) sufficiently large.
With the increasingly frequent exchanges between countries, my country’s demand for high-quality English translators has greatly increased. However, an important problem we are currently facing is that China’s translation talents are far behind the demand. An important reason for this phenomenon is that the traditional translation teaching is difficult to cultivate translators who can meet the market demand. Therefore, it is necessary to improve the traditional translation teaching mode. Translation teaching for English majors is an important part of translation teaching. Therefore, after evaluating the speech characteristics and speech data, this document first proposes a translation classification error detection model based on mfcc-rf. The acoustic function of the extracted 39 dimensional Mel inverse spectral coefficient is the input of the random forest classifier, and a classification error detection model is established. By analyzing the experimental results, the MFCC radio frequency translation error detection model has achieved high classification error detection accuracy under three types of errors (rising, falling and shortening). The experimental results show that, with semantic similarity as the design principle of distractors, using the word vector training method of the context word prediction model to automatically generate distractors can ultimately improve the comprehensive training efficiency of college English majors’ translation ability.
A node in the \(n\)-dimensional hypercube \(Q_n\) is called an odd node (resp. an even node) if the sum of all digits of the node is odd (resp. even). Let \(F\subset E(Q_n)\) and let \(L\) be a linear forest in \(Q_n-F\) such that \(|E(L)|+|F|\leq n-2\) for \(n\geq 2\). Let \(x\) be an odd node and \(y\) an even node in \(Q_n\) such that none of the paths in \(L\) has \(x\) or \(y\) as internal node or both of them as end nodes. In this note, we prove that there is a Hamiltonian path between \(x\) and \(y\) passing through \(L\) in \(Q_n-F\). The upper bound \(n-2\) on \(|E(L)|+|F|\) is sharp.
As a product of the revolutionary war years, red culture, with its strong vitality, strong cohesion and extraordinary charm, with its incomparable positive energy, resists vulgar and flattering culture, promotes people to rebuild their faith, purify their minds, stimulate their motivation, and promote the process of cultural power. Yan’an, represented by red culture, is rich in resources. This is the holy land of Chinese revolution, the first batch of famous historical and cultural cities named by the State Council, and the three major education bases of patriotism, revolutionary tradition, and Yan’an spirit. The development and utilization of such resources have great political, cultural, educational and economic values. This research is based on the development of red culture, and uses the distributed machine learning system to realize in the system architecture of parameter server. In the distributed system set in this study, node downtime and network interruption are random. When the parameter server system adopts static scheduling, it leads to poor scalability and robustness. The experimental results show that under the intelligent simulation of machine learning system, the development of red culture resources meets the expected assumptions, and the accuracy of the model is relatively high.
In this paper, we introduce a class of restricted symmetric permutations, called half-exceeded symmetric permutations. We deduce the enumerative formula of the permutations of \(\{1,2,\ldots,2n\}\) and give it a refinement according to the distribution of the inverse pairs. As a consequence, we obtain new combinatorial interpretations of some well-known sequences, such as Stirling numbers of the second kind and ordered Bell numbers. Moreover, we introduce the ordered Stirling number of the second kind and establish a combinatorial proof of the recursive relation of the sequence.
The total labeling of a graph \(G=(V,E)\) is a bijection from the union of the vertex set and the edge set of \(G\) to the set \(\{1,2,\dots,|V(G)|+|E(G)|\}\). The edge-weight of an edge under a total labeling is the sum of the label of the edge and the labels of the end vertices of that edge. The vertex-weight of a vertex under a total labeling is the sum of the label of the vertex and the labels of all the edges incident with that vertex. A total labeling is called edge-magic or vertex-magic when all the edge-weights or all the vertex-weights are the same, respectively. When all the edge-weights or all the vertex-weights are different then a total labeling is called edge-antimagic or vertex-antimagic total, respectively.
In this paper we deal with the problem of finding a~total labeling of some classes of graphs that is simultaneously vertex-magic and edge-antimagic or simultaneously vertex-antimagic and edge-magic, respectively.
We show several results for stars, paths and cycles.
This study presents a pioneering federated multi-modal data classification model tailored for smart optical cable monitoring systems. By harnessing federated learning techniques, the model ensures data privacy while achieving performance on par with centralized models. Through comprehensive experiments spanning various modalities, including vision and auditory data, our approach showcases promising outcomes, as evidenced by accuracy and precision metrics. Furthermore, comparative analyses with centralized models highlight the superior data security and reduced network strain offered by federated learning. Moreover, we delineate the design and deployment of a smart optical cable monitoring system leveraging edge computing, accentuating the pivotal role of information technology in elevating operational efficiency within the cable monitoring domain. Through meticulous analysis and simulations, our proposed system adeptly monitors environmental variables, thereby bolstering safety and efficiency in smart optical cable monitoring applications.
The created public art sculpture is a material form that expresses the public spirit of the city. This paper proposes a deep model capable of enhancing the aesthetic quality of public art sculptures. The model uses the inverse mapping network of the augmented network to weaken the restriction of paired data sets required for training, and at the same time designs an effective loss function, that is, constructs the color and texture losses that are actively learned in training through generative adversarial rules, and enhances generative sculpture. The total variational loss of smoothness that improves the aesthetic quality of the sculpture to some extent. On this basis, this paper improves the design idea of content consistency loss. Experiments on the interaction between public art sculptures and the urban environment and the enhancement of aesthetics.
With the increasing scale of college enrollment and the increasing complexity of college teaching management, college finance department should innovate the traditional financial management mode while adapting to the reform of teaching management, and make use of the openness and real-time characteristics of Internet to improve the quality of college financial management and reduce the risk of college financial management. To this end, this paper designs a university financial system based on multi-scale deep learning. In the hardware design, the system adds multiple sensors and scans all the information in the financial database using a coordinator. In the software design, the weights that can connect the financial information of the same attribute are set by establishing a database form; according to the multilayer perceptual network topology, a full interconnection model based on multi-scale deep learning is designed to realize the system’s deep extraction of data. The experimental results show that the financial risk is based on the risk warning capability for university finance, and compared with the system under the traditional design, the university finance system designed in this time has the most categories of financial information parameters extracted.
This work suggests predicting student performance using a Gaussian process model classification in order to address the issue that the prediction approach is too complex and the data set involved is too huge in the process of predicting students’ performance. In order to prevent overfitting, a sample set consisting of the three typical test outcomes from 465 undergraduate College English students is divided into training and test sets. The cross-validation technique is used in this study. According to the findings, Gaussian process model classification can accurately predict 92% of the test set with a prediction model, and it can also forecast students’ final exam marks based on their typical quiz scores. Furthermore, it is discovered that the prediction accuracy increases with the sample set’s distance from the normal distribution; this prediction accuracy rises to 96% when test scores with less than 60 points are taken out of the analysis.
Fix integers \(k, b, q\) with \(k \ge 2\), \(b \ge 0\), \(q \ge 2\). Define the function \(p\) to be: \(p(x) = kx + b\). We call a set \(S\) of integers \emph{\((k, b, q)\)-linear-free} if \(x \in S\) implies \(p^i(x) \notin S\) for all \(i = 1, 2, \dots, q-1\), where \(p^i(x) = p(p^{i-1}(x))\) and \(p^0(x) = x\). Such a set \(S\) is maximal in \([n] := \{1, 2, \dots, n\}\) if \(S \cup \{t\}, \forall t \in [n] \setminus S\) is not \((k, b, q)\)-linear-free. Let \(M_{k, b, q}(n)\) be the set of all maximal \((k, b, q)\)-linear-free subsets of \([n]\), and define \(g_{k, b, q}(n) = \min_{S \in M_{k, b, q}(n)} |S|\) and \(f_{k, b, q}(n) = \max_{S \in M_{k, b, q}(n)} |S|\). In this paper, formulae for \(g_{k, b, q}(n)\) and \(f_{k, b, q}(n)\) are proposed. Also, it is proven that there is at least one maximal \((k, b, q)\)-linear-free subset of \([n]\) with exactly \(x\) elements for any integer \(x\) between \(g_{k, b, q}(n)\) and \(f_{k, b, q}(n)\), inclusively.
Nanoparticles have potential applications in a wide range of fields, including electronics, medicine and material research, because of their remarkable and exceptional attributes. Carbon nanocones are planar carbon networks with mostly hexagonal faces and a few non-hexagonal faces (mostly pentagons) in the core. Two types of nanocone configurations are possible: symmetric and asymmetric, depending on where the pentagons are positioned within the structure. In addition to being a good substitute for carbon nanotubes, carbon nanocones have made an identity for themselves in a number of fields, including biosensing, electrochemical sensing, biofuel cells, supercapacitors, gas storage devices, and biomedical applications. Their astonishing chemical and physical attributes have made them well-known and widely accepted in the fields of condensed matter physics, chemistry, material science, and nanotechnology. Mathematical and chemical breakthroughs were made possible by the concept of modeling a chemical structure as a chemical graph and quantitatively analyzing the related graph using molecular descriptors. Molecular descriptors are useful in many areas of chemistry, biology, computer science, and other sciences because they allow for the analysis of chemical structures without the need for experiments. In this work, the quotient graph approach is used to establish the distance based descriptors of symmetrically configured two-pentagonal and three-pentagonal carbon nanocones.
A kite \(K\) is a graph which can be obtained by joining an edge to any vertex of \(K_3\). A kite with edge set \(\{ab,\,bc,\,ca,\,cd\}\) can be denoted as \((a,\,b,\,c;\,cd)\). If every vertex of a kite in the decomposition lies in different partite sets, then we say that a kite decomposition of a multipartite graph is a gregarious kite decomposition. In this manuscript, it is shown that there exists a decomposition of \((K_m \otimes \overline{K}_n) \times (K_r \otimes \overline{K}_s) \) into gregarious kites if and only if
\[
n^2 s^2 m(m-1)r(r-1) \equiv 0 \pmod{8},
\]
where \(\otimes\) and \(\times\) denote the wreath product and tensor product of graphs respectively. We denote a gregarious kite decomposition as \(\it GK\)-decomposition.
With the rapid development of my country’s socialist market economy, the system of joint and several liability has been established in my country’s civil and commercial law and is playing an increasingly important role. There are also problems such as scattered regulations and contradictory laws and regulations at the level. Since there is no unified application principle established in judicial practice, the litigation burden caused by the recovery lawsuit also wastes a lot of trial resources. Dimensional key features distinguish confusing charges. Use regular expression technology to extract key content such as fact descriptions, defendants’ charges, relevant laws and regulations in legal documents and create JSON format documents; use stammer word segmentation and stop word list to remove stop words; use Word2Vec algorithm to represent text into vector form , establish a judicial judgment prediction model and an optimization model, and through experimental comparison, it is concluded that the performance of the model after adding focal loss is improved by 1.82%, 0.45%, 1.62%, and 1.62% compared with the cross entropy loss, and the final accuracy of the optimized model is 84.78%. , the precision rate is 87%, the recall rate is 85%, and the F1 value is 85%. The system is expected to assist judicial workers in classifying crimes with joint liability and reduce the burden of judicial workers reading many legal documents to classify crimes.
The evolution of computer science and the innovations in language teaching methodologies have paved the way for computer-assisted language learning (CALL) technology to tackle pertinent challenges. While existing CALL systems primarily emphasize vocabulary and grammar acquisition, their evaluation mechanisms often rely on a limited set of criteria, resulting in a simplistic assessment of learners’ pronunciation skills. This oversight underscores the need for a more comprehensive approach. In response, this study targets Chinese college students’ English oral proficiency and aims to enhance the conventional computerized evaluation method. Our approach involves integrating multiple assessment parameters, including pitch, speed, rhythm, and intonation. For instance, pitch assessment is grounded on frequency central feature parameters, while speech speed evaluation considers speech duration, thus enriching the evaluation framework. Through experimental validation, the efficacy of our method in evaluating pitch, speed, rhythm, and intonation has been substantiated, reaffirming its reliability.
The common bills in life include VAT invoices, taxi invoices, train invoices, plane itineraries, machine-printed invoices, etc. Most of these common bills are presented in the form of fixed form templates, so template matching can be used. , for a certain fixed template bill, manually set the rules to determine the spatial position of the key area, extract the corresponding text information, or build a model with logical semantic relationship and spatial relative relationship between the bill texts of different attributes, from the global image of the image. Identify the required key text information in the text information. However, these methods are either limited by fixed ticket templates, or cannot guarantee considerable accuracy. The electronicization of paper invoices mainly needs to go through the steps of text detection, bill recognition and text recognition. Based on this, this paper adopts the DL method. Construct a financial bill recognition model and combine experiments to explore the effectiveness and superiority of the model. The results show that our model can achieve a recognition accuracy rate of up to 91\%, and also achieve a 26\% improvement in recognition speed.
The maximum-weight perfect matching inverse issue in graph theory and text clustering are the two primary topics of this study. We suggest a novel approach to text clustering that makes use of self-encoders and BERT embeddings for feature extraction in order to increase clustering accuracy. According to experimental results, our technique enhances the clustering results greatly and performs well on numerous short text datasets. In the context of graph theory, we examine the unit paradigm inverse issue of maximum-weight perfect matching with value constraints and provide a robust polynomial-time method for its solution. In addition to effectively solving the maximum-weight perfect matching inverse issue, our technique can also produce the best weight vector configuration scheme for real-world uses. In conclusion, our work has advanced the domains of text clustering and graph theory significantly, offering fresh approaches and theoretical underpinnings for future investigations.
A brief survey on mutually orthogonal resolutions of some combinatorial designs is presented. Some \((2,w)\)-threshold schemes from mutually orthogonal resolutions of these designs are also obtained.
In the era of social media platforms like Douyin, preserving the essence of traditional Chinese culture while adapting it to contemporary trends is crucial for its continued relevance and vitality. This paper delves into the practical implications of leveraging social media for cultural communication, emphasizing communication strategies tailored to platforms like Douyin. It introduces two novel algorithms for generating Douyin information dissemination trees: one based on retweeting relationships and another optimized for rapid dissemination. Comparative experiments assess the performance of these algorithms and analyze the node distribution within dissemination trees, aiming to enhance the dissemination power of traditional culture and foster its inheritance and innovation.
The “Three Rural Issues” has always been the top priority of my country’s economic development, which related to the construction process of modern agriculture, the development effect of agricultural economy and the development speed of the national economy. In recent years, the state and local governments have taken the construction of new countryside as the starting point, seriously discussed many problems faced by the agricultural economy in the process of development, and took targeted measures to effectively solve them, which better promoted the construction of new countryside. Agriculture plays an important role in the national economy and is the foundation of national economic development. Under the background of new rural construction, we must strengthen the management of agricultural economy. This paper analyzes the main contents, characteristics and existing problems of agricultural sustainable development under the background of new rural construction, and puts forward solutions that hope to be discussed by a wide range of partners.
Zhou, Xu and Sun [S. Zhou, Y. Xu, Z. Sun, Degree conditions for fractional \((a,b,k)\)-critical covered graphs, Information Processing Letters 152(2019)105838] defined the concept of a fractional \((a,b,k)\)-critical covered graph, namely, a graph \(G\) is a fractional \((a,b,k)\)-critical covered graph if after removing any \(k\) vertices of \(G\), the remaining graph of \(G\) is a fractional \([a,b]\)-covered graph. In this paper, we prove that a graph \(G\) with \(\delta(G)\geq2+k\) is fractional \((2,b,k)\)-critical covered if \(bind(G)>\frac{b+k}{b-1}\), where \(k\geq0\) and \(b\geq2+k\) are two integers.
In this paper, we study the submodular hitting set problem (SHSP), which is a variant of the hitting set problem. In the SHSP, we are given a supergraph \(H = (V, \mathcal{C})\) and a nonnegative submodular function on the set \(2^{V}\). The objective is to determine a vertex subset to cover all hyperedges such that the cost of submodular covering is minimized. Our main work is to present a rounding algorithm and a primal-dual algorithm respectively for the SHSP and prove that they both have the approximation ratio \(k\), where \(k\) is the maximum number of vertices in all hyperedges.
In recent years, there is a lot of interest in the topic of conveying the groups of planar graphs with an unvarying metric dimension. A few types of planar graphs have recently had their locating number (or metric dimension) determined, and an outstanding problem concerning these graphs was brought up that: Illustrate the types of planar graphs \(\Upsilon\) that can be generated from a graph \(\Phi\) through the addition of more edges to \(\Phi\), such that \(dim(\Phi)=dim(\Upsilon)\) and \(\mathbb{V}(\Phi)=\mathbb{V}(\Upsilon)\). While proceeding in a similar directives, we identify two families of radially identical planar graphs with unaltered metric dimension in this study: \(\digamma_{n,m}\) and \(\gimel_{n,m}\). We do this by establishing that \(dim(\digamma_{n,m})=dim(\gimel_{n,m})\) and \(\mathbb{V}(\digamma_{n,m})=\mathbb{V}(\gimel_{n,m})\), respectively. We acquire another family of a radially symmetrical plane graph (i.e., \(\daleth_{n,m}\)) with a constant metric dimension. We show that all the vertices of these classes of the plane graphs can potentially be identified with just three well-chosen nodes.
The purpose of this work was to use machine learning classification models and hyperspectral camera technologies to create a model of surface damage to garlic. 140 of the 184 garlic plants of which 44 were used for test validation were pre-treated for surface damage. First, we examined the data in ENVI under various damage scenarios using the normalised vegetation index (NDVI) approach. 579 pixels were then chosen for the training of the logistic regression model. Finally, we used 54 garlic bulbs to practically validate the model. Although tiny regions could not be precisely identified, the mouldy portion of the garlic’s surface could be identified using the NDVI technique. 90% accuracy was attained using the 90% classification model constructed using the logistic regression approach. Garlic’s surface damage, even at first mild ones, was correctly identified. The creation of this model for identifying garlic damage lowers the cost of detecting garlic damage and broadens the use of hyperspectral technologies in garlic detection.
In this paper, the sensor is applied to the collection of rock parameter data. Aiming at the classification and evaluation of stability (i.e. rock quality), an attribute recognition model for the classification and evaluation of surrounding rock quality of underground engineering is established. Using multi-source data fusion and orthogonal numerical simulation test methods, the effects of rock mechanics parameters on the horizontal convergence of the tunnel, the settlement of the vault and the plastic zone coefficient are studied. Six factors (elastic modulus, Poisson’s ratio, internal friction angle, tensile strength, cohesion and density) and three levels of orthogonal experimental solutions were selected. The method of defining similar weight by using similar number to determine the weight of evaluation index, so as to calculate the comprehensive attribute measure, and apply confidence criteria to identify the stability of rock samples. Through the analysis and evaluation of rock mass quality classification of underground engineering, the application of the model and the evaluation method of rock mass quality classification are explained. The test results match the orthogonal test results; Considering the stability of tunnel envelope, the horizontal convergence, vault settlement and plastic zone coefficient after excavation should be comprehensively considered.
This paper proposes a comprehensive framework for testing and evaluating automatic ambulances, crucial for ensuring their reliability and safety in real-world scenarios. The framework includes designing test scenarios with varying complexity, covering environmental factors like road conditions, weather, and obstacles. An evaluation index system is introduced, comprising driving security, ride comfort, intelligence, and efficiency. Methodologies for calculating indicator weights, using the CRITIC and AHP methods, are presented to ensure fair evaluation. Additionally, evaluation methods including qualitative and quantitative techniques, such as grey correlation theory, are discussed. The test results show that the assessment results of the traditional fuzzy comprehensive evaluation method and the grey correlation theory evaluation method are highly consistent. The change in vehicle speed has less of an effect on accuracy during the real-time assessment process when the time interval is set to 0.1s, and the evaluation time of 0.098s can satisfy the requirement that the planning time of autonomous driving vehicles be shorter than 200 ms.
In recent years, the use of smart data analysis method to predict the stock price is financial technology; important issues in the field of finch. However, there are many technical indicators and human subjective factors will affect the stock price forecast, so we must effectively grasp the important influence indicators to improve the accuracy of stock price forecast. Therefore, this study uses four machine learning algorithms to predict and analyze the stock price fluctuation through the screening process of technical indicators, and then selects the important technical indicators. In addition, due to the uncertainty and fuzziness of the attributes of technical indicators and human subjective judgment, this study uses the fuzzy inference method to construct the fuzzy inference system to predict the rise and fall of stock price, and proposes the prediction method of the range of the rise and fall of stock price. Finally, this paper makes an empirical analysis on the stock price data of three companies. The results show that the accuracy of stock price forecast is more than 82.13%, and the average accuracy of stock price forecast is more than 83%. Therefore, the fuzzy inference prediction system proposed in this study not only has the theoretical basis, but also can effectively predict the trend and range of stock price, which has practical value and contribution to investors.
Removing clouds is an essential preprocessing step in analyzing remote sensing images, as cloud-based overlays commonly occur in optical remote sensing images and can significantly limit the usability of the acquired data. Deep learning has exhibited remarkable progress in remote sensing, encompassing scene classification and change detection tasks. Nevertheless, the appli-cation of deep learning techniques to cloud removal in remote sensing images is currently con-strained by the limited availability of training datasets explicitly tailored for neural networks. This paper presents the Remote sensing Image Cloud rEmoving dataset (RICE) to address this challenge and proposes baseline models incorporating a convolutional attention mechanism, which has demonstrated superior performance in identifying and restoring cloud-affected regions, with quantitative results indicating a 3.08% improvement in accuracy over traditional methods. This mechanism empowers the network to comprehend better the spatial structure, local details, and inter-channel correlations within remote sensing images, thus effectively addressing the diverse distributions of clouds. Moreover, by integrating this attention mechanism, our models achieve a crucial comparison advantage, outperforming existing state-of-the-art techniques in terms of both visual quality and quantitative metrics. We propose adopting the Learned Per-ceptual Image Patch Similarity metric, which emphasizes perceptual similarity, to evaluate the quality of cloud-free images generated by the models. Our work not only contributes to advancing cloud removal techniques in remote sensing but also provides a comprehensive evaluation framework for assessing the fidelity of the generated images.
Let \(g,f:V(G)\rightarrow\{0,1,2,3,\cdots\}\) be two functions satisfying \(g(x)\leq f(x)\) for every \(x\in V(G)\). A \((g,f)\)-factor of \(G\) is
defined as a spanning subgraph \(F\) of \(G\) such that \(g(x)\leq d_F(x)\leq f(x)\) for every \(x\in V(G)\). An \((f,f)\)-factor is simply called
an \(f\)-factor. Let \(\varphi\) be a nonnegative integer-valued function defined on \(V(G)\). Set
\[
D_{g,f}^{even}=\Big\{\varphi: g(x)\leq\varphi(x)\leq f(x) \text{ for every } x\in V(G) \text{ and } \sum\limits_{x\in V(G)}\varphi(x) \text{ is even}\Big\}.
\]
If for each \(\varphi\in D_{g,f}^{even}\), \(G\) admits a \(\varphi\)-factor, then we say that \(G\) admits all \((g,f)\)-factors. All \((g,f)\)-factors
are said to be all \([1,k]\)-factors if \(g(x)\equiv1\) and \(f(x)\equiv k\) for any \(x\in V(G)\). In this paper, we verify that for a connected multigraph
\(G\) satisfying \(N_G(X)=V(G) \text{ or } |N_G(X)|>\Big(1+\frac{1}{k+1}\Big)|X|-1\) for every \(X\subset V(G)\), \(kG\) admits all \([1,k]\)-factors, where
\(k\geq2\) is an integer and \(kG\) denotes the graph derived from \(G\) by replacing every edge of \(G\) with \(k\) parallel edges.
We consider finitely presented groups \(G_{mn}\) as follows:
\[
G_{mn}=\left\langle x, y \mid {x^{m}}={y^{n}}=1, {[x, y]^{x}}=[x, y], {[x, y]^{y}}=[x, y] \right\rangle m, n\ge 2.
\]
In this paper, we first study the groups \(G_{mn}\). Then by using the properties of \(G_{mm}\) and \(t-\)Fibonacci sequences in
finitely generated groups, we show that the period of \(t-\)Fibonacci sequences in \(G_{mm}\) are a multiple of \(K(t, m)\). In particular for \(t \geq 3\) and \(p=2\), we prove \({{LEN}_{t}({{G}_{pp}})}= 2K(t,p)\).
In this paper, we consider a degree sum condition sufficient to imply the existence of \(k\) vertex-disjoint chorded cycles in a graph \(G\).
Let \(\sigma_4(G)\) be the minimum degree sum of four independent vertices of \(G\).
We prove that if \(G\) is a graph of order at least \(11k+7\) and \(\sigma_4(G)\ge 12k-3\) with \(k\ge 1\),
then \(G\) contains \(k\) vertex-disjoint chorded cycles.
We also show that the degree sum condition on \(\sigma_4(G)\) is sharp.
This study addresses challenges in rural planning amid economic growth and the implementation of rural revitalization policies. The aim is to enhance the integration of cultural and ecological elements in rural areas, combating issues such as the fading village atmosphere and incomplete agricultural chains. The research focuses on optimizing the random forest algorithm to explore innovative approaches to landscape planning and design for rural human settlements. Using the moving window method, the study computes two-dimensional and three-dimensional landscape indices in surrounding villages of Beijing, conducting a multi-scale analysis of the living environment. Power function fitting indicates an optimal window size of approximately 700 meters for studying the relationship between art patterns and three-dimensional landscape patterns in the rural area. The findings offer insights into improving rural living environments through effective landscape planning and design influenced by artistic modes.
In the major cities with many high-rise buildings in contemporary China, land resources are becoming more and more scarce, and the urban ecological environment is in urgent need of recycling, and due to the blind imitation of Western culture and design mode and the neglect of China traditional regional culture, the urban landscape lacks interaction, resonance, and sense of belonging with citizens, and the phenomenon of landscape similarity emerges in various cities, focusing on the landscape space of urban complexes. There are also these problems. Urban residents urgently need a third space that can adjust their physical, mental, and spiritual needs. How to design an urban complex landscape that meets the aesthetic needs and humanistic needs of contemporary cities and has regional characteristics has become the first important task of my research. Folk art is an artistic treasure created by the working people in their production and life. Folk art is the embodiment of cultural regionality and the foundation of national culture. It awakens people’s awareness of the importance of local culture, awakens people’s sense of belonging, and is closer to the local public life. Today, the living soil and social and humanistic environment of folk art are in the process of urbanization in China, and there is a trend of gradual disappearance of lifestyle changes. How to make the contemporary urban complex landscape an organic soil for the survival, expression, application, and development of folk art is an important task in contemporary urban landscape design. Based on optimization, related concepts such as symbols, folk art symbols, urban complexes, urban complex landscape design, etc. have been sorted out. The relevant experimental results show that the construction land accuracy of the logistic regression model based on genetic algorithm has increased from 78.0% to 85.3%. kappa increased from 74.5% to 81.2%. Research shows that the logistic regression parameter optimization method based on genetic algorithm has better simulation effect than the conventional logistic regression method and is more suitable for the situation of unbalanced data distribution and many data features in the simulation of large-scale urban land dynamic changes.
Digital services, including healthcare, among others, have recently seen a massive volume of complicated data that arrives rapidly due to a rapid increase in the number of smart devices, focusing on the needs of regional emerging economic development and industrial structure adjustment, this paper explores the dynamic adjustment of a major in which schools, governments, enterprises and international cooperation participate in the development of regional emerging economies. mechanism. Based on the concept of future-oriented development, formulate a development plan for the legal profession, build a community of government, school, enterprise and international cooperation, promote the vigorous implementation of engineering practice education, and cultivate high-quality, high-level, international graduates, and to form the school-running characteristics of law majors in local application-oriented undergraduate colleges.
Given a connected simple undirected graph \(G=(V,E)\), a subset \(S\) of \(V\) is \(P_3\)-convex if each vertex of \(G\) not in \(S\) has at most one neighbor in \(S\). The \(P_3\)-convex hull \(\langle S\rangle\) of \(S\) is the smallest \(P_3\)-convex set containing \(S\). A Carathéodory set of \(G\) is a set \(S\subseteq V\) such that \(\langle S \rangle \setminus \bigcup_{w\in S} \langle S\setminus \{w\} \rangle\) is non-empty. The Carathéodory number of \(G\), denoted by \(C(G)\), is the largest cardinality of a Carathéodory set of \(G\). In this paper, we settle the conjecture posed by Barbosa et al. appeared in [SIAM J. Discrete Math. 26 (2012) 929–939] in the affirmative, which states that for a claw-free graph \(G\) of order \(n(G)\), the Carathéodory number \(C(G)\) of the \(P_3\)-convexity satisfies \(C(G) \leq \frac{2n(G)+6}{5}\). Furthermore, we determine all graphs attaining the bound.
The key factor that promotes Vocational students development is the development of movement, which requires children to have excellent motor skills to develop their intellectual level, physical function and social adaptability in daily study and life. Data mining technology is an economical and practical core technology, which obtains useful information for the service system from a large amount of data. However, many teaching deficiencies in this area are prevalent in the field of early childhood education. In the current research on the content of Vocational students physical activities, a large amount of data information needs the support of data mining technology. This paper aims at how to combine data mining technology to study the content of Vocational students sports activities from the perspective of movement development, establish a decision support system for Vocational students sports activities, and conduct experiments on Vocational students sports activities from the perspective of action scientific arrangement and implementation of activity content, carry out empirical research on the content of Vocational students sports activities.
When using airborne LiDAR point clouds for city modelling and road extraction, point cloud classification is a crucial step. There are numerous ways for classifying point clouds, but there are still issues like redundant multi-dimensional feature vector data and poor point cloud classification in intricate situations. A point cloud classification method built on the fusing of multikernel feature vectors is suggested as a solution to these issues. The technique employs random forest to classify point cloud data by merging colour information, and it extracts feature vectors based on point primitives and object primitives, respectively. In this study, a densely populated area was chosen as the study area. Light airborne LIDAR mounted on a delta wing was used to collect point cloud data at a low altitude (170 m) over a dense cross-course. The point cloud data were then combined, corrected, and enhanced with texture data, and the houses were vectorized on the point cloud. The accuracy of the results was then assessed. With a median inaccuracy of 4.8 cm and a point cloud data collection rate of 83.3%, using airborne LIDAR to measure house corners can significantly lighten the labour associated with external house corner measurements.This test extracts the texture information of point cloud data through the efficient processing of high-density point cloud data, providing a reference for the application of texture information of airborne LIDAR data and a clear understanding of its accuracy.
One of the fundamental properties of the hypercube \( Q_n \) is that it is bipancyclic as \( Q_n \) has a cycle of length \( l \) for every even integer \( l \) with \( 4 \leq l \leq 2^n \). We consider the following problem of generalizing this property: For a given integer \( k \) with \( 3 \leq k \leq n \), determine all integers \( l \) for which there exists an \( l \)-vertex, \( k \)-regular subgraph of \( Q_n \) that is both \( k \)-connected and bipancyclic. The solution to this problem is known for \( k = 3 \) and \( k = 4 \). In this paper, we solve the problem for \( k = 5 \). We prove that \( Q_n \) contains a \( 5 \)-regular subgraph on \( l \) vertices that is both \( 5 \)-connected and bipancyclic if and only if \( l \in \{32, 48\} \) or \( l \) is an even integer satisfying \( 52 \leq l \leq 2^n \). For general \( k \), we establish that every \( k \)-regular subgraph of \( Q_n \) has \( 2^k, 2^k + 2^{k-1} \) or at least \( 2^k + 2^{k-1} + 2^{k-3} \) vertices.
Coded caching technology can better alleviate network traffic congestion. Since many of the centralized coded caching schemes now in use have high subpacketization, which makes scheme implementation more challenging, coded caching schemes with low subpacketization offer a wider range of practical applications. It has been demonstrated that the coded caching scheme can be achieved by creating a combinatorial structure named placement delivery array (PDA). In this work, we employ vector space over a finite field to obtain a class of PDA, calculate its parameters, and consequently achieve a coded caching scheme with low subpacketization. Subsequently, we acquire a new MN scheme and compare it with the new scheme developed in this study. The subpacketization \(F\) of the new scheme has significant advantages. Lastly, the number of users \(K\), cache fraction \(\frac{M}{N}\), and subpacketization \(F\) have advantages to some extent at the expense of partial transmission rate \(R\) when compared to the coded caching scheme in other articles.
We continue the study of Token Sliding (reconfiguration) graphs of independent sets initiated by the authors in an earlier paper [Graphs Comb. 39.3, 59, 2023]. Two of the topics in that paper were to study which graphs \(G\) are Token Sliding graphs and which properties of a graph are inherited by a Token Sliding graph. In this paper, we continue this study specializing in the case of when \(G\) and/or its Token Sliding graph \(\mathsf{TS}_k(G)\) is a tree or forest, where \(k\) is the size of the independent sets considered. We consider two problems. The first is to find necessary and sufficient conditions on \(G\) for \(\mathsf{TS}_k(G)\) to be a forest. The second is to find necessary and sufficient conditions for a tree or forest to be a Token Sliding graph. For the first problem, we give a forbidden subgraph characterization for the cases of \(k=2,3\). For the second problem, we show that for every \(k\)-ary tree \(T\) there is a graph \(G\) for which \(\mathsf{TS}_{k+1}(G)\) is isomorphic to \(T\). A number of other results are given along with a join operation that aids in the construction of \(\mathsf{TS}_k\)-graphs.
In this paper, we introduce graceful and near graceful labellings of several families of windmills. In particular, we use Skolem-type sequences to prove (near) graceful labellings exist for windmills with \(C_{3}\) and \(C_{4}\) vanes, and infinite families of \(3,5\)-windmills and \(3,6\)-windmills. Furthermore, we offer a new solution showing that the graph obtained from the union of \(t\) 5-cycles with one vertex in common (\(C_{5}^{t}\)) is graceful if and only if \(t \equiv 0, 3 \pmod{4}\) and near graceful when \(t \equiv 1, 2 \pmod{4}\).
We study groups generated by sets of pattern avoiding permutations. In the first part of the paper, we prove some general results concerning the structure of such groups. In particular, we consider the sequence \((G_n)_{n \geq 0}\), where \(G_n\) is the group generated by a subset of the symmetric group \(S_n\) consisting of permutations that avoid a given set of patterns. We analyze under which conditions the sequence \((G_n)_{n \geq 0}\) is eventually constant. Moreover, we find a set of patterns such that \((G_n)_{n \geq 0}\) is eventually equal to an assigned symmetric group. Furthermore, we show that any non-trivial simple group cannot be obtained in this way and describe all the non-trivial abelian groups that arise in this way. In the second part of the paper, we carry out a case-by-case analysis of groups generated by permutations avoiding a few short patterns.
We consider the eccentric graph of a graph \(G\), denoted by \(\mathrm{ecc}(G)\), which has the same vertex set as \(G\), and two vertices in the eccentric graph are adjacent if and only if their distance in \(G\) is equal to the eccentricity of one of them. In this paper, we present a fundamental requirement for the isomorphism between \(\mathrm{ecc}(G)\) and the complement of \(G\), and show that the previous necessary condition given in the literature is inadequate. Also, we obtain that the diameter of \(\mathrm{ecc}(T)\) is at most 3 for any tree and get some characterizations of the eccentric graph of trees.
Let \(G\) be a finite simple undirected \((p, q)\)-graph, with vertex set \(V(G)\) and edge set \(E(G)\) such that \(p = |V(G)|\) and \(q = |E(G)|\). A super edge-magic total labeling \(f\) of \(G\) is a bijection \(f \colon V(G) \cup E(G) \longrightarrow \{1, 2, \dots, p+q\}\) such that for all edges \(uv \in E(G)\), \(f(u) + f(v) + f(uv) = c(f)\), where \(c(f)\) is called a magic constant, and \(f(V(G)) = \{1, \dots, p\}\). The minimum of all \(c(f)\), where the minimum is taken over all the super edge-magic total labelings \(f\) of \(G\), is defined to be the super edge-magic total strength of the graph \(G\). In this article, we work on certain classes of unicyclic graphs and provide evidence to conjecture that the super edge-magic total strength of a certain family of unicyclic \((p, q)\)-graphs is equal to \(2q + \frac{n+3}{2}\).
For a poset \(P = C_a \times C_b\), a subset \(A \subseteq P\) is called a chain blocker for \(P\) if \(A\) is inclusion-wise minimal with the property that every maximal chain in \(P\) contains at least one element of \(A\), where \(C_i\) is the chain \(1 < \cdots < i\). In this article, we define the shelter of the poset \(P\) to give a complete description of all chain blockers of \(C_5 \times C_b\) for \(b \geq 1\).
This project aims at investigating properties of channel detecting codes on specific domains \(1^+0^+\). We focus on the transmission channel with deletion errors. Firstly, we discuss properties of channels with deletion errors. We propose a certain kind of code that is a channel detecting (abbr. \(\gamma\)-detecting) code for the channel \(\gamma = \delta(m, N)\) where \(m < N\). The characteristic of this \(\gamma\)-detecting code is considered. One method is provided to construct \(\gamma\)-detecting code. Finally, we also study a kind of special channel code named \(\tau(m, N)\)-srp code.
A chemical structure specifies the molecular geometry of a given molecule or solid in the form of atom arrangements. One way to analyze its properties is to simulate its formation as a product of two or more simpler graphs. In this article, we take this idea to find upper and lower bounds for the generalized Randić index \(\mathcal{R}_{\alpha}\) of four types of graph products, using combinatorial inequalities. We finish this paper by providing the bounds for \(\mathcal{R}_{\alpha}\) of a line graph and rooted product of graphs.
Let \(G\) be a \((p, q)\) graph. Let \(f: V(G) \to \{1, 2, \ldots, k\}\) be a map where \(k \in \mathbb{N}\) is a variable and \(k > 1\). For each edge \(uv\), assign the label \(\gcd(f(u), f(v))\). \(f\) is called \(k\)-Total prime cordial labeling of \(G\) if \(\left|t_{f}(i) – t_{f}(j)\right| \leq 1\), \(i, j \in \{1, 2, \ldots, k\}\) where \(t_{f}(x)\) denotes the total number of vertices and edges labeled with \(x\). A graph with a \(k\)-total prime cordial labeling is called \(k\)-total prime cordial graph. In this paper, we investigate the 4-total prime cordial labeling of some graphs like dragon, Möbius ladder, and corona of some graphs.
Let \(G = (V, E)\) be a graph with vertex set \(V\) and edge set \(E\). An edge labeling \(f: E \to Z_{2}\) induces a vertex labeling \(f^{+} : V \to Z_{2}\) defined by \( f^{+}(v) \equiv \sum_{uv \in E} f(uv) \pmod 2 \), for each vertex \(v \in V\). For \(i \in Z_{2}\), let \( v_{f}(i) = |\{v \in V : f^+(v) = i\}| \) and \( e_{f}(i) = |\{e \in E : f(e) = i\}| \). An edge labeling \(f\) of a graph \(G\) is said to be edge-friendly if \( |e_{f}(1) – e_{f}(0)| \le 1 \). The set \(\{v_f(1) – v_f(0) : f \text{ is an edge-friendly labeling of } G\}\) is called the full edge-friendly index set of \(G\). In this paper, we shall determine the full edge-friendly index sets of one point union of cycles.
After the Chartrand definition of graph labeling, since 1988 many graph families have been labeled through mathematical techniques. A basic approach in those labelings was to find a pattern among the labels and then prove them using sequences and series formulae. In 2018, Asim applied computer-based algorithms to overcome this limitation and label such families where mathematical solutions were either not available or the solution was not optimum. Asim et al. in 2018 introduced the algorithmic solution in the area of edge irregular labeling for computing a better upper-bound of the complete graph \(es(K_n)\) and a tight upper-bound for the complete \(m\)-ary tree \({es(T}_{m,h})\) using computer-based experiments. Later on, more problems like complete bipartite and circulant graphs were solved using the same technique. Algorithmic solutions opened a new horizon for researchers to customize these algorithms for other types of labeling and for more complex graphs. In this article, to compute edge irregular \(k\)-labeling of star \(S_{m,n}\) and banana tree \({BT}_{m,n}\), new algorithms are designed, and results are obtained by executing them on computers. To validate the results of computer-based experiments with mathematical theorems, inductive reasoning is adopted. Tabulated results are analyzed using the law of double inequality and it is concluded that both families of trees observe the property of edge irregularity strength and are tight for \(\left\lceil \frac{|V|}{2} \right\rceil\)-labeling.
A graph \(G\) is called a fractional ID-\((g,f)\)-factor-critical covered graph if for any independent set \(I\) of \(G\) and for every edge \(e \in E(G-I)\), \(G-I\) has a fractional \((g,f)\)-factor \(h\) such that \(h(e) = 1\). We give a sufficient condition using degree condition for a graph to be a fractional ID-\((g,f)\)-factor-critical covered graph. Our main result is an extension of Zhou, Bian, and Wu’s previous result [S. Zhou, Q. Bian, J. Wu, A result on fractional ID-\(k\)-factor-critical graphs, Journal of Combinatorial Mathematics and Combinatorial Computing 87(2013) 229–236] and Yashima’s previous result [T. Yashima, A degree condition for graphs to be fractional ID-\([a,b]\)-factor-critical, Australasian Journal of Combinatorics 65(2016) 191–199].
Generally, all the models discussed so far are continuous time models. The continuous time models are quite apt at explaining the phenomena they are trying to predict and have known methods to get information from these type of models. But these models are not accurate for the physical systems which are observed over discreet time periods or which have non-continuous phenomena embedded in them, like production of new generation. Some species like salmon have non-overlapping generation characteristics since they have an annual spawning season and are born each year at a certain time. The discrete models are much more apt in describing the nature’s complex dynamics than the continuous models. A discrete-time modified Leslie-Gower system with double Allee effect is studied in this paper. The stability analysis of interior fixed points is performed. Using center manifold theorem it is shown that the system under consideration exhibits period-doubling and Neimark-Sacker bifurcations. The numerical simulations are provided to illustrate the consistency of the theoretical results.
We investigate the Sombor indices for a diverse group of nonsteroidal anti-inflammatory drugs (NSAIDs) to understand their molecular architecture and physicochemical properties. By utilizing quantitative structure-property relationship (QSPR) modeling, we establish mathematical models linking Sombor indices to key pharmacodynamic and toxicological parameters. Our study sheds light on how the molecular composition of NSAIDs influences their drug profiles and biological behavior, offering valuable insights for drug development and safety assessment.
In this paper, the relations of maximum degree energy and maximum reserve degree energy of a complete graph after removing a vertex have been shown to be proportional to the energy of the complete graph. The results of splitting the graph and shadow graphs are also presented for the complete graph after removing a vertex.
Based on the Hermitian adjacency matrices of second kind introduced by Mohar [1] and weighted adjacency matrices introduced in [2], we define a kind of index weighted Hermitian adjacency matrices of mixed graphs. In this paper we characterize the structure of mixed graphs which are cospectral to their underlying graphs, then we determine a upper bound on the spectral radius of mixed graphs with maximum degree \(\Delta\), and characterize the corresponding extremal graphs.
Modified group divisible designs MGD\((k, \lambda, m, n)\) are extensively studied because of an intriguing combinatorial structure that they possess and their applications. In this paper, we present a generalization of MGDs called GMGD\((k, \lambda_1, \lambda_2, m, n)\), and we provide some elementary results and constructions of some special cases of GMGDs. In addition, we show that the necessary conditions are sufficient for the existence of a GMGD\((3, \lambda, 2\lambda, m, n)\) for any positive integer \(\lambda\), and a GMGD\((3, 2, 3, m, n)\). Though not a general result, the construction of a GMGD\((3, 3, 2, 2, 6)\) given in the paper is worth mentioning in the abstract. Along with another example of a GMGD\((3, 3, 2, 2, 4)\), and \(n\) to \(tn\) construction, we have families of GMGD\((3, 3\lambda, 2\lambda, 2, n)\)s for \(n = 4t\) or \(6t\) when \(t \equiv 0, 1 \pmod 3\), for any positive integer \(\lambda\).
In this article, we define \(q\)-generalized Fibonacci polynomials and \(q\)-generalized Lucas polynomials using \(q\)-binomial coefficient and obtain their recursive properties. In addition, we introduce generalized \(q\)-Fibonacci matrix and generalized \(q\)-Lucas matrix, then we derive their basic identities. We define \((k,q,t)\)-symmetric generalized Fibonacci matrix and \((k,q,t)\)-symmetric generalized Lucas matrix, then we give the Cholesky factorization of these matrices. Finally, we give determinantal and permanental representations of these new polynomial sequences.
We show that connected, bicyclic graphs on nine edges with at least one cycle other than \(C_3\) decompose the complete graphs \(K_{18k}\) and \(K_{18k+1}\), for \(k\geq1\), when the necessary conditions allow for such a decomposition. This complements previous results by Freyberg, Froncek, Jeffries, Jensen, and Sailstad on connected bicyclic triangular graphs.
In the realm of graph theory, recent developments have introduced novel concepts, notably the \(\nu\varepsilon\)-degree and \(\varepsilon\nu\)-degree, offering expedited computations compared to traditional degree-based topological indices (TIs). These TIs serve as indispensable molecular descriptors for assessing chemical compound characteristics. This manuscript aims to meticulously compute a spectrum of TIs for silicon carbide \(SiC_{4}\)-\(I[r,s]\), with a specific focus on the \(\varepsilon\nu\)-degree Zagreb index, the \(\nu\varepsilon\)-degree Geometric-Arithmetic index, the \(\varepsilon\nu\)-degree Randić index, the \(\nu\varepsilon\)-degree Atom-bond connectivity index, the \(\nu\varepsilon\)-degree Harmonic index, and the \(\nu\varepsilon\)-degree Sum connectivity index. This study contributes to the ongoing advancement of graph theory applications in chemical compound analysis, elucidating the nuanced structural properties inherent in silicon carbide molecules.
Graph theory has experienced notable growth due to its foundational role in applied mathematics and computer science, influencing fields like combinatorial optimization, biochemistry, physics, electrical engineering (particularly in communication networks and coding theory), and operational research (with scheduling applications). This paper focuses on computing topological properties, especially in molecular structures, with a specific emphasis on the nanotube \(HAC_{5}C_{7}[w,t]\).
Let \(\beta_{H}\) denote the orbit graph of a finite group \(H\). Let \(\zeta\) be the set of commuting elements in \(H\) with order two. An orbit graph is a simple undirected graph where non-central orbits are represented as vertices in \(\zeta\), and two vertices in \(\zeta\) are connected by an edge if they are conjugate. In this article, we explore the Laplacian energy and signless Laplacian energy of orbit graphs associated with dihedral groups of order $2w$ and quaternion groups of order \(2^{w}\).
In this paper, we introduce the concept of the generalized \(3\)-rainbow dominating function of a graph \(G\). This function assigns an arbitrary subset of three colors to each vertex of the graph with the condition that every vertex (including its neighbors) must have access to all three colors within its closed neighborhood. The minimum sum of assigned colors over all vertices of G is defined as the \(g_{3}\)-rainbow domination number, denoted by \(\gamma_{g3r}\). We present a linear-time algorithm to determine a minimum generalized 3-rainbow dominating set for several graph classes: trees, paths \((P_n)\), cycles \((C_n)\), stars (\(K_1,n)\), generalized Petersen graphs \((GP(n,2)\), GP \((n,3))\), and honeycomb networks \((HC(n))\).
Stanley considered Dyck paths where each maximal run of down-steps to the \(x\)-axis has odd length; they are also enumerated by (shifted) Catalan numbers. Prefixes of these combinatorial objects are enumerated using the kernel method. A more challenging version of skew Dyck paths combined with Stanley’s restriction is also considered.
For \(r=1,2,…, 6\), we obtain generating functions \(F^{(r)}_{k}(y)\) for words over the alphabet \([k]\), where \(y\) tracks the number of parts and \([y^n]\) is the total number of distinct adjacent \(r\)-tuples in words with \(n\) parts. In order to develop these generating functions for \(1\le r\le 3\), we make use of intuitive decompositions but for larger values of \(r\), we switch to the cluster analysis method for decorated texts that was introduced by Bassino et al. Finally, we account for the coefficients of these generating functions in terms of Stirling set numbers. This is done by putting forward the full triangle of coefficients for all the sub-cases where \(r=5\) and 6. This latter is shown to depend on both periodicity and number of letters used in the \(r\)-tuples.
We consider the following variant of the round-robin scheduling problem: \(2n\) people play a number of rounds in which two opposing teams of \(n\) players are reassembled in each round. Each two players should play at least once in the same team, and each two players should play at least once in opposing teams. We provide an explicit formula for calculating the minimal numbers of rounds needed to satisfy both conditions. Moreover, we also show how one can construct the corresponding playing schedules.
Two binary structures \(\mathfrak{R}\) and \(\mathfrak{R’}\) on the same vertex set \(V\) are \((\leq k)\)-hypomorphic for a positive integer \(k\) if, for every set \(K\) of at most \(k\) vertices, the two binary structures induced by \(\mathfrak{R}\) and \(\mathfrak{R’}\) on \(K\) are isomorphic. A binary structure \(\mathfrak{R}\) is \((\leq k)\)-reconstructible if every binary
structure \(\mathfrak{R’}\) that is \((\leq k)\)-hypomorphic to \(\mathfrak{R}\) is isomorphic to \(\mathfrak{R}\). In this paper, we describe the pairs of \((\leq 3)\)-hypomorphic posets and the pairs of \((\leq 3)\)-hypomorphic bichains. As a consequence, we characterize the \((\leq 3)\)-reconstructible posets and the \((\leq 3)\)-reconstructible bichains. This answers a question suggested by Y. Boudabbous and C. Delhommé during a personal communication.
A tremendous amount of drug experiments revealed that there exists a strong inherent relation between the molecular structures of drugs and their biomedical and pharmacology characteristics. Due to the effectiveness for pharmaceutical and medical scientists of their ability to grasp the biological and chemical characteristics of new drugs, analysis of the bond incident degree (BID) indices is significant of testing the chemical and pharmacological characteristics of drug molecular structures that can make up the defects of chemical and medicine experiments and can provide the theoretical basis for the manufacturing of drugs in pharmaceutical engineering. Such tricks are widely welcomed in developing areas where enough money is lacked to afford sufficient equipment, relevant chemical reagents, and human resources which are required to investigate the performance and the side effects of existing new drugs. This work is devoted to establishing a general expression for calculating the bond incident degree (BID) indices of the line graphs of various well-known chemical structures in drugs, based on the drug molecular structure analysis and edge dividing technique, which is quite common in drug molecular graphs.
In this paper, we introduce a graph structure, called component intersection graph, on a finite dimensional vector space \(\mathbb{V}\). The connectivity, diameter, maximal independent sets, clique number, chromatic number of component intersection graph have been studied.
A linear system is a pair \((P,\mathcal{L})\) where \(\mathcal{L}\) is a finite family of subsets on a finite ground set \(P\) such that any two subsets of \(\mathcal{L}\) share at most one element. Furthermore, if for every two subsets of \(\mathcal{L}\) share exactly one element, the linear system is called intersecting. A linear system \((P,\mathcal{L})\) has rank \(r\) if the maximum size of any element of \(\mathcal{L}\) is \(r\). By \(\gamma(P,\mathcal{L})\) and \(\nu_2(P,\mathcal{L})\) we denote the size of the minimum dominating set and the maximum 2-packing of a linear system \((P,\mathcal{L})\), respectively. It is known that any intersecting linear system \((P,\mathcal{L})\) of rank \(r\) is such that \(\gamma(P,\mathcal{L})\leq r-1\). Li et al. in [S. Li, L. Kang, E. Shan and Y. Dong, The finite projective plane and the 5-Uniform linear intersecting hypergraphs with domination number four, Graphs and 34 Combinatorics (2018) , no.~5, 931–945.] proved that every intersecting linear system of rank 5 satisfying \(\gamma(P,\mathcal{L})=4\) can be constructed from a 4-uniform intersecting linear subsystem \((P^\prime,\mathcal{L}^\prime)\) of the projective plane of order 3 satisfying \(\tau(P^\prime,\mathcal{L}^\prime)=\nu_2(P^\prime,\mathcal{L}^\prime)=4\), where \(\tau(P^\prime,\mathcal{L}^\prime)\) is the transversal number of \((P^\prime,\mathcal{L}^\prime)\). In this paper, we give an alternative proof of this result given by Li et al., giving a complete characterization of these 4-uniform intersecting linear subsystems. Moreover, we prove a general case, that is, we prove if $q$ is an odd prime power and \((P,\mathcal{L})\) is an intersecting linear system of rank \((q+2)\) satisfying \(\gamma(P,\mathcal{L})=q+1\), then this linear system can be constructed from a spanning \((q+1)\)-uniform intersecting linear subsystem \((P^\prime,\mathcal{L}^\prime)\) of the projective plane of order \(q\) satisfying \(\tau(P^\prime,\mathcal{L}^\prime)=\nu_2(P^\prime,\mathcal{L}^\prime)=q+1\).
We classify all near hexagons of order \((3,t)\) that contain a big quad. We show that, up to isomorphism, there are ten such near hexagons.
Let \(G=(V,E)\) be a simple graph. A vertex \(v\in V(G)\) ve-dominates every edge \(uv\) incident to \(v\), as well as every edge adjacent to these incident edges. A set
\(D\subseteq V(G)\) is a vertex-edge dominating set if every edge of \(E(G)\) is ve-dominated by a vertex of \(D.\) The MINIMUM VERTEX-EDGE DOMINATION problem is to find a vertex-edge dominating set of minimum cardinality. A linear time algorithm to find the minimum vertex-edge dominating set for proper interval graphs is proposed. The vertex-edge domination problem is proved to be APX-complete for bounded-free graphs and NP-Complete for Chordal bipartite and Undirected Path graphs.
In this paper, we investigate the \((d,1)\)-total labelling of generalized Petersen graphs \(P(n,k)\) for \(d\geq 3\). We find that the \((d,1)\)-total number of \(P(n,k)\) with \(d\geq 3\) is \(d+3\) for even \(n\) and odd \(k\) or even \(n\) and \(k=\frac{n}{2}\), and \(d+4\) for all other cases.
By employing Kummer and Thomae transformations, we examine four classes of nonterminating \(_3F_2\)(1)-series with five integer parameters. Several new summation formulae are established in closed form.
Let \(c\) be a proper \(k\)-coloring of a connected graph \(G\) and \(\pi=\{S_{1},S_{2},\ldots,S_{k}\}\) be an ordered partition of the vertex set \(V(G)\) into the resulting color classes, where \(S_{i}\) is the set of all vertices that receive the color \(i\). For a vertex \(v\) of \(G\), the color code \(c_{\pi}(v)\) of \(v\) with respect to \(\pi\) is the ordered \(k\)-tuple \(c_{\pi}(v)=(d(v,S_{1}),d(v,S_{2}),\ldots,d(v,S_{k}))\), where \(d(v,S_{i})=min\{d(v,u):\textit{ } u\in S_{i}\}\) for \(1\leqslant i \leqslant k\). If all distinct vertices of \(G\) have different color codes, then \(c\) is called a locating coloring of \(G\). The locating chromatic number is the minimum number of colors needed in a locating coloring. In this paper, we determine the locating-chromatic number for the middle graphs of Path, Cycle, Wheel, Star, Gear and Helm graphs.
There has been significant research dedicated towards computing the crossing numbers of families of graphs resulting from the Cartesian products of small graphs with arbitrarily large paths, cycles and stars. For graphs with four or fewer vertices, these have all been computed, but there are still various gaps for graphs with five or more vertices. We contribute to this field by determining the crossing numbers for fifteen such families.
A graph \(G\) with vertex set \(V = V(G)\) and edge set \(E = E(G)\) is harmonious if there exists a harmonious labeling of \(G\); which is an injective function \(f:V(G) \rightarrow \mathbf{Z}_m\) provided that whenever \(e_1, e_2 \in E\) are distinct with endpoints \(u_1,v_1\) and \(u_2,v_2\), respectively, then \(f(u_1) + f(v_1) \not\equiv f(u_2) + f(v_2) (\hbox{mod } m )\). Using basic group theory, we prove in a different manner an already established result that a disjoint union of an odd cycle and a path is harmonious provided their lengths satisfy certain conditions. We apply the same basic idea to establish that, under the same conditions, a disjoint union of an odd cycle with a certain starlike tree is harmonious (where a starlike tree consists of a central vertex that is adjacent to an endpoint of a certain number of fixed length paths). Finally, we extend the latter result to include specifying that the central vertex in the tree be adjacent to different vertices in each of the \(t\)-many \(s\)-paths.
Under the background of my country’s new rural construction and the implementation of the rural revitalization strategy, the rural economy has ushered in an unprecedented opportunity for development. Agricultural economic management plays a catalytic role in providing direction guidance for rural economic development, promoting sustainable rural economic development, and providing a good environment for rural economic development. However, there are some drawbacks in agricultural economic management, which are mainly reflected in the imperfect agricultural economic management system and the lag in information infrastructure construction. In view of this, the author puts forward corresponding the advanced VAR model from the aspects of improving the agricultural economic management system, improving the application level of information technology, and improving the quality of the agricultural economic management team. Research shows that: through effective management of agricultural economy, the obstacles to agricultural economic development can be reduced from the source, thereby promoting the healthy and sustainable development of rural economy.
This paper mainly focuses on the algorithms related to local path planning and path tracking control of unmanned vehicles in the process of obstacle avoidance. By introducing the temporal dimension as a reference, the perceptual results are projected onto the 3D spatio-temporal navigation map by combining the multi-target behavior prediction and other means; by increasing the temporal dimension, the static obstacles and dynamic obstacles are unified into the same parameter space. Under this parameter space, the front-end A* path search initializes the unified B spline curve control points, designs the trajectory cost function and performs nonlinear optimization to generate a spatio-temporal trajectory that satisfies the safety collision-free and vehicle motion constraints (speed and acceleration limits), thus transforming the decision and planning problem under the two-dimensional fence dynamic physical space into a static scene decision and planning problem under the three-dimensional spatio-temporal space. Through simulation verification, the whole process of the proposed trajectory planning method takes 51.27ms on average, which meets the driving requirements of driverless cars. In addition, by adjusting the search conditions of the A* algorithm, its overall planning efficiency is improved by 27.86% compared with the search speed of the traditional algorithm. The actual feeling and data results from the real vehicle experiments show its good tracking effect, which verifies the effectiveness and practicality of the algorithm proposed in this paper.
Based on the visual servo technology, this paper focuses on the visual tracking algorithm of moving objects and the dynamic grasping control method of robots, and realizes the automatic loading and unloading of moving workpieces to improve production efficiency. Firstly, aiming at the difficulties in the selection of high-dimensional features extracted by visual servo, this paper proposes a training method of generation countermeasure network based on heuristic algorithm by using the efficient search ability of heuristic algorithm. Secondly, we use image processing technology to realize real-time recognition and location of workpieces under complex background. According to the positioning results, an adaptive dual rate unscented Kalman filter visual tracking algorithm is proposed to solve the problem of delay and multi sampling rate in visual servo, and realize visual tracking of moving objects. The experimental results show that the proposed visual tracking algorithm has better stability and real-time performance.
For any positive integer \(h\), a graph \(G=(V,E)\) is said to be \(h\)-magic if there exists a labeling \(l:E(G)\to \mathbb{Z}_h -\{0\}\) such that the induced vertex set labeling \(\ l^+ : V(G) \to \mathbb{Z}_h \) defined by
\[
l^+ (v)=\sum_{uv \in E(G)} \ l(uv)
\]
is a constant map. The integer-magic spectrum of a graph \(G\), denoted by \(IM(G)\), is the set of all \(h \in \mathbb{N}\) for which \(G\) is \(h\)-magic. So far, only the integer-magic spectra of trees of diameter at most five have been determined. In this paper, we determine the integer-magic spectra of trees of diameter six and higher.
A total Roman \(\{2\}\)-dominating function on a graph \(G = (V,E)\) is a function \(f:V\rightarrow\{0,1,2\}\) with the properties that (i) for every vertex \({v}\in V\) with \(f({v})=0\), \(f(N({v}))\ge2\) and (ii) the set of vertices with \(f({v})>0\) induces a subgraph with no isolated vertices. The weight of a total Roman \(\{2\}\)-dominating function is the value \(f(V)=\sum_{{v}\in V}f({v})\), and the minimum weight of a total Roman \(\{2\}\)-dominating function is called the total Roman \(\{2\}\)-domination number and denoted by \(\gamma_{tR2}(G)\). In this paper, we prove that for every graph \(G\) of order \(n\) with minimum degree at least two, \(\gamma_{tR2}({G})\leq \frac{5n}{6}\).
The penetration of virtual classroom teaching into German teaching is the presentation of teaching innovation in the information age. In this work, we explore the necessity of applying virtual classroom teaching in German classrooms and the effective strategies of German teaching innovation in virtual classrooms, to provide some suggestions for the reform of German teaching. First, the GPS trajectories are transformed into a sequence of hotspot regions using the spatiotemporal properties of GPS points. Then, a sequential pattern mining algorithm of asynchronous cycles with multiple minimum supports based on pattern growth is adopted, and the sequential patterns of asynchronous cycles are deeply recursively mined according to the multiple minimum supports. Experiments show that the proposed IoT-assisted teaching scheme can effectively integrate equipment resources, mine spatiotemporal information, and help students and teachers establish a new educational method of integrating space and land. Compared with the baseline, it can fully exploit the characteristics of German.
The limitations of existing procedures make it difficult to locate and identify old subterranean culverts in urban infrastructure management. In order to effectively manage urban infrastructure, subterranean pipe culverts must be accurately located and detected. In this research, we investigate the method of computing the shortest distance from the point to the ellipse and propose a pipeline collision detection method based on the projection of the direction of the common perpendicular. In the positioning accuracy test, we simulate the detection of straight and curved paths and obtain satisfactory results; the experimental results show that the detection errors are within acceptable limits for different azimuth and bending angles; in the correctness test, we compared with AutoCAD and ArcGIS, and found that the algorithm in this paper shows superiority in collision detection, especially when dealing with complex spatial relationships and large amounts of data, with evident efficiency advantages. Through theoretical analysis and experimental verification, we demonstrate the effectiveness and reliability of the method.
We identified, via a computer search, 143 excluded minors of the spindle surface, the space formed by the identification of two points of the sphere. Per our search, any additional excluded minors must have at least 12 vertices and 28 edges. We also identified 847 topological obstructions for the spindle surface. We conjecture that our lists of excluded minors and topological obstructions are complete.
This paper analyzes the prediction model of enterprise human resource demand based on Internet of Things (IoT) technology and data mining technology. It also analyzes the impact of the company’s growth scale and other key factors on the demand for human resources, tries to establish a coupling factor model of enterprise development and economic benefits, and then analyzes and forecasts the enterprise’s personnel structure and quality structure. The experimental results show that the optimized human resource demand forecasting model integrates the advantages of the grey system model in data processing, can mine the inherent laws of unorganized data information, and provides a certain convenience for forecasting. Through the linear mapping and processing of sample data, the input and output reflect a kind of correlation, thus changing the fault tolerance of information, making the prediction in the calculation process more accurate, and its comprehensive accuracy can exceed 92.5%.
Low efficiency and poor accuracy are caused by missing data in traditional 3D reconstruction methods. This study suggests a new 3D point cloud recognition technique for substation equipment based on 3D laser scanning point clouds, which combines the k-nearest neighbour (KNN) classification algorithm and particle swarm optimisation (PSO) algorithm, to address these issues. The particle swarm optimisation algorithm optimises the coefficient weights of each subspace feature. The k-nearest neighbour classification algorithm is then used to finish the classification. To confirm the superiority and accuracy of the suggested approach, the impact of the point cloud subspace’s size and loss rate on the recognition effect is examined experimentally and contrasted with the enhanced iterative nearest point algorithm. With an average recognition time of 0.19 seconds and a recognition accuracy of over 95\%, the experimental results demonstrate the method’s good performance in terms of efficiency and accuracy, opening up a wide range of potential applications.
Users may receive personalised information services and decision support from personalised recommendations. In this paper, a hybrid algorithm-based personalised recommendation approach for learning English is proposed. The user model is created by merging user interest tags, and the Person Rank algorithm is then recommended based on user information. Second, the question-and-answer model is created once the question-and-answer data has been labelled, and the Problem Rank algorithm is suggested in accordance with the question-and-answer data. Then, the approach of tag-based recommendation, comparable user recommendation, and multi-dimensional sliding window are used to construct the recommendation algorithm model. The experimental findings demonstrate that, following the model’s training with the gradient descent technique, the recommendation accuracy is steady at around 0.78, the suggested information can accommodate users who are learning English, and the personalised recommendation effect is enhanced.
In this paper, we introduce the edge version of doubly resolving set of a graph which is based on the edge distances of the graph. As a main result, we computed the minimum cardinality \(\psi_E\) of edge version of doubly resolving sets of family of \(n\)-sunlet graph \(S_n\) and prism graph \(Y_n\).
Consider the simple connected graph G with vertex set V(G) and edge set E(G). A graph \(G\) can be resolved by \(R\) if each vertex’s representation of distances to the other vertices in \(R\) uniquely identifies it. The minimum cardinality of the set \(R\) is the metric dimension of \(G\). The length of the shortest path between any two vertices, x, y in V(G), is signified by the distance symbol d(x, y). An ordered k-tuple \(r(x/R)=(d(x,z_1),d(x,\ z_2),…,d(x,z_k))\) represents representation of \(x\) with respect to \(R\) for an ordered subset \(R={\{z}_1,z_2,z_3…,z_k\}\) of vertices and vertex \(x\) in a connected graph. Metric dimension is used in a wide range of contexts where connection, distance, and connectedness are essential factors. It facilitates understanding the structure and dynamics of complex networks and problems relating to robotics network design, navigation, optimization, and facility location. Robots can optimize their localization and navigation methods using a small number of reference sites due to the pertinent idea of metric dimension. As a result, many robotic applications, such as collaborative robotics, autonomous navigation, and environment mapping, are more accurate, efficient, and resilient. A claw-free cubic graph (CCG) is one in which no induced subgraph is a claw. CCG proves helpful in various fields, including optimization, network design, and algorithm development. They offer intriguing structural and algorithmic properties. Developing algorithms and results for claw-free graphs frequently has applications in solving of challenging real-world situations. The metric dimension of a couple of claw-free cubic graphs (CCG), a string of diamonds (SOD), and a ring of diamonds (ROD) will be determined in this work.
Using blockchain technology to handle the entire chain of digital copyrights in digital libraries not only helps to improve the economy, validity, and fairness of the libraries’ digital resource offerings, but it also increases the revenue of digital copyright subjects in a sustainable manner. In this work, a decentralized, secure, and traceable digital copyright transaction system is designed and implemented using blockchain technology. The system serves creators, administrators, and subscribers through its user layer, business model layer, and Fabric network layer. To guarantee the accuracy and integrity of transaction data, smart contracts are used for the registration of digital works, transaction supervision, and smart contract execution. Fabric Composer is used in the development of the system and offers good scalability. The system still has issues with privacy protection, increasing performance, and complying with laws and regulations. It is anticipated that the digital copyright transaction system will advance in the area of digital copyright protection as blockchain technology develops.
As computer and mechanical automation technologies advance, machine vision-based non-destructive testing technology finds use in a multitude of domains. Non-destructive testing technologies can be used on apple sorting equipment to decrease apple damage while simultaneously increasing sorting efficiency. As a result, the apple sorting machine’s image identification system now incorporates machine vision technology. The automatic classification of apple grades is accomplished by gathering, processing, extracting, and computing the contour features of apple photographs using preset sorting levels. The automatic control system then sorts apples of different grades to designated locations, thus achieving the automation of apple sorting. Tests were run on the sorting machine’s image recognition system to confirm the solution’s viability. The outcomes demonstrate that the sorting machine can effectively classify fruit automatically based on their perimeter, which is important for fruit sorting automation.
The gearbox gearbox transmission system, which is the foundation of a new energy vehicle, is responsible for the crucial duty of power transmission. In reality, the reducer gearbox system is the primary source of noise inside cars because of the design of the system, mistakes made during manufacturing and assembly, and gear engagement impulses. The research target is the second-stage retarder gearbox system of a new energy vehicle. A three-dimensional model of the retarder gearbox system is created using the Romax software.Static and dynamic analyses were carried out in Romax software based on the five typical conditions of start, acceleration, equal speed, deceleration, and stop in order to derive performance data such as maximum contact and bending stresses of the gears, single-position length load distribution, gearbox error, etc. In the NVH analysis, the system’s vibration acceleration was ascertained using the findings of the gearbox error analysis. In order to provide comparative data for vibration and noise reduction of gear modification, the comparative study analyses the data output results under various working conditions and analyses the relationship between gear engagement force and gear vibration.
Let \(G\) be a connected graph. A pebbling move is defined as taking two pebbles from one vertex and the placing one pebble to an adjacent vertex and throwing away the another pebble. A dominating set \(D\) of a graph \(G=(V,E)\) is a non-split dominating set if the induced graph \(\) is connected. The Non-split Domination Cover(NDC) pebbling number, \(\psi_{ns}(G)\), of a graph $G$ is the minimum of pebbles that must be placed on \(V(G)\) such that after a sequence of pebbling moves, the set of vertices with a pebble forms a non-split dominating set of \(G\), regardless of the initial configuration of pebbles. We discuss some basic results and determine \(\psi_{ns}\) for some families of standard graphs.
Graph theory is playing vital role in almost every field of our routine life. You make a conference call with your friends by using vertices (yourself and your friends) and edges (network connection). You construct a printed grid floor with different faces in your home by the help of graph theory. Authors in this study are using labelling of graphs and applying it in choosing best friends around you. The helping graphs in this article will be plane graphs which will be labelling under \(\Bbbk-\)labelling \(\mathrm{M}\) of kind \((\lambda,\mu,\nu)\). This study can be applied in many fields of everyday life.
A complex Hadamard matrix is a matrix \(H_n \in {\{\omega^i | 1\leq i \leq m \}}^{n\times n}\) of order \(n\), where \(\omega\) is a primitive \(m^{th}\) root of unity, that satisfies \(H_n{H}^{*}_n=n{I_{n}}\), where \(H_n^{*}\) denotes the complex conjugate transpose of \(H_n\). We show that the Scarpis technique for constructing classic Hadamard matrices generalizes to Butson-type complex Hadamard matrices.
With the rapid development of the country’s economy, politics, and culture, China has swiftly ascended to the ranks of global powers. Its participation in international organizations, including the WTO, has significantly bolstered its global standing and diplomatic ties, making it an indispensable player in international politics. Meanwhile, domestically, China has implemented numerous initiatives aimed at improving the lives of its citizens, such as anti-corruption campaigns, efforts to uphold integrity, crackdowns on criminal organizations, and poverty alleviation programs. As a result, the well-being of the populace has seen a steady increase. Furthermore, China has embarked on a new era of education characterized by its unique attributes, with civic education platforms experiencing comprehensive development. This paper examines these developments through text and knowledge mapping, assessing the efficacy of this approach within the framework of course ideology and politics.
This work suggests predicting student performance using a Gaussian process model classification in order to address the issue that the prediction approach is too complex and the data set involved is too huge in the process of predicting students’ performance. In order to prevent overfitting, a sample set consisting of the three typical test outcomes from 465 undergraduate College English students is divided into training and test sets. The cross-validation technique is used in this study. According to the findings, Gaussian process model classification can accurately predict 92\% of the test set with a prediction model, and it can also forecast students’ final exam marks based on their typical quiz scores. Furthermore, it is discovered that the prediction accuracy increases with the sample set’s distance from the normal distribution; this prediction accuracy rises to 96\% when test scores with less than 60 points are taken out of the analysis.
Let \(\varepsilon_{0}\), \(\varepsilon_{1}\) be two linear homogenous equations, each with at least three variables and coefficients not all the same sign. Define the \(2\)-color off-diagonal Rado number \(R_2(\varepsilon_{0},\varepsilon_{1})\) to be the smallest \(N\) such that for any 2-coloring of \([1,N]\), it must admit a monochromatic solution to \(\varepsilon_{0}\) of the first color or a monochromatic solution to \(\varepsilon_{1}\) of the second color. Mayers and Robertson gave the exact \(2\)-color off-diagonal Rado numbers \(R_2(x+qy=z,x+sy=z). \) Xia and Yao established the formulas for \(R_2(3x+3y=z,3x+qy=z) \) and \(R_2(2x+3y=z,2x+2qy=z) \). In this paper, we determine the exact numbers \(R_2(2x+qy=2z,2x+sy=2z)\), where \(q, s\) are odd integers with \(q>s\geq1\).
Let \(X\) be bipartite mixed graph and for a unit complex number \(\alpha\), \(H_\alpha\) be its \(\alpha\)-hermitian adjacency matrix. If \(X\) has a unique perfect matching, then \(H_\alpha\) has a hermitian inverse \(H_\alpha^{-1}\). In this paper we give a full description of the entries of \(H_\alpha^{-1}\) in terms of the paths between the vertices. Furthermore, for \(\alpha\) equals the primitive third root of unity \(\gamma\) and for a unicyclic bipartite graph \(X\) with unique perfect matching, we characterize when \(H_\gamma^{-1}\) is \(\pm 1\) diagonally similar to \(\gamma\)-hermitian adjacency matrix of a mixed graph. Through our work, we have provided a new construction for the \(\pm 1\) diagonal matrix.
Student-centeredness is a teaching theory proposed by British and American scholars in linguistics, psycholinguistics, applied linguistics, and second language acquisition theory. “The student-centered approach is different from the traditional teacher-centered approach, but it is implemented in a teacher-led environment. In this study, word2vec, paragraph2vec, pos2vec and LDA (latent dirichlet allocation) are combined to form a semantic representation vector for college business English translation. The key point of the college business English translation reform is to update the concept and theoretical understanding, so as to improve the teachers’ business English teaching theory and teaching practice, and to do a good job of college business English translation reform. Finally, it is shown that the proposed intelligent evaluation framework is more accurate than the traditional method in terms of automatic grading and rubric generation for college business English translation.
We study real algebras admitting reflections which commute. In dimension two, we show that two commuting reflections coincide and we specify the two and four-dimensional real algebras cases. We characterize real algebras of division of two-dimensional to third power-associative having a reflection. Finally We give a characterization in four-dimensional, the unitary real algebras of division at third power-associative having two reflections that commute. In eight-dimensional, we give an example of algebra so the group of automorphisms contains a subgroup isomorphic to \(\mathbb{Z}_2\times\mathbb{Z}_2\).
Let \(G(V,E)\) be a simple graph of order \(n\) with vertex set \(V\) and edge set \(E\). Let \((u, v)\) denote an unordered vertex pair of distinct vertices of \(G\). For a vertex \(u \in G,\) let \(N(u)\) be the set of all vertices of \(G\) which are adjacent to \(u\) in \(G.\) Then for \(0\leq i \leq n-1\), the \(i\)-equi neighbor set of \(G\) is defined as: \(N_{e}(G,i)=\{(u,v):u, v\in V, u\neq v\) and \(|N(u)|=|N(v)|=i\}.\) The equi-neighbor polynomial \(N_{e}[G;x]\) of \(G\) is defined as \(N_{e}[G;x]=\sum_{i=0}^{(n-1)} |N_{e}(G,i)| x^{i}.\) In this paper we discuss the equi-neighbor polynomial of graphs obtained by some binary graph operations.
The presence of unknown synchronization characteristics, unclear instability mechanism, and various fault mode evolution laws, lacking corresponding theoretical support and analysis methods and instability criteria, are defined with clear physical concepts. It is still impossible to systematically understand the transient synchronization mechanism of the wind power grid-connected system from the perspective of the whole fault stage. Therefore, this study uniformly reveals its temporary synchronous stability problem and proposes a large/small disturbance adaptive synchronous stability control method, which improves the dynamic characteristics of the wind turbine through the control of the inverter itself to improve the system stability—using different scenarios, such as single doubly-fed wind turbines. The experimental results show that the small disturbance on the AC side significantly impacts the system characteristics, followed by a bit of annoyance on the DC side. The DC side fault will cause a change in system frequency characteristics, especially at the receiving end. However, compared with the Voltage Source Converters-High Voltage Direct Current (VSC-HVDC)system, Modular Multilevel Converters-High Voltage Direct Current (MMC-HVDC) systems operate at a much higher frequency and produce less low-frequency harmonics. This makes them less likely to induce subsynchronous oscillations in the system.
With China’s educational reform, college physical education teaching mode has also made some innovations. Sports club is a new modern education model developed on the basis of traditional physical education courses. It provides students with more choices and is convenient for autonomous learning, thus forming a student-centered engineering education model. With the background of sports reform, this paper investigates and analyzes the reform of sports club system in universities, puts forward specific implementation means to stimulate the development process of sports reform in universities in China, puts forward data analysis schemes, and analyzes and guides the reform of sports club system. The specific research results show that our reform plan has been recognized by 92% of students and 94% of teachers.
Paths that consist of up-steps of one unit and down-steps of \(k\) units, being bounded below by a horizontal line \(-t\), behave like \(t+1\) ordered tuples of \(k\)-Dyck paths, provided that \(t\le k\). We describe the general case, allowing \(t\) also to be larger. Arguments are bijective and/or analytic.
The lens array is a multi-functional optical element, which can modulate the incident light such as diffusion, beam shaping, light splitting, and optical focusing, thereby achieving large viewing angle, low aberration, small distortion, high temporal resolution, and infinite depth of field. Meanwhile, it has important application potential in the form, intelligence and integration of op-to electronic devices and optical systems. In this paper, the optical principle and development history of lens arrays are introduced, and the lens array fabrication technologies such as ink jet printing, laser direct writing, screen printing, photo lithography, photo polymerization, hot melt reflow and chemical vapor deposition are reviewed. The application progress of lens arrays in imaging sensing, illumination light source, display and photovoltaic fields is presented. And this paper prospected the development direction of lens arrays, and discussed the development trends and future challenges of new directions such as curved lenses, superimposed compound eye systems, and the combination of lenses and new op-to electronic materials.
Innovation and entrepreneurship and education have become an important topic in China’s higher education. Based on pedagogy theory, this paper divides innovation and entrepreneurship education in universities into three levels: ideological education, innovative education and entrepreneurial education. Innovation is the content of higher education, and it is also the ability that contemporary college students must have. Only with education can there be innovation, only with innovation can there be entrepreneurship, and only with entrepreneurship can there be innovation. This is of great significance to the development of multi-level education, universal education, innovation and entrepreneurship education, and the improvement of education, teaching and child-rearing levels. In order to promote the optimization practice of college students’ innovation and entrepreneurship education, this paper designs a software system which is convenient for college students’ project application, project implementation, data verification and progress report. At the same time, it can help people review and select team members, thus greatly improving management efficiency.
People’s aesthetic requirements for landscape environment are improving, and we can also see very beautiful as well as characteristic urban parks, street side green areas and scenic spots with certain aesthetic value around us, and we can find that people’s demand for the living environment they live in regarding beauty is also strengthening. The synergistic development of edge computing and cloud computing is an important development trend in the future, and integrating them into landscape design is an inevitable choice and requirement for developing gardens and building a beautiful China. Based on this, this study first proposes a methodological framework based on machine learning to model and predict GSS, and then proposes a data-driven multi-style terrain synthesis method. The experimental results prove that the optimized landscape perception model optimizes the landscape path aesthetics according to the relevant theories and actual cases of landscape planning and construction.
Multimode fibre optic communication systems, employing mode/mode group multiplexing, present challenges in accurately identifying numerous modes and mode groups for improved performance. In this study, we propose an intelligent identification model utilizing a fully convolutional neural network (CNN) to precisely identify multimode fibre modes and their clusters. The model is simulated and experimentally validated, considering noise influences on linear polarisation modes. Using a platform with OM2 multimode fibre and a multiplane optical conversion mode multiplexer, we capture optical field information for 10 modes and their corresponding mode groups. Extensive data are employed for training and validation, achieving a 100% recognition rate for all modes and mode groups in experiments. Notably, when employing a 44-photodetector array, an impressive 98.3% recognition efficiency is attained, showcasing the potential of deep learning in advancing multimode fibre optic communication systems.
To address the human activity recognition problem and its application in practical situations, a CNN-LSTM hybrid neural network model capable of automatically extracting sensor data features and memorizing temporal activity data is designed and improved by integrating CNN and gated recurrent units as a variant of RNN. A multi-channel spatiotemporal fusion network-based two-person interaction behavior recognition method is proposed for two-person skeletal sequential behavior recognition. Firstly, a viewpoint invariant feature extraction method is used to extract two-player skeleton features, then a two-layer cascaded spatiotemporal fusion network model is designed, and finally, a multi-channel spatiotemporal fusion network is used to learn multiple sets of two-player skeleton features separately to obtain multi-channel fusion features, and the fusion features are used to recognize the interaction behavior, and the weights are shared among the channels. Applying the algorithm in the paper to the UCF101 dataset for experiments, the accuracy of the two-person cross-object experiment can reach 96.42% and the accuracy of the cross-view experiment can reach 97.46%. The method in the paper shows better performance in two-player interaction behavior recognition compared to typical methods in this field.
Taekwondo behavior recognition has become a popular study issue in the past few decades due to its vast range of applications in the visual realm. The research of Taekwondo behavior recognition based on skeleton sequences has received increasing attention in recent years due to the widespread use of depth sensors and the development of real-time skeleton estimate methods based on depth images. In order to characterize the behavioral sequences, the majority of research work currently in existence extracts the spatial domain information of various skeleton joints within frames and the temporal domain information of the skeleton joints between frames. However, this research work ignores the fact that different joints and postures play different roles in determining the behavioral categories. Consequently, this paper presents a spatio-temporal weighted gesture Taekwondo features-based approach for Taekwondo recognition that employs a bilinear classifier to iteratively compute the weights of the static gestures and joint points relative to the action category in order to identify the joint points and gestures with high information content; concurrently, this paper introduces dynamic temporal regularization and Fourier time pyramid algorithms for temporal modeling in order to provide a better temporal analysis of the behavioural features, and ultimately employs support vector machines to complete the behavioural classification. According to experimental results on several datasets, this strategy outperforms certain other methods in terms of recognition accuracy and is highly competitive.
A large amount of course data has been accumulated in the long-term teaching activities of universities. It is of great research value to use the data resources to analyze the course teaching status and provide decision support for improving the course teaching quality. In this paper, we design and implement a course evaluation system based on association rules and cluster analysis, analyze the functional requirements of the course evaluation system, and pre-process the course evaluation data. Students’ performance data are analyzed by FP-growth association rules, and then clustered by K-means, which can improve the accuracy of data evaluation.The evaluation index system of university English teaching quality under the concept of “Thinking and Government” is established. With the results of the sample survey, the main problems of the evaluation method are summarized and analyzed, and corresponding suggestions are put forward, which provide an important reference for promoting the reform of college English course.
Depression is a clinical disease, mainly accompanied by mood or emotional abnormalities, mainly depression, slow thinking, often accompanied by emotional abnormalities, cognitive behavior, psychophysiological and interpersonal changes or disorders. Here, using static and task-state MRI data, we present a comprehensive study of abnormal neural activity in patients with depression through spatiotemporal, static, and dynamic measures, demonstrating its validity as an underlying biological trait. In order to effectively study the role of emotion regulation in depression, a brain dynamic network synthesis method based on support vector machine model and community detection algorithm was established. We selected data on the mental state of 45 patients from a hospital’s psychiatric disease control center. They had no history of hearing impairment and normal (or corrected) vision. All procedures are agreed in writing by each participant. The results show that this method can effectively reduce the depression degree of the subjects, and the multi-level features of the integration of task activation and task regulation connection reach 81% (\(\mathrm{<}\)0.0010, surrogate test) and 83% (\(\mathrm{<}\)0.0016, surrogate test), respectively. The recovery of its depressive psychological state has a significant impact. Numerous studies have used various forms of emotional stimuli to reveal abnormal behaviors and neural responses in multi-channel emotional processing in patients with depression, providing valuable insights into the mechanism of multi-channel emotion regulation in depression.
In order to provide users with better recommendations, it is particularly important to analyze the behavior of users tagging different resources. In this paper, an attention mechanism based on deep learning is designed to effectively capture the features related to the user’s long-term interests and current interests in the session simultaneously, and alleviate the impact of the user’s interest drift that is difficult to deal with by the current session recommendation algorithm on the recommendation accuracy. The main community discovery algorithms are applied to the clustering analysis of the label system, and their performances on different data sets are compared. Besides, we design a personalized recommendation algorithm for the label system. The experimental results show that the proposed algorithm can find the interests of different users and improve the quality of the recommendation system.
Nowadays, people look at a brand not only to see the value of the brand itself, but to understand the cultural value conveyed behind the brand and experience the connotation of the brand culture. Human-computer interaction technology has also gained more application space with the development of the times. Therefore, a psychological model of brand culture based on human-computer interaction was designed in this paper, and a survey of related content was conducted. In terms of user satisfaction survey, it was concluded that the use of the brand culture mental model based on human-computer interaction technology could greatly improve users’ satisfaction with brand culture and make more people love brand culture; in terms of user participation survey, it was concluded that the brand culture mental model designed based on human-computer interaction technology could achieve better profit results at 21:00 on Sunday. Finally, a survey of user stickiness was carried out, and the test results showed that the brand culture mental model based on human-computer interaction technology established the stickiness between users and brands.
Cities are highly concentrated areas of human civilization, the contradiction between urban development and resources and environment has become increasingly prominent. Inefficient use of energy and land resources, shortage of water resources, and environmental pollution are threatening the healthy development of cities. In this paper, the signal reconstruction algorithm and measurement matrix design in the compressed sensing theory are mainly studied. Aiming at the problems of green city environmental monitoring and landscape design, signal underestimation or overestimation caused by the fixed selection step in the iterative process of sparse adaptive matching tracking algorithm, The threshold idea is introduced into atomic selection, and a variable step size strategy is proposed based on the change of step size. The experimental results show that the establishment of the green city environment monitoring and landscape design model system dynamically changes the network topology, so that data can be transmitted in the mobile ad hoc network.
Deep learning is based on scientific educational psychology theory and is an important concept in contemporary learning theory. Therefore, combining in-depth learning with teaching of political courses, to explore teaching strategies of college political courses based on students’ in-depth learning, requirements for implementing new curriculum standards for cultivating students’ core literacy of disciplines, and cultivating students who meet development requirements of times. High-quality talents are of great value and significance. Through questionnaires and sample interviews, this paper focuses on analyzing specific measures for improvement from four aspects: sufficient teaching preparation, effective teaching implementation, scientific teaching evaluation and normalized teaching reflection. It is highly effective and feasible to increase level of students’ deep learning ability to more than 14.65%.
The development of information society requires the reform of traditional English education. The progress of science and technology, especially the progress of Internet information technology, has penetrated into the English teaching system, reconstructed the relationship between the elements of the English teaching system, and provided technical support for the reform of English teaching. These two aspects are the internal and external impetus of English teaching reform. According to the theory of knowledge construction and multimodal information fusion, this research establishes a user-centered knowledge space, which can respond to user needs quickly, emphasizes the integration and integration of multimodal subject knowledge in resource organization, and expresses multi-dimensional relevance in functional form. The experimental results show that the optimized BOPPPS English teaching model is conducive to improving students’ participation in English classroom interaction. In the new information integration technology environment, students are more likely to put forward opinions or suggestions, and the transformation relationship of students’ interactive behavior becomes more complex.
At present, countries all over the world attach great importance to cultural works. These works have become an important engine of economic development and can make good contributions to economic growth. The traditional tracing control scheme of cultural creation works has some problems, such as incomplete information collection, critical point of unit tracing, information fraud, centralized data storage and so on. At the same time, there seems to be a series of problems that can be solved. This paper analyzes the current situation of the review data of cultural creation products, and puts forward the review and analysis scheme of cultural creation works based on large-scale data algorithm and block chain technology. In addition, by combining department chain technology with intranet and traditional database system, an information database about the supply chain of cultural products in crop departments is established. The processing, logistics and sales information and the information of participants are interrelated.
This paper analyzed the components of the connotation of innovation ability, then constructs a linear spatial model of innovation and entrepreneurship ability, proposes a multi-objective function model of the utilization efficiency and allocation efficiency of education resources, and uses the grey correlation algorithm The experimental simulation and model solution are carried out. The simulation results show that, through the optimization, the utilization efficiency and allocation efficiency of the educational resources for innovation and entrepreneurship for all are increased by 18.72% and 20.98% respectively, and tend to be in equilibrium, which can achieve the optimization of educational resources allocation. Among all people, the correlation value with ideal entrepreneurship is 0.3177, achieving the most excellent innovation and entrepreneurship education.
IMO Member State audits aim to identify non-compliant behavior with the requirements of relevant instruments, enabling the implementation of corrective measures to enhance performance. However, the complexity and diversity of IMO instruments’ requirements result in low evaluation effectiveness and efficiency in current assessment methods of implementation of IMO instruments. To address this challenge, this study proposes a meta-learning model based on prototype networks, focusing on the corrective measures outlined in consolidated audit summary reports approved and issued by the IMO Secretariat. The suggested model conducts meta-learning using small samples, offering a swift and straightforward assessment method. It facilitates the fine classification of corrective measures, providing a way for the consistent and effective assessment of various countries’ current implementation practices. Empirical results of two strategies demonstrate improved classification accuracy. In comparison with traditional manual evaluation, the proposed method achieves accuracy value 71.61% and 65.78% in two strategies respectively. Furthermore, the model exhibits varying prediction accuracy across different articles and demonstrates robust generalization capabilities, highlighting its practicality.
A mapping \(l : E(G) \rightarrow A\), where \(A\) is an abelian group written additively, is called an edge labeling of the graph \(G\). For every positive integer \(h \geqslant 2\), a graph \(G\) is said to be zero-sum \(h\)-magic if there is an edge labeling \(l\) from \(E(G)\) to \(\mathbb{Z}_{h} \backslash \{0\}\) such that \(s(v) = \sum_{uv\in E(G)}l(uv) = 0\) for every vertex \(v \in V(G)\). In 2014, Saieed Akbari, Farhad Rahmati and Sanaz Zare proved that if \(r\) \((r\not= 5)\) is odd and \(G\) is a \(2\)-edge connected \(r\)-regular graph, \(G\) admits a zero-sum 3-magic labeling, and they also conjectured that every 5-regular graph admits a zero-sum \(3\)-magic. In this paper, we first prove that every 5-regular graph with every edge contained in a triangle must have a perfect matching, and then we denote the edge set of the perfect maching by \(EM\), and we make a labeling \(l : E(EM) \rightarrow {2}\), and \(E(E(G) – EM) \rightarrow {1}\). Thus we can easily see this labeling is a zero-sum 3-magic, confirming the above conjecture with a moderate condition.
Let \(\Gamma_{G}\) be the orbit graph of \(G\), with non-central orbits in the subset of order two commuting elements in \(G\), and the vertices of \(\Gamma_{G}\) connected if they are conjugate. The main objective of this study is to compute several topological indices for the orbit graph of a dihedral group, including the Wiener index, the Zagreb index, the Schultz index, and others. We also develop a relationship between the Wiener index and the other indices for the dihedral group’s orbit graph. Furthermore, their polynomial has been computed as well.
\(Y_k\)-tree is defined as \((v_1, v_2,\ldots, v_{k-1};\, v_{k-2} v_k)\) by taking their vertices as \((v_1,\,v_2,\ldots,\,v_k)\) and edges as \(\{(v_1v_2, v_2v_3,\ldots, v_{k-2}v_{k-1})\cup (v_{k-2}v_k)\}\). It is also represented as \((P_ {k-1} +e)\). One can obtain the necessary condition as \(mn(m-1)(n-1)\equiv 0 \pmod {2(k-1)}\), for \(k \geq 5\) to establish a \(Y_k\)-tree decomposition in \(K_m \times K_n\). Here the tensor product is denoted by \(\times\). In this manuscript, it is shown that a \(Y_5\)-tree (gregarious \(Y_5\)-tree) decomposition exists in \(K_m \times K_n\), if and only if, \(mn(m-1)(n-1)\equiv 0 \pmod8\).
For graphs \(F\) and \(H\), the proper Ramsey Number \(PR(F,H)\) is the smallest integer \(n\) so that any \(\chi'(H)\)-edge-coloring on \(K_n\) contains either a monochrome \(F\) or a properly colored \(H\). We determine the proper Ramsey number of \(K_3\) against \(C_3\) and \(C_5\).
We have constructed Block structured Hadamard matrices in which odd number of blocks are used in a row (column). These matrices are different than those introduced by Agaian. Generalised forms of arrays developed by Goethals-Seidel, Wallis-Whiteman and Seberry-Balonin heve been employed. Such types of matrices are applicable in the constructions of nested group divisible designs.
The primary challenge in credit analysis revolves around uncovering the correlation between repayment terms and yield to maturity, constituting the interest rate term structure-an essential model for corporate credit term evaluation. Presently, interest rate term structures are predominantly examined through economic theoretical models and quantitative models. However, predicting treasury bond yields remains a challenging task for both approaches. Leveraging the clustering analysis algorithm theory and the attributes of an insurance company’s customer database, this paper enhances the K-means clustering algorithm, specifically addressing the selection of initial cluster centers in extensive sample environments. Utilizing the robust data fitting and analytical capabilities of the Gaussian process mixture model, the study applies this methodology to model and forecast Treasury yields. Additionally, the research incorporates customer credit data from a property insurance company to investigate the application of clustering algorithms in the analysis of insurance customer credit.
In this paper, we propose a method for effectively evaluating the quality of business English teaching in colleges and universities. The approach is based on a multimodal neural network model integrated with grey correlation analysis. By employing the optimal data clustering criterion, we identify teaching quality evaluation indices. Subsequently, we establish a teaching quality evaluation index system using a genetic algorithm (GA) optimized Radial Basis Function (RBF) neural network. Grey correlation analysis is then applied to assess the quality of business English teaching by considering the relationship between the correlation degree and the evaluation level. The results indicate a correlation degree exceeding 0.90, signifying excellent teaching quality. The reliability of the selected evaluation indicators, assessed through retesting, surpasses 0.700, validating the evaluation results.
A crucial component of kindergarten instruction, collective teaching activities are a good way to educate young children on their overall development. The language field is one of the subjects taught in kindergarten, and it has to do with how kids learn to read, write, and speak. In order to improve teachers’ comprehension of children’s emotional reactions and language, this paper combines quantitative and qualitative methods to observe and analyze the quality of current language collective teaching activities in kindergartens. It also suggests knowledge logic and psychological logic for grasping the content of language collective teaching in kindergartens. To improve the quality of language teaching in kindergartens, it is crucial to adopt a variety of teaching strategies and organizational techniques, provide the proper tools and materials for language learning, pay attention to the key experiences of children learning the language, and enhance learning quality.
In the new era characterized by the modernization of national governance, fair competition is the inherent requirement of building a modern market system. However, the abuse of administrative power by administrative organs to excessively interfere in free-market competition is widespread, seriously damaging the market competition order in China. To avoid the unreasonable intervention of administrative organs in the market economy, restrain the administrative acts of administrative organs, and form a highly “competitive” market environment, the fair competition review system came into being. With the rapid development of blockchain technology, new ideas are provided for the research of fair-trade protocols. Aiming at the system performance bottleneck and high-cost problems caused by centralized processing in traditional fair transaction schemes based on trusted third parties, a fair transaction scheme based on fuzzy signature is proposed. In the proposed scheme, the signature model uses concurrent signature, and both parties hold their own key numbers, which are released through blockchain transactions to bind their signatures. In the whole process, both parties can complete the contract signing without the assistance of a centralized third party. Based on analyzing the security of the proposed scheme, the performance of the proposed scheme is further compared with other similar schemes of the same kind, which shows that the proposed scheme has higher computational efficiency.
Selecting the user comment information of short videos with top 2 likes in the top 50 topics about public cultural services in Shake App as the research object, and facilitating video platforms to identify the high and low quality of the videos and make reasonable promotion arrangements by predicting the short-term playback volume of pop-up videos and analyzing the influencing factors, which is conducive to improving the platform’s pop-up video services and economic benefits. The data related to B station videos are captured, and feature selection and different algorithms are combined to construct random forest model, XG Boost model and LSTM model to predict the playback volume of the pop-up videos, and compare and analyze the effects of different feature combinations on the experimental results. The results show that the prediction accuracy of the random forest model is higher than that of the XG Boost model and the LSTM model, and the features of the pop-up video itself have the greatest influence on the playback volume, while the features of the video markup text have a smaller degree of influence on the playback volume.
The rapid economic progress and widespread use of sophisticated technology elevate the output value per kWh of electricity consumed, underscoring the paramount importance of maintaining an uninterrupted and dependable power supply to avoid substantial economic losses for consumers and society. Investigating the reliability of urban distribution systems emerges not only as a pivotal factor in enhancing power supply quality but also as a cornerstone of electric power modernization, significantly impacting production, technology, and management within the industry while bolstering its economic and social benefits. This study adopts a multifaceted approach: firstly, establishing a methodology for grid-side storage capacity distribution to mitigate substation load factors and implement peak shaving, thereby minimizing load discrepancies. Secondly, it develops a mathematical model encompassing diverse user distributions, employing analytical techniques to derive reliability indices and optimal segment numbers tailored to different user distributions. The research proposes segment optimization based on user distributions, considering both economic viability and reliability, showcasing an interdisciplinary amalgamation of combinatorial principles and scientific computing methodologies. This approach aims to optimize segment distribution, enhancing the reliability and economic feasibility of urban distribution networks through advanced mathematical and computational techniques.
This study introduces a novel approach to address deficiencies in prior teaching quality assessment systems by establishing a mathematical model for evaluation. Utilizing a neural network trained via a particle swarm optimization algorithm (PSO), the method develops a BP (Backpropagation) model fine-tuned by PSO to capture the intricate relationships among diverse indicators influencing teachers’ teaching quality assessment and resulting evaluations. Empirical findings highlight the effectiveness of artificial neural networks in constructing a comprehensive evaluation framework accommodating a wide spectrum of systematic assessments. This approach not only optimizes teaching methodologies but also augments overall teaching efficacy and the quality of educational delivery in a holistic manner. Moreover, it fosters the cultivation of multifaceted individuals proficient in English application skills, contributing to the development of high-quality talent in practical and complex domains. The convergence of advanced mathematical modeling techniques and computational methods, alongside the utilization of numerous indicators, aligns with combinatorial principles, exploring the permutations and relationships of diverse factors impacting teaching quality assessment.
In this paper, we addresses the growing importance of enterprise equipment asset management efficiency. Proposing an advanced approach rooted in combinatorial principles and scientific computing, the study introduces a comprehensive evaluation model for equipment value. Overcoming the limitations of traditional models, a fuzzy algorithm establishes a three-dimensional cross-compound element, encompassing equipment reliability, stability, and accuracy. Hierarchical analysis and the entropy power method determine weights for evaluation indexes, facilitating a quantitative assessment of measurement and production equipment health. Validation through a real energy meter production line demonstrates the model’s effectiveness in comparison to real defect rates. This innovative evaluation model not only offers asset managers a new method for assessing equipment assets but also presents a forward-looking strategy for enterprises to enhance their asset management proficiency, emphasizing the synergies between combinatorics and scientific computing in addressing contemporary economic challenges.
Let \(G = (V, E)\) be a graph with \(n\) vertices. A bijection \(f : V \to \{1, 2, \dots, n\}\) is called a distance magic labeling of \(G\) if there exists an integer \(k\) such that \(\sum_{u \in N(v)}f (u) = k\) for all \(v \in V\), where \(N(v)\) is the set of all vertices adjacent to \(v\). Any graph which admits a distance magic labeling is a distance magic graph. The existence of regular distance magic graphs of even order was solved completely in a paper by Fronček, Kovář, and Kovářová. In two recent papers, the existence of \(4\)-regular and of \((n-3)\)-regular distance magic graphs of odd order was also settled completely. In this paper, we provide a similar classification of all feasible odd orders of \(r\)-regular distance magic graphs when \(r=6,8,10,12\). Even though some nonexistence proofs for small orders are done by brute force enumeration, all the existence proofs are constructive.
A good set on \(k\) vertices is a vertex induced subgraph of the hypercube \(Q_n\) that has the maximum number of edges. The long-lasting problem of characterizing graphs that are cover graphs of lattices is NP-complete. This paper constructs and studies lattice theoretic properties of a class of lattices whose cover graphs are isomorphic to good sets.
Combinatorial mathematics is a versatile field that can provide valuable insights and techniques in various aspects of artificial intelligence and educational research. We focus our attention on the exploration of the mechanism of the role of teachers’ emotional labor In this paper, we merge two parts of data, predicted and formally administered, based on the optimization and management of artificial intelligence English teachers’ emotional labor for the corresponding statistical analysis. Yes individual college English teachers are working for non-interpersonal issues for emotional regulation, temporarily restraining anger and cursing impulses, and communicating with students in a pleasant manner. In the case study of this paper, a teacher repeatedly failed in teaching, but he restrained his frustration and continued to work hard, and finally finished.
In order to determine the optimal scale for urban ride-hailing services and taxis while promoting their sustainable growth, we have developed a Lotka-Volterra evolutionary model that accounts for the competitive, cooperative, and mixed dynamics between these two entities. This model is rooted in the theory of synergistic evolution and is supported by data simulation and analysis. By employing this model, we can identify the appropriate size for urban ride-hailing services and taxis when they reach equilibrium under different environmental conditions. The study’s findings reveal that the evolutionary outcomes of online ride-hailing services and traditional taxis are closely linked to the competitive impact coefficient and the cooperative effect coefficient. In highly competitive environments, intense rivalry can lead to the elimination of the less competitive party, while the dominant player ultimately attains a specific size threshold. As competition moderates, both entities can achieve a balanced and stable coexistence in the market. In cooperative environments, both online ride-hailing services and traditional taxis have more room for development, which facilitates the integration of existing and innovative business models. In environments marked by competition, the development trends of both entities mirror those in competitive settings, but cooperation can slow down the decline of the less competitive party. In conclusion, we propose strategies to foster fair competition between online ride-hailing services and traditional taxis, consider the coexistence of old and new business models, and promote their integrated development.
A vertex labeling \(\xi\) of a graph \(\chi\) is referred to as a ‘vertex equitable labeling (VEq.)’ if the induced edge weights, obtained by summing the labels of the end vertices, satisfy the following condition: the absolute difference in the number of vertices \(v\) and \(u\) with labels \(\xi(v)= a\) and \(\xi(u)= b\) (where \(a,\ b\in Z\)) is approximately \(1\), considering a given set \(A\) that consists of the first \(\lceil \frac{q}{2} \rceil\) non negative integers. A graph \(\chi\) that admits a vertex equitable labeling (VEq.) is termed a ‘vertex equitable’ graph. In this manuscript, we have demonstrated that graphs related to cycles and paths are examples of vertex-equitable graphs.
Network theory is the study of graphs such as representing equilibrium relationships or unequal relationships between different objects. A network can be defined as a graph where nodes and / or margins have attributes (e.g. words). Topological index of a graph is a number that helps to understand its topology and a topological index is known as irregularity index if it is greater than zero and topological index of graph is equal to zero if and only if graph is regular. The irregularity indices are used for computational analysis of nonregular graph topological composition. In this paper, we aim to compute topological invariants of some computer related graph networks. We computed various irregularities indices for the graphs of OTIS swapped network \(OP_a\) and Biswapped Networks \(Bsw(Pa).\)
Let \(G=(V,\,E)\) be a simple graph with vertex set \(V(G)\) and edge set \(E(G)\). The Lanzhou index of a graph \(G\) is defined by \(Lz(G)=\sum\limits_{u \in V(G)} d_u^2\overline{d}_u\), where \(d_u\) (\(\overline{d}_u \) resp.) denotes the degree of the vertex \(u\) in \(G\) (\(\overline{G}\), the complement graph of \(G\) resp.). It has predictive powers to provide insights of chemical relevant properties of chemical graph structures. In this paper we discuss some properties of Lanzhou index. Several inequalities having lower and upper bound for the Lanzhou index in terms of first, second and third Zagreb indices, radius of graph, eccentric connectivity index, Schultz index, inverse sum indeg index and symmetric division deg index, are discussed. At the end the Lanzhou index of corona and join of graphs have been derived.
We define an extremal \((r|\chi)\)-graph as an \(r\)-regular graph with chromatic number \(\chi\) of minimum order. We show that the Turán graphs \(T_{ak,k}\), the antihole graphs and the graphs \(K_k\times K_2\) are extremal in this sense. We also study extremal Cayley \((r|\chi)\)-graphs and we exhibit several \((r|\chi)\)-graph constructions arising from Turán graphs.
A dominating broadcast of a graph \(G\) is a function \(f : V(G) \rightarrow \lbrace 0, 1, 2, \dots ,\text{diam}(G)\rbrace\) such that \(f(v) \leqslant e(v)\) for all \(v \in V(G)\), where \(e(v)\) is the eccentricity of \(v\), and for every vertex \(u \in V(G)\), there exists a vertex \(v\) with \(f(v) > 0\) and \(\text{d}(u,v) \leqslant f(v)\). The cost of \(f\) is \(\sum_{v \in V(G)} f(v)\). The minimum of costs over all the dominating broadcasts of \(G\) is called the broadcast domination number \(\gamma_{b}(G)\) of \(G\). A graph $G$ is said to be radial if \(\gamma_{b}(G)=\text{rad}(G)\). In this article, we give tight upper and lower bounds for the broadcast domination number of the line graph \(L(G)\) of \(G\), in terms of \(\gamma_{b}(G)\), and improve the upper bound of the same for the line graphs of trees. We present a necessary and sufficient condition for radial line graphs of central trees, and exhibit constructions of infinitely many central trees \(T\) for which \(L(T)\) is radial. We give a characterization for radial line graphs of trees, and show that the line graphs of the \(i\)-subdivision graph of \(K_{1,n}\) and a subclass of caterpillars are radial. Also, we show that \(\gamma_{b}(L(C))=\gamma(L(C))\) for any caterpillar \(C\).
In this paper we introduce the concept of independent fixed connected geodetic number and investigate its behaviours on some standard graphs. Lower and upper bounds are found for the above number and we characterize the suitable graphs achieving these bounds. We also define two new parameters connected geo-independent number and upper connected geo-independent number of a graph. Few characterization and realization results are formulated for the new parameters. Finally an open problem is posed.
Let \(E(H)\) and \(V(H)\) denote the edge set and the vertex set of the simple connected graph \(H\), respectively. The mixed metric dimension of the graph \(H\) is the graph invariant, which is the mixture of two important graph parameters, the edge metric dimension and the metric dimension. In this article, we compute the mixed metric dimension for the two families of the plane graphs viz., the Web graph \(\mathbb{W}_{n}\) and the Prism allied graph \(\mathbb{D}_{n}^{t}\). We show that the mixed metric dimension is non-constant unbounded for these two families of the plane graph. Moreover, for the Web graph \(\mathbb{W}_{n}\) and the Prism allied graph \(\mathbb{D}_{n}^{t}\), we unveil that the mixed metric basis set \(M_{G}^{m}\) is independent.
Consider a total labeling \(\xi\) of a graph \(G\). For every two different edges \(e\) and \(f\) of \(G\), let \(wt(e) \neq wt(f)\) where weight of \(e = xy\) is defined as \(wt(e)=|\xi(e) – \xi(x) – \xi(y)|\). Then \(\xi\) is called edge irregular total absolute difference \(k\)-labeling of \(G\). Let \(k\) be the minimum integer for which there is a graph \(G\) with edge irregular total absolute difference labeling. This \(k\) is called the total absolute difference edge irregularity strength of the graph \(G\), denoted \(tades(G)\). We compute \(tades\) of \(SC_{n}\), disjoint union of grid and zigzag graph.
A total dominator coloring of \(G\) without isolated vertex is a proper coloring of the vertices of \(G\) in which each vertex of \(G\) is adjacent to every vertex of some color class. The total dominator chromatic number \(\chi^t_d(G)\) of \(G\) is the minimum number of colors among all total dominator coloring of \(G\). In this paper, we will give the polynomial time algorithms to computing the total dominator coloring number for \(P_4\)-reducible and \(P_4\)-tidy graphs.
