At present, the physical training of public security police has not formed a unified training system in the country, and various places ignore the cultivation of other aspects of the ability to take skill training as the leading role, and solve the problem of how to train through the construction of the system, so as to ensure that the physical training of public security police is carried out effectively. This paper explores the impact of physical training on college students’ professionalism in public security colleges, constructs the K nearest neighbor classification algorithm, and introduces the relevant activation function to deal with more complex students’ physical training exercise trajectories. ATT-DAN multitarget tracking model is constructed to extract the feature information of college students’ physical fitness training, obtain the target movement trajectory, and parameterize the representation of students’ physical fitness training programs. The correlation ranges of frequency, average score, highest grade score of physical fitness training and occupational ability were between 0.415~0.632, 0.452~0.769, 0.412~0.715, respectively, and the credibility and stability of the occupational ability characteristics were good. Meanwhile, the linear regression of the two showed that the correlation P value of age, 30-second deep squat, pull-up, 3200 meters, and 15-second repetitive straddle with occupational ability was less than 0.05, and there was a positive correlation between the two.

Power system simulation training is one of the important means to improve the quality of operators and ensure the safe and effective operation of power systems. Research based on digital twin technology, combined with configuration algorithms to give the substation integration diagram model generation method, developed a smart substation virtual training system. The intelligent monitoring is studied, the digital twin-based substation output voltage anomaly detection method is designed using the tracking differentiator method, and finally the simulation test of the intelligent substation virtual training system is carried out. The analysis shows that the voltage anomaly detection method in this paper is highly accurate and can extract the voltage anomaly waveform, and the offset rate of its collected signal is significantly lower than that of the comparison method (11.58%~14.84%), which is only 0.54%. The training test of fast distance protection, differential protection and zero sequence protection verifies the feasibility and effectiveness of the virtual training system in practical application. The platform can effectively promote the reform of applied electric power practice courses and provide a backbone for the training of new power system talents.

The rapid development of information technology has put forward higher requirements for teachers, and the traditional training model is difficult to meet the demand. The article constructs a teacher digital competency framework based on the ASTD model, realizes the division of teachers’ professional competence, and explains the professional core connotation of teacher digital competency in detail. A personalized resource recommendation model for teachers is constructed using artificial intelligence technology, which provides accurate recommendations for teachers through candidate resource extraction and learning resource screening. At the same time, with the help of Google Cloud Services digital tools, the design of teachers’ digital teaching and research activities was accomplished, and communication and cooperation with users in the virtual community was promoted. The combination of the two is integrated into the development of teachers’ professional skills to enhance their teaching competence. The mean values of accuracy, applicability, timeliness, personalization, and diversity of learning resource recommendations under artificial intelligence technology ranged from 4.123 to 4.544, with good recommendation performance. The Google Cloud Services platform can promote teaching and research exchange activities among teachers. The use of artificial intelligence and digital tools makes teachers improve their professional skills in knowledge base, instructional design, teaching and research between 24.04% and 91.00%, and with their intervention, teacher competency shows significant improvement.

Under the environment of plateau alpine region, the new model of substitute construction separating government construction and management functions has gained great development in barracks construction, which significantly improves the risk management level of barracks facilities to some extent. From the significance of barracks facilities construction guarantee in highland alpine area, the article proposes a risk identification framework for the substitute construction unit of Someplace facilities in highland alpine area based on the whole life cycle of engineering projects. Combined with the risk identification framework, the risk evaluation index system of the agency construction unit is constructed, and then the AHP hierarchical analysis method is introduced to solve the weight of the indexes, and combined with the fuzzy comprehensive evaluation method, the AHP-FCM evaluation model is constructed. A barracks facilities project in a camp area is selected as a case study, and Company T is used as the research object to carry out data analysis of its risk degree using the AHPFCM model. In the construction of barracks facilities in highland and alpine areas, the biggest risk faced by the construction unit is the project implementation stage, the weight of which reaches 29.93%, and the fuzzy comprehensive evaluation of Company T’s risk score is 3.182, which is between medium and large risks. Therefore, the agency needs to examine and check its own risk factors in time, in order to lay a solid foundation for ensuring the smooth implementation of the agency project of barracks facilities in highland alpine areas.

The study is based on the important role of graph theory in the teaching of physical dance and aesthetic education, integrating the concept of graph theory into it and designing the training path of physical dance and aesthetic education based on graph theory. Taking two classes in a university as the research object, the teaching experiment is conducted to compare their physical quality and course performance after the experiment, and the aesthetic education evaluation index system is constructed, and the index weights are measured using the combination assignment method to carry out the comprehensive scoring. After the experiment, the students improved in physical quality, course grades and aesthetic effect, and as far as the students of traditional teaching class are concerned, the experimental students improved in course grades and aesthetic effect by 18.17% and 7.52% respectively. The teaching practice of integrating the concept of graph theory and the curriculum of physical education dance and aesthetic education not only embodies the concept of cross-disciplinary teaching, but more importantly improves the physical quality, physical education dance level and aesthetic effect of students in colleges and universities, and provides a reference for the teaching reform of physical education dance and aesthetic education in colleges and universities.

Transportation demand is gradually increasing and road traffic congestion is becoming more and more serious. Traffic state prediction is one of the important bases for accurate traffic management and control. This paper investigates a traffic state prediction method based on a deep learning algorithm fusing spatio-temporal graphical convolutional networks, and explores the law of path selection decision-making of pedestrians under different traffic flow prediction and guidance strategies, and analyzes the effect of the implementation of the information guidance policy by traffic managers in realistic scenarios using evolutionary game theory. The simulation results combined with the traffic simulation model show that the traffic state prediction method proposed in this paper is more effective compared with other models. The evolution results are more reasonable when the value of the path adjustment rate in the replicated dynamic model is the inverse of the number of iterations. In the perceptual error analysis, when the value of perceptual error 1 is taken to be too large, i.e., when the perceptual error of the first type of travelers is small and small, it tends to be a deterministic choice. Finally, a traffic simulation model is implemented to validate the performance of the proposed model and propose congestion mitigation strategies.

The construction of information resource management system is a promotion for upgrading industrial structure and enhancing independent innovation capability. Based on the city-level data of a region from 2010 to 2022, the evaluation index system of information resource management system and regional economic development mode is established, and the variables are measured according to the collected data using gray correlation analysis method. Then double machine learning method is applied to explore the influence effect of information resource management system on regional economic development model. The baseline regression analysis reveals that the information resource management system can promote the regional economic development model, with a regression coefficient of 0.029, and the conclusion still holds after the stability test. The heterogeneity results show that regions with better economic foundation (0.067) and peripheral cities (0.036) are more significantly affected by the positive spillover effect of the information resource management system. This paper combines machine learning algorithms with traditional causal inference to explore the role path of information resource management system to promote regional economic development model, which provides empirical evidence and decision-making reference for promoting regional economic development.

With the rapid development of the global cruise transportation industry and the worldwide increase of cruise ship transportation year by year, fire accidents on passenger and roll-on/roll-off ships (P/ROCs) pose a serious threat to economic properties. The article establishes a fire model of a passenger-roller ship carrying electric vehicles using the basic equation of dynamics, a large eddy simulation model, and a mixed fraction combustion model. The mesh division is used to improve the solving accuracy of the kinetic equations. The fire simulation conditions of the electric vehicle carried by a passenger-roller ship are designed to analyze the fire combustion characteristics of the passenger-roller ship transported in terms of wind speed, fire intensity, and ignition power in multiple dimensions using the FDS simulation software as a carrier. Based on the YOLOv5s network and combined with the improved non-great suppression algorithm, a statistical model for target detection of electric vehicles carried by a passenger-roller ship is designed, and the corresponding loss function is designed. When the external ambient wind speed was increased from 0.5 m/s to 6.5 m/s, the maximum temperature at the fire center of the electric vehicle carried by the passenger-roller ship was reduced from 883.93°C to 748.57°C. The improved YOLOv5s model has the highest mAP of 96.67% on the target detection of EVs after fire damage and an accuracy of 92.96% for counting the number of EVs after fire. The state of electric vehicles after fire damage can be obtained under fire dynamics simulation, and the target detection and quantity counting of electric vehicles can be effectively realized by combining deep learning technology.

Background: Ultraviolet radiation (UVR) causes premature skin aging. Litchi seed (LS) is considered a natural plant extract with potential antioxidant, anti-aging and anti-inflammatory properties. However, the mechanisms of LS’s protective effects on skin photoaging remain unclear. Objective: This study aims to perform a rapid and efficient virtual screening of the main targets and possible mechanisms of the protective effect of LS on skin photoaging through network pharmacology, bioinformatics and molecular docking. Methods: The primary active compounds and their corresponding targets of LS were obtained from the TCMSP, STP, and UniProt databases. Concurrently, photoaging-related targets were mined from the GEO, GeneCards, and OMIM databases. “LS-photoaging” targets were identified using Venn diagrams created with R software. Protein-protein interaction (PPI) networks and “compound-target-disease” networks were constructed and analyzed using Cytoscape. GO and KEGG pathway enrichment analyses were then performed to predict the protective mechanisms of LS against skin photoaging. Finally, key targets and active compounds were validated through molecular docking using AutoDock Vina. Results: The screening identified 368 targets of LS active compounds and 872 photoaging-related targets. Network topology analysis revealed 87 common targets, with AKT1, IL6, TP53, and CASP3 as core targets. Enrichment analysis reveals that LS can modulate the ROS/MAPK/AP-1 pathway, thereby inhibiting inflammatory responses and reducing oxidative stress, which leads to a decrease in pro-inflammatory factors. Additionally, it promotes collagen restoration by suppressing the expression of MMPs. Molecular docking validation demonstrated a strong binding affinity between the core targets and the key compounds. Conclusion: LS shows potential for treating photoaging by counteracting inflammation and oxidative stress, regulating collagen and lipid metabolism, and inhibiting apoptosis.

The Tradable Green Certificate (TGC) system scientifically guides renewable energy investment by internalising the positive externalities of renewable electricity. With the promotion of energy transition, the demand for TGC has increased significantly, and the scale of market players has gradually expanded. Market players will imitate other players’ trading strategies for reasons such as herd mentality, which is manifested as herd behaviour. If TGC market players ignore high-quality information and blindly imitate the behaviour of other players, it will limit the diffusion of effective information in the market and reduce the pricing efficiency of the market. Therefore, this paper explores the emergence law of herd behaviour in the TGC market based on a hybrid system dynamic model, with a view to providing theoretical and methodological support for the immediate identification of market risk. This paper portrays the emergence process of herd behaviour of TGC trading subjects, and analyses the emergence law through multi-scenario computational experiments. The results show that (1) herd behavior will emerge from all kinds of strategy subjects and there is a positive feedback relationship between the emergence speed and the return difference between subjects. (2) The emergence of herd behaviour of fundamental strategy subjects has scale and structural effects, and only when the initial imitation scale of such subjects reaches 40% or the market share is less than 50%, will the emergence of herd behaviour, and the depth of its emergence shows an ‘S’ type growth. (3) The herd mentality and the weakening of cognitive bias of TGC trading subjects will reduce the emergence speed of herd behaviour, but have almost no effect on the depth of emergence.