The presented article develops the detailed analysis of battery performance degradation profiles for EVs, based on operational data collected in real-world use. Based on data points gathered for 150 vehicles over 24 months, we have developed and then validated an integrated degradation prediction model incorporating several degradation mechanisms. Our study applies a novel hybrid approach that will combine physics-based principles with data-driven methods for outlining the battery aging profile. The model proposed in this paper realizes a better prediction performance of 94.3% under different operational conditions and thus proves to be considerably superior to the existing techniques. Indeed, the change of temperature and charging behavior becomes the main influence factor with the correlation coefficient of 0.85 and 0.78, respectively. After applying the proposed model to a fleet management system, there are 32.4% maintenance cost reduction and 15.8% increasing of the cycle life for batteries. It represents in detail the continuous degradation assessment and predictive maintenance framework, validated on different vehicle platforms under varying operational conditions. These findings provide valuable inputs related to the improvement of battery management strategies and life extension of a battery in electric vehicle applications, hence benefiting theoretical understanding and practical application in electric vehicle battery management.

It has identified and presented a unified machine-learning-based malware defense system that can handle dynamic features in cyber-security challenges. This approach will leverage recent deep learning models, ensembles, and automatic generation of defense strategies to construct an effective and adaptive framework for malware detection and mitigation. These results tend to indicate significant gains compared with traditional signature-based approaches, whereby known malware detection rates reached 99.2%, and zero-day vulnerabilities reached 87.5%. The system also recorded an extra 68% reduction in false positives after one month of operations due to the adaptive learning component, while real-time detection features yielded less than a one-second response time for 95% of the threatened records. The generated defense strategy module can demonstrate a 92% success rate in the automated mitigation or containment of identified threats. The paper further presents that even with such advances, much potential still exists for optimizing resource use, enhancing model interpretability, and building more robust defenses against adversarial attacks. It enhances the area of cybersecurity and adds a new dimension by showing the capability of AI-enabled methodology to create much more efficient, agile, and flexible malware protection systems-thereby paving the way for more advanced cybersecurity innovations.

This paper presents a hardware encryption system based on FPGA (Field-Programmable Gate Array) implementing the elliptic curve cryptography algorithm. Using FPGA as the core control unit, IoT (Internet of Things) data transmission terminals are connected to FPGA-specific external interfaces via USB/SPI interfaces. Data collected into the FPGA undergoes encryption and decryption using the FPGA’s internal hardware resources. The encrypted data is then converted into TCP/IP protocol packets and transmitted to a cloud server through the FPGA’s internal Ethernet interface circuit module. A detailed analysis and design of the hardware implementation of the elliptic curve encryption algorithm are provided. Simulation validation of the point multiplication algorithm was conducted on a computer platform with a quad-core 3.2GHz processor and 8GB of memory, using the Xilinx 5vlx20tff323 chip. The simulation results indicate that the maximum execution frequency reached 372.686 MHz, with a single point multiplication operation completed in 3328 . This significantly enhances the processing speed of the algorithm, bearing significant theoretical value and practical implications for advancing the security of the IoT ecosystem.

Aiming at the current problems of low level of intelligent development and backward infrastructure in the countryside, this paper proposes a multi-objective optimization model for rural construction. According to the overall principle of optimization and the current situation of rural infrastructure construction, model assumptions, objective functions and constraints are determined. Facing the problem of calculating the optimal values of the four objective functions, NSGA-II method is chosen to solve and analyze the problem. NSGA-II algorithm is calculated in 100 iterations, and the optimal solutions of the four objective functions are 0.813, 0.943, 0.852, and 0.886, which are better than NSGA and GA algorithms in terms of performance. In order to improve the intelligent development of the countryside, two targeted development proposals are put forward.

With the progress of modern technology, smart wearable devices have been gradually applied in the field of sports. This paper focuses on the experiments of motion recognition of the main joints realized by convolutional neural network-assisted smart wearable devices. Using smart wearable devices to feature extraction of a variety of sports signals, using GAF algorithm for sports signal image coding, and using convolutional neural network and gated recurrent unit, a CNN-GRU-based motion recognition method is proposed. Through the training and evaluation experiments of the model, it is found that the average accuracy of the CNN-GRU model training and testing is higher than 96%, and the loss value is lower than 1.5%, and the performance of sports recognition is better than that of CNN and CNN-LSTM models. Meanwhile, it presents excellent performance in the recognition of sports with different classifications and different signal durations, reaching 97.02% and 92.63% accuracy in the recognition of three and four types of sports, respectively, and the distribution of the values of human body indexes in different sports in the case study presents a certain degree of regularity, which verifies the effectiveness and feasibility of the CNN-GRU model in different application scenarios. It also shows that the method has great development potential in the field of intelligent sports.

This paper constructs a heterogeneous network adjacency matrix containing multiple user relationships from the connotation of professional organizations and other guides to individual behaviors covered by the take-read mechanism. The GAT algorithm is used to learn the embedding of its heterogeneous network in order to obtain the embedding vectors of user nodes, which serves as the basis for the analysis of the spreading influence of group behavior. An event recognition method based on word embedding and hierarchical cohesive clustering is proposed to analyze the recognition and evolution of social media essay-carrying behavioral events (group behavioral events) for complex networks. We point out that the distribution of group behavior affects the dynamics of information dissemination, set the adoption threshold parameter of the group, and analyze the dissemination pattern of individuals’ (individual information) participation in essay-reading behaviors. Analyze the emergence and evolution of thesis-reading behavior in social media, and explore the influence of individual’s own attributes and the attitude of neighboring nodes on the evolution of group behavioral events in complex networks. The spreading degree analysis is conducted for different relational social media bandwagon behaviors. When =0.6 and =0.8, the individual’s decision is supported by the neighbor’s viewpoints, and the users who have already participated in the paper band-reading activities have a strong attraction to the individual. When the strong degree increases to a certain value, the individual decides to participate in the dissertation banding activity, at which point the individual is no longer influenced by the external environment. The degree of the initial node for the propagation of thesis banding behavior in random networks and small-world networks is linearly and negatively correlated with the percentage of the information audience.

This paper constructs an improved Changsha city brand image communication model on the basis of the traditional contagion model, and studies the communication effect of Changsha in the process of city brand image transformation from “online star city” to “long-term famous city”. By summarizing and analyzing the current situation of Changsha’s city brand image communication, the evaluation index system of Changsha’s city brand image communication effectiveness is constructed, and the collected evaluation index data are downscaled using principal component analysis. The support vector regression machine combined with differential evolution algorithm is used to quantitatively analyze the communication benefits of Changsha city brand image. The improved city brand image communication model in this paper has a higher accuracy compared with the traditional contagion model, and can accurately grasp the communication effect of Changsha city brand image. The average relative error of the support vector regression machine model in the quantitative analysis of communication benefits for the test samples from 2020 to 2023 is only 1.53%, which is 27.86% lower than that of the BP neural network model. It strongly demonstrates the effectiveness of the regression model selected based on the communication big data in this paper, and provides a useful reference for accurately measuring the communication benefits of Changsha’s city brand image.

Under the background of carbon peak carbon neutrality, the competition among ports is not only the competition among terminal scale, throughput, and service level, but also the competition of low energy consumption and low pollution, and with the development of China’s carbon trading mechanism, the cost of carbon emission has become more and more a part of the enterprise that cannot be ignored. In this paper, the berths and shore bridges of the port are taken as the target variables, and the fuel consumption in the process of ships traveling to the port is inferred according to the assumed conditions, and the BAP model under the carbon peak carbon neutrality is deduced, and the relevant constraints are proposed. The initial population is randomly generated, and the first generation of offspring population is obtained through the selection, crossover and mutation operations of multi-objective genetic algorithm, which then continues until the end conditions of the program are satisfied. Through the empirical method, comparing the effect of carbon cost optimization scheme generated by multi-objective genetic algorithm and traditional method, the value of the objective function under the multi-objective genetic algorithm model decreased by 10.48%, the operation cost of the port decreased by 4.54%, the cost of the ship’s in-port time decreased by 24.9%, and the ship’s average in-port time decreased by 11.01%, as compared with the traditional allocation scheme. The multi-objective genetic optimization model of berth shore bridge considering carbon cost can shorten the ship’s time in port, which reduces the carbon emission from the side and achieves the promotion purpose of green port. In the model sensitivity analysis, with the increase of carbon trading price, the four indicators F, F1, F2 and T also showed linear growth, with the growth rate of 17.24%, 18.44%, 14.37% and 18.02%, respectively, and the model sensitivity is good.

Participatory culture, as one of the characteristics of audience performance in the current communication environment, provides imaginative space for stimulating the power of audience participation in the communication of non-heritage culture, and at the same time provides new thinking direction and inspiration for the current communication of non-heritage culture. In this paper, we mainly apply recurrent neural networks to model sequence data, and control the flow of information by adding special gating structures, so as to be able to effectively memorize and process long sequence data. Self-attention is constructed so that the network can better focus on the important parts of the sequence while ignoring the irrelevant information in the sequence. Identify non-heritage communication behaviors based on time-series data, and model non-heritage cultural communication behaviors based on the length of time the behaviors occur under the framework of situational awareness. The research experimental model is designed, relevant hypotheses are proposed, and examined through empirical evidence. The number of borrowings by visitors under 18 years old, which is the main group of visitors, declined from 737 in 2016 to 357 in 2022, with an overall decline of 51.56%, and the overall visiting behavior also showed a declining trend. In order to test the mediating role of perceived value in the relationship between interactive behavior and the communication effect of intangible cultural heritage, the benchmark model M3 model was constructed with the communication effect as the dependent variable and gender and whether the only child was the controlling variable, and the independent variables “interactive behavior” and “perceived value” were added on this basis, and the perceived value had a significant positive impact on the communication effect, β=0.485, p<0.001. The influence of interactive behavior on communication effect remains significant, at this time the β-value is 0.487 and p<0.001, the mediating role of perceived value between interactive behavior and non-heritage culture communication effect.

Science and technology innovation talents are the center of gravity of the national strategic power, which is crucial for promoting social development and scientific and technological progress. The purpose of this paper is to study the scientific and technological innovation talents of power grid enterprises, build the evaluation index system of scientific and technological innovation talents with reference to the CIPP model, select a power grid enterprise to analyze the examples, and use the fuzzy AHP model to evaluate its scientific and technological innovation talents training. Then build the role mechanism model of science and technology innovation talent cultivation, conduct regression analysis of the influence factors of science and technology innovation talent cultivation, and verify the research hypothesis. The evaluation results of the STI talents of the sample grid enterprises range from 3.6 to 4.0 points, and the evaluation grades are all good, confirming the practicality of the proposed STI talent evaluation method. Except for years of education, high focus in research field and teamwork, the selected personal factors, organizational factors and environmental factors have positive and significant effects on the quality of STI talents training. It is suggested that power grid enterprises improve and promote the development of the training system of scientific and technological innovation talents by building a training and development channel, developing a layered training model, innovating training methods as well as building a research platform.