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With the rapid development of smart grids, the strategic behavior evolution in user-side electricity market transactions has become increasingly complex. To explore the dynamic evolution mechanisms in this area, this paper systematically reviews the application of evolutionary game theory in user-side electricity markets, focusing on its unique advantages in modeling multi-agent interactions and dynamic strategy optimization. While evolutionary game theory excels in explaining the formation of long-term stable strategies, it faces limitations when dealing with real-time dynamic changes and high-dimensional state spaces. Thus, this paper further investigates the integration of deep reinforcement learning, particularly the deep Q-learning network (DQN), with evolutionary game theory, aiming to enhance its adaptability in electricity market applications. The introduction of the DQN enables market participants to perform adaptive strategy optimization in rapidly changing environments, thereby more effectively responding to supply–demand fluctuations in electricity markets. Through simulations based on a multi-agent model, this study reveals the dynamic characteristics of strategy evolution under different market conditions, highlighting the changing interaction patterns among participants in complex market environments. In summary, this comprehensive review not only demonstrates the broad applicability of evolutionary game theory in user-side electricity markets but also extends its potential in real-time decision making through the integration of modern algorithms, providing new theoretical foundations and practical insights for future market optimization and policy formulation. [ABSTRACT FROM AUTHOR]

The power grid is an important industry that is crucial to national security and economic development, and its importance in society continues to grow. As an emerging concept, financial sharing enables internal resource sharing and optimization, thereby improving the efficiency and effectiveness of asset management. This study investigates and analyzes the current situation of asset management in large-scale electricity enterprises in X Province, China, and proposes a comprehensive asset management strategy optimization plan based on the concept of financial sharing. The proposed plan integrates management models such as PDCA and designs an entire information management architecture to enhance resource utilization efficiency, reduce environmental pollution risks, and optimize asset allocation and operational decisions. In addition, it also utilizes the status of assets to assess the risks associated with fixed assets in the power grid. The results indicate that the asset risk assessment method under the concept of financial sharing can reduce power grid asset losses, effectively enhance the competitiveness and sustainable development capabilities of electricity enterprises. [ABSTRACT FROM AUTHOR]

With the continuous deepening of electricity marketization reform and the large-scale integration of clean energy into the system, the problem of transmission congestion in the power system is frequent, and research on congestion management methods has become a major focus. Therefore, this paper studies the optimization strategy of transmission congestion management in the power spot market. Firstly, the existing congestion management mechanisms were sorted out from two aspects: dynamic management mechanisms, and market steady-state mechanisms. At the same time, the advantages and disadvantages of various mechanisms were summarized. Then, optimization strategies for congestion management were proposed from three aspects: cost optimization, surplus diversion, and risk optimization. Firstly, based on the ATC reduction method and rescheduling method, a congestion cost optimization strategy is proposed. Secondly, a congestion cost pool and multidimensional allocation indicators, are established and a congestion surplus diversion strategy is proposed. Thirdly, a two-level market for transmission rights trading has been constructed to optimize congestion risk. Finally, a case study was conducted in a certain province in eastern and western China, and the results showed that: 1) Considering the rescheduling method and ATC reduction method to optimize congestion costs can reduce congestion costs by 8.22% and 18.83%, compared to only considering the scheduling method and using ATC reduction method. 2) Establishing a congestion cost pool and multi-dimensional allocation indicators to channel congestion surplus can form price guidance signals. 3) Constructing a two-level market for transmission rights trading can increase the efficiency of system operators by 62.75%. [ABSTRACT FROM AUTHOR]

Reinforcement learning has been used extensively to learn the low-level tactical choices during gameplay; however, less effort is invested in the strategic decisions governing the effective engagement of a diverse set of opponents. In this paper, a two-tier reinforcement learning model is created to play competitive games and effectively engage in matches with different opponents to maximize earnings. The multi-agent environment has four types of learners, which vary in their ability to learn gameplay directly (tactics) and their ability to learn to bet or withdraw from gameplay (strategy). The players are tested in three different competitive games: Connect 4, Dots and Boxes, and Tic-Tac-Toe. Analyzing the behavior of players as they progress from naivety to game mastery reveals some interesting features: (1) learners who optimize strategy and tactics outperform all learners, (2) learners who initially optimize their strategy to engage in matches outperform those who focus on optimizing tactical gameplay, and (3) the advantage of strategy optimization versus tactical gameplay optimization diminishes as more games are played. A reinforcement learning model with a dual learning scheme presents possible applications in adversarial scenarios where both strategic and tactical learning are critical. We present detailed results in a systematic manner, providing strong support for our claim.

The object of this research is the process of compiler optimization, as it is essential in modern software development, particularly in functional programming languages like Lambda Calculus. Optimization strategies directly impact interpreter and compiler performance, influencing resource efficiency and program execution. While functional programming compilers have garnered less attention regarding optimization efforts than their object-oriented counterparts, Lambda Calculus’s complexity poses unique challenges. Bridging this gap requires innovative approaches like leveraging machine learning techniques to enhance optimization strategies. This study focuses on leveraging machine learning to bridge the optimization gap in functional programming, particularly within the context of Lambda Calculus. This study delves into the extraction features from Lambda terms related to reduction strategies by applying machine learning. Previous research has explored various approaches, including analyzing reduction step complexities and using sequence analysis Artificial Neural Networks (ANNs) with simplified term representation. This research aims to develop a methodology for extracting comprehensive term data and providing insights into optimal reduction priorities by employing Large Language Models (LLMs). Tasks were set to generate embeddings from Lambda terms using LLMs, train ANN models to predict reduction steps, and compare results with simplified term representations. This study employs a sophisticated blend of machine learning algorithms and deep learning models as a method of analyzing and predicting optimal reduction paths in Lambda Calculus terms. The result of this study is a method that showed improvement in determining the number of reduction steps by using embeddings. Conclusions: The findings of this research offer significant implications for further advancements in compiler and interpreter optimization. This study paves the way for future research to enhance compiler efficiency by demonstrating the efficacy of employing LLMs to prioritize normalization strategies. Using machine learning in functional programming optimization opens avenues for dynamic optimization strategies and comprehensive analysis of program features.

In the information age, there's a growing need to improve eldercare services for the mobile elderly population. Current Chinese eldercare often separates medical and nursing care, leading to low resource use. This study aims to integrate community healthcare with data analysis and intelligent coordination to meet the floating elderly's needs. Using a Stacking model, it identifies key indicators and develops a mobile terminal based community healthcare model. Results show that primary indicators are crucial, scoring between 4.48−5.00, with secondary and tertiary indicators also significant. The KMO value is 0.93, confirming the model's validity. Compared to traditional methods, this new approach enhances accuracy by 7%, offering a valuable framework for community-based eldercare integration in China. HIGHLIGHTS This research mainly focuses on the integration model of the elderly floating population and community health care.; The experimental results showed that the model proposed the importance of community health care indicators for the elderly floating population, with a distribution of 4.48–5.00 and a full score of 52.17–100%.;

The work focuses on the optimization of management strategies of energy supply chain in oil–gas enterprises to find out the optimal management strategy and overcome the fragility of the traditional energy supply system. Back propagation neural network (BPNN) is applied to energy supply predicting and energy security early warning in oil–gas enterprises, and the security early warning system is designed based on the auxiliary cognitive system. The security early warning system is used to predict the peak value of oil and gas supply, and then the detection performances and the data security transmission performances of different models are compared and analysed. Finally, a comprehensive evaluation model for security management of oil–gas supply chain based on the neural network is established. It is found that compared with convolutional neural network (CNNs), the improved BPNN model algorithm proposed shows increased accuracy by 5.4%, reduced model training time and test time, and improved accuracy and recall rate. The accuracy, recall rate, and F1 value of the proposed model are 88.48%, 75.88%, and 96.21%, respectively, which were obviously higher than those of the CNN. It suggests that the improved security management model of oil–gas supply chain shows better recognition and prediction accuracy. Based on the quantitative and qualitative analysis results, the optimization suggestions for management strategies in oil–gas enterprises are put forward, which is of strategic significance for improving the security of oil–gas supply. [ABSTRACT FROM AUTHOR]

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The object of this research is the process of compiler optimization, as it is essential in modern software development, particularly in functional programming languages like Lambda Calculus. Optimization strategies directly impact interpreter and compiler performance, influencing resource efficiency and program execution. While functional programming compilers have garnered less attention regarding optimization efforts than their object-oriented counterparts, Lambda Calculus’s complexity poses unique challenges. Bridging this gap requires innovative approaches like leveraging machine learning techniques to enhance optimization strategies. This study focuses on leveraging machine learning to bridge the optimization gap in functional programming, particularly within the context of Lambda Calculus. This study delves into the extraction features from Lambda terms related to reduction strategies by applying machine learning. Previous research has explored various approaches, including analyzing reduction step complexities and using sequence analysis Artificial Neural Networks (ANNs) with simplified term representation. This research aims to develop a methodology for extracting comprehensive term data and providing insights into optimal reduction priorities by employing Large Language Models (LLMs). Tasks were set to generate embeddings from Lambda terms using LLMs, train ANN models to predict reduction steps, and compare results with simplified term representations. This study employs a sophisticated blend of machine learning algorithms and deep learning models as a method of analyzing and predicting optimal reduction paths in Lambda Calculus terms. The result of this study is a method that showed improvement in determining the number of reduction steps by using embeddings. Conclusions: The findings of this research offer significant implications for further advancements in compiler and interpreter optimization. This study paves the way for future research to enhance compiler efficiency by demonstrating the efficacy of employing LLMs to prioritize normalization strategies. Using machine learning in functional programming optimization opens avenues for dynamic optimization strategies and comprehensive analysis of program features. [ABSTRACT FROM AUTHOR]

Background: Emergency observation unit in China are characterized by a high number of patients, complexity of diseases, and instability of patient conditions, leading to low patient satisfaction. The Kano model is an effective method widely used to identify customer demands and improve service quality to enhance customer satisfaction. However, its application in emergency observation unit has been studied less. This study aims to design a questionnaire based on the Kano model and identify the demands of emergency observation patients to determine priorities for improvements in the emergency observation unit and improve patient satisfaction. Methods: A cross-sectional study was conducted in Guizhou Provincial People's Hospital from March 21st, 2023, to May 20th, 2023. A convenient sampling method was used to recruit 100 patients from the emergency observation unit, who completed a questionnaire designed based on the Kano model to assess their demands for care service. Data were analyzed using IBM SPSS Statistics 28.0 software. The element selection line and sensitivity analysis were used to determine factors for patient service demand improvement. Results: A total of 13 patient service demands for improvement were screened out from 19 service demands, including 1 item of must-be quality (M), 11 item of one-dimensional quality (O), and 1 item of attractive quality (A), These attributes showed significant differences in patients’ sociodemographic characteristics. Conclusion: The Kano model is a valuable tool for identifying the characteristics of patients' service demands, and the element screening method can be employed to establish the hierarchy of these demands. These results offer crucial direction for creating forthcoming nursing management initiatives in emergency observation unit.

The power grid is an important industry that is crucial to national security and economic development, and its importance in society continues to grow. As an emerging concept, financial sharing enables internal resource sharing and optimization, thereby improving the efficiency and effectiveness of asset management. This study investigates and analyzes the current situation of asset management in large-scale electricity enterprises in X Province, China, and proposes a comprehensive asset management strategy optimization plan based on the concept of financial sharing. The proposed plan integrates management models such as PDCA and designs an entire information management architecture to enhance resource utilization efficiency, reduce environmental pollution risks, and optimize asset allocation and operational decisions. In addition, it also utilizes the status of assets to assess the risks associated with fixed assets in the power grid. The results indicate that the asset risk assessment method under the concept of financial sharing can reduce power grid asset losses, effectively enhance the competitiveness and sustainable development capabilities of electricity enterprises.

With the rapid development of smart grids, the strategic behavior evolution in user-side electricity market transactions has become increasingly complex. To explore the dynamic evolution mechanisms in this area, this paper systematically reviews the application of evolutionary game theory in user-side electricity markets, focusing on its unique advantages in modeling multi-agent interactions and dynamic strategy optimization. While evolutionary game theory excels in explaining the formation of long-term stable strategies, it faces limitations when dealing with real-time dynamic changes and high-dimensional state spaces. Thus, this paper further investigates the integration of deep reinforcement learning, particularly the deep Q-learning network (DQN), with evolutionary game theory, aiming to enhance its adaptability in electricity market applications. The introduction of the DQN enables market participants to perform adaptive strategy optimization in rapidly changing environments, thereby more effectively responding to supply–demand fluctuations in electricity markets. Through simulations based on a multi-agent model, this study reveals the dynamic characteristics of strategy evolution under different market conditions, highlighting the changing interaction patterns among participants in complex market environments. In summary, this comprehensive review not only demonstrates the broad applicability of evolutionary game theory in user-side electricity markets but also extends its potential in real-time decision making through the integration of modern algorithms, providing new theoretical foundations and practical insights for future market optimization and policy formulation.

As an important component of new western land-sea corridor, the construction of Pinglu Canal will effectively alleviated waiting time and congestion costs and enhance the reliability and resilience of the regional maritime transport network in the post-pandemic era in particular. From the perspective of competition and cooperation game, this paper investigates typical transportation routes from the port of Jakarta in Indonesia to the port of Nanning in China from the key factors of the changes in freight volume and the evolution of profits and subsidies, considering local government subsidies, environmental costs, marine emissions and other critical factors. The results demonstrated that in the centralized strategies adopted by two transport route operators, as the volume of goods transported through Pinglu Canal increased, so the corresponding profits increased. The increase in subsidies also contributed to generating the volume of freight through Pinglu Canal, but the social welfare under the decentralized strategy adopted by both transport route operators was more effective than that of the centralized strategy. [ABSTRACT FROM AUTHOR]

With the development of the Internet, social media has brought new shopping experiences to consumers, and enterprises have joined the wave of social media marketing, which has led consumers to buy their wishes through social media. This paper analyzes the reconstruction of marketing activities in terms of information dissemination, audience expansion, and community building. According to the behavior of social media users, the key indicators of media marketing include the consumer’s awareness of media marketing, consumer behavior, and how these indicators affect the willingness to buy. The article presents the direction for adjusting the social media influence marketing strategy. Marketers must take into account the comprehensive value of the affected people when selecting the influence. At the same time, the cognitive effect of social media on channel optimization is significantly affected by the elimination of costs, and optimizing channels also benefits marketing effectiveness.

To improve the teaching effect of computer courses, make students master computer knowledge, and improve computer operation skills, this paper proposes a computer teaching model of continuous, discrete methods in colleges and universities. This paper investigates the teaching mode of traditional computer teaching methods in colleges and universities and proposes a continuous, discrete computer teaching model for the drawbacks of traditional computer teaching methods. The discrete learning teaching matrix and teaching process are constructed using the information entropy-based rough set discretization algorithm. Moreover, the teaching effects are compared with traditional methods regarding students’ learning interests, homework completion, and test scores. In terms of homework completion rate: the correct homework rate of the class using the discrete learning method was 20% higher than that of the class using the traditional teaching method.

Cross-border e-commerce has developed rapidly with network globalization, convenience, and mobility characteristics, and its operation mode is highly popular in the international trade industry. This paper analyzes cross-border e-commerce marketing strategies based on the SCOR model, aiming to seek scientific and reasonable methods to improve cross-border e-commerce companies’ precision marketing speed and accuracy and gain greater advantages in the fiercely competitive environment. This paper uses the SCOR model to identify the possible risks of marketing in the cross-border e-commerce industry. The characteristics of the supply chain are studied, and the relevant risk factors are identified from a holistic perspective. By comparing the two risk assessment methods of the fuzzy integrated evaluation method and the BP wavelet neural network model, the advantages of the BP wavelet neural network model in this paper are proved, and then the method is chosen as the risk assessment method and verified by example.

Background: Emergency observation unit in China are characterized by a high number of patients, complexity of diseases, and instability of patient conditions, leading to low patient satisfaction. The Kano model is an effective method widely used to identify customer demands and improve service quality to enhance customer satisfaction. However, its application in emergency observation unit has been studied less. This study aims to design a questionnaire based on the Kano model and identify the demands of emergency observation patients to determine priorities for improvements in the emergency observation unit and improve patient satisfaction., Methods: A cross-sectional study was conducted in Guizhou Provincial People's Hospital from March 21st, 2023, to May 20th, 2023. A convenient sampling method was used to recruit 100 patients from the emergency observation unit, who completed a questionnaire designed based on the Kano model to assess their demands for care service. Data were analyzed using IBM SPSS Statistics 28.0 software. The element selection line and sensitivity analysis were used to determine factors for patient service demand improvement., Results: A total of 13 patient service demands for improvement were screened out from 19 service demands, including 1 item of must-be quality (M), 11 item of one-dimensional quality (O), and 1 item of attractive quality (A), These attributes showed significant differences in patients' sociodemographic characteristics., Conclusion: The Kano model is a valuable tool for identifying the characteristics of patients' service demands, and the element screening method can be employed to establish the hierarchy of these demands. These results offer crucial direction for creating forthcoming nursing management initiatives in emergency observation unit., Competing Interests: The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (© 2024 The Authors. Published by Elsevier Ltd.)