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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]

Copyright of Control Theory & Applications / Kongzhi Lilun Yu Yinyong is the property of Editorial Department of Control Theory & Applications and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

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]

Imperfect maintenance policy is always required and carried out on the equipment parts under limited maintenance resources. The maintenance strategy optimization of the imperfect maintenance level for each equipment part becomes difficult for large parts number or high imperfect maintenance levels. To solve the imperfect maintenance strategy optimization problem of equipment with multiple parts and maintenance levels, a repeated cooperative game theory based optimization algorithm was proposed in this research. Considering the imperfect maintenance level of each equipment part as the optimization variable and maintenance cost minimization as the optimization objective, a mathematical model of imperfect maintenance of equipment with multiple parts connected in series was fabricated under the constraints of limited maintenance time and minimal required reliability. Further, considering the administrators corresponding to each equipment part as the game players, the equipment imperfect maintenance model was mapped into a cooperative game scenario. Via constructing the strategy space, setting the payoff in each round of the cooperative game, the updated maintenance plan in each round will move towards the global optimum via repeated game. Moreover, the performance of the proposed method was tested via case and parameter studies, and compared with traditional optimization methods including genetic algorithm method. The analysis results show that the proposed cooperative game algorithm provides overall more accurate optimization results, and enhances the optimization efficiency. The research could provide more practical guidelines for imperfect maintenance strategy optimization of equipment in engineering applications. [ABSTRACT FROM AUTHOR]

With the deep development of tight reservoir in Mahu Sag, the trend of rising water cut during flowback concerns engineers, and its control mechanism is not yet clear. For this purpose, the integrated numerical model of horizontal well pattern from fracturing to production was established, and its applicability has been demonstrated. Then the flowback performance from child wells to parent wells and single well to well pattern was simulated, and the optimization method of reasonable flowback strategy was discussed. The results show that the formation pressure coefficient decreases as well patterns were put into production year by year, so that the seepage driving force of the matrix is weakened. The pressure-sensitive reservoir is also accompanied by the decrease of permeability, resulting in the increase of seepage resistance, which is the key factor causing the prolongation of flowback period. With the synchronous fracturing mode of well patterns, the stimulated reservoir volume (SRV) is greatly increased compared with that of single well, which improves the reservoir recovery. However, when the well spacing is less than 200 m, well interference is easy to occur, resulting in the rapid entry and outflow of fracturing fluid, and the increased water cut during flowback. Additionally, the well patterns in target reservoir should adopt a drawdown management after fracturing, with an aggressive flowback in the early stage and a slow flowback in the middle and late stage. With pressure depletion in different development stages, the pressure drop rate should be further slowed down to ensure stable liquid supply from matrix. This research can provide a theoretical guidance for optimizing the flowback strategy of tight oil wells in Mahu sag. [ABSTRACT FROM AUTHOR]

The dual carbon target is a two-stage carbon reduction goal proposed by China, while the bridge maintenance strategy does not consider the need for sustainable development. Therefore, this article studies the optimization of bridge maintenance timing under China's dual carbon goals. Firstly, this paper aims to minimize the total cost of maintenance and carbon emissions, considering the continuous effects of carbon pricing and emissions in the context of the dual carbon goals. The CHINAGEM-E model is employed to predict carbon prices, and a preventive maintenance decision-making method for highway bridges is established. Secondly, based on the theory of material residual strength, a degradation model for the technical condition of highway bridges is constructed. Finally, an in-depth case analysis of an in-service highway bridge is conducted to derive optimal maintenance solutions under three scenarios. In comparison to scenarios considering only maintenance costs or those based on benchmark carbon prices, the comprehensive maintenance cost under the dual carbon targets is the highest. In the total maintenance cost, carbon emission costs constitute over 50%, emphasizing the need for increased attention to carbon emission cost studies in future maintenance research. The methodology proposed in this paper is the first to connect carbon prices with the timing of preventive maintenance for bridges, providing a more scientific and sustainable basis for future highway bridge maintenance decisions. [ABSTRACT FROM AUTHOR]

Major sudden disasters, such as floods, earthquakes, and fires, often cause significant casualties. Emergency evacuation is crucial in mitigating these impacts. Different types of disaster incidents vary significantly in terms of impact scope, suddenness, and urgency. Each type of disaster possesses distinct characteristics, necessitating varying requirements for emergency evacuation. Consequently, we conducted a bibliometric analysis and visual mapping of evacuation processes in major natural disasters from 2004-2023, analyzing 7213 publications from the Web of Science database via VOSviewer and ArcGIS. Our study identified three developmental phases: an initial phase (pre-2011) with 1169 publications, a growth phase (2012-2018) with 2772 publications, and an expansion phase (post-2019) with 3335 publications. This study provides a comprehensive review and classification of emergency evacuation theories and methods in major disaster scenarios. It emphasizes the necessity of assessing the scope and intensity of different types of major emergent disasters, defining and simulating the affected behaviors of the influenced populations, and formulating differentiated emergency evacuation strategies accordingly. Keyword analysis reveals two main trends supporting these findings: an increasing focus on complex evacuation modeling and simulation techniques, manifested in the application of various simulation-optimized microscopic and macroscopic models such as cellular automata, social force models, agent-based models, pedestrian flow, and network flow models, enhancing disaster understanding and prediction capabilities; and the strategic development of tailored evacuation strategies for specific disaster contexts, thereby improving disaster response efficiency. Three key future pathways for safety evacuation research are outlined: refining evacuation behavior models for greater accuracy, improving the coordination of complex, multi-level evacuation procedures, and integrating indoor and outdoor evacuation strategies more seamlessly. It establishes a forward-looking framework for advancing safety evacuation studies in major emergencies.

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.

Copyright of Journal of Railway Science & Engineering is the property of Journal of Railway Science & Engineering Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

ObjectiveTo compare the differences between residents who did and who did not participate in a community colorectal cancer screening based on the results of their colorectal colonoscopy and explore the reasons.MethodsThe residents who underwent a colonoscopy in a central hospital in Shanghai from 2017 to 2020 were divided into two groups according to whether they had been screened in the community， and t test and χ2 test were used to compare the results of the colonoscopy （detection of lesions） of the examinees with different ages， genders， whether they had a history of colorectal cancer， and whether they had been screened in the community. The correlation between whether they had participated in the community screening and the detection of lesions was analyzed by the logistic regression model.ResultsFrom 2017 to 2020， the hospital had performed a colonoscopy for 6 389 people， and 3 623 lesions were detected， with a detection rate of 56.71%. There were 413 residents who had been screened in the community， accounting for 6.46% of the total number of those receiving a colonoscopy. 243 patients were found with pathological changes， with a detection rate of 58.84%. Compared with the residents who did not participate in the community screening， the proportion of adenoma and polyp was higher in those who had participated in the screening （χ2=50.44， P

In the generation process of reinforcement learning decision model, due to the complex environment and incomplete observation of state information, the classical proximal policy optimization algorithm faces problems such as low exploration and utilization efficiency and poor effect of generated strategies, this paper proposes an MNAEK-PPO (proximal policy optimization based on maximum number of arrival & expert knowledge algorithm) based on curiosity mechanism. Focusing on the difficult problem of exploring the strategy space, by constructing the exploration frequency matrix of the agent in the training process, the exploration frequency is treated as an internal reward to participate in the agent's reinforcement learning and training process. In addition, expert knowledge is added to assist the agent in making decisions. Through experiments in the intelligent battlefield simulation environment, the best construction method of internal rewards in MNAEK-PPO is determined, and a series of comparative experiments are carried out. The experimental results show that MNAEK-PPO greatly improves the exploration efficiency of decision space, and the convergence speed and game score are significantly improved, which provides a new solution for promoting the application and development of deep reinforcement learning in the generation of intelligent tactical strategies. [ABSTRACT FROM AUTHOR]

To achieve the ambitious goal of net-zero by 2050, the power sector in many countries has been increasingly using renewable energy sources (RES). The power system balance will be significantly challenged due to the intermittency of RES. Lithium-ion battery energy storage systems (BESS) can effectively address the challenge by providing flexibility to fill the mismatch between the intermittent supply and the varying demand, generating revenues for BESS to payback the initial investment. However, every grid participation also results in an inevitable lifetime loss. From the asset owner’s perspective, therefore, it is important to balance the short-term revenue from the fluctuating electrical market with the long-term battery life loss. To achieve this in this study, first, a novel online-applied battery life model is established, which is computationally efficient to comprehensively consider the battery’s historical aging, state of charge (SOC), charge–discharge rate (Crate). Then, an online-applied sliding-window dynamic programming (SWDP) strategy is proposed to optimize short-term grid service revenues and long-term battery life losses. The SWDP is also compared with an offline-applied dynamic programming (DP) strategy. The simulation results show that the cost due to battery life decay is significant, which indicates the importance of tracking the battery life loss in the battery asset management. Additionally, the prediction accuracy of electricity price significantly influences the performance of SWDP strategy. The BESS’s total profit can reduce by half in the considered case study if the electricity prediction error is 12.5%.

Copyright of Journal of Computer Engineering & Applications is the property of Beijing Journal of Computer Engineering & Applications Journal Co Ltd. and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

With the rapid development of Internet technology, many platforms provide online music services for users. Faced with a large number of music education resources, users are often difficult to choose. A system architecture based on the combination of edge computing (EC) and cloud computing (CC) is proposed to optimize the scheduling strategy of educational resources. The present work first briefly introduces the combination and advantages of EC and CC technology, and analyzes the demand for educational resource scheduling. The contribution and innovation of the research are to introduce EC into the technical scheme of piano music resource scheduling to improve the network and system architecture of the platform. The application results show that the resource utilization rate of the proposed multi-round iterative resource scheduling algorithm is 29% higher than that of the basic genetic algorithm (GA) and ant colony optimization (ACO), the user waiting time is reduced by 15%, and the system efficiency is improved by 23%. This shows that, compared with the traditional resource scheduling strategy, the proposed resource scheduling strategy can reduce delay, improve system efficiency and resource utilization, and improve service level. The research results contribute to support new teaching forms, improve platform application performance and intelligent level, and further improve user experience. [ABSTRACT FROM AUTHOR]

Developing low-carbon economy is an inherent requirement for China's sustainable development, and digital economy is an important engine for China's economy to reduce carbon emission intensity in the new development stage. Identifying the industrial evolution law of China's digital economy is helpful to diagnose its development opportunity, tap the 'green potential' and provide strategic support for the digital economy to play the low-carbon effect. Based on the data of China's digital industrialization and industry digitization from 2001 to 2020, the development level of China's digital economy industry was analyzed by logistic curve estimation, and the evolution stage of China's digital economy industry was determined by the characteristic points of the curve. The results showed that, before 2008, China's digital economy industry was in the germination period; from 2009 to 2035, China's digital economy industry is in the period of accelerating growth; from 2036 to 2062, China's digital economy industry will be in the mature stage; after 2062, it will enter a period of decline. The above analysis results are basically in line with the requirements of China's 'strategic plan to become a high-income country by 2035' and 'strategic goal of achieving carbon neutrality by 2060'. Finally, according to the carbon emission reduction development demand of China's digital economy in the 'growth period', it is pointed out that it is urgent for China to continue to promote the construction of digital infrastructure, achieve green digital technology innovation, improve the social digital literacy level and increase the proportion of renewable energy application. [ABSTRACT FROM AUTHOR]

As power systems are undergoing a significant transformation with more uncertainties, less inertia and closer to operation limits, there is increasing risk of large outages. Thus, there is an imperative need to enhance grid emergency control to maintain system reliability and security. Towards this end, great progress has been made in developing deep reinforcement learning (DRL) based grid control solutions in recent years. However, existing DRL-based solutions have two main limitations: 1) they cannot handle well with a wide range of grid operation conditions, system parameters, and contingencies; 2) they generally lack the ability to fast adapt to new grid operation conditions, system parameters, and contingencies, limiting their applicability for real-world applications. In this paper, we mitigate these limitations by developing a novel deep meta-reinforcement learning (DMRL) algorithm. The DMRL combines the meta strategy optimization together with DRL, and trains policies modulated by a latent space that can quickly adapt to new scenarios. We test the developed DMRL algorithm on the IEEE 300-bus system. We demonstrate fast adaptation of the meta-trained DRL polices with latent variables to new operating conditions and scenarios using the proposed method, which achieves superior performance compared to the state-of-the-art DRL and model predictive control (MPC) methods. [ABSTRACT FROM AUTHOR]

With the rapid advancement of manufacturing in China, robot machining technology has become a popular research subject. An increasing number of robots are currently being used to perform complex tasks during manual operation, e.g., the grinding of large components using multi-robot systems and robot teleoperation in dangerous environments, and machining conditions have evolved from a single open mode to a multisystem closed mode. Because the environment is constantly changing with multiple systems interacting with each other, traditional methods, such as mechanism modeling and programming are no longer applicable. Intelligent learning models, such as deep learning, transfer learning, reinforcement learning, and imitation learning, have been widely used; thus, skill learning and strategy optimization have become the focus of research on robot machining. Skill learning in robot machining can use robotic flexibility to learn skills under unknown working conditions, and machining strategy research can optimize processing quality under complex working conditions. Additionally, skill learning and strategy optimization combined with an intelligent learning model demonstrate excellent performance for data characteristics learning, multisystem transformation, and environment perception, thus compensating for the shortcomings of the traditional research field. This paper summarizes the state-of-the-art in skill learning and strategy optimization research from the perspectives of feature processing, skill learning, strategy, and model optimization of robot grinding and polishing, in which deep learning, transfer learning, reinforcement learning, and imitation learning models are integrated into skill learning and strategy optimization during robot grinding and polishing. Finally, this paper describes future development trends in skill learning and strategy optimization based on an intelligent learning model in the system knowledge transfer and nonstructural environment autonomous processing. [ABSTRACT FROM AUTHOR]

With the rapid economic growth and increased national income year by year, individuals and families have an increasingly greater demand for financial products. Banks' sales of financial products have become a new economic profit growth point for major banks. Based on consumers' psychological perception, the influencing factors of consumers' behavior in purchasing bank financial products are studied. The influencing factor model path of consumer purchase behavior is constructed to find out the factors affecting consumers' purchase of bank financial products and formulate appropriate promotion strategies according to the influencing factors. Through the research methods of literature analysis, small-scale in-depth interview, questionnaire surveys, and statistical analysis, this exploration selects four variables: independent variable, mediator, control variable, and dependent variable. They are influencing factors of purchasing bank financial products (perceived convenience, risk value of bank financial products, satisfaction of purchasing communication process), consumers' willingness to buy bank financial products, consumers' personal characteristics and consumers' behavior of purchasing bank financial products. Meanwhile, based on 196 valid questionnaires, regression analysis is carried out through a regression model. The results show that the three influencing factors of consumers' purchase of bank financial products-perceived convenience, risk value of bank financial products, and satisfaction with the purchase communication process significantly impact consumers' purchase of bank financial products. They can put forward specific promotion suggestions for banks. This exploration aims to study the optimization of bank financial product promotion strategy from the perspective of consumer psychological perception to provide a reference for subsequent related research. [ABSTRACT FROM AUTHOR]

Municipal water mains are built with a target service age of several decades. In such a long life, breaks can occur, even multiple times. Water mains can be maintained before or right at breaks. The former is referred to as the preventive strategy, whereas the latter is the corrective strategy. Depending on the costs of repair, replacement, and failure consequence, different strategies should typically be implemented in order to achieve the optimal watermain management in terms of life cycle costs. This study aims to investigate the optimal scenarios for the two strategies based on a two-time-scale (TTS) point process used to model the deterioration of water mains. The corrective strategy is to determine the optimal number n , where upon the n -th break, implementing a replacement for water main is justified, compared to a minimal repair. The preventive strategy is to determine the optimal replacement time in terms of pipe survival probability P s . Monte Carlo simulations are used to investigate the optimal n and P s considering a number of influential factors, including model parameters of the intensity function and ratios of maintenance, replacement, and consequence costs. Then, the full distributions of the life cycle costs are characterized with the mean of total life cycle costs being the target for optimization. Last, a case study is illustrated to demonstrate the application of both strategies in real water systems. An important finding is that with a typical pipe diameter of 400 mm and length of 200 m, the optimal n is typically less than five, and the optimal P s is below 50%. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Engineering for Thermal Energy & Power / Reneng Dongli Gongcheng is the property of Journal of Engineering for Thermal Energy & Power and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Abstract Background Risk‐stratified endoscopic screening (RSES), which offers endoscopy to those with a high risk of esophageal cancer, has the potential to increase effectiveness and reduce endoscopic demands compared with the universal screening strategy (i.e., endoscopic screening for all targets without risk prediction). Evidence of RSES in high‐risk areas of China is limited. This study aimed to estimate whether RSES based on a 22‐score esophageal squamous cell carcinoma (ESCC) risk prediction model could optimize the universal endoscopic screening strategy for ESCC screening in high‐risk areas of China. Methods Eight epidemiological variables in the ESCC risk prediction model were collected retrospectively from 26,618 individuals aged 40‐69 from three high‐risk areas of China who underwent endoscopic screening between May 2015 and July 2017. The model's performance was estimated using the area under the curve (AUC). Participants were categorized into a high‐risk group and a low‐risk group with a cutoff score having sensitivities of both ESCC and severe dysplasia and above (SDA) at more than 90.0%. Results The ESCC risk prediction model had an AUC of 0.80 (95% confidence interval: 0.75–0.84) in this external population. We found that a score of 8 (ranging from 0 to 22) had a sensitivity of 94.2% for ESCC and 92.5% for SDA. The RSES strategy using this threshold score would allow 50.6% of endoscopies to be avoided and save approximately US$ 0.59 million compared to universal endoscopic screening among 26,618 participants. In addition, a higher prevalence of SDA (1.7% vs. 0.9%), a lower number need to screen (60 vs. 111), and a lower average cost per detected SDA (US$ 3.22 thousand vs. US$ 5.45 thousand) could have been obtained by the RSES strategy. Conclusions The RSES strategy based on individual risk has the potential to optimize the universal endoscopic screening strategy in ESCC high‐risk areas of China.

The phase change material (PCM) radiator has good temperature control ability in photovoltaic thermal management. The cooling capacity of these PCM radiators can be further improved by adding metal foam. In this study, numerical simulations were conducted to optimize and analyze different extension strategies of a metal foam composite PCM (MF-PCM) radiator to cooling the concentrating photovoltaic systems. Seven extension methods were investigated to select the optimal method, and five extension sizes were studied the impacts on the liquid fraction of the PCM. The average temperature and temperature uniformity of photovoltaic cells were comprehensively evaluated. The results showed that among the seven extension methods examined, although the upper and lower equal proportion extension did not significantly reduce the average temperature of photovoltaic cell, provided more uniform longitudinal thermal diffusion of the photovoltaic panel along the radiator. This method reduced the temperature uniformity index by 77.72 %, and achieved the best temperature uniformity. Moreover, compared to the initial size, when the extension size is 0.6x, the temperature uniformity is slightly reduced, but the average temperature of the cell is also reduced by 24.22 %. In summary, the MF-PCM radiator has the best extension strategy when using the upper and lower equal proportion extension method with an extension size of 0.6x. [ABSTRACT FROM AUTHOR]

The aircraft fuel system is one of the core systems of the aircraft and is essential to the safe and reliable flight of aircraft. In order to solve the problem of the offset of the aircraft centroid due to the unreasonable distribution of fuel supply from multiple fuel tanks during the longitudinal flight of aircraft, this paper analyzes the principle of aircraft fuel supply and establishes a fuel centroid model for the longitudinal motion of aircraft. On this basis, a complex method is adopted. The optimal fuel supply strategy of aircraft is determined in real time according to the aircraft pitch angle and the fuel volume of each fuel tank. The simulation results show that after the required centroid of a given aircraft changes, the fuel supply strategy determined by the method in this paper can control the offset of the aircraft centroid within a reasonable range, and realize the dynamic adjustment of the fuel supply strategy according to the required centroid. The method has theoretical and practical engineering application value. [ABSTRACT FROM AUTHOR]

Summary: Bridges play an important role in the highway transportation network. There is an increasing concern that highway bridges have been suffering from structural degradation and deficiency due to severe environment, overloading, initial structural defects, and other factors. Ensuring the safety of a large number of regional bridges at the network level becomes a major challenge. This study proposes an entire data‐driven condition assessment framework for network‐level bridges, including data integration, condition assessment, and maintenance management. The periodic bridge inspection reports in China could provide quite a few condition‐related information to reveal the time‐variant bridge condition deterioration and effect of maintenance behaviors. The proposed framework is applied to a real highway bridge network located in Hebei province, China. Inspection data are obtained from thousands of bridges over quite a few years. The established regional deterioration model could effectively predict the bridge future condition based on the extracted hidden features. The regional maintenance strategies are optimized to satisfy the economic and performance constraints, which allows bridge managers to select the most appropriate one for implementation. It is shown that the proposed data‐driven approach can provide a guideline to bridge managers to estimate the future condition and allocate the maintenance resources. [ABSTRACT FROM AUTHOR]

Consecutive-k-out-of-n (Con/k/n) system, a reconfigurable system, can improve the system performance by adjusting the redundancy and assignment of components. This paper aims to determine the optimal defensive strategy of Con/k/n systems under external risks. The defensive capability of Con/k/n systems is evaluated based on real-time system reliability, and a defensive importance measure (DIM) is constructed to optimize components’ redundancy locally. To solve the proposed optimization model effectively, a DIM-based genetic algorithm (DIGA) is developed by integrating the advantages of DIM’s local search with the global search ability of the classical genetic algorithm (CGA). The numerical experiment under 36 scenarios illustrates that DIGA is more effective than CGA verified by average defensive capability, robustness, and convergence generations. Moreover, the redundancy distribution analysis of Con/k/5 systems in the optimal defensive strategy shows that the redundancy of F(G) systems is in a spaced (continuous) way under spacing k-1 risk or continuous k risk. [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., 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.)

Municipal water mains are built with a target service age of several decades. In such a long life, breaks can occur, even multiple times. Water mains can be maintained before or right at breaks. The former is referred to as the preventive strategy, whereas the latter is the corrective strategy. Depending on the costs of repair, replacement, and failure consequence, different strategies should typically be implemented in order to achieve the optimal watermain management in terms of life cycle costs. This study aims to investigate the optimal scenarios for the two strategies based on a two-time-scale (TTS) point process used to model the deterioration of water mains. The corrective strategy is to determine the optimal number n, where upon the n-th break, implementing a replacement for water main is justified, compared to a minimal repair. The preventive strategy is to determine the optimal replacement time in terms of pipe survival probability Ps. Monte Carlo simulations are used to investigate the optimal n and Ps considering a number of influential factors, including model parameters of the intensity function and ratios of maintenance, replacement, and consequence costs. Then, the full distributions of the life cycle costs are characterized with the mean of total life cycle costs being the target for optimization. Last, a case study is illustrated to demonstrate the application of both strategies in real water systems. An important finding is that with a typical pipe diameter of 400 mm and length of 200 m, the optimal n is typically less than five, and the optimal Ps is below 50%.

Copyright of Systems Engineering & Electronics is the property of Journal of Systems Engineering & Electronics Editorial Department and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Intelligent storage is an important part of intelligent logistics, and it is the development trend of modern warehousing and logistics. Based on the characteristics of flood and drought prevention materials and their intelligent storage characteristics, a decision model of intelligent storage location allocation with the multi objectives of shelf stability and storage efficiency was set up, the multi-objective optimization problem was transformed into a single objective optimization problem by using the weight method. A case study was made on the facilities planning and location allocation in the intelligent storage area of the provincial flood and drought prevention materials at Zhenjiang Sub Center Warehouse of Jiangsu Water Conservancy and Flood Control Material Reserve Center, then the intelligent storage location allocation of the provincial flood and drought prevention materials was optimized based on the MATLAB software and the good results had been achieved. [ABSTRACT FROM AUTHOR]

Recently, using unmanned platforms to perform target search tasks has received extensive attention and research. The complexity of the search scenario and the unpredictability of the target movement pose significant challenges for unmanned platforms to perform the search task. Developing efficient search strategies is crucial for their success. In this study, we model the problem as a Partially Observable Markov Decision Process (POMDP) and propose a Hierarchical Deep Q Network with Multi-criteria Negative Feedback method named MNF-HDQN to solve the problem efficiently. The MNF-HDQN incorporates point and area evaluations instead of the original sparsity calculation to enhance the cooperation competence of unmanned platforms in searching for a target in various search tasks. Additionally, the integration of convex polygon theory into reward shaping and the design of a new two-stage search strategy encouragement function further improve the performance of the proposed method. We conduct detailed experiments to verify the superiority of the MNF-HDQN. And experimental results show that our method provides a high successful search rate in a shorter timestep compared with state-of-the-art baselines. This advantage is more evident when the search scenario is more complex or the target movement is more unpredictable. • We propose a new reinforcement learning method for the multi-agent target search strategy optimization problem. • We design a novel two-stage reward function based on convex polygon theory to handle reward sparsity. • Our method develops a hindsight experience replay with multi-criteria negative feedback, the new feedback evaluation that combines two special indicators. • Experimental results demonstrate the superiority and the generalization ability of MNF-HDQN. [ABSTRACT FROM AUTHOR]

The functional and structural characteristics of civil engineering works, in particular bridges, influence the performance of transport infrastructure. Remote sensing technology and other advanced technologies could help bridge managers review structural conditions and deteriorations through bridge inspection. This paper proposes an artificial intelligence-based methodology to solve the condition assessment of regional bridges and optimize their maintenance schemes. It includes data integration, condition assessment, and maintenance optimization. Data from bridge inspection reports is the main source of this data-driven approach, which could provide a substantial amount og condition-related information to reveal the time-variant bridge condition deterioration and effect of maintenance behaviors. The regional bridge condition deterioration model is established by neural networks, and the impact of the maintenance scheme on the future condition of bridges is quantified. Given the need to manage limited resources and ensure safety and functionality, adequate maintenance schemes for regional bridges are optimized with genetic algorithms. The proposed data-driven methodology is applied to real regional highway bridges. The regional inspection information is obtained with the help of emerging technologies. The established structural deterioration models achieve up to 85% prediction accuracy. The obtained optimal maintenance schemes could be chosen according to actual structural conditions, maintenance requirements, and total budget. Data-driven decision support can substantially aid in smart and efficient maintenance planning of road bridges.