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The construction process of offshore wind farms involves multiple complexities, which is very complex to be scheduled manually, and the coordinating and optimized scheduling not only decreases project construction costs but also increases the construction speed. The impact of meteorological conditions on offshore wind power construction has been considered, and optimizing resource-allocation strategies under complex influencing factors has been analyzed. Then, a comprehensive strategy optimization index system is developed, which includes key indicators, such as the minimum working hours, resource-allocation-optimization rate, window period utilization rate, and cost–benefit ratio. Additionally, an offshore wind power resource-allocation-optimization model is formulated based on discrete event simulation (DES). A statistical analysis of each optimization index was performed using this model to assess the impact of resource-allocation strategies. The simulation results demonstrate that the model can not only simulate the construction process of offshore wind farms and monitor the state of wind turbines, personnel, and meteorological conditions in real time but also accurately calculate key indicators, such as the minimum working hours, resource-allocation-optimization rate, window period utilization rate, and cost–benefit ratio. This strategy effectively enhances resource-allocation efficiency during the wind farm installation phase and improves the overall construction process efficiency. [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.

Emergency communication is a key link to ensure the stable operation of communication networks. And large-scale multiple-input multiple-output (MIMO) technology can significantly improve coverage of communication systems. This work aims to optimize the backhaul process through deep neural network to improve the response speed and data transmission efficiency of base stations. Specifically, the proposed method uses deep neural network to represent the backhaul process of base stations, and explores data transmission under high efficiency and low delay. Simulation experiments are designed to verify performance advantages of the proposed method in large-scale MIMO wireless ad hoc networking scenarios, by using three comparison metrics. The experimental results show that the proposed method has a significant performance improvement in data transmission rate, system stability and resource utilization compared with traditional methods. Besides, the feasibility and effectiveness of the algorithm in practical application are verified by simulation and actual environment test. To sum up, the proposed algorithm can provide a new direction for the technical research in the field of emergency communication. [ABSTRACT FROM AUTHOR]

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]

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]

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]

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.

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 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]

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.

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.

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.

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

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%.

Proton Exchange Membrane Fuel Cell (PEMFC) is a prominent application of hydrogen energy in fields such as mobile devices, vehicles and small-scale energy systems. This study experimentally investigates transient response of the automotive PEMFC when starting with different load under various operating conditions, which significantly influences durability. The key finding is that dynamic behavior follows a "two-stage" response mode under drier conditions. The first stage is related to the diffusion processes of cathode gas and the second stage is determined by the membrane rehydration process. Under wetter conditions, dynamic behavior exhibits a "three-stage" response mode with a larger voltage fluctuation and longer startup time. The contributors to the first two stages are the same as the "two-stage" response mode and the third stage is determined by the degree of cathode flooding. Results indicate that increasing cathode stoichiometry, cathode humidity, and temperature obviously enables PEMFC to startup more stable and faster and with a higher load. Besides, a step loading strategy with a gradually reducing magnitude achieves an optimal balance between response time and voltage fluctuation at the target load, with a moderate startup time of 9.6 s and minimal voltage fluctuation of 0.009 V. [Display omitted] • Dynamic behavior is divided into 2 modes: 2-stage response and 3-stage response. • Effects of different operating parameters on dynamic behavior when startup. • Balance response time and voltage fluctuation by gradually reducing load magnitude. [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]

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.

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]

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]

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.

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]

This paper attempts to develop an efficient route planning algorithm to guide the operations of the multi-helicopter search and rescue in emergency. Route planning model of multi-helicopter cooperative search and rescue activity was established first, based on preference ordering of search and rescue objectives, as well as behavioral model of rescue helicopter and on-board detector. Given the route planning model, a multi-helicopter search and rescue route planning general algorithm was developed. The operation mechanism of ant colony algorithm was improved by introducing cooperative modes and the pheromone updating mechanism into existing methods. Furthermore, two cooperative search and rescue modes were studied: one is Overall Cooperative Search and Rescue Mode (OCSARM), in which many ants search and rescue the same region all together; the other is Blocking Cooperative Search and Rescue Mode (BCSARM), which partitions the region into small blocks and appoints helicopter with corresponding performance capabilities. Simulated experiments were developed to test the operability of proposed multi-helicopter search and rescue route planning algorithm. The comparison with existing algorithm shows that the algorithm proposed in this paper reduces computational complexity and evidently enhances algorithm efficiency. Results also indicate that this algorithm not only has the capability of comparing efficiency of two search and rescue modes in different mission requirements but also helps select search and rescue modes before rescue operation. [ABSTRACT FROM AUTHOR]

Green building has emerged as a new type of building to mitigate the conflict between the rapid expansion of buildings and the deteriorating ecological environment, thus promoting ecologically sustainable development of building projects. However, the development of green building suffers from issues such as high initial costs and complicated process. In this study, a system dynamics (SD) approach has been used to investigate green building development (GBD) in China. The validity of the GBD-SD model has been verified and further applied to simulate GBD in the city of Wuhan. Three problems in current GBD system of Wuhan have been identified based on analysis of simulated results: (1) slow-paced GBD; (2) imbalanced green building supply and demand; and (3) low overall green level. Furthermore, three strategies have been proposed accordingly to solve those three problems. This study provides a GBD-SD model to comprehensively understand the dynamic relationships between participants in a GBD system and may shed light on sustainable development of green buildings for policy-makers. [ABSTRACT FROM AUTHOR]

Air-cooled proton exchange membrane fuel cell (PEMFC) is a promising electrochemical device in fields of unmanned aerial and light-duty ground vehicles， with the virtues of simple system, low parasitic power and low cost. However, the significantly uneven distribution of internal reaction state of air-cooled PEMFC greatly limits the output performance and stability of the cells. In this study, a PCB test board for the in-situ observation of cell temperature and current density is designed and applied in fuel cells. The result shows that when the average current density is 500 mA/cm2，the difference of temperature and current density reach 20 °C and 400 mA/cm2, respectively. For the optimization of uniformity of reaction state, the effects of hydrogen pulsing interval, hydrogen supply mode, fan configuration and air inlet speed are experimentally explored. Based on the observed results of the PCB test board, optimization proposals of control strategy for air-cooled PEMFC are given: 1) A 30-s pulsing interval of hydrogen is preferred due to its little changes on current density distribution while significant increasement of hydrogen utilization rate. 2) The proposed bidirectional hydrogen flow improves the reaction uniformity, and reduces the fluctuation of current density during hydrogen pulsing discharge; 3) Compared to two paralleled fans, employing a single fan helps to improve the temperature uniformity, with the standard deviation of temperature from 8.9 °C to 6.7 °C; 4) Medium velocity of air inlet speed is preferred due to poor temperature uniformity at low velocity, and low water content at high velocity. • A PCB test board is designed to obtain the internal reaction state of fuel cell. • The uneven distributed temperature and current density in fuel cell is analyzed. • Optimization proposals of control strategy of air-cooled PEMFC are raised. [ABSTRACT FROM AUTHOR]

In this paper we propose an optimization scheme for a selling strategy of an electricity producer who in advance decides on the share of electricity sold on the day-ahead market. The remaining part is sold on the complementary (intraday/balancing) market. To this end, we use probabilistic forecasts of the future selling price distribution. Next, we find an optimal share of electricity sold on the day-ahead market using one of the three objectives: maximization of the overall profit, minimization of the sellers risk, or maximization of the median of portfolio values. Using data from the Polish day-ahead and balancing markets, we show that the assumed objective is achieved, as compared to the naive strategy of selling the whole produced electricity only on the day-ahead market. However, an increase of the profit is associated with a significant increase of the risk.

Nowadays, anyone carrying a mobile device can enjoy the various location-based services provided by the Internet of Things (IoT). ‘Aggregate nearest neighbor query’ is a new type of location-based query which asks the question, ‘what is the best location for a given group of people to gather?’ There are numerous, promising applications for this type of query, but it needs to be done in a secure and private way. Therefore, a trajectory privacy-preserving scheme, based on a trusted anonymous server (TAS) is proposed. Specifically, in the snapshot queries, the TAS generates a group request that satisfies the spatial K-anonymity for the group of users—to prevent the location-based service provider (LSP) from an inference attack—and in continuous queries, the TAS determines whether the group request needs to be resent by detecting whether the users will leave their secure areas, so as to reduce the probability that the LSP reconstructs the users’ real trajectories. Furthermore, an aggregate nearest neighbor query algorithm based on strategy optimization, is adopted, to minimize the overhead of the LSP. The response speed of the results is improved by narrowing the search scope of the points of interest (POIs) and speeding up the prune of the non-nearest neighbors. The security analysis and simulation results demonstrated that our proposed scheme could protect the users’ location and trajectory privacy, and the response speed and communication overhead of the service, were superior to other peer algorithms, both in the snapshot and continuous queries.

This paper proposes a simple and easily optimizable mathematical representation of an energy management strategy (EMS) for the hybrid energy storage system (HESS) in EV. The power of each device in the HESS is provided as a continuous function of load power called γ . Two strategies based on the proposed method, one incorporating fixed coefficients of the γ function (GBS) and one with coefficients optimized by a genetic algorithm (GAS) in real-time using a backward time window, are tested and compared to the rule-based strategy (RBS) and battery storage system. The calculations are made for an electric car with a LiFePO 4 battery-supercapacitor HESS. The analyzed parameters are: energy consumption, RMS and maximum current rates of the battery, and the cycle cost of an EV with HESS and a battery-powered EV. The analysis is made in dependence on drive cycle speed and an internal resistance of the battery module. The obtained results show that the GBS and the GAS are able to reduce the RMS current rate by 40% in the NEDC in comparison to battery-powered EV, as well as that maximum current rates do not exceed nominal values. The GAS aims at the minimization of energy consumption. It obtains best results in low speed cycles. [ABSTRACT FROM AUTHOR]

• An advanced price change prediction method is proposed for crude oil futures. • A sophisticated trading strategy is designed based on the price change prediction. • The proposed method outperformed all the benchmark methods. • The proposed decision-supporting system is interpreted by the SHAP approach. In the abundant literature about crude oil futures price forecasting, researchers generally predicted the crude oil price movements from the perspective of only a single timeframe. In addition, the trading strategies of their trading models were generally designed to be less sophisticated, and their prediction models lacked interpretability. To fill these gaps, a price direction fused prediction and trading approach has been proposed for high-frequency prediction of the Chinese crude oil futures. In the proposed approach, the MTXGBoost (Multiple Timeframes eXtreme Gradient Boosting) is developed and utilized for predictions fusion under multiple timeframes, and the NSGA-II (Non-dominated Sorting Genetic Algorithm-II) is integrated for trading strategy optimization. Moreover, the SHAP (Shapley Additive exPlanation) approach is also employed to interpret how the proposed approach made predictions. Experimental results show that the approach proposed in this research averagely produced a direction prediction accuracy of 78.69%, an accumulated return of 23.17%, and a maximum drawdown of 1.00%, demonstrating that it can produce an excellent profit with small trading risks. Therefore, the proposed approach can be employed as an intelligent, efficient, and reliable decision support system for market investors, energy-related companies, and government departments to make crude oil related decisions. [ABSTRACT FROM AUTHOR]

With the development of cloud storage system and its application in complex environment, its data security has been more and more attention. On the one hand, node crashes or external invasion are likely to lead to incomplete data; on the other hand, when the data is incomplete, because the cloud service provider deliberately concealed or other factors, the user cannot be promptly informed of the change. In view of the above problems, this paper makes a deep research, and puts forward a secure storage system based on how to ensure the data availability when data integrity and data are not complete. In this paper, we begin with the availability of data; the research focuses on the confidentiality of data, the loss of data recovery and data recovery. In this paper, we propose a data secure storage scheme based on Tornado codes (DSBT) by combining the technique of symmetric encryption and erasure codes. Program uses boot password to solve the traditional data encryption in the problem of key preservation and management; system design by correcting Tornado data redundancy code delete code in order to solve problems and recover lost data; through a hash keyed to Tornado code with error correction function so as to solve the problem of data tampering. On this basis, the paper continues to carry out research on data retrieval (POR). Based on the classic POR algorithm based on BLS short signature, the trusted log is introduced, and the trusted log is used to provide the user with the test results. Finally, combined with the DSBT scheme, the computational efficiency of the POR algorithm is optimized, which has nothing to do with the file size, which can achieve the calculation complexity of the constant level. According to the above scheme, this paper implements a secure cloud storage prototype system based on Cassandra. The test shows that the system can provide strong data loss recovery ability, effectively resist the Byzantine fault, in the back of the desirable detection ability is also prominent, but also has very high computation efficiency, especially in the face of large files. This paper studies the modeling and analysis methods of some key problems of data security in cloud storage, such as encryption storage, integrity verification, access control, and verification and so on. Through the data segmentation and refinement rules algorithm to optimize the access control strategy, using the data label verification cloud data integrity, using replica strategy to ensure the data availability, the height of authentication to strengthen security, attribute encryption method using signcryption technology to improve the algorithm efficiency, the use of time encryption and DHT network to ensure that the cipher text and key to delete the data, so as to establish a security scheme for cloud storage has the characteristics of privacy protection. [ABSTRACT FROM AUTHOR]

During the past 70 years, prestressed-concrete cylinder pipe (PCCP) has been used to construct approximately 19,000 mi of US water transmission mains. Numerous unanticipated performance shortfalls, caused largely by the actions of one manufacturer during the 1970s, resulted in concerns among utilities related to the use of PCCP that continue to this day. An emerging PCCP risk management strategy is to use the combination of electromagnetic inspection to gauge the current extent of deterioration, finite-element-based structural analysis to gauge the significance of deterioration relative to risk of failure, and acoustic monitoring to gauge the rate of deterioration. This article examines the state of the art as it relates to PCCP risk assessment and provides constructive input related to the limitations associated with the various technologies currently being used. Additionally, suggestions for improvements and ways to get the most out of a risk assessment are given. [ABSTRACT FROM AUTHOR]

• An evaluation index for water injection strategies is proposed. • An optimization method to the trade-off of wate/fuel consumption is presented. • Optimization on different water injection strategies is performed. • Water consumption reduced by 73% under the space cost injection strategy. For the requirements of rigorous CO 2 and emissions regulations, the water injection technique is a promising solution to improve fuel economy. The establishment of an optimum water injection strategy is still a challenge when this technique is applied as a method of thermal efficiency enhancement. The present study proposes an evaluation index for the energy conservation potential of water injection strategies, which optimizes water consumption and minimizes the negative influence on energy conservation. An evaluation approach based on Brent's method is adopted to search the optimum combination parameters of objection function, in which constraints consider fuel cost, usage cost, and space cost. A comprehensive vehicle dynamics model is established and validated to evaluate the energy conservation potential of proposed water injection strategies. The results present the proposed index provides an effective approach to assessing the trade-off relationship between water and fuel consumption. The fuel cost strategy improves fuel economy by 5.2% along the WLTC driving cycle, but this strategy consumes a large amount of water (1.08 L/100 km). In contrast, the proposed usage and space cost strategies save water consumption to 0.58 L/100 km and 0.29 L/100 km, respectively, although their ability to fuel conservation reduces to 4.8% and 4.5%, correspondingly. The comparison results provide considerable guidance on the selection of water injection strategies for vehicle designers and customers according to their purposes and demands. [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. [ABSTRACT FROM AUTHOR]

The combination of different propulsion and energy storage systems for hybrid vehicles is changing the focus in the field of powertrain calibration. Shorter time-to-market as well as stricter legal requirements regarding the validation of Real Driving Emissions (RDE) require the adaptation of current procedures and the implementation of new technologies in the powertrain development process. In order to achieve highest efficiencies and lowest pollutant emissions at the same time, the layout and calibration of the control strategies for the powertrain and the exhaust gas aftertreatment system must be precisely matched. An optimal operating strategy must take into account possible trade-offs in fuel consumption and emission levels, both under highly dynamic engine operation and under extended environmental operating conditions. To achieve this with a high degree of statistical certainty, the combination of advanced methods and the use of virtual test benches offers significant potential. An approach for such a combination is presented in this paper. Together with a Hardware-in-the-Loop (HiL) test bench, the novel methodology enables a targeted calibration process, specifically designed to address calibration challenges of hybridized powertrains. Virtual tests executed on a HiL test bench are used to efficiently generate data characterizing the behavior of the system under various conditions with a statistically based evaluation identifying white spots in measurement data, used for calibration and emission validation. In addition, critical sequences are identified in terms of emission intensity, fuel consumption or component conditions. Dedicated test scenarios are generated and applied on the HiL test bench, which take into account the state of the system and are adjusted depending on it. The example of one emission calibration use case is used to illustrate the benefits of using a HiL platform, which achieves approximately 20% reduction in calibration time by only showing differences of less than 2% for fuel consumption and emission levels compared to real vehicle tests. [ABSTRACT FROM AUTHOR]

In this paper we propose an optimization scheme for a selling strategy of an electricity producer who in advance decides on the share of electricity sold on the day-ahead market. The remaining part is sold on the complementary (intraday/balancing) market. To this end, we use probabilistic forecasts of the future selling price distribution. Next, we find an optimal share of electricity sold on the day-ahead market using one of the three objectives: maximization of the overall profit, minimization of the sellers risk, or maximization of the median of portfolio values. Using data from the Polish day-ahead and balancing markets, we show that the assumed objective is achieved, as compared to the naive strategy of selling the whole produced electricity only on the day-ahead market. However, an increase of the profit is associated with a significant increase of the risk. [ABSTRACT FROM AUTHOR]

A distributed energy system (DES) can integrate renewable resources and achieve efficient energy utilization, and it is a common form of energy system for isolated systems. However, a multienergy complementary energy supply scheme makes operating a DES complicated. To ensure the system operates efficiently, it is necessary to study its operation strategy. However, currently, the research on DES operation optimization mostly is in the optimization result itself, hence lacking the transferring of the optimized operation strategy to engineering applications, which is difficult to apply to the actual energy saving operation. Therefore, this paper takes the DES of a scientific research station in Antarctica as an example when it comes to the optimization of its operation strategy, extracting the control logic for engineering applications from the optimized operation strategy. Aiming for the lowest primary energy consumption of the DES across an entire year, a model for optimal operation is presented and formulated as a Mixed Integer Linear Program (MILP), and further solved by the General Algebraic Modeling System (GAMS). The operation strategy of the DES is optimized and the energy saving space is analyzed. Furthermore, through strategy interpretation, the available control logic of the project is extracted from the optimized strategy, and the extraction effect is verified. The results show that the primary energy consumption of the optimized operation strategy can be reduced by 11.8% when compared with the original operation strategy. By using the engineering control logic extracted from the optimized operation strategy to adjust the original operation strategy, the primary energy consumption of the system can be reduced by 9.6%. The current study can provide a scientific and reasonable scheduling strategy for the actual operation of a DES and provide guidance and reference for the optimal operation of an existing DES. [ABSTRACT FROM AUTHOR]

Nowadays, anyone carrying a mobile device can enjoy the various location-based services provided by the Internet of Things (IoT). 'Aggregate nearest neighbor query' is a new type of location-based query which asks the question, 'what is the best location for a given group of people to gather?' There are numerous, promising applications for this type of query, but it needs to be done in a secure and private way. Therefore, a trajectory privacy-preserving scheme, based on a trusted anonymous server (TAS) is proposed. Specifically, in the snapshot queries, the TAS generates a group request that satisfies the spatial K-anonymity for the group of users—to prevent the location-based service provider (LSP) from an inference attack—and in continuous queries, the TAS determines whether the group request needs to be resent by detecting whether the users will leave their secure areas, so as to reduce the probability that the LSP reconstructs the users' real trajectories. Furthermore, an aggregate nearest neighbor query algorithm based on strategy optimization, is adopted, to minimize the overhead of the LSP. The response speed of the results is improved by narrowing the search scope of the points of interest (POIs) and speeding up the prune of the non-nearest neighbors. The security analysis and simulation results demonstrated that our proposed scheme could protect the users' location and trajectory privacy, and the response speed and communication overhead of the service, were superior to other peer algorithms, both in the snapshot and continuous queries. [ABSTRACT FROM AUTHOR]