Search Results (526)

As a key enabling technology for energy transition, the smart grid is propelling the global power system to evolve toward greater efficiency, reliability, and sustainability. Based on the three-dimensional analysis framework of “technology–policy–application”, this study systematically sorts out the technical architecture, regional development mode, and typical application scenarios of the smart grid, revealing the multi-dimensional challenges that it faces. By using the methods of literature review, cross-national case comparison, and technology–policy collaborative analysis, the differentiated paths of China, the United States, and Europe in the development of smart grids are compared, aiming to promote the integration and development of smart grid technologies. From a technical perspective, this paper proposes a collaborative framework comprising the perception layer, network layer, and decision-making layer. Additionally, it analyzes the integration pathways of critical technologies, including sensors, communication protocols, and artificial intelligence. At the policy level, by comparing the differentiated characteristics in policy orientation and market mechanisms among China, the United States, and Europe, the complementarity between government-led and market-driven approaches is pointed out. At the application level, this study validates the practical value of smart grids in optimizing energy management, enhancing power supply reliability, and promoting renewable energy consumption through case analyses in urban smart energy systems, rural electrification, and industrial sectors. Further research indicates that insufficient technical standardization, data security risks, and the lack of policy coordination are the core bottlenecks restricting the large-scale development of smart grids. This paper proposes that a new type of intelligent and resilient power system needs to be constructed through technological innovation, policy coordination, and international cooperation, providing theoretical references and practical paths for energy transition. Full article

This study introduces a novel tripartite evolutionary game model to analyze the strategic interactions among electric vehicle (EV) aggregators, local governments, and EV users in vehicle–grid interaction (VGI) markets. The core novelty lies in capturing bounded rationality and dynamic decision-making across the three stakeholders, revealing how policy incentives and market mechanisms drive the transition from disordered charging to bidirectional VGI. Key findings include the following: (1) The system exhibits five stable equilibrium points, corresponding to three distinct developmental phases of the VGI market: disordered charging (V0G), unidirectional VGI (V1G), and bidirectional VGI (V2G). (2) Peak–valley price differences are the primary driver for transitioning from V0G to V1G. (3) EV aggregators’ willingness to adopt V2G is influenced by upgrade costs, while local governments’ subsidy strategies depend on peak-shaving benefits and regulatory costs. (4) Increasing the subsidy differential between V1G and V2G accelerates market evolution toward V2G. The framework offers actionable policy insights for sustainable VGI development, while advancing evolutionary game theory applications in energy systems. Full article

Data is the starting point for generating information and knowledge in the decision-making process. Open data, which is information disclosed free of charge through open licenses and reusable formats, has great potential for value creation. Therefore, the objective of this research is to evaluate Spanish autonomous communities’ open data initiatives in a category of information of vital importance: housing. The methodology employed was a population analysis of datasets labeled as housing, followed by a necessary data cleansing process due to the identification of various errors, which reduced the number of labeled datasets from 1000 to 599. Only 12 of the 17 autonomous communities provided this type of information. The analysis of the results reveals that autonomous communities’ approaches to open data initiatives are highly heterogeneous and that the supply is irregular, with the Basque Country accounting for 70% of the datasets considered in the research. The creation of an indicator that equally assesses the existence of information and file formats (breadth and reusability) continues to identify the Basque Country as the undisputed leader, with Catalonia and Cantabria in second and third place, the only autonomous communities to exceed 50 points out of a possible 100. The study concludes by highlighting that the lack of uniformity in the formulation and implementation of open data policies will limit the use of information and, consequently, its value. Therefore, a series of recommendations is issued in this regard. Full article

This study explores modernization strategies for existing district heating (DH) networks to enhance their efficiency and sustainability, focusing on achieving net-zero emissions in urban heating systems. Building upon a literature review and expert interviews, we developed a strategic decision-support framework that outlines distinct strategies for retrofitting district heating grids and includes a portfolio analysis. This framework serves as a tool to guide DH operators and stakeholders in selecting well-founded modernization pathways by considering technical, economic, and social dimensions. The review identifies several promising measures, such as reducing operational temperatures at substations, implementing optimized substations, integrating renewable and waste heat sources, implementing thermal energy storage (TES), deploying smart metering and monitoring infrastructure, and expanding networks while addressing public concerns. Additionally, the review highlights the importance of stakeholder engagement and policy support in successfully implementing these strategies. The developed strategic decision-support framework helps practitioners select a tailored modernization strategy aligned with the local context. Furthermore, the findings show the necessity of adopting a comprehensive approach that combines technical upgrades with robust stakeholder involvement and supportive policy measures to facilitate the transition to sustainable urban heating solutions. For example, the development of decision-support tools enables stakeholders to systematically evaluate and select grid modernization strategies, directly helping to reduce transmission losses and lower greenhouse gas (GHG) emissions contributing to climate goals and enhancing energy security. Indeed, as shown in the reviewed literature, retrofitting high-temperature district heating networks with low-temperature distribution and integrating renewables can lead to near-complete decarbonization of the supplied heat. Additionally, integrating advanced digital technologies, such as smart grid systems, can enhance grid efficiency and enable a greater share of variable renewable energy thus supporting national decarbonization targets. Further investigation could point to the most determining context factors for best choices to improve the sustainability and efficiency of existing DH systems. Full article

This study investigates the value identification and assessment of architectural heritage in Fujian Overseas Chinese New Village. As representative 20th-century settlements of returned overseas Chinese, these villages demonstrate distinctive architectural integration of Southeast Asian and Minnan architectural traditions while preserving historical memories of diasporic communities, though systematic evaluation remains lacking. An innovative multidimensional assessment framework combining qualitative and quantitative approaches was developed, with spatial analysis and value evaluation conducted on 247 representative structures employing Kernel Density Estimation (KDE), Delphi method, and Analytic Hierarchy Process (AHP). Three primary findings emerged: (1) Spatial distribution patterns revealed core-periphery clustering characteristics, with Xiamen and Zhangzhou forming high-density cores (23.5% concentration ratio) showing KDE values of 4.138–4.976, reflecting historical migration networks and policy-driven site selection logic. (2) Heritage values were categorized into seven dimensions, with historical significance (0.2904), artistic merit (0.1602), and functional utility (0.1638) identified as primary value drivers. (3) A four-tier evaluation system quantified heritage significance through weighted indices, demonstrating 53.89% dominance of intrinsic value components, with historical and cultural factors contributing 29.04% and 18.52% respectively. Assessment outcomes indicated 23.5% of structures scoring above 80 points, particularly highlighting Xiamen’s comprehensive preservation value. This research advances traditional conservation paradigms through its pioneering “value identification–quantitative assessment–conservation and utilization” closed-loop model, providing methodological innovation applicable to similar Overseas Chinese communities. The developed framework fills critical research gaps in the systematic evaluation of Southern Min diaspora architecture while establishing quantitative parameters for decision-making synergy between cultural preservation and urban–rural development. By transcending conventional single-dimensional approaches, this study offers replicable analytical tools for differentiated conservation strategies and policy formulation. Full article

The construction of transition rules is the core and difficulty faced by the cellular automata (CA) model. Dynamic mining of transition rules can more accurately simulate urban land use change. By introducing a graph attention network (GAT) to mine CA model transition rules, the temporal and spatial dynamics of the model are increased based on the construction of a real-time dynamic graph structure. At the same time, by adding an agent-based model (ABM) to the CA model, the simulation evolution of different human decision-making behaviors can be achieved. Based on this, an urban expansion scenario prediction (UESP) model has been proposed: (1) the UESP model employs a multi-head attention mechanism to dynamically capture high-order spatial dependencies, supporting the efficient processing of large-scale datasets with over 50,000 points of interest (POIs); (2) it incorporates the behaviors of agents such as residents, governments, and transportation systems to more realistically reflect human micro-level decision-making; and (3) by integrating macro-structural learning with micro-behavioral modeling, it effectively addresses the existing limitations in representing high-order spatial relationships and human decision-making processes in urban expansion simulations. Based on the policy context of the Outline of the Beijing–Tianjin–Hebei (BTH) Coordinated Development Plan, four development scenarios were designed to simulate construction land change by 2030. The results show that (1) the UESP model achieved an overall accuracy of 0.925, a Kappa coefficient of 0.878, and a FoM index of 0.048, outperforming traditional models, with the FoM being 3.5% higher; (2) through multi-scenario simulation prediction, it is found that under the scenario of ecological conservation and farmland protection, forest and grassland increase by 3142 km², and cultivated land increases by 896 km², with construction land showing a concentrated growth trend; and (3) the expansion of construction land will mainly occur at the expense of farmland, concentrated around Beijing, Tianjin, Tangshan, Shijiazhuang, and southern core cities in Hebei, forming a “core-driven, axis-extended, and cluster-expanded” spatial pattern. Full article

Maintenance 4.0 studies have become a focus for managers and employees when developing effective and efficient maintenance policies. In this study, the barriers to Maintenance 4.0 applications in the textile industry are investigated, and these barriers are weighted using the Stepwise Weight Assessment Ratio Analysis (SWARA) method based on Pythagorean fuzzy numbers. Solutions to address these barriers are presented. As a result of this study, Organizational and Managerial emerged as the most important main criterion. Operational was identified as the second most significant main criterion, followed by Technical Competence. Data-Related and Cybersecurity ranked fourth in terms of importance. On the other hand, Human Resources and Training and Financial were found to be the least important main criteria. These two criteria received lower importance scores compared to the others, with Financial being the criterion with the lowest overall significance. Sensitivity analyses were performed for six different scenarios by changing the importance weights of the decision-makers. The ranking of the criteria only slightly changed with the weights; this means that the results obtained in Case 1 are robust and reliable. Even in Case 6, where the expert weight ratios were completely reversed, the results did not change significantly. This highlights an important point regarding the reliability of the assessment. Full article

This study conducts a macro-level comparative analysis of the eco-efficiency in the agricultural sectors of the European Union (EU) member states and Türkiye from 2003 to 2022. By treating countries as decision-making units, this research offers a holistic overview of how national-level inputs and outputs shape the aggregate performance, focusing on the trade-offs between economic value generation and environmental pressures. An input-oriented Data Envelopment Analysis (DEA) model, based on Variable Returns to Scale (VRS), was employed. The model employs three inputs—compensation of employees (COE), energy consumption (EC), and gross fixed capital formation (GFC)—and two outputs—agricultural gross domestic product (GDP) and GHG emissions (GGEs). All variables were normalized by agricultural land area per country to account for scale differences. The findings reveal significant disparities in the eco-efficiency across countries and over time. Notably, Türkiye consistently demonstrated a high performance, frequently serving as a benchmark. In contrast, several Eastern European countries exhibited lower scores, suggesting significant room for structural improvement at the national level. The results point to the considerable potential for reducing energy and labor inputs in many countries. Instead of offering specific policy prescriptions, this study provides a diagnostic tool that identifies national-level performance gaps, informs policy discussions on resource allocation, and highlights priority areas for more detailed investigation. Full article

For the formation reconfiguration of fixed-wing unmanned aerial vehicles (UAVs), a hierarchical control decision-making method considering both convergence and optimality is studied. To begin with, the dynamic model of the fixed-wing UAVs is established, and the formation reconfiguration control problem formally constructed. Subsequently, based on information such as the initial positions of the UAVs and the expected geometric configuration, an integer programming issue is formulated to determine the destinations of the UAVs. After completing the aforementioned preparations, by incorporating the concept of hierarchical games, the formation guidance and control problem is consequently reformulated as a multiplayer Stackelberg–Nash game (SNG). Through rigorous analysis, the optimality of using the Stackelberg–Nash equilibrium solution as the UAV control commands was demonstrated. Furthermore, a novel policy iteration (PI) algorithm for solving this equilibrium based on fixed-point iteration is proposed. To guarantee the accurate execution of the control commands, an auxiliary control system is designed, thereby forming a closed-loop real-time control decision-making mechanism. The numerical simulation results illustrate that the UAVs can rapidly switch to the desired formation configuration, thus validating the effectiveness of the proposed method. Full article

Food loss and waste are critical challenges in Taiwan’s agri-food supply chain, deteriorating security and resource efficiency. By employing the best–worst method (BWM), a multi-criteria decision-making model was developed in this study to evaluate the severity of losses and wastes. Combining literature review results with expert survey analysis results, key loss points, and mitigation strategies were identified to enhance sustainability and efficiency in Taiwan’s agricultural food system. Among the seven stages of the agricultural food supply chain, supermarket waste (16.95%) was identified as the severest, followed by government policies (16.63%), restaurant waste (15.35%), processing loss (14.71%), production site loss (13.64%), household waste (11.93%), and logistics/storage/distribution loss (10.79%). In the subcategories of each supply chain stage, the eight severe issues were identified as “Inadequate planning and control of overall production and marketing policies” under government policies, “Adverse climate conditions” and “Imbalance in production and marketing” under production site loss, “Inaccurate market demand forecasting” and “Poor inventory management at supermarkets” under supermarket waste, and “Improper storage management of ingredients leading to spoilage” as well as “Inability to accurately forecast demand due to menu diversity” under restaurant waste. The least severe issues included “Poor production techniques” under production site loss. Other minor issues included “Inefficient use of ingredients due to poor cooking skills”, “Festive culture and traditional customs”, and “Suboptimal food labeling design”, all of which contributed to household waste. Based on these findings, we proposed recommendations to mitigate food loss and waste in Taiwan’s agricultural food supply chain from practical, policy, and academic perspectives. The results of this study serve as a reference for relevant organizations and stakeholders. Full article

Port shipping collaboration is vital to greener, more resilient trade, yet decisions remain siloed and uncertain. This study develops a Bayesian network model grounded in empirical data from major Chinese ports, aiming to systematically analyze and enhance port shipping collaborative capacity. The methodology integrates expert knowledge and structural learning algorithms to construct a Directed Acyclic Graph (DAG), representing complex multi-stakeholder interactions among port enterprises, shipping companies, customers, and governmental bodies. Through forward and backward probabilistic inference, the study quantifies how coordinated improvements yield substantial synergistic benefits. Five leverage points stand out: customer engagement in green supply chains, perceived service quality, port digital information integration, multilateral trading maturity, and strict policy enforcement. A newly revealed feedback loop between digital integration and enforcement extends Emerson et al.’s collaborative governance framework, highlighting “digital-era connectivity” as a critical governance dimension and offering managers a focused, evidence-based action agenda. Full article

Water security is a basic requirement of a region’s residents and also an important point of discussion worldwide. The middle route of the south-to-north water diversion project (MR-SNWDP) represents the most extensive inter-basin water allocation scheme globally. It is the major water resource for the Beijing–Tianjin–Hebei region, and its security is of great significance. In this study, 28 indicators including society, nature, and economy were selected from the water sources of the MR-SNWDP from 2000 to 2017. According to the Drivers-Pressures-States-Impact-Response (DPSIR) framework principle, the entropy weight method was used for weight calculation, and the comprehensive evaluation method was used for evaluating the water security of the water sources of the MR-SNWDP. This study showed that the total loss of nonpoint source pollution (NPSP) in the water source showed a trend of slow growth, except in 2007. Over the past 18 years, the proportion of pollution from three NPSP sources, livestock, and poultry (LP) breeding industry, planting industry, and living sources, were 44.56%, 40.33%, and 15.11%, respectively. The main driving force of water security in all the areas of the water source was the total net income per capita of farmers. The main pressure was the amount of LP breeding and the amount of fertilizer application. The largest impact indicators were NPSP gray water footprint and soil erosion area, and water conservancy investment was the most effective response measure. Overall, the state of the water source safety was relatively stable, showing an overall upward trend, and it had remained at Grade III except for in 2005, 2006, and 2011. The state of water safety in all areas except Shiyan City was relatively stable, where the state of water safety had fluctuated greatly. Based on the assessment findings, implications for policy and decision-making suggestions for sustainable management of the water sources of the MR-SNWDP resources are put forward. Agricultural cultivation in water source areas should reduce the application of chemical fertilizers and accelerate the promotion of agricultural intensification. Water source areas should minimize retail livestock and poultry farming and promote ecological agriculture. The government should increase investment in water conservancy and return farmland to forests and grasslands, and at the same time strengthen the education of farmers’ awareness of environmental protection. The evaluation system of this study combined indicators such as the impact of agricultural nonpoint source pollution on water bodies, which is innovative and provides a reference for the water safety evaluation system. Full article

Interdisciplinary decision-making significantly influences both the therapeutic potential and clinical outcomes, shaping clinical attitudes and management strategies. As the integration between endodontic and restorative-prosthetic considerations becomes increasingly prevalent, it is essential to understand how different dental specialists, particularly general dental practitioners, prosthodontists and endodontists, approach clinical decision-making and collaborate to optimize patient care. Objectives: This study aims to identify practice disparities in post-endodontic crown placement to inform national policy reforms, including standardised timing protocols and interdisciplinary referral criteria. Methods: A structured questionnaire was distributed to dentists practicing in Romania, yielding 238 collected responses. Results: Substantial variability was found in clinical approaches: diagnostic imaging preferences indicated frequent use of periapical radiography (83.49%) and CBCT (53.67%). Over 70% expressed high confidence in CBCT’s diagnostic precision, significantly higher than periapical radiography (Wilcoxon Signed-Rank test, p < 0.00001). A statistically significant majority (69.3%, binomial test, p < 0.001) preferred delaying definitive crown placement until radiographic healing of periapical lesions. Logistic regression analysis showed endodontists were significantly less likely to choose invasive treatments compared to other specialists (p = 0.027). Although clinicians widely recognize the significance of prosthetic planning, its early integration into the overall treatment strategy has been inconsistent. Conclusions: This study points out the necessity for standardised guidelines that clearly integrate prosthetic planning into endodontic decision-making, enhancing predictability and tooth preservation. Full article

Food security indices are widely used to support decision making and provide a structured assessment of countries’ capacities to withstand global environmental and economic crises. However, these indices have inherent limitations, including potential biases in ranking and a lack of structural insights into food system dynamics. This study presents a systematic approach that combines elastic-net regression-based feature selection and two-step clustering to address some of these limitations and equip decision makers with structured procedures for making informed decisions and supporting food system management. The mathematical and operational procedures of the proposed approach were demonstrated through an illustrative example using the EIU dataset of 94 countries. The study investigated the sensitivity of composite indicators to extreme data points, relative weights, and dimensionality reduction. After applying elastic-net regression, 15 indicators were selected for Model 1 (M1) and 9 for Model 2 (M2) from an initial set of 25 indicators. Subsequently, two-step clustering grouped the countries into four distinct clusters, reflecting combinations of food system characteristics and income levels. The results demonstrate that countries with industrialized, consolidated food systems and high per capita income tend to exhibit greater food security. Conversely, countries with rural or traditional food systems and low-income levels are more vulnerable to food insecurity. By incorporating statistical rigor and empirical structure discovery, this methodology addresses key limitations of existing indices. It provides an adaptive, transparent framework that informs targeted policy by linking the structural characteristics of food systems to tangible food security outcomes. Full article

With the widespread adoption of electric vehicles (EVs), optimizing their charging and discharging strategies to improve energy efficiency and extend battery life has become a focal point of current research. Traditional charging and discharging strategies often fail to adequately consider the battery’s state of health (SOH), resulting in accelerated battery aging and decreased efficiency. In response, this paper proposes a safe reinforcement learning–based optimization method for EV charging and discharging strategies, aimed at minimizing charging and discharging costs while accounting for battery SOH. First, a novel battery health status prediction model based on physics-informed hybrid neural networks (PHNN) is designed. Then, the EV charging and discharging decision-making problem, considering battery health status, is formulated as a constrained Markov decision process, and an interior-point policy optimization (IPO) algorithm based on long short-term memory (LSTM) neural networks is proposed to solve it. The algorithm filters out strategies that violate constraints by introducing a logarithmic barrier function. Finally, the experimental results demonstrate that the proposed method significantly enhances battery life while maintaining maximum economic benefits during the EV charging and discharging process. This research provides a novel solution for intelligent and personalized charging strategies for EVs, which is of great significance for promoting the sustainable development of new energy vehicles. Full article