Search Results (6,425)

Rapid urbanization has led to habitat fragmentation in peri-urban areas, continuously eroding the ecological foundation of sustainable food systems in urban–rural transition zones and posing a real threat to regional food security. Against the backdrop of urbanization disturbances, traditional nature-based solutions have limitations in addressing socioecological nonlinear responses, whereas digital tools offer new governance pathways for enhancing food system resilience. To elucidate the intrinsic mechanisms through which digital technology empowers the resilience of peri-urban food systems, this study, which is grounded in ecological wisdom theory, constructs a system dynamics model that integrates “digital technology-ecological perception-ecological wisdom capital” in a three-dimensional linkage. This model simulates the dynamic process through which sustainable food systems in urban–rural transition zones resist the risks of habitat fragmentation and achieve synergistic steady-state evolution. According to the simulation results, a synthesized steady-state transition in sustainable food systems can be regarded as a self-organizing phase transition process. During resource metabolism, system elements show strong nonlinear symbiotic and mutually beneficial features. Further, there is a significant time-lag effect on improving food system resilience through digital technology empowerment and policy coordination. Also, the effects of governance are not immediately visible. Further, as an important instrumental empowerment carrier, urban–rural spatial and information barriers can be broken through means like digital ecological monitoring. Moderate investment in this regard can promote the acceleration of the system’s self-organizing phase transition. Also, this can enhance resilience against disturbance from habitat fragmentation while ensuring food production and supply. Finally, the ecological carrying capacity of core food production spaces does not increase monotonically. This means that the system possesses an adaptive cyclical fluctuation mechanism, with a periodic oscillatory evolution of carrying capacity. This study breaks through static analytical paradigms, fills the quantitative research gap on the resilience evolution of peri-urban food systems driven by the integration of digital technology and ecological wisdom, and can provide scientific evidence and decision-making support for food–ecological collaborative governance in China’s urban–rural transition zones. Full article

As global photovoltaic (PV) installation capacity grows rapidly, the environmental pollution and resource waste from the large-scale end-of-life (EOL) wave have drawn increasing attention. Traditional disposal methods such as landfilling and incineration are no longer viable, making green recycling a logical path for the PV industry. This paper reviews recent progress in the disassembly and recycling of EOL crystalline silicon (c-Si) PV modules. It first describes the structural material composition of c-Si PV modules and summarizes global recycling policies and regulatory frameworks. It then analyzes the mechanisms and process parameters of major delamination technologies, including mechanical crushing, pyrolysis, thermal cutting, high-voltage pulse fragmentation, solvent-based approaches, and laser peeling. Methods for purifying silicon and recovering precious metals such as silver and copper are also covered. Finally, key challenges in the recycling field and future development trends are discussed, with the aim of supporting the advancement of c-Si PV recycling technologies and the sustainable development of related industrial chains. Full article

Ensuring the long-term sustainable utilisation of supplemented cropland has become a critical challenge for implementing China’s requisition–compensation balance of farmland (RCBF) policy, particularly in the fragmented hilly and mountainous regions of Southwest China. Existing studies generally evaluate land suitability and utilisation performance separately, making it difficult to identify mismatches between theoretical suitability and actual utilisation and thereby limiting targeted regulation. To address this limitation, this study proposes a suitability–utilisation deviation identification (SUDI) framework, which integrates four sequential analytical components: three-dimensional suitability assessment, suitability–utilisation deviation identification, driving mechanism analysis, and sustainable regulation. Taking Beibei District of Chongqing as a case study, supplemented cropland parcels were identified using the 2020–2024 land change survey data. A three-dimensional suitability evaluation system incorporating production, ecological, and utilisation attributes was established to quantify theoretical land suitability. Actual utilisation performance was characterised using the land economic utilisation coefficient, and suitability–utilisation deviation was identified through residual analysis between theoretical suitability and utilisation intensity. A Bayesian-optimised Extreme Gradient Boosting-SHAP (XGBoost-SHAP) model was subsequently employed to reveal the nonlinear effects and interaction mechanisms of the driving factors. The results indicate the following: (1) supplemented cropland in Beibei District is predominantly characterised by medium-to-high suitability, with high-suitability patches exhibiting a mosaic spatial pattern of local aggregation and overall dispersion; (2) suitability–utilisation deviation is dominated by under-utilised plots, whereas well-matched and over-intensified plots account for substantially smaller proportions, indicating that insufficient realisation of land suitability is the prevailing utilisation pattern; and (3) the land economic utilisation coefficient is the dominant factor driving suitability–utilisation deviation, while high-standard farmland construction and plot area exhibit significant mitigating effects. Moreover, significant interaction effects between utilisation intensity and location-related variables reveal that unfavourable spatial conditions amplify deviation risk under intensive land use. The proposed SUDI framework extends conventional suitability assessment by explicitly linking suitability evaluation with utilisation performance, driving mechanism analysis, and differentiated regulation. It provides a transferable analytical framework for diagnosing suitability–utilisation mismatch and supports dynamic management and sustainable utilisation of supplemented cropland in fragmented hilly and mountainous regions. Full article

Background: The rapid integration of Artificial Intelligence (AI), specifically Clinical Decision Support Systems (CDSS), into healthcare offers substantial efficiency but introduces critical ethical and legal challenges, particularly the perpetuation of systemic bias against older adults (“Digital Ageism”). While technological advances may improve care, they can violate fundamental bioethical principles when models are trained on unrepresentative data. Aim: This article argues that traditional clinical risk-management models are structurally insufficient to address opaque algorithmic bias and presents a conceptual, multidimensional governance framework designed to prevent the codification of human ageism into AI infrastructure. Methods: Drawing on systemic failures observed during the COVID-19 pandemic, the normative model integrates legal and governance standards aligned with the EU AI Act, Explainable AI (XAI) tools, and a three-phase implementation protocol. Results: To illustrate potential application without overburdening medical staff, the article introduces a theoretical Targeted Escalation Protocol and an Autonomous High-Load Safety Mode. The latter applies deterministic hardcoded constraints to contain age-dominant outputs during acute surges while preserving attending-clinician authority. The framework is explored through an Intensive Care Unit (ICU) thought experiment. Conclusions: The framework provides a structured roadmap for policymakers, ethicists, and healthcare administrators to move from reactive defensive medicine toward proactive ethical safety, safeguarding the dignity of the aging population while aiming to mitigate institutional legal exposure. Full article

The Tianjin Container Freight Index (TCI) is a key benchmark for container shipping prices in northern China and is strongly driven by macroeconomic conditions and trade fluctuations. This study proposes a PCC–CNN–GRU hybrid deep learning framework for TCI forecasting using 25,870 daily observations from 13 April 2015, to 1 January 2024. The model combines Pearson Correlation Coefficient (PCC)-based feature selection, convolutional neural networks (CNN) for local temporal feature extraction, and gated recurrent units (GRU) for capturing long-term dependencies, thereby addressing the nonlinear and nonstationary characteristics of TCI data. Empirical results show that the proposed model achieves an R² of 91.24%, outperforming standalone CNN, GRU, and classical ARIMA and VAR models. The model demonstrates strong robustness to structural changes and noise, enhancing its suitability for complex market environments. The integrated framework provides reliable forecasting support for shipping companies, logistics planners, and policymakers in pricing, capacity planning, and sustainable maritime operations. This study contributes to the growing integration of intelligent forecasting methods with regional freight index analysis and supports the digital transformation of the container shipping industry. Full article

This paper investigates the multifaceted macroeconomic drivers of vehicle electrification in India and correlates them with micro-level corporate financial returns using a rigorous dual-stage econometric framework. Stage 1 employs a Newey–West time-series estimator on monthly observations to evaluate aggregate consumer demand elasticities across the automotive sector. Stage 2 utilizes a fixed effects panel specification with clustered standard errors across 10 major Indian automotive manufacturers over a four-year fiscal horizon. Stage 1 results demonstrate that short-run variations in Brent crude prices lack joint predictive power over domestic retail metrics ($F=0.89,p=0.4166$), supporting the thesis that state-owned OMC price-smoothing insulates short-term market dynamics from global oil shocks. Conversely, Stage 2 panel estimations prove that annual global Brent crude fluctuations yield no significant contemporaneous margin shocks. However, expanding annual EV market penetration exerts a substantive negative impact ($\beta =-2.49,p=0.107$) bordering statistical significance on corporate operating profit margins. This operational decoupling reflects a prominent industry ‘J-curve’, where accelerating consumer adoption cycles are countered by heavy front-loaded capital expenditures, asset re-tooling, and unoptimized economies of scale. These findings provide critical direct and indirect strategic insights for organizational stakeholders and policymakers navigating transitional capital cycles in emerging markets. Full article

Digital Twin (DT) technologies are increasingly transforming maritime and port systems by enabling real-time monitoring, predictive analytics, operational optimisation, and sustainability-oriented decision-making. Despite growing academic and industrial interest, the integration of DTs into sustainable maritime ecosystems remains fragmented, particularly in assessing digital maturity and sustainability performance. This study follows the PRISMA 2020 methodology to systematically review 26 peer-reviewed studies from Scopus and Web of Science. Through thematic synthesis, based on inductive coding and the identification of recurring patterns across the reviewed studies, four Digital Twin dimensions and a four-level maturity framework supported by Key Performance Indicators (KPIs) were derived: smart port development, artificial intelligence integration, energy optimisation, and environmental governance. Based on the findings, the study proposes a DT Maturity Framework for sustainable maritime systems, structured across progressive levels of technological integration, operational intelligence, sustainability alignment, and governance capacity. The framework provides an operational, multidimensional approach to assessing DT maturity across heterogeneous maritime ecosystems. The study contributes to the emerging literature on sustainable maritime digitalisation by offering a systematic conceptual synthesis that can support future research, strategic planning, and policy development in smart and sustainable port ecosystems. The proposed framework constitutes a conceptual assessment model whose empirical validation is identified as a priority for future research. As a literature-derived conceptual assessment model, the proposed framework requires empirical validation before its maturity levels and associated KPIs can be considered empirically established. Full article

Large-scale maritime activity, persistent shipping incidents, and complex marine environments continue to place substantial demands on maritime search and rescue (MSAR). Current MSAR systems do not fully capitalize on the complementary strengths of unmanned aerial vehicles (UAVs) and satellites for collaborative tracking and rescue support. Existing air-space collaboration technologies suffer from two critical limitations: (1) rigid processes, including fixed task allocation, pre-determined path planning without real-time environmental adaptation, and isolated satellite–UAV decision-making, and (2) long task completion cycles, mainly because many methods are adapted to wide-area, long-duration military tracking scenarios. They therefore provide limited support for the dynamic flexibility required in MSAR. This study proposes a Proximal Policy Optimization (PPO)-based air-space collaborative tracking method for maritime moving targets to address these shortcomings and enhance air-space cooperation in MSAR operations. The core implementation of the method includes: (1) integration of target drift forecasting, satellite orbit prediction, UAV task allocation, and path planning into a unified reinforcement learning framework to reduce isolated single-platform decision-making; (2) the adoption of PPO to generate dynamic and flexible air-space collaborative tracking strategies that adjust satellite observation angles and scanning ranges, as well as UAV altitude, speed, and heading according to real-time target, environmental, and platform states; and (3) the design of a multi-dimensional reward function that balances target proximity, energy efficiency, coverage overlap, and inter-platform cooperation to guide strategy optimization. Simulation experiments include system-feasibility verification, baseline-controller comparison, PPO hyperparameter screening, and cross-scenario evaluation. Under idealized communication and payload-matching assumptions, the method enables coordinated tracking of maritime moving targets in simulated MSAR scenarios. In the standardized evaluation, PPO achieved an 11.9% higher mean evaluation episode return, 11.2% lower aggregate UAV energy consumption, and a 9.92-percentage-point greater endurance margin than DDPG. Hyperparameter screening compared candidate learning rates, discount factors, and training budgets, informing the PPO configuration for the subsequent six-scenario evaluation. Across the six controlled scenarios, rewards stabilized after approximately 1400 steps, while action magnitudes varied among regions. These results indicate that the proposed method has potential to enhance air-space collaborative tracking for MSAR decision support. Full article

This study evaluates the economic feasibility of standalone Battery Energy Storage Systems (BESS) for energy arbitrage in the Portuguese day-ahead electricity market. A Mixed-Integer Linear Programming (MILP) model is developed to optimize the operation of BESS configurations with varying durations (2, 4, 6, and 8 h), using historical price data from 2020 to 2024. The model incorporates realistic operational constraints, and the resulting arbitrage revenues are analyzed under multiple cost scenarios. Additionally, the study performs a Net Present Value (NPV) analysis using average and year-specific price profiles and three different scenarios to assess long-term investment viability. Consistent with current market access conditions in Portugal, the analysis focuses exclusively on day-ahead market arbitrage, and alternative revenue streams (intraday, real-time, ancillary services) are discussed qualitatively due to limited liquidity and restricted participation rules. The results reveal that although BESS can generate positive cash flows in recent high-volatility years, all configurations yield negative NPVs under current cost structures and market conditions. Even with optimistic cost reductions, breakeven is not achieved, indicating that standalone arbitrage remains financially not viable. These findings highlight the importance of cost optimization and the need for complementary revenue streams or policy support to make such investments feasible in Portugal. Full article

Historic gardens are among the cultural heritage assets most vulnerable to climate change because their significance depends on the continuous interaction between living ecological systems, designed landscapes and cultural values. Although international policies increasingly advocate the integration of climate adaptation into heritage management, operational methodologies capable of systematically incorporating climate risk into the conservation planning of historic gardens remain limited. This paper develops the Historic Garden Climate Risk Management Framework (HG-CRMF), a multi-scale, heritage-centered methodological framework designed to integrate climate risk management within existing Conservation Management Plans (CMPs). The framework was developed through the synthesis of international heritage conservation principles, climate adaptation policies, environmental risk assessment methodologies and adaptive governance approaches. Its principal innovation lies in combining strategic conservation planning at the scale of the historic garden with operational implementation through Garden Conservation Units (GCUs), spatially coherent heritage entities that enable site-specific assessment of heritage values, climate hazards, exposure, vulnerability, adaptation measures and monitoring indicators. The HG-CRMF provides a structured workflow linking heritage significance, climate risk assessment, adaptation planning, monitoring and adaptive governance within a single decision-support methodology. Rather than constituting a stand-alone adaptation strategy, the framework strengthens existing conservation planning by embedding climate-informed decision-making into routine management processes. Full article

Background: Mental health issues among university students are a growing global concern, including in Malaysia. This exploratory study aims to examine mental health support systems in Malaysian universities using a dyadic perspective, capturing both students’ and counsellors’ experiences. This addresses a key gap in the literature, which has largely relied on single-perspective accounts. Methods: A qualitative exploratory design was employed using purposive sampling. Semi-structured interviews were conducted with 15 students receiving mental health support and 10 university counsellors. The data were analysed using hybrid deductive–inductive thematic analysis. Results: Three themes emerged from the findings. The first theme showed that students sought support once distress affected their daily functioning, valuing trust, safety, and emotional guidance, while counsellors described a parallel process of assessing student needs and facilitating longer term recovery. The second theme identified barriers shared by both groups, namely limited awareness of services and stigma, alongside additional constraints reported only by counsellors, including staffing shortages, unclear referral pathways, and limited institutional support. The third theme centred on strategies for improvement, including early assessment, awareness campaigns, digital accessibility, resilience building, and stronger collaboration among students, counsellors, and university stakeholders. Conclusions: This study contributes to the literature by integrating three complementary theoretical perspectives to explain university students’ mental health support experiences, support networks, and help-seeking behaviour. The findings also provide practical guidance for university administrators, counsellors, and policymakers seeking to develop more accessible, proactive, and student-centred mental health support systems. As the study is based on qualitative, cross-sectional data, the findings should be interpreted as indicative rather than causal. Nonetheless, they suggest that strengthening such initiatives may contribute to more supportive and responsive mental health provision within higher education institutions. Full article

Background: The health sector is getting transformed with the usage of AI, be it diagnosis, treatment planning, disease prediction, and or health system management. Research in this field has picked up in the last few years, which was made possible with the emergence of machine learning, natural language processing and the increasing number of e-health records. Objectives: The study aims to investigate the current trends in the implementation of artificial intelligence (AI) applications in medical settings by investigating the global scientific output/landscape on this theme, such as the annual publication trends, country-wise contributions, and publishing patterns. Methods: The current study is based on systematic review by combining bibliometric analysis and cluster analysis using VOSviewer version 1.6.20, R software version 4.5.0, and Biblioshiny (Bibliometrix package in R) along with preferred reporting items for systematic reviews and meta analyses (PRISMA), 2020 which provides transparency and rigorous visualization to examine the articles published in English on the use of AI in healthcare, after the onset of COVID-19 till date i.e., from 2020 to 2026 on the Scopus database. Results: Using the relevant search string, 5940 documents were identified between 2020 and 2026, 1434 were included for analysis after screening and relevant filters. The publications have increased remarkably after 2020 on this theme and more than half of the publications have their roots in the discipline of Medicine. The USA, China, and the United Kingdom have contributed the most to the volume of research. Natural language processing and diagnosis are the emerging themes. The Journal of Medical Internet Research, BMC Medical Informatics and Decision Making, Computers in Biology and Medicine, IEEE Journal of Biomedical and Health Informatics, Frontiers in Public Health, and Digital Health are some of the most influential sources in the field. Li J and Liu X are among the authors with remarkable local impact. Conclusions: The work aims to assist investigators, health care professionals, and policymakers to learn about modern trends and focus on critical areas of future research and collaboration in AI-enhanced health care. The limitation of the study is that it considered only the Scopus database but it has opened up opportunities for researchers for analysis using other databases such as Dimensions, Lens, and PubMed. Also, this review is considering the publication record since the onset of COVID-19 but a comparative analysis of pre and post-pandemic studies can also be conducted to get a holistic view of drastic collaboration of research in this field. Discussions: The findings suggest that the role of artificial intelligence in health care has paramount over recent years, with other supporting technologies but a technologically hesitant population as well as low acceptance of AI due to ethical issues, cannot be ignored for ensuring efficiency in the health sector. Full article

Shifting dietary patterns is necessary to meet global environmental and health targets. Legumes represent a nutritious and viable protein source. Yet, consumption remains low among consumer groups due to low familiarity and inferior sensory perceptions. Sensory conditioning has proven effective in increasing acceptance of unfamiliar foods and may have applicability in shaping future legume preferences. The study compared Mere-Exposure, Flavor–Flavor and Flavor–Nutrient learning strategies in their effectiveness in increasing consumer preferences for legumes in a Danish adult sample (N = 70). Participants filled out a questionnaire before and after a three-week at-home meal intervention. Fava beans were used as the legume case, and all participants, were exposed to 12 fava bean test stimuli. Participants in the Mere-Exposure group experienced a significant increase in preferences, while the Flavor–Flavor and Flavor–Nutrient groups did not. The Mere-Exposure group also experienced an increase in liking of the appearance and flavor of fava beans. By showcasing the potential of a widely applicable sensory strategy, the study highlights how sensory conditioned learning might support the adoption of more sustainable foods in an adult sample. The results provide insights for health and nutritional personnel, researchers and policymakers seeking to promote sustainable diets. Full article

China pushes forward two key national strategies, namely common prosperity and Healthy China. Many papers have discussed how urban common prosperity and urban healthy development evolve across space, yet most of these works cannot match the practical needs of local high-quality urban construction in Guangdong. This study takes various cities in Guangdong Province as the research area and utilizes multi-source big data from 2010 to 2025 so that it constructs a multidimensional evaluation system for the two major systems. Subsequently, this research employs methods including a deep weighted neural network to conduct empirical analysis. The results show that the urban common prosperity index in Guangdong Province exhibits an overall continuous upward trend. However, common prosperity gradually deviates from the development pattern that matches the economic driving forces. Meanwhile, the prominent advantages of urban healthy development gradually shift from eastern Guangdong to the core area of the Pearl River Delta, which establishes an overwhelming leading position for the Pearl River Delta. Furthermore, the regional development level plays a certain promoting role in healthy development. Spatially, common prosperity and urban healthy development exhibit phased non-linear characteristics, which encompass synchronous growth, growth rate divergence, and trend deviation. This study provides empirical evidence and practical support for Guangdong Province so that policymakers can comprehensively advance common prosperity and healthy city construction. Ultimately, these efforts optimize the regional development layout, which promotes high-quality and sustainable urban development. Full article

Renewable energy communities (RECs) are increasingly recognised as important instruments for accelerating the transition towards decentralised, low-carbon energy systems. However, technological progress and supportive policies alone cannot ensure their success, as participation depends largely on citizens’ behavioural readiness. This study investigates the behavioural determinants of participation in renewable energy communities by extending the Theory of Planned Behaviour (TPB) with four motivational dimensions: environmental, economic, technical, and social. A quantitative cross-sectional survey of 174 household electricity users in Slovenia was analysed using Partial Least Squares Structural Equation Modelling (PLS-SEM). The findings show that environmental and economic motivations were positively associated with Attitudes, Technical Motivation was positively associated with Perceived Behavioural Control, and Social Motivation was positively associated with Subjective Norms. These TPB constructs are positively associated with behavioural readiness to participate in renewable energy communities. A supplementary exploratory multi-group analysis suggested possible differences between prosumers and conventional consumers. Prosumer status reflected individual household renewable electricity production and not verified REC membership, while Behavioural Readiness captured prospective stated intention and willingness rather than observed participation. However, because the prosumer subgroup comprised only 15 respondents, these group-specific patterns should be interpreted cautiously and require confirmation in larger and more balanced samples. The study extends the Theory of Planned Behaviour by integrating a multidimensional motivational framework and conceptualises participation in renewable energy communities as a socio-technical behavioural process. The findings provide empirically informed insights for policymakers, municipalities, and renewable energy community developers seeking to support citizens’ behavioural readiness to participate in renewable energy communities. Full article