Search Results (3,896)

With the increasing integration of distributed energy sources, fault restoration in power distribution systems faces challenges in terms of real-time performance and adaptability. To effectively manage the uncertainty and volatility of distributed generation, this paper proposes a rapid self-healing strategy based on a dynamic operational grid. By enabling real-time topological reconfiguration and utilizing adaptive resource allocation, the proposed method accommodates the inherent fluctuations of distributed energy sources. First, a dynamic grid weighted graph theory model is constructed, and an emergency control strategy combining particle preprocessing and stepwise optimization is designed to achieve rapid fault response. Then, a “primary-secondary” two-tier restoration mechanism is established: the primary layer integrates the Floyd algorithm with optimized adaptive dynamic programming to achieve millisecond-level restoration of critical loads; the secondary layer employs an improved particle swarm algorithm incorporating Lévy flight perturbations and adaptive weighting to maximize the restoration of general loads. Simulations on a 56-node system demonstrate that this method achieves 100% restoration of critical loads under various fault scenarios. Even under extreme conditions, it can restore 90.88% of secondary loads and 44.63% of tertiary loads, forming a self-healing system characterized by “second-level detection and minute-level restoration,” thereby significantly enhancing system resilience. Full article

Rising heatwaves threaten urban sustainability, necessitating a shift toward heat resilience. This study examines 38 cities across China’s three major coastal urban agglomerations (2016–2024) to quantify dynamic resilience responses. Utilizing a dual-threshold identification method and the Baidu Search Index to construct a Standardized Stress Index (SSI), the research evaluates urban heat vulnerability (UHV) through an exposure–sensitivity–adaptive capacity framework while applying NMF and machine learning models (XGBoost/SHAP) to analyze spatiotemporal heterogeneity. The results show that heatwave pressures peaked in 2022–2023, with Jing–Jin–Ji’s UHV evolving from localized clusters toward regional homogenization. Regional UHV profiles reveal that Jing–Jin–Ji is constrained by population pressures, the Yangtze River Delta (YRD) by resource allocation, and the Pearl River Delta by industrial attributes; notably, the YRD’s systematic coordination effectively offsets structural vulnerability. Furthermore, the optimized XGBoost model achieves strong predictive performance (R² = 0.673), revealing that core factors like summer heat exposure intensity (SHE, 25.65% importance) trigger sharp non-linear surges in social stress upon crossing critical inflection thresholds (e.g., SHE at −0.10). The conclusion will lead to the formulation of differentiated, forward-looking climate adaptation strategies to enhance urban resilience across major regions. Full article

Background: Liver diseases are an increasing public health concern in Morocco; reliable national population-based estimates of liver fibrosis and cirrhosis in Morocco are currently unavailable. Existing evidence is largely limited to selected high-risk groups and hospital-based cohorts. Generating reliable prevalence data is crucial for designing evidence-based screening pathways, targeting high-risk groups and informing prevention and treatment policies. Objectives: Our aim was to comprehensively review studies on the prevalence of liver fibrosis and cirrhosis in Morocco, focusing on characterizing study populations, specifically high-risk populations and hospital-based cohorts, diagnostic methods and thresholds used. The review also summarizes hospital-based cirrhosis cohorts without merging them with prevalence estimates, and identifies gaps in the literature, particularly the absence of population-based prevalence studies and national epidemiological data in Morocco. Methods: The study systematically reviewed literature up to 26 October 2025, including studies conducted in Morocco among high-risk populations or hospital-based cirrhosis cohorts, using multiple databases. Two reviewers independently screened and extracted data, assessing bias with the Joanna Briggs Institute (JBI) checklist. Due to heterogeneity in study populations and diagnostic approaches, a narrative synthesis was performed. Hospital-based cohorts were analyzed separately to provide contextual information and were not included in prevalence estimates. Results: From 1198 records, four Moroccan studies providing prevalence data on liver fibrosis and cirrhosis were included, primarily involving patients with hepatitis C, HIV, or rheumatoid arthritis. Additionally, three hospital-based cirrhosis cohorts were incorporated for a contextual analysis of disease severity and complications. In total, seven studies were included, with prevalence and hospital-based data analyzed separately to ensure clarity. Conclusions: Current evidence on liver disease in Morocco is limited but suggests a significant burden among high-risk groups. The findings highlight major gaps in national epidemiological data and underscore the urgent need for comprehensive nationwide data and improved diagnostic tools to guide effective screening, prevention, and resource allocation. Full article

This study examines how FUN&EAT’s “AI+Unmanned” strategy affects system resilience in sustainable operations management. This study is based on a mixed design, combining a case study with a survey study, and uses 499 valid samples and tests the effects of AI-driven decision-making capability, resource allocation flexibility, risk forecasting ability, system synergy capability, and resource optimization ability. The results show that all five factors have significant positive effects on system resilience. Resource optimization ability has the strongest effect, followed by AI-driven decision-making capability. The mediation results show that risk forecasting ability partially mediates the effects of system synergy capability and resource allocation flexibility on system resilience. However, risk forecasting ability does not mediate the effects of resource optimization ability and AI-driven decision-making capability. The findings indicate that FUN&EAT can improve operational resilience through intelligent decision-making, flexible resource allocation, risk prediction, system coordination, and resource optimization. Full article

Accurate forecasting of urban water demand is essential for scientific regulation and sustainable management of water resources, particularly in complex DMA (District Metered Area) environments. This study proposes an integrated regional water demand prediction framework that combines CEEMDAN decomposition with deep learning techniques. CEEMDAN is first applied to decompose the original water demand time series into multiple Intrinsic Mode Functions (IMFs), effectively extracting multi-scale features and mitigating non-stationarity and complexity. A hybrid Transformer-BiLSTM model is then constructed to capture global dependencies, nonlinear dynamics, and bidirectional temporal features. Experimental results demonstrate that the proposed CEEMDAN-Transformer-BiLSTM model significantly outperforms various benchmark models in terms of prediction accuracy, robustness, and generalization across different DMAs. This research provides a new perspective for modeling complex water resource time series and offers theoretical and practical support for optimizing urban water allocation and achieving sustainable management, while laying a foundation for future work involving external driving factors, enhanced model interpretability, and dynamic regulation mechanisms. Full article

Proton exchange membrane fuel cell–battery hybrid power systems provide an effective solution to overcome the limited endurance of battery-powered multirotor unmanned aerial vehicles. However, the highly transient power demands of quadcopter platforms, combined with balance-of-plant losses and operational constraints, create significant challenges for reliable energy management. This study proposes a degradation-aware stress-mitigation model predictive control-based energy management framework to maximize mission endurance under realistic conditions. A control-oriented, physics-consistent model is developed using manufacturer polarization data from a 500 W Aerostak proton exchange membrane fuel cell. The model captures polarization behavior, balance-of-plant loads, battery dynamics, and direct current-bus power balance. The model predictive control strategy optimally allocates power by maintaining direct current-bus stability, regulating battery state-of-charge within safe limits, and constraining fuel cell power ramp rates to mitigate degradation. High-fidelity simulations are conducted under stochastic wind disturbances and mission-dependent load profiles, including takeoff, climb, cruise, and maneuvering phases. The results show continuous power delivery without unmet load demand. The hybrid system achieves a flight endurance of 220–224 min, consuming a total of 89.99 g of hydrogen at an average rate of 0.398–0.412 g/min, indicating a notable reduction under the considered operating conditions. Additionally, long-term analysis indicates that over 97% of initial endurance is preserved after 100 cycles, demonstrating robustness against fuel cell aging. An analytical real-time feasibility assessment further indicates that the control-oriented formulation is compatible with the computational resources of typical unmanned aerial vehicle-class onboard processors, while the integration of adaptive and robust predictive control techniques is identified as a direction for future work. Full article

Umbrella species can facilitate efficient biodiversity conservation, as their targeted protection effectively safeguards co-occurring species and entire ecosystems in conservation management. To verify Hainan gibbon (Nomascus hainanus) as an umbrella species for sympatric endangered species, this study evaluated the conservation efficacy of the Hainan four-eyed turtle (Sacalia insulensis) through multidimensional spatiotemporal analysis. By comparing the population data collected using cage-trapping methods from 2005 to 2025, the S. insulensis population in the Bawangling region of the Hainan Tropical Rainforest National Park has exhibited obvious recovery, from no initial records to 25 individuals (mean relative population density: 0.0404 individuals/cage-day). Its growth trajectory is closely linked to the continuous enhancement of conservation efforts for Hainan gibbons, confirming umbrella effects stemming from poaching suppression and habitat restoration. However, when compared with recent surveys in other park regions, significant spatial limitations were revealed that the mean relative population densities at Yinggeling (0.01 individuals/cage-day) and Diaoluoshan (0.0064 individuals/cage-day) were only 25.74% and 15.84%, respectively. Additionally, juveniles constituted only 12% of Bawangling’s population, indicating that while the umbrella strategy delivers meaningful conservation outcomes, supplementary targeted initiatives are still needed to improve efficiency in resolving the species reproductive bottlenecks (e.g., nesting habitat quality). Those findings demonstrate that umbrella species conservation can establish an ecological conservation force field; however, its effectiveness is constrained by unequal resource allocation and species niche differences. Future efforts should overcome the spatial and ecological limitations of single-species umbrella effects by using smart patrol networks, secondary umbrella species mechanisms, and dedicated management plans. Full article

This review examines the relationship among foreign direct investment (FDI), trade openness, and economic growth, with emphasis on the channels through which external integration influences development outcomes. The literature generally suggests that FDI can raise growth through capital accumulation, technology transfer, productivity gains, and stronger linkages with domestic firms, while trade openness can promote growth by expanding market access, increasing competition, and improving resource allocation. However, the evidence is not uniform: some studies report that trade openness is the main driver of growth, while others find that FDI has a stronger effect, or that both variables matter only under favorable macroeconomic, institutional, and financial conditions. This review synthesizes theoretical arguments and empirical findings, identifies major transmission mechanisms and conditional factors, and highlights the policy environment needed for FDI and trade liberalization to translate into sustained economic growth. Full article

Indoor visible light communication (VLC) has attracted increasing attention as a promising wireless access technology because of its large unlicensed bandwidth and dual functionality of illumination and data transmission. However, practical VLC systems are vulnerable to line-of-sight (LoS) blockage caused by user mobility, human shadowing, and indoor obstacles, which may degrade link reliability and service continuity. Although hybrid VLC/RF networks can improve robustness by using RF transmission as a backup link, excessive RF fallback under severe optical blockage may overload the bandwidth-limited RF interface and reduce the service quality of RF-associated users. To address this issue, this paper investigates a risk-aware illumination-constrained resource allocation scheme for hybrid VLC/RF indoor networks under random optical blockage. A unified system model is developed by considering Lambertian optical propagation, random optical blockage, RF backup transmission, and working-plane illumination constraints. Based on this model, a joint user association and power allocation problem is formulated under QoS, transmit-power, and illumination requirements. The proposed scheme evaluates VLC service utility under blockage uncertainty, controls RF fallback to avoid excessive backup-link loading, allocates VLC/RF transmission power, and performs illumination feasibility adjustment to preserve the required lighting level. Simulation results show that, under severe blockage conditions, the proposed scheme reduces the outage probability to approximately 0.26, compared with 0.68 for VLC-only transmission and 0.47 for threshold-based VLC/RF switching. For a 20-user network, the proposed scheme achieves an average sum rate of approximately 277 Mbps, maintains a 100% illumination compliance ratio, and achieves higher energy efficiency than the benchmark schemes. Further RF backup analysis shows that the proposed scheme can maintain the service quality of RF-associated users by avoiding excessive RF fallback. These results demonstrate the effectiveness of the proposed framework for reliable and illumination-feasible hybrid VLC/RF indoor communication. Full article

China has achieved rapid industrial development, but at the same time, the problems of tightening energy supply and environmental degradation have become increasingly prominent. Improving green total factor productivity (GTFP) has become a central task in China’s industrial transformation. Using industrial input–output data for 30 Chinese provinces from 2008 to 2020, this paper innovatively extends the Hsieh–Klenow framework by introducing land, energy and environmental pollution as input factors, and combines it with a spatial Durbin model to quantify industrial GTFP losses caused by energy misallocation and factor price distortions and to examine their spatial spillover effects. The results show that resource misallocation has generated significant inter-provincial industrial GTFP losses, with loss rates ranging from 13.96% to 32.57%, among which energy misallocation is the most important source. By comparing effective GTFP under a scenario without energy distortions with the observed GTFP, it can be found that eliminating energy misallocation would increase the average level of China’s inter-provincial industrial GTFP by about 40% during the sample period. Spatially, GTFP losses exhibit significant clustering, mainly concentrated in the northwest region, particularly in the Yellow River Basin, while lower losses are observed in the Yangtze River Basin and the southeastern coastal regions. In addition, industrial GTFP losses show significant spatial correlation and spillover effects. Environmental regulation can reduce local GTFP losses, but it may increase losses in neighboring regions through mechanisms such as industrial relocation or pollution spillovers. To support the sustainable development of China’s industry, these findings indicate the need for more efficient energy allocation, faster technological upgrading and talent investment, as well as stricter and more targeted environmental regulation. Full article

In green credit markets, information asymmetry and corporate greenwashing increasingly undermine the efficiency of resource allocation, while traditional assessment models relying on static, self-reported environmental data fail to impose effective constraints. To address this limitation, this paper develops a dynamic corporate carbon credit evaluation framework by integrating multiple sources of physical (hard) signals and embeds it into commercial banks’ credit management systems. Anchored in multi-source power-carbon signals (e.g., carbon intensity and compliance records), the framework integrates verifiable physical metrics with ESG disclosures via a Bayesian AHP–CRITIC weighting scheme to construct a dual-dimensional classification scheme (“Credit Rating–Green Label”). It further embeds carbon credit scores into dynamic adjustments to credit limits and differentiated interest rate pricing, forming an integrated risk management mechanism. Empirically, a stratified validation strategy is adopted. Analysis based on a sample of 3327 firms shows that the proposed framework achieves a classification consistency of 81.3%, significantly outperforming both a financial-only baseline model (46.8%) and models based on voluntary carbon disclosure (61.4%). Ablation studies further confirm that physical (hard) signal indicators contribute substantially to ranking stability. Moreover, panel regression analysis, based on 36,185 firm-year observations from 3327 firms over the period 2000–2023, demonstrates that carbon credit scores have robust predictive power for future financial distress. Overall, the proposed framework offers a sustainable, data-driven approach to green credit risk management. Full article

Promoting the high-quality development of farmers’ specialized cooperatives and narrowing regional development gaps is critical for advancing China’s rural revitalization strategy. Based on provincial panel data covering 30 Chinese regions from 2015 to 2023, this paper constructs a five-dimensional evaluation index system including standardized operation, operational performance, service scope, driving effect, and industrial upgrading, and adopts the entropy weight method to quantify the comprehensive development level of cooperatives. By combining spatial autocorrelation, kernel density estimation, the Dagum Gini coefficient and the Geodetector model, this paper explores the spatio-temporal evolution, regional disparities and multi-factor coupled driving mechanism of cooperative development. The main findings are as follows: (1) While the total quantity of cooperatives keeps expanding nationwide, their overall development level presents an evolutionary feature of declining first and then rising; industrial upgrading gradually becomes a new growth engine, whereas operational performance and driving effect slip downward. (2) The spatial layout of cooperatives maintains a typical pyramid structure; high-value agglomeration shifts from the Yangtze River Delta to southeast coastal regions, and low-value clusters are persistently concentrated in Northeast China. (3) The overall Dagum Gini coefficient reflects widening-then-shrinking regional gaps, and intra-eastern provincial differences constitute the primary source of nationwide spatial divergence. (4) Household consumption and rural labor force stock serve as core driving factors; regional economic development, agricultural production efficiency, rural human capital and land resource allocation form a coupled driving system, and all explanatory variables show mutual enhancement effects without offsetting interactions. Targeted policy suggestions are put forward to realize balanced and high-quality development of farmers’ specialized cooperatives across China. Full article

This study addresses the unclear regulation capability boundaries and resource dispatching challenges caused by the heterogeneity and market volatility of dispersed energy storage resources. A flexibility evaluation method for aggregated multi-type energy storage stations in multi-market environments is proposed. The approach first constructs an operation model to maximize revenues from both energy and frequency regulation (FR) markets, which informs the capacity allocation between the two. Next, a Minkowski-based aggregation method for heterogeneous resources is proposed to characterize the physical boundaries of aggregated energy storage in the power–energy–time space. Finally, peak regulation and frequency regulation response rates are defined as indicators to quantitatively analyze the dynamic relationship between price signals and the flexibility potential of energy storage. The results demonstrate that that the method can clearly delineate the physical boundaries of the energy storage system and effectively evaluate the flexibility of the energy storage system. This indicates that the method can guide grid operators in optimizing resource allocation and improving system flexibility. Full article

Identifying mineral deposits with minimal environmental impact requires the optimization of heterogeneous datasets. This study validates a rapid geospatial exploration methodology using statistical distances to identify patterns in limited raw data. The approach was applied to a Cu-Ag stratabound deposit in Tiltil, Metropolitan Region, Chile. The method consists of processing diverse spatial variables to generate similarity maps based on user-defined criteria, utilizing a statistical comparison of variable distributions between known mineralized zones, such as El Olivo, Esmeralda, and El Manzano, and unexplored areas. Results demonstrate that the application of statistical distances effectively delineates high-probability mineralization zones, where all 12 generated targets coincided with previously documented mineralized bodies. Specifically, the Total Variation Distance (TVD) yielded the highest precision and contrast for target discrimination. This methodology proves effective for small-scale mining exploration and is potentially adaptable to copper porphyry systems at district and regional scales, significantly optimizing resource allocation in early-stage exploration. Full article

As a core policy tool for promoting green economic transformation and high-quality development, green finance has significantly optimized the allocation of resources for corporate green innovation, thereby inevitably influencing the efficiency of green innovation in the manufacturing sector. Using the “Green Finance Policy Pilot Program” as a case study, this study employs a multi-period difference-in-differences (DID) model and robustness tests to examine the impact of green finance policies on the green innovation efficiency of Chinese manufacturing firms. The results indicate that green finance policies help enhance the green innovation efficiency of manufacturing firms. Mechanism analysis reveals that green finance policies enhance firms’ green innovation efficiency by alleviating financing constraints for green innovation, improving the quality of environmental information disclosure, and promoting collaborative green technology innovation. Heterogeneity results indicate that the positive correlation between green finance policies and firms’ green innovation efficiency is particularly pronounced among large-scale firms and firms in regions with high levels of green development. This study not only enriches the literature on the micro-level effects of green finance but also provides valuable insights for governments and firms seeking to enhance green innovation efficiency. Full article