Search Results (1,896)

Hydrogen refueling stations (HRSs) are a critical enabling infrastructure for fuel cell electric vehicles (FCEVs), yet their deployment in dense metropolitan areas often faces a dual challenge: limited travel-time accessibility for users and low public acceptance driven by perceived safety risks. This study develops an integrated, city-scale framework to quantify HRS accessibility and resident acceptance and to identify expansion priorities for Seoul, South Korea. We combine (i) an online perception survey of 1000 adult residents (October 2024) capturing environmental awareness, perceived safety, siting preferences, and willingness-to-travel distance; (ii) spatial demand data on FCEV registrations by administrative dong (n = 2443 vehicles, 2022); and (iii) network-based travel-time analysis using the Seoul road network and the current HRS supply (n = 10, 2024). Accessibility is evaluated under three travel-time thresholds (10, 15, and 20 min), with service-area delineation and demand-weighted underserved-area diagnosis. Candidate expansion sites are generated and screened using operational and regulatory constraints (e.g., site area and proximity to protected facilities), followed by a p-median location-allocation optimization to select five additional sites that minimize demand-weighted travel impedance. Results indicate that, under the 20 min threshold (7.7 km at an average operating speed of 23.1 km/h), 50 of 425 dongs (11.8%) and 244 of 2443 FCEVs (10.0%) are outside the baseline service coverage. After adding five sites (total n = 15), underserved dongs decrease to 5 (1.2%) and underserved FCEVs to 26 (1.1%) for the 20 min threshold, with consistent improvements across shorter thresholds. Survey responses further reveal that only 12.5% of respondents perceive HRSs as safe, while 46.5% report a maximum willingness-to-travel distance of up to 5 km, underscoring the need for both accessibility enhancement and risk-aware communication. The proposed workflow offers a transparent, reproducible approach to support equitable and risk-informed HRS planning by jointly considering network accessibility, demand distribution, and social acceptance, thereby contributing to sustainable urban mobility, low-carbon transport transition, and socially acceptable hydrogen infrastructure deployment. Beyond local accessibility improvement, the study is framed in the broader context of sustainability, as equitable and socially acceptable hydrogen refueling infrastructure can support low-carbon urban transport transitions and more resilient metropolitan energy-mobility systems. Full article

This study addresses the need for intelligent condition monitoring in high-complexity medical imaging systems by proposing a smart sensing architecture for the Revolution EVO Computed Tomography (CT) scanner. Ensuring operational reliability and minimizing unexpected downtime remain critical challenges in advanced CT platforms, motivating the integration of distributed sensing and data-driven analytics. System logs spanning August 2024 to October 2025 were processed into 10-min intervals and converted into a structured dataset comprising 76 features derived from operational events, scanning parameters, and temporal dynamics. Two supervised learning models, the Support Vector Machine (SVM) and Artificial Neural Network (ANN), were trained to identify abnormal operating conditions. Both models delivered excellent classification performance, achieving an accuracy of 0.973. The SVM demonstrated balanced precision, recall, and F1-score metrics of 0.973, while the ANN outperformed in ranking and sensitivity to anomalies with an AUROC of 0.993 and an AUPRC of 0.976. This framework highlights the potential of sensor-driven machine learning in enabling early detection of system anomalies and optimizing maintenance planning within clinical CT environments. Full article

Decentralized sustainable microgrids are emerging as a promising approach for addressing the increasing complexity of modern power systems while ensuring reliable and efficient operation. A fundamental driver of this transition is the partitioning of distribution networks into self-sufficient microgrids supported by the effective integration of Distributed Energy Resources (DERs) and Energy Storage Systems (ESSs), enabling improved power flow management and enhanced voltage stability. In this regard, this paper proposes a tri-stage optimization framework designed to segment power distribution systems into multiple self-sustaining microgrids while maintaining optimal network performance. In the first stage, the distribution grid is partitioned into microgrid clusters based on electrical distance metrics and bus correlation analysis. The second stage focuses on the optimal sizing and operational management of DERs and ESSs within each identified microgrid to ensure energy self-sufficiency and minimize greenhouse gas (GHG) emissions. In the third stage, an optimal resource allocation strategy is implemented, where the resources determined in the previous stage are optimally placed within the distribution network to achieve optimal power flow, reduce system losses, and maintain voltage stability under worst-case operating conditions. The proposed framework is validated using the IEEE 33-bus test system. Simulation results demonstrate its effectiveness in multi-microgrid classification, coordinated planning, and resource allocation, highlighting its superiority in enhancing system performance and resilience. Full article

Effective supply chain planning increasingly requires balancing cost-efficiency with environmental responsibility, particularly as organisations face growing pressure to reduce the carbon footprint of logistics operations. This study develops a mixed-integer linear programming model to optimise inventory and transportation decisions in a multi-echelon distribution network comprising a central warehouse, regional warehouses, and retailers. The model integrates a continuous-review (r,Q) replenishment policy, stochastic demand, safety stock requirements, transportation lead times, and stockout behaviour, enabling a detailed representation of operational dynamics under uncertainty and environmental concerns. Unlike most sustainable inventory models—which typically treat environmental impacts and replenishment control separately or rely on simplified service assumptions—this study provides an integrated framework that jointly embeds (r,Q) policies, stochastic demand, stockouts and distance-based CO₂ metrics within a unified optimisation structure. The model advances prior work by explicitly integrating continuous-review (r,Q) replenishment policies with distance-based CO₂ metrics under stochastic demand, a combination rarely addressed in sustainable multi-echelon inventory models. A multi-objective formulation captures the trade-off between economic performance and CO₂ emissions, allowing the identification of Pareto-efficient strategies that reconcile financial and environmental goals. Reducing emissions by over 90% requires an additional cost of only about 4%, demonstrating that substantial emission reductions can be achieved at relatively low additional cost. The findings offer practical insights for managers seeking to design more sustainable and cost-effective distribution policies, highlighting the value of integrated optimisation approaches in contemporary logistics systems. Full article

The imbalance of urban public transport networks often leads to traffic congestion. Traditional planning prioritizes system optimization and single-mode travel, neglecting interactions between different modes. From an economic perspective and based on passenger travel behavior, this paper constructs a reasonable path set for multimodal networks. Using information entropy, it establishes multidimensional indicators including site path diversity entropy, destination regional entropy vectors, and weighted comprehensive entropy. Regional aggregation and coefficient of variation analyze internal balance, while scatter plots and the Gini coefficient measure global resource allocation equity. ArcGIS Pro 3.4.3 is employed for spatial analysis and visualization. An empirical study of Beijing’s six central districts reveals significant spatial heterogeneity in path distribution across functional zones: working areas exhibit concentric patterns, commercial areas form corridor agglomerations, residential areas have the highest entropy values, and transport hubs are relatively balanced. Cluster analysis based on entropy vectors effectively identifies commuter, residential, and hub station types. Some hubs show an ideal “high richness, low imbalance” state, while areas like Beijing Railway Station exhibit “low richness, high imbalance.” The Gini coefficient of 0.1864 indicates relatively balanced public transport resources overall. The “route-region-demand” collaborative analysis framework constructed in this study achieves a paradigm shift from static network structure to dynamic human-oriented evaluation, providing methodological support for equity assessment, network optimization, and resource allocation in multimodal public transport networks, and can contribute to the equitable and balanced sustainable development of public transport. Full article

The rapid integration of renewable energy is increasing the need for fast and sustained load-side frequency regulation, and public electric vehicle (EV) charging networks are promising providers. Their participation, however, is constrained by the volatile charging demand and strict service requirements, which make it difficult to balance regulation revenue with charging quality. This paper proposes a three-layer coordinated framework for multi-site charging networks participating in secondary frequency regulation, comprising market bidding, rolling planning, and fast-response tracking. At the market layer, baseline charging schedules are co-optimized with symmetric regulation capacity bids. At the planning layer, completion margin and progress protection constraints are introduced as tractable service safeguards that preserve charging continuity and deadline compliance. At the execution layer, coordinator-assisted distributed station-level tracking and charger-level urgency-aware allocation track automatic generation control (AGC) commands while correcting the charging progress in real time. The station-level problem is decomposed into local box-constrained subproblems coordinated by a scalar dual signal, enabling real-time AGC tracking with limited inter-station information exchange. Case studies on a reproducible simulated network with 20 stations and 600 chargers show that the proposed method improves ancillary service benefits while maintaining strong tracking performance and markedly improving the charging continuity, deadline compliance, and spatial load balance. Full article

The accurate and explainable prediction of carbon emissions is crucial for the efficient operation of hybrid and electrified transportation systems and their integration with energy grids. An Explainable Spatio-Temporal Graph Neural Network (EST-GNN) is proposed for highly precise CO₂ emission forecasting using Lévy Flight-guided Optuna optimization. By modelling vehicles and their operational characteristics as nodes in a dynamic graph, the proposed framework can jointly learn timing and spatial correlations while sustaining interpretability. The accuracy of the EST-GNN model is compared with models based on one-hot encoded features, SMOTE-enhanced datasets, and ensemble regressors. Using a real-world dataset of 7385 vehicle registrations with 12 predictive features experiments are conducted. When applied the EST-GNN model outperformed all baseline and traditional models achieving the highest reliability (R² = 0.98754) while solving competitive error metrics (RMSE = 6.55, MAE = 2.556). There is strong indication that reasonable machine learning (ML) models can be used accurately to confirm their suitability for resource-prevented and real-time applications, while predictable ML techniques have relatively low reliability. The optimal solution ensures scalability, robustness, and independence of the deployment environment. The distribution analysis of best performing models develops the ability of EST-GNN, which accounts for the largest proportion of best results across evaluation metrics. To achieve superior predictive accuracy, graph-based learning, explainability, and advanced hyperparameter optimization are combined. EST-GNN provides a powerful tool for analyzing fleet emission levels, making energy-aware decisions, and planning sustainable transportation, while ML models continue to be a useful complement for deployment states with high computation costs and quick responses. Full article

Accurate photovoltaic (PV) power forecasting is essential for the efficient operation of residential energy systems and microgrids, as reliable short-term predictions enable improved energy scheduling, demand management, and operational planning in distributed energy environments. In this study, one-hour-ahead forecasting of residential PV power generation is investigated using real-world data collected from multiple households within an Irish energy community. Several deep-learning architectures, including long short-term memory (LSTM), gated recurrent unit (GRU), convolutional neural networks (CNN), CNN–LSTM hybrid networks, and attention-based LSTM models, are evaluated and compared with a seasonal autoregressive integrated moving average (SARIMA) statistical model. A sliding-window approach is employed to transform the PV time series into a supervised learning problem. To ensure statistical robustness, deep-learning models are evaluated using a multi-run framework, and results are reported as mean ± standard deviation based on MAE, RMSE, MAPE, and R² metrics across multiple households. The results indicate that deep-learning models achieve consistently strong forecasting performance, with GRU frequently providing the most reliable predictions across several households. For instance, in House 5, GRU achieved an RMSE of 142.02 ± 1.87 W and an R² of 0.694 ± 0.008, while in Houses 11 and 13 it attained R² values of 0.837 ± 0.002 and 0.835 0.08, respectively. However, performance varied across households, reflecting the influence of data variability and generation patterns on model effectiveness. In comparison, the SARIMA model demonstrated competitive performance and, in certain cases, outperformed deep-learning models. For example, in House 4, it achieved the lowest RMSE of 90.68 W and the highest R² of 0.709. Overall, these findings highlight that while deep-learning models offer greater adaptability and stability, statistical models remain effective for more regular PV generation patterns. Consequently, the study emphasizes the importance of evaluating forecasting models under realistic household-level conditions and demonstrates that both deep-learning and statistical approaches can provide short-term PV forecasting. Full article

Accurately identifying urban functional zones and revealing their spatial interaction characteristics is crucial for understanding urban operational mechanisms and optimizing spatial layouts. Addressing the limitations of traditional research in simultaneously capturing static functional attributes and dynamic resident travel behaviors, this study takes the central urban area of Lhasa as the research object, integrating ride-hailing trajectory data with Point of Interest (POI) data to conduct research on urban functional zone identification and spatial interaction characteristics. First, Thiessen polygons were used to quantify the spatial influence range of POIs, and an address matching algorithm was employed to associate ride-hailing origins and destinations (ODs) with POIs. A weighted land use intensity index was constructed, and functional zones were precisely identified using information entropy and K-Means clustering. Secondly, with basic research units as nodes and OD flows as edges, a directed weighted spatial interaction network was constructed. Complex-network indicators and the Infomap community detection algorithm were utilized to analyze network characteristics, node importance, and community interaction patterns. The results show that: (1) The functional mixing degree in the study area exhibits a pattern of “highly composite core, relatively differentiated periphery.” Eight functional zone types, including commercial–residential mixed, science–education–culture, and transportation service zones, were ultimately identified. Residential areas form the base, while the core area features multi-functional agglomeration. (2) The spatial interaction network exhibits typical small-world effects, while its degree distribution is better characterized by a lognormal distribution rather than a power law. Node importance is dominated by betweenness centrality, with Lhasa Station, the Potala Palace, and core commercial areas constituting key hubs. (3) The network can be divided into four functionally coupled communities: the core multi-functional area, the western industry–residence integrated area, the eastern science–education-dominated area, and the southern transportation hub area, forming a “core leading, two wings supporting” center–subcenter spatial organization pattern. This study verifies the effectiveness of integrating trajectory and POI data for identifying urban functional zones and provides a new perspective for understanding the spatial structure and planning of plateau cities. Full article

The growing complexity of human systems and the increasing frequency of climate-driven hazards have transformed some disasters from isolated events into cascading phenomena which propagate through critical infrastructure networks, disrupting essential services and amplifying systemic risk. This work examines the impacts of extreme storms and subsequent flooding on critical entities as defined under the new EU Directive (Critical Entities Resilience, CER). This study introduces a structured Critical Entities Disruption Database—Greece (CEDD-GR), as a methodological framework for systematically recording and analysing disruptions to critical entities, and applies it to the case of Storm Daniel (2023), one of the most severe flood events recorded in Greece. The analysis identified direct impacts across eight of the eleven sectors defined in the CER Directive, namely, energy, transport, health, drinking water, wastewater, public administration, digital infrastructure and food production, processing and distribution. A total of 21 different types of critical entities were documented, revealing the mechanisms through which failures affected different subsectors. The results underscore the systemic fragility of critical entities when exposed to extreme storms, compound flooding, and mass wasting processes (landslides, ground subsidence) and highlight the need for integrated resilience planning in line with the CER framework. Full article

Satellite constellations are transforming space systems from isolated spacecraft into networked, software-defined platforms capable of on-orbit perception, decision making, and adaptation. Yet many of the existing AI studies remain centered on single-satellite inference, while constellation-scale autonomy introduces fundamentally new algorithmic requirements: learning and coordination under dynamic inter-satellite connectivity, strict SWaP-C limits, radiation-induced faults, non-IID data, concept drift, and safety-critical operational constraints. This survey consolidates the emerging field of on-orbit space AI through three complementary paradigms: (i) federated learning for cross-satellite training, personalization, and secure aggregation; (ii) multi-agent algorithms for cooperative planning, resource allocation, scheduling, formation control, and collision avoidance; and (iii) collaborative sensing and distributed inference for multi-satellite fusion, tracking, split/early-exit inference, and cross-layer co-design with constellation networking. We provide a system-level view and a taxonomy that unifies collaboration architectures, temporal mechanisms, and trust models. Full article

This paper addresses the optimal management of photovoltaic (PV) systems and distribution static synchronous compensators (D-STATCOMs) in modern electrical distribution networks. A mixed-integer nonlinear programming (MINLP) model is formulated which co-optimizes device placement, sizing, and multi-period dispatch to minimize the total annualized system costs while satisfying AC power flow and operational constraints. To solve this challenging problem, a decomposition methodology is proposed, wherein the binary location decisions for the PVs and D-STATCOMs are treated as predefined inputs, upon the basis of site selections commonly reported in the literature. With the integer variables fixed, the problem is reduced to a continuous nonlinear programming (NLP) subproblem for optimal capacity sizing and operational scheduling, which is solved using the interior point optimizer (IPOPT) via the Julia/JuMP environment. The core contribution of this work lies in its comprehensive demonstration of the economic superiority of variable reactive power injection over conventional fixed compensation schemes. Through numerical validation on standard 33- and 69-bus test systems, it is shown that a variable D-STATCOM operation yields substantial and consistent economic gains. Compared to optimized fixed-injection solutions, variable injection provides additional annual savings averaging USD 120,516 (33-bus feeder) and USD 125,620 (69-bus grid), corresponding to a further 3.4% reduction in total costs. These benefits prove robust across different device location sets identified by various metaheuristic algorithms, and they scale effectively to larger network topologies. The results demonstrate that transitioning to variable power injection is not merely an incremental improvement but a fundamental advancement for achieving techno-economic optimality in distribution system planning. The proposed methodology provides utilities with a computationally efficient framework for determining near-optimal PV and D-STATCOM management strategies by first fixing deployment locations based on established planning insights and then rigorously optimizing sizing and dispatch, in order to maximize economic returns while ensuring reliable network operation. Full article

Under the evolving modern power system, user-side load structures and consumption behaviors are becoming increasingly diversified, while grid–load interactions are growing more frequent. These changes pose new challenges for load characteristic analysis amid the energy transition. The advancement of metering automation has generated massive power consumption data, offering rich insights for load modeling and applications. This paper presents a comprehensive review of intelligent analysis and applications of load characteristics in new power systems. It begins by outlining a systematic framework for load analysis and summarizing relevant public datasets. Then, it synthesizes existing research from three perspectives: load feature analysis and user profiling, interactive aggregation behaviors under market mechanisms, and practical applications. Finally, this paper discusses platform development practices for big data and highlights future research challenges and directions. Full article

Water distribution networks are part of critical infrastructure, and ensuring a rapid return to service after failures is vital for public health and economic resilience. While numerous studies have quantified failure rates and examined factors that influence the duration of repairs, the seasonal variability of repair time itself has received little attention. This study analyses 264 monthly observations (January 2004–December 2025) from a large urban water supply system in south-eastern Poland. We evaluate the seasonality of failure counts, average repair time per event, and the total labour hours needed to restore service. Methods include descriptive statistics, seasonal indices, non-parametric tests, kernel density estimation, parametric distribution fitting, empirical exceedance curves of monthly mean repair duration, and time-series decomposition. The results show a pronounced seasonal pattern in the number of failures and total labour hours, with peaks in winter and minima in spring, whereas the monthly mean repair duration remained stable at approximately 8 h and showed no significant seasonal variation. Among the positive-support candidate distributions, the log-normal model provided a slightly better fit than the Weibull model. Empirical exceedance analysis and non-parametric tests confirmed no significant differences in monthly mean repair duration between seasons or calendar months. Decomposition reveals a small downward trend in total repair hours after 2010. These findings provide new insights for maintenance planning and indicate that winter workload peaks are driven primarily by higher failure counts rather than by longer mean repair duration. Full article

Underwater dynamic target detection, classification, localization, and tracking (DCLT) is central to maritime surveillance and monitoring and increasingly relies on distributed AUV-based robotic sonar networks operating in passive listening and, when required, cooperative multistatic modes. Achieving a robust performance in realistic oceans remains challenging, because sensor placement must adapt to time-varying acoustic conditions and target priors while preserving acoustic communication connectivity, and because frequent reconfiguration under dynamic currents makes classical large-scale planning computationally expensive. This paper presents an integrated deep reinforcement learning (DRL)-based framework for passive-stage sonar placement and dynamic reconfiguration in distributed AUV networks. First, we cast placement as a constructive finite-horizon Markov decision process (MDP) and train a Proximal Policy Optimization (PPO) agent to sequentially build a collision-free layout on a discretized surveillance grid. The terminal reward is formulated to jointly optimize the environment-aware detection performance, computed from BELLHOP-based transmission loss models, and global network connectivity, quantified using algebraic connectivity. Second, to enable time-critical reconfiguration, we estimate flow-aware motion costs for all AUV–destination pairs using a PPO with a Long Short-Term Memory (LSTM) trajectory policy trained for partial observability. The learned policy can be deployed onboard, allowing each AUV to refine its path online using locally sensed currents, improving robustness to ocean-model uncertainty. The resulting cost matrix is solved via an efficient zero-element assignment method to obtain the optimal one-to-one reassignment. In the reported simulation studies, the proposed Sequential PPO placement method achieves a final reward 16–21% higher than Particle Swarm Optimization (PSO) and 2–3.7% higher than the Genetic Algorithm (GA), while the proposed PPO + LSTM planner reduces average travel time by 30.44% compared with A*. The proposed closed-loop architecture supports frequent re-optimization, scalable fleet operation, and a seamless transition to communication-supported cooperative multistatic tracking after detection, enabling efficient, adaptive DCLT in dynamic marine environments. Full article