Search Results (6,487)

District heating networks (DHNs) are critical infrastructure prone to physical failures such as leakage-related faults, which cause significant energy and financial losses. Traditional physics-based monitoring methods are computationally expensive and require the continual recalibration of complex mathematical models, while standard data-driven approaches often fail due to the scarcity of real-world sensor data. This study addresses these challenges by proposing a topology-aware graph neural network (GNN) architecture for fault localization. The methodology follows a two-stage process: first, a graph attention-based architecture is designed and optimized using a synthetic dataset to effectively capture multi-step neighborhood dependencies. Second, the model is adapted and evaluated on a physically simulated dataset of a real urban DHN, comprising 187 nodes and 42,570 operational states. The problem is formulated as a multi-class classification task across supply and return subnets. The results demonstrate high predictive performance, achieving an accuracy of 96% on the supply subnet and 91% on the return subnet. Analysis of prediction errors reveals a strong bias towards local topological mistakes, indicating the model’s ability to capture the physical propagation of disturbances. These findings highlight the efficacy of GNNs in handling sparse data and exploiting network topology for robust DHN monitoring. Full article

Small ruminant production in Kosovo is predominantly extensive, and biosecurity practices remain poorly characterized. The emergence of Peste des Petits Ruminants (PPR) in Europe (beginning in 2024) and the first confirmed case in Kosovo (July 2025) highlight the urgent need for baseline biosecurity data to inform disease control. A cross-sectional pilot study was conducted on 63 small ruminant farms (53 meat-producing, 10 dairy-producing) across seven municipalities in Kosovo between September 2025 and February 2026. Biosecurity practices were assessed using the Biocheck.UGent™ questionnaire during direct on-farm visits. External (Ext) biosecurity scores (preventing pathogen introduction) were higher (p < 0.0001) than internal (Int) scores (limiting spread within farms). For external biosecurity, the highest scores were observed for purchase and reproduction (Ext A), intermediate scores existed for feed and water (Ext C) and visitors and farm workers (Ext D), and the lowest scores were found for transport and carcass removal (Ext B) and infrastructure (Ext E). For internal biosecurity, the highest scores were observed for lamb/kid management (Int H) and dairy management (Int I), followed by the management of adult animals (Int J); work organization (Int K) and reproduction management (Int G) formed an intermediate-low cluster, whereas disease management (Int F) scored the lowest. Benchmarking against the Biocheck.UGent™ worldwide database (predominantly intensive systems, thus not directly comparable) indicated that internal biosecurity and overall biosecurity levels were lower than the benchmark, while external biosecurity was comparable for some components. Given the convenience sample (36.4% response rate), findings are exploratory and are not directly generalizable. Larger herd size was positively correlated with external (ρ = 0.54, p < 0.0001), internal (ρ = 0.35, p = 0.005), and overall (ρ = 0.57, p < 0.0001) biosecurity scores. This first empirical biosecurity assessment of small ruminant farms in Kosovo reveals critical gaps in transport hygiene, disease management, and reproductive management pathways that enable PPR spread and perpetuate endemic zoonoses. The positive association between herd size and biosecurity may indicate structural barriers and/or knowledge gaps for small farms. Current biosecurity tools, designed for intensive systems, require adaptation for extensive production systems. These findings provide a baseline for targeted interventions, policy development, and validation of context-appropriate biosecurity instruments in Kosovo and similar extensive systems globally. Full article

Autonomous ship maneuvering in harbor environments is increasingly supported by advances in model predictive control, reinforcement learning, digital twins, multi-sensor fusion, berth allocation, and multi-agent coordination. However, these developments are often studied as separate technological domains, while real harbor autonomy requires coordinated operation across vessels, port infrastructure, regulatory systems, cybersecurity mechanisms, and human supervisory processes. This study presents an architecture-oriented critical review of autonomous ship and harbor maneuvering research published between 2015 and May 2026. The review synthesizes literature from control engineering, maritime artificial intelligence, sensor fusion, digital twins, port logistics, cyber-physical systems, regulation, cybersecurity, and human–AI supervision. The analysis introduces two conceptual contributions: a layered cyber-physical taxonomy and an integration maturity model. The taxonomy organizes autonomous harbor maneuvering into seven interdependent layers: physical dynamics, perception and sensor fusion, prediction and state estimation, control, decision and coordination, digital twin federation, and regulatory–supervisory governance. The maturity model distinguishes isolated vessel autonomy, assisted coordination, shared digital synchronization, agent-based coordination, and fully federated maritime cyber-physical autonomy. The reviewed evidence shows substantial progress in individual layers, especially control, perception, digital twins, and berth allocation. However, major gaps remain in cross-layer synchronization, semantic interoperability, regulation-aware decision-making, cybersecurity integration, and validated ship–shore federation. To address these gaps, this study proposes a Federated Maritime Cyber-Physical Architecture for autonomous harbor maneuvering. The architecture integrates vessel autonomy cores, port intelligence cores, semantic federation middleware, agent-based negotiation, regulatory verification, cybersecurity safeguards, and human supervisory interfaces. This review argues that future progress in autonomous harbor operations depends not only on stronger algorithms, but on interoperable, explainable, regulation-aware, and cyber-resilient ship–shore ecosystems. Full article

Oil and gas pipelines are critical infrastructures that require continuous and reliable monitoring to detect leaks, pressure anomalies, corrosion, and unauthorized activities. Wireless sensor networks (WSNs) have emerged as an effective solution for large-scale pipeline monitoring due to their low deployment cost and real-time sensing capabilities. However, the resource-constrained nature of sensor nodes and the open wireless communication environment expose pipeline monitoring systems to various routing attacks, for example, blackhole, sinkhole, selective forwarding, and false data injection attacks, while simultaneously demanding strict energy efficiency to prolong network lifetime. In this paper, we propose SEER-PM (Secure and Energy-Efficient Routing for Pipeline Monitoring): a novel protocol that integrates an Artificial neural network (ANN)-based trust mechanism with energy-aware routing metrics. SEER-PM dynamically evaluates node trustworthiness based on packet forwarding behavior, residual energy, and signal consistency. By training the ANN on historical behavioral data, the system accurately detects malicious nodes with high precision. Simulation results demonstrate that SEER-PM outperforms existing secure routing protocols (Sec-AODV and T-LEACH) in terms of packet delivery ratio (PDR) by 14%, detection rate by 9.5%, and network lifetime by 12% under heavy attack scenarios. The proposed protocol enhances the reliability, security, and sustainability of pipeline monitoring WSNs operating in harsh and remote environments. Full article

Urban water insecurity is an increasingly critical challenge in rapidly urbanizing regions of the Global South, driven by population growth, environmental degradation, infrastructure limitations, and institutional constraints. Kathmandu Valley, Nepal, exemplifies these interconnected pressures. This study presents a systematic review of 45 peer-reviewed and selected grey literature sources published between 2000 and 2025, conducted in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. Studies were included if they examined drinking water contamination, public health risks, household coping practices, wastewater-related exposure, or governance dynamics in Kathmandu Valley, Nepal. Findings were synthesized using a narrative thematic approach. The review identifies widespread contamination across municipal supply systems, groundwater, tanker water, traditional water sources, and household-stored water. Microbial contamination, particularly total coliforms, fecal coliforms, and Escherichia coli, emerged as the most consistently reported and immediate public health concern. Chemical and physicochemical contaminants, including ammonia, iron, arsenic, nitrate, and turbidity, were also widely reported, especially in shallow and deep groundwater systems. Seasonal dynamics further influenced exposure risks, with increased microbial contamination during monsoon periods and greater dependence on alternative and less regulated water sources during dry seasons. The findings further indicate that unsafe water exposure is associated with a substantial burden of waterborne diseases and emerging risks such as antimicrobial resistance. Although household water treatment practices reduced contamination in some cases, risks often persisted due to recontamination during storage and handling. These burdens disproportionately affected marginalized and peri-urban populations with limited access to safe and reliable water infrastructure. The review also highlights persistent governance challenges, including institutional fragmentation, weak regulatory enforcement, inadequate infrastructure investment, and growing dependence on informal water supply systems. Together, these conditions contribute to a hybrid urban water system in which formal and informal sources coexist without consistent quality control. Overall, the evidence demonstrates that water insecurity in Kathmandu Valley is a systemic condition shaped by the interaction of environmental contamination, unequal exposure, household coping limitations, and fragmented governance. By integrating environmental, public health, and governance evidence, this review advances understanding of urban water insecurity in rapidly urbanizing contexts and highlights the need for integrated, equity-oriented, and governance-informed interventions. These findings have broader relevance for cities across the Global South experiencing similar environmental and infrastructural pressures. Full article

Oil-dependent urban regions face persistent ecological and societal issues following extraction, including land degradation and infrastructural neglect. Despite the discourse on environmental justice and extractivism, a research gap exists regarding the transition of post-extractive cities from recognizing environmental harm to implementing territorial rehabilitation strategies. This study examines Poza Rica, Mexico, a critical city in the oil industry, as a case study for restorative justice and urban transition after extraction. Utilizing a qualitative case study approach with planning documents, technical reports, environmental regulations, spatial data, and community input, the research evaluates the territorial impacts of seventy years of oil extraction and explores restoration pathways. The results indicate a landscape characterized by abandoned wells, environmental liabilities, and the integration of former extraction zones into urban areas. In the Tampico–Misantla Basin, 49.5% of wells remain inactive, with only 2.7% meeting contemporary closure standards. In Poza Rica, nearly 98% of urban growth from 1997 to 2016 occurred in regions previously linked to oil extraction. The article posits that restorative justice in post-extractive cities necessitates more than mere financial restitution. It advocates for a territorial restitution framework centred on remediation, economic transformation, and community governance, illustrating how former extraction sites can evolve into assets for urban resilience and sustainable development. Full article

Topological data analysis (TDA) has evolved into a flexible and robust paradigm for obtaining qualitative, geometry-inspired insights from high-dimensional, noisy, and complex data. Grounded in algebraic topology, geometry, statistics, and machine learning (ML), TDA provides multiscale descriptions through persistent homology, Mapper (a graph-based method that summarizes the shape of high-dimensional data), and related topological signatures that are often inaccessible to standard linear and metric methods. In recent years, and especially during 2024–2025, TDA has expanded rapidly across science, engineering, biomedical research, and socio-economic studies, while also being integrated with modern learning paradigms such as deep learning (DL) and graph learning. This survey summarizes recent developments in TDA using a carefully selected set of articles, with emphasis on 2024–2025. We first present the mathematical and computational foundations of TDA, covering simplicial complexes, filtrations, persistent homology, the Mapper algorithm, and computational advances such as data simplification, stability, and efficiency. We then review applications in time series and dynamical systems, biomedical imaging and precision medicine, engineering and physical sciences, finance and risk analysis, DL and interpretability, and security and critical infrastructure systems. Throughout, we highlight how TDA can extract informative features, function as a model component, and provide a conceptual lens for studying complex systems. However, the survey also emphasizes recurrent failure patterns: TDA performance is highly sensitive to filtration, embedding, and vectorization choices; aggressive simplification can dilute or remove informative topological signals; and integration into standard ML workflows still lacks uniform validation and reporting protocols. We conclude by outlining key challenges—including scalability, statistical foundations, interpretability, and compatibility with rapidly evolving artificial intelligence (AI) paradigms—and by identifying directions for future research. The survey also provides a unifying design perspective for TDA systems, highlighting methodological trade-offs and emerging research directions for integrating topology with modern ML. Full article

Introduction: River fragmentation caused by hydropower infrastructure remains a primary threat to aquatic biodiversity, creating a critical need for fish passage solutions that can adapt to high environmental variability. Although adaptive management (AM) has the potential to significantly improve longitudinal connectivity and ecological resilience, its application in real-world fishway operations is currently limited. Objective: This study aims to present and validate a flexible AM framework designed to optimize fish passage by integrating low-cost monitoring systems with automated data processing and predictive modeling. Methodology: The proposed system combines a sensor network for real-time water-level and environmental monitoring with biological performance data obtained through Passive Integrated Transponder (PIT) technology. These data were processed locally using edge computing. Over a two-year period, weekly aggregated data were used to develop Random Forest models to identify the primary drivers of fish movement. Results: The final model successfully identified five key drivers: luminosity, water temperature, and three nested hydraulic parameters at the fishway’s upstream section. Validation at a vertical-slot fishway in Vadocondes (Duero River, Spain) showed that retrospective optimization—specifically adjusting sluice-gate regulation—could increase downstream water levels and reduce drops at the first cross wall. This adjustment demonstrated a substantial increase in predicted fish passage without requiring changes to the hydropower plant’s core operation. Conclusions: The framework is highly flexible and transferable to other regulated river systems. However, its success is contingent upon the definition of clear ecological objectives and the seamless integration of monitoring results into the day-to-day operation of river infrastructure. Full article

Mass–balance discrepancies exist between estimated land-based inputs and observed marine plastic inventories. While current global mass–balance models predominantly treat the open ocean as a passive terminal sink, they overlook the rapid expansion of offshore and deep-sea industrial frontiers. This review identifies offshore and deep-sea activities as active, in situ emission nodes of microplastics (MPs). Through a bibliometric analysis and numerical descriptions of studies, we document that direct offshore emissions are underrepresented in the current literature. By synthesizing these limited quantitative data, preliminary metrics indicate localized MP enrichment signals and elevated biological exposure near specific offshore infrastructures. Furthermore, plastics released directly into the marine environment bypass terrestrial weathering, undergoing distinct multiscale aging pathways governed by the complex interplay of wave-induced physical fragmentation bounded by critical size thresholds, UV-driven chemical photo-oxidation, and biological interactions. We conclude that refining global plastic budgets supports moving toward an integrated ocean-industrial framework. However, the synthesis remains constrained by data scarcity and high methodological heterogeneity across different environmental matrices. Future strategies must prioritize standardized in situ flux quantification and the incorporation of MP emission risks into offshore Environmental Impact Assessments. Full article

This study develops and validates risk-based emergency response scenarios for Biosafety Level 2 (BSL-2) and Animal Biosafety Level 2 (ABSL-2) facilities. Utilizing Bow-tie analysis, three multidimensional scenarios were constructed: infrastructure failure, biosecurity breach, and compound disaster. Four domain experts independently evaluated the scripts using the Content Validity Index (CVI), with an absolute consensus threshold of I-CVI = 1.00. To address operational gaps identified during initial evaluations, the revised protocols were strictly aligned with the Taiwan Centers for Disease Control (CDC) mandatory reporting thresholds for high-hazard incidents. Furthermore, the scripts explicitly defined the Incident Command System (ICS) to prevent communication fragmentation and integrated the NC3Rs tunnel handling technique to minimize occupational bite risks. Following these targeted refinements, all items achieved absolute expert consensus. This research translates static biosafety regulations into dynamic, stress-tested training tools. By providing a standardized instrument for resilience assessment, this study equips frontline personnel with the critical capacity to navigate cascading crises while strictly adhering to a “life safety first” paradigm. Full article

Resource allocation for cognitive Vehicle-to-Everything (V2X) networks is challenging due to dynamic spectrum sharing, strong interference coupling, and stringent latency constraints for safety-critical Vehicle-to-Vehicle (V2V) traffic. Although recent Multi-Agent Reinforcement Learning (MARL) approaches report promising gains, many evaluations are conducted at limited and fixed network scales, which restricts insights into scalability under dense spectrum reuse. This paper investigates cooperative multi-agent learning for interference-aware and deadline-constrained V2X resource management. We propose a Q-value Transformation (QTRAN)-based value decomposition framework under centralized training with decentralized execution (CTDE) for joint resource-block and power allocation among V2V agents. The proposed approach is implemented in a realistic V2V/V2I simulator incorporating Manhattan grid mobility, fast fading, explicit cross-tier and co-channel interference, and per-link payload/deadline dynamics. Beyond communication-level performance, improved timely delivery of V2V safety messages can support cooperative maneuvering, collision avoidance, platooning, and infrastructure-assisted traffic management. Extensive simulations across varying numbers of V2V agents benchmark QTRAN against independent learning baselines including MARL and centralized single-agent learning (SARL). Results show that QTRAN improves performance compared with the selected learning baselines and enhances the throughput–reliability trade-off under interference-coupled spectrum reuse. For instance, at $N_{\mathrm{V}2\mathrm{V}}=20$, QTRAN achieves a V2V rate of $0.194\pm 0.004$ and a V2I rate of $9.117\pm 0.213$, while reaching a V2V success rate of $0.812\pm 0.017$ with a low Deadline Miss Ratio of $0.001\pm 0.000$. At higher density ($N_{\mathrm{V}2\mathrm{V}}=50$), QTRAN sustains strong reliability (V2V success rate of $0.719\pm 0.006$ and Completion Ratio of $0.716\pm 0.006$) while maintaining competitive infrastructure throughput (V2I rate of $9.251\pm 0.114$). These results indicate that QTRAN effectively captures non-linear interference interactions, enabling coordinated decentralized spectrum and power decisions under the adopted density-based evaluation setting, thereby enhancing V2V reliability and throughput in cognitive Internet of Vehicles. Full article

Accurate three-dimensional characterization of turbulent flows in natural waterways is essential for the effective design of tidal farms and other critical infrastructure situated along or across rivers. High-fidelity predictions based on the large-eddy simulation (LES) method capture the necessary physics but incur computational costs that hinder rapid scenario testing. Statistically, a relatively long history of instantaneous flow fields is required to generate reliable turbulence statistics, e.g., mean velocity and Reynolds stresses, of river flow. Such a requirement often incurs high simulation runtime and data storage costs. This study seeks to develop a neural network surrogate model that learns from a limited number of instantaneous flow realizations and approximates the outputs of the corresponding time-averaged fields with LES-level accuracy. Such a surrogate would eliminate the need to accumulate extensive ensembles, enabling faster hydrodynamic assessment and making LES-informed analyses more accessible for practical engineering decisions. Full article

Industrial control systems (ICS), which manage critical infrastructure such as power grids and water treatment, are increasingly exposed to cyber threats and operational faults as their connectivity to external networks grows. AI-based anomaly detection has emerged as a key defense, yet three limitations restrict its practical deployment: (i) detected anomalies are treated uniformly without distinguishing between transient faults and intentional attacks, hindering tailored incident response; (ii) the trade-off between detection accuracy and the false-positive rate burdens experts with extensive manual triage and delays prompt action; and (iii) prevailing feature-attribution Explainable AI (XAI) techniques such as SHAP and LIME produce fragmented sensor-level explanations and fail to capture correlations among sensors in time-series data, undermining trust in model decisions. To address these gaps, this paper proposes a graph-based deep learning framework that (a) defines anomaly types in terms of the anomalous-sensor ratio measured before and after smoothing—which operationalizes the correlation-maintenance principle that faults keep coupled sensors jointly anomalous while attacks isolate them—enabling explicit separation of faults, attacks, false positives, and false negatives; (b) identifies ambiguous decisions near the detection threshold as candidate false alarms via dynamic threshold smoothing; and (c) provides correlation-aware graph visualizations for intuitive interpretation. Experiments on the Secure Water Treatment (SWaT) dataset center on this post-detection layer: built on a standard graph-based detector (F1-score 0.787 at Top-K = 10) that serves only as the substrate, the categorization separates faults from attacks, and the subsequent ambiguity analysis identifies false negatives with 83% precision and false positives with 73% precision. By separating attacks from faults and surfacing high-likelihood false alarms together with intuitive sensor-correlation explanations, the proposed approach reduces analyst workload and supports more reliable, prioritized incident response in ICS environments. Full article

The Kubuqi Desert serves as a critical zone for both renewable energy development and ecological management in China. Large-scale photovoltaic (PV) deployment has fundamentally altered the regional underlying surface, impacting near-surface wind–sand dynamics. To elucidate these disturbance mechanisms, we selected three representative surfaces—a PV area, a resource base, and Qixing Lake—and conducted field observations from September to December 2023 using meteorological towers and wind erosion sensors. Results indicate that all surfaces significantly attenuated near-surface wind speeds by over 30% through modified flow field structures. A strong linear positive correlation existed between wind speed and friction velocity (R² ≈ 0.99). Notably, for the same friction velocity, the actual wind speed required to initiate sand movement was lowest in the PV zone (high k) and highest at Qixing Lake (low k), signifying enhanced surface stability due to PV infrastructure and moisture. Threshold analysis revealed distinct initiation speeds: >6.0 m·s⁻¹ in peripheral quicksand, >4.3 m·s⁻¹ in inter-panel zones, and >4.6 m·s⁻¹ beneath panels. The tilted PV panels accelerate airflow downward, generating cyclonic vortices that intensify sand particle impacts under and between panels. This study reveals the tri-dimensional mechanism of wind regulation–sand suppression–stability enhancement, providing theoretical support for mitigating wind–sand disasters while advancing green energy in desert regions. Full article

The rapid growth of web-based services has intensified the need for reliable mechanisms to distinguish malicious Uniform Resource Locators (URLs) from legitimate ones. Phishing campaigns, malware distribution networks, and defacement operations increasingly rely on deceptive web addresses to compromise unsuspecting users and critical infrastructure. Accurate URL classification plays a critical role in mitigating phishing attacks, malware distribution, and other cyber threats. This study presents a machine learning framework for detecting malicious URLs in cybersecurity applications. This study presents a comprehensive empirical evaluation of multiple machine learning and deep learning approaches for URL classification under two experimental settings: training with the complete feature set and training with a reduced subset obtained through Particle Swarm Optimization (PSO). The framework incorporates advanced feature engineering techniques that capture domain-specific characteristics of malicious URLs. Seventeen classifiers, encompassing traditional ensemble methods, neural architectures, and hybrid stacking configurations, were evaluated on a publicly available dataset of 651,191 URL samples retrieved from Kaggle. The PSO reduced the original ten-feature space to seven discriminative features, representing a 30% dimensionality reduction. Experimental results demonstrate that all-feature models consistently outperformed their PSO-reduced counterparts, with Random Forest achieving the highest classification accuracy of 91.90% and an F1-score of 0.9165. The findings offer empirical grounding for the design of computationally efficient URL threat detection systems and provide actionable directions for future research in adversarial machine learning and real-time cybersecurity pipelines. Full article