Search Results (6,103)

Online identification of HV cable circuits is vital for routine inspection and maintenance, yet existing passive electromagnetic wave injection methods are limited to offline operations. To fill the gap and achieve the online identification of HV cable circuits, an online circuit identification methodology based on sheath current temporal characteristics and deep embedded clustering is proposed. First, an equivalent circuit model of the multi-circuit cross-bonded cable sheath was built to deduce the temporal similarity of sheath currents within the same circuit, establishing the identification criterion. Second, the robustness of the temporal similarity under various operating conditions was verified via simulation based on the Dynamic Time Warping (DTW) distance. Then, a combined model of Temporal Convolutional Network Autoencoder (TCN-AE) and K-medoids was established to transform circuit identification into a temporal clustering problem of sheath currents, realizing circuit determination by synchronously monitoring the time-series sheath current data of multi-circuit HV cross-bonded cables. The method was verified on a full-scale 110 kV cable test platform. The results show that the identification accuracy reached 95.37%, and the proposed method can effectively identify the circuits of cross-bonded cables with high robustness against the domain gap, having significant engineering application value. Full article

Artificial-intelligence-driven wayside monitoring has become a promising solution for early identification of railway wheel flats, enabling safer operations and more efficient maintenance planning. This study introduces a comparative investigation of supervised and unsupervised machine learning strategies for wheel flat identification, with particular emphasis on real-time applicability and sensor cost reduction. Support Vector Machines (SVMs) and k-means clustering are evaluated as representative supervised and unsupervised approaches using vibration data obtained from numerically simulated train–track interactions under realistic operating conditions, including train speeds of 120 km/h and 200 km/h and multiple wheel flat severities. A key contribution of this work is the proposal of a simplified supervised classification framework that directly exploits Auto-Regressive features extracted from rail-mounted accelerometers, eliminating the need for feature normalization and multi-sensor data fusion. This simplification significantly reduces computational effort, making the approach suitable for real-time deployment in operational railway environments. In parallel, a systematic sensitivity analysis is conducted to assess the influence of sensor placement and to identify the minimum sensor configuration required to achieve reliable damage classification. The outputs from the current study show that an SVM emerges with more accurate defect classification than the k-means clustering, allowing a wayside system with fewer sensors. Full article

This study presents the use of a Battery Energy Storage System (BESS) and a thermal power plant to enhance Primary Frequency Regulation (PFR) in a power system. This integration seeks to mitigate operational challenges, such as the reduction in system inertia and frequency regulation, which are heightened when increasing renewable energy use in power grids with high hydroelectric generation. The proposed solution enables thermal generators to operate at optimal capacity, while the BESS provides a rapid frequency response, thereby enhancing operational efficiency and compliance with national standards. The process was structured in five stages: criteria definition, analysis, design, models, and evaluation. A comprehensive methodological approach was adopted, including dynamic system modeling and BESS sizing based on regulatory parameters. The method was tested with real data from a thermal plant under the conditions of the Colombian electricity market. The simulation results highlight the effectiveness of the proposed BESS, with a response time of approximately 0.6 s and regulation maintenance for over 30 s, reducing mechanical stress and preventing frequency overshoot. The control strategy was designed to maintain the energy neutrality of the BESS, thereby stabilizing its state of charge over the operational horizon. The results show that the BESS targets high-frequency transients and the generator focuses on low-frequency adjustments, managed by an Energy Management System (EMS) with a unified control approach. Full article

Urban flooding represents an increasingly critical challenge in rapidly urbanizing cities, where high-density development and climate variability intensify hydrological vulnerability. This article presents an analytically focused case study of Shenzhen, a national Sponge City pilot, to examine not only whether nature-based interventions are associated with flood-resilience gains but also under what spatial, institutional, and governance conditions such gains emerge. The study adopts a qualitative mixed-methods case-study design based on secondary sources, integrating observed flood-event records, reported hydrological and water-quality indicators, model-based projections, and systematic policy analysis. Drawing on data from 2006–2020, the analysis explicitly distinguishes observed outcomes, reported performance indicators, and inferred effects, addressing a key methodological limitation in existing Sponge City assessments. Results indicate that, within designated pilot zones, Sponge City interventions are associated with reduced surface runoff, attenuated peak flows, and reported improvements in pollutant filtration, particularly where green infrastructure density and monitoring capacity are high. However, these performance patterns are spatially uneven and mediated by governance constraints, including institutional fragmentation and maintenance capacity. The principal contribution of the study lies in identifying governance–infrastructure mechanisms that condition Sponge City performance and scalability. By treating Shenzhen as a critical rather than representative case, the article offers analytically transferable insights into the effectiveness, durability, and limits of nature-based flood-management strategies in high-capacity urban contexts. Full article

Submarine cable systems are essential for intercontinental connectivity and the integration of offshore renewable energy into onshore grids. The reliability of these systems depends on a well-coordinated life cycle process that integrates installation, monitoring, and maintenance technologies. This review synthesizes the key components of submarine communication and power cables, highlighting the processes involved in route survey, cable laying, and burial under complex seabed conditions. The major factors contributing to damage are typically classified into natural hazards and human activities. Particular attention is given to fault diagnosis techniques, including optical time domain reflectometry (OTDR) and time domain reflectometry (TDR). Additionally, practical workflows and processes for fault location and cable repair are outlined. By structuring advancements across installation, monitoring, and maintenance processes, this review offers a comprehensive technical reference for researchers and practitioners, while emphasizing emerging trends aimed at enhancing system resilience, real-time situational awareness, and rapid response, thus supporting global digitalization and the transition to clean energy. Full article

High-voltage circuit breakers (HVCBs) are critical switching devices whose mechanical reliability directly affects power system safety and operational continuity. Accurate fault diagnosis remains challenging due to nonlinear vibration characteristics and the sensitivity of support vector machines (SVMs) to hyperparameter selection. To address this issue, a multi-strategy improved dung beetle optimization–support vector machine (MIDBO–SVM) framework is proposed for vibration-based mechanical fault diagnosis. Frequency-domain features are extracted from vibration signals using the fast Fourier transform to characterize fault-related spectral variations. A multi-strategy improved dung beetle optimization (MIDBO) algorithm incorporating chaotic initialization, adaptive search regulation, and mutation enhancement is developed to improve population diversity, global exploration, and convergence stability. The optimized MIDBO is used to determine the penalty and kernel parameters of the SVM, constructing a robust and well-generalized diagnostic model. Experimental results show that MIDBO–SVM achieves a diagnostic accuracy of 96.67%, outperforming conventional SVM (86.25%) and random forest (89.17%). The proposed method also demonstrates faster convergence and maintains accuracy above 86% under imbalanced sample conditions, confirming its robustness and generalization capability. These advantages contribute to more reliable mechanical condition assessment and improved maintenance decision support for HVCBs. Full article

The neuronal cell adhesion molecule NrCAM is widely expressed in the nervous system across the lifespan and has important physiological functions in the development of neuronal circuits through axonal growth and guidance and formation and maintenance of synapses in the cortex. NrCAM gene polymorphisms are associated with vulnerability to neuropsychiatric disorders such as schizophrenia, as well as vulnerability to substance use disorders. We investigated the effects of acute and chronic stress and the effects of systemic administration of AMPAR antagonist CNQX and NMDAR antagonist MK-801 on delay discounting in male NrCAM knockout (KO) mice and their wild-type littermate controls (WT). Under the no-stress condition, no discounting differences were found. Acute stress increased discounting and impulsivity in WTs but not in NrCAM KO mice. Chronic stress increased discounting and impulsivity in both genotypes. CNQX increased impulsive choice in WT controls but not in NrCAM KOs; impulsive choice decreased in both genotypes after MK-801 administration. Relative to WTs, NrCAM KOs had more neuronal activation in the prelimbic and orbitofrontal cortices. In NrCAM KO mice, a low dose of MK-801 decreased neuronal activation in the ventral orbitofrontal cortex and increased activation in the accumbens shell and core. These results indicate differential effects of genotype, stress, and response to glutamatergic drugs and support a role for NrCAM in stress-induced behavioral alterations relevant to addiction and psychiatric disorders. Full article

Limosilactobacillus fermentum MC1 is an exopolysaccharide (EPS)-producing strain with previously determined probiotic potential in vitro. This study aimed to investigate the in vivo capacity of the MC1 strain or its EPSs to modulate intestinal microbiota and assess its anti-inflammatory effects in both healthy and dysbiotic conditions. Therefore, Lb. fermentum MC1 and its EPSs were administered to a mouse model of dextran sulfate sodium (DSS)-induced colitis (DIC) and to a healthy group, and the effects were observed. Microbiome analysis was used to detect taxonomic differences between treatments. According to the results, administration of the MC1 strain and MC1-EPSs significantly altered gut microbiome composition at different taxonomic levels. The most notable effect was an increased relative abundance of Firmicutes and decreased levels of Candidatus saccharibacteria. Llb. fermentum MC1, and its EPS administration positively affected several disease parameters: reduced disease activity index (DAI), reduced mouse colitis histology index (MCHI), reduced expression of inflammation-related genes and levels of bleeding, and induced polarization of M1 macrophages to the M2-like macrophage phenotype in the DIC mice. These results, along with those related to the induction of antioxidant enzymes and changes in NF-κB-related gene expression, suggest that strain MC1 and MC1-EPSs could be further investigated for their capacity to alleviate DSS-induced histopathological changes and modulate pro-inflammatory cytokine gene expression in colon tissue, which positively correlates with the secretion of inflammatory cytokines, the delay of intestinal inflammation and the maintenance of intestinal barrier function. The obtained data provide a basis for further research into the potential application of intact or microencapsulated Llb. fermentum MC1 cells and its EPSs in colitis therapy. Full article

Urban green space (UGS) constitutes critical ecological infrastructure for climate adaptation and sustainable urban transitions. This review synthesizes the conceptual evolution of UGS, elucidating the coupled dynamics driven by anthropogenic interventions and climatic forces. We highlight that UGS has evolved from spontaneous vegetation to systematically planned infrastructure, serving dual cultural and ecological functions. While human drivers—spanning policy frameworks, species selection, and maintenance regimes—dictate the spatial morphology of UGS, climatic conditions and extreme weather events modulate vegetation resilience and performance, creating distinct bioclimatic patterns, particularly within Chinese cities. Collectively, these forces govern the structural integrity and ecosystem performance of UGS. Methodologically, this study combines a bibliometric analysis of Web of Science publications from 2000 to 2025 with a PRISMA-based systematic literature review and a semi-quantitative synthesis of recent empirical studies. The bibliometric analysis provides a global overview of research hotspots and thematic evolution in UGS research, while the in-depth synthesis and factor prioritization primarily focus on China-based studies published between 2021 and 2025. By integrating evidence on both human activities and climatic factors, this review clarifies the dominant driving mechanisms shaping UGS under rapid urbanization and climate change, while situating China-specific findings within the broader international literature. Although UGS delivers well-documented benefits for microclimate regulation and social well-being, accelerating urbanization and increasing climate complexity pressures indicate that existing management approaches could be further enhanced to meet emerging demands. Consequently, future UGS development should shift from quantitative expansion to qualitative optimization and spatial equity. We propose a research agenda prioritizing cross-climate comparative frameworks, smart maintenance technologies, and inclusive governance to bolster UGS resilience, thereby advancing long-term sustainable development goals. Full article

This study presents a systematic review of Explainable Artificial Intelligence (XAI) applications in Transportation Infrastructure Management (TIM), focusing on predictive maintenance of safety-critical assets such as railways and bridges. A predefined review protocol was implemented, and peer-reviewed literature was systematically retrieved from Web of Science and Scopus covering the period 2015 to March 2025. Using structured Boolean search logic and clearly defined inclusion and exclusion criteria—requiring explicit integration of explainability within AI-driven infrastructure maintenance—450 records were initially identified, screened in multiple stages, and refined to 163 eligible studies for detailed analysis. Through structured data extraction and thematic synthesis, the review develops a taxonomy of model-specific, model-agnostic, hybrid, and human-centered XAI approaches while identifying recurring challenges including heterogeneous multi-modal data environments, lack of standardized interpretability metrics, computational constraints in real-time deployment, limited robustness validation under field conditions, and unresolved performance–interpretability trade-offs. The findings demonstrate systematic growth in XAI-driven predictive maintenance research and highlight the need for domain-specific benchmarks, hybrid interpretable architectures, digital twin-assisted validation, and edge-enabled explainable systems to enable scalable, transparent, and regulation-ready infrastructure management aligned with Industry 5.0. Full article

This paper investigates the specific positioning accuracies and uncertainties associated with the measurement of acoustic leakage noise correlation (LNC) in underground pressurized water mains, treating them as acoustic waveguides. It begins by identifying three key intrinsic sources of measurement errors: (1) the speed of acoustic waves in the water mains as influenced by pipe material, wall thickness, modulus of elasticity, and bulk modulus; (2) the distance between the two accelerometers used for correlation; (3) the time delay from the point of leakage to the accelerometers. A mathematical uncertainty model was developed to compute sensitivity coefficients, enabling the propagation of measurement errors from these sources. This was validated through seven sets of full-scale experiments conducted at Q-Leak, a 25,000 sq. ft. test site in Hong Kong. This study ultimately quantified and assessed the contributions of individual error sources to the overall uncertainty, allowing for the prioritization of factors that have the most significant impact in various scenarios. The findings reveal that Young’s modulus and pipe wall thickness are the primary factors affecting measurements for both plastic and metal pipes. Additionally, a universal in-house program, “LNC uncertainty calculator,” was developed to provide insights into the buffer ranges for confirming suspected leak locations while considering constraints within the uncertainty budget. This research highlights the critical but often overlooked area of uncertainty modeling in leak detection for pressurized buried water mains, offering valuable insights intended to enhance operational strategies and maintenance practices within the industry. This research provides a robust framework for understanding the accuracy of leak detection. This means operators can better interpret the reliability of their measurements, leading to consistent decision-making across different situations and minimizing the risk of misidentifying the presence or absence of leakage. In addition, the insights gained from prioritizing factors that affect measurement accuracy allow engineers and operators to make informed decisions about where to focus their resources and efforts. This can lead to more effective maintenance strategies that are tailored to specific conditions, thereby optimizing operational efficiency. Full article

High-strength low-alloy (HSLA) steels in marine environments suffer from microbiologically influenced corrosion (MIC) and hydrogen-assisted degradation. This study investigates the synergistic effects of sulfate-reducing bacterial biofilms, mechanical stress, and seawater chemistry on HSLA AH36 steel using electrochemical, microstructural, and magnetic Barkhausen noise (MBN) monitoring. Under multiparametric exposure (80% yield strength tensile stress, Desulfovibrio vulgaris, 28 days), biotic samples exhibited sustained 1.88× corrosion acceleration despite 86% sulfate depletion. Magnetic Barkhausen noise RMS amplitude (MBN_RMS) peaked at day 7 (612 ± 38 mV/mm) at pit depths of only 20–50 μm, detecting subsurface hydrogen damage before macroscopic failure. Quantitative correlations (R² ≥ 0.99) between MBN_RMS and cumulative mass loss revealed distinctive linear relationships in abiotic conditions and nonlinear cubic polynomials in biotic conditions, providing a non-destructive signature diagnostic of hydrogen-assisted MIC. Directional anisotropy analysis (parallel vs. perpendicular fields) showed that hydrogen-induced damage produces isotropic magnetic signatures (anisotropy ratio: 1.27 → 1.15), enabling discrimination between hydrogen embrittlement and stress-controlled degradation. The integration of portable MBN measurements with electrochemical monitoring establishes a quantitative framework for real-time structural health assessment and predictive maintenance of HSLA steels in maritime applications. Full article

Nowadays, the building sector is responsible for 30% of the global final energy demand and 37% of global energy and process emissions. In this context, industrial buildings account for 33% of global final energy consumption, representing one of the most energy-intensive sectors. The challenging European goal of achieving a carbon-free economy by 2050 is not reachable without intervening on the existing building stock. This research study aims to propose several retrofitting measures implemented in existing Italian industrial facilities to ameliorate energy and environmental performance, as well as to guarantee better indoor thermal conditions for workers. These interventions deal with both external envelope interventions and conditioning system improvements, along with their possible combination, to identify the most cost-effective solutions. A life cycle cost (LCC) analysis is performed to assess and compare the different redevelopment measures to identify the advisable ones considering the initial investment expenditure and operational and maintenance costs during a life span of 20 years. To define the cost-effective solution, different synthetic indexes are considered in the analysis. A sensitivity analysis is conducted on the discount rate and the operational life of the building (20 years). Redevelopment measures concerning conditioning systems seem to be the most advantageous ones in terms of operational energy savings and payback period evaluation if renewables are installed. The latter possibly makes industrial buildings carbon-neutral. The interventions on the external envelope allow buildings to meet the current Italian regulations in terms of thermodynamic properties, even if they affect the operational cost to a lesser extent. Full article

Dynamic positioning (DP) systems are essential to maritime operations, as they ensure precise station keeping. Yet human error remains a major contributor to DP incidents, often interacting with technical failures and environmental conditions. This study proposes an adaptive probabilistic framework to characterise human-error contributions to DP risk and support targeted mitigation. We compare integrated Bayesian network (BN)/fuzzy analytic hierarchy process (AHP) and Bayesian network (BN)/Dempster–Shafer (D-S) theory to model causal relationships, aggregate uncertain expert judgements, and prioritise risk factors. Historical incident narratives, accident reports, and expert elicitation inform the model to analyse failure propagation and quantify factor contributions. In a representative DP case application, insufficient training, operator fatigue, and reduced situational awareness—together with software anomalies and adverse environmental loads—emerge as dominant contributors; BN backward analysis corroborates their diagnostic relevance. The approach yields actionable insights for risk reduction, including tailored training programmes, strengthened safety protocols, and integration of real-time monitoring. It provides an auditable, updateable basis for scenario-based training, software/maintenance assurance, and environment-aware operating envelopes, and is readily extendable as new evidence becomes available. Overall, the framework offers practical value for improving safety, operational continuity, and system resilience in DP operations. Full article

This paper presents a transient performance comparison of a flat-plate solar collector (FPSC) and a sun-tracked parabolic trough collector (PTC) with a double U-tube receiver. Both collectors were modeled using in-house transient mathematical models and validated against experimental data obtained from a dedicated test stand. After validation, annual simulations were conducted for Kraków, Poland, using hourly meteorological data from the PVGIS database. The analysis focused on the long-term thermal and economic performance of both collector types under identical boundary conditions. The electricity demand of the tracking system was included using a constant motor power assumption. A simplified techno-economic evaluation was performed using the Levelized Cost of Heat (LCOH), accounting for investment costs, operating and maintenance expenses, auxiliary electricity consumption, system degradation, and cost escalation over a 20-year lifetime. For a comparable aperture area, the calculated LCOH amounted to 0.096 EUR/kWh for the sun-tracked PTC and 0.041 EUR/kWh for the stationary FPSC. The results indicate that, despite higher thermal performance, the examined PTC configuration is not economically competitive for low-temperature heat production under the assumed cost structure, mainly due to its higher investment cost. Full article