Search Results (32,726)

Digital Twin (DT) technology is increasingly recognised as a promising approach for predictive and optimised railway maintenance; however, its current applications remain fragmented and lack systematic evaluation across railway domains. This study aims to critically review DT-enabled monitoring, analysis, and maintenance decision-support systems in railway engineering, while identifying key research gaps and future directions. A DT is defined in this study as an integrated cyber–physical system comprising a physical asset, its virtual representation, and continuous bidirectional data exchange enabling real-time monitoring, prediction, and decision-making. A systematic and transparent review methodology was adopted to select 34 representative peer-reviewed studies published between 2020 and 2025, focusing explicitly on DT applications in railway infrastructure and operations. Among these, a subset of 10 key studies was further analysed in greater depth based on their level of technical implementation, data integration capability, and relevance to predictive maintenance applications, which cover multiple domains, including track systems, rolling stock, bridges, and communication networks. Results show that DT-based approaches can enhance fault detection, enable condition-based and predictive maintenance, and reduce reliance on manual inspections. However, significant limitations remain. Most studies are conceptual or pilot-scale, with limited validation under real operating conditions. Key challenges include a lack of standardisation and interoperability, constraints in real-time scalability, data governance and cybersecurity issues, and insufficient integration of multi-source sensing and advanced analytics. This review provides a structured synthesis of current DT implementations in railway systems and highlights critical gaps that must be addressed to enable scalable, reliable, and fully integrated DT-driven maintenance frameworks. Full article

Anomaly detection is crucial in maintaining the safety, reliability, and optimal performance of complex systems across diverse domains, such as industrial manufacturing, cybersecurity, and autonomous systems. While conventional methods typically handle single data modalities, recently, there has been an increase in the application of multimodal detection in dynamic real-world environments. This paper presents a comprehensive review of recent research at the intersection of agentic artificial intelligence and large language-based multimodal anomaly detection. We systematically analyze and categorize existing studies based on the agent architecture, reasoning capabilities, tool integration, and modality scope. The main contribution of this work is a novel taxonomy that unifies agentic and multimodal anomaly detection methods, alongside benchmark datasets, evaluation methods, key challenges, and mitigation strategies. Furthermore, we identify major open issues, including data alignment, scalability, reliability, explainability, and evaluation standardization. Finally, we outline future research directions, with a particular emphasis on trustworthy autonomous agents, efficient multimodal fusion, human-in-the-loop systems, and real-world deployment in safety-critical applications. Full article

Aiming at the rain protection problem of outdoor precision instruments such as total stations, this paper designs an environmentally adaptive intelligent protection system based on D-S evidence fusion and state machine control. In terms of mechanical structure, the protective cover is designed as a sinking type, which not only improves the safety of the equipment but also avoids the shielding problem of the working surface compared with the traditional upward-lifting structure. The system collects data from multi-source meteorological sensors and uses D-S evidence theory for fusion decision-making. To alleviate decision conflicts in high-conflict scenarios, a conflict-guided dynamic weight adjustment strategy is introduced. Combined with a dual-layer finite state machine, the system realizes coordinated control between environmental perception and protection actions and can activate protection within 30 s under severe weather. Simulation results show that the improved method increases the response speed by 41.9–62.5% compared with traditional D-S fusion in weather transition conditions. In a 28-day field test, the system achieves a daily protection success rate of 96.4% and 100% reliability in critical weather transitions. The proposed system can provide reliable support for the all-weather safe operation of field precision measurement equipment. Full article

Gypsum-based fire protection relies on thermally activated dehydration, where chemically bound water is released and evaporated, thereby providing an endothermic heat sink that delays heat penetration through assemblies. In parallel, inorganic hydrated salts are increasingly used as flame-retardant additives in gypsum-based systems to enhance heat absorption over specific temperature ranges. Fire simulation tools and performance-based fire engineering approaches require reliable kinetic data and reaction enthalpies that can be implemented as coupled thermal–chemical source terms. However, additive-specific kinetic datasets remain limited, particularly under restricted vapor exchange conditions representative of porous construction materials. This work investigates the thermal decomposition behavior and dehydration kinetics of Aluminum Trihydrate (Al(OH)₃, ATH), Magnesium Hydroxide (Mg(OH)₂, MDH), Calcium Aluminate Sulfate (3CaO·Al₂O₃·3CaSO₄·32H₂O, CAS), and Magnesium Sulfate Heptahydrate (MgSO₄·7H₂O, ESM) with emphasis on vapor-restricted conditions representative of confined porous systems. Differential scanning calorimetry (DSC) experiments were conducted at three heating rates (2, 10, and 20 K/min for MDH, CAS and ESM and 20, 40 and 60 K/min for GB-ATH) up to 600 °C using pinhole crucibles to simulate autogenous vapor pressure. The thermal analysis indicates that ATH and MDH exhibit predominantly single-step dehydration behavior, while ESM shows a complex multi-step mechanism. Although CAS presents a single dominant thermal peak in the DSC signal, the isoconversional analysis reveals a multi-stage reaction behavior, demonstrating that peak-based interpretation alone may be insufficient for such systems. Kinetic parameters were determined using both model-free (Starink) and model-fitting approaches in accordance with the recommendations of the Kinetics Committee of the International Confederation for Thermal Analysis and Calorimetry (ICTAC). All reactions were consistently described using the Avrami–Erofeev model as an effective phenomenological representation of the conversion behavior. The extracted kinetic triplets were validated through numerical simulations, showing good agreement with experimental conversion and reaction rate data. The resulting kinetic parameters and dehydration enthalpies provide a physically consistent dataset for the description of dehydration processes under restricted vapor exchange. These results support the development of thermochemical models for gypsum-based systems; however, their transferability to full-scale assemblies remains subject to validation under coupled heat- and mass-transfer conditions. Full article

Groundwater sustains human activity in arid crystalline terrains where surface water is scarce and hydrogeological data are limited. However, most groundwater potential mapping approaches depend on deterministic weighting methods without quantifying model variability. This study describes an uncertainty-aware Remote Sensing and Geographic Information Systems (RS–GIS) framework to delineate groundwater potential zones in the Wadi Arab Watershed, Northeastern Sudan. Nine thematic factors—geology and lithology, rainfall, slope, drainage density, lineament density, soil, land use/land cover, topographic wetness index, and height above nearest drainage—were integrated using the Analytical Hierarchy Process (AHP), with acceptable consistency (Consistency Ratio (CR) < 0.1). To address subjectivity in weights, a Dirichlet-based Monte Carlo simulation (500 iterations) was implemented to perturb AHP weights whilst preserving compositional constraints. The resulting Groundwater Potential Index (GWPI) classified 32.69% of the watershed as high to very high potential, primarily associated with alluvial deposits and fractured crystalline rocks. Model validation using Receiver Operating Characteristic (ROC) analysis yielded an Area Under the Curve (AUC) of 0.704, indicating acceptable predictive performance. Uncertainty assessment showed low spatial variability (mean standard deviation (SD) = 0.215) and stable exceedance probabilities, verifying the robustness of predicted high-potential zones. The proposed probabilistic AHP framework augments decision reliability and provides a transferable, cost-effective tool for groundwater planning in data-limited arid basement environments. Full article

Noise disturbances can cause conflicts in several areas, such as residences, civil constructions, highways, subways, and airports, measured by different scales of acoustic comfort for community well-being evaluation. These disturbances also have signatures such as frequency, amplitude, and temporal patterns to compare acoustic comfort with real-time parameters. In addition, acoustic sensors should be chosen based on accuracy, price, and calibration method, and acoustic insulation should be applied with the aim of achieving reliable measurements in indoor and outdoor environments for sustainable urban living. In some situations, the lack of noise control can lead to several human disorders, from hearing loss to cardiovascular complications. Therefore, legislation and regulation should be carefully studied and applied to achieve an equilibrium between energy-efficient and healthy building designs in entertainment, work, and rest activities with measured parameters visualized through the design of interface tools that should enable the collection and organization of sound data, with proper presentation for the final user. Finally, intellectual property registrations bring recent industrial applications with aspects of noise mitigation. All these features constitute noise disturbance mitigation in a multi-dimensional integration framework of technology, health, and law to improve the quality of life in human-centric environments. Full article

Accurate medium-horizon load forecasting and risk-aware unit commitment are critical for high-load power systems. This study develops an integrated prediction–optimization framework that couples 744 h recursive load forecasting with uncertainty-aware scheduling. In the forecasting stage, a CNN-LSTM model is tuned by the Dung Beetle Optimizer (DBO), while Monte Carlo Dropout is retained during inference to generate probabilistic trajectories and time-varying prediction intervals. In the scheduling stage, these forecast-derived intervals are embedded into a mixed-integer linear robust unit commitment model through a dynamic uncertainty budget. Using real-world load data from Southern China, the proposed method achieves average RMSE, MAE, MAPE, and R² values of 2941 kW, 2137 kW, 4.33%, and 0.97, respectively. Relative to SARIMA and Informer, the average RMSE is reduced by 48.1% and 26.0%, respectively, while point-forecasting performance remains competitive with XGBoost. The proposed model also provides the best overall interval quality, with average PINAW and Winkler Score values of 0.19 and 17,049, outperforming XGBoost, CNN-LSTM, and Informer. In the scheduling study, the proposed robust strategy reduces average EENS and LOLH to 68.6 kWh and 0.0454 h, respectively, and yields the lowest average generalized total cost of CNY 97.30 million, compared with 124.69 million CNY for the deterministic benchmark and CNY 99.66 million for the chance-constrained benchmark. These results show that forecast uncertainty can be effectively translated into more reliable and economical scheduling decisions. Full article

Forest fires pose a considerable threat to the security of ecosystems and human society, rendering accurate assessments of fire severity critical for ecological recovery and effective fire management. The differenced Normalized Burn Ratio (dNBR) has been employed to evaluate forest fire severity; however, it presents notable uncertainties owing to variations in data sources, temporal phases, and environmental factors. To address these challenges, this study analyzed 10 forest fires occurring between 2006 and 2023 in central Yunnan Province, China. First, a rapid sampling method utilizing very high-resolution imagery was developed to assess the performance of dNBR classification under varying conditions. Second, the study identified the optimal post-fire observation window and compared classification thresholds and accuracy between Landsat and Sentinel-2 imagery in assessing fire severity. Finally, the research explored the impacts of topographic correction and pre-fire vegetation differences on classification outcomes. The findings revealed the following: (1) Imagery captured in the spring of the fire year, characterized by minimal vegetation interference, demonstrated the highest classification stability and superior capability for identifying high-severity burns. (2) Landsat outperformed Sentinel-2 in regional accuracy (0.92 vs. 0.87), and direct threshold transfer between sensors resulted in a 39% underestimation of high-severity areas, underscoring the necessity for sensor-specific calibration. (3) Topographic correction provided limited practical benefits, merely yielding a marginal improvement in accuracy (+1.44%) with the SCS+C model in steep terrain, and was generally unnecessary. (4) The influence of pre-fire vegetation was discovered to be threshold-dependent: dNBR performed reliably in forests with pre-fire NDVI > 0.5, while adjusted approaches were solely recommended for sparse or heterogeneous vegetation. Overall, this study establishes a systematic framework for optimizing dNBR-based severity assessment, enhancing its accuracy and operational utility in forest fire management. Full article

High-resolution soil depth maps are valuable for environmental modelling, yet reliable data remains scarce in the tropics. This study evaluates the feasibility of mapping depth to bedrock (DTB) in Sri Lanka using a legacy dataset (n = 88) and global environmental covariates (n = 247). A robust machine learning workflow was employed—including feature selection, hyperparameter tuning, and a stacked ensemble of four algorithms (Random Forest, XGBoost, Cubist, SVM)—to test the limits of global data for local mapping. Despite rigorous optimization, the final ensemble model achieved a performance of R² = 0.197 (RMSE = 35.4 cm) under spatial cross-validation. While still modest, this result significantly outperforms existing global products and quantifies the “prediction gap” inherent in using ~1 km resolution global covariates to model micro-scale soil variability. An initial exploration involved log-transforming the target variable; however, following rigorous testing, the untransformed depth was modelled directly to avoid bias in back-transformation. A robustness experiment was further conducted, reducing predictors from 24 to 12, which degraded performance, confirming that the model captures complex, physically meaningful climatic interactions rather than fitting noise. The study concludes that while global covariates can capture regional meso-scale trends (explaining ~20% of variance), they are insufficient for resolving local micro-relief (<50 m). The resulting map and uncertainty products provide a critical “baseline” for national planning, but effectively demonstrate that future improvements will require investment in higher-resolution local covariates (e.g., LiDAR) rather than more complex algorithms. Full article

Vehicle-to-grid (V2G) technology has emerged as a key enabler for coupling large-scale electric vehicle (EV) deployment with the operation of smart energy systems. By allowing bidirectional power and information exchange between EVs and the grid, V2G transforms EVs from passive loads into distributed energy resources capable of supporting grid flexibility, reliability, and renewable energy integration. However, the practical realization of V2G remains challenged by technical complexity, system coordination, user participation, and regulatory constraints. This paper presents a comprehensive review of V2G integration from a system-level perspective. Rather than focusing solely on individual technologies, the review examines how V2G is embedded within smart energy systems, emphasizing the interactions among EVs, aggregators, grid operators, energy markets, and end users. Key enabling technologies, including bidirectional charging, aggregation mechanisms, communication frameworks, and data-driven control strategies, are discussed in relation to their system-level roles and limitations. The impacts of V2G on grid operation, energy management, and market participation are analyzed, with particular attention to reliability, battery lifetime, and user trust. Furthermore, this review identifies critical open issues that hinder large-scale deployment, spanning infrastructure readiness, standardization, economic incentives, and cybersecurity. Emerging application scenarios, such as building-integrated V2G, fleet-based services, and artificial intelligence (AI) supported coordination, are also discussed to illustrate potential evolution pathways. By synthesizing technological developments with system-level impacts and unresolved challenges, this paper aims to provide a structured reference for researchers, system planners, and policymakers seeking to advance the integration of V2G into future smart energy systems. Full article

Background: The Mobility and Quality of Life-7 Dimension (MobQoL-7D) is a new patient-reported outcome measure for mobility-related quality of life. Our aim was to translate and test a Dutch-language version. Methods: A cross-sectional psychometric evaluation study was undertaken. The sampling frame was community-dwelling adults living in the Netherlands who had a long-term (>6 months) mobility impairment. Participants were recruited through a Dutch research agency, and data were collected via online survey. Statistical and psychometric analyses were undertaken to assess the interpretability, validity and reliability of the MobQoL-7D Dutch, including assessment of missing data, floor/ceiling effects, test–retest reliability, structural validity, known-group validity and convergent validity. Results: n = 308 respondents completed the survey; sub-group sample sizes ranged from n = 29 to n = 87. No issues with missing data were found. Despite ceiling effects per item (ranging from 23.1% to 56.5%), there were no floor/ceiling effects for overall index values (12.3% and 0%, respectively). The findings show excellent test–retest reliability of the index value over a two-week period (n = 37; ICC = 0.95), and potential discriminative ability to detect differences between known groups. Factor analyses confirmed unidimensionality. Conclusions: The results provide promising evidence of the validity and reliability of the MobQoL-7D Dutch; further research is needed to confirm these findings. Full article

A water company’s water supply network is responsible for distributing good-quality water in quantities that meet customer needs, ensuring proper operation of the water supply network to ensure adequate pressure at the receiving points, efficiently repairing faults, and planning and executing maintenance, modernization, and expansion work. Managing a water supply network is a complex and complex process. A crucial challenge in water company management is detecting and locating hidden water leaks in the water supply network. Leak location in water distribution networks is a key challenge for utilities, as undetected leaks lead to water losses, increased energy consumption, and reduced service reliability. With the development of cyber-physical systems (CPSs), the integration of physical infrastructure with real-time digital monitoring has enabled more adaptive and responsive water operations. Data-driven decision-making in CPS in the water industry leverages classification and regression trees (C&RTs) to analyze real-time sensor data—such as pressure, flow, and consumption—to classify system states and predict potential faults. By transforming operational data into interpretable decision rules, C&RTs enable automated and timely maintenance actions that improve reliability, reduce water loss, and support intelligent infrastructure management. The aim of this study is to develop and evaluate AI-based optimization methods to enhance sustainability, efficiency, and resilience in the water industry by enabling autonomous, data-driven decision-making within CPSs, supporting smart industry practices, and addressing practical challenges associated with the actual implementation of smart water management solutions using simple solutions such as C&RTs. The accuracy of the best classifier was 86.15%. Further research will focus on using other types of decision trees that will improve classification accuracy. Full article

(1) Background: The “Bider” marking refers to the symmetrical black stripes distributed on the shoulder blades of Dun Mongolian horses, representing an ancestral trait of significant genetic value. However, the molecular mechanisms underlying its formation remain unclear. This study aims to elucidate the molecular basis of these markings by comparing transcriptomic differences in skin tissues from variously pigmented areas of Mongolian horses’ “Bider” patterns. (2) Methods: Using three Dun Mongolian horses as subjects, skin tissue samples were collected from their shoulders (dark-marked and light-marked areas), dorsal midline, and croup regions for transcriptome sequencing. Differentially expressed genes were identified based on sequencing data, followed by Gene Ontology (GO) functional annotation and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis. Key findings were validated through quantitative reverse transcription polymerase chain reaction (qRT-PCR). (3) Results: The sequencing yielded approximately 893 million high-quality clean reads, with an overall alignment rate exceeding 96%. A total of 140 to 775 differentially expressed genes were identified. GO enrichment analysis revealed that these genes were significantly enriched in biological processes related to pigment metabolism, skin and hair follicle development, signal transduction (including calcium and cyclic guanosine monophosphate (cGMP) signaling), and immune regulation. KEGG analysis further indicated that multiple pathways closely associated with pigment regulation, including the calcium signaling pathway, tyrosine metabolism, cyclic adenosine monophosphate (cAMP) signaling pathway, and melanoma pathway, were significantly enriched across different tissue comparison groups, suggesting their potential key roles in coat color phenotype formation. The reliability of the sequencing data was corroborated by the results of qRT-PCR validation. (4) Conclusions: This study conducted a transcriptome analysis of skin samples from various pigmented regions of the Dun Mongolian horse’s Bider marking, revealing that the formation of this marking is associated with the differential expression of numerous genes and is co-regulated by multiple pigment-related signaling pathways. Full article

Restless legs syndrome (RLS) is one of the most prevalent sleep disorders, yet its diagnosis continues to rely almost entirely on subjective symptom descriptions. This persistent dependence on phenomenology reflects the absence of reliable biological markers to aid in the process of diagnosis or monitoring. However, there is accumulating molecular evidence that suggests that RLS is associated with systemic biological alterations. These extend beyond the traditional paradigm of iron deficiency. The present scoping review synthesizes the current research on circulating serum biomarkers investigated in RLS outside classical iron indices. A comprehensive search of PubMed, Scopus, and Web of Science databases identified 1050 records, of which 50 studies met eligibility criteria and were included. In the processing of data, clusters emerged into several recurring biological domains, including dysregulated iron regulatory signaling (hepcidin), low-grade immune activation, oxidative stress, and neuroaxonal injury markers. High-throughput omics studies reveal molecular network perturbations involving inflammatory pathways, complement activation, metabolic signaling, and cellular stress responses. Biomarker associations appear stronger when linked to objective motor burden. These findings suggest that RLS may involve multifarious molecular changes detectable in the serum. Consequently, this can support the transition from symptom-based diagnosis toward biomarker-informed stratification, which may enable more precise disease characterization and improved diagnostic accuracy. Full article

Understanding crop water use is essential for improving agricultural water management and ensuring sustainable food production, especially in regions with limited water resources. Evapotranspiration (ET) is a key component of the hydrological cycle, directly influencing irrigation planning and crop productivity. However, accurately estimating ET at local scales remains a challenge due to the limitations of conventional measurement methods and the difficulty of integrating high-resolution remote sensing data. This study investigates the estimation of terrestrial evapotranspiration (ET) in a sugarcane cultivation area located in the northern coastal region of Paraíba, Brazil, using meteorological data and aerial images acquired by an Unmanned Aerial Vehicle (UAV). We adapted the PT-JPL model to estimate ET at the local scale, using thermal and multispectral imagery obtained from UAVs. Data validation was performed using surface energy balance measurements obtained from a micrometeorological tower, thereby enabling comparison of estimated and observed ET values. The results demonstrated strong correlations between modeled predictions and field measurements of net radiation (R² = 0.85), with performance metrics indicating moderate reliability for local-scale simulated ET when compared to flux-tower-based ET (R² = 0.48; RMSE ≈ 0.045 mm/30 min). This research highlights the potential of integrating UAV-based remote sensing with the PT-JPL model to improve understanding of crop water use, support irrigation management, and contribute to sustainable agricultural practices. Full article