Search Results (5,361)

Against the backdrop of global energy transition and the widespread adoption of Hydrogen-Blended Natural Gas (HBNG), the safety of urban gas pipeline networks faces severe challenges. This paper systematically reviews the research progress of numerical simulation in the field of natural gas pipeline safety, focusing on its core supporting roles throughout the “Leakage–Dispersion–Evolution–Consequence” disaster chain. First, it analyzes the kinetic modeling of high-pressure leakage holes and property corrections based on real gas equations of state, elaborating on the numerical characterization of HBNG multi-component transport. Second, it compares the dispersion mechanisms and environmental coupling modeling methods in typical scenarios such as buried porous media, confined spaces in utility tunnels, underwater environments, and urban building clusters. Third, it reviews leakage monitoring technologies based on physical field simulation and data-driven approaches (e.g., Convolutional Neural Network, Long Short-Term Memory), emphasizing the value of numerical simulation in constructing digital twin training sets. Furthermore, it explores the dynamic evolution of explosion flame–shock wave interactions and the evaluation models for secondary disaster consequences. Finally, the current research status of grid-based risk pre-warning and emergency response strategies is summarized. In conclusion, numerical simulation is not only a robust method for precisely quantifying and characterizing complex physical mechanisms but also a critical technological foundation for building smart and resilient energy cities. Future research should focus on the deep coupling of multi-physics fields, physics-informed learning, and the development of system-level integrated defense systems. Full article

Lubricating oil monitoring provides continuous health information for early fault warning and maintenance decision-making in industrial gas turbines. However, real-world multi-sensor monitoring streams exhibit pronounced nonstationary thermodynamic drifts that often obscure subtle high-frequency residuals containing critical incipient degradation signatures. Prevailing data-driven monitoring models typically struggle to separate these macroscopic trends from stochastic wear-related fluctuations, and their restrictive distributional assumptions are often inadequate for the heteroscedastic and heavy-tailed nature of industrial residuals. To address these challenges, this study proposes ResAD-Net, a framework for early fault warning in nonstationary multi-sensor oil monitoring that combines trend–residual decoupling, trend-conditioned residual modeling, and residual-domain dependency learning. Specifically, a signal trend–residual decoupling strategy is adopted to separate slowly varying operational trends from stochastic residual fluctuations captured by the sensors, thereby exposing residual information that is more sensitive to incipient degradation. On this basis, a trend-conditioned diffusion model is introduced to characterize state-dependent, skewed residual distributions and generate residual sample ensembles for nonstationary monitoring. Meanwhile, a graph-based variational autoencoder is employed to learn latent intersensor dependency structures from the residual domain, providing diagnostic cues for temporal risk evolution analysis and sensor-level inspection. Experiments on a real-world industrial oil-monitoring record show that the proposed framework achieves an average F1-score of 0.985 with no observed false positives in the predefined pre-alarm reference interval of the finite test set. In addition to accurate anomaly detection, ResAD-Net captures early residual distributional shifts before clear macroscopic deviations emerge and provides diagnostic association cues for interpreting oil-monitoring changes around the system-level alarm. Full article

The water–energy–food (WEF) system acts as a critical nexus of social–ecological systems. However, rapid urbanization has intensified the regional imbalance in the supply and demand of ecosystem services (ESs). Clarifying the spatiotemporal matching of ecosystem services supply and demand (ESSD) within the WEF framework and revealing the driving mechanisms behind such imbalances are essential to formulating reasonable zoning schemes and targeted optimization strategies for the coordinated development of the regional WEF system. Taking Zhejiang Province as a case study, this research uses water yield (WY), carbon sequestration (CS), and grain production (GP) to characterize the WEF nexus system. It uses the InVEST model to assess WY and CS, applies spatial allocation methods to characterize GP, and integrates socioeconomic data to quantify the demand for the above three ESs. All indicators were standardized and integrated with equal weights to further clarify the comprehensive levels of ESSD. By integrating the Geodetector and K-Means clustering methods, the study analyzes the supply–demand matching of ecosystem services and its driving mechanisms in Zhejiang Province during this period, thereby exploring ecological management zoning and optimization strategies within the WEF system. The study findings indicate that: (1) From the supply perspective, Zhejiang Province’s WY services demonstrate a trend of elevated activity in the southwest and diminished presence in the northeast; high values for CS services are predominantly found in the vegetation-rich areas of the northwest, while high values for GP services are clustered in the northern Zhejiang Plain; from the demand perspective, high values for all three ESs in Zhejiang Province are primarily located in economically active, densely populated urban areas. (2) The correlation between ESSD within Zhejiang Province’s WEF system exhibits significant spatial heterogeneity and is driven by the combined effects of natural and socioeconomic factors, with the interaction between these two factors often producing a synergistic effect. Specifically, annual average precipitation and population density are the dominant factors influencing WY services, NDVI and human footprint are the dominant factors influencing CS services, and population density and GDP are the dominant factors influencing GP services. (3) From 2000 to 2020, the supply–demand ratio for comprehensive ESs in Zhejiang Province generally followed a pattern of being lower in the east and higher in the west. The supply–demand imbalance of ESs intensified in the core areas of eastern cities, whereas the western regions maintained a relatively sound supply–demand balance. (4) The study classifies the counties in Zhejiang Province into four ecological management zones—ecological stable zones, ecological conservation zones, ecological control zones, and ecological restoration zones—and explores differentiated approaches to optimizing these zones and implementing control strategies. Full article

To improve the sustainability of intelligent building operation and enhance grid adaptability in the presence of uncertainty, this paper presents a coordinated optimization method that jointly exploits virtual energy storage and waste heat recovery. A thermal modeling framework is developed to represent the coupling relationships among air conditioning operation, waste heat utilization, and indoor comfort requirements. On this basis, building thermal inertia is incorporated into an IDM-informed two-stage robust optimization framework, where distributional bounds derived from the Imprecise Dirichlet Model are transformed into data-driven interval uncertainty sets for wind–photovoltaic output and outdoor temperature. To make the model computationally tractable, the column-and-constraint generation method is employed for iterative solution. Numerical results verify that the proposed method can effectively unlock the flexibility of the cooling system and improve the utilization of recoverable heat resources while maintaining acceptable indoor comfort, even under adverse operating conditions. Overall, the proposed strategy strengthens system resilience, reduces carbon-related operational pressure, and provides more dependable demand-side support for secure power system operation. Full article

This paper presents a multidimensional analysis of Historic Building Information Modeling (HBIM) segmentation, offering a roadmap towards standardization, a key dimension towards broader adoption within the Cultural Heritage (CH) sector. HBIM faces multiple challenges related to the lack of standardized protocols and varying definitions of Level of Detail (LOD) across applications. Amid the advancements of the fourth industrial revolution, integrating Building Information Modeling (BIM) improves sustainability and digital governance, aligning with the sustainable development agenda. Despite increasing academic interest, the implementation of HBIM remains limited, primarily due to the complexities and heterogeneities inherent in CH artifacts. This study begins with a purely qualitative strategy. Then, it introduces multidimensional and hierarchical clustering analysis to classify the unique characteristics of various HBIM applications such as segmentation, input, and data-capturing media. At the same time, it is a tool for fine-tuning keyword-based selection criteria, which is crucial in systematic or semi-systematic surveys in HBIM segmentation. The thematic analysis output is interrupted just before the conceptualization step, and theme extraction is diverted to correspondence analysis implemented in R, an open-source statistical package. Among the key findings of this paper is the classification of four distinct HBIM application clusters, revealing how specific workflows align with data acquisition methods, input formats, and Level of Detail (LOD) requirements. The analysis exposes critical standardization bottlenecks hindering wider-scale industry adoption, highlighting that challenges are domain-specific. Strong evidence shows that 3D modeling has not reached the required maturity level, with persisting challenges distributed non-uniformly within the applications spectrum. Finally, AI-driven automation relates with poor LOD outcome. Full article

India exhibits substantial variation in neonatal mortality across regions and socioeconomic groups. This study used nationally representative survey data (2019–2021) to examine wealth-based inequalities in neonatal mortality. Socioeconomic disparities were assessed using Erreygers’ Normalized Concentration Index (ECI) and concentration curves, with subgroup analyses by residence, state development status (Empowered Action Group (EAG) vs. non-EAG), district typology, and region. Inequality was further decomposed using the Wagstaff method. Analysis of 176,843 most recent live births revealed marked rural–urban disparities, with neonatal mortality in rural areas (18.3 per 1000 live births) 1.6 times higher than in urban areas (11.5). Neonatal mortality was significantly concentrated among poorer households (ECI: −0.0123; p < 0.001), with greater inequality in urban areas, EAG states, and non-aspirational districts. Regional variation was evident, with the highest inequality in the Western and Central regions. Decomposition analysis showed that inequality was primarily driven by adverse household conditions and maternal risk factors concentrated among poorer populations. Key contributors included unclean cooking fuel, higher parity, large family size, normal delivery and inadequate antenatal care. These findings highlight the need for equality-focused strategies addressing both social determinants and gaps in access to quality maternal and newborn care. Full article

Arboviral diseases are emerging as important public health threats in Saudi Arabia, driven by rapid urbanization, climate variability, the expansion of Aedes aegypti populations, international travel, and large-scale religious mass gatherings. Dengue virus remains the most established arboviral infection in the Kingdom, particularly in the southwestern regions such as Jazan and the western urban centers of Makkah and Jeddah, where ecological and climatic conditions are conducive to sustained vector survival and transmission. This review synthesizes current evidence on the epidemiology, vector ecology, climatic determinants, diagnostics, and prevention strategies of arboviral diseases in Saudi Arabia. Particular attention is paid to the impacts of rising temperatures, changes in rainfall patterns, urban heat island effects, population mobility, and cross-border movement on vector expansion and disease emergence. The review also identifies gaps in surveillance, diagnostics, insecticide resistance monitoring, and integrated vector management programs. Emerging preparedness strategies include climate-informed early warning systems, Geographic Information System-based risk mapping, multiplex molecular diagnostics, genomic surveillance, and community-based vector control. The review emphasizes the importance of implementing a One Health approach that combines data on humans, the environment, entomology, and climate. Currently, sustained endemic transmission of chikungunya and Zika viruses has not been conclusively demonstrated in Saudi Arabia, but increased environmental suitability and connectivity with other areas highlight the need for proactive surveillance and preparedness. Full article

Neuroinflammation in multiple sclerosis (MS) is driven by the infiltration of myelin-reactive T cells into the central nervous system (CNS). Interferon-β (IFN-β) is one of the earliest disease-modifying treatments (DMTs) approved for MS and remains widely used in special populations (pregnant and elderly patients) owing to its favorable safety profile. However, the exact mechanism of action of this drug and reliable biomarkers of treatment response remain unclear. Transcriptomic profiling and data integration approaches offer powerful tools for investigating complex patterns of regulation and molecular mechanisms underlying therapeutic efficacy. In this study, we performed an integrative analysis of openly available transcriptomic datasets to characterize IFN-β-induced gene expression changes in MS patients. By combining data from large independent cohorts, we identified a 43-gene transcriptional signature consistently associated with IFN-β treatment across disease stages, including progressive MS. To explore the relevance of this signature, we cross-referenced the 43-gene signature with publicly available expression quantitative trait loci (eQTL) datasets to determine whether these genes could be influenced by known MS-associated risk variants highlighting Interferon-Induced Transmembrane Protein 3 (IFITM3) as a candidate molecular mediator of MS. This integrative approach provides new insights into IFN-β-driven immune modulation and supports the development of therapeutic strategies for MS. Full article

Game-based learning (GBL) has gained widespread attention as an innovative pedagogical approach, yet its potential to enhance students’ metacognitive learning remains underexplored. Guided by self-regulated learning (SRL) theory, the review investigates how GBL design features, such as goal-setting, real-time feedback, progress visualization, and reflection tools, scaffold students’ planning, monitoring, and evaluation strategies. A systematic search across Web of Science, Scopus, and ProQuest identified the studies, which included data from physical classrooms, online learning environments, and mixed settings. This scoping review synthesizes evidence from 11 peer-reviewed studies conducted between 2015 and 2025 to evaluate the impact of GBL on metacognitive learning in primary and junior middle school contexts. Findings reveal that GBL effectively supports metacognitive learning through real-time feedback and progress indicators, though planning and evaluation scaffolds are less comprehensively addressed. Furthermore, digital trace data and behavioral logs are emerging as robust tools for assessing metacognitive processes, offering deeper insights than self-reports alone. However, the review identifies critical gaps, including insufficient focus on junior middle school students, limited representation of non-STEM disciplines, and uneven theoretical grounding across studies. The findings underscore the need for theory-driven design and balanced scaffolding to maximize GBL’s potential in fostering metacognitive competence. This study also provides practical insights for educators to foster students’ metacognitive learning by effectively integrating games into educational practices. Full article

Smart museums increasingly rely on digital media, interactive installations, artificial intelligence, augmented reality, and virtual reality to support cultural communication and visitor engagement. However, existing studies have mainly examined specific technologies, usability, or visitor satisfaction, while a systematic and quantitative framework for comparing interactive experience across different smart museums remains limited. To address this gap, this study proposes a hybrid multi-criteria decision-making framework for evaluating smart museum interactive experience. Based on the Strategic Experiential Modules, an evaluation system consisting of five dimensions—Sense, Feel, Think, Act, and Relate—and sixteen indicators was constructed. The Analytic Hierarchy Process was used to determine subjective weights from expert judgments, the entropy method was applied to capture the data-driven dispersion characteristics of expert evaluation data, and a game-theoretic combination weighting strategy was used to integrate the two weighting results. Subsequently, the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) was employed to compare five representative smart museum cases. The results show that Zhejiang Provincial Museum achieved the highest relative closeness value (C_i = 0.9891), followed by Shanghai Museum (C_i = 0.8457) and Hunan Museum (C_i = 0.5326). Robustness analysis further showed that the ranking order remained consistent under entropy weights, AHP weights, average weights, and game-theoretic combined weights. The Friedman test indicated no significant difference in the relative closeness coefficients across weighting schemes (χ² = 1.200, p = 0.753). These findings indicate that the proposed framework can effectively identify relative strengths and weaknesses in smart museum interactive experience and provide a replicable decision-support tool for experience-oriented museum design and optimization. Full article

In the context of subtropical cities, the slow-moving environment of HOD (Hospital-Oriented Development) faces the dual challenges of spatial fragmentation and an extreme hot and humid climate, which also restricts the outdoor space’s thermal environment performance. Taking the Macau Light Rapid Transit (LRT) Union Hospital Station as an example, this study constructs a “topology-climate” dual quantitative assessment framework that integrates space syntax and parametric universal thermal climate index (UTCI) simulation. In response to the current problems of mixed pedestrian and vehicular traffic and high-intensity heat radiation, a comprehensive intervention strategy combining three-dimensional stitching and spatial optimization is proposed. The results show that: (1) The implantation of three-dimensional corridors improved the spatial integration of the core area of the site by 67.0%, significantly optimizing network connectivity. (2) During the extreme high-temperature period of daytime (9:00–18:00) in summer and autumn, the intervention strategy precisely opened up a continuous low-heat-stress linear shade zone through the synergistic mechanism of building projection shadows, physical shading of connecting corridors, (landscape shading effect, original evaporation removed). (3) The study confirms that landscape-coupled shading layout is the most effective method, reducing potential pedestrian heat exposure across the entire area, while the three-dimensional connecting corridors precisely control the thermal environment of core walkways. Together, these two elements construct a “topology-climate” optimization framework, achieving a synergistic improvement in spatial accessibility and simulated thermal comfort performance under standard meteorological input and quantitatively verifying the optimization effectiveness of the tiered intervention scheme. This study provides a data-driven decision-making basis for optimizing potential walking thermal conditions for vulnerable groups and reshaping the space’s potential to improve microclimate via shading design of medical hub areas and also provides a scientific paradigm for TOD microclimate planning focused on shading-based thermal environment optimization. Full article

This study investigates the hydroclimatic impacts of climate change on the Göynük Stream Basin, a snow-fed tributary within the Euphrates River Basin, utilizing flow, precipitation, and temperature data from 1975 to 2022. The Göynük Stream Basin is characterized by high-altitude, harsh continental conditions, with its flow regime heavily influenced by snowmelt, rendering it particularly sensitive to climate change. Employing a suite of trend analysis methods, including Mann–Kendall, Spearman Rho, Theil–Sen, Şen-Innovative Trend Analysis (ITA), and Innovative Polygon Trend Analysis (IPTA), the research evaluated annual and seasonal data from one stream and four meteorological stations across multiple significance levels (90%, 95%, 99%). Unlike conventional hydroclimatic studies based solely on monotonic trend detection, this study integrates classical trend tests, innovative trend approaches, temporal regime-based analysis (RAPS), and machine learning techniques within a unified assessment framework to evaluate both hydroclimatic variability and runoff predictability under climate change conditions. Key findings indicate a significant decline in annual flow rates by approximately 9.37%, with a notable decrease in maximum flow rates evidenced by a negative trend slope of −0.2726 m³/s/year. While precipitation trends were generally decreasing, temperature data exhibited significant increases, especially during winter and spring. Seasonal analysis revealed substantial flow reductions in summer and autumn, coupled with an earlier timing of the annual maximum flow, shifting from mid-May to late March/early April, suggesting earlier snowmelt. The study concludes that the Göynük Stream Basin is experiencing increasing hydroclimatic pressures attributable to climate change. These insights are crucial for water resource management and serve as a guideline for similar snow-fed sub-basins within the broader Euphrates River Basin. Furthermore, the integration of a machine learning approach, utilizing meteorological and seasonal data, demonstrated strong monthly runoff prediction capabilities with NRMSE of 4.11% and R² equal to 0.951. Feature importance analysis highlighted seasonality and temperature as primary predictive factors. However, a marked decline in model accuracy after 2011 was observed, indicating a non-stationarity in the hydroclimatic system, likely driven by climate change impacts and underscoring the need for adaptive management strategies. Full article

In underwater target recognition, the scattering characteristics of small targets are weak and highly sensitive to observation angles, posing significant challenges to achieving stable and robust recognition in complex environments. Existing methods are mainly data-driven and rely on closed-set classifiers, which often lack physical interpretability and show limited generalization under different observation conditions. To address these issues, a physics-guided deep embedding framework for underwater target recognition is proposed. Firstly, an encoder–decoder network is designed to learn representative and physically consistent scattering features from measured echo frequency spectra. The encoder is then extracted to construct a Triplet-based embedding model, which maps high-dimensional scattering spectra into a discriminative low-dimensional feature space. In the embedding space, a similarity-based decision strategy is further adopted to replace the traditional classifier, and recognition is achieved by evaluating the relationships among embedded features. Experimental results show that the proposed method achieves robust recognition performance under varying observation angles and establishes an interpretable connection between scattering characteristics and recognition results. The proposed framework provides an effective way to combine physics-guided feature learning with deep embedding methods for underwater target recognition. Full article

The present work proposes a phase change material-based defrosting system (PCM-DS) for vapor compression refrigeration systems (VCRSs). The primary objective is to determine the optimal PCM mass and refrigerant mass flow rate required to melt 1 kg of accumulated evaporator ice. A steady-state macroscopic thermodynamic model, governed by global energy balances and driven by experimental boundary conditions, evaluates the VCRS in both cooling and defrosting operating modes. The PCM-DS is not installed on the experimental setup. The latter is used to obtain experimental data to be used as inputs in the steady-state model. Among the three candidates investigated (OM42, OM46, OM48), OM42 was selected for minimizing system mass and volume constraints. Results demonstrate that integrating the PCM-DS induces only a 3% reduction in the theoretical coefficient of performance (COP) compared with a 5.6% reduction in the case of using the electric heater defrosting (EHD). The core innovation of this work involves proposing and evaluating three distinct energy storage management strategies: unique superheating, unique bypass, and intermittent bypass. The results show that the highest COP is obtained for unique superheating (2.93), followed by unique bypass (2.82) and intermittent bypass (2.81). The work conducted proves the theoretical feasibility of such PCM-DS. Full article

A reinforcement-learning-based wall-modeled large-eddy simulation (RL-WMLES) framework is proposed to improve the physical consistency of near-wall turbulence predictions. In this approach, a reinforcement learning agent is coupled with the WMLES solver to dynamically adjust a compensating stress term, with the objective of enforcing agreement between the LES solution and the law of the wall. The agent is trained using the proximal policy optimization (PPO) algorithm, where the state is defined as the discrepancy between the near-wall LES velocity and the wall-model prediction, and the action corresponds to modifying a parameterized support viscosity distribution. The proposed method is implemented within a high-performance CFD solver and trained on turbulent channel flow. Numerical results demonstrate that the trained agent effectively reduces the log-layer mismatch and significantly improves the accuracy of near-wall velocity predictions. Furthermore, the RL-WMLES framework exhibits a degree of generalization capability: the trained agent performs robustly with varying levels of numerical dissipation and Reynolds numbers. By introducing a simple interpolation strategy, the same agent can be successfully applied to configurations with different matching locations. Overall, the RL-WMLES framework provides a flexible and data-driven approach for enforcing physical constraints in turbulence modeling. The method shows strong potential for extension to more complex flows. Full article