Search Results (2,244)

This study addresses the optimal scheduling of a proton exchange membrane fuel cell (PEMFC)-based building combined cooling, heating, and power (CCHP) system, aiming to improve operational efficiency and flexibility under coupled electricity–thermal–cooling demands and load uncertainty. A physics-informed scheduling environment was developed using component models and multi-energy balance constraints, including a PEMFC with waste-heat recovery, a lithium bromide absorption chiller, a reversible heat pump with condenser heat recovery to thermal storage, a battery energy storage system, and a hot-water thermal storage tank. The dispatch problem was formulated as a Markov decision process and solved using deep reinforcement learning with TD3; performance was evaluated on typical summer and winter days, and robustness was tested by generating 100 scenarios with 30% demand perturbations. The results show that TD3 learns coordinated multi-energy dispatch patterns consistent with seasonal operation and reduces hydrogen consumption relative to a rule-based strategy under uncertainty while requiring millisecond-level inference time. Dynamic programming achieved slightly lower hydrogen consumption but incurred orders-of-magnitude higher computation time. Overall, TD3 provides a practical trade-off between near-optimal performance, robustness, and real-time applicability for PEMFC-based building CCHP scheduling. Full article

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

This study addresses a critical methodological gap in evaluating building envelope performance in hot, arid climates, the overreliance on annual energy indicators, which fail to capture transient thermal behavior during peak-load periods. In such environments, instantaneous heat gains, their intensity, and temporal distribution are decisive factors for cooling demand, occupant comfort, and grid stability. To overcome this limitation, a dynamic evaluation framework—the Thermal Adaptation Rating (TAC) system—is proposed. TAC integrates three interrelated indices—peak temperature reduction (ΔT_peak), relative peak cooling load reduction (ΔP_peak, %), and peak thermal delay (Δt_delay), representing thermal damping, load intensity mitigation, and temporal redistribution, respectively. A typical residential building in Karbala was modeled in DesignBuilder using the EnergyPlus engine, with inputs documented and calibration performed against real consumption data following ASHRAE standards (MBE and CV(RMSE)) to ensure reliability. The study examined advanced envelope systems, including thermochromic glass (TG), phase-change materials (PCMs), aerogel materials (AMs), and hybrid combinations. Results revealed that while AM achieved the greatest annual energy savings, its impact on instantaneous cooling load was limited. PCM, by contrast, effectively mitigated and delayed peak loads, enhancing thermal comfort (PMV/PPD). Hybrid systems, particularly TG-PCM, delivered the most balanced performance, simultaneously reducing peak cooling load and shifting its occurrence to reshape the cooling demand curve during critical periods. These findings demonstrate that annual indices alone are insufficient for evaluating envelope performance in extreme climates. Peak-condition analysis, expressed in terms of instantaneous cooling load, as operationalized through TAC, provides a more accurate representation of thermal behavior and offers a practical tool to guide envelope design decisions in hot, dry regions. Full article

Sandstone of the Lower Cretaceous Luohe Formation is the main water inrush source in the Binchang Mining Area in the southwestern Ordos Basin. Its sedimentary environment and provenance features are critical for local coal development and safe mining. The Luohe Formation at Xiaozhuang Coal Mine comprises three vertical members: the lower member dominated by coarse- to medium-grained sandstones, the middle member mainly composed of fine-grained sandstones, and the upper member characterized by interbedded fine- to medium-grained sandstones and sandy conglomerates. This subdivision newly identifies a complete hydrodynamic evolutionary cycle of depositional environments from high-energy to low-energy and back to high-energy conditions. Integrated petrographic observations and analyses of major and rare earth elements first confirm that the tectonic affinity of the Luohe Formation progressively shifted from a passive continental margin to an active continental margin, accompanied by a corresponding transition in sediment provenance from the North China Craton to a magmatic arc source region. Trace element compositions precisely indicate that the Luohe Formation was deposited in a fluvial freshwater environment under hot, arid, and oxidizing conditions, thus providing new constraints on the paleoenvironmental evolution of the region. Full article

This paper presents a collaborative optimization design methodology aimed at improving heat dissipation efficiency through the modulation of microstructural variations. The approach addresses the thermal protection requirements of high-temperature components, such as ceramic matrix composite turbine blades, which are subjected to complex and elevated thermal loads. Through the integration of numerical simulation and experimental validation, a bidirectional mapping model linking carbon nanotube (CNT) content with the macroscopic anisotropic thermal conductivity of the material was developed. Furthermore, a thermal conduction analysis and optimization framework for Ceramic Matrix Composite (CMC) high-temperature components under non-uniform thermal loads was established. This study expands the adjustable range of the material’s thermal conductivity by allowing flexible modulation of carbon nanotube content. The results demonstrate that this methodology effectively enhances the heat dissipation capacity of CMC materials in extreme thermal environments: the maximum surface temperature of the optimized flat plate is reduced by 8.96%, the peak temperature gradient is lowered by 46.64%, and the maximum thermal stress is decreased by 38.17%. This research provides new insights into the comprehensive integration of thermal dissipation requirements for CMC hot components. Full article

Designing sustainable pedestrian infrastructure in hyper-arid cultural landscapes requires balancing visitor experience, heritage protection, and environmental constraints. This study develops a statistically grounded model for planning sustainable walking trails in Al-Ula, Saudi Arabia, using multi-spectral remote sensing data integrated with expert-based evaluation. A GIS-based Multi-Criteria Decision-Making (MCDM) framework was applied to assess topographic slope, vegetation cover (NDVI), built-up density (NDBI), Land Surface Temperature (LST), and solar exposure. Indicator weights were validated through a three-round Delphi survey involving fifteen experts. The results indicate strong consensus among experts, identifying LST (21%) and slope (20%) as the most influential determinants of trail suitability in desert environments. These findings highlight the critical role of thermal stress in shaping safe and sustainable pedestrian mobility in hot climates. The optimized 44.5 km trail network, classified into three difficulty levels, improves energetic efficiency by reducing caloric expenditure by 24% compared to conventional routing. In addition, the proposed network has the potential to reduce carbon emissions associated with heritage-related travel by approximately 75% through modal shift from vehicles to walking. The framework provides a practical decision-support tool for planners seeking to develop low-carbon, climate-responsive tourism infrastructure aligned with the objectives of Saudi Arabia’s Vision 2030. Full article

High-resolution Köppen–Geiger projections indicate that several cold desert (BWk) regions are likely to transition toward hot desert (BWh) regimes during the twenty-first century, challenging the environmental logic of vernacular architecture. Despite extensive simulation-based research on passive cooling in established BWh contexts, limited attention has been given to climate-type transition zones and to the morphological continuity of traditional housing systems. This study investigates the adaptive capacity of Bukhara’s courtyard houses under projected BWk–BWh reclassification. Employing an analytical generalization approach, the research integrates systematic literature mapping, typological morphological analysis, and a threshold-based compatibility matrix. Findings reveal that climate transition produces a form of morphological discontinuity by weakening diurnal discharge assumptions embedded in high thermal mass systems. However, courtyard typologies retain a resilient passive core when recalibrated through microclimatic amplification strategies. The proposed staged adaptation framework contributes a heritage-sensitive decision model that reconciles climatic performance with spatial integrity, offering transferable guidance for cli-mate-intensifying desert regions. Full article

Due to their characteristics of a high power-to-weight ratio, stringent lightweight requirements, and harsh working environments, straight face gears are prone to issues such as tooth fracture and inadequate fatigue strength. Meanwhile, because of the lack of fatigue information and weak fatigue life prediction method, the fatigue life of face gears cannot be effectively evaluated. In this study, the key technologies involved in the hot rolling forming process, fatigue experiments, and numerical modeling of straight face gears are studied. A technical foundation for straight face gears formed by hot rolling processing is established, and a fatigue experiment of the hot rolling forming of straight face gears is carried out. Due to the lack of information on fatigue experiments, a numerical prediction model is constructed. Sample expansion is carried out using a BP neural network–Bootstrap model to calculate the reliable lifespan of hot-rolled straight face gears, and fatigue life prediction for hot-rolled straight face gears is completed via the improved GM(1,1,λ) model based on the artificial bee colony algorithm, and thus the accurate evaluation of the fatigue life of rolling forming face gears is realized. The feasibility and superiority of the improved fatigue life prediction model are demonstrated by comparing it with the traditional prediction model and experimental results. The theoretical basis and technical support for the research of the fatigue resistance and installation application of face gears are provided. Full article

To investigate the aging behavior of high-temperature-vulcanized silicone rubber (HTV-SiR) used in composite insulator sheds under coupled electrical, thermal, humidity, and mechanical stresses, accelerated aging tests were conducted to emulate the service conditions of ±800 kV ultra-high-voltage direct current (UHVDC) systems in Guangzhou, China. The physicochemical, mechanical, and electrical properties of the specimens were systematically characterized. The results show simultaneous degradation of both electrical and mechanical performance. In particular, the tensile strength exhibits a significant monotonic decrease and drops to 49.52% of its initial value under the most severe condition (0.5 kV·mm⁻¹ and 5% tensile strain) after 75 days. In contrast, the DC breakdown strength shows a non-monotonic “rise-then-fall” trend and decreases more markedly with increasing tensile strain. To address the one-shot and destructive nature of tensile testing and the associated statistical uncertainties, a lifetime prediction framework was developed by integrating a generalized Eyring acceleration relation with a stochastic degradation process. Under representative service conditions of 0.09 kV·mm⁻¹ and 0.2% tensile strain, the predicted lifetimes corresponding to failure probabilities of 10%, 75%, and 90% are 1.77, 9.08, and 17.90 years, respectively. The applicability of the model is supported by field-aged specimens. These findings provide a mechanistically grounded and reliability-oriented basis for condition assessment, lifetime-margin evaluation, material screening, and maintenance planning of UHVDC composite insulators operating in hot–humid environments. Full article

Background: Bikram yoga, a form of hot yoga practiced in heated environments, has been associated with improvements in flexibility, body composition, and overall well-being. However, longitudinal evidence on its effects in adult women remains limited. Obesity/metabolic syndrome (MetS) is highly prevalent among adult women worldwide, with estimates exceeding 40% in middle-aged populations, underscoring the need for low-impact interventions targeting adiposity and age-related metabolic risks. This study evaluated the effects of 6-month Bikram yoga on body fat percentage (%BF) in adult women, with age-stratified analyses. Methods: Twenty-two women (20–65 years) participated in a structured Bikram yoga program consisting of three weekly sessions (90 min, 26 postures + 2 breathing exercises, 40 °C, 40% humidity) over six months. Anthropometric assessments (8 skinfolds, 5 body circumferences, weight, and height) were conducted at T0, T1 (~45 days), T2 (~90 days), and T3 (6 months). %BF was estimated using multiple validated prediction equations integrated into the Exercise Science Toolkit. Results: A significant and progressive reduction in %BF was observed across the sample: −3.71% at T1 (p < 0.0001) and −6.07 at T3 (p < 0.0001) compared to the baseline. Positive outcomes were consistent across all age subgroups: subgroup A (20–35 years, T3 −6.62%), subgroup B (36–50 years, T3 −5.96%), and subgroup C (51–65 years, T3 −5.39%). Decreased inter-subject variability (SD) suggests a similar direction of change among participants. Conclusions: Regular Bikram yoga practice (three sessions per week for six months) was associated with significantly and consistently reduced %BF among adult women aged 20–65, exceeding the clinical threshold (>5%) for metabolic benefits. Effects were evident after six weeks and remained across all age subgroups, suggesting that Bikram yoga may represent an effective, low-impact intervention for health promotion and active aging. Full article

This work deals with a practical method of using crumb rubber resulting from waste tires to produce modified bitumen via a wet mixing method for road construction in Iraq. Due to wide variation in temperatures and over-loading traffic in Iraq, rutting deformation is the most observed structural pavement problem. Also, tire wear and tear are higher in Iraq than in other countries due to high temperature and dry weather most of the year, which makes considerable amounts of waste tire piles easily accessible. Utilizing this waste material could be crucial to the environment and economy of the country, as well as to the sustainability of resources. Using waste tire materials as bitumen modifiers in the production of hot mix asphalt is a widely practiced experiment, although it is applied differently depending on the weather, type of bitumen used, and its availability. In the methodology of this research, it is suggested to modify asphalt grades 60/70 by a certain amount of crumb rubber (5–20%). The modified asphalt and asphalt grade 40/50 were used in preparing two types of asphalt concretes to examine their volumetric properties and evaluate their rutting behavior. The results for both mixtures were compared to the Iraqi General Specifications for Roads and Bridges (SORB/R9). The findings showed significant improvements in Marshall stability and flow, as well as in the percentages of voids satisfied in the modified mixture. After using rubberized asphalt in the mixture, the rutting depth was recorded below 20 mm and decreased by 30% and 26% at temperatures of 40 °C and 60 °C, respectively, compared to the controlled mixture. Full article

This study examines the gap concerning occupants’ perceived thermal comfort and objectively measured indoor conditions in a green university building in Riyadh. The purpose is to assess occupant satisfaction with thermal conditions, compare subjective responses with physical measurements, and derive design and operational implications for educational buildings in hot-arid climates. The primary aim was to assess occupant satisfaction with indoor thermal conditions and to measure key environmental parameters to provide a thorough assessment of thermal comfort. A cross-sectional approach was used, combining subjective data from the Center for the Built Environment (CBE) Occupant Indoor Environmental Quality (IEQ) survey with objective measurements of air temperature, relative humidity, mean radiant temperature, and air velocity, which were documented over five consecutive working days during the mid-winter period in Riyadh. These parameters were explored using the CBE Thermal Comfort Tool to calculate Predicted Mean Vote (PMV) and Predicted Percentage Dissatisfied (PPD) indices. Statistical analyses examined the relationship between occupant-reported comfort and measured environmental conditions. Results showed that only 36% of occupants reported satisfaction with thermal comfort, while 48% expressed dissatisfaction. In contrast, objective measurements indicated stable indoor conditions within recommended comfort ranges (average temperature 23 °C, humidity 30–34%, MRT 24 °C, air velocity 0.5–1.0 m/s), with PMV values near neutral (−0.2 to 0.0) and PPD below 6%. The observed discrepancy highlights the influence of regional climate, individual adaptability, and perceived control. These findings emphasize the need to integrate both subjective feedback and objective measurements to develop occupant-centered strategies that enhance comfort and well-being in sustainable educational buildings in hot-arid climates. Full article

Hot and humid climates challenge conventional residential designs in maintaining thermal comfort, often leading to a heavy reliance on energy-intensive mechanical cooling. This dependence increases operational costs and contributes to elevated carbon emissions. In rapidly urbanising regions such as Selangor, Malaysia, climate-responsive and sustainable design strategies are urgently needed. This study evaluates the effectiveness of passive design strategies in enhancing indoor thermal comfort in naturally ventilated residential buildings using a three-case study methodology. Empirical field measurements were conducted to examine the influence of shading, building orientation, natural ventilation, and material selection on operative temperature T_op and perceived comfort. The findings indicate that integrating passive strategies significantly improves indoor thermal conditions. Residence A, incorporating effective cross-ventilation and thermal mass, achieved the lowest operative temperature range of 28.5 °C to 29.8 °C, remaining within the 90% adaptive comfort band, with favourable air velocities between 0.45 and 0.65 m/s. In contrast, Residence B recorded higher operative temperatures from 29.5 °C to 31.2 °C, up to 1.4 °C warmer than Residence A, due to mean radiant temperatures exceeding 31 °C and a near-stagnant airflow below 0.10 m/s. Although Residence C demonstrated moderated radiant temperatures between 28.2 °C and 29.5 °C through effective envelope design, operative temperatures remained warm, ranging from 29.0 °C to 30.5 °C, due to severely restricted air velocities below 0.05 m/s. Overall, the results demonstrate that combinations of low air velocity (<0.10 m/s) and elevated mean radiant temperature (>30 °C) consistently drive operative conditions beyond the upper 90% adaptive comfort threshold, confirming ventilation effectiveness is the primary control factor of thermal acceptability in tropical residential environments. Full article

In this study, we employed forced convective cooling under the fan-cooling garment (FC condition) and conductive cooling under the neck cooling device (NC condition) in a hot environment during intermittent exercise to compare their effects on thermophysiological and subjective responses. Cooling was examined under two conditions: continuous application throughout both exercise and rest periods (Experiment 1) and application solely during rest periods (Experiment 2). As different participant groups were utilized for each experiment, the effects of cooling timing were interpreted in an exploratory manner. No differences were observed between conditions at baseline. In the FC condition, whole-body heat dissipation (HF_mean) significantly increased (p < 0.05), particularly during the recovery phase, and was associated with significant suppression of mean skin temperature rise (p < 0.05) and enhanced thermal comfort. Conversely, although localized heat dissipation at the neck (HF_neck) significantly increased under the NC condition, its effects on whole-body heat dissipation and mean skin temperature were limited. No consistent differences were observed between cooling conditions in axillary temperature or heart rate responses. These results suggest that forced convective cooling, which facilitates ventilation within clothing, and localized conductive cooling exhibit distinct thermal response characteristics. This study provides fundamental comparative data under controlled conditions, contributing to the understanding of the response characteristics of wearable cooling devices. Full article

Artificial intelligence (AI) can help students accelerate assignment completion, but it may also foster cognitive outsourcing and learning detached from authentic contexts. This paper presents E3-HOT, a conceptual framework that leverages embodied intelligence to sustain learners’ cognitive agency and higher-order thinking for sustainable learning, aligned with SDG 4 (Sustainable Development Goal 4) and its emphasis on inclusive and equitable quality education and lifelong learning. Using an iterative conceptual synthesis, we distill three embodied pathways—situational embedding, embodied participation, and cognitive creation—and translate them into a practical system design with a three-module E3 core. It includes a virtual–real integrated learning environment for rich scenarios, embodied interaction for action and sensing, and an intelligent core that provides bounded and teacher-controlled support. To facilitate equitable adoption across resource-diverse settings, we specify multi-fidelity enactment options and an auditable set of evidence artifacts for subsequent expert review and future validation studies. We further provide an illustrative university human–AI design project that outlines a week-by-week workflow and corresponding evidence plan, presented as a worked example rather than a report of an implemented study. E3-HOT offers a traceable design-and-evidence blueprint without claiming measured learning gains. Full article