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Post-Consumer Recycled (PCR) ETFE Foil: Recycling of the Chelsea and Westminster Hospital ETFE Roof
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AI-Driven Modeling of Window Opening Behavior in Kindergarten Classrooms: A Case Study during the Transitional Season in the Cold Region of China
No full textWith increasing awareness of energy conservation and environmental protection, optimizing indoor air quality and energy consumption through rational control of window opening behavior (WOB) has become a crucial issue in building design and environmental management. However, existing research primarily focuses on buildings for adults, with relatively few studies on buildings for children, particularly those used by children aged 3–6. Moreover, previous studies often overlook the impact of functional differences between buildings on occupant behavior patterns. This study focuses on a kindergarten and proposes an event-based method for analyzing and modeling WOB. The results show that events such as arrival, class, and departure are associated with higher frequencies of window opening (exceeding 50%), whereas events such as dietary activity, indoor/outdoor activity, sleep, and tidying exhibit lower probabilities. WOB is more sensitive to indoor air quality during events with higher student activity (e.g., class, dietary activity, and indoor activity), resulting in more frequent ventilation. In terms of modeling, the Random Forest (RF) algorithm achieved higher prediction accuracy than Logistic Regression (LR) and Support Vector Machine (SVM). To reduce the complexity associated with multi-model integration, a stacking model was introduced, further enhancing predictive performance. Finally, the generalizability of the proposed method was validated using office building data from the ASHRAE occupant behavior database, achieving a maximum accuracy improvement of 3.87%. This study presents a novel approach for modeling WOB in functional buildings such as kindergartens and provides theoretical support for energy efficiency optimization and indoor air quality management
Modeling Engineering Reliability Data Using the Modified Generalized Exponential Distribution
No full textThis study introduces the modified generalized exponential (MGEx) distribution, a flexible probabilistic model designed to capture complex failure behaviors commonly encountered in engineering systems. The MGEx distribution accommodates various hazard rate shapes, including unimodal, increasing, and decreasing forms, making it suitable for modeling reliability and lifetime data. We derive the mathematical properties of the model and focus on computational techniques for parameter estimation using multiple frequentist approaches. To evaluate the numerical performance of these methods, we conduct extensive Monte Carlo simulations, comparing their accuracy and robustness through partial and overall ranking metrics. The practical utility of the MGEx model is demonstrated by applying it to two real-world engineering datasets. The results highlight the model’s potential in enhancing simulation accuracy and supporting data-driven decision-making in reliability analysis, system design, and failure prediction tasks.OPEN ACCESS Received: 05/07/2025 Accepted: 27/10/2025 Published: 23/01/202
AI-Driven Multi-Dimensional Computing Power Scheduling: Adaptive Resource Coordination via Task Demand Prediction in Heterogeneous Clusters
No full textThis paper introduces an AI-driven computing power scheduling framework that innovatively integrates multidimensional resource optimization with machine-learning-based task-demand prediction to significantly enhance computational efficiency and resource utilization. Unlike prior works that primarily focus on Graphics Processing Unit (GPU) allocation, our method pioneers a holistic resource-coordination mechanism that dynamically balances GPU, memory, Central Processing Unit (CPU), and other critical resources according to their joint impact on task performance and cluster efficiency. A core innovation is our data-driven resource predictor, which autonomously analyzes historical task patterns and forecasts future demand, enabling the scheduler to adaptively scale resources so that user over-provisioning is reduced and under-allocation bottlenecks are avoided. Experimental validation demonstrates that this closed-loop prediction–scheduling paradigm achieves breakthroughs in both scale and efficiency: a 25.7% increase in concurrent task deployment, a 15.6% increase in task completion rate, and substantial relative utilization improvements of 7.5% for GPU and 8.0% for memory, outperforming conventional single-resource optimization approaches. These advancements establish a new direction for intelligent resource management in large-scale heterogeneous computing environments.OPEN ACCESS Received: 15/05/2025 Accepted: 11/10/2025 Published: 23/01/202
Numerical Simulation Analysis of Influence of Wind and Temperature on Deformation of Constructed Zhaidi River High Bridge Pier
No full textBridges are key projects of high-grade Expressways in mountainous areas. The verticality of bridge piers with a height of more than 100 m is crucial to ensure the safety and stability of bridge projects. When a pier construction is completed but the upper beam structure has not yet been connected (socalled after construction or constructed), the verticality of the pier is most likely to vary due to some factors. Based on the Zhaidi River Bridge project of Yunnan Zhenhe Expressway, considering the natural environment of the bridge site, three wind force intensity levels (Beaufort Scale 8, 10, and 12) and two climate conditions (high temperature and high radiation in summer, and low temperature and low radiation in winter) were identified; and the wind-induced deformation, temperature-induced deformation, and wind-temperature coupled deformation of the constructed main pier of the Zhaidi River Bridge with a height of 112.60 m were simulated with ANSYS Workbench numerical simulation platform. The simulation results show that: the influence of wind on pier deformation is much greater than that of ambient temperature variation; the influence of solar radiation on temperature-induced deformation of the bridge pier is much greater than that of air temperature variation; the temperature-induced deformation of the pier body under low temperature and low radiation condition in winter is greater than that under high temperature and high radiation condition in summer; the directional effect of the superposition of wind-induced deformation and temperature-induced deformation is more significant under low temperature and low radiation condition in winter.OPEN ACCESS Received: 11/04/2025 Accepted: 16/10/2025 Published: 23/01/202
