This study investigates the structural resilience of skylights integrated with perforated aluminum panels (Mashrabiya) in healthcare facilities under arid climate conditions. This study is based on a real, constructed skylight system installed in a healthcare facility, offering a rare field-based assessment rather than a purely theoretical or simulated model. The motivation behind this work stems from the need for energy-efficient, structurally robust, and aesthetically appealing skylight systems capable of withstanding extreme wind loads. Unlike conventional skylight studies, this research introduces a comprehensive numerical modelling approach in SAP2000 that incorporates nonlinear geometric effects, second-order buckling analysis, and stress concentration factors for perforated panels. The study evaluates deflections, stresses, and demand-to-capacity ratios (DCRs) under a wind load of 1.2 kPa, in accordance with local Standards. A key novelty of this study is the integration of perforated panels as both aesthetic and functional elements, enhancing structural performance by dissipating wind-induced stresses. The results indicate that the maximum DCR is 0.46, ensuring a significant safety margin, while the perforated panels exhibit a maximum stress of 41.05 MPa, well below the allowable limit of 160 MPa. Additionally, a mesh sensitivity analysis was conducted to optimise computational accuracy while balancing efficiency. The perforated aluminium panels (Mashrabiya) serve a dual function, enhancing aesthetics and acting as structural elements to redistribute and dissipate wind-induced stresses, a novel approach in skylight system design. This research advances wind-resistant façade design in arid climates by offering practical recommendations for optimising skylight configurations. Future work should focus on experimental validation through wind testing, real-time load monitoring, and parametric studies to support further structural optimisation. Unlike prior parametric or purely analytical works, this paper is based on a field‑installed skylight structure, using full‑scale geometry and validated against site‑measured boundary conditions. The study bridges the gap between experimental contexts and numerical modelling, thereby offering both practical and methodological novelty.

Porous silicon carbide (SiC) ceramic membranes require extremely high sintering temperatures, limiting their large‑scale adoption in cost-effective water and wastewater treatment. Incorporating NaA zeolite residue as a sintering aid markedly lowers the sintering temperature and promotes industrial waste valorization, but the coupled effects of multiple compositional and processing parameters remain poorly understood. In this study, we developed a skip-connection multi‑path multilayer perceptron (skip@M‑MLP) framework, together with several conventional machine learning models, to predict porosity and bending strength using a curated dataset from the literature. A hybrid data augmentation strategy combining Gaussian noise and linear interpolation was applied to address small‑sample limitations, and model interpretability was achieved via Shapley Additive Explanations (SHAP) and partial dependence plots (PDPs). The skip@M‑MLP achieved the highest accuracy among all tested models (R² up to 0.8716) and revealed that activated carbon content is the dominant factor governing the porosity–strength trade‑off, followed by sintering temperature and SiC fraction. These insights link data‑driven predictions with membrane engineering, providing a theoretical basis and quantitative guidance toward the potential scalable for the low-carbon fabrication of robust SiC membranes explicitly tailored for advanced water treatment processes. • ML optimizes NaA-assisted low-temp sintering of SiC water filtration membranes. • Hybrid data augmentation overcomes small-sample limits in membrane engineering. • Skip-connection MLP accurately predicts the porosity-strength of membranes. • Interpretable AI guides cost-effective design of water treatment membranes.

Optimization is the key to obtaining efficient utilization of resources in structural design. Due to the complex nature of truss systems, this study presents a method based on metaheuristic modelling that minimises structural weight under stress and frequency constraints. Two new algorithms, the Red Kite Optimization Algorithm (ROA) and Secretary Bird Optimization Algorithm (SBOA), are utilized on five benchmark trusses with 10, 18, 37, 72, and 200-bar trusses. Both algorithms are evaluated against benchmarks in the literature. The results indicate that SBOA always reaches a lighter optimal. Designs with reducing structural weight ranging from 0.02% to 0.15% compared to ROA, and up to 6%–8% as compared to conventional algorithms. In addition, SBOA can achieve 15%–20% faster convergence speed and 10%–18% reduction in computational time with a smaller standard deviation over independent runs, which demonstrates its robustness and reliability. It is indicated that the adaptive exploration mechanism of SBOA, especially its Levy flight–based search strategy, can obviously improve optimization performance for low- and high-dimensional trusses. The research has implications in the context of promoting bio-inspired optimization techniques by demonstrating the viability of SBOA, a reliable model for large-scale structural design that provides significant enhancements in performance and convergence behavior.

Abstract This study developed machine learning models to predict the swelling pressure (P S ) and swelling potential (SP) of fibre-reinforced expansive soils using a compiled dataset of 187 experimental specimens collected from published literature. The input variables included fibre characteristics (length, aspect ratio, content, tensile strength), soil consistency and compaction parameters (liquid limit, plasticity index, fine content, maximum dry density, optimum moisture content), and swelling characteristics of untreated soil (P S0 and SP 0 ). Tree-based gradient boosting models and artificial neural networks were implemented and evaluated using an 80% training and 20% testing data split. Model performance was assessed using determination coefficient (R²), root mean square error (RMSE), mean absolute error, and additional statistical indices. The gradient boosting model achieved the highest predictive accuracy, with testing R² values of 0.974 and 0.972 and RMSE values of 14.2 kPa and 0.82% for P S and SP, respectively. Feature importance analysis showed that untreated soil swelling parameters (P S0 and SP 0 ) were the dominant predictors governing swelling behaviour after fibre reinforcement. The results demonstrated that machine learning models, particularly gradient boosting methods, provided reliable and accurate prediction of swelling characteristics, enabling efficient evaluation and design of fibre-reinforced expansive soils.

Abstract This paper introduces a framework for designing resilient deck‐to‐pier connections that aims to address bridge vulnerability to tsunami loading. This framework presents a novel application of displacement design methods. Analytical and numerical models were developed for use in the framework, which enable the proposed bridge deck‐to‐pier connection response to be characterized. The numerical model uses nonlinear tsunami pushover analyses to simulate the superstructure response, while the analytical model uses a simplified formulation of the monolithic beam analogy. Validation was conducted using experimental data, which demonstrated that both models could accurately replicate the response of mild steel, stainless steel, and glass fiber‐reinforced polymer connections under tsunami uplift. Finally, a study was undertaken using the numerical model to extend the conclusions of the experimental testing. The findings of this numerical study highlighted that these connections can be customized for different situations to optimize bridge resilience to tsunami loading.

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Prognostics of the health degradation of lithium-ion batteries plays a crucial role in the electrification of transportation systems. Data-driven approaches have been widely adopted for battery health forecasting, but the need for labeled data and the domain-specific nature of the prediction model limit the deployment of these approaches in real-world applications. In this study, we unlock the potential of large language models as a generalized approach for lithium-ion battery state-of-health forecasting. We demonstrate the feasibility of applying large language models in a few-shot and zero-shot learning setups, where the model is capable of forecasting the battery health degradation given proper guided prompts without the need for fine-tuning. Extensive experiments are conducted to evaluate the prediction performance with various usage setups considered. The results indicate that both few-shot and zero-shot learning setups yield satisfactory performance with the lowest root-mean-square error of [Formula: see text] achieved. In addition, this research examines various future operational conditions provided in the prompt and their impact on the prediction performance. The findings of this study provide insights into the potential of large language models as a generalized approach for lithium-ion battery health prognostics.

The Landscape of Outstanding Features of “Islands and Cliffs near Slankamen” (ICS) is a protected area in the Danube region, characterized by diverse forest and wetland habitats. Different forms of sustainable tourism (SUTO) can be developed in this area, including nature-based tourism, ecotourism, and scientific tourism. This study aims to examine the impact of SUTO dimensions on residents’ satisfaction in the settlements of Stari Slankamen and Novi Slankamen. The research is based on the Prism of Sustainability (PoS) model, which includes ecological, economic, socio-cultural, and institutional dimensions. A total of 1030 inhabitants participated in the survey. The results show that all four dimensions have a statistically significant impact on residents’ satisfaction. The economic and institutional dimensions have a stronger influence, while the socio-cultural and ecological dimensions were evaluated more positively by respondents. The results indicate the need for better coordination of tourism development and management activities in order to achieve a balance between nature protection, economic benefits, and the needs of the local community.