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.

Socio-cultural tourism factors include folk music, cuisine and gastronomic brands, domestic handicrafts, crafts, folk customs, events, local tourist culture and cultural–historical heritage, language, social life of residents, and other factors. Important natural factors are the geographical and tourist location, features of relief, hydrographic potential, types of climates, plant and animal species, and others. Socio-cultural factors, together with natural factors, can create the basic characteristics of a destination. This research used the two landscapes of outstanding features (LOFs) that are part of the wider area of Serbia’s capital city, Belgrade. The selected areas are the main excursion and tourist centers, which possess significant natural and cultural characteristics for the development of sustainable tourism (STO). The main characteristics of these LOFs are forest ecosystems, which have an impact on tourism and recreation. The article used a quantitative methodology, based on the survey technique, which was used to collect data. A total of 1120 respondents were surveyed. Respondents expressed their views on claims related to space factors, which can influence the development of tourism and recreation. By analyzing the results, it can be concluded that there is an impact of factors on satisfaction with STO.

The cosmic-ray (CR) electrons and positrons in space are of great significance for studying the origin and propagation of cosmic-rays. The satellite-borne experiment DArk Matter Particle Explorer (DAMPE) has been used to measure the separate electron and positron spectra, as well as the positron fraction. In this work, the Earth's magnetic field is used to distinguish CR electrons and positrons, as the DAMPE detector does not carry an onboard magnet. The energy range for the measurements is from 10 to 20 GeV, being currently limited at high energy by the zenith pointing orientation of DAMPE. The results are consistent with previous measurements based on the magnetic spectrometer by AMS-02 and PAMELA, while the results of Fermi-LAT seem then to be systematically shifted to larger values.

Abstract This study examines the implementation and application of Artificial Intelligence (AI) methodologies for estimating the Remaining Useful Life (RUL) of civil infrastructure assets, with the aim of supporting more effective civil infrastructure maintenance and management practices. A total of 90 publications were reviewed. Although not all were directly related to civil infrastructure RUL, the overall body of work reveals a continuous research activity since 2014, reflecting growing interest in data-driven deterioration forecasting. A key motivation for this review is to identify AI approaches that have been successfully applied to structured datasets and that demonstrate potential for practical integration into civil engineering asset-management environments. While advanced AI techniques exist, their adoption in civil infrastructure engineering and maintenance management remains limited, and many real-world systems require methods that balance predictive capability with interpretability, robustness, and compatibility with existing workflows. This study discusses a range of AI approaches—including Deep Learning (DL), Machine Learning (ML) ensemble regression, and hybrid models—highlighting their ability to capture complex degradation processes and their potential to enhance durability predictions. Challenges such as data quality, generalisability and interpretability of AI models, and the difficulty of embedding advanced analytics into current maintenance systems are identified. Opportunities for future research include improving model stability against noise, leveraging diverse data sources, addressing class imbalance, quantifying predictive uncertainty, exploring alternative degradation models, and integrating maintenance actions within RUL prediction frameworks. Overall, the findings underscore the increasing role of AI in asset-life prediction and highlight the need for approaches that remain technically sound while being feasible for implementation in civil real infrastructure-management settings.