N’Landu Dikumbwa^1,*, Scott Tshibang Nawej², Gabriel Mutundo Teteka², Benjamin Mayaka Kibwila³, Jules Aloni Komanda³

Revue Internationale de Géomatique, Vol.35, pp. 13-30, 2026, DOI:10.32604/rig.2026.068019 - 05 February 2026

Abstract Rainfall data from four weather stations, quite far from each other, but located in the Zambezian phytogeographic region, were analysed for the research for indices of climate change. Two variables, rainfall and the annual number of rainy days, were considered. The rainfall data examined are 114 years for Luanda (1901–2014), 106 years for Lubumbashi (1916–2021), respectively, 54 and 41 years for Huambo (1961–2014) and Boma (1981–2021); 100 years (1921–2021) for the annual number of rainy days for only the Lubumbashi weather station. The results were a widespread decline in rainfall at all weather stations. Despite… More >

Hasan Kaan Kucukerdem^*

Phyton-International Journal of Experimental Botany, Vol.95, No.1, 2026, DOI:10.32604/phyton.2026.074188 - 30 January 2026

Abstract Root-zone temperature (RZT) strongly affects plant growth, nutrient uptake and tolerance to environmental stress, making its regulation a key challenge in greenhouse cultivation in cold climates. This study aimed to assess the potential of passive techniques, namely black polyethylene mulch and row covers, for modifying RZT dynamics in lettuce (Lactuca sativa L.) production and to evaluate the predictive performance of the eXtreme Gradient Boosting (XGBoost) algorithm. Experiments were conducted in Iğdır, Türkiye, over a 61-day period, with soil temperature continuously monitored at depths of 1–30 cm under mulched and non-mulched conditions, alongside measurements of greenhouse air… More >

Yue Cui¹, Ziqing Yang², Hao Gao^1,*

International Journal of Mental Health Promotion, Vol.28, No.1, 2026, DOI:10.32604/ijmhp.2025.072986 - 28 January 2026

Abstract Background: In the Chinese context, the impact of short video applications on the psychological well-being of older adults is contested. While often examined through a pathological lens of addiction, this perspective may overlook paradoxical, context-dependent positive outcomes. Therefore, the main objective of this study is to challenge the traditional Compensatory Internet Use Theory by proposing and testing a chained mediation model that explores a paradoxical pathway from social support to life satisfaction via problematic social media use. Methods: Data were collected between July and August 2025 via the Credamo online survey platform, yielding 384 valid responses… More >

Shengkang Zhang¹, Yong Jin^2,*, Soon Poh Yap^1,*, Haoyun Fan¹, Shiyuan Li³, Ahmed El-Shafie⁴, Zainah Ibrahim¹, Amr El-Dieb⁵

CMES-Computer Modeling in Engineering & Sciences, Vol.146, No.1, 2026, DOI:10.32604/cmes.2025.075351 - 29 January 2026

Abstract Concrete-filled steel tubes (CFST) are widely utilized in civil engineering due to their superior load-bearing capacity, ductility, and seismic resistance. However, existing design codes, such as AISC and Eurocode 4, tend to be excessively conservative as they fail to account for the composite action between the steel tube and the concrete core. To address this limitation, this study proposes a hybrid model that integrates XGBoost with the Pied Kingfisher Optimizer (PKO), a nature-inspired algorithm, to enhance the accuracy of shear strength prediction for CFST columns. Additionally, quantile regression is employed to construct prediction intervals for… More >

Guangyu Xu^1,2, Yuxin Liu¹, Bo Yang¹, Siyu Lu^3,*, Chao Liu⁴, Junmin Lyu⁵, Wenfeng Zheng^1,*

CMES-Computer Modeling in Engineering & Sciences, Vol.146, No.1, 2026, DOI:10.32604/cmes.2025.074938 - 29 January 2026

Abstract Soft-tissue motion introduces significant challenges in robotic teleoperation, especially in medical scenarios where precise target tracking is critical. Latency across sensing, computation, and actuation chains leads to degraded tracking performance, particularly around high-acceleration segments and trajectory inflection points. This study investigates machine learning-based predictive compensation for latency mitigation in soft-tissue tracking. Three models—autoregressive (AR), long short-term memory (LSTM), and temporal convolutional network (TCN)—were implemented and evaluated on both synthetic and real datasets. By aligning the prediction horizon with the end-to-end system delay, we demonstrate that prediction-based compensation significantly reduces tracking errors. Among the models, TCN More >

Bin Ou^1,2,3,4, Haoquan Chi^1,3, Xu’an Qian^1,3, Shuyan Fu^1,3, Zhirui Miao^1,3, Dingzhu Zhao^1,3,*

CMES-Computer Modeling in Engineering & Sciences, Vol.146, No.1, 2026, DOI:10.32604/cmes.2026.074757 - 29 January 2026

Abstract Deformation prediction for extra-high arch dams is highly important for ensuring their safe operation. To address the challenges of complex monitoring data, the uneven spatial distribution of deformation, and the construction and optimization of a prediction model for deformation prediction, a multipoint ultrahigh arch dam deformation prediction model, namely, the CEEMDAN-KPCA-GSWOA-KELM, which is based on a clustering partition, is proposed. First, the monitoring data are preprocessed via variational mode decomposition (VMD) and wavelet denoising (WT), which effectively filters out noise and improves the signal-to-noise ratio of the data, providing high-quality input data for subsequent prediction… More > Graphic Abstract

Maddina Dinesh Kumar^1,#, S. U. Mamatha², Khalid Masood³, Nehad Ali Shah^4,#, Se-Jin Yook^1,*

CMES-Computer Modeling in Engineering & Sciences, Vol.146, No.1, 2026, DOI:10.32604/cmes.2025.074680 - 29 January 2026

Abstract Fluid dynamic research on rectangular and trapezoidal fins is aimed at increasing heat transfer by means of large surfaces. The trapezoidal cavity form is compared with its thermal and flow performance, and it is revealed that trapezoidal fins tend to be more efficient, particularly when material optimization is critical. Motivated by the increasing need for sustainable energy management, this work analyses the thermal performance of inclined trapezoidal and rectangular porous fins utilising a unique hybrid nanofluid. The effectiveness of nanoparticles in a working fluid is primarily determined by their thermophysical properties; hence, optimising these properties… More >

Menwa Alshammeri^1,2,*, Noshina Tariq³, NZ Jhanji^4,5, Mamoona Humayun⁶, Muhammad Attique Khan⁷

CMES-Computer Modeling in Engineering & Sciences, Vol.146, No.1, 2026, DOI:10.32604/cmes.2025.074627 - 29 January 2026

Abstract Artificial Intelligence (AI) is changing healthcare by helping with diagnosis. However, for doctors to trust AI tools, they need to be both accurate and easy to understand. In this study, we created a new machine learning system for the early detection of Autism Spectrum Disorder (ASD) in children. Our main goal was to build a model that is not only good at predicting ASD but also clear in its reasoning. For this, we combined several different models, including Random Forest, XGBoost, and Neural Networks, into a single, more powerful framework. We used two different types More >

Guebin Choi^*

CMES-Computer Modeling in Engineering & Sciences, Vol.146, No.1, 2026, DOI:10.32604/cmes.2025.073985 - 29 January 2026

Abstract This study proposes a novel forecasting framework that simultaneously captures the strong periodicity and irregular meteorological fluctuations inherent in solar radiation time series. Existing approaches typically define inter-regional correlations using either simple correlation coefficients or distance-based measures when applying spatio-temporal graph neural networks (STGNNs). However, such definitions are prone to generating spurious correlations due to the dominance of periodic structures. To address this limitation, we adopt the Elastic-Band Transform (EBT) to decompose solar radiation into periodic and amplitude-modulated components, which are then modeled independently with separate graph neural networks. The periodic component, characterized by strong More >

Marwa M. Eid^1,2,*, Amel Ali Alhussan³, Ebrahim A. Mattar⁴, Nima Khodadadi^5,*, El-Sayed M. El-Kenawy^6,7

CMES-Computer Modeling in Engineering & Sciences, Vol.146, No.1, 2026, DOI:10.32604/cmes.2025.073555 - 29 January 2026

Abstract Accurate prediction of concrete compressive strength is fundamental for optimizing mix designs, improving material utilization, and ensuring structural safety in modern construction. Traditional empirical methods often fail to capture the non-linear relationships among concrete constituents, especially with the growing use of supplementary cementitious materials and recycled aggregates. This study presents an integrated machine learning framework for concrete strength prediction, combining advanced regression models—namely CatBoost—with metaheuristic optimization algorithms, with a particular focus on the Somersaulting Spider Optimizer (SSO). A comprehensive dataset encompassing diverse mix proportions and material types was used to evaluate baseline machine learning models,… More >