Bai Xiao^1,*, Jingjun Bu¹, Binbin Du², Yulin Ge², Jian Gao²

Energy Engineering, Vol.123, No.10, 2026, DOI:10.32604/ee.2026.081408 - 30 August 2026

Abstract In order to solve the problem of the mismatch between the supply of electric vehicle charging stations and charging demand of electric vehicle charging stations caused by the rapid growth of electric vehicles, and the difficulty in determining the optimal site and capacity of electric vehicle charging stations, an optimisation method for the siting and sizing of electric vehicle charging stations considering the accessibility of user charging was proposed. Firstly, the road network topology structure is established in the GIS environment, and the shortest time path of the user is planned by establishing the road… More >

Nadira Mokarroma¹, Imrul Mosaddek Ahmed¹, Md. Romij Uddin², Md. Shihab Uddine Khan^3,*, Zakaria Alam^4,*, Nahid Afridi⁵, Sadia Afroz Ritu⁶, Md. Motiar Rohman⁷, Suman Biswas⁸, Abul Fazal Mohammad Shamim Ahsan¹, A. A. M. Mohammad Mustakim¹, Hela Znazen⁹, Ahmed Gaber¹⁰, Akbar Hossain^11,*

Phyton-International Journal of Experimental Botany, Vol.95, No.8, 2026, DOI:10.32604/phyton.2026.086460 - 28 August 2026

Abstract Heat stress is a major abiotic constraint limiting barley (Hordeum vulgare L.) productivity in regions experiencing rising temperatures. This study evaluated the genetic variability and morphophysiological as well as yield responses of 50 barley genotypes under control and heat-stressed conditions to identify superior lines for thermotolerance breeding. A completely randomized design (CRD) with replications was used, and data was collected for ten morpho-physiological and yield traits. Analysis of variance (ANOVA) indicated highly significant (p ≤ 0.001) effects of genotype, treatment, and their interaction on most of the measured traits. Wide phenotypic variation was observed for grain yield… More >

Jia Chen¹, Zhicheng Liao¹, Mengdi Hou², Jianbo Huang^1,3,*

CMES-Computer Modeling in Engineering & Sciences, Vol.148, No.2, 2026, DOI:10.32604/cmes.2026.085729 - 28 August 2026

Abstract Extrusion-based 3D-printed concrete (3DPC) imposes a dual constraint on mix design: fresh-state printability and hardened compressive strength must both be maintained within a narrow water-to-binder window, making data-driven prediction tools essential for reducing experimental iteration. This study evaluates 20 regression algorithms on 254 experimental records spanning plain printable mortars to high-fibre reinforced composites (CS: 11.1–189.0 MPa). Four physically motivated composite variables encoding cement blend potency, cumulative supplementary cementitious material (SCM) substitution, fibre volumetric stiffness, and water-to-sand ratio are constructed; Boruta-based selection retains 11 of 17 candidate features. CatBoost achieves the highest 30-run mean performance ($R^2=0.8968\pm 0.0505$;… More >

Xiao-Juan Li, Yu Zhang^*, Xing-She Zhou, Meng-Jie Li, Xin-Yue Liu

CMES-Computer Modeling in Engineering & Sciences, Vol.148, No.2, 2026, DOI:10.32604/cmes.2026.085689 - 28 August 2026

Abstract Sensor-equipped Unmanned Aerial Vehicles (UAVs) are increasingly deployed for collaborative aerial sensing, yet selecting an optimal subgroup from a heterogeneous fleet remains challenging. Existing approaches rank individual UAVs by fixed, isolated metrics (e.g., sensor type, residual energy) and deploy them sequentially, failing to quantify task-specific performance under coupled operational uncertainties arising from platform heterogeneity, sensor configuration, and environmental dynamics. To address this, we propose Near-Optimal Group UAV Selection (OGU), a capability-driven modeling method. Rather than directly manipulating raw, heterogeneous hardware parameters, OGU aggregates each UAV–sensor unit into a capability entity characterized by intrinsic task-oriented attributes… More >

Yih-Tzoo Chen¹, Thi-Hien Dao^2,*

CMES-Computer Modeling in Engineering & Sciences, Vol.148, No.2, 2026, DOI:10.32604/cmes.2026.084193 - 28 August 2026

Abstract Construction strategy selection is a context-dependent decision problem in which multiple conflicting criteria must be considered under changing project conditions. Conventional multi-criteria decision-making (MCDM) methods generally produce rankings for predefined decision matrices but do not learn transferable preference structures across project scenarios. This study proposes a hybrid framework integrating T-spherical fuzzy sets, the Criteria Importance Through Intercriteria Correlation (CRITIC) method, the Combined Compromise Solution (CoCoSo) method, and RankNet-based pairwise learning. Fifty construction scenarios were designed using six contextual variables, and eight construction strategies were evaluated against eight criteria. Linguistic evaluations were transformed using a seven-level… More >

Dong-Fang Wu^1,2, Jiaojiao Deng^1,2, Zhiwei Ye^1,2,*, Rong Gao^1,2, Fan Ma^1,2, Dingfeng Song^1,2

CMC-Computers, Materials & Continua, Vol.89, No.1, 2026, DOI:10.32604/cmc.2026.085308 - 13 August 2026

Abstract Anomaly detection is used to identify data points deviating from normal patterns and plays a significant role in fields such as fault diagnosis, biomedicine, and cybersecurity. However, anomaly detection tasks in practical applications often involve high-dimensional data which presents challenges such as severe feature redundancy, hidden anomaly patterns, and difficulty in capturing uncertainty. These issues make it difficult to effectively identify anomalies, thereby limiting the model’s discriminative power and stability. To address these challenges, we propose a high-dimensional anomaly detection model, MSFO-EIF, integrating multi-strategy feature optimization with rough set modeling. First, in the feature space… More >

Sultan Shutyan Albalawi¹, Mohd Yamani Idna Idris^1,2,*, Ainuddin Wahid Bin Abdul Wahab¹

CMC-Computers, Materials & Continua, Vol.89, No.1, 2026, DOI:10.32604/cmc.2026.083860 - 13 August 2026

Abstract As Distributed Denial-of-Service (DDoS) attacks grow in size and complexity, standard intrusion detection systems are hitting a wall. Most struggle to generalize well, require too much computational power, or lack model diversity. To tackle this, we developed TS-SCHO-TSE, a unified hybrid framework for DDoS detection. Unlike traditional methods that treat feature selection and ensemble building as isolated, step-by-step tasks, our approach combines everything. We map feature masks, classifier states, voting weights, and hyperparameters into a single mixed discrete-continuous search space to optimize them all at once. We tested our system against classical functions (F1–F23), the More >

Israt Jahan Munny¹, Anup Majumder², Bibhas Roy Chowdhury Piyas^3,*, Fahmid Al Farid^4,5,*, Md. Rafsan Jani², Fatama Jannat Tisha³, Israt Jahan³, Abu Saleh Musa Miah⁶, Hezerul Abdul Karim^4,*

CMC-Computers, Materials & Continua, Vol.89, No.1, 2026, DOI:10.32604/cmc.2026.083167 - 13 August 2026

Abstract Orange is one of the most economically significant citrus crops worldwide, which is essential for the global food distribution network and supports rural livelihoods. However, its high susceptibility to destructive diseases results in substantial yield losses and long-term economic damage. Despite recent advances in smart agriculture, early and precise disease diagnosis remains challenging due to visual resemblance among disease symptoms, high computational cost, and limited model interpretability. To overcome these difficulties, we introduce a novel lightweight and Region of Interest (ROI)-guided explainable machine learning framework to identify orange disease that integrates a strategic feature selection… More >

Yiyan Zhang^1,*, Yi Xin², Qin Li²

CMES-Computer Modeling in Engineering & Sciences, Vol.148, No.1, 2026, DOI:10.32604/cmes.2026.082841 - 27 July 2026

Abstract Medical data has specificity compared to other fields of data, and the description of medical data characteristics is still in a qualitative stage. This study included 293 sub-datasets of 138 independent datasets. First, data preprocessing was performed using methods such as incomplete data removal, inconsistent data normalization, and data integration. Then, the characteristics of 293 research datasets were quantified using 26 indicators in three categories: simple indicators, statistical indicators, and informational indicators. Furthermore, statistical analysis was performed on the above-mentioned quantitative characteristics, and stepwise regression and decision tree methods were used for modeling learning. The… More >

Wenchang Yu¹, Xiaoqin Ma^1,2, Zheqing Zhang¹, Kezhong Lu^1,2,*

CMC-Computers, Materials & Continua, Vol.88, No.3, 2026, DOI:10.32604/cmc.2026.083713 - 23 July 2026

Abstract Feature selection grounded in neighborhood rough sets has attracted sustained research attention owing to its principled treatment of classification uncertainty. However, existing forward greedy algorithms typically evaluate uncertainty over the entire object universe at each iteration, resulting in prohibitive computational complexity on large-scale datasets. To address this inefficiency, we introduce a new uncertainty index built upon Boundary Object Sets (BOS). BOS are defined as objects whose neighborhood granules intersect with multiple decision classes, thereby capturing intrinsic classification ambiguity. The proposed measure quantifies the proportion of these boundary objects relative to the total universe size. Grounded More >