Rahul Kumar¹, Faladrum Sharma², Pradeep Kumar Karsh^3,*

CMES-Computer Modeling in Engineering & Sciences, Vol.148, No.2, 2026, DOI:10.32604/cmes.2026.086198 - 28 August 2026

Abstract The increasing environmental concerns after the United Nations’ push towards sustainable development goals (SDGs) and depletion of non-renewable resources have accelerated the global pursuit of sustainable materials. Within this framework, bio-based polymer composites have gained considerable attention for their ability to balance mechanical performance, cost-effectiveness, and environmental responsibility. Biofibres/fillers-based polymer composites reinforced with natural fibres like jute, bamboo, coconut coir, pineapple leaf fibre (PALF), and flax offer an attractive combination of mechanical performance, cost-effectiveness, and environmental sustainability. Moreover, additive manufacturing (3D printing), coupled with machine learning, digital twins, and data-driven material design, is transforming the… More >

Jinghao Yang¹, Haitao Yu^2,*, Jian Zhou³, Qingyu Yang¹, Yaokang Zhang³, Hailong Gong³

CMES-Computer Modeling in Engineering & Sciences, Vol.148, No.2, 2026, DOI:10.32604/cmes.2026.086135 - 28 August 2026

Abstract Large and complex structures on stepped slope sites are still commonly evaluated using simplified fixed-base assumptions, which may not adequately reflect the effects of soil-structure interaction (SSI). To address this issue, this study develops a coupled three-dimensional nonlinear finite element model in which the surrounding soil domain, foundation system, and superstructure are considered simultaneously, so as to capture soil-structure interaction effects under stepped topographic conditions. A corresponding fixed-base model is further established for comparison, and the approach is applied to the Ground Transportation Center (GTC) at Kunming Airport, a large transportation hub founded on a… More >

Ziheng Lu¹, Yingfeng Cai^1,*, Wei Dong¹, Hai Wang^2,*, Long Chen¹

CMES-Computer Modeling in Engineering & Sciences, Vol.148, No.2, 2026, DOI:10.32604/cmes.2026.086003 - 28 August 2026

Abstract Vision-language-action models have recently gained attention in autonomous driving, as language can express high-level behavioral intent beyond geometric waypoints. However, existing planners often treat language as an external command or auxiliary explanatory signal, while the hidden states of generated control instructions are rarely aligned explicitly with the latent variables that produce executable trajectories. This paper presents MALT-Drive, a moment-aligned language-to-trajectory end-to-end planning framework for autonomous driving. Given front-camera visual input, ego-state history, route priors, and a driving-oriented VQA prompt, MALT-Drive first autoregressively generates a concise control instruction from a controlled instruction space and then decodes… More >

Haitao Liu^1,*, Xuyang Wang¹, Shengcheng Quan¹, Qiaosheng Guo², Aichun Wang³, Lie Yang¹, Tingfang Zhang¹, Xiaojian Wu^1,*

CMES-Computer Modeling in Engineering & Sciences, Vol.148, No.2, 2026, DOI:10.32604/cmes.2026.085747 - 28 August 2026

Abstract Rapid advances in information and automation technologies have accelerated the development of smart manufacturing, thereby heightening the importance of reliable fault diagnosis for mechanical equipment. Although neural network-based algorithms are widely adopted in industrial applications due to their strong feature extraction and classification capabilities, their deployment in safety-critical fields such as aerospace remains limited. This limitation mainly arises from poor model interpretability and a heavy reliance on large-scale labeled training data. To address these challenges, this paper proposes an interpretable neural network framework that integrates discrete wavelet transform (DWT) with neural networks. Specifically, discrete wavelet… 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 >

Maryam Omar Abdullah Sawad¹, Said Jadid Abdulkadir^1,2,*, Hitham Seddig Alhussian^1,2, Majdy Mohamed Eltayeb Eltahir³

CMES-Computer Modeling in Engineering & Sciences, Vol.148, No.2, 2026, DOI:10.32604/cmes.2026.085472 - 28 August 2026

Abstract The increasing complexity and heterogeneity of cyberattacks targeting Internet of Things (IoT) environments, driven by the diversity of interconnected nodes and communication channels, necessitate the development of more advanced and intelligent cyber defence techniques. However, the most effective methods are Machine Learning (ML)-based and Deep Learning (DL)-based intrusion detection systems (IDS), which perform well but still face significant limitations and challenges. To address these issues, Deep Reinforcement Learning (DRL) has been proposed in recent years to automatically resolve the issues by detecting attacks in IoT environments. Therefore, this Systematic Literature Review (SLR) presents an up-to-date… More >

Masood Karamoozian¹, Zarrin Mahdavipour², Mohammed Ameen³, Faisal Binzagr⁴, Abdolraheem Khader^2,*, Ahmed Hamza Osman³, Ali Ahmed⁴

CMES-Computer Modeling in Engineering & Sciences, Vol.148, No.2, 2026, DOI:10.32604/cmes.2026.085421 - 28 August 2026

Abstract Achieving net-zero carbon emissions in the building sector requires computational tools that are simultaneously efficient, physically rigorous, and scalable to complex multi-domain design spaces. Traditional physics-based simulators such as EnergyPlus and finite-element analysis (FEM) packages deliver high fidelity but are computationally prohibitive for large-scale parametric exploration and real-time multi-objective optimization. This study introduces a hybrid Physics-Informed Graph Neural Network (PI-GNN) surrogate that directly leverages semantic Building Information Modeling (BIM) data to enable rapid, accurate, and physically consistent multi-physics prediction and optimization of prefabricated modular buildings. Building components and their physical and functional relationships are encoded… More >

Yu-Feng Yu^1,*, Xiaoying Tan¹, Qirong Wu¹, Guoxia Xu²

CMES-Computer Modeling in Engineering & Sciences, Vol.148, No.2, 2026, DOI:10.32604/cmes.2026.085413 - 28 August 2026

Abstract To tackle critical visual tracking difficulties arising in UAV tracking tasks, including frequent target occlusion and abrupt fast motion during high-altitude inspection, we propose an adaptive correlation filter tracking algorithm incorporating a motion smoothing module and adaptive residual regularization, named MACF. The tracker is constructed via multi-strategy fusion of two elaborately designed components at the algorithmic modeling level. First, we design a Motion Smoothing Module (MSM) that conducts weighted fusion of historical motion trends in the modeling pipeline. It suppresses search window jitter arising from instantaneous positioning errors and lowers target drift risk by providing More >

Wen-Chin Hsu, Yi-Cheng Chen^*, Yi-Hsuan Kuo

CMES-Computer Modeling in Engineering & Sciences, Vol.148, No.2, 2026, DOI:10.32604/cmes.2026.085408 - 28 August 2026

Abstract Due to the structural complexity of Chinese characters, the occurrence of homophones and visual similarity among glyphs directly increases the difficulties presented in Chinese spell checking (CSC). These factors also indicate the importance of the connection between CSC and context-dependency. In this study, a novel framework, the Summarization-Enhanced Chinese Spell Checking (abbreviated as SE-CSC) model, is proposed, which integrates phonetic and glyph embeddings to further enhance context awareness in error detection and correction. We utilize sentence-level summarization features to augment and generate an error-guided mask that can effectively detect errors and derive more precise corrections. More >