Sanjog Chhetri Sapkota^1,2, Liborio Cavaleri³, Ajaya Khatri⁴, Siddhi Pandey⁵, Satish Paudel⁶, Panagiotis G. Asteris^7,*

CMES-Computer Modeling in Engineering & Sciences, Vol.146, No.1, 2026, DOI:10.32604/cmes.2025.069691 - 29 January 2026

Abstract 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%… More >

Haotian Zhang, Jing Li^*, Ming Zhu, Zhiyong Zhao, Hongli Su, Liming Sun

CMC-Computers, Materials & Continua, Vol.86, No.3, 2026, DOI:10.32604/cmc.2025.072381 - 12 January 2026

Abstract In the wireless energy transmission service composition optimization problem, a key challenge is accurately capturing users’ preferences for service criteria under complex influencing factors, and optimally selecting a composition solution under their budget constraints. Existing studies typically evaluate satisfaction solely based on energy transmission capacity, while overlooking critical factors such as price and trustworthiness of the provider, leading to a mismatch between optimization outcomes and user needs. To address this gap, we construct a user satisfaction evaluation model for multi-user and multi-provider scenarios, systematically incorporating service price, transmission capacity, and trustworthiness into the satisfaction assessment… More >

Shaoping Xiao^1,*, Zhaoan Wang¹, Junchao Li², Caden Noeller¹, Jiefeng Jiang³, Jun Wang⁴

CMC-Computers, Materials & Continua, Vol.86, No.2, pp. 1-62, 2026, DOI:10.32604/cmc.2025.072146 - 09 December 2025

Abstract The integration of human factors into artificial intelligence (AI) systems has emerged as a critical research frontier, particularly in reinforcement learning (RL), where human-AI interaction (HAII) presents both opportunities and challenges. As RL continues to demonstrate remarkable success in model-free and partially observable environments, its real-world deployment increasingly requires effective collaboration with human operators and stakeholders. This article systematically examines HAII techniques in RL through both theoretical analysis and practical case studies. We establish a conceptual framework built upon three fundamental pillars of effective human-AI collaboration: computational trust modeling, system usability, and decision understandability. Our… More >

Songsong Zhang¹, Huazhong Jin^1,2,*, Zhiwei Ye^1,2, Jia Yang^1,2, Jixin Zhang^1,2, Dongfang Wu^1,2, Xiao Zheng^1,2, Dingfeng Song¹

CMC-Computers, Materials & Continua, Vol.86, No.1, pp. 1-19, 2026, DOI:10.32604/cmc.2025.068044 - 10 November 2025

Abstract Multi-label feature selection (MFS) is a crucial dimensionality reduction technique aimed at identifying informative features associated with multiple labels. However, traditional centralized methods face significant challenges in privacy-sensitive and distributed settings, often neglecting label dependencies and suffering from low computational efficiency. To address these issues, we introduce a novel framework, Fed-MFSDHBCPSO—federated MFS via dual-layer hybrid breeding cooperative particle swarm optimization algorithm with manifold and sparsity regularization (DHBCPSO-MSR). Leveraging the federated learning paradigm, Fed-MFSDHBCPSO allows clients to perform local feature selection (FS) using DHBCPSO-MSR. Locally selected feature subsets are encrypted with differential privacy (DP) and transmitted… More >

Mujahid Islam¹, Fateh Ali^1,*, Xinlong Feng^1,*, M. Zahid², Sana Naz Maqbool¹

CMES-Computer Modeling in Engineering & Sciences, Vol.145, No.3, pp. 3511-3561, 2025, DOI:10.32604/cmes.2025.073678 - 23 December 2025

Abstract Roll coating is a vital industrial process used in printing, packaging, and polymer film production, where maintaining a uniform coating is critical for product quality and efficiency. This work models non-isothermal Carreau fluid flow between a rotating roll and a stationary wall under fixed boundary constraints to evaluate how non-Newtonian and thermal effects influence coating performance. The governing equations are transformed into non-dimensional form and simplified using lubrication approximation theory. Approximate analytical solutions are obtained via the perturbation technique, while numerical results are computed using both the finite difference method and the BVP-Midrich technique. Furthermore, More >

Rodolphe Griset^1,#,*, Ioannis Lavdas^2,§, Jiří Guth Jarkovský³

Journal of Quantum Computing, Vol.7, pp. 81-105, 2025, DOI:10.32604/jqc.2025.066064 - 08 December 2025

Abstract Quantum search has emerged as one of the most promising fields in quantum computing. State-of-the-art quantum search algorithms enable the search for specific elements in a distribution by monotonically increasing the density of these elements relative to the rest of the distribution. These kinds of algorithms demonstrate a theoretical quadratic speed-up on the number of queries compared to classical search algorithms in unstructured spaces. Unfortunately, the major part of the existing literature applies quantum search to problems whose size grows exponentially with the input size without exploiting any specific problem structure, rendering this kind of… More >

Tianfeng Li^*, Yonghua Fan

The International Conference on Computational & Experimental Engineering and Sciences, Vol.33, No.4, pp. 1-1, 2025, DOI:10.32604/icces.2025.011408

Abstract A fixed-time anti-saturation backstepping guidance law (FTABGL) is designed for interceptor under acceleration input constraints. Firstly, an adaptive fixed-time anti-saturation compensator (AFAC) is proposed to ensure the stability of saturated system and drive it to faster leave the saturated region. Compared with conventional anti-saturation compensators, the auxiliary variable of AFAC is able to realize faster response speed and higher convergent precision when saturation disappears, which avoids the impact on convergent characteristics of original tracking error. In addition, the novel adaptive law in AFAC can further shorten the duration of saturation and improve the convergent speed… More >

Abubakar Elsafi^1,*, Arafat Abdulgader Mohammed Elhag², Lubna A. Gabralla³, Ali Ahmed⁴, Ashraf Osman Ibrahim⁵

CMES-Computer Modeling in Engineering & Sciences, Vol.145, No.1, pp. 621-647, 2025, DOI:10.32604/cmes.2025.072726 - 30 October 2025

Abstract Energy efficiency stands as an essential factor when implementing deep reinforcement learning (DRL) policies for robotic control systems. Standard algorithms, including Deep Deterministic Policy Gradient (DDPG), primarily optimize task rewards but at the cost of excessively high energy consumption, making them impractical for real-world robotic systems. To address this limitation, we propose Physics-Informed DDPG (PI-DDPG), which integrates physics-based energy penalties to develop energy-efficient yet high-performing control policies. The proposed method introduces adaptive physics-informed constraints through a dynamic weighting factor (), enabling policies that balance reward maximization with energy savings. Our motivation is to overcome the… More >

Alex Segura^*, Joaquim Meléndez

CMES-Computer Modeling in Engineering & Sciences, Vol.145, No.1, pp. 649-672, 2025, DOI:10.32604/cmes.2025.070954 - 30 October 2025

Abstract The increasing use of renewable energy sources, combined with the increase in electricity demand, has highlighted the importance of energy flexibility management in electrical grids. Energy flexibility is the capacity that generators and consumers have to change production and/or consumption to support grid operation, ensuring the stability and efficiency of the grid. Thus, Local Flexibility Markets (LFMs) are market-oriented mechanisms operated at different time horizons that support flexibility provision and trading at the distribution level, where the Distribution System Operators (DSOs) are the flexibility-demanding actors, and prosumers are the flexibility providers. This paper investigates the… More >

Md Sabir Hossain¹, Md Mahfuzur Rahman^1,2,*, Mufti Mahmud^1,3

CMES-Computer Modeling in Engineering & Sciences, Vol.145, No.1, pp. 1087-1116, 2025, DOI:10.32604/cmes.2025.068779 - 30 October 2025

Abstract This article presents a human fall detection system that addresses two critical challenges: privacy preservation and detection accuracy. We propose a comprehensive framework that integrates state-of-the-art machine learning models, multimodal data fusion, federated learning (FL), and Karush-Kuhn-Tucker (KKT)-based resource optimization. The system fuses data from wearable sensors and cameras using Gramian Angular Field (GAF) encoding to capture rich spatial-temporal features. To protect sensitive data, we adopt a privacy-preserving FL setup, where model training occurs locally on client devices without transferring raw data. A custom convolutional neural network (CNN) is designed to extract robust features from More > Graphic Abstract