Mohammad A Al Khaldy¹, Ahmad Nabot², Ahmad Al-Qerem^3,*, Mohammad Alauthman⁴, Amina Salhi^5,*, Suhaila Abuowaida⁶, Naceur Chihaoui⁷

CMES-Computer Modeling in Engineering & Sciences, Vol.145, No.1, pp. 673-697, 2025, DOI:10.32604/cmes.2025.070004 - 30 October 2025

Abstract The exponential growth of Internet of Things (IoT) devices has created unprecedented challenges in data processing and resource management for time-critical applications. Traditional cloud computing paradigms cannot meet the stringent latency requirements of modern IoT systems, while pure edge computing faces resource constraints that limit processing capabilities. This paper addresses these challenges by proposing a novel Deep Reinforcement Learning (DRL)-enhanced priority-based scheduling framework for hybrid edge-cloud computing environments. Our approach integrates adaptive priority assignment with a two-level concurrency control protocol that ensures both optimal performance and data consistency. The framework introduces three key innovations: (1)… More >

Nurlan Zhangabay^1,*, Marco Bonopera^2,*, Konstantin Avramov³, Maryna Chernobryvko³, Svetlana Buganova⁴

CMES-Computer Modeling in Engineering & Sciences, Vol.145, No.1, pp. 241-261, 2025, DOI:10.32604/cmes.2025.069544 - 30 October 2025

Abstract This research extends ongoing efforts to develop methods for reinforcing damaged main gas pipelines to prevent catastrophic failure. This study establishes the use of scaled-down experimental models for assessing the dynamic strength of damaged pipeline sections reinforced with wire wrapping or composite sleeves. A generalized dynamic model is introduced for numerical simulation to evaluate the effectiveness of reinforcement techniques. The model incorporates the elastoplastic behavior of pipe and wire materials, the influence of temperature on mechanical properties, the contact interaction between the pipe and the reinforcement components (including pretensioning), and local material failure under transient… More >

Yusheng Jian, Xiujiang Shi^*, Xiaoxiao Li, Zehong Cai, Bailing Guan, Xiqun Lu

The International Conference on Computational & Experimental Engineering and Sciences, Vol.33, No.3, pp. 1-1, 2025, DOI:10.32604/icces.2025.011085

Abstract The optimization of the lubricant supply quantity contributes to minimizing energy losses and wastage. Stearic acid is commonly used for boundary lubrication as an organic friction modifier. To enhance the performance of hydrodynamic bearings running with limited lubricant supply (LLS), under conditions of limited oil supply, the effect of stearic acid oiliness additive was studied on the relationship between oil film thickness of PAO10 (polyα-olefin) and inclination angle of the slider with an optical test rig for measuring the film lubrication in the slider-on-disc contact. The results showed that the film thickness presented an overall… More >

Jiayun Ding, Bin Chen^*, Yutong Lei, Wei Zhang

Energy Engineering, Vol.122, No.11, pp. 4561-4577, 2025, DOI:10.32604/ee.2025.067574 - 27 October 2025

Abstract In the field of low-carbon building systems, the combination of renewable energy and hydrogen energy systems is gradually gaining prominence. However, the uncertainty of supply and demand and the multi-energy flow coupling characteristics of this system pose challenges for its optimized scheduling. In light of this, this study focuses on electro-thermal-hydrogen trigeneration systems, first modelling the system’s scheduling optimization problem as a Markov decision process, thereby transforming it into a sequential decision problem. Based on this, this paper proposes a reinforcement learning algorithm based on deep deterministic policy gradient improvement, aiming to minimize system operating… More >

Diyar Wirya Omar Ameenulhakeem^*, Osman Nuri Uçan

CMC-Computers, Materials & Continua, Vol.85, No.3, pp. 5671-5702, 2025, DOI:10.32604/cmc.2025.070422 - 23 October 2025

Abstract Face antispoofing has received a lot of attention because it plays a role in strengthening the security of face recognition systems. Face recognition is commonly used for authentication in surveillance applications. However, attackers try to compromise these systems by using spoofing techniques such as using photos or videos of users to gain access to services or information. Many existing methods for face spoofing face difficulties when dealing with new scenarios, especially when there are variations in background, lighting, and other environmental factors. Recent advancements in deep learning with multi-modality methods have shown their effectiveness in… More >

Imran Ahmed^1,#, Misbah Ahmad^2,3,#, Gwanggil Jeon^4,5,*

CMC-Computers, Materials & Continua, Vol.85, No.3, pp. 4237-4273, 2025, DOI:10.32604/cmc.2025.068319 - 23 October 2025

Abstract The rapid convergence of Information and Communication Technologies (ICT), driven by advancements in 5G/6G networks, cloud computing, Artificial Intelligence (AI), and the Internet of Things (IoT), is reshaping modern digital ecosystems. As massive, distributed data streams are generated across edge devices and network layers, there is a growing need for intelligent, privacy-preserving AI solutions that can operate efficiently at the network edge. Federated Learning (FL) enables decentralized model training without transferring sensitive data, addressing key challenges around privacy, bandwidth, and latency. Despite its benefits in enhancing efficiency, real-time analytics, and regulatory compliance, FL adoption faces… More >

Jiao Chen¹, Xiao Wang^1,*, Zhiqin He¹, Yi Chen², Chao Ma¹

CMC-Computers, Materials & Continua, Vol.85, No.3, pp. 5423-5450, 2025, DOI:10.32604/cmc.2025.067952 - 23 October 2025

Abstract This research proposes an innovative solution to the inherent challenges faced by landslide displacement prediction models based on data-driven methods, such as the need for extensive historical datasets for training, the reliance on manual feature selection, and the difficulty in effectively utilizing landslide historical data. We have developed a dual-channel deep learning prediction model that integrates multimodal decomposition and an attention mechanism to overcome these challenges and improve prediction performance. The proposed methodology follows a three-stage framework: (1) Empirical Mode Decomposition (EMD) effectively segregates cumulative displacement and feature factors; (2) We have developed a Double… More >

Ming Chen^1,*, Yanfei Niu², Ping Qi¹, Fucheng Wang¹

CMC-Computers, Materials & Continua, Vol.85, No.3, pp. 5015-5035, 2025, DOI:10.32604/cmc.2025.067951 - 23 October 2025

Abstract This research addresses the critical challenge of enhancing satellite images captured under low-light conditions, which suffer from severely degraded quality, including a lack of detail, poor contrast, and low usability. Overcoming this limitation is essential for maximizing the value of satellite imagery in downstream computer vision tasks (e.g., spacecraft on-orbit connection, spacecraft surface repair, space debris capture) that rely on clear visual information. Our key novelty lies in an unsupervised generative adversarial network featuring two main contributions: (1) an improved U-Net (IU-Net) generator with multi-scale feature fusion in the contracting path for richer semantic feature… More >

Qin Qin¹, Xuan Jiang^1,*, Jinhua Jiang¹, Dongfang Zhao¹, Zimei Tu¹, Zhiwei Shen²

CMC-Computers, Materials & Continua, Vol.85, No.3, pp. 4849-4867, 2025, DOI:10.32604/cmc.2025.067919 - 23 October 2025

Abstract The effectiveness of industrial character recognition on cast steel is often compromised by factors such as corrosion, surface defects, and low contrast, which hinder the extraction of reliable visual information. The problem is further compounded by the scarcity of large-scale annotated datasets and complex noise patterns in real-world factory environments. This makes conventional OCR techniques and standard deep learning models unreliable. To address these limitations, this study proposes a unified framework that integrates adaptive image preprocessing with collaborative reasoning among LLMs. A Biorthogonal 4.4 (bior4.4) wavelet transform is adaptively tuned using DE to enhance character… More >

Yifan Zhu^1,2, Fengxiang Xu^1,2,*, Zhen Zou^1,2, Zhengpao Liu^1,2, Xiaokun Dai^1,2

The International Conference on Computational & Experimental Engineering and Sciences, Vol.33, No.2, pp. 1-2, 2025, DOI:10.32604/icces.2025.010641

Abstract Conventional energy-absorbing mechanical metamaterials primarily dissipate energy through irreversible plastic deformation, buckling, or fragmentation. Their applications are limited by structural fractures caused by 45° shear stresses and their suitability only for single-use impact protection, lacking the capability for repeated energy absorption. Inspired by the cancellous bone of the human skull, a Tangent Arc Curve Structure (TACS) was proposed in this study, followed by the modeling and fabrication of four types of 3D-TACSs: tensile, tensile-rotational, orthogonal, and diagonal. The shear resistance and repeatable energy absorption capabilities of TACS were systematically investigated through theoretical analysis, compression experiments,… More >