Danping Niu¹, Yuan Ping^1,*, Chun Guo², Xiaojun Wang³, Bin Hao⁴

CMC-Computers, Materials & Continua, Vol.86, No.3, 2026, DOI:10.32604/cmc.2025.073630 - 12 January 2026

Abstract With the increasing complexity of malware attack techniques, traditional detection methods face significant challenges, such as privacy preservation, data heterogeneity, and lacking category information. To address these issues, we propose Federated Dynamic Prototype Learning (FedDPL) for malware classification by integrating Federated Learning with a specifically designed K-means. Under the Federated Learning framework, model training occurs locally without data sharing, effectively protecting user data privacy and preventing the leakage of sensitive information. Furthermore, to tackle the challenges of data heterogeneity and the lack of category information, FedDPL introduces a dynamic prototype learning mechanism, which adaptively adjusts the More >

Chao Li^1,3,#, Jianing Wang^1,3,#, Caichang Ding^2,*, Zhiwei Ye^1,3

CMC-Computers, Materials & Continua, Vol.86, No.3, 2026, DOI:10.32604/cmc.2025.071242 - 12 January 2026

Abstract Underwater images frequently suffer from chromatic distortion, blurred details, and low contrast, posing significant challenges for enhancement. This paper introduces AquaTree, a novel underwater image enhancement (UIE) method that reformulates the task as a Markov Decision Process (MDP) through the integration of Monte Carlo Tree Search (MCTS) and deep reinforcement learning (DRL). The framework employs an action space of 25 enhancement operators, strategically grouped for basic attribute adjustment, color component balance, correction, and deblurring. Exploration within MCTS is guided by a dual-branch convolutional network, enabling intelligent sequential operator selection. Our core contributions include: (1) a More >

Tsu-Yang Wu¹, Chengyuan Yu¹, Yanan Zhao², Saru Kumari³, Chien-Ming Chen^1,*

CMC-Computers, Materials & Continua, Vol.86, No.3, 2026, DOI:10.32604/cmc.2025.069139 - 12 January 2026

Abstract With the increasing complexity of logistics operations, traditional static vehicle routing models are no longer sufficient. In practice, customer demands often arise dynamically, and multi-depot systems are commonly used to improve efficiency. This paper first introduces a vehicle routing problem with the goal of minimizing operating costs in a multi-depot environment with dynamic demand. New customers appear in the delivery process at any time and are periodically optimized according to time slices. Then, we propose a scheduling system TS-DPU based on an improved ant colony algorithm TS-ACO to solve this problem. The classical ant colony More >

Wu Feng^1,2,*, Tengku Anita Raja Hussin¹, Xu Yang³

Structural Durability & Health Monitoring, Vol.20, No.1, 2026, DOI:10.32604/sdhm.2025.071664 - 08 January 2026

Abstract This study investigates the thermo–mechanical behavior of C40 concrete and reinforced concrete subjected to elevated temperatures up to 700°C by integrating experimental testing and advanced numerical modeling. A temperature-indexed Concrete Damage Plasticity (CDP) framework incorporating bond–slip effects was developed in Abaqus to capture both global stress–strain responses and localized damage evolution. Uniaxial compression tests on thermally exposed cylinders provided residual strength data and failure observations for model calibration and validation. Results demonstrated a distinct two-stage degradation regime: moderate stiffness and strength reduction up to ~400°C, followed by sharp deterioration beyond 500°C–600°C, with residual capacity at… More >

Abu Tayab^1,*, Yanwen Li¹, Ahmad Syed², Ghanshyam G. Tejani^3,4,*, Doaa Sami Khafaga⁵, El-Sayed M. El-kenawy⁶, Amel Ali Alhussan⁷, Marwa M. Eid^8,9

CMC-Computers, Materials & Continua, Vol.86, No.2, pp. 1-27, 2026, DOI:10.32604/cmc.2025.070583 - 09 December 2025

Abstract Autonomous connected vehicles (ACV) involve advanced control strategies to effectively balance safety, efficiency, energy consumption, and passenger comfort. This research introduces a deep reinforcement learning (DRL)-based car-following (CF) framework employing the Deep Deterministic Policy Gradient (DDPG) algorithm, which integrates a multi-objective reward function that balances the four goals while maintaining safe policy learning. Utilizing real-world driving data from the highD dataset, the proposed model learns adaptive speed control policies suitable for dynamic traffic scenarios. The performance of the DRL-based model is evaluated against a traditional model predictive control-adaptive cruise control (MPC-ACC) controller. Results show that the… More >

Bin Zhang^*, Zhancheng Xu

CMC-Computers, Materials & Continua, Vol.86, No.2, pp. 1-12, 2026, DOI:10.32604/cmc.2025.069979 - 09 December 2025

Abstract This study aimed to enhance the performance of semantic segmentation for autonomous driving by improving the 2DPASS model. Two novel improvements were proposed and implemented in this paper: dynamically adjusting the loss function ratio and integrating an attention mechanism (CBAM). First, the loss function weights were adjusted dynamically. The grid search method is used for deciding the best ratio of 7:3. It gives greater emphasis to the cross-entropy loss, which resulted in better segmentation performance. Second, CBAM was applied at different layers of the 2D encoder. Heatmap analysis revealed that introducing it after the second… More >

Huaxiong Liao^1,2, Xiangxuan Zhong², Xueqi Chen², Yirui Huang³, Yuwei Lin², Jing Zhang^2,*, Bruce Gu⁴

CMC-Computers, Materials & Continua, Vol.86, No.1, pp. 1-21, 2026, DOI:10.32604/cmc.2025.069208 - 10 November 2025

Abstract The generation of synthetic trajectories has become essential in various fields for analyzing complex movement patterns. However, the use of real-world trajectory data poses significant privacy risks, such as location re-identification and correlation attacks. To address these challenges, privacy-preserving trajectory generation methods are critical for applications relying on sensitive location data. This paper introduces DPIL-Traj, an advanced framework designed to generate synthetic trajectories while achieving a superior balance between data utility and privacy preservation. Firstly, the framework incorporates Differential Privacy Clustering, which anonymizes trajectory data by applying differential privacy techniques that add noise, ensuring the… More >

H. A. Rather^1,*, J. B. A. Wahid², M. A. Dar³, L. Guganathan⁴, U. A. Dar⁵, P. Arularasan⁶, S. E. I. Yagoub⁷, L. G. Amin⁷

Chalcogenide Letters, Vol.22, No.5, pp. 461-468, 2025, DOI:10.15251/CL.2025.225.461

Abstract SnSe powdered nanoparticles (NPs) are prepared using the co-precipitation method. The powdered NPs were studied using X-ray diffraction (XRD), UV-absorbance spectroscopy, and scanning electron microscopy (SEM) characterization techniques. The XRD result indicates that NPs are orthorhombic with a crystalline size of 4 nm for TS-1, 6 nm for TS-2, and 13 nm for TS-3, respectively. The SEM images show the surface morphology of the prepared NPs is not fully spherical, but semi-flower-like. The optical properties of the powdered NPs are found by UV-Vis absorbance spectroscopy, in which the highest absorbance was found between 200 nm 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 >

Mujeebu Rehman¹, Qinghua Liu¹, Ali Ghulam², Tariq Ahmad³, Jawad Khan^4,*, Dildar Hussain^5,*, Yeong Hyeon Gu⁵

CMES-Computer Modeling in Engineering & Sciences, Vol.145, No.1, pp. 779-805, 2025, DOI:10.32604/cmes.2025.067412 - 30 October 2025

Abstract Identifying druggable proteins, which are capable of binding therapeutic compounds, remains a critical and resource-intensive challenge in drug discovery. To address this, we propose CEL-IDP (Comparison of Ensemble Learning Methods for Identification of Druggable Proteins), a computational framework combining three feature extraction methods Dipeptide Deviation from Expected Mean (DDE), Enhanced Amino Acid Composition (EAAC), and Enhanced Grouped Amino Acid Composition (EGAAC) with ensemble learning strategies (Bagging, Boosting, Stacking) to classify druggable proteins from sequence data. DDE captures dipeptide frequency deviations, EAAC encodes positional amino acid information, and EGAAC groups residues by physicochemical properties to generate… More >