Haotian Wu¹, Jiaming Pei², Jinhai Li^3,*

CMC-Computers, Materials & Continua, Vol.86, No.1, pp. 1-20, 2026, DOI:10.32604/cmc.2025.069873 - 10 November 2025

Abstract With the increasing complexity of vehicular networks and the proliferation of connected vehicles, Federated Learning (FL) has emerged as a critical framework for decentralized model training while preserving data privacy. However, efficient client selection and adaptive weight allocation in heterogeneous and non-IID environments remain challenging. To address these issues, we propose Federated Learning with Client Selection and Adaptive Weighting (FedCW), a novel algorithm that leverages adaptive client selection and dynamic weight allocation for optimizing model convergence in real-time vehicular networks. FedCW selects clients based on their Euclidean distance from the global model and dynamically adjusts More >

Ghadah Naif Alwakid^*

CMC-Computers, Materials & Continua, Vol.86, No.1, pp. 1-25, 2026, DOI:10.32604/cmc.2025.068666 - 10 November 2025

Abstract Alzheimer’s Disease (AD) is a progressive neurodegenerative disorder that significantly affects cognitive function, making early and accurate diagnosis essential. Traditional Deep Learning (DL)-based approaches often struggle with low-contrast MRI images, class imbalance, and suboptimal feature extraction. This paper develops a Hybrid DL system that unites MobileNetV2 with adaptive classification methods to boost Alzheimer’s diagnosis by processing MRI scans. Image enhancement is done using Contrast-Limited Adaptive Histogram Equalization (CLAHE) and Enhanced Super-Resolution Generative Adversarial Networks (ESRGAN). A classification robustness enhancement system integrates class weighting techniques and a Matthews Correlation Coefficient (MCC)-based evaluation method into the design.… More >

Yu Tao¹, Tian Yin², Yang Jie^1,*

Journal on Artificial Intelligence, Vol.7, pp. 549-570, 2025, DOI:10.32604/jai.2025.072727 - 01 December 2025

Abstract To address the issue of insufficient accuracy in attitude estimation using Inertial Measurement Units (IMU), this paper proposes a multi-sensor fusion attitude estimation method based on an improved Error-State Kalman Filter (ESKF). Several adaptive mechanisms are introduced within the standard ESKF framework: first, the process noise covariance is dynamically adjusted based on gyroscope angular velocity to enhance the algorithm’s adaptability under both static and dynamic conditions; second, the Sage-Husa algorithm is employed to estimate the measurement noise covariance of the accelerometer and magnetometer in real-time, mitigating disturbances caused by external accelerations and magnetic fields. Additionally,… More >

Sewwandi Tennakoon¹, Eric Danso¹, Zhenjie Zhao^2,*

Journal on Artificial Intelligence, Vol.7, pp. 517-547, 2025, DOI:10.32604/jai.2025.072628 - 01 December 2025

Abstract Generative Artificial Intelligence (GenAI) systems have achieved remarkable capabilities across text, code, and image generation; however, their outputs remain prone to errors, hallucinations, and biases. Users often overtrust these outputs due to limited transparency, which can lead to misuse and decision errors. This study addresses the challenge of calibrating trust in GenAI through a human centered testing framework enhanced with adaptive explainability. We introduce a methodology that adjusts explanations dynamically according to user expertise, model output confidence, and contextual risk factors, providing guidance that is informative but not overwhelming. The framework was evaluated using outputs… More >

Shuxin Tan¹, Wei Yan², Lei Zhao¹, Xianglin Zhang^3,*, Ziqiang Man², Yu Lu², Teng Liu², Gaoyue Zhong², Weiqun Liu², Linjun Shi³

Energy Engineering, Vol.122, No.12, pp. 5175-5197, 2025, DOI:10.32604/ee.2025.068692 - 27 November 2025

Abstract The integration of large-scale renewable energy introduces frequency instability challenges due to inherent intermittency. While doubly-fed pumped storage units (DFPSUs) offer frequency regulation potential in pumping mode, conventional strategies fail to address hydraulic-mechanical coupling dynamics and operational constraints, limiting their effectiveness. This paper presents an innovative primary frequency control strategy for double-fed pumped storage units (DFPSUs) operating in pumping mode, integrating an adaptive parameter calculation method. This method is constrained by operational speed and power limits, addressing key performance factors. A dynamic model that incorporates the reversible pump-turbine characteristics is developed to translate frequency deviations… More >

Junyu Ge¹, Yifan Wang¹, Hong Li^1,2,*

The International Conference on Computational & Experimental Engineering and Sciences, Vol.33, No.4, pp. 1-3, 2025, DOI:10.32604/icces.2025.011446

Abstract The advancement of untethered and adaptive robotic systems necessitates the development of compact, efficient, and silent pneumatic power sources [1,2]. Traditional pneumatic actuation relies on bulky compressors or external gas reservoirs, limiting their practical applications in mobile and autonomous systems [3,4]. This work presents a novel electrochemical pneumatic battery (EPB) that exploits electrochemical driven gas generation to achieve controlled and energy-efficient pneumatic actuation, offering a viable alternative to conventional air supply methods. The EPB operates through an electrochemical redox mechanism based on a zinc-oxygen battery [5–7], enabling reversible gas storage and controlled pressure modulation. This… More >

Tian Tian¹, Huayi Wei^2,*

The International Conference on Computational & Experimental Engineering and Sciences, Vol.33, No.4, pp. 1-2, 2025, DOI:10.32604/icces.2025.011175

Abstract Brittle fracture is a critical failure mode in structural materials, and accurately simulating its evolution is essential for engineering design, material performance evaluation, and failure prediction. Traditional numerical methods, however, face significant challenges when dealing with higher-order fracture models and complex fracture behaviors. To overcome these challenges, this study proposes an innovative simulation framework based on higher-order finite element methods and adaptive mesh refinement, effectively balancing computational efficiency and simulation accuracy.
The research first develops a higher-order finite element method for the continuum damage fracture phase-field model. By incorporating higher-order finite element techniques, the proposed method… More >

Abdulnaser M. Alshoaibi^*, Yahya Ali Fageehi

CMES-Computer Modeling in Engineering & Sciences, Vol.145, No.1, pp. 189-214, 2025, DOI:10.32604/cmes.2025.071583 - 30 October 2025

Abstract Fatigue crack growth is a critical phenomenon in engineering structures, accounting for a significant percentage of structural failures across various industries. Accurate prediction of crack initiation, propagation paths, and fatigue life is essential for ensuring structural integrity and optimizing maintenance schedules. This paper presents a comprehensive finite element approach for simulating two-dimensional fatigue crack growth under linear elastic conditions with adaptive mesh generation. The source code for the program was developed in Fortran 95 and compiled with Visual Fortran. To achieve high-fidelity simulations, the methodology integrates several key features: it employs an automatic, adaptive meshing… More >

Yuan Mei^1,2, Daokui Li^1,2, Shiming Zhou^1,2,*, Zhibin Shen^1,2

CMES-Computer Modeling in Engineering & Sciences, Vol.145, No.1, pp. 153-187, 2025, DOI:10.32604/cmes.2025.070252 - 30 October 2025

Abstract Viscoelastic solids, such as composite propellants, exhibit significant time and rate dependencies, and their fracture processes display high levels of nonlinearity. However, the correlation between crack propagation and viscoelastic energy dissipation in these materials remains unclear. Therefore, accurately modeling and understanding of their fracture behavior is crucial for relevant engineering applications. This study proposes a novel viscoelastic phase-field model. In the numerical implementation, the adopted adaptive time-stepping iterative strategy effectively accelerates the coupling iteration efficiency between the phase-field and the displacement field. Moreover, all unknown parameters in the model, including the form of the phase-field More >

Jahfar Khan Said Baz¹, Peng Zhang^2,3,*, Mian Muhammad Kamal⁴, Heba G. Mohamed⁵, Muhammad Sheraz⁶, Teong Chee Chuah^6,*

CMES-Computer Modeling in Engineering & Sciences, Vol.145, No.1, pp. 947-969, 2025, DOI:10.32604/cmes.2025.069956 - 30 October 2025

Abstract Multiple Object Tracking (MOT) is essential for applications such as autonomous driving, surveillance, and analytics; However, challenges such as occlusion, low-resolution imaging, and identity switches remain persistent. We propose HAMOT, a hierarchical adaptive multi-object tracker that solves these challenges with a novel, unified framework. Unlike previous methods that rely on isolated components, HAMOT incorporates a Swin Transformer-based Adaptive Enhancement (STAE) module—comprising Scene-Adaptive Transformer Enhancement and Confidence-Adaptive Feature Refinement—to improve detection under low-visibility conditions. The hierarchical Dynamic Graph Neural Network with Temporal Attention (DGNN-TA) models both short- and long-term associations, and the Adaptive Unscented Kalman Filter… More >