Tien-Tuan Dao¹, Lan-Nhi Tran-Ngoc^2,3, Trong-Pham Nguyen-Huu^2,3, Khanh-Linh Dinh-Bui^2,3, Nhat-Minh Nguyen^2,3, Ngoc-Bich Le^2,3, Tan-Nhu Nguyen^2,3,*

CMC-Computers, Materials & Continua, Vol.84, No.2, pp. 3345-3369, 2025, DOI:10.32604/cmc.2025.065279 - 03 July 2025

Abstract Skull structures are important for biomechanical head simulations, but they are mostly reconstructed from medical images. These reconstruction methods harm the human body and have a long processing time. Currently, skull structures can be straightforwardly predicted from the head, but a full head shape must be available. Most scanning devices can only capture the face shape. Consequently, a method that can quickly predict the full skull structures from the face is necessary. In this study, a novel face-to-skull prediction procedure is introduced. Given a three-dimensional (3-D) face shape, a skull mesh could be predicted so… More >

Jie He, Dongmei Zhao, Tao Liu^*, Qingfeng Zou, Jian’an Xie

CMC-Computers, Materials & Continua, Vol.84, No.2, pp. 2733-2749, 2025, DOI:10.32604/cmc.2025.065205 - 03 July 2025

Abstract Robot navigation in complex crowd service scenarios, such as medical logistics and commercial guidance, requires a dynamic balance between safety and efficiency, while the traditional fixed reward mechanism lacks environmental adaptability and struggles to adapt to the variability of crowd density and pedestrian motion patterns. This paper proposes a navigation method that integrates spatiotemporal risk field modeling and adaptive reward optimization, aiming to improve the robot’s decision-making ability in diverse crowd scenarios through dynamic risk assessment and nonlinear weight adjustment. We construct a spatiotemporal risk field model based on a Gaussian kernel function by combining… More >

Haiyan Long¹, Chonghao Zhang², Xudong Qiu³, Hai Chen^2,*, Gang Chen^4,*

CMC-Computers, Materials & Continua, Vol.84, No.2, pp. 2975-2988, 2025, DOI:10.32604/cmc.2025.065047 - 03 July 2025

Abstract 3D sparse convolution has emerged as a pivotal technique for efficient voxel-based perception in autonomous systems, enabling selective feature extraction from non-empty voxels while suppressing computational waste. Despite its theoretical efficiency advantages, practical implementations face under-explored limitations: the fixed geometric patterns of conventional sparse convolutional kernels inevitably process non-contributory positions during sliding-window operations, particularly in regions with uneven point cloud density. To address this, we propose Hierarchical Shape Pruning for 3D Sparse Convolution (HSP-S), which dynamically eliminates redundant kernel stripes through layer-adaptive thresholding. Unlike static soft pruning methods, HSP-S maintains trainable sparsity patterns by progressively… More >

Fan Li¹, Hongxue Liu², Yongsong Li², Leilei Chen², Haojie Lian^1,*

CMES-Computer Modeling in Engineering & Sciences, Vol.143, No.3, pp. 2785-2809, 2025, DOI:10.32604/cmes.2025.066001 - 30 June 2025

Abstract This study explores a sensitivity analysis method based on the boundary element method (BEM) to address the computational complexity in acoustic analysis with ground reflection problems. The advantages of BEM in acoustic simulations and its high computational cost in broadband problems are examined. To improve efficiency, a Taylor series expansion is applied to decouple frequency-dependent terms in BEM. Additionally, the Second-Order Arnoldi (SOAR) model order reduction method is integrated to reduce computational costs and enhance numerical stability. Furthermore, an isogeometric sensitivity boundary integral equation is formulated using the direct differentiation method, incorporating Cauchy principal value More >

Yu-Shiuan Tsai^*, Yuk-Hang Sit

CMES-Computer Modeling in Engineering & Sciences, Vol.143, No.3, pp. 3065-3090, 2025, DOI:10.32604/cmes.2025.064783 - 30 June 2025

Abstract To improve small object detection and trajectory estimation from an aerial moving perspective, we propose the Aerial View Attention-PRB (AVA-PRB) model. AVA-PRB integrates two attention mechanisms—Coordinate Attention (CA) and the Convolutional Block Attention Module (CBAM)—to enhance detection accuracy. Additionally, Shape-IoU is employed as the loss function to refine localization precision. Our model further incorporates an adaptive feature fusion mechanism, which optimizes multi-scale object representation, ensuring robust tracking in complex aerial environments. We evaluate the performance of AVA-PRB on two benchmark datasets: Aerial Person Detection and VisDrone2019-Det. The model achieves 60.9% mAP@0.5 on the Aerial Person… More >

Jing Hu^*, Xiaoyu Zhang

Frontiers in Heat and Mass Transfer, Vol.23, No.3, pp. 957-974, 2025, DOI:10.32604/fhmt.2025.064892 - 30 June 2025

Abstract As a core power device in strategic industries such as new energy power generation and electric vehicles, the thermal reliability of IGBT modules directly determines the performance and lifetime of the whole system. A synergistic optimization structure of “inlet plate-channel spoiler columns” is proposed for the local hot spot problem during the operation of Insulated Gate Bipolar Transistor (IGBT), combined with the inherent defect of uneven flow distribution of the traditional U-type liquid cooling plate in this paper. The influences of the shape, height (H), and spacing from the spoiler column (b) of the plate on… More > Graphic Abstract

Dongfeng Yang, Ruirui Zhang, Chuang Liu^*, Guoliang Bian

Energy Engineering, Vol.122, No.7, pp. 2673-2694, 2025, DOI:10.32604/ee.2025.062154 - 27 June 2025

Abstract Under the current long-term electricity market mechanism, new energy and thermal power face issues such as deviation assessment and compression of generation space. The profitability of market players is limited. Simultaneously, the cooperation model among various energy sources will have a direct impact on the alliance’s revenue and the equity of income distribution within the alliance. Therefore, integrating new energy with thermal power units into an integrated multi-energy complementary system to participate in the long-term electricity market holds significant potential. To simulate and evaluate the benefits and internal distribution methods of a multi-energy complementary system… More >

Hussam Qushtom, Ahmad Hasasneh^*, Sari Masri

CMC-Computers, Materials & Continua, Vol.84, No.1, pp. 1379-1395, 2025, DOI:10.32604/cmc.2025.061995 - 09 June 2025

Abstract This study presents an enhanced convolutional neural network (CNN) model integrated with Explainable Artificial Intelligence (XAI) techniques for accurate prediction and interpretation of wheat crop diseases. The aim is to streamline the detection process while offering transparent insights into the model’s decision-making to support effective disease management. To evaluate the model, a dataset was collected from wheat fields in Kotli, Azad Kashmir, Pakistan, and tested across multiple data splits. The proposed model demonstrates improved stability, faster convergence, and higher classification accuracy. The results show significant improvements in prediction accuracy and stability compared to prior works,… More >

Namal Rathnayake¹, Jeevani Jayasinghe^2,3, Rashmi Semasinghe², Upaka Rathnayake^4,*

CMES-Computer Modeling in Engineering & Sciences, Vol.143, No.2, pp. 2287-2305, 2025, DOI:10.32604/cmes.2025.064464 - 30 May 2025

Abstract In this study, a machine learning-based predictive model was developed for the Musa petti Wind Farm in Sri Lanka to address the need for localized forecasting solutions. Using data on wind speed, air temperature, nacelle position, and actual power, lagged features were generated to capture temporal dependencies. Among 24 evaluated models, the ensemble bagging approach achieved the best performance, with R² values of 0.89 at 0 min and 0.75 at 60 min. Shapley Additive exPlanations (SHAP) analysis revealed that while wind speed is the primary driver for short-term predictions, air temperature and nacelle position become more More >

Shuqin Zhang¹, Zihao Wang^1,*, Xinyu Su²

CMC-Computers, Materials & Continua, Vol.83, No.3, pp. 5781-5809, 2025, DOI:10.32604/cmc.2025.062080 - 19 May 2025

Abstract The methods of network attacks have become increasingly sophisticated, rendering traditional cybersecurity defense mechanisms insufficient to address novel and complex threats effectively. In recent years, artificial intelligence has achieved significant progress in the field of network security. However, many challenges and issues remain, particularly regarding the interpretability of deep learning and ensemble learning algorithms. To address the challenge of enhancing the interpretability of network attack prediction models, this paper proposes a method that combines Light Gradient Boosting Machine (LGBM) and SHapley Additive exPlanations (SHAP). LGBM is employed to model anomalous fluctuations in various network indicators,… More >