Seyong Jin¹, Muhammad Fayaz², L. Minh Dang³, Hyoung-Kyu Song³, Hyeonjoon Moon^2,*

CMC-Computers, Materials & Continua, Vol.86, No.1, pp. 1-23, 2026, DOI:10.32604/cmc.2025.070667 - 10 November 2025

Abstract Brain tumors require precise segmentation for diagnosis and treatment plans due to their complex morphology and heterogeneous characteristics. While MRI-based automatic brain tumor segmentation technology reduces the burden on medical staff and provides quantitative information, existing methodologies and recent models still struggle to accurately capture and classify the fine boundaries and diverse morphologies of tumors. In order to address these challenges and maximize the performance of brain tumor segmentation, this research introduces a novel SwinUNETR-based model by integrating a new decoder block, the Hierarchical Channel-wise Attention Decoder (HCAD), into a powerful SwinUNETR encoder. The HCAD… More >

Haixia Wang, Huan Zhang, Xiuling Wang, Xule Xin, Zhiguo Zhang^*

CMC-Computers, Materials & Continua, Vol.86, No.1, pp. 1-20, 2026, DOI:10.32604/cmc.2025.069340 - 10 November 2025

Abstract Accurately counting dense objects in complex and diverse backgrounds is a significant challenge in computer vision, with applications ranging from crowd counting to various other object counting tasks. To address this, we propose HUANNet (High-Resolution Unified Attention Network), a convolutional neural network designed to capture both local features and rich semantic information through a high-resolution representation learning framework, while optimizing computational distribution across parallel branches. HUANNet introduces three core modules: the High-Resolution Attention Module (HRAM), which enhances feature extraction by optimizing multi-resolution feature fusion; the Unified Multi-Scale Attention Module (UMAM), which integrates spatial, channel, and More >

Longfei Gao^*, Weidong Wang, Dieyun Ke

CMC-Computers, Materials & Continua, Vol.86, No.1, pp. 1-15, 2026, DOI:10.32604/cmc.2025.068873 - 10 November 2025

Abstract At present, energy consumption is one of the main bottlenecks in autonomous mobile robot development. To address the challenge of high energy consumption in path planning for autonomous mobile robots navigating unknown and complex environments, this paper proposes an Attention-Enhanced Dueling Deep Q-Network (AD-Dueling DQN), which integrates a multi-head attention mechanism and a prioritized experience replay strategy into a Dueling-DQN reinforcement learning framework. A multi-objective reward function, centered on energy efficiency, is designed to comprehensively consider path length, terrain slope, motion smoothness, and obstacle avoidance, enabling optimal low-energy trajectory generation in 3D space from the… More >

Lijuan Huang¹, Xianyi Liu², Jinping Liu^2,*, Pengfei Xu^2,*

CMC-Computers, Materials & Continua, Vol.86, No.1, pp. 1-20, 2026, DOI:10.32604/cmc.2025.068818 - 10 November 2025

Abstract The ubiquity of mobile devices has driven advancements in mobile object detection. However, challenges in multi-scale object detection in open, complex environments persist due to limited computational resources. Traditional approaches like network compression, quantization, and lightweight design often sacrifice accuracy or feature representation robustness. This article introduces the Fast Multi-scale Channel Shuffling Network (FMCSNet), a novel lightweight detection model optimized for mobile devices. FMCSNet integrates a fully convolutional Multilayer Perceptron (MLP) module, offering global perception without significantly increasing parameters, effectively bridging the gap between CNNs and Vision Transformers. FMCSNet achieves a delicate balance between computation… More >

Zhenpeng Jiang, Qingquan Liu^*, Ende Wang

CMC-Computers, Materials & Continua, Vol.86, No.1, pp. 1-18, 2026, DOI:10.32604/cmc.2025.068780 - 10 November 2025

Abstract Rapidly-exploring Random Tree (RRT) and its variants have become foundational in path-planning research, yet in complex three-dimensional off-road environments their uniform blind sampling and limited safety guarantees lead to slow convergence and force an unfavorable trade-off between path quality and traversal safety. To address these challenges, we introduce HS-APF-RRT*, a novel algorithm that fuses layered sampling, an enhanced Artificial Potential Field (APF), and a dynamic neighborhood-expansion mechanism. First, the workspace is hierarchically partitioned into macro, meso, and micro sampling layers, progressively biasing random samples toward safer, lower-energy regions. Second, we augment the traditional APF by More >

Omar Darwish¹, Shorouq Al-Eidi², Abdallah Al-Shorman¹, Majdi Maabreh³, Anas Alsobeh⁴, Plamen Zahariev⁵, Yahya Tashtoush^6,*

CMC-Computers, Materials & Continua, Vol.86, No.1, pp. 1-21, 2026, DOI:10.32604/cmc.2025.068266 - 10 November 2025

Abstract Covert timing channels (CTC) exploit network resources to establish hidden communication pathways, posing significant risks to data security and policy compliance. Therefore, detecting such hidden and dangerous threats remains one of the security challenges. This paper proposes LinguTimeX, a new framework that combines natural language processing with artificial intelligence, along with explainable Artificial Intelligence (AI) not only to detect CTC but also to provide insights into the decision process. LinguTimeX performs multidimensional feature extraction by fusing linguistic attributes with temporal network patterns to identify covert channels precisely. LinguTimeX demonstrates strong effectiveness in detecting CTC across… More >

Zhenhao Zhang^1,*, Yong Tian^1,2, Yuanzhi Cao¹, Tao Chen¹

The International Conference on Computational & Experimental Engineering and Sciences, Vol.34, No.1, pp. 1-1, 2025, DOI:10.32604/icces.2025.010846

Abstract Accurate calculation of the failure probability of structural components was crucial for full-probability level structural design. However, current design codes typically use uniform design factors, which fail to accurately reflect the true failure probability of structures. In this paper, based on the direct probability integral method (DPIM) and combining different design parameter iterative calculation strategies, the full-probabilistic design methods for single-parameter and multi-parameter were proposed, and their accuracy advantages in structural reliability design were verified by engineering examples. Furthermore, this study compares the partial factor method, the design value method, the direct probability design method,… More >

Chuandong Lin^*

The International Conference on Computational & Experimental Engineering and Sciences, Vol.34, No.1, pp. 1-1, 2025, DOI: 10.32604/icces.2025.010544

Abstract Multiphase flows with thermodynamic nonequilibrium effects are encountered in various engineering and natural systems, such as bubbly flows, droplet-laden flows, and phase change processes. To accurately model and simulate such complex flows, a Discrete Boltzmann Method (DBM) is introduced in this report. The DBM is a kinetic-based approach that can capture the dynamics of multiple phases and their interactions, including phase change, mass transfer, and energy exchange. The method is validated through simulations of multiphase flows with phase change, showing good agreement with analytical solutions. The capability of the DBM to handle thermodynamic nonequilibrium effects… More >

Md. Injamum-Ul-Hoque^1,2, Md. Mahfuzur Rahman², Nayan Chandra Howlader³, Soosan Tavakoli⁴, Md. Mezanur Rahman⁵, Shahin Imran⁶, Mallesham Bulle⁷, S. M. Ahsan^2,4,*, Hyong Woo Choi^4,8,*

Phyton-International Journal of Experimental Botany, Vol.94, No.12, pp. 3805-3826, 2025, DOI:10.32604/phyton.2025.069827 - 29 December 2025

Abstract Cannabis sativa is highly valued for its use in fiber production, medicine, and recreational products. Its secondary metabolites (SM) are renowned for their wide range of health benefits and psychoactive properties. While much of the existing research has focused on cannabinoid production in the plant’s aerial parts, particularly the leaves and flowers, the root system remains understudied in terms of its SM profile. One promising in vitro approach for metabolite production involves the use of ‘hairy roots (HRs)’. These roots mimic the phytochemical profile of native roots but grow more efficiently and yield higher quantities of metabolites.… More >

Xiaoyan Zhuo¹, Yukun Ji¹, Yatao Ren^1,*, Xuehui Wang², Hong Qi¹

Frontiers in Heat and Mass Transfer, Vol.23, No.6, pp. 1701-1720, 2025, DOI:10.32604/fhmt.2025.074045 - 31 December 2025

Abstract Flow and heat transfer characteristics during droplet impact on hot walls are pivotal for elucidating the mechanisms of spray cooling and exploring pathways for heat transfer enhancement. When the wall temperature exceeds the Leidenfrost point, a vapor film forms between the droplet and the wall, rendering the heat transfer process highly complex. Furthermore, for droplet impact on curved walls, the presence of curvature introduces additional factors that modify the spreading behavior of the droplet and necessitate in-depth analysis. Therefore, this work investigates the flow and heat transfer dynamics of droplet impact on hot planes and… More >