Nien-Tsu Hu^1,*, Hsiang-Tung Kao¹, Chin-Sheng Chen¹, Shih-Hao Chang²

CMC-Computers, Materials & Continua, Vol.87, No.1, 2026, DOI:10.32604/cmc.2025.074993 - 10 February 2026

Abstract Trajectory tracking for nonlinear robotic systems remains a fundamental yet challenging problem in control engineering, particularly when both precision and efficiency must be ensured. Conventional control methods are often effective for stabilization but may not directly optimize long-term performance. To address this limitation, this study develops an integrated framework that combines optimal control principles with reinforcement learning for a single-link robotic manipulator. The proposed scheme adopts an actor–critic structure, where the critic network approximates the value function associated with the Hamilton–Jacobi–Bellman equation, and the actor network generates near-optimal control signals in real time. This dual… More >

Shu-Yin Chiang^*, Shin-En Huang

CMC-Computers, Materials & Continua, Vol.87, No.1, 2026, DOI:10.32604/cmc.2025.074528 - 10 February 2026

Abstract This paper presents an intelligent patrol and security robot integrating 2D LiDAR and RGB-D vision sensors to achieve semantic simultaneous localization and mapping (SLAM), real-time object recognition, and dynamic obstacle avoidance. The system employs the YOLOv7 deep-learning framework for semantic detection and SLAM for localization and mapping, fusing geometric and visual data to build a high-fidelity 2D semantic map. This map enables the robot to identify and project object information for improved situational awareness. Experimental results show that object recognition reached 95.4% mAP@0.5. Semantic completeness increased from 68.7% (single view) to 94.1% (multi-view) with an More >

Naresh Kumar¹, Anuj Kumar^1,*, Abhishek K. Mishra^2,*

CMC-Computers, Materials & Continua, Vol.87, No.1, 2026, DOI:10.32604/cmc.2025.074430 - 10 February 2026

Abstract Density functional theory (DFT) calculations were employed to investigate the adsorption behavior of NH₃, AsH₃, PH₃, CO₂, and CH₄ molecules on both pristine and mono-vacancy phosphorene sheets. The pristine phosphorene surface shows weak physisorption with all the gas molecules, inducing only minor changes in its structural and electronic properties. However, the introduction of mono-vacancies significantly enhances the interaction strength with NH₃, PH₃, CO₂, and CH₄. These variations are attributed to substantial charge redistribution and orbital hybridization in the presence of defects. The defective phosphorene sheet also exhibits enhanced adsorption energies, along with favorable sensitivity and recovery characteristics, highlighting its potential More >

Chang Xu^1,*, Xianbo Yin², Zhiyong Zhou¹, Bomin Liu¹

CMC-Computers, Materials & Continua, Vol.87, No.1, 2026, DOI:10.32604/cmc.2026.074417 - 10 February 2026

Abstract This study presents a hybrid CNN-Transformer model for real-time recognition of affective tactile biosignals. The proposed framework combines convolutional neural networks (CNNs) to extract spatial and local temporal features with the Transformer encoder that captures long-range dependencies in time-series data through multi-head attention. Model performance was evaluated on two widely used tactile biosignal datasets, HAART and CoST, which contain diverse affective touch gestures recorded from pressure sensor arrays. The CNN-Transformer model achieved recognition rates of 93.33% on HAART and 80.89% on CoST, outperforming existing methods on both benchmarks. By incorporating temporal windowing, the model enables More >

Weiguo Pan¹, Songjie Du^2,*, Bingxin Xu¹, Bin Zhang¹, Hongzhe Liu¹

CMC-Computers, Materials & Continua, Vol.87, No.1, 2026, DOI:10.32604/cmc.2025.073599 - 10 February 2026

Abstract Traffic sign detection is a critical component of driving systems. Single-stage network-based traffic sign detection algorithms, renowned for their fast detection speeds and high accuracy, have become the dominant approach in current practices. However, in complex and dynamic traffic scenes, particularly with smaller traffic sign objects, challenges such as missed and false detections can lead to reduced overall detection accuracy. To address this issue, this paper proposes a detection algorithm that integrates edge and shape information. Recognizing that traffic signs have specific shapes and distinct edge contours, this paper introduces an edge feature extraction branch More >

Feihong Xu¹, Jianbo Wu^1,*, Qing An^1,*, Fei Zhu^1,2, Zhaoyang Han³, Saru Kumari⁴

CMC-Computers, Materials & Continua, Vol.87, No.1, 2026, DOI:10.32604/cmc.2025.073441 - 10 February 2026

Abstract The large-scale deployment of Internet of Things (IoT) technology across various aspects of daily life has significantly propelled the intelligent development of society. Among them, the integration of IoT and named data networks (NDNs) reduces network complexity and provides practical directions for content-oriented network design. However, ensuring data integrity in NDN-IoT applications remains a challenging issue. Very recently, Wang et al. (Entropy, 27(5), 471(2025)) designed a certificateless aggregate signature (CLAS) scheme for NDN-IoT environments. Wang et al. stated that their construction was provably secure under various types of security attacks. Using theoretical analysis methods, in… More >

Jian Huang¹, Mingwei Li¹, Yulong Tian¹, Yi Yao², Hao Han^1,*

CMC-Computers, Materials & Continua, Vol.87, No.1, 2026, DOI:10.32604/cmc.2025.073201 - 10 February 2026

Abstract With the popularization of new technologies, telephone fraud has become the main means of stealing money and personal identity information. Taking inspiration from the website authentication mechanism, we propose an end-to-end data modem scheme that transmits the caller’s digital certificates through a voice channel for the recipient to verify the caller’s identity. Encoding useful information through voice channels is very difficult without the assistance of telecommunications providers. For example, speech activity detection may quickly classify encoded signals as non-speech signals and reject input waveforms. To address this issue, we propose a novel modulation method based… More >

Myunghoon Roh¹, Katalin Parti², Diego Gomez-Baya^3,*, Cheryl E. Sanders⁴, Elizabeth K. Englander⁵

International Journal of Mental Health Promotion, Vol.28, No.1, 2026, DOI:10.32604/ijmhp.2025.072605 - 28 January 2026

Abstract Background: Bullying is a current social and educational problem with detrimental consequences in adolescence and later life stages. Previous research has explored the risk or protective factor at different socio-ecological levels, but further integration is needed to examine the relationships of family characteristics. This study examines how parenting style and attachment relate to adolescents’ bullying and cyberbullying, and whether parental phubbing mediates these links. Methods: Grounded in social bonding theory, we surveyed a cross-sectional convenience sample of U.S. college students (N = 545; Mean_age = 19.60, SD = 1.41) who retrospectively reported middle/high-school experiences from Massachusetts, Colorado,… More >

Mohammed Al-Mahbashi^1,2,*, Ali Ahmed³, Abdolraheem Khader^4,*, Shakeel Ahmad³, Mohamed A. Damos⁵, Ahmed Abdu⁶

CMES-Computer Modeling in Engineering & Sciences, Vol.146, No.1, 2026, DOI:10.32604/cmes.2025.075909 - 29 January 2026

Abstract Reliable detection of traffic signs and lights (TSLs) at long range and under varying illumination is essential for improving the perception and safety of autonomous driving systems (ADS). Traditional object detection models often exhibit significant performance degradation in real-world environments characterized by high dynamic range and complex lighting conditions. To overcome these limitations, this research presents FED-YOLOv10s, an improved and lightweight object detection framework based on You Only look Once v10 (YOLOv10). The proposed model integrates a C2f-Faster block derived from FasterNet to reduce parameters and floating-point operations, an Efficient Multiscale Attention (EMA) mechanism to More >

Nuh Azginoglu^*

CMES-Computer Modeling in Engineering & Sciences, Vol.146, No.1, 2026, DOI:10.32604/cmes.2026.075834 - 29 January 2026

Abstract Breast cancer screening programs rely heavily on mammography for early detection; however, diagnostic performance is strongly affected by inter-reader variability, breast density, and the limitations of conventional computer-aided detection systems. Recent advances in deep learning have enabled more robust and scalable solutions for large-scale screening, yet a systematic comparison of modern object detection architectures on nationally representative datasets remains limited. This study presents a comprehensive quantitative comparison of prominent deep learning–based object detection architectures for Artificial Intelligence-assisted mammography analysis using the MammosighTR dataset, developed within the Turkish National Breast Cancer Screening Program. The dataset comprises… More >