Moneerah Alotaibi^*

CMC-Computers, Materials & Continua, Vol.86, No.1, pp. 1-20, 2026, DOI:10.32604/cmc.2025.069195 - 10 November 2025

Abstract Advanced traffic monitoring systems encounter substantial challenges in vehicle detection and classification due to the limitations of conventional methods, which often demand extensive computational resources and struggle with diverse data acquisition techniques. This research presents a novel approach for vehicle classification and recognition in aerial image sequences, integrating multiple advanced techniques to enhance detection accuracy. The proposed model begins with preprocessing using Multiscale Retinex (MSR) to enhance image quality, followed by Expectation-Maximization (EM) Segmentation for precise foreground object identification. Vehicle detection is performed using the state-of-the-art YOLOv10 framework, while feature extraction incorporates Maximally Stable Extremal… More >

Anna Elisa Silva, Eduardo Thiago Formigari, João Pedro Mayer Camacho Araújo, Dagoberto de Oliveira Silva, Jürgen Andreaus, Eduardo Guilherme Cividini Neiva^*

Journal of Renewable Materials, Vol.13, No.10, pp. 1899-1912, 2025, DOI:10.32604/jrm.2025.02025-0130 - 22 October 2025

Abstract Here, we report a comprehensive study on the characterization of cotton biomass residue, its conversion into carbon-based materials via pyrolysis, and its application as an electrochemical sensor for ascorbic acid (AA). The compositions, morphologies, and structures of the resulting materials were investigated using XRD, FTIR, TGA, SEM, and EDS. Pyrolysis was carried out in an air atmosphere at different temperatures (300°C and 400°C) and durations (1, 60, and 240 min), leading to the transformation of lignocellulosic cotton residue into carbon-based materials embedded with inorganic nanoparticles, including carbonates, sulfates, chlorates, and phosphates of potassium, calcium, and… More > Graphic Abstract

Ana M. Vela-AlcáNtara¹, Diego J. HernáNdez-SáNchez^1,2, Elisa Tamariz^1,*

BIOCELL, Vol.49, No.9, pp. 1663-1695, 2025, DOI:10.32604/biocell.2025.066201 - 25 September 2025

Abstract Cells are exposed to various mechanical forces, including extracellular and intracellular forces such as stiffness, tension, compression, viscosity, and shear stress, which regulate cell biology. The process of transducing mechanical stimuli into biochemical signals is termed mechanotransduction. These mechanical forces can regulate protein and gene expression, thereby impacting cell morphology, adhesion, proliferation, apoptosis, and migration. During cancer development, significant changes in extracellular and intracellular mechanical properties occur, resulting in altered mechanical inputs to which cells are exposed. MicroRNAs (miRNAs), key post-transcriptional regulators of gene and protein expression, are increasingly recognized as mechanosensitive molecules involved in More >

Nadhmi A. Gazem¹, Sultan Noman Qasem^2,3, Umair Naeem⁴, Shahid Latif⁵, Ibtehal Nafea⁶, Faisal Saeed⁷, Mujeeb Ur Rehman^8,*

CMES-Computer Modeling in Engineering & Sciences, Vol.144, No.1, pp. 407-427, 2025, DOI:10.32604/cmes.2025.066517 - 31 July 2025

Abstract Falls are a leading cause of injury and morbidity among older adults, especially those with Alzheimer’s disease (AD), who face increased risks due to cognitive decline, gait instability, and impaired spatial awareness. While wearable sensor-based fall detection systems offer promising solutions, their effectiveness is often hindered by domain shifts resulting from variations in sensor placement, sampling frequencies, and discrepancies in dataset distributions. To address these challenges, this paper proposes a novel unsupervised domain adaptation (UDA) framework specifically designed for cross-dataset fall detection in Alzheimer’s disease (AD) patients, utilizing advanced transfer learning to enhance generalizability. The… More >

Asaad Algarni¹, Iqra Aijaz Abro², Mohammed Alshehri³, Yahya AlQahtani⁴, Abdulmonem Alshahrani⁴, Hui Liu^5,*

CMC-Computers, Materials & Continua, Vol.84, No.3, pp. 5879-5896, 2025, DOI:10.32604/cmc.2025.064601 - 30 July 2025

Abstract Inertial Sensor-based Daily Activity Recognition (IS-DAR) requires adaptable, data-efficient methods for effective multi-sensor use. This study presents an advanced detection system using body-worn sensors to accurately recognize activities. A structured pipeline enhances IS-DAR by applying signal preprocessing, feature extraction and optimization, followed by classification. Before segmentation, a Chebyshev filter removes noise, and Blackman windowing improves signal representation. Discriminative features—Gaussian Mixture Model (GMM) with Mel-Frequency Cepstral Coefficients (MFCC), spectral entropy, quaternion-based features, and Gammatone Cepstral Coefficients (GCC)—are fused to expand the feature space. Unlike existing approaches, the proposed IS-DAR system uniquely integrates diverse handcrafted features using… More >

Kunlin Wu, Jingcheng Xiao, Junwei Li, Yifan Wang^*

Journal of Polymer Materials, Vol.42, No.2, pp. 277-305, 2025, DOI:10.32604/jpm.2025.065269 - 14 July 2025

Abstract The integration of 3D-printed hydrogels in soft robotics enables the creation of flexible, adaptable, and biocompatible systems. Hydrogels, with their high-water content and responsiveness to stimuli, are suitable for actuators, sensors, and robotic systems that require safe interaction and precise manipulation. Unlike traditional techniques, 3D printing offers enhanced capabilities in tailoring structural complexity, resolution, and integrated functionality, enabling the direct fabrication of hydrogel systems with programmed mechanical and functional properties. In this perspective, we explore the evolving role of 3D-printed hydrogels in soft robotics, covering their material composition, fabrication techniques, and diverse applications. We highlight More >

Zainab Ouardirhi^1,2,*, Mostapha Zbakh², Sidi Ahmed Mahmoudi¹

CMES-Computer Modeling in Engineering & Sciences, Vol.143, No.3, pp. 2509-2571, 2025, DOI:10.32604/cmes.2025.064283 - 30 June 2025

Abstract Object detection in occluded environments remains a core challenge in computer vision (CV), especially in domains such as autonomous driving and robotics. While Convolutional Neural Network (CNN)-based two-dimensional (2D) and three-dimensional (3D) object detection methods have made significant progress, they often fall short under severe occlusion due to depth ambiguities in 2D imagery and the high cost and deployment limitations of 3D sensors such as Light Detection and Ranging (LiDAR). This paper presents a comparative review of recent 2D and 3D detection models, focusing on their occlusion-handling capabilities and the impact of sensor modalities such More >

Duc-Nghia Tran¹, Viet-Manh Do^1,2, Manh-Tuyen Vi^3,*, Duc-Tan Tran^3,*

CMC-Computers, Materials & Continua, Vol.83, No.2, pp. 2525-2553, 2025, DOI:10.32604/cmc.2025.062590 - 16 April 2025

Abstract This study focuses on the design and validation of a behavior classification system for cattle using behavioral data collected through accelerometer sensors. Data collection and behavioral analysis are achieved using machine learning (ML) algorithms through accelerometer sensors. However, behavioral analysis poses challenges due to the complexity of cow activities. The task becomes more challenging in a real-time behavioral analysis system with the requirement for shorter data windows and energy constraints. Shorter windows may lack sufficient information, reducing algorithm performance. Additionally, the sensor’s position on the cows may shift during practical use, altering the collected accelerometer… More >

Zhe Gong¹, Ge Yan², Jie Ma¹, Chang-Lin Yan², Fu-Kang Shen¹, Hu Li³, Hua-Ping Wang^1,*

Structural Durability & Health Monitoring, Vol.19, No.3, pp. 473-498, 2025, DOI:10.32604/sdhm.2024.057916 - 03 April 2025

Abstract Vacuum pressure vessels are one of the critical components in the aerospace field, and understanding the mechanical behavior feature is particularly important for safe operation. Therefore, it is meaningful to obtain the stress and strain distributions in the key positions of the vacuum tank, which can contribute to the safe performance assessment, operation efficiency, and fault analysis. Hence, this paper provides the distribution characteristics and variation rules of stress and tank strain of vacuum under different internal and external pressures through the elastic theoretical analysis and iteration method. The quasi-distributed fiber Bragg grating (FBG) sensors… More >

Tabassum Kanwal¹, Saif Ur Rehman^1,*, Azhar Imran², Haitham A. Mahmoud³

Computer Systems Science and Engineering, Vol.49, pp. 185-212, 2025, DOI:10.32604/csse.2024.056535 - 10 January 2025

Abstract This study presents an energy-efficient Internet of Things (IoT)-based wireless sensor network (WSN) framework for autonomous data validation in remote environmental monitoring. We address two critical challenges in WSNs: ensuring data reliability and optimizing energy consumption. Our novel approach integrates an artificial neural network (ANN)-based multi-fault detection algorithm with an energy-efficient IoT-WSN architecture. The proposed ANN model is designed to simultaneously detect multiple fault types, including spike faults, stuck-at faults, outliers, and out-of-range faults. We collected sensor data at 5-minute intervals over three months, using temperature and humidity sensors. The ANN was trained on 70%… More >