Ayesha Kausar^*

Journal of Polymer Materials, Vol.43, No.1, 2026, DOI:10.32604/jpm.2026.078233 - 03 April 2026

Abstract Purpose of this novel review article is to unfold the current scientific worth of high performance polymer nanocomposite nanofibers, owing to growing scientific interests in this field. Accordingly, this state-of-the-art manuscript has been systematically categorized into distinct sections related to (i) fundamentals of carbonaceous nanoreinforcements, (ii) design-structure-property-performance aspects of different categories of polymer nanocomposite nanofibers (conducting polymers, thermoplastics, and thermosets with carbonaceous nanofillers (carbon nanotubes, graphene, fullerene), and then (iii) existing scientific worth (energy devices, electronics, space/defense, environmental sectors), future prospects, challenges, and conclusions. As per literature to date, polymer/carbonaceous nanocomposite nanofibers had myriad of advantageous physical… More > Graphic Abstract

Wael A. Altabey^*

Structural Durability & Health Monitoring, Vol.20, No.2, 2026, DOI:10.32604/sdhm.2025.073949 - 31 March 2026

Abstract The study of long-term pavement performance is a fundamental topic in the field of highway engineering. Through comprehensive and in-depth research on the pavement system, the previous scattered, one-sided, superficial, and perceptual knowledge and experience are summarized and sublimated into a systematic and complete engineering theory, thereby providing powerful guidance and assistance for the practice of pavement design, construction, maintenance, operation, and management. In this research, the mentoring system deployment technology for automatic monitoring is carried out for long-term pavement performance. By burying a variety of sensors in different parts of the road surface, base,… More >

Rama Gautam^1,*, Nikhil Marriwala¹, Reeta Devi¹, Dhariya Singh Arya²

CMC-Computers, Materials & Continua, Vol.87, No.2, 2026, DOI:10.32604/cmc.2026.076136 - 12 March 2026

Abstract In this study, the multi-scale (meso and macro) modelling was used to predict the electric response of the material. Porosity was introduced through a sugar-templating process to enhance compressibility and sensitivity. Mean-field homogenization was employed to predict the electrical conductivity of the nanocomposites, which was validated experimentally through I–V characterisation, confirming stable Ohmic behavior. The homogenised material parameters were incorporated into COMSOL Multiphysics to simulate diaphragm deflection and capacitance variation under applied pressure. Experimental results showed a linear and stable capacitance response at the force magnitude of 0–7 N. The Graphene nanoplatelets (GnP)–Polydimethylsiloxane (PDMS) sensor demonstrated More >

Shtwai Alsubai¹, Abdullah Al Hejaili², Najib Ben Aoun^3,4,*, Amina Salhi⁵, Vincent Karovič^6,*

CMC-Computers, Materials & Continua, Vol.87, No.2, 2026, DOI:10.32604/cmc.2026.074057 - 12 March 2026

Abstract The concept of Human Activity Recognition (HAR) is integral to applications based on Internet of Things (IoT)-enabled devices, particularly in healthcare, fitness tracking, and smart environments. The streams of data from wearable sensors are rich in information, yet their high dimensionality and variability pose a significant challenge to proper classification. To address this problem, this paper proposes hybrid architectures that integrate traditional machine learning models with a deep neural network (DNN) to deliver improved performance and enhanced capabilities for HAR tasks. Multi-sensor HAR data were used to systematically test several hybrid models, including: RF +… More >

Hafiz Burhan Ul Haq¹, Waseem Akram², Haroon ur Rashid Kayani³, Khalid Mahmood^4,*, Chihhsiong Shih⁵, Rupak Kharel^6,7, Amina Salhi⁸

CMC-Computers, Materials & Continua, Vol.87, No.2, 2026, DOI:10.32604/cmc.2025.073188 - 12 March 2026

Abstract The Internet of Things (IoT) is a new model that evolved with the rapid progress of advanced technology and gained tremendous popularity due to its applications. Anomaly detection has widely attracted researchers’ attention in the last few years, and its effects on diverse applications. This review article covers the various methods and tools developed to perform the task efficiently and automatically in a smart city. In this work, we present a comprehensive literature review (2011 onwards) of three major types of anomalies: network anomalies, sensor anomalies, and video-based anomalies, along with their methods and software… 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 >

Benyu Wang^*, Ke Chen, Bingjian Wang^#,*

Structural Durability & Health Monitoring, Vol.20, No.1, 2026, DOI:10.32604/sdhm.2025.070137 - 08 January 2026

Abstract To examine stress redistribution phenomena in bridges subjected to varying operational conditions, this study conducts a comprehensive analysis of three years of monitoring data from a 153-m double-deck road–rail steel arch bridge. An initial statistical comparison of sensor data distributions reveals clear temporal variations in stress redistribution patterns. XGBoost (eXtreme Gradient Boosting), a gradient-boosting machine learning (ML) algorithm, was employed not only for predictive modeling but also to uncover the underlying mechanisms of stress evolution. Unlike traditional numerical models that rely on extensive assumptions and idealizations, XGBoost effectively captures nonlinear and time-varying relationships between stress… More >

Rashid Jahangir^1,*, Nazik Alturki², Muhammad Asif Nauman³, Faiqa Hanif¹

CMC-Computers, Materials & Continua, Vol.86, No.2, pp. 1-20, 2026, DOI:10.32604/cmc.2025.071574 - 09 December 2025

Abstract Human activity recognition (HAR) is a method to predict human activities from sensor signals using machine learning (ML) techniques. HAR systems have several applications in various domains, including medicine, surveillance, behavioral monitoring, and posture analysis. Extraction of suitable information from sensor data is an important part of the HAR process to recognize activities accurately. Several research studies on HAR have utilized Mel frequency cepstral coefficients (MFCCs) because of their effectiveness in capturing the periodic pattern of sensor signals. However, existing MFCC-based approaches often fail to capture sufficient temporal variability, which limits their ability to distinguish… More >

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 >

R. Rajeev^a,b,*, C. M. S. Negi^a

Chalcogenide Letters, Vol.22, No.5, pp. 469-480, 2025, DOI:10.15251/CL.2025.225.469

Abstract Cobalt-doped cadmium sulphide nanoparticles of semiconductors (CDs: Co NPs) were synthesised using various cobalt concentrations utilising a microwave-assisted approach. Debye-Scherer equation revealed the nanoparticles' size range to be between 2 and 4 nm. Diffraction from X-rays revealed a zinc mix structure. According to the structure in the optical bandgap energies indicates that, doping has systematically raised the bandgap energy as the doping concentration raises. The composition of the nanoparticles which was verified by EDAX, validated the effective integration of cobalt into the CdS structure. The detection of different functional and vibrational groups was performed at More >