Sathish Rao Udupi, Gururaj Bolar, Manjunath Shettar^*, Ashwini Bhat

CMC-Computers, Materials & Continua, Vol.87, No.3, 2026, DOI:10.32604/cmc.2026.078574 - 09 April 2026

Abstract Glass fiber-reinforced polymer composites (GFRPCs) are extensively utilized in the aerospace, automotive, and structural sectors; nevertheless, their heterogeneous and abrasive characteristics result in rapid tool wear during drilling. Drill flank wear among various wear mechanisms notably influences hole quality and dimensional accuracy. This research investigates the impact of spindle speed, feed rate, and drill diameter on flank wear during dry drilling of GFRPC laminates with high-speed steel (HSS) twist drills. A full-factorial design with 81 experiments is used to create a comprehensive dataset. ANOVA indicates that spindle speed is the dominant factor affecting wear changes,… More >

Sihan Wang¹, Luyao Liu², Xingjun Wang^3,*, Yifan Liu^3,*

CMC-Computers, Materials & Continua, Vol.87, No.3, 2026, DOI:10.32604/cmc.2026.077791 - 09 April 2026

Abstract This paper presents a novel dual-stage approach for efficient gait phase recognition and trajectory prediction, tailored for the operation of wearable devices such as exoskeletons. By leveraging dynamic template matching techniques and addressing their computational challenges, we propose an innovative algorithm that significantly enhances both prediction accuracy and computational efficiency. The approach integrates Dynamic Time Warping-KMeans (DTW-KM) template selection in the offline phase and a Soft Constraint Weighted (SCW) template matching technique in the online phase. In the offline stage, the DTW-KM method extracts diverse and generalizable gait patterns from a database, establishing a robust More >

Hye-Min Kwon, Sung-Jun Lee, Chang-Lae Kim^*

CMC-Computers, Materials & Continua, Vol.87, No.3, 2026, DOI:10.32604/cmc.2026.077155 - 09 April 2026

Abstract UV-curable acrylic polymers are promising for advanced coating applications; however, they suffer from low mechanical strength and wear resistance. This study investigated the effects of zinc oxide (ZnO) nanoparticle incorporation (0, 1, 3, and 5 wt.%) on mechanical, surface, and tribological properties of UV-curable acrylic polymer nanocomposites. The elastic modulus increased from 9.41 MPa (bare polymer) to 22.39 MPa (5 wt.% ZnO), a 138% improvement. X-ray diffraction (XRD) analysis confirmed the formation of a crystalline region at the polymer-ZnO interface, with crystallite sizes reaching 121.94 nm compared to 7.95 nm for the bare-polymer. Surface roughness More >

Chan-Woo Kim^#, Sung-Jun Lee^#, Chang-Lae Kim^*

CMC-Computers, Materials & Continua, Vol.87, No.3, 2026, DOI:10.32604/cmc.2026.077143 - 09 April 2026

Abstract Epoxy resins are widely used as protective coatings due to their excellent adhesion and chemical resistance; however, their inherent brittleness and susceptibility to shear stress-induced crack propagation limit their tribological performance. This study investigates the stress distribution mechanisms governing the wear resistance of solvent-textured epoxy coatings using finite element analysis (FEA) and experimental validation. Three solvents with distinct volatilities—acetone, methyl ethyl ketone (MEK), and ethyl acetate (EA)—generated characteristic surface morphologies through Marangoni convection, with roughness ranging from Ra = 0.17 μm (EA) to 0.66 μm (acetone). X-ray diffraction (XRD) and Fourier-transform infrared (FT-IR) spectroscopy confirmed… More >

Wei Kang^1,*, Juan Jin²

Chalcogenide Letters, Vol.23, No.3, 2026, DOI:10.32604/cl.2026.077859 - 03 April 2026

Abstract A sulfate–chloride electrolyte was used to deposit alloy layers on AISI 1018 coupons while co-introducing ZnS nanoparticles (5–20 g/L) to tune microstructure and durability under combined saline exposure and dry sliding. Cross-sectional SEM confirmed continuous deposits of ~24–26 μm, and XRD indicated γ-phase dominance with pronounced grain refinement: crystallite size decreased from 42 ± 3 nm (particle-free) to 28 ± 2 nm at 15 g/L. In 3.5 wt% NaCl, polarization data showed a progressive ennoblement of E_corr from −1.032 to −0.981 V (vs. SCE) together with a reduction of i_corr from 8.47 to 1.82 μA/cm² at 15… 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 >

Luca Giammichele^*, Valerio D’Alessandro, Matteo Falone, Renato Ricci

Frontiers in Heat and Mass Transfer, Vol.24, No.1, 2026, DOI:10.32604/fhmt.2026.072535 - 28 February 2026

Abstract The airborne diffusion of saliva droplets during respiratory activities is one of the major factors in the spread of infections. During the COVID-19 pandemic, the use of protective face masks was essential to reduce the risk of infection and spread of SARS-CoV-2. The face mask is able to significantly reduce the saliva droplet emission in front of the person. However, the use of masks also produces a particle leakage towards the back of the person, which could increase the infection risk of people behind the subject. Most of the experimental investigations applied invasive and/or complex… 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 >

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 >

Du-Yi Wang¹, Shih-Chen Shi^1,*, Dieter Rahmadiawan^1,2

Journal of Polymer Materials, Vol.42, No.4, pp. 1075-1095, 2025, DOI:10.32604/jpm.2025.072263 - 26 December 2025

Abstract Polymethyl methacrylate (PMMA) is widely used in diverse applications such as protective components (e.g., automotive lamp covers and structural casings), optical devices, and biomedical products, owing to its lightweight nature and impact resistance. However, its surface hardness and wear resistance remain insufficient under prolonged exposure to abrasive environments. In this study, a multi-filler strategy with nano-silica (SiO₂), brominated lignin (Br-Lignin), and cellulose nanocrystals (CNCs) was employed to enhance PMMA tribological properties. SiO₂ provided localized reinforcement, Br-Lignin established stable network structures, and CNCs improved compactness, enabling strong synergistic effects. As a result, the composites achieved up to More >