Chiwan Ahn¹, Daehee Kim^2,*, Seongkeun Park^3,*

CMC-Computers, Materials & Continua, Vol.87, No.1, 2026, DOI:10.32604/cmc.2025.073330 - 10 February 2026

Abstract This paper proposes a deep learning-based 3D LiDAR perception framework designed for applications such as autonomous robots and vehicles. To address the high dependency on large-scale annotated data—an inherent limitation of deep learning models—this study introduces a hybrid perception architecture that incorporates expert-driven LiDAR processing techniques into the deep neural network. Traditional 3D LiDAR processing methods typically remove ground planes and apply distance- or density-based clustering for object detection. In this work, such expert knowledge is encoded as feature-level inputs and fused with the deep network, thereby mitigating the data dependency issue of conventional learning-based… More >

Nataliya Kazantseva^1,2,*, Nikolai Saharov¹, Denis Davydov^1,2, Nikolai Popov², Maxim Il’inikh¹

CMC-Computers, Materials & Continua, Vol.87, No.1, 2026, DOI:10.32604/cmc.2026.073078 - 10 February 2026

Abstract Because of the developed surface of the Triply Periodic Minimum Surface (TPMS) structures, polylactide (PLA) products with a TPMS structure are thought to be promising bio soluble implants with the potential for targeted drug delivery. For implants, mechanical properties are key performance characteristics, so understanding the deformation and failure mechanisms is essential for selecting the appropriate implant structure. The deformation and fracture processes in PLA samples with different interior architectures have been studied through computer simulation and experimental research. Two TPMS topologies, the Schwarz Diamond and Gyroid architectures, were used for the sample construction by… More >

Zeyu Wang¹, Jian-Hong Wang^1,*, Kuo-Chun Hsu^2,*

CMC-Computers, Materials & Continua, Vol.87, No.1, 2026, DOI:10.32604/cmc.2025.072881 - 10 February 2026

Abstract In daily life, keyword spotting plays an important role in human-computer interaction. However, noise often interferes with the extraction of time-frequency information, and achieving both computational efficiency and recognition accuracy on resource-constrained devices such as mobile terminals remains a major challenge. To address this, we propose a novel time-frequency dual-branch parallel residual network, which integrates a Dual-Branch Broadcast Residual module and a Time-Frequency Coordinate Attention module. The time-domain and frequency-domain branches are designed in parallel to independently extract temporal and spectral features, effectively avoiding the potential information loss caused by serial stacking, while enhancing information… More >

Zujun Ding, Shuaichao Wu, Chenliang Ji, Xinyu Feng, Yuanyuan Shi, Baolian Liu, Wan Chen, Qiuchan Bai, Hengrui Zhou, Hui Huang, Jie Ji^*

Energy Engineering, Vol.123, No.2, 2026, DOI:10.32604/ee.2025.068106 - 27 January 2026

Abstract In the quest to enhance energy efficiency and reduce environmental impact in the transportation sector, the recovery of waste heat from diesel engines has become a critical area of focus. This study provided an exhaustive thermodynamic analysis optimizing Organic Rankine Cycle (ORC) systems for waste heat recovery from diesel engines. The study assessed the performance of five candidate working fluids—R11, R123, R113, R245fa, and R141b—under a range of operating conditions, specifically varying overheat temperatures and evaporation pressures. The results indicated that the choice of working fluid substantially influences the system’s exergetic efficiency, net output power,… More >

Sha Hu^1,2,#, Wenjing Wang^1,#, Qianfang Hu^3,#, Rujuan Zheng^1,2, Qinghe Huang^1,2, Hui Shi^1,2, Xinyuan Ding^3,*, Wenjuan Wang^1,2,*, Zengyan Zhu^1,2,*

Oncology Research, Vol.34, No.2, 2026, DOI:10.32604/or.2025.068967 - 19 January 2026

Abstract Objectives: Acquired resistance to paclitaxel represents a critical barrier to the effective chemotherapy of non-small cell lung cancer (NSCLC). The present study aimed to elucidate the molecular and pharmacological mechanisms promoting paclitaxel resistance in NSCLC and to explore potential strategies for overcoming this resistance. Methods: Here, we report an integrated pharmacological and analytical approach to quantify paclitaxel disposition and overcome resistance in a A549/TAX cell model (paclitaxel-resistant A549 cells). Results: Cell counting kit-8 (CCK-8) assay, colony formation, and apoptosis assays confirmed that A549/TAX cells exhibited marked resistance to paclitaxel relative to parental A549 cells. Based on… More >

Ashna Abduljabbar Haji^*, Ranj S. Abdullah^*

Energy Engineering, Vol.122, No.12, pp. 5111-5127, 2025, DOI:10.32604/ee.2025.073367 - 27 November 2025

Abstract The hybrid photovoltaic solar-assisted heat pump are primarily used to generate electricity and provide thermal energy for heating applications. This study investigates the performance enhancement of a hybrid Photovoltaic Thermal Solar-Assisted Heat Pump (PV/T-SAHP) system integrated with a solar tracking mechanism. The system was simulated using TRNSYS to evaluate its monthly electrical output and coefficient of performance (COP) of the heat pump system over a year. The results showed a significant improvement in energy generation and efficiency compared to a conventional PV/T system without SAHP system. Overall, the solar tracking configuration of the PV/T-SAHP generated… More > Graphic Abstract

Su Jin Shin¹, Sang Uk Shin^2,*

CMES-Computer Modeling in Engineering & Sciences, Vol.145, No.2, pp. 2787-2819, 2025, DOI:10.32604/cmes.2025.072602 - 26 November 2025

Abstract To reconstruct vehicle accidents, data from the time of the incident—such as pre-collision speed and collision point—is essential. This data is collected and generated through various sensors installed in the vehicle. However, it may contain sensitive information about the vehicle owner. Consequently, vehicle owners tend to be reluctant to provide their vehicle data due to concerns about personal information exposure. Therefore, extensive research has been conducted on secure vehicle data trading models. Existing models primarily utilize centralized approaches, leading to issues such as single points of failure, data leakage, and manipulation. To address these problems,… More >

Xuekun Shao^1,#, Cheng Wang^2,#,*, Mengru Zhang², Yi Wang², Zhuoya Qiu¹, Wen Cai¹, Ruiliang Zhu³, Ping Wang^2,*

BIOCELL, Vol.49, No.11, pp. 2181-2194, 2025, DOI:10.32604/biocell.2025.070859 - 24 November 2025

Abstract Objectives: PI3K plays a pivotal role in the inflammatory response by modulating the production and release of inflammatory factors. Pik3cb is one of the subunits of PI3K, and its specific role in myocardium inflammation remains unelucidated. This study aimed to investigate the role of Pik3cb in the inflammatory response and to elucidate the underlying mechanism. Methods: An inflammation model was established using H9c2 cells treated with LPS and ATP, and Pik3cb expression was evaluated in this model system. Subsequently, an overexpression model was constructed by transfecting cells with a Pik3cb overexpression plasmid, after which the… More > Graphic Abstract

Palak Bari, Gurnur Bedi, Khushi Joshi, Anupama Jawale^*

Journal on Artificial Intelligence, Vol.7, pp. 499-508, 2025, DOI:10.32604/jai.2025.072531 - 17 November 2025

Abstract This study investigates sarcasm detection in text using a dataset of 8095 sentences compiled from MUStARD and HuggingFace repositories, balanced across sarcastic and non-sarcastic classes. A sequential baseline model (LSTM) is compared with transformer-based models (RoBERTa and XLNet), integrated with attention mechanisms. Transformers were chosen for their proven ability to capture long-range contextual dependencies, whereas LSTM serves as a traditional benchmark for sequential modeling. Experimental results show that RoBERTa achieves 0.87 accuracy, XLNet 0.83, and LSTM 0.52. These findings confirm that transformer architectures significantly outperform recurrent models in sarcasm detection. Future work will incorporate multimodal More >

Xinrui Ma, Shizhi Chen^*

Structural Durability & Health Monitoring, Vol.19, No.6, pp. 1745-1767, 2025, DOI:10.32604/sdhm.2025.071148 - 17 November 2025

Abstract The rapid and accurate assessment of structural damage following an earthquake is crucial for effective emergency response and post-disaster recovery. Traditional manual inspection methods are often slow, labor-intensive, and prone to human error. To address these challenges, this study proposes STPEIC (Swin Transformer-based Framework for Interpretable Post-Earthquake Structural Classification), an automated deep learning framework designed for analyzing post-earthquake images. STPEIC performs two key tasks: structural components classification and damage level classification. By leveraging the hierarchical attention mechanisms of the Swin Transformer (Shifted Window Transformer), the model achieves 85.4% accuracy in structural component classification and 85.1% More >