Ashuai Du^1,#, Dianbin Guo^2,#, Dongbo Yuan^3,#, Kai Li⁴, Yuanyuan Luo⁴, Songsong Tan³, Xuchao Dai⁴, Bo Yu⁵, Wanxiang You⁶, Junjie Zhao⁷, Bo Yan³, Kehua Jiang^3,*, Xiaofei Fan^2,*, Jianguo Zhu^3,*,*

Oncology Research, Vol.34, No.5, 2026, DOI:10.32604/or.2026.076716 - 22 April 2026

Abstract Background: Bisphenol A (BPA) is a widely used industrial chemical and endocrine-disrupting compound, and accumulating evidence suggests that it may contribute to prostate cancer progression; however, the underlying molecular mechanisms remain incompletely elucidated. This study aimed to elucidate the molecular targets and signaling pathways underlying BPA-induced prostate cancer progression. Methods: In this study, an integrated strategy combining network toxicology, molecular docking, and molecular dynamics simulations was employed to identify potential BPA-related targets and signaling pathways involved in prostate cancer. Candidate targets were retrieved from public databases, followed by protein-protein interaction network analysis to screen key… More >

Abdin Abdin¹, Nicola Bianchi^1,*, Andrea Voltan², Walter Faedo², Piero Cazzavillan², Alessandro Biason²

Energy Engineering, Vol.123, No.3, 2026, DOI:10.32604/ee.2025.073434 - 27 February 2026

Abstract Synchronous reluctance motors (SynRM) are widely employed in industrial applications due to their high robustness, low cost, and absence of permanent magnets. In recent years, significant research efforts have focused on improving the controllability and efficiency of SynRM. Accurate rotor position information is essential for the controller to generate appropriate current and voltage references corresponding to the desired speed and load torque. Shaft-mounted position sensors are generally undesirable because of their high cost, sensitivity to harsh operating conditions, maintenance requirements, and reduced reliability in environments characterized by high vibration. Consequently, sensorless control techniques that estimate… 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 >

Yewei Xiao, Xin Du^*, Wei Zeng

CMC-Computers, Materials & Continua, Vol.86, No.3, 2026, DOI:10.32604/cmc.2025.072145 - 12 January 2026

Abstract Audio-visual speech recognition (AVSR), which integrates audio and visual modalities to improve recognition performance and robustness in noisy or adverse acoustic conditions, has attracted significant research interest. However, Conformer-based architectures remain computational expensive due to the quadratic increase in the spatial and temporal complexity of their softmax-based attention mechanisms with sequence length. In addition, Conformer-based architectures may not provide sufficient flexibility for modeling local dependencies at different granularities. To mitigate these limitations, this study introduces a novel AVSR framework based on a ReLU-based Sparse and Grouped Conformer (RSG-Conformer) architecture. Specifically, we propose a Global-enhanced Sparse… More >

Ihsan Qadir¹, Usama Naeem², Ahmed Nouman³, Aamir Raza⁴, Jun Wu^1,*

Revue Internationale de Géomatique, Vol.34, pp. 831-853, 2025, DOI:10.32604/rig.2025.069020 - 10 November 2025

Abstract The Jhelum River Basin in Pakistan has experienced recurrent and severe flooding over the past several decades, leading to substantial economic losses, infrastructure damage, and socio-environmental disruptions. This study uses multi-temporal satellite remote sensing data with historical hydrological records to map the spatial and temporal dynamics of major flood events occurring between 1988 and 2019. By utilizing satellite imagery from Landsat 5, Landsat 8, and Sentinel-2, key flood events were analyzed through the application of water indices such as the Normalized Difference Water Index (NDWI) and the Modified NDWI (MNDWI) to delineate flood extents. Historical… More >

Hussain Dawood¹, Marriam Nawaz², Tahira Nazir³, Ali Javed², Abdul Khader Jilani Saudagar^4,*, Hatoon S. AlSagri⁴

CMES-Computer Modeling in Engineering & Sciences, Vol.144, No.1, pp. 429-459, 2025, DOI:10.32604/cmes.2025.066415 - 31 July 2025

Abstract Reliable human action recognition (HAR) in video sequences is critical for a wide range of applications, such as security surveillance, healthcare monitoring, and human-computer interaction. Several automated systems have been designed for this purpose; however, existing methods often struggle to effectively integrate spatial and temporal information from input samples such as 2-stream networks or 3D convolutional neural networks (CNNs), which limits their accuracy in discriminating numerous human actions. Therefore, this study introduces a novel deep-learning framework called the ARNet, designed for robust HAR. ARNet consists of two main modules, namely, a refined InceptionResNet-V2-based CNN and… More >

Zongqi Li¹, Hongwei Zhao^2,*, Jianyong Guo²

CMES-Computer Modeling in Engineering & Sciences, Vol.144, No.1, pp. 345-357, 2025, DOI:10.32604/cmes.2025.066175 - 31 July 2025

Abstract Monitoring the condition of road infrastructure is crucial for maintaining its structural integrity and ensuring safe transportation. This study proposes a deep learning framework based on Temporal Convolutional Networks (TCN) integrated with Adaptive Parametric Rectified Linear Unit (APReLU) to predict future road subbase strain trends. Our model leverages time-series strain data collected from embedded triaxial sensors within a national highway, spanning August 2021 to June 2022, to forecast strain dynamics critical for proactive maintenance planning. The TCN-APReLU architecture combines dilated causal convolutions to capture long-term dependencies and APReLU activation functions to adaptively model nonlinear strain More >

Nawaf Waqas¹, Muhammad Islam^2,*, Muhammad Yahya³, Shabana Habib⁴, Mohammed Aloraini², Sheroz Khan⁵

CMC-Computers, Materials & Continua, Vol.84, No.2, pp. 3029-3051, 2025, DOI:10.32604/cmc.2025.064660 - 03 July 2025

Abstract Deep learning (DL), derived from the domain of Artificial Neural Networks (ANN), forms one of the most essential components of modern deep learning algorithms. DL segmentation models rely on layer-by-layer convolution-based feature representation, guided by forward and backward propagation. A critical aspect of this process is the selection of an appropriate activation function (AF) to ensure robust model learning. However, existing activation functions often fail to effectively address the vanishing gradient problem or are complicated by the need for manual parameter tuning. Most current research on activation function design focuses on classification tasks using natural… More >

Siji Chen¹, Gang Chen², Wei Wang², Han Liu¹, Mukun Ouyang¹, Wanhong Zhang¹, Lianghua Zhang¹, Wei Tang¹, Shilai Hu^2,*

Energy Engineering, Vol.122, No.7, pp. 2819-2843, 2025, DOI:10.32604/ee.2025.065481 - 27 June 2025

Abstract The construction and operation of sulfur-containing gas storage are often more difficult than a non-sulfur storage facility due to the need to prevent environmental contamination from H₂S leaks, as well as the corrosive effects of H₂S on production facilities. Rapid elutriation of H₂S from the reservoir during the construction of the gas storage is an effective way to avoid these problems. However, the existing H₂S elutriation method has low efficiency and high economic cost, which limits the development of reconstructed gas storage of sulfur-containing gas reservoirs. To improve the efficiency of H₂S elutriation in sulfur-containing gas reservoirs and… More >

Shaoming Qiu, Jingjie He, Yan Wang^*, Bicong E

CMC-Computers, Materials & Continua, Vol.83, No.3, pp. 4879-4898, 2025, DOI:10.32604/cmc.2025.061532 - 19 May 2025

Abstract Software defect prediction (SDP) aims to find a reliable method to predict defects in specific software projects and help software engineers allocate limited resources to release high-quality software products. Software defect prediction can be effectively performed using traditional features, but there are some redundant or irrelevant features in them (the presence or absence of this feature has little effect on the prediction results). These problems can be solved using feature selection. However, existing feature selection methods have shortcomings such as insignificant dimensionality reduction effect and low classification accuracy of the selected optimal feature subset. In… More >