MD. MAHMUDUL HASAN^1,2, SHAH MD. WASIN^2,3, MISHU RAHMAN^2,4, EVA AZME^1,2, MD. SAQLINE MOSTAQ⁵, MD. MAHEDI HASAN NAHID⁶, NOR MOHAMMAD⁷, FARJANA AFRIN TANJUM⁸, MD. ANAMUL HAQUE⁵, MD ASHIQ MAHMUD^5,*, MOHAMMAD NURUL AMIN^5,*

Oncology Research, DOI:10.32604/or.2025.062778

Abstract Background: Breast cancer still stands to be the foremost contributor to cancer-related incidence and mortality in women globally accounting for about 14% of all female cancer-related deaths worldwide. This research seeks to illustrate the mechanisms and clinical findings of natural products against breast cancer treatment. Methodology: Required data for this review article was retrieved employing several readily obtainable search databases, including Web of Science^® (Thomson Reuters, USA), PubMed^® (U.S. National Library of Medicine, USA), and SciVerse Scopus^® (Elsevier Properties S.A., USA), taking into consideration certain search terms like “breast cancer,” “natural products against breast cancer,” and “Clinically… More > Graphic Abstract

HYEON JI KIM^1,#, BO KYUNG JOO^1,#, JIN-SEOK BYUN^2,3,*, DO-YEON KIM^1,3,*

Oncology Research, DOI:10.32604/or.2025.062207

Abstract Head and neck squamous cell carcinoma (HNSCC) is a highly aggressive and devastating disease arising primarily from the mucosal epithelium of the oral cavity, pharynx, and larynx. HNSCC ranks as the sixth most common cancer worldwide, carrying significant morbidity and mortality. HPV-positive HNSCC can be partially prevented with the FDA-approved HPV vaccine and generally exhibits a more favorable prognosis compared to HPV-negative cases. However, effective screening and treatment approaches remain elusive for HPV-negative HNSCC. While precancerous lesions may precede invasive cancer in certain situations, most patients present with advanced disease without prior indication of precancerous More >

Qiulan Gu^1,2, Mei Zhao^1,2,*

International Journal of Mental Health Promotion, DOI:10.32604/ijmhp.2025.062796

Abstract Objectives: With the widespread adoption of smartphones, parental phubbing behaviors have become increasingly prevalent, potentially affecting preschool children’s development. Current research primarily focuses on adolescent populations, while the mechanisms through which parental phubbing and authoritarian parenting style influence preschool children’s behavioral problems within the Chinese cultural context remain to be explored. Our investigation seeks to examine the factors contributing to behavioral difficulties among children of preschool age and provide theoretical guidance for prevention. Methods: In our research, we utilized a convenience sampling approach to collect data from parents whose children (n = 612) were between… More >

Shahina Akter^1,2, Muhammad Amer Qureshi³, Mohammad Ferdows^1,*

Frontiers in Heat and Mass Transfer, DOI:10.32604/fhmt.2025.062311

Abstract This study explores free convective heat transfer in an electrically conducting nanofluid flow over a moving semi-infinite flat plate under the influence of an induced magnetic field and viscous dissipation. The velocity and magnetic field vectors are aligned at a distance from the plate. The Spectral Relaxation Method (SRM) is used to numerically solve the coupled nonlinear partial differential equations, analyzing the effects of the Eckert number on heat and mass transfer. Various nanofluids containing , , , and nanoparticles are examined to assess how external magnetic fields influence fluid behavior. Key parameters, including the More >

Haichao Zhou^*, Wei Zhang, Tinghui Huang, Haoran Li

FDMP-Fluid Dynamics & Materials Processing, DOI:10.32604/fdmp.2025.064991

Abstract The configuration of a spoiler plays a crucial role in the aerodynamics of a vehicle. In particular, investigating the impact of spoiler design on aerodynamic performance is essential for effectively reducing drag and optimizing efficiency. This study focuses on the 35° Ahmed body as the test model and examines six different spoiler types mounted on its slant surface. Using the Lattice Boltzmann Method (LBM) in XFlow and the Large Eddy Simulation (LES) technique, the aerodynamic effects of these spoilers were analyzed. The numerical approach was validated against published experimental data. Results indicate that aerodynamic drag More >

David He^1,*, Miao He², Jay Yoon³

CMES-Computer Modeling in Engineering & Sciences, DOI:10.32604/cmes.2025.063975

Abstract Full ceramic bearings are mission-critical components in oil-free environments, such as food processing, semiconductor manufacturing, and medical applications. Developing effective fault diagnosis methods for these bearings is essential to ensuring operational reliability and preventing costly failures. Traditional supervised deep learning approaches have demonstrated promise in fault detection, but their dependence on large labeled datasets poses significant challenges in industrial settings where fault-labeled data is scarce. This paper introduces a few-shot learning approach for full ceramic bearing fault diagnosis by leveraging the pre-trained GPT-2 model. Large language models (LLMs) like GPT-2, pre-trained on diverse textual data,… More >

Dawei Xu^1,2,3, Yue Lv¹, Min Wang¹, Baokun Zheng^4,*, Jian Zhao^1,3, Jiaxuan Yu⁵

CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2025.064833

Abstract Network intrusion detection systems (IDS) are a prevalent method for safeguarding network traffic against attacks. However, existing IDS primarily depend on machine learning (ML) models, which are vulnerable to evasion through adversarial examples. In recent years, the Wasserstein Generative Adversarial Network (WGAN), based on Wasserstein distance, has been extensively utilized to generate adversarial examples. Nevertheless, several challenges persist: (1) WGAN experiences the mode collapse problem when generating multi-category network traffic data, leading to subpar quality and insufficient diversity in the generated data; (2) Due to unstable training processes, the authenticity of the data produced by… More >

Mingliang Deng¹, Zhao Zhang^1,*, Hongyan Zhou², Xuebo Chen²

CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2025.064509

Abstract To fully explore the potential features contained in power load data, an innovative short-term power load forecasting method that integrates data mining and deep learning techniques is proposed. Firstly, a density peak fast search algorithm optimized by time series weighting factors is used to cluster and analyze load data, accurately dividing subsets of data into different categories. Secondly, introducing convolutional block attention mechanism into the bidirectional gated recurrent unit (BiGRU) structure significantly enhances its ability to extract key features. On this basis, in order to make the model more accurately adapt to the dynamic changes… More >

Jinhai Wang¹, Junwei Xue¹, Hongyan Zhang², Hui Xiao^3,4, Huiling Wei^3,4, Mingyou Chen^3,4, Jiang Liao², Lufeng Luo^3,4,*

CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2025.064489

Abstract Defect detection based on computer vision is a critical component in ensuring the quality of industrial products. However, existing detection methods encounter several challenges in practical applications, including the scarcity of labeled samples, limited adaptability of pre-trained models, and the data heterogeneity in distributed environments. To address these issues, this research proposes an unsupervised defect detection method, FLAME (Federated Learning with Adaptive Multi-Model Embeddings). The method comprises three stages: (1) Feature learning stage: this work proposes FADE (Feature-Adaptive Domain-Specific Embeddings), a framework employs Gaussian noise injection to simulate defective patterns and implements a feature discriminator… More >

Rongwei Yu¹, Jie Yin^1,*, Jingyi Xiang¹, Qiyun Shao², Lina Wang¹

CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2025.064438

Abstract With the emergence of new attack techniques, traffic classifiers usually fail to maintain the expected performance in real-world network environments. In order to have sufficient generalizability to deal with unknown malicious samples, they require a large number of new samples for retraining. Considering the cost of data collection and labeling, data augmentation is an ideal solution. We propose an optimized noise-based traffic data augmentation system, ONTDAS. The system uses a gradient-based searching algorithm and an improved Bayesian optimizer to obtain optimized noise. The noise is injected into the original samples for data augmentation. Then, an More >