CHENG CHENG^1,2,3, CHAO SHI^1,2, SHANG WU^1,2, WEIXING WU³, JINGPING LI^1,2, SINUO GAO^1,2, MENG HAN³, YIMIN WANG³, XIANGMEI ZHANG^2,4,*, YUNJIANG LIU^1,2,*

Oncology Research, DOI:10.32604/or.2025.061637

Abstract Objectives: While programmed cell death 1 (PD-1) inhibitors have improved cancer treatment, the function and mechanisms of programmed cell death ligand 1 (PD-L1), particularly when expressed by cancer cells, remain unclear. This study aims to explore the role of PD-L1 within breast cancer cells and identify key targets for future immunotherapy. Methods: RNA-seq was performed on breast cancer cells with silenced PD-L1 to screen for differentially expressed genes, followed by bioinformatics analysis. Clinical specimens from breast cancer patients undergoing primary surgery without preoperative treatment were collected, along with in vitro analysis to validate the potential mechanism. Results:… More >

Duc Hoa Pham, Bijender Kumar, Samia Adil, Jaehwan Kim^*

Journal of Renewable Materials, DOI:10.32604/jrm.2025.02025-0040

Abstract Increasing bio-based content in composites without compromising performance is an urgent duty toward sustainability and carbon neutralization. This paper reports high bio-content thermoset resin and cellulose long filament (CLF)-reinforced green composite by using a semi-bio imine-based hardener. The hardener (VDDM) is synthesized from DDM grafted with vanillin, and a bio-based vanillyl alcohol epoxy (VAE) monomer is used to prepare highly bio-based thermoset resin. The VAE-VDDM thermoset shows high tensile strength and Young’s modulus up to 94.8 MPa and 2.69 GPa, respectively. Further, a natural fiber-reinforced green composite is made with CLF and the prepared VAE-VDDM More >

Kun Li¹, Tianfu Li^2,*, Xiuwei Wang¹, Qingchun Meng¹, Zhenjie Wang¹, Jinyang Luo^1,2, Zhaohui Wang¹, Yuedong Yao²

Energy Engineering, DOI:10.32604/ee.2025.062876

Abstract CO₂ flooding for enhanced oil recovery (EOR) not only enables underground carbon storage but also plays a critical role in tertiary oil recovery. However, its displacement efficiency is constrained by whether CO₂ and crude oil achieve miscibility, necessitating precise prediction of the minimum miscibility pressure (MMP) for CO₂-oil systems. Traditional methods, such as experimental measurements and empirical correlations, face challenges including time-consuming procedures and limited applicability. In contrast, artificial intelligence (AI) algorithms have emerged as superior alternatives due to their efficiency, broad applicability, and high prediction accuracy. This study employs four AI algorithms—Random Forest Regression (RFR), Genetic… More >

Dongfeng Yang, Ruirui Zhang, Chuang Liu^*, Guoliang Bian

Energy Engineering, DOI:10.32604/ee.2025.062154

Abstract Under the current long-term electricity market mechanism, new energy and thermal power face issues such as deviation assessment and compression of generation space. The profitability of market players is limited. Simultaneously, the cooperation model among various energy sources will have a direct impact on the alliance’s revenue and the equity of income distribution within the alliance. Therefore, integrating new energy with thermal power units into an integrated multi-energy complementary system to participate in the long-term electricity market holds significant potential. To simulate and evaluate the benefits and internal distribution methods of a multi-energy complementary system… More >

Peiying Zhang^1,2,*, Yihong Yu^1,2, Jing Liu³, Chong Lv^1,2, Lizhuang Tan^4,5, Yulin Zhang^6,7,8

CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2025.064654

Abstract As Internet of Things (IoT) technologies continue to evolve at an unprecedented pace, intelligent big data control and information systems have become critical enablers for organizational digital transformation, facilitating data-driven decision making, fostering innovation ecosystems, and maintaining operational stability. In this study, we propose an advanced deployment algorithm for Service Function Chaining (SFC) that leverages an enhanced Practical Byzantine Fault Tolerance (PBFT) mechanism. The main goal is to tackle the issues of security and resource efficiency in SFC implementation across diverse network settings. By integrating blockchain technology and Deep Reinforcement Learning (DRL), our algorithm not… More >

Van-Ha Hoang¹, Jong Weon Lee¹, Chun-Su Park^2,*

CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2025.063468

Abstract Fire can cause significant damage to the environment, economy, and human lives. If fire can be detected early, the damage can be minimized. Advances in technology, particularly in computer vision powered by deep learning, have enabled automated fire detection in images and videos. Several deep learning models have been developed for object detection, including applications in fire and smoke detection. This study focuses on optimizing the training hyperparameters of YOLOv8 and YOLOv10 models using Bayesian Tuning (BT). Experimental results on the large-scale D-Fire dataset demonstrate that this approach enhances detection performance. Specifically, the proposed approach… More >

Mingen Zhong¹, Kaibo Yang^1,*, Ziji Xiao¹, Jiawei Tan², Kang Fan², Zhiying Deng¹, Mengli Zhou¹

CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2025.063383

Abstract With the rapid urbanization and exponential population growth in China, two-wheeled vehicles have become a popular mode of transportation, particularly for short-distance travel. However, due to a lack of safety awareness, traffic violations by two-wheeled vehicle riders have become a widespread concern, contributing to urban traffic risks. Currently, significant human and material resources are being allocated to monitor and intercept non-compliant riders to ensure safe driving behavior. To enhance the safety, efficiency, and cost-effectiveness of traffic monitoring, automated detection systems based on image processing algorithms can be employed to identify traffic violations from eye-level video… More >

Junfeng Lin¹, Jialin Ma^1,*, Wei Chen^1,2, Hao Wang¹, Weiguo Ding¹, Mingyao Tang¹

CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2025.062895

Abstract The cross-modal person re-identification task aims to match visible and infrared images of the same individual. The main challenges in this field arise from significant modality differences between individuals and the lack of high-quality cross-modal correspondence methods. Existing approaches often attempt to establish modality correspondence by extracting shared features across different modalities. However, these methods tend to focus on local information extraction and fail to fully leverage the global identity information in the cross-modal features, resulting in limited correspondence accuracy and suboptimal matching performance. To address this issue, we propose a quadratic graph matching method… More >

Ronglei Hu¹, Chuce He^1,2, Sihui Liu¹, Dong Yao¹, Xiuying Li¹, Xiaoyi Duan^1,*

CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2025.062542

Abstract Ciphertext data retrieval in cloud databases suffers from some critical limitations, such as inadequate security measures, disorganized key management practices, and insufficient retrieval access control capabilities. To address these problems, this paper proposes an enhanced Fully Homomorphic Encryption (FHE) algorithm based on an improved DGHV algorithm, coupled with an optimized ciphertext retrieval scheme. Our specific contributions are outlined as follows: First, we employ an authorization code to verify the user’s retrieval authority and perform hierarchical access control on cloud storage data. Second, a triple-key encryption mechanism, which separates the data encryption key, retrieval authorization key, More >

Louyi Jiang^1,#, Sulei Wang^1,#, Jiang Xie¹, Haiya Wang², Wei Shao^3,*

CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2025.061759

Abstract Carotid artery plaques represent a major contributor to the morbidity and mortality associated with cerebrovascular disease, and their clinical significance is largely determined by the risk linked to plaque vulnerability. Therefore, classifying plaque risk constitutes one of the most critical tasks in the clinical management of this condition. While classification models derived from individual medical centers have been extensively investigated, these single-center models often fail to generalize well to multi-center data due to variations in ultrasound images caused by differences in physician expertise and equipment. To address this limitation, a Dual-Classifier Label Correction Network model… More >