Se Ha Jang^1,2,#, Hyung Seok Kim^3,#, Jung Woo Eun^1,*

Oncology Research, Vol.34, No.1, 2026, DOI:10.32604/or.2025.072240 - 30 December 2025

Abstract Hepatocellular carcinoma (HCC) remains one of the most prevalent and lethal malignancies worldwide. Long non-coding RNAs (lncRNAs) have emerged as crucial regulators of gene expression and cancer progression, yet the functional diversity of RP11-derived lncRNAs—originally mapped to bacterial artificial chromosome (BAC) clones from the Roswell Park Cancer Institute—has only recently begun to be appreciated. This mini-review aims to systematically synthesize current findings on RP11-derived lncRNAs in HCC, outlining their genomic origins, molecular mechanisms, and biological significance. We highlight their roles in metabolic reprogramming, microRNA network modulation, and tumor progression, as well as their diagnostic and More >

Jianfa Wu^1,2,3, Huang Chen³, Sihong Wang^1,2, Lei Peng^1,2, Xiaoying Hu^1,2, Zhou Liu^1,2,*

Oncology Research, Vol.34, No.1, 2026, DOI:10.32604/or.2025.070542 - 30 December 2025

Abstract Objectives: Ribosomal protein S6 kinase A2 (RPS6KA2) has been identified as a potential prognostic biomarker in several cancers, including breast cancer, glioblastoma, and prostate cancer. However, its functional significance in ovarian cancer is not well characterized. This study was designed to explore the therapeutic relevance of modulating RPS6KA2 in the context of ovarian cancer, particularly in relation to cisplatin resistance. Methods: The expression levels of RPS6KA2 and key regulators involved in autophagy and ferroptosis were assessed using quantitative reverse transcription-PCR, immunofluorescence staining, immunohistochemistry, and western blotting. Prognostic associations were conducted using the Kaplan-Meier Plotter database.… More >

Heping Qi, Wenyao Sun^*, Yi Zhao, Xiaoyi Qian, Xingyu Jiang

Energy Engineering, Vol.123, No.1, 2026, DOI:10.32604/ee.2025.069603 - 27 December 2025

Abstract Virtual power plant (VPP) integrates a variety of distributed renewable energy and energy storage to participate in electricity market transactions, promote the consumption of renewable energy, and improve economic efficiency. In this paper, aiming at the uncertainty of distributed wind power and photovoltaic output, considering the coupling relationship between power, carbon trading, and green card market, the optimal operation model and bidding scheme of VPP in spot market, carbon trading market, and green card market are established. On this basis, through the Shapley value and independent risk contribution theory in cooperative game theory, the quantitative… More > Graphic Abstract

Changcheng Li^1,2, Weimeng Chang^1,2, Dahai Zhang^1,*, Jinghan He¹

Energy Engineering, Vol.123, No.1, 2026, DOI:10.32604/ee.2025.069389 - 27 December 2025

Abstract Effective partitioning is crucial for enabling parallel restoration of power systems after blackouts. This paper proposes a novel partitioning method based on deep reinforcement learning. First, the partitioning decision process is formulated as a Markov decision process (MDP) model to maximize the modularity. Corresponding key partitioning constraints on parallel restoration are considered. Second, based on the partitioning objective and constraints, the reward function of the partitioning MDP model is set by adopting a relative deviation normalization scheme to reduce mutual interference between the reward and penalty in the reward function. The soft bonus scaling mechanism… More >

Yong Li, Yuxuan Chen^*, Jiahui He, Guowei He, Chenxi Dai, Jingjing Tong, Wenting Lei

Energy Engineering, Vol.123, No.1, 2026, DOI:10.32604/ee.2025.069257 - 27 December 2025

Abstract Ensuring reliable power supply in urban distribution networks is a complex and critical task. To address the increased demand during extreme scenarios, this paper proposes an optimal dispatch strategy that considers the coordination with virtual power plants (VPPs). The proposed strategy improves system flexibility and responsiveness by optimizing the power adjustment of flexible resources. In the proposed strategy, the Gaussian Process Regression (GPR) is firstly employed to determine the adjustable range of aggregated power within the VPP, facilitating an assessment of its potential contribution to power supply support. Then, an optimal dispatch model based on More > Graphic Abstract

Bindi Saurabh Thakkar¹, Pradeep Kumar Karsh^2,*

CMC-Computers, Materials & Continua, Vol.86, No.2, pp. 1-22, 2026, DOI:10.32604/cmc.2025.072839 - 09 December 2025

Abstract This study investigates the uncertain dynamic characterization of hybrid composite plates by employing advanced machine-assisted finite element methodologies. Hybrid composites, widely used in aerospace, automotive, and structural applications, often face variability in material properties, geometric configurations, and manufacturing processes, leading to uncertainty in their dynamic response. To address this, three surrogate-based machine learning approaches like radial basis function (RBF), multivariate adaptive regression splines (MARS), and polynomial neural networks (PNN) are integrated with a finite element framework to efficiently capture the stochastic behavior of these plates. The research focuses on predicting the first three natural frequencies… More >

Shih-Lin Lin^*, Cheng-Wei Li

CMC-Computers, Materials & Continua, Vol.86, No.2, pp. 1-20, 2026, DOI:10.32604/cmc.2025.071915 - 09 December 2025

Abstract This study focuses on developing a deep learning model capable of recognizing vehicle brands and models, integrated with a law enforcement intelligence platform to overcome the limitations of existing license plate recognition techniques—particularly in handling counterfeit, obscured, or absent plates. The research first entailed collecting, annotating, and classifying images of various vehicle models, leveraging image processing and feature extraction methodologies to train the model on Microsoft Custom Vision. Experimental results indicate that, for most brands and models, the system achieves stable and relatively high performance in Precision, Recall, and Average Precision (AP). Furthermore, simulated tests… More >

Chang Su, Liangliang Zhao^*, Dongbing Xiang

CMC-Computers, Materials & Continua, Vol.86, No.2, pp. 1-24, 2026, DOI:10.32604/cmc.2025.071319 - 09 December 2025

Abstract To address low learning efficiency and inadequate path safety in spraying robot navigation within complex obstacle-rich environments—with dense, dynamic, unpredictable obstacles challenging conventional methods—this paper proposes a hybrid algorithm integrating Q-learning and improved A*-Artificial Potential Field (A-APF). Centered on the Q-learning framework, the algorithm leverages safety-oriented guidance generated by A-APF and employs a dynamic coordination mechanism that adaptively balances exploration and exploitation. The proposed system comprises four core modules: (1) an environment modeling module that constructs grid-based obstacle maps; (2) an A-APF module that combines heuristic search from A* algorithm with repulsive force strategies from… More >

Eman Alsalmi, Abeer Alhuzali^*, Areej Alhothali

CMC-Computers, Materials & Continua, Vol.86, No.2, pp. 1-20, 2026, DOI:10.32604/cmc.2025.071012 - 09 December 2025

Abstract Log anomaly detection is essential for maintaining the reliability and security of large-scale networked systems. Most traditional techniques rely on log parsing in the reprocessing stage and utilize handcrafted features that limit their adaptability across various systems. In this study, we propose a hybrid model, BertGCN, that integrates BERT-based contextual embedding with Graph Convolutional Networks (GCNs) to identify anomalies in raw system logs, thereby eliminating the need for log parsing. The BERT module captures semantic representations of log messages, while the GCN models the structural relationships among log entries through a text-based graph. This combination More >

Weiping Zeng¹, Xiangping Bryce Zhai^1,2,3,*, Cheng Sun¹, Liusha Jiang^1,2, Yicong Du³, Xuefeng Yan^1,3

CMC-Computers, Materials & Continua, Vol.86, No.2, pp. 1-15, 2026, DOI:10.32604/cmc.2025.070777 - 09 December 2025

Abstract With the expanding applications of unmanned aerial vehicles (UAVs), precise flight evaluation has emerged as a critical enabler for efficient path planning, directly impacting operational performance and safety. Traditional path planning algorithms typically combine Dubins curves with local optimization to minimize trajectory length under 3D spatial constraints. However, these methods often overlook the correlation between pilot control quality and UAV flight dynamics, limiting their adaptability in complex scenarios. In this paper, we propose an intelligent flight evaluation model specifically designed to enhance multi-waypoint trajectory optimization algorithms. Our model leverages a decision tree to integrate attitude More >