Sai Xu^1,*, Jun Liu^1,*, Shengyu Huang¹, Zhi Li²

CMC-Computers, Materials & Continua, Vol.86, No.3, 2026, DOI:10.32604/cmc.2025.071865 - 12 January 2026

Abstract In scenarios where ground-based cloud computing infrastructure is unavailable, unmanned aerial vehicles (UAVs) act as mobile edge computing (MEC) servers to provide on-demand computation services for ground terminals. To address the challenge of jointly optimizing task scheduling and UAV trajectory under limited resources and high mobility of UAVs, this paper presents PER-MATD3, a multi-agent deep reinforcement learning algorithm with prioritized experience replay (PER) into the Centralized Training with Decentralized Execution (CTDE) framework. Specifically, PER-MATD3 enables each agent to learn a decentralized policy using only local observations during execution, while leveraging a shared replay buffer with More >

Chao Li^1,3,#, Jianing Wang^1,3,#, Caichang Ding^2,*, Zhiwei Ye^1,3

CMC-Computers, Materials & Continua, Vol.86, No.3, 2026, DOI:10.32604/cmc.2025.071242 - 12 January 2026

Abstract Underwater images frequently suffer from chromatic distortion, blurred details, and low contrast, posing significant challenges for enhancement. This paper introduces AquaTree, a novel underwater image enhancement (UIE) method that reformulates the task as a Markov Decision Process (MDP) through the integration of Monte Carlo Tree Search (MCTS) and deep reinforcement learning (DRL). The framework employs an action space of 25 enhancement operators, strategically grouped for basic attribute adjustment, color component balance, correction, and deblurring. Exploration within MCTS is guided by a dual-branch convolutional network, enabling intelligent sequential operator selection. Our core contributions include: (1) a More >

Manaliben Amin^*

Journal on Artificial Intelligence, Vol.8, pp. 1-18, 2026, DOI:10.32604/jai.2026.073895 - 07 January 2026

Abstract Artificial intelligence (AI) is steadily making its way into pharmaceutical validation, where it promises faster documentation, smarter testing strategies, and better handling of deviations. These gains are attractive, but in a regulated environment speed is never enough. Regulators want assurance that every system is reliable, that decisions are explainable, and that human accountability remains central. This paper sets out a Human-in-the-Loop (HITL) AI approach for Computer System Validation (CSV) and Computer Software Assurance (CSA). It relies on explainable AI (XAI) tools but keeps structured human review in place, so automation can be used without creating… More >

Keiko Yanagihara^1,*, Masato Yoshida², Kensaku Awaji², Tamami Yamakawa¹, Sena Kato¹, Miki Tamura¹, Koji Nagata³

Oncology Research, Vol.34, No.1, 2026, DOI:10.32604/or.2025.073891 - 30 December 2025

Abstract Background: Cyclin-dependent kinase 4/6 (CDK4/6) inhibitors have transformed the management of hormone receptor–positive/HER2–negative (HR+/HER2–) advanced breast cancer, yet evidence for elderly or poor-performance patients remains limited. This study examined real-world outcomes of palbociclib plus endocrine therapy in Asian patients, with additional subgroup analyses by age and performance status. Methods: We retrospectively analyzed 46 consecutive Asian patients with recurrent or de novo HR+/HER2− breast cancer treated with first-line palbociclib plus ET between April 2021 and March 2025. The primary endpoint was progression-free survival (PFS). Secondary endpoints included overall response rate (ORR), disease control rate (DCR), and safety.… More >

Cheng-Hsun Chuang^1,2,3,#, Ping-Kun Tsai^3,4,5,6,#, Shih-Wen Kao^7,8, Yu-Hsun Wang^8,9,*, Chao-Bin Yeh^1,2,3,*

Oncology Research, Vol.34, No.1, 2026, DOI:10.32604/or.2025.072875 - 30 December 2025

Abstract Background: To determine whether initiating a glucagon-like peptide-1 receptor agonist (GLP-1 RA) within 3 months of type 2 diabetes (T2DM) diagnosis alters the subsequent risk of overall and site-specific cancer and whether this association differs by baseline body-mass index (BMI). Methods: This retrospective cohort study used electronic health records from the TriNetX U.S. research network. Adults aged 20 years or older diagnosed with T2DM between 2016 and 2024 were included if they received any hypoglycemic agents within 3 months before and after diagnosis. Following 1:1 propensity score matching, both the GLP-1 RA user and non-user… More > Graphic Abstract

K. Naga Venkata Siva¹, Damodhar Reddy², P. Krishna Murthy³, Kiran Kumar Pulamolu⁴, M. Dharani⁵, T. Venkatakrishnamoorthy^6,*

Energy Engineering, Vol.123, No.1, 2026, DOI:10.32604/ee.2025.072982 - 27 December 2025

Abstract Conventional multilevel inverters often suffer from high harmonic distortion and increased design complexity due to the need for numerous power semiconductor components, particularly at elevated voltage levels. Addressing these shortcomings, this work presents a robust 15-level Packed U Cell (PUC) inverter topology designed for renewable energy and grid-connected applications. The proposed system integrates a sensor less proportional-resonant (PR) controller with an advanced carrier-based pulse width modulation scheme. This approach efficiently balances capacitor voltage, minimizes steady-state error, and strongly suppresses both zero and third-order harmonics resulting in reduced total harmonic distortion and enhanced voltage regulation. Additionally, More >

Zeyu Chen¹, Jian Sun^2,*, Zhengda Huan¹, Ziyi Zhang¹

CMC-Computers, Materials & Continua, Vol.86, No.2, pp. 1-17, 2026, DOI:10.32604/cmc.2025.071182 - 09 December 2025

Abstract To address the issues of poor adaptability in resource allocation and low multi-agent cooperation efficiency in Joint Radar and Communication (JRC) systems under dynamic environments, an intelligent optimization framework integrating Deep Reinforcement Learning (DRL) and Graph Neural Network (GNN) is proposed. This framework models resource allocation as a Partially Observable Markov Game (POMG), designs a weighted reward function to balance radar and communication efficiencies, adopts the Multi-Agent Proximal Policy Optimization (MAPPO) framework, and integrates Graph Convolutional Networks (GCN) and Graph Sample and Aggregate (GraphSAGE) to optimize information interaction. Simulations show that, compared with traditional methods More >

Zhongyun Tang^1,2,3, Hanyi Xu², Haiyang Hu^1,3,*

CMC-Computers, Materials & Continua, Vol.86, No.2, pp. 1-29, 2026, DOI:10.32604/cmc.2025.070616 - 09 December 2025

Abstract With the deep integration of smart manufacturing and IoT technologies, higher demands are placed on the intelligence and real-time performance of industrial equipment fault detection. For industrial fans, base bolt loosening faults are difficult to identify through conventional spectrum analysis, and the extreme scarcity of fault data leads to limited training datasets, making traditional deep learning methods inaccurate in fault identification and incapable of detecting loosening severity. This paper employs Bayesian Learning by training on a small fault dataset collected from the actual operation of axial-flow fans in a factory to obtain posterior distribution. This More >

Raghavendra Kulkarni¹, Venkata Satya Suresh kumar Kondeti¹, Binu Sudhakaran Pillai², Surendran Rajendran^3,*

CMC-Computers, Materials & Continua, Vol.86, No.1, pp. 1-23, 2026, DOI:10.32604/cmc.2025.069835 - 10 November 2025

Abstract The next-generation RAN, known as Open Radio Access Network (ORAN), allows for several advantages, including cost-effectiveness, network flexibility, and interoperability. Now ORAN applications, utilising machine learning (ML) and artificial intelligence (AI) techniques, have become standard practice. The need for Federated Learning (FL) for ML model training in ORAN environments is heightened by the modularised structure of the ORAN architecture and the shortcomings of conventional ML techniques. However, the traditional plaintext model update sharing of FL in multi-BS contexts is susceptible to privacy violations such as deep-leakage gradient assaults and inference. Therefore, this research presents a… More >

Lincong Zhang¹, Yuqing Liu¹, Kefeng Wei², Weinan Zhao¹, Bo Qian^1,*

CMC-Computers, Materials & Continua, Vol.86, No.1, pp. 1-18, 2026, DOI:10.32604/cmc.2025.069108 - 10 November 2025

Abstract In the field of edge computing, achieving low-latency computational task offloading with limited resources is a critical research challenge, particularly in resource-constrained and latency-sensitive vehicular network environments where rapid response is mandatory for safety-critical applications. In scenarios where edge servers are sparsely deployed, the lack of coordination and information sharing often leads to load imbalance, thereby increasing system latency. Furthermore, in regions without edge server coverage, tasks must be processed locally, which further exacerbates latency issues. To address these challenges, we propose a novel and efficient Deep Reinforcement Learning (DRL)-based approach aimed at minimizing average… More >