Jaeseung Lee¹, Jehyeok Rew^2,*

CMC-Computers, Materials & Continua, Vol.87, No.3, 2026, DOI:10.32604/cmc.2026.077691 - 09 April 2026

Abstract The accelerating transition toward electrified mobility has positioned electric vehicles (EVs) as a primary technology in modern transportation systems. In this context, ensuring the reliability of EV drive motors (EVDMs) becomes increasingly critical, given their central role in propulsion performance and operational safety. Accurate and interpretable fault diagnosis of EVDMs is therefore essential for enabling effective maintenance and supporting the broader sustainability and resilience of EVs. This study presents a novel framework that combines decision tree-based fault classification with a multi-agent large language model (LLM) interpretation architecture to deliver transparent and human-readable diagnostic explanations. The… More >

Taimoor Hassan¹, Ibrar Hussain^1,*, Hafiz Mahfooz Ul Haque², Hamid Turab Mirza³, Muhammad Nadeem Ali⁴, Byung-Seo Kim^4,*, Changheun Oh⁴

CMC-Computers, Materials & Continua, Vol.87, No.3, 2026, DOI:10.32604/cmc.2026.077510 - 09 April 2026

Abstract The expeditious proliferation of the smart computing paradigm has a remarkable upsurge towards Artificial Intelligence (AI) assistive reasoning with the incorporation of context-awareness. Context-awareness plays a significant role in fulfilling users’ needs whenever and wherever needed. Context-aware systems acquire contextual information from sensors/embedded sensors using smart gadgets and/or systems, perform reasoning using reinforcement learning (RL) or other reasoning techniques, and then adapt behavior. The core intention of using an RL-based reasoning strategy is to train agents to take the right actions at the right time and in the right place. Generally, agents are rewarded for… More >

Pratik Goswami^1,#, Hamid Naseem^2,#, Khizar Abbas^3,*, Kwonhue Choi^1,*

CMC-Computers, Materials & Continua, Vol.87, No.3, 2026, DOI:10.32604/cmc.2026.077012 - 09 April 2026

Abstract In a distributed edge computing environment, Internet of Things (IoT) and Vehicular-IoT (V-IoT) devices communicate through Wireless Sensor Networks (WSNs) by collecting and transmitting data from different environments. Although energy efficiency is always a critical challenge in WSN due to limited battery power, along with the demand for fast communication over edge devices in 5G and beyond 5G scenarios. Therefore, to overcome the challenges, an advanced hierarchical agent-based power management scheme is proposed for WSNs that optimizes energy distribution while maintaining reliable communication. The proposed model employs Master Agents (MAs), Coordination Agents (CoAs), and Task More >

Longquan Ma¹, Huarong Zhao^1,*, Liqin Zhou¹, Linbo Xie^1,*, Hongnian Yu²

CMC-Computers, Materials & Continua, Vol.87, No.3, 2026, DOI:10.32604/cmc.2026.075679 - 09 April 2026

Abstract This paper studies a sampling-based dynamic event-triggered fixed-time bipartite formation algorithm for a class of continuous-time multi-agent systems with communication constraints. First, a periodic sampling mechanism is designed to reduce the system’s communication frequency. Then, a dynamic event-triggered control algorithm based on auxiliary variables is developed for sampled-data systems to further reduce the system’s triggering frequency. Next, to enhance the convergence speed of the dynamic event-triggered control method, a dynamic event-triggered fixed-time bipartite formation control scheme is investigated. Finally, using Lyapunov stability theory, signed graph theory, and relevant inequalities, a rigorous theoretical proof of the More >

Xin Yu, Xi Fang^*

CMC-Computers, Materials & Continua, Vol.87, No.3, 2026, DOI:10.32604/cmc.2026.075607 - 09 April 2026

Abstract This paper addresses the challenging problem of multi-agent dynamic target pursuit under stringent communication constraints (including delays and range limits), where the agile targets are non-cooperative and free from such limitations. To tackle this, we propose CRS-DQN, a novel Deep Q-Network algorithm designed for this scenario. CRS-DQN enables agents to learn effective pursuit strategies through deep reinforcement learning despite partial observability and constrained information sharing. Simulation experiments systematically evaluate the impact of key parameters. The results show that pursuit performance degrades monotonically with increased communication delay. In contrast, the communication radius exhibits a non-linear effect: More >

Fangxiang Hu¹, Qi Fu^1,2,*, Shiwen Zhang¹, Jing Huang¹

CMC-Computers, Materials & Continua, Vol.87, No.3, 2026, DOI:10.32604/cmc.2026.074154 - 09 April 2026

Abstract In the Internet of Vehicles (IoV) environment, the growing demand for computational resources from diverse vehicular applications often exceeds the capabilities of intelligent connected vehicles. Traditional approaches, which rely on one or more computational resources within the cloud-edge-device computing model, struggle to ensure overall service quality when handling high-density traffic flows and large-scale tasks. To address this issue, we propose a computational offloading scheme based on a cloud-edge-device collaborative 6G IoV edge computing model, namely, Multi-Agent Deep Reinforcement Learning-based and Server-weighted scoring Selection (MADRLSS), which aims to optimize dynamic offloading decisions and resource allocation. The… More >

Abiodun Abiola^*, Francisca Segura, José Manuel Andújar, Antonio Javier Barragán

CMES-Computer Modeling in Engineering & Sciences, Vol.146, No.3, 2026, DOI:10.32604/cmes.2025.070788 - 30 March 2026

Abstract This paper presents an artificial intelligence (AI)-based observer that combines fuzzy logic and neural networks to detect abnormalities in sensors embedded in an electrolyser. Electrolysers are hydrogen production plants that require effective maintenance to guarantee suitable operation, prevent degradation, and avoid loss of efficiency. In this sense, predictive maintenance arises as one of the most advisable techniques for maintenance in electrolysers by using sensor data to predict potential abnormalities. However, if the sensor fails, there will be an incorrect forecasting of abnormalities. Among the different types of operational faults that sensors can present are drift-related… More >

Denisse A. Gutierrez^*, Elisa Robles-Escajeda, Jose A. Lopez-Saenz, Robert A. Kirken, Edgar A. Borrego, Ana P. Betancourt, Soumya Nair, Sourav Roy, Armando Varela-Ramirez, Renato J. Aguilera^*

Oncology Research, Vol.34, No.4, 2026, DOI:10.32604/or.2026.074945 - 23 March 2026

Abstract Objectives: Drug resistance is the major determinant of chemotherapy failure, leading to relapse and tumor progression, demonstrating the urgent need for novel antineoplastic drugs. This study aimed to evaluate the anticancer potential of two novel pyrazole derivatives, P3C.1 and P3C.2, and to elucidate their mechanism of action in cancer cells. Methods: The cytotoxicity of the compounds was evaluated across 27 different cancer cell lines via a nuclear staining assay. Subsequent flow cytometric and biochemical analyses were performed to assess reactive oxygen species (ROS) generation, apoptosis induction, mitochondrial integrity, and cell cycle progression. Additional studies included… More >

Go Sato¹, Shusaku Egami^1,2, Yasuyuki Tahara¹, Akihiko Ohsuga¹, Yuichi Sei^1,*

CMC-Computers, Materials & Continua, Vol.87, No.2, 2026, DOI:10.32604/cmc.2026.078188 - 12 March 2026

Abstract While Large Language Models (LLMs) possess the capability to perform a wide range of tasks, security attacks known as prompt injection and jailbreaking remain critical challenges. Existing defense approaches addressing this problem face challenges such as the over-refusal of prompts that contain harmful vocabulary but are semantically benign, and the limited accuracy improvement in machine learning-based approaches due to the ease of distinguishing benign prompts in existing datasets. Therefore, we propose a multi-LLM agent framework aimed at achieving both the accurate rejection of harmful prompts and appropriate responses to benign prompts. Distinct from prior studies,… More >

Mohammad Nauman^*

CMC-Computers, Materials & Continua, Vol.87, No.2, 2026, DOI:10.32604/cmc.2026.077259 - 12 March 2026

Abstract Large Language Models (LLMs) are increasingly utilized for semantic understanding and reasoning, yet their use in sensitive settings is limited by privacy concerns. This paper presents In-Mig, a mobile-agent architecture that integrates LLM reasoning within agents that can migrate across organizational venues. Unlike centralized approaches, In-Mig performs reasoning in situ, ensuring that raw data remains within institutional boundaries while allowing for cross-venue synthesis. The architecture features a policy-scoped memory model, utility-driven route planning, and cryptographic trust enforcement. A prototype using JADE for mobility and quantized Mistral-7B demonstrates practical feasibility. Evaluation across various scenarios shows that In-Mig achieves More >