Nai-Wei Lo¹, Cheng-I Lin², Chih-Chieh Chang^3,*, Chi-Yang Chang⁴, Tran Thi Luu Ly¹

CMES-Computer Modeling in Engineering & Sciences, Vol.147, No.1, 2026, DOI:10.32604/cmes.2026.077316 - 27 April 2026

Abstract The growing frequency of malicious attacks on Internet of Things (IoT) devices has rendered conventional approaches with static label-dependent risk assessment models obsolete, especially when coping with unknown and continuously evolving threats. To mitigate these challenges, a novel dynamic trust evaluation framework approach is proposed in this work. The proposed framework utilized unsupervised learning and zero-knowledge proofs to assess device risks in complex environments adaptively, with an accuracy rate of 98.96% for normal clustering and 95.39% for anomalies. K-means clustering algorithm is leveraged to distinguish risk patterns with an additional Decision Tree classification algorithm to More >

Serhiy Serbin^1,*, Bohdan Lychko², Kateryna Burunsuz¹

Energy Engineering, Vol.123, No.5, 2026, DOI:10.32604/ee.2026.076966 - 27 April 2026

Abstract This study investigates the use of ammonia as a carbon-free fuel for gas turbines in decarbonized hybrid energy systems. The objective is to predict the emission characteristics of a gas turbine combustion chamber operating on gaseous ammonia by employing detailed combustion kinetics. The chamber is modeled as a network of chemical reactors to simulate the primary reaction zone and the secondary air-mixing zone. The model is based on solving mass and energy conservation equations for chemically reacting flows. Four high-temperature ammonia oxidation mechanisms, comprising 71 to 286 chemical reactions, were used as kinetic schemes. New More >

Xiang Xiao, Peng Zhang^*, Yuan Yuan, Zhiyuan Feng, Kui Hu, Yuan Xu, Yunhuang Zhang, Guoming Liu

Energy Engineering, Vol.123, No.5, 2026, DOI:10.32604/ee.2026.073741 - 27 April 2026

Abstract As a promising solution to the challenges of future clean and reliable energy supply, the Gas-Cooled Micro-Reactor (GCMR) has attracted increasing attention due to its potential for decentralized power generation, carbon-free operation, and flexible deployment in remote or extreme environments. As a novel reactor concept, the GCMR offers advantages such as compact size, inherent safety, and high thermal efficiency. However, conventional core calculation methods face significant challenges due to the complex geometric configurations, heterogeneous material distribution, and pronounced neutron leakage characteristics of the GCMR. This study proposes a diffusion-based homogenization method for GCMR analysis. First,… More > Graphic Abstract

Carlo Ronsini^1,*, Giuseppe Cucinella¹, Maria Cristina Solazzo¹, Mariano Catello Di Donna¹, Cono Scaffa¹, Stefano Cianci², Manuela Ludovisi³, Sandro Pignata⁴, Vito Chiantera¹

Oncology Research, Vol.34, No.5, 2026, DOI:10.32604/or.2026.074934 - 22 April 2026

Abstract Background: The optimal sequencing of surgery and chemotherapy in advanced epithelial ovarian cancer remains debated. While primary debulking surgery (PDS) has been considered the standard approach, recent randomized trials have questioned its survival advantage over neoadjuvant chemotherapy (NACT) followed by interval debulking surgery (IDS). The study aimed to systematically evaluate phase III randomized controlled trials comparing PDS and NACT. Methods: Following PRISMA guidelines (PROSPERO ID 1169057), PubMed and Scopus were systematically searched in October 2025 for phase III randomized clinical trials evaluating cytoreductive strategies in ovarian carcinoma. Only full-text English studies reporting overall survival (OS)… More >

Aktham Mestareehi^1,2,*

Oncology Research, Vol.34, No.5, 2026, DOI:10.32604/or.2026.073745 - 22 April 2026

Abstract Objective: Hepatocellular carcinoma (HCC) is a leading cause of cancer-related mortality worldwide, largely due to late diagnosis, molecular heterogeneity, and limited prognostic biomarkers. Aberrant protein phosphorylation plays a critical role in cancer progression by regulating DNA damage response, cell cycle control, and signaling pathways; however, the prognostic relevance of phosphorylation events in key DNA topology–related proteins remains incompletely understood. This study aimed to investigate the prognostic significance of phosphorylation of TOP1, TOP2A, TOP2B, and C1orf35 in HCC and to characterize their associated molecular features to identify potential diagnostic and therapeutic biomarkers. Methods: Publicly available HCC… More > Graphic Abstract

Harri Renney^1,*, Maxim Nethercott¹, Nathan Renney², Peter Hayes¹

Journal on Artificial Intelligence, Vol.8, pp. 231-257, 2026, DOI:10.32604/jai.2026.078487 - 17 April 2026

Abstract This paper provides systemisation on the emerging design patterns and paradigms for Large Language Model (LLM)-enabled multi-agent systems (MAS), evaluating their practical utility across various domains, bridging academic research and industry practice. We define key architectural components, including agent orchestration, communication mechanisms, and control-flow strategies, and demonstrate how these enable rapid development of modular, domain-adaptive solutions. Three real-world case studies are tested in controlled, containerised pilots in telecommunications security, national heritage asset management, and utilities customer service automation. Initial empirical results show that, for these case studies, prototypes were delivered within two weeks and pilot-ready More >

Benedict Djouboussi^1,*, Elie Fute Tagne^1,2

Journal of Cyber Security, Vol.8, pp. 171-187, 2026, DOI:10.32604/jcs.2026.079617 - 15 April 2026

Abstract The Distributed Network Protocol 3 (DNP3) is widely deployed in SCADA-based microgrids; however, it was not originally designed to meet the cybersecurity requirements of modern decentralized energy infrastructures. Although DNP3 Secure Authentication (DNP3-SA) introduces HMAC-based session-level protection, it does not ensure fragment-level integrity, leaving the protocol vulnerable to fragmentation disruption, replay attacks, and sequence manipulation. Such vulnerabilities can cause desynchronization between master and outstation devices, compromising the operational reliability of distributed energy resources. This paper proposes DNP3Chain, a blockchain-enabled framework that provides real-time fragment-level validation and enforces end-to-end message integrity in DNP3 communications. An OpenDNP3-based… More >

Hao Li^1,2, Zhoujingzi Qiu^1,2, Jianxiao Zou^1,2, Haojie Wu¹, Shicai Fan^1,2,*

CMC-Computers, Materials & Continua, Vol.87, No.3, 2026, DOI:10.32604/cmc.2026.076784 - 09 April 2026

Abstract Self-supervised monocular depth estimation has attracted considerable attention due to its ability to learn without ground-truth depth annotations and its strong scalability. However, existing approaches still suffer from inaccurate object boundaries and limited inference efficiency. To address these issues, we present a Lightweight Conditional Diffusion Model for Monocular Depth Estimation (LCDM-Mono). The proposed framework integrates an efficient diffusion inference strategy with a knowledge distillation scheme, enabling the model to generate high-quality depth maps with only two sampling steps during inference. This design substantially reduces computational overhead and ensures real-time performance on resource-constrained platforms. In addition, More >

Trong-Thua Huynh^1,*, De-Thu Huynh², Du-Thang Phu¹, Hong-Son Nguyen¹, Quoc H. Nguyen³

CMC-Computers, Materials & Continua, Vol.87, No.3, 2026, DOI:10.32604/cmc.2026.076623 - 09 April 2026

Abstract This paper introduces MobiIris, a lightweight deep network for mobile iris recognition that enhances attention and specifically addresses the balance between accuracy and efficiency on devices with limited resources. The proposed model is based on the large version of MobileNetV3 and adds more spatial attention blocks and an embedding-based head that was trained using margin-based triplet learning, enabling fine-grained modeling of iris textures in a compact representation. To further improve discriminability, we design a training pipeline that combines dynamic-margin triplet loss, a staged hard/semi-hard negative mining strategy, and feature-level knowledge distillation from a ResNet-50 teacher.… More >

Zuoquan Zhu^*, Menghan Wang, Xinyu Li, Mengxin Zhao

CMC-Computers, Materials & Continua, Vol.87, No.3, 2026, DOI:10.32604/cmc.2026.076418 - 09 April 2026

Abstract Investigating the deformation behavior of graphene-reinforced composite structures holds significant engineering implications, while the rapid advancement of machine learning has introduced new technical approaches to structural bending analysis. In this study, we investigate the mechanical bending behavior of graphene origami (GOri)-enabled auxetic metamaterial beams using a physics-informed neural network (PINN). GOri-enabled auxetic metamaterials represent an innovative composite system characterized by a negative Poisson’s ratio (NPR) and superior mechanical performance. Here, we propose a composite beam model incorporating the modified coupled stress theory (MCST) and employing the PINN method to solve higher-order bending governing equations. Compared More >