Kamaljeet¹, Abhishek Vijukumar¹, Hardik Kumar^2,*, Shilpa Debnath², Sourabh Kosey¹

European Cytokine Network, Vol.37, No.1, pp. 13-24, 2026, DOI:10.32604/ecn.2026.078458 - 13 April 2026

Abstract Cytokine storm syndromes have become a much-invoked concept to describe severe immunopathology in infectious, inflammatory, and iatrogenic diseases, but the concept is poorly defined and often mechanistically imprecise. High levels of systemic cytokines have often been viewed as indicators of immune dysfunction, and based on this notion, therapeutic interventions focused on general cytokine inhibition are proposed. Nevertheless, a growing number of clinical and experimental data dispute the notion that hypercytokinemia is necessarily pathological. The present paper reconsiders cytokine storm syndromes in the light of an evolutionary, systems-immunology model, and suggests that most cytokine amplification conditions… More >

Kuen-Suan Chen^1,2,3, Tsai-Sung Lin⁴, Ruey-Chyn Tsaur^4,*, Minh T. N. Nguyen⁵

CMC-Computers, Materials & Continua, Vol.87, No.3, 2026, DOI:10.32604/cmc.2026.079476 - 09 April 2026

Abstract As artificial intelligence, the Internet of Things, edge computing, and blockchain are increasingly integrated into long-term care (LTC) services, policymakers face complex and often non-compensatory trade-offs among affordability, workforce sustainability, service reliability, and data governance. Conventional compensatory evaluation models tend to mask critical structural weaknesses and limiting their usefulness for Smart LTC policy assessment. This study proposes and applies a Fuzzy Trade-Off-Aware Scoring with Conflicts (Fuzzy TASC) framework to evaluate Smart LTC system performance. Four digital-integration configurations—conventional cloud-based LTC, AI+IoT, AI+Edge, and AI+Blockchain—were compared across 12 OECD countries. A Monte Carlo perturbation procedure was incorporated… More >

Sathish Rao Udupi, Gururaj Bolar, Manjunath Shettar^*, Ashwini Bhat

CMC-Computers, Materials & Continua, Vol.87, No.3, 2026, DOI:10.32604/cmc.2026.078574 - 09 April 2026

Abstract Glass fiber-reinforced polymer composites (GFRPCs) are extensively utilized in the aerospace, automotive, and structural sectors; nevertheless, their heterogeneous and abrasive characteristics result in rapid tool wear during drilling. Drill flank wear among various wear mechanisms notably influences hole quality and dimensional accuracy. This research investigates the impact of spindle speed, feed rate, and drill diameter on flank wear during dry drilling of GFRPC laminates with high-speed steel (HSS) twist drills. A full-factorial design with 81 experiments is used to create a comprehensive dataset. ANOVA indicates that spindle speed is the dominant factor affecting wear changes,… More >

Kexin Jiang^1,2, Yong Fan², Liang Wen¹, Zhigang Xie¹, Enzhi Dong¹, Bo Zhu¹, Zhonghua Cheng^1,*

CMC-Computers, Materials & Continua, Vol.87, No.3, 2026, DOI:10.32604/cmc.2026.077392 - 09 April 2026

Abstract With the growing deployment of unmanned aerial vehicles (UAVs), reliable engine health state assessment (HSA) requires methods that are interpretable, auditable, and transferable under noisy data and varying operating conditions. This paper proposes an AHP-enhanced, data-driven HSA framework that builds a unified health vector from four indicators—remaining useful life (RUL) health, absolute state, relative degradation, and condition health. Indicator weights are derived using AHP with consistency checking, and the resulting continuous health index is mapped through nonlinear stretching and four-level thresholds to produce actionable health grades. Experiments on the NASA CMAPSS benchmark (FD001) evaluate conventional More >

Tsu-Yang Wu¹, Yehai Xue¹, Haonan Li², Saru Kumari³, Lip Yee Por^2,*

CMC-Computers, Materials & Continua, Vol.87, No.3, 2026, DOI:10.32604/cmc.2026.077332 - 09 April 2026

Abstract Blockchain technology, characterized by decentralization, transparency, and immutability, has been widely applied in areas such as supply chain tracking, medical data management, and the Internet of Things. However, single blockchain systems suffer from limitations in performance, scalability, and cross-chain interoperability, giving rise to the issue of “blockchain silos.” To address the challenges of data and asset circulation among heterogeneous blockchain networks, both academia and industry have proposed multi-blockchain architectures. In this paper, we categorize current multi-blockchain systems from a network topology perspective into four types: parallel architecture, hierarchical architecture, hybrid architecture, and multi-blockchain networks. We… More >

Faten S. Alamri¹, Muhammad Amjad Raza^2,3, Abeer Rashad Mirdad⁴, Adil Ali Saleem², Tanzila Saba^4,*

CMC-Computers, Materials & Continua, Vol.87, No.3, 2026, DOI:10.32604/cmc.2026.077084 - 09 April 2026

Abstract Rapid expansion of Industrial Internet of Things (IIoT) systems has heightened the vulnerability of critical infrastructure to sophisticated malware attacks. Traditional signature-based detection methods are ineffective against evolving threats, and many machine learning models fail to capture temporal behavior, offer interpretability, or operate efficiently in resource-constrained environments. This study proposes HMF-Net, a Hierarchical Multi-Feature Network, for accurate, interpretable, and efficient IIoT malware detection. HMF-Net combines hierarchical VT-Tag embedding (HVTE) to model semantic behavioral information, temporal detection ratio analysis (TDRA) to capture confidence variations for polymorphic malware, and static structural binary features. These features are fused… More >

Dmytro Tymoshchuk^1,*, Oleh Yasniy¹, Iryna Didych², Pavlo Maruschak^3,*, Yuri Lapusta⁴

CMC-Computers, Materials & Continua, Vol.87, No.3, 2026, DOI:10.32604/cmc.2026.077062 - 09 April 2026

Abstract This article presents an approach to predicting the hysteresis behavior of shape memory alloys (SMAs) using a Temporal Convolutional Network (TCN) deep learning model, followed by the interpretation of the results using Explainable Artificial Intelligence (XAI) methods. The experimental dataset was prepared based on cyclic loading tests of nickel-titanium wire at loading frequencies of 0.3, 0.5, 1, 3, and 5 Hz. For training, validation, and testing, 100–250 loading-unloading cycles were used. The input features of the models were stress σ (MPa), cycle number (Cycle parameter), and loading-unloading stage indicator, while the output variable was strain… More >

Guanfeng Wang^1,2, Xuliang Yao^1,*, Jingfang Wang¹, Yongxin Sun², Jincheng Geng², Erlou Shi², Zhili Zhou^3,*

CMC-Computers, Materials & Continua, Vol.87, No.3, 2026, DOI:10.32604/cmc.2026.076846 - 09 April 2026

Abstract In a real-world industrial system, it is challenging to reduce interference from operating conditions and extract high-quality feature data. To address these issues, this paper proposes a time-domain feature generation and analysis (FGA) scheme, which designs a grouping-aggregation (GA) scheme and an index decomposition (ID) method to extract and analyze high-quality feature data for industrial systems. The FGA represents a designed GA-based hybrid algorithm collaborative architecture, which overcomes the limitations of single algorithms and enables joint feature extraction from multi-condition, multi-scale industrial data. Simultaneously, FGA incorporates a feedback-driven threshold self-calibration mechanism, integrating LSTM-VAE to dynamically… More >

Yan-Hao Huang^*, Yu-Tse Huang

CMC-Computers, Materials & Continua, Vol.87, No.3, 2026, DOI:10.32604/cmc.2026.076800 - 09 April 2026

Abstract Diabetic Retinopathy (DR) is a common microvascular complication of diabetes that progressively damages the retinal blood vessels and, without timely treatment, can lead to irreversible vision loss. In clinical practice, DR is typically diagnosed by ophthalmologists through visual inspection of fundus images, a process that is time-consuming and prone to inter- and intra-observer variability. Recent advances in artificial intelligence, particularly Convolutional Neural Networks (CNNs) and Transformer-based models, have shown strong potential for automated medical image classification and decision support. In this study, we propose a Position-Wise Attention-Enhanced Vision Transformer (PWAE-ViT), which integrates a positional attention… More >

Hitesh Mohapatra^*

CMC-Computers, Materials & Continua, Vol.87, No.3, 2026, DOI:10.32604/cmc.2026.076726 - 09 April 2026

Abstract This review examines current approaches to real-time decision-making and task optimization in Internet of Things systems through the application of machine learning models deployed at the network edge. Existing literature shows that edge-based distributed intelligence reduces cloud dependency. It addresses transmission latency, device energy use, and bandwidth limits. Recent optimization strategies employ dynamic task offloading mechanisms to determine optimal workload placement across local devices and edge servers without centralized coordination. Empirical findings from the literature indicate performance improvements with latency reductions of approximately 32.8% and energy efficiency gains of 27.4% compared to conventional cloud-centric models.… More >