Darlington Chigozie Okeke^*

Journal of Cyber Security, Vol.8, pp. 319-356, 2026, DOI:10.32604/jcs.2026.080477 - 08 June 2026

Abstract Background: The evolution of modern networked systems in complexity, volume, and diversity has markedly increased the cyber-attack area. Conventional signature-based intrusion detection systems (IDS) will no longer be adequate for identifying advanced threats. A data-driven, adaptive approach that can identify malicious network activity is provided by machine learning (ML) techniques. This review aims to study, compare, and analyze ML-based approaches in IDS and improve the security defense mechanism. Methods: This systematic review followed the PRISMA 2020 guidelines. ML-based IDS peer-reviewed papers were identified from five scientific databases. Abstracts, full texts, and titles were filtered using… More >

Bernard P. Zeigler^*

Digital Engineering and Digital Twin, Vol.4, pp. 1-26, 2026, DOI:10.32604/dedt.2026.082492 - 02 June 2026

Abstract As Digital Twin (DT) applications expand into complex, dynamic environments, a formal methodology is lacking to ensure that the embedded digital models remain adequate for specific stakeholder goals over time. This article introduces the Experimental Frame–System Under Test (EFSUT) methodology, providing a principled foundation for linking high-level stakeholder questions to the specific models capable of answering them. EFSUT organizes the digital engineering process around three core constructs: stakeholder questions, experimental frames that formalize observational requirements, and models related through morphisms. This structure allows developers to reason about model choice, reduction, and adequacy with technical rigor… More >

Seham Hassan Salman¹, Sarmad Mahdi Ali¹, Hawraa Hadi^2,*, Dhufr Hadi³

Chalcogenide Letters, Vol.23, No.5, 2026, DOI:10.32604/cl.2026.081302 - 02 June 2026

Abstract Films of (SnS) with a thickness of 400 nm were deposited by the thermal evaporation technique to investigate the influence of annealing temperature on their Physics and CO₂ gas-sensing characteristics. The deposited films were annealed at 200, 300, and 400°C. Structural characterization was performed using XRD, and the obtained results revealed that all samples possessed an orthorhombic crystal structure with a preferred orientation (301). The annealing treatment significantly improved the crystallinity of the films and reduced structural defects and lattice strain. Surface morphology investigations were performed using atomic force microscopy (AFM), which revealed noticeable modifications in… More >

Priyanka Halder, Gopikrishnan Sundaram^*

Journal on Internet of Things, Vol.8, pp. 127-153, 2026, DOI:10.32604/jiot.2026.073962 - 29 May 2026

Abstract The need for intelligent learning frameworks that can function under stringent limitations relating to privacy, energy, scalability, and trust has increased due to the Internet of Things’ (IoT) and the Internet of Artificial Things’ (IoAT) explosive expansion. Federated Learning (FL), which allows collaborative model training without sharing raw data, has become a potential approach. Non-IID data delivery, inconsistent client engagement, vulnerability to poisoning assaults, and low resource knowledge are among of the significant obstacles that FL alone must overcome. Blockchain integration adds extra overhead in terms of latency, energy consumption, and scalability, but it has… More >

Ting He^*, Dong Chen, Liqiong Chen, Kun Huang, Haoyu Jia

FDMP-Fluid Dynamics & Materials Processing, Vol.22, No.5, 2026, DOI:10.32604/fdmp.2026.080326 - 27 May 2026

Abstract The global transition toward sustainable energy systems underscores the strategic importance of methane (CH₄)–carbon dioxide (CO₂) mixtures in cryogenic applications. In Liquefied Natural Gas (LNG) processing and Carbon Capture, Utilization, and Storage (CCUS) networks, such mixtures are routinely exposed to low-temperature environments where phase stability becomes critical. Under these conditions, the unintended formation of solid CO₂ (dry ice) within pipelines poses significant engineering challenges, including flow blockage and potential equipment damage. Ensuring flow assurance therefore demands a rigorous understanding of the coupling between thermodynamic phase transitions and complex hydrodynamic behavior. This paper presents a comprehensive review of More >

Xiang Li^1,2, Jie Zhang^1,2,*, Yuxin Cheng^1,2, Jiaohao Xie^1,2, Zhaoqi Xiong^1,2

FDMP-Fluid Dynamics & Materials Processing, Vol.22, No.5, 2026, DOI:10.32604/fdmp.2026.079253 - 27 May 2026

Abstract Current prediction methods for wellbore temperature and pressure in gas storage injection–production wells are commonly based on the simplifying assumption of pure methane, thereby neglecting the multi-component nature of real natural gas and limiting predictive accuracy. To overcome this shortcoming, this study develops a comprehensive model for the coupled temperature and pressure fields in wellbores transporting multi-component natural gas mixtures. The proposed framework explicitly accounts for compositional effects by integrating key thermophysical properties, including density, viscosity, compressibility factor, and Joule–Thomson coefficient, into the governing flow equations, thereby enhancing the fidelity of the ensuing injection and More >

Penghui Zhang^1,2, Nan Lin^2,*, Yang Wang^1,*, Ming Sun², Sixi Zha¹, Zongjie Zhou¹, Chenglin Li³

FDMP-Fluid Dynamics & Materials Processing, Vol.22, No.5, 2026, DOI:10.32604/fdmp.2026.078553 - 27 May 2026

Abstract Erosion-corrosion in refining and chemical plant pipelines remains a persistent integrity concern, particularly in straight sections located downstream of elbows, which are rarely prioritized in inspection programs that typically focus on elbows and tees despite their well-known vulnerability. In these downstream regions, developing flow structures can sustain wall impingement and liquid film formation, leading to progressive material loss that is often underestimated in practice. This work examines a representative industrial pipeline through a combined approach based on computational fluid dynamics (CFD) simulations and controlled experimental validation to resolve the hydrodynamic behavior in the straight pipe… More >

Marco Roccetti^*

CMES-Computer Modeling in Engineering & Sciences, Vol.147, No.2, 2026, DOI:10.32604/cmes.2026.082258 - 27 May 2026

Abstract Geospatial health risk signals, characterized by associations with high magnitude statistical significance, may frequently originate from circumscribed observational data streams. When these signals are fueled by massive N-size datasets, the large dimensional scale of the sample can induce a misleading interpretation of local evidence as a statistically significant risk inflation. The objective of this study is to verify whether such health risk configurations constitute geospatial structural artifacts: namely, stochastic distortions generated by the spatial information of local health repositories that, despite their massive scale, may remain fundamentally distant from broader contextual realities. To this aim,… More >

Yenjou Wang¹, Chihtan Cheng², Jia-Wei Chang^3,*

CMES-Computer Modeling in Engineering & Sciences, Vol.147, No.2, 2026, DOI:10.32604/cmes.2026.080888 - 27 May 2026

Abstract As large language models (LLMs) become increasingly integrated into enterprise decision-making processes, structural pressures such as version drift, cross-source evidence integration, and regulatory accountability have shifted the primary challenge from isolated generative performance to system-level consistency, traceability, and governability. This paper systematically reviews key technological developments relevant to enterprise requirements, including document perception, retrieval-augmented generation (RAG), hybrid RAG-KG architectures, fine-grained attribution evaluation, and multi-agent coordination. The analysis demonstrates that the main obstacle to enterprise LLM adoption is not model capability, but rather the structural gap between fragmented technical modules and the need for high-reliability decision-making. More >

Ezz El-Din Hemdan¹, Haitham Elwahsh^2,3, Samah Alshathri^4,*, Amged Sayed^5,6,*

CMES-Computer Modeling in Engineering & Sciences, Vol.147, No.2, 2026, DOI:10.32604/cmes.2026.079305 - 27 May 2026

Abstract Ensuring robust methods for maintaining high levels of medical data security is crucial in the Medical Internet of Things (IoT) for the protection of sensitive patient data during real-time transmission and analysis. Electroencephalography (EEG) signals in medical IoT systems are transmitted through cloud and edge networks, which create risks of cyber threats, unauthorized access, and data breaches. Consequently, there is an urgent need for efficient encryption methods to ensure the confidentiality of EEG signals during classification and prediction processes, as several state-of-the-art models either neglect security during classification or suffer from increased computational overhead that… More >