Dakshnamoorthy Manivannan^*

Journal of Cyber Security, Vol.8, pp. 397-468, 2026, DOI:10.32604/jcs.2026.082741 - 06 July 2026

Abstract Ransomware has emerged as one of the most disruptive and financially damaging forms of cybercrime, affecting individuals, enterprises, and critical infrastructures worldwide. Over the past decade, ransomware attacks have evolved from simple file-encryption malware to sophisticated, multi-stage campaigns involving data exfiltration, double extortion, and ransomware-as-a-service (RaaS) ecosystems. In response, a large body of research has proposed diverse techniques for detecting, preventing, mitigating, and recovering from ransomware attacks. This paper presents a comprehensive survey of ransomware research spanning behavioral and runtime detection, machine learning and deep learning-based approaches, network and SDN-based detection, platform-specific defenses for mobile… More >

Hsing-Ju Wu^1,2, Yu-Chieh Tsai^3,*, Hung-Yu Lin^2,4,*

European Cytokine Network, Vol.37, No.2, pp. 121-135, 2026, DOI:10.32604/ecn.2026.079012 - 30 June 2026

Abstract Backgrounds: Lymph node metastasis is a critical determinant of breast cancer prognosis, yet the specific microenvironmental cytokines driving this process remain elusive. This study aims to identify key prognostic cytokines linking nodal metastasis to tumor microenvironment (TME) remodeling and to evaluate their clinical utility. Methods: A predefined panel of 176 microenvironmental genes was evaluated using differential expression analysis and the Least Absolute Shrinkage and Selection Operator (LASSO) algorithm on the TCGA-BRCA cohort to identify optimal predictors of nodal metastasis. Prognostic value was assessed via Kaplan-Meier, subgroup, and multivariate Cox regression analyses, and validated across five… More > Graphic Abstract

Manish Kewalramani¹, Refka Ghodhbani², Arsalan Mahmoodzadeh^3,*, Abdulaziz Alghamdi⁴, Faten Khalid Karim⁵, Abed Alanazi⁶, Abdullah Alqahtani⁶, Shtwai Alsubai⁶, Mounir Ltifi⁷

CMES-Computer Modeling in Engineering & Sciences, Vol.147, No.3, 2026, DOI:10.32604/cmes.2026.084696 - 30 June 2026

Abstract Concrete manufacturing consumes vast quantities of natural resources and contributes significantly to environmental degradation and carbon emissions. Therefore, integrating recycled waste substances into concrete has become a crucial approach to fostering eco-friendly building practices and supporting circular economy concepts. This study investigates the potential of incorporating recycled aluminum beverage can crumbs (RABCC) as a partial replacement for natural coarse aggregates (NCA) in concrete mixtures, focusing on its impact on compressive strength (CS) and the feasibility of its application in structural concrete. A comprehensive experimental program was conducted to assess the mechanical properties of concrete with… More >

Soran Manguri^1,2, Kasim Mermerdaş¹, Briar Esmail^3,4, Ahmed Manguri^2,*

CMES-Computer Modeling in Engineering & Sciences, Vol.147, No.3, 2026, DOI:10.32604/cmes.2026.083537 - 30 June 2026

Abstract Geopolymer materials are increasingly recognized as sustainable alternatives to conventional cementitious materials due to their lower environmental impact and promising engineering performance. Recent studies have demonstrated that incorporating nanomaterials can further enhance the properties of geopolymer systems. In particular, nano-silica has been reported to significantly improve the mechanical performance of geopolymer materials. However, accurate prediction of compressive strength remains challenging because of the complex nonlinear interactions among mix design parameters, activator chemistry, and curing conditions. This study develops a machine learning framework to predict the 28-day compressive strength of nanosilica-modified hybrid geopolymer mortar using a… More >

Yunlong Teng¹, Ying Liu², Jianhong Liang¹, Jinshang Luo^3,*

CMES-Computer Modeling in Engineering & Sciences, Vol.147, No.3, 2026, DOI:10.32604/cmes.2026.082594 - 30 June 2026

Abstract Over the past decades, surrogate model-aided reliability analysis approaches grounded in active learning have undergone extensive development. However, Gaussian process models like Kriging suffer from severe computational burdens when handling high-dimensional problems or large samples. Conversely, machine learning algorithms such as extreme learning machines exhibit high computational efficiency but lack variance output and stability, making them difficult to employ for adaptive active learning strategies. To address these limitations, this study proposes a population Monte Carlo method based on an adaptive closed neuron extreme learning machine. First, a closed neuron strategy uses a consistency metric to… More >

Cristina Roxana Popa¹, Maria Tănase^2,*, Gheorghe Brănoiu³, Elena-Emilia Sirbu^4,5, Cătălina Călin⁴

CMES-Computer Modeling in Engineering & Sciences, Vol.147, No.3, 2026, DOI:10.32604/cmes.2026.082244 - 30 June 2026

Abstract Glass-fiber-reinforced polymer (GFRP) pipes are increasingly used in aggressive environments due to their high corrosion resistance and favorable mechanical properties. However, long-term exposure to saline environments and elevated temperatures can lead to degradation of their structural performance. This study investigates the influence of accelerated saltwater aging on the tensile behavior and structural characteristics of GFRP pipes and proposes machine-learning-based predictive models for the ultimate tensile strength (UTS). Experimental specimens were immersed in a 3.5% NaCl solution under controlled temperature and exposure time conditions. Tensile testing revealed that the unexposed samples exhibited a maximum UTS of… More >

Naseer Muhammad Khan^1,2, Liqiang Ma^3,*, Majid Khan⁴, Sajjad Hussain⁵, Waleed Inqiad⁶, Tariq Feroze², Danial Jahed Armaghani^7,*

CMES-Computer Modeling in Engineering & Sciences, Vol.147, No.3, 2026, DOI:10.32604/cmes.2026.081660 - 30 June 2026

Abstract Rock fracture behavior under stress is vital for risk evaluation in underground engineering excavation because the presence of water can significantly increase the extent of cracks and fractures in rock, leading to structural damage. This can result in catastrophic failures, including rock bursts, coal bursts, and water inrush. Hence, reliable prediction of rock damage and fracture processes is still lacking, which, in turn, enables the safe and efficient conduct of engineering projects in rock-mass environments. Thus, this study examines both dry and saturated sandstone samples under loading using Infrared Radiation (IR), Acoustic Emission (AE) monitoring,… More >

Jaffar Hussain¹, Tahira Nazir¹, Junaid Rashid^2,*, Jungeun Kim^3,*

CMES-Computer Modeling in Engineering & Sciences, Vol.147, No.3, 2026, DOI:10.32604/cmes.2026.081617 - 30 June 2026

Abstract Early detection of diabetic retinopathy (DR), media haze (MH), optic disc cupping (ODC), and glaucoma is crucial for preventing vision loss. However, timely diagnosis is often constrained by limited specialist availability and high diagnostic costs. This study proposes a You Only Look Once (YOLO)-based deep learning (DL) framework for the automated classification of fundus images into disease-specific categories. We unified diverse annotations from the Retinal Fundus Multi-Disease image Dataset (RFMiD), RFMiD2.0, and the DR Fundus Image Dataset (DR-FID) by standardizing annotation files and class labels. A custom filtering module was used to isolate single-pathology cases,… More >

Samuel Martínez-Gutiérrez^1,*, Carlos Gutiérrez¹, Alejandro Merino¹, Diego García-Álvarez², Daniel Sarabia¹

CMES-Computer Modeling in Engineering & Sciences, Vol.147, No.3, 2026, DOI:10.32604/cmes.2026.081247 - 30 June 2026

Abstract Accurate modelling of power production in wind power systems is essential for optimizing their real-time operation and meeting technical or economic objectives. However, the precise modelling of wind turbine power output remains challenging, particularly when relying on conventional parametric models, which often struggle to capture complex or non-linear behaviors. This paper compares three modelling approaches to estimate the power produced by a real wind turbine (a Senvion MM82/2050 located in France): one parametric, based on analytical expressions of the power coefficient C_P(λ, β); another nonparametric, which uses Gaussian processes (GP) to probabilistically model the relationship between… More >

Maram Fahaad Almufareh^1,*, Samabia Tehsin^2,*

CMES-Computer Modeling in Engineering & Sciences, Vol.147, No.3, 2026, DOI:10.32604/cmes.2026.081129 - 30 June 2026

Abstract This article presents a Hierarchical Pathway-Masked Attention Autoencoder (H-PAAE), a biologically inspired representation-learning framework that enables explainable AI-guided cancer diagnosis. The model directly integrates the curated MSigDB Hallmark pathways, introducing pathway-constrained information flow and mechanistic interpretability through multi-level attention mechanisms. Based on TCGA RNA-seq data from 33 tumor types, H-PAAE compresses approximately 20,000 genes into a 128-dimensional latent space while preserving biologically meaningful structure. When used with XGBoost classification, H-PAAE delivers 92.37% test accuracy and 99.38% macro-AUROC with robust cross-validation results (92.5 ± 0.6%). SHAP analysis identifies a small number of key latent features, corresponding More >