Hongyu Zhao^1,2, Shengsheng Wang^1,2,*

CMC-Computers, Materials & Continua, Vol.86, No.1, pp. 1-25, 2026, DOI:10.32604/cmc.2025.068426 - 10 November 2025

Abstract Driven by advancements in mobile internet technology, images have become a crucial data medium. Ensuring the security of image information during transmission has thus emerged as an urgent challenge. This study proposes a novel image encryption algorithm specifically designed for grayscale image security. This research introduces a new Cantor diagonal matrix permutation method. The proposed permutation method uses row and column index sequences to control the Cantor diagonal matrix, where the row and column index sequences are generated by a spatiotemporal chaotic system named coupled map lattice (CML). The high initial value sensitivity of the… More >

Tariq Ali^1,2,*, Muhammad Ayaz^1,2, Mohammad Hijji², Imran Baig³, MI Mohamed Ershath⁴, Saleh Albelwi²

CMES-Computer Modeling in Engineering & Sciences, Vol.145, No.3, pp. 3839-3858, 2025, DOI:10.32604/cmes.2025.073169 - 23 December 2025

Abstract The integration of renewable energy sources into electricity markets presents significant challenges due to the inherent variability and uncertainty of power generation from wind, solar, and other renewables. Accurate forecasting is crucial for ensuring grid stability, optimizing market operations, and minimizing economic risks. This paper introduces a hybrid forecasting framework incorporating fractional-order statistical models, fractal-based feature engineering, and deep learning architectures to improve renewable energy forecasting accuracy. Fractional autoregressive integrated moving average (FARIMA) and fractional exponential smoothing (FETS) models are explored for capturing long-memory dependencies in energy time-series data. Additionally, multifractal detrended fluctuation analysis (MFDFA) More >

Xiaolin Wang, Richeng Liu^*, Kai Qiu, Zhongzhong Liu, Shisen Zhao, Shuchen Li

CMES-Computer Modeling in Engineering & Sciences, Vol.144, No.1, pp. 289-307, 2025, DOI:10.32604/cmes.2025.066716 - 31 July 2025

Abstract Gas-liquid two-phase flow in fractal porous media is pivotal for engineering applications, yet it remains challenging to be accurately characterized due to complex microstructure-flow interactions. This study establishes a pore-scale numerical framework integrating Monte Carlo-generated fractal porous media with Volume of Fluid (VOF) simulations to unravel the coupling among pore distribution characterized by fractal dimension (D_f), flow dynamics, and displacement efficiency. A pore-scale model based on the computed tomography (CT) microstructure of Berea sandstone is established, and the simulation results are compared with experimental data. Good agreement is found in phase distribution, breakthrough behavior, and flow… More >

Manal Adil Murad¹, Shayma Adil Murad^2,*, Thabet Abdeljawad^3,4,5,6,*, Aziz Khan³, D. K. Almutairi⁷

CMES-Computer Modeling in Engineering & Sciences, Vol.143, No.3, pp. 3377-3409, 2025, DOI:10.32604/cmes.2025.064245 - 30 June 2025

Abstract Asthma is the most common allergic disorder and represents a significant global public health problem. Strong evidence suggests a link between ascariasis and asthma. This study aims primarily to determine the prevalence of Ascaris lumbricoides infection among various risk factors, to assess blood parameters, levels of immunoglobulin E (IgE) and interleukin-4 (IL-4), and to explore the relationship between ascariasis and asthma in affected individuals. The secondary objective is to examine a fractal-fractional mathematical model that describes the four stages of the life cycle of Ascaris infection, specifically within the framework of the Caputo-Fabrizio derivative. A… More >

Mohammed Althubyani^1,*, Nidal E. Taha², Khdija O. Taha², Rasmiyah A. Alharb², Sayed Saber^1,3

CMES-Computer Modeling in Engineering & Sciences, Vol.143, No.3, pp. 3491-3521, 2025, DOI:10.32604/cmes.2025.061640 - 30 June 2025

Abstract This study investigates the dynamics of pneumococcal pneumonia using a novel fractal-fractional Susceptible-Carrier-Infected-Recovered model formulated with the Atangana-Baleanu in Caputo (ABC) sense. Unlike traditional epidemiological models that rely on classical or Caputo fractional derivatives, the proposed model incorporates nonlocal memory effects, hereditary properties, and complex transmission dynamics through fractal-fractional calculus. The Atangana-Baleanu operator, with its non-singular Mittag-Leffler kernel, ensures a more realistic representation of disease progression compared to classical integer-order models and singular kernel-based fractional models. The study establishes the existence and uniqueness of the proposed system and conducts a comprehensive stability analysis, including local More >

Giuseppe Carota¹, Lucia Di Pietro^2,3, Vincenzo Cardaci⁴, Anna Privitera^1,2, Francesco Bellia¹, Valentina Di Pietro⁵, Giuseppe Lazzarino¹, Barbara Tavazzi⁶, Angela Maria Amorini¹, Giacomo Lazzarino⁶, Giuseppe Caruso^6,7,*

BIOCELL, Vol.49, No.4, pp. 563-578, 2025, DOI:10.32604/biocell.2025.062176 - 30 April 2025

Abstract Carnosine (β-alanyl-L-histidine) is a naturally occurring endogenous peptide widely distributed in excitable tissues, such as the heart and brain. Over the years, several beneficial effects of carnosine have been discussed well in scientific literature. In particular, this dipeptide is well-known for its antioxidant, anti-inflammatory, and anti-aggregation activities. It is of great interest in the context of numerous systemic and neurodegenerative diseases, besides performing important “side activities” such as metal chelation and pH-buffering. Despite a plethora of preclinical and clinical data supporting carnosine’s therapeutic potential, researchers are still searching for new pharmacological targets that better highlight More >

Weiping Ouyang^1,2, Luoyi Huang^3,*, Jinghua Liu^3,*, Hongzhong Zhang^1,2

Energy Engineering, Vol.122, No.4, pp. 1491-1509, 2025, DOI:10.32604/ee.2025.061171 - 31 March 2025

Abstract Hydraulic fracturing is a crucial technique for efficient development of coal reservoirs. Coal rocks typically contain a high density of natural fractures, which serve as conduits for fracturing fluid. Upon injection, the fluid infiltrates these natural fractures and leaks out, resulting in complex fracture morphology. The prediction of hydraulic fracture network propagation for coal reservoirs has important practical significance for evaluating hydraulic fracturing. This study proposes a novel inversion method for predicting fracture networks in coal reservoirs, explicitly considering the distribution of natural fractures. The method incorporates three distinct natural fracture opening modes and employs… More >

Nataliya Shakhovska, Volodymyr Shymanskyi^*, Maksym Prymachenko

CMC-Computers, Materials & Continua, Vol.82, No.3, pp. 4469-4484, 2025, DOI:10.32604/cmc.2025.062675 - 06 March 2025

Abstract Time series forecasting is important in the fields of finance, energy, and meteorology, but traditional methods often fail to cope with the complex nonlinear and nonstationary processes of real data. In this paper, we propose the FractalNet-LSTM model, which combines fractal convolutional units with recurrent long short-term memory (LSTM) layers to model time series efficiently. To test the effectiveness of the model, data with complex structures and patterns, in particular, with seasonal and cyclical effects, were used. To better demonstrate the obtained results and the formed conclusions, the model performance was shown on the datasets More >

Mariam Ishtiaq^1,2, Jong-Un Won^1,2,*

CMC-Computers, Materials & Continua, Vol.82, No.3, pp. 5343-5361, 2025, DOI:10.32604/cmc.2025.061466 - 06 March 2025

Abstract Fire detection has held stringent importance in computer vision for over half a century. The development of early fire detection strategies is pivotal to the realization of safe and smart cities, inhabitable in the future. However, the development of optimal fire and smoke detection models is hindered by limitations like publicly available datasets, lack of diversity, and class imbalance. In this work, we explore the possible ways forward to overcome these challenges posed by available datasets. We study the impact of a class-balanced dataset to improve the fire detection capability of state-of-the-art (SOTA) vision-based models and propose… More >

Mohammed A. El-Shorbagy¹, Anas Bouaouda^2,*, Laith Abualigah^3,4, Fatma A. Hashim^5,6

CMES-Computer Modeling in Engineering & Sciences, Vol.142, No.3, pp. 2339-2404, 2025, DOI:10.32604/cmes.2025.061028 - 03 March 2025

Abstract With the rapid advancements in technology and science, optimization theory and algorithms have become increasingly important. A wide range of real-world problems is classified as optimization challenges, and meta-heuristic algorithms have shown remarkable effectiveness in solving these challenges across diverse domains, such as machine learning, process control, and engineering design, showcasing their capability to address complex optimization problems. The Stochastic Fractal Search (SFS) algorithm is one of the most popular meta-heuristic optimization methods inspired by the fractal growth patterns of natural materials. Since its introduction by Hamid Salimi in 2015, SFS has garnered significant attention… More >