Kelly Leite dos Santos Castro Assis¹, Druval Santos de Sá¹, Bruno da Silva Marques¹, Carolina Carvalho de Mello¹, João Lucas Marques Barros², Henrique Carvalhais Milanezi², João Batista Oliveira dos Santos², Carlos Alberto Franchini¹, Bráulio Soares Archanjo¹, Carlos Alberto Achete¹, Adriana Maria da Silva^1,*

Journal of Renewable Materials, Vol.14, No.7, 2026, DOI:10.32604/jrm.2026.02025-0233 - 28 July 2026

Abstract The transition toward a circular economy and zero-waste strategies has driven increasing interest in biomass-derived carbon materials as sustainable alternatives to conventional catalyst supports. Agricultural and industrial residues can be converted into porous carbons with high surface area, tunable porosity, and rich surface chemistry, enabling waste valorization and stabilization of metal species ranging from nanoparticles to single atoms. These properties support their application across heterogeneous catalysis, photocatalysis, and electrochemical systems, revealing cross-cutting opportunities among these fields. Despite these advantages, challenges remain, including feedstock heterogeneity, energy-intensive processing, scalability limitations, and the lack of standardized methodologies. This More > Graphic Abstract

Nuhu Lawal^1,2, Adekunle Adeleke^2,3, Petrus Nzerem^2,4, Chizoma Adewumi^2,5, Frank Ogundolie^2,6, Esther Anosike-Francis^2,3, Waliyi Adeleke^2,3, Seun Jesuloluwa^2,3,*

Journal of Renewable Materials, Vol.14, No.7, 2026, DOI:10.32604/jrm.2026.02025-0203 - 28 July 2026

Abstract The environmental concerns of petroleum-based plastics, including their non-biodegradability, contribution to pollution, and reliance on finite fossil resources, have motivated growing global interest in biodegradable alternatives, with starch-based bioplastics emerging as a promising solution due to their renewability, biodegradability, cost-effectiveness, and compatibility with existing processing technologies. This review synthesizes recent developments, challenges, and analytical techniques related to starch-based bioplastics. It examines the physicochemical properties of starch, modification methods such as plasticization, blending, and chemical treatments, and key production techniques including extrusion, injection molding, and 3D printing. Mechanical, thermal, and barrier properties are evaluated through standardized More >

Siti Norasmah Surip¹, Wan Nor Raihan Wan Jaafar^1,*, Jaka Fajar Fatriansyah², Ing Kong³

Journal of Renewable Materials, Vol.14, No.7, 2026, DOI:10.32604/jrm.2025.02025-0182 - 28 July 2026

Abstract Most studies on kenaf fibre composites focus on the bast due to its higher fibre yield and strength, while the core is often neglected. In this work, Polylactic Acid (PLA) matrix was reinforced with both kenaf bast and core fibres at a 49:1 wt% ratio. The fibres were chemically treated and cryo-crushed to improve bonding and dispersion. Mechanical testing revealed that treated Kenaf Core Composites (KCC) exhibited comparable flexural and impact properties to Kenaf Bast Composites (KBC), with flexural strengths of 46.19 and 46.52 MPa, respectively, and impact strengths of 5.8 and 4.4 J/m. Meanwhile, More >

Xuan Kien Pham¹, Van Dat Nguyen^2,*, Van Thanh Phan^3,*, Duc Trien Nguyen^4,*

CMES-Computer Modeling in Engineering & Sciences, Vol.148, No.1, 2026, DOI:10.32604/cmes.2026.086196 - 27 July 2026

Abstract Accurate energy consumption forecasting faces two major challenges: limited historical data and complex consumption patterns. To address these challenges, this study proposes a new hybrid framework named the Decomposition-based Grey-Neuro-Fuzzy Architecture (DeGNA). The model first uses the Denton method to convert limited annual records into high-frequency monthly data. Next, it applies STL decomposition to separate the data into trend, seasonal and residuals components. A rolling-window GM(1,1) model is then used to predict the main growth trend, while a GWO-optimized ANFIS model uses economic indicators (IIP and FDI) to forecast complex seasonal changes. This study evaluates… More >

Adrián Bañuls-Arias, Cipriano Galindo, Ana Cruz-Martín, Manuel Castellano-Quero, Juan M. Gandarias, Juan-Antonio Fernández-Madrigal, Vicente Arévalo-Espejo^*

CMES-Computer Modeling in Engineering & Sciences, Vol.148, No.1, 2026, DOI:10.32604/cmes.2026.085976 - 27 July 2026

Abstract The basic operation of a mobile robot is navigating to some target, avoiding collisions and possibly minimizing other criteria. A diversity of methods have been developed since the past century, and the research is still active, but there is one aspect that is often neglected: the duration of the steps in which computational devices divide the navigation process. Usually, it is set heuristically to a small, constant value for sampling observations frequently enough to ensure safety; however, each robot and environment has particularities that can make such a fixed timestep sub-optimal under some criteria. This… More >

Guangyu Xu^1,2, Yuxi Ban¹, Legend Zhang³, Junmin Lyu³, Feng Bao⁴, Wenfeng Zheng^1,3,*

CMES-Computer Modeling in Engineering & Sciences, Vol.148, No.1, 2026, DOI:10.32604/cmes.2026.085484 - 27 July 2026

Abstract High-resolution remote sensing semantic segmentation is a fundamental task in Geospatial Artificial Intelligence (GeoAI). Existing CNN-based methods are effective for local and multiscale feature extraction but often lack progressive cross-scale semantic propagation, while attention- and Transformer-based methods improve global spatial modeling but generally ignore frequency-domain regularities. To address these limitations, this study proposes a Multiscale Long-Distance Feature Aggregation Network (MLFANet), a unified spatial-frequency segmentation framework for high-resolution remote sensing imagery. MLFANet introduces three key components: a Multiscale Global Dependency Extraction module for cascaded cross-scale contextual refinement, an FFT-based frequency-domain branch with learnable global filtering for… More >

Ali Raza^1,*, Umar Shafique¹, Marek Lampart¹, Dumitru Baleanu², Emad Fadhal³, Hadil Alhazmi⁴

CMES-Computer Modeling in Engineering & Sciences, Vol.148, No.1, 2026, DOI:10.32604/cmes.2026.084939 - 27 July 2026

Abstract Kawasaki disease (KD) is an acute, self-limited pediatric vasculitis of unknown etiology and is one of the leading causes of acquired coronary artery complications in children. Endothelial dysfunction, vascular endothelial growth factor (VEGF) activity, adhesion molecule/chemokine activation, and inflammatory cytokine responses play important roles in its pathogenesis. This paper presents a delay differential equation model with stochastic perturbations to study lesion-level inflammatory mechanisms involved in Kawasaki disease pathogenesis. The model describes interactions among healthy endothelial cells, vascular endothelial growth factor (VEGF), adhesion molecules/chemokines, and inflammatory cytokine activity. Mathematically, endothelial-cell injury promotes VEGF production, VEGF contributes… More >

Sachin Tom, Eva Gutheil^*

CMES-Computer Modeling in Engineering & Sciences, Vol.148, No.1, 2026, DOI:10.32604/cmes.2026.084886 - 27 July 2026

Abstract A single droplet heating, vaporization, and detailed combustion model is developed for pure p-xylene (p-C₈H₁₀) in hot air. p-C₈H₁₀ is a combustible solvent in precursor solutions, for instance, with titanium tetraisopropoxide (TTIP) for the production of TiO2 nanoparticles. In the present one-dimensional mathematical model, a spherically symmetric p-xylene droplet in hot air is considered, resolving both the droplet (liquid phase) and the ambience (gas phase). The calculation of the vaporization rate includes the Stefan velocity at the droplet surface. In the gas phase, a detailed chemical reaction scheme is used. Elementary reactions are… More >

Tanzila Saba¹, Muhammad Mujahid¹, Faten S. Alamri^2,*, Roaa Khalil Mohamed Ali Abed³

CMES-Computer Modeling in Engineering & Sciences, Vol.148, No.1, 2026, DOI:10.32604/cmes.2026.084816 - 27 July 2026

Abstract In the era of artificial intelligence, pattern recognition techniques have become fundamental in advancing medical image processing, diagnosis, and automated disease classification systems. Among various clinical challenges, breast cancer is the second most dangerous leading cause of death in women worldwide. Early and accurate detection of breast cancer is crucial to develop advanced diagnostic methods to control further loss or reduce mortality rates. This study proposes a dynamic graph multi-scale network for breast cancer diagnosis, integrated with multi-scale convolutional feature extraction, a squeeze-and-excitation block, and a graph convolutional network to jointly model local spatial features… More > Graphic Abstract

Yunus Celik^*, Burhan Necati Kiziloglu

CMES-Computer Modeling in Engineering & Sciences, Vol.148, No.1, 2026, DOI:10.32604/cmes.2026.084744 - 27 July 2026

Abstract This study investigates the passive amplification of aerodynamic excitation forces through coordinated bluff-body geometric modifications to quantify the vortex-induced vibration (VIV) energy harvesting potential of stationary cylinders in the laminar regime. Two-dimensional laminar simulations on fixed bodies isolate geometric effects from structural feedback. Circumferential biomimetic grooves are first optimised on a circular baseline at Re=200 using a Taguchi orthogonal array (L9), identifying groove amplitude as the dominant control parameter and selecting N=24, Amp=5% as the optimal configuration, which yields a 21% increase in the root-mean-square lift coefficient (Cℓ,rms) and… More >