Kangjia Fan¹, Biao He^2,*, Shahab Hosseini³, Seyed Yaser Mousavi Siamakani^4,*

CMES-Computer Modeling in Engineering & Sciences, Vol.148, No.2, 2026, DOI:10.32604/cmes.2026.084187 - 28 August 2026

Abstract Mining activities are associated with environmental side effects, which can be successfully predicted and strategies proposed for mitigating their adverse impacts. The cleaner production policies of green blasting focus on ecological issues related to mining operations and reduction plans. As a prediction part of this policy, this research proposed a mathematical model named Gene Expression Programming (GEP) to accurately predict the factors that generated pollutions, i.e., total suspended particles (TSP), particles dust with an analogous aerodynamic diameter of less than 10 μm (PM₁₀), and dust emission distance due to mine blasting (DEMB), simultaneously. As the reduction… More > Graphic Abstract

Merve Sinem Karahan^*, Mehmet Karaköse

Journal of Quantum Computing, Vol.8, pp. 101-121, 2026, DOI:10.32604/jqc.2026.078969 - 21 August 2026

Abstract Accurate prediction of carbon emissions is essential for developing sustainable environmental policies and mitigating global warming. Road transportation represents one of the major sources of global CO₂ emissions due to its dependence on fossil fuels. This study presents a comparative framework that evaluates classical machine learning models alongside a hybrid quantum–classical learning architecture for vehicle-based CO₂ emission prediction. A large-scale vehicle emissions dataset containing 7385 samples collected over approximately seven years was obtained from the official open-data platform of the Government of Canada. Key vehicle characteristics, including engine size, fuel consumption, transmission type, and vehicle class,… More >

Yurong Zhao, Daozhi Chen^*, Yu Yin^*

Energy Engineering, Vol.123, No.8, 2026, DOI:10.32604/ee.2026.075847 - 12 July 2026

Abstract In order to investigate the impacts of government policy and market environment on the development of renewable energy, this paper constructs a computable general equilibrium (CGE) model to simulate the impacts of policy scenarios, market scenarios and policy-market scenarios on renewable energy output and investment, energy structure, economy and environment based on China’s input-output extension table in 2020. The results show that: the subsidy can optimize the energy structure and power structure, and non-hydropower renewable energy represented by wind power and solar power will become a new force of energy supply in China; the market… More >

Riad Mollik Babu¹, Md Shafiul Alam^2,*, Md. Hasibur Rahman³, Mohammad Ali², Md. Alamgir Hossain⁴, Md. Arifuzzaman⁵

CMES-Computer Modeling in Engineering & Sciences, Vol.147, No.3, 2026, DOI:10.32604/cmes.2026.082366 - 30 June 2026

Abstract The increasing penetration of solar photovoltaic (PV) systems into power grids poses challenges due to their inherent intermittency and variability, which can compromise grid stability and reliability. Hybridizing solar PV with wind energy and battery energy storage system (BESS) offers a promising solution by leveraging resource complementarity and providing fast frequency response. This study presents a techno-economic and environmental assessment of a hybrid renewable energy system. Wind turbines and a BESS are integrated with the existing 7.5 MW Sirajganj Solar PV Power Plant in Bangladesh. The proposed hybrid configuration is evaluated using real-world operational data… 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 >

Media Nadhim Mahmood¹, Oday I. Abdullah^1,2,3,*, Amani I. Altmimi⁴

Energy Engineering, Vol.123, No.7, 2026, DOI:10.32604/ee.2026.078421 - 18 June 2026

Abstract The highly important requirement for achieving urban sustainability for any city is the availability of renewable energy, as reducing carbon emissions is considered one of the most important factors in improving the quality of life and health of people in green cities. The main objective of this research is to provide an in-depth study and analysis of the role of renewable energies, especially solar energies, in promoting sustainable development in cities around the world, in general, and in Iraq in particular. Strategies for using clean energy sources in a hybrid manner, such as solar energy,… More >

Pavlo Bekhta^1,2,*, Iryna Lytvyn¹

Journal of Renewable Materials, Vol.14, No.5, 2026, DOI:10.32604/jrm.2026.02026-0035 - 28 May 2026

Abstract The modification of melamine–urea–formaldehyde (MUF) adhesives with lignosulfonates (LS) represents a promising strategy for developing more sustainable wood-based panels. However, the influence of the counterion type remains poorly understood. In this study, the effect of lignosulfonate counterions on adhesives performance and properties of MUF-bonded particleboards was investigated, with a focus on sodium (NaLS) and magnesium (MgLS) lignosulfonates incorporated at 2.5%, 5.0%, and 7.5%. Adhesives performance was characterized by measuring dry solids content, dynamic viscosity, gelation time, and pH. The produced particleboards were evaluated in terms of density, bending strength, modulus of elasticity, internal bond strength… More > Graphic Abstract

Marcelo Dario Rodas Britez^1,*, Francesco Ghionda², Vasileios Tatsis³, Dimosthenis Ioannidis³

Energy Engineering, Vol.123, No.6, 2026, DOI:10.32604/ee.2026.078082 - 27 May 2026

Abstract Current energy systems are increasingly complex, considering multi-energy systems, the integration of non-programmable renewable energy sources, and the simultaneous evaluation of multiple evaluation objectives (i.e., costs vs carbon dioxide emissions). This complexity opens the opportunity to explore optimization algorithms as assistance for systematic and automatic management of energy systems. The implementation of a multi-energy system poses multiple challenges, including managing multiple energy vectors with different technologies applied across energy production, energy storage, and renewable energy sources. Also, multi-objective evaluation should be considered to manage reductions in costs and carbon dioxide emissions. Therefore, this paper proposes… More >

Lihua Zhong¹, Feng Pan¹, Yuyao Yang¹, Lei Feng¹, Jinghe Jiang², Guo Lin², Xiaoshun Zhang^3,*

Energy Engineering, Vol.123, No.6, 2026, DOI:10.32604/ee.2025.073240 - 27 May 2026

Abstract This paper addresses the challenge of efficiently calculating dynamic carbon emission factors (CEFs) in large-scale power systems. Traditional methods that rely on direct matrix inversion are computationally intensive and become impractical for networks with thousands of nodes. To overcome this limitation, a fast and scalable computational framework is proposed based on the incomplete LU (ILU) preconditioned biconjugate gradient stabilized (BiCGSTAB) iterative solver. The proposed approach formulates the nodal CEF model as a sparse linear system and employs Krylov subspace acceleration with ILU preconditioning to enhance convergence and numerical stability. The method is applied to synthetic… More > Graphic Abstract

Ovidijus Grigas^*, Rytis Maskeliūnas

CMES-Computer Modeling in Engineering & Sciences, Vol.147, No.1, 2026, DOI:10.32604/cmes.2026.077324 - 27 April 2026

Abstract Early detection of Mild Cognitive Impairment (MCI) with FDG-PET is essential for timely Alzheimer’s disease intervention. However, PET image quality is limited by low spatial resolution, partial volume effects, and Poisson noise. Standard enhancement methods, such as Bilateral filtering or Contrast Limited Adaptive Histogram Equalization (CLAHE), can increase contrast but often introduce heavy noise or distort image texture, while deep learning methods may produce hallucinated structures. We propose a fully data-adaptive, non-learned 3D enhancement framework whose output is deterministic for a given input volume, that combines Laplacian-based local contrast modulation with a gradient-gated Difference-of-Gaussians (DoG)… More >