Holy Oghenewona Ovwiurhobo¹, Marius O. Eji², Adil Alshoaibi³, Ndanduleni Lethole⁴, Chawki Awada³, Shumaila Islam³, Nisrin Alnaim³, Fabian I. Ezema^1,4,*

Chalcogenide Letters, Vol.23, No.8, 2026, DOI:10.32604/cl.2026.086712 - 18 September 2026

Abstract Several studies have reported a growing interest in nanomaterials beyond conventional graphite, driven by the rapid global demand for sustainable, high-performance energy storage. Among these materials, two-dimensional (2D) transition metal chalcogenides (TMCs), molybdenum- and tin-based systems such as molybdenum disulfide (MoS₂) and tin (IV) disulfide (SnS₂) in particular, have emerged as promising candidates for next-generation electrochemical energy storage devices (EESDs). This is owing to their unique X-M-X sandwich architectures, tunable electronic properties, and versatile intercalation chemistry. Despite several studies on 2D TMCs and their applications in EESDs, a gap still exists, as there is no comprehensive… More > Graphic Abstract

Nashat Nawafleh^*, Faris M. Al-Oqla

CMC-Computers, Materials & Continua, Vol.89, No.2, 2026, DOI:10.32604/cmc.2026.087255 - 15 September 2026

Abstract In order to mimic the thermal properties of various natural fibers, this research presents a novel prediction framework that combines Fuzzy Logic (FL) with Grey Wolf Optimization (GWO). While the GWO technique ensures mathematical correctness by fine-tuning membership function parameters, this research uses a hybrid fuzzy model to outline nonlinear relationships between fiber components and thermal performance, which significantly reduces the need for extensive, trial-and-error laboratory testing. In this study, moisture, cellulose, and hemicellulose levels are predicted to be used to identify the finest natural fibers for biomaterial uses. An optimization methodology is seen by More >

Chuanlin Jian¹, Yuanchen Sun², Xiangcheng Liu¹, Xuming Huang³, Samaneh Beheshti Kashi⁴, Xing Hu^1,*

CMC-Computers, Materials & Continua, Vol.89, No.2, 2026, DOI:10.32604/cmc.2026.086441 - 15 September 2026

Abstract Heterogeneous clusters now carry most artificial-intelligence, scientific-computing, cloud, and edge-assisted workloads, yet scheduling them well remains difficult: workload semantics, hardware capability, queue state, and energy behavior are tightly coupled. Existing schedulers, whether heuristic, learning-based, or built on reinforcement learning, tend to rely on coarse resource requests, overlook the compatibility between task types and node classes, or carry a heavy training and deployment cost. This paper presents Classification-Aware Scheduling for Heterogeneous clusters (CASH), a confidence-calibrated, classification-aware scheduling framework that integrates workload profiling, deep feature crossing, gradient-boosted boundary modeling, and risk-aware heterogeneous resource mapping. CASH constructs a… More >

Hui Zhang¹, Mangang Xie^1,*, Baozhen An², Jing Wei¹

CMC-Computers, Materials & Continua, Vol.89, No.2, 2026, DOI:10.32604/cmc.2026.086401 - 15 September 2026

Abstract Peak age of information (PAoI) and energy consumption (EC) are conflicting yet critical metrics in mobile edge computing (MEC)-assisted vehicular networks. Most existing studies overlook the joint effects of sensing, transmission, and computation. The main contributions of this work are threefold. First, we derive novel analytical expressions for the average PAoI and average EC under all three strategies, explicitly accounting for the energy and delay costs across the entire data processing chain. Second, we demonstrate that the partial computation offloading strategy is superior, effectively balancing the low latency of local processing with the high power More >

Yusra Mansoor¹, Huma Jamshed^1,*, Mohammed Khouj², Muhammad I. Masud^2,*, Urooj Waheed¹, Abdul Wahid Memon³, Najeeb Ur Rehman Malik^4,*, Touqeer Ahmed Jumani⁵

CMC-Computers, Materials & Continua, Vol.89, No.2, 2026, DOI:10.32604/cmc.2026.085975 - 15 September 2026

Abstract The increasing demand for sustainable and energy-aware agricultural practices due to climate change, limited natural resources, and increasing food requirements has accelerated the adoption of Internet of Things (IoT) and machine learning (ML) technologies in smart farming. This study proposes a solar-powered IoT and ML-based framework for energy-aware crop suitability prediction in precision agriculture. The system employs low-power IoT sensors to continuously monitor important environmental and soil parameters, including temperature, humidity, soil moisture, soil temperature, heat index, nitrogen (N), phosphorus (P), and potassium (K) levels. To reduce dependence on conventional energy sources, the framework is… More >

Yunis Torun^1,*, Burak Seckin¹, Rukiye Karakis²

CMC-Computers, Materials & Continua, Vol.89, No.2, 2026, DOI:10.32604/cmc.2026.085198 - 15 September 2026

Abstract Marble classification has traditionally relied on human visual inspection, where operators assess color, texture, and pattern alignment to determine quality. However, this manual process is subjective, inconsistent, and inefficient for large-scale industrial applications. To address these limitations, this study proposes DeepMarbleVision, a texture-aware ensemble deep learning framework with energy-layer-based feature fusion for marble quality classification. A real-world dataset was created using the MarbleVision system, including three marble quality classes acquired from an industrial marble classification environment. The proposed approach integrates energy-layer-based feature fusion into TCNN variants of AlexNet, ResNet, and DenseNet, which were initialized through… More >

Nidhika Chauhan^1,*, Navneet Kaur¹, Jawad Khan², Younhyun Jung², Haleem Farman³, Ahmed Sedik^3,4, Sohaib Bin Altaf Khattak³

CMC-Computers, Materials & Continua, Vol.89, No.2, 2026, DOI:10.32604/cmc.2026.084063 - 15 September 2026

Abstract The exponential growth of cloud data centers necessitates highly efficient resource allocation and task migration strategies. However, multi-dimensional memory fragmentation severely limits the efficacy of standard scheduling algorithms under heavy-tailed, real-world workloads. This paper proposes Fuzzy-SLW, a hybrid swarm-intelligence architecture that integrates a Mamdani fuzzy-inference pre-filter with a distributed Spark Lion-Whale Optimization (SLWO) core via Apache Spark. The fuzzy pre-filter mathematically prunes the search space using non-compressible hardware constraints, while the Spark execution model resolves the traditional serial bottleneck of swarm intelligence. Evaluated within a discrete-event environment utilizing the Google Cluster Trace (2019), Fuzzy-SLW demonstrates… More >

Guofen Rui¹, Xiangyu Hu², Jingyu Cao^3,4,*, Yibo Zhang², Yangyang Zhu², Haifei Chen^1,2,*

Frontiers in Heat and Mass Transfer, Vol.24, No.4, 2026, DOI:10.32604/fhmt.2026.086281 - 31 August 2026

Abstract Spectral beam splitting is a promising approach for thermally decoupling photovoltaic and photothermal processes in PV/T systems. However, existing liquid spectral splitters still suffer from insufficient short-wavelength absorption and/or the high cost of noble-metal nanoparticles. In this study, a water-based Cu@SiO₂ nanofluid was developed as a low-cost absorption/transmission spectral splitting filter for monocrystalline silicon PV/T systems. The full solar spectrum considered in this system covers both photovoltaic and thermal utilization, while 750–1000 nm was selected only as the target transmission window for the c-Si cell. This window was chosen because c-Si cells can effectively utilize this… More >

Perihan Hazel Kaya^1,*, Coşkun Kuş², Mustafa Çoklu³, Mustafa Göktuğ Kaya⁴

Energy Engineering, Vol.123, No.10, 2026, DOI:10.32604/ee.2026.084804 - 30 August 2026

Abstract This study examines the relationship between energy consumption, renewable energy supply, and economic growth in BRICS countries over the period 2000–2023 within the framework of panel data analysis. In the analysis, economic growth is represented by gross domestic product, while total energy consumption and renewable energy supply are used as the main energy-related variables. The study contributes to the literature by evaluating the energy-growth nexus together with both total energy use and renewable energy dynamics in BRICS economies. Since BRICS countries may be affected by common global shocks, cross-sectional dependence is considered in the empirical… More >

Viktoriia Mykytenko^1,*, Veronika Khudolei², Oleksandr Hurin³, Halyna Kryshtal⁴, Svetlana Mishchenko⁵, Roman Iskiv⁶

Energy Engineering, Vol.123, No.10, 2026, DOI:10.32604/ee.2026.084053 - 30 August 2026

Abstract This study develops a conceptual and analytical approach to assessing energy system (ES) resilience under conditions of a low-carbon transition, with particular attention to the role of operational constraints and adaptive capacity. The study demonstrates that, under conditions of polycrisis transformation, ES resilience cannot be adequately evaluated solely through technical and technological indicators, but requires the integration of functional, institutional, and operational parameters. An integrated resilience model is proposed in which operational constraints are interpreted as an endogenous structural factor that determines the boundary conditions of system functioning and shapes ES development trajectories. The methodological… More > Graphic Abstract