Lei Zhao¹, Lihui Ma², Junwen Chen³, Pan Zhang², Jiang Bian^4,*, Dong Sun²

Energy Engineering, Vol.123, No.2, 2026, DOI:10.32604/ee.2025.070290 - 27 January 2026

Abstract Shock waves in the nozzle during supersonic separation under different conditions can disrupt the flow field’s thermodynamic equilibrium. While it contributes to the recovery of pressure energy, it also leads to the dissipation of mechanical energy. This study aimed to investigate the effects of changes in back pressure on the shock wave position and its subsequent impact on the refrigeration performance of nozzles. A mathematical model for the supersonic gas in a nozzle was established and evaluated via experiments. The results show that when the back pressure is less than 0.2 MPa, no shock wave… More >

Mehran Karimi¹, Hesamodin Heidarigoujani¹, Mehdi Jahangiri^1,*, Milad Torabi Anaraki², Daryosh Mohamadi Janaki³

Energy Engineering, Vol.123, No.2, 2026, DOI:10.32604/ee.2025.070190 - 27 January 2026

Abstract The technical, economic, and environmental performance of solar hot-water (SWH) systems for Swedish residential apartments—where approximately 80% of household energy is devoted to space heating and sanitary hot-water production—was assessed. Two collector types, flat plate (FP) and evacuated tube (ET), were simulated in TSOL Pro 5.5 for five major cities (Stockholm, Göteborg, Malmö, Uppsala, Linköping). Climatic data and cold-water temperatures were sourced from Meteonorm 7.1, and economic parameters were derived from recent national statistics and literature. All calculations explicitly accounted for heat losses from collectors, storage tanks, and internal and external piping systems, and established… More >

Chenxuan Zhang, Yongqing Qi^*, Ximin Cao, Yanchi Zhang

Energy Engineering, Vol.123, No.2, 2026, DOI:10.32604/ee.2025.069878 - 27 January 2026

Abstract To enhance the low-carbon economic efficiency and increase the utilization of renewable energy within integrated energy systems (IES), this paper proposes a low-carbon dispatch model integrating power-to-gas (P2G), carbon capture and storage (CCS), hydrogen fuel cell (HFC), and combined heat and power (CHP). The P2G process is refined into a two-stage structure, and HFC is introduced to enhance hydrogen utilization. Together with CCS and CHP, these devices form a multi-energy conversion system coupling electricity, heat, cooling, and gas. A ladder-type carbon trading approach is adopted to flexibly manage carbon output by leveraging marginal cost adjustments.… More >

Boda Zhang^1,*, Fuhua Luo¹, Yunhao Yu¹, Chameiling Di¹, Ruibin Wen¹, Fei Chen²

Energy Engineering, Vol.123, No.2, 2026, DOI:10.32604/ee.2025.069323 - 27 January 2026

Abstract The increasing intelligence of power systems is transforming distribution networks into Cyber-Physical Distribution Systems (CPDS). While enabling advanced functionalities, the tight interdependence between cyber and physical layers introduces significant security challenges and amplifies operational risks. To address these critical issues, this paper proposes a comprehensive risk assessment framework that explicitly incorporates the physical dependence of information systems. A Bayesian attack graph is employed to quantitatively evaluate the likelihood of successful cyber attacks. By analyzing the critical scenario of fault current path misjudgment, we define novel system-level and node-level risk coupling indices to precisely measure the… More >

Miriam Di Matteo^*, Domiziana Vespasiano, Gianluigi Lo Basso, Costanza Vittoria Fiorini, Andrea Vallati

Energy Engineering, Vol.123, No.2, 2026, DOI:10.32604/ee.2025.064353 - 27 January 2026

Abstract Decarbonising the building sector, particularly residential heating, represents a critical challenge for achieving carbon-neutral energy systems. Efficient solutions must integrate both technological performance and renewable energy sources while considering operational constraints of existing systems. This study investigates a hybrid heating system combining a natural gas boiler (NGB) with an air-to-water heat pump (AWHP), evaluated through a combination of laboratory experiments and dynamic modelling. A prototype developed in the Electrical and Energy Engineering Laboratory enabled the characterization of both heat generators, the collection of experimental data, and the calibration of a MATLAB/Simulink model, including emissions and… More >

Hosam O. Elansary¹, Naveed A. Noor², Syed M. Ahmad³, Humza Riaz³, Sohail Mumtaz^4,*

Chalcogenide Letters, Vol.23, No.1, 2026, DOI:10.32604/cl.2026.076592 - 26 January 2026

Abstract Ferrites are remarkable compounds for energy harvesting and spintronic applications. For this purpose, mechanically stable, thermodynamic, photo-catalytic, and ferromagnetic characteristics of ferrites Al₂Mn(S/Se)₄ have been investigated significantly using PBEsol-GGA and modified Becke Johnson potential (TB-mBJ). In order to determine structural stability, we calculate formation energy (E_f) and Born stability criteria that confirm the structural stability of the Al₂Mn(S/Se)₄. 2D and 3D plots of Poisson’s ratio (υ) and linear compressibility are also used to indicate the stability of these materials. Additionally, thermodynamic characteristics reveal that both ferrites are stable. Spin-polarized electronic properties indicate that both ferrites are ferromagnetic More >

Jiale Hu, Fan Chen^*, Yue Yang, Man Wang

Journal on Artificial Intelligence, Vol.8, pp. 51-64, 2026, DOI:10.32604/jai.2026.075257 - 22 January 2026

Abstract To enhance the operational performance of energy storage systems (ESS), this paper proposes an optimal dispatch strategy that jointly considers reliability and economic efficiency. First, we formulate a cost-minimization model that includes ESS dispatch costs, wind and photovoltaic (PV) curtailment costs, and load loss costs, while explicitly enforcing power supply reliability constraints. Next, we develop a comprehensive evaluation indicator system that integrates reliability, economic performance, renewable-energy utilization, and ESS technical indicators, thereby addressing the limitations of single-indicator assessments. Finally, a case study using real data from a region in China shows that the proposed strategy More >

Ahmed Awad Mohamed¹, Eslam Abdelhakim Seyam^2,*, Ahmed R. Elsaeed³, Laith Abualigah⁴, Aseel Smerat^5,6, Ahmed M. AbdelMouty⁷, Hosam E. Refaat⁸

CMC-Computers, Materials & Continua, Vol.86, No.3, 2026, DOI:10.32604/cmc.2025.073171 - 12 January 2026

Abstract In recent years, fog computing has become an important environment for dealing with the Internet of Things. Fog computing was developed to handle large-scale big data by scheduling tasks via cloud computing. Task scheduling is crucial for efficiently handling IoT user requests, thereby improving system performance, cost, and energy consumption across nodes in cloud computing. With the large amount of data and user requests, achieving the optimal solution to the task scheduling problem is challenging, particularly in terms of cost and energy efficiency. In this paper, we develop novel strategies to save energy consumption across… More >

Rayeesa Mehmood^*, Sergei Koltcov, Anton Surkov, Vera Ignatenko

CMC-Computers, Materials & Continua, Vol.86, No.3, 2026, DOI:10.32604/cmc.2025.072735 - 12 January 2026

Abstract Activation pruning reduces neural network complexity by eliminating low-importance neuron activations, yet identifying the critical pruning threshold—beyond which accuracy rapidly deteriorates—remains computationally expensive and typically requires exhaustive search. We introduce a thermodynamics-inspired framework that treats activation distributions as energy-filtered physical systems and employs the free energy of activations as a principled evaluation metric. Phase-transition–like phenomena in the free-energy profile—such as extrema, inflection points, and curvature changes—yield reliable estimates of the critical pruning threshold, providing a theoretically grounded means of predicting sharp accuracy degradation. To further enhance efficiency, we propose a renormalized free energy technique that More >

Tahir Mahmood^1,*, Muhammad Waseem Ashraf^1,*, Shahzadi Tayyaba², Muhammad Munir³, Babiker M. A. Abdel-Banat³, Hassan Ali Dinar³

CMC-Computers, Materials & Continua, Vol.86, No.3, 2026, DOI:10.32604/cmc.2025.072436 - 12 January 2026

Abstract Artificial intelligence (AI) based models have been used to predict the structural, optical, mechanical, and electrochemical properties of zinc oxide/graphene oxide nanocomposites. Machine learning (ML) models such as Artificial Neural Networks (ANN), Support Vector Regression (SVR), Multilayer Perceptron (MLP), and hybrid, along with fuzzy logic tools, were applied to predict the different properties like wavelength at maximum intensity (444 nm), crystallite size (17.50 nm), and optical bandgap (2.85 eV). While some other properties, such as energy density, power density, and charge transfer resistance, were also predicted with the help of datasets of 1000 (80:20). In… More >