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  The Influence of Various Training Algorithms on the Effectiveness of Thermal Conductivity Prediction for Mgo-GO/Water–Ethylene Glycol Hybrid Nanofluid: A More Effective Method for Network Training

  (Elsevier B.V., 2026) Singh, Narinderjit Singh Sawaran; Alaloosi, Waleed; Hussein, Muntadher Abed; Qasim, Ali Abdul Karim; Jasim, Dheyaa J.; Sabri, Laith S.; Alrawashdeh, Albara Ibrahim; Taner, Mahmut; Salahshour, Soheil; Ali Eftekhari, S.

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  Accurate prediction of the thermal conductivity of hybrid nanofluids is essential for the design and optimization of advanced thermal management systems. In the present study, an artificial neural network framework was developed to predict the thermal conductivity of magnesium oxide–graphene oxide/water–ethylene glycol hybrid nanofluids using experimentally measured data. A total of 45 experimental datasets were generated by varying the temperature from 20 to 60 ◦C and the nanoparticle volume fraction from 0 to 0.20 vol.%. A feedforward multilayer perceptron network was constructed, and ten backpropagation training algorithms were systematically evaluated to identify the optimum predictive model. Among the investigated algorithms, the Levenberg–Marquardt algorithm exhibited the highest predictive performance, achieving a mean squared error of 1.976 × 10⁻⁶, a root mean square error of 1.395 × 10⁻³ W/m⋅K, a correlation coefficient of 0.9965, and a coefficient of determination of 0.9920. The robustness and generalization capability of the developed model were further confirmed through regression analysis, residual analysis, and 5-fold cross-validation, which yielded root mean square errors ranging from 8.38 × 10⁻⁴ to 4.73 × 10⁻³ W/m⋅K. A comparison with support vector regression, random forest, and Gaussian process regression demonstrated that the proposed model achieved highly competitive predictive accuracy while maintaining stable performance across different validation datasets. Furthermore, global Sobol sensitivity analysis identified nanoparticle volume fraction as the dominant governing parameter, whereas temperature exerted a considerably smaller influence on thermal conductivity. The observed enhancement in thermal conductivity was primarily attributed to the formation of conductive particle networks and improved interfacial heat transport associated with increasing nanoparticle loading. The proposed framework provided an accurate, robust, and computationally efficient methodology for predicting the thermophysical behavior of hybrid nanofluids and can be readily extended to estimate other thermophysical properties using appropriate experimental datasets.

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  Thermal Efficiency Enhancement in Battery Thermal Management Systems by Incorporating Phase Change Materials in Nano-Encapsulated Form into the Base Fluid

  (Elsevier Ltd, 2026) Xie, Maoqing; Wang, Leigang; Ding, Kexin; Taner, Mahmut; Salahshour, Soheil; Seçer, Aydın

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  The increasing variety of mechanisms and sources for energy extraction has drawn considerable attention to the long-term storage of energy. Phase change materials (PCMs) can retain heat for extended periods. This research presents a case study of lithium battery thermal management (BTM) via numerical simulation. We aim to store energy and enhance heat transfer by utilizing nano-encapsulated phase change materials (NEPCMs). We performed simulations of both steady and unsteady forced convection using C++ − based OpenFOAM solvers. The pimpleFOAM and simpleFOAM solvers have been improved to incorporate the effects of NEPCMs within the governing equations. The simulations demonstrate that, under both steady and unsteady conditions with high heat flux, the addition of NEPCM particles reduces the battery surface temperature. Specifically, integrating 5% nanoparticles into water results in a decrease in temperature of up to 2 K in steady-state simulations and up to 4 K in unsteady-state simulations. However, at elevated Reynolds numbers, the presence of nanoparticles diminishes pressure. Moreover, incorporating NEPCMs into water promotes a more uniform temperature distribution across the battery cell surface. In particular, adding 5% NEPCM to water resulted in a nearly 36% reduction in UT values.

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  Local Governments as Missing Actors in Occupational Safety Governance

  (INST SOCIAL SCIENCES, Kraljice Natalije 45 (Narodnog Fronta 45), Belgrade 11000, SERBIA, 2026) Koçali, Kaan

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  Occupational safety and health (OSH) outcomes have been increasingly influenced not only by legal frameworks and workplace-level practices, but also by the governance capacity of institutions that act at the local or regional levels. This study aims to explore the role of local governments in multi-level OSH governance systems, which is an important knowledge gap in safety science literature where local governments have often been viewed as peripheral or supplementary actors. A qualitative comparative study approach is used in this study, with documentary study findings from Germany, Serbia, and Türkiye aimed at exploring how OSH governance influences local governments in OSH prevention, coordination, and crisis management. The findings of this study have revealed that European countries with multi-level governance systems that integrate local governments using formal mandates, coordination mechanisms, or preventive infrastructure have been more effective in OSH prevention, coordination, and crisis management. However, in the case of Türkiye, there is a highly centralized OSH system with limited integration of local governments, which is likely to have limited OSH prevention-oriented practices. The study on Serbia reveals that there is limited integration of local governments in OSH governance in the country, but with limited institutionalization. The findings of this study have revealed that local governments have been an important but not well-integrated part of OSH governance. A key recommendation of this study concerns the need of developing local institutional capacity in OSH governance.

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  Green Liquefied Hydrogen Production with Multi Energy Generation by Solar–Wind Cogeneration System Incorporating Htcorcs and Peme–Claude Cycle: Techno Economic Environmental Analysis and Ann-Assisted Optimization

  (Elsevier Ltd, 2026) Li, Yonghui; Nutakki, Tirumala Uday Kumar; Zhang, Haibo; Khlifi, Mohamed Arbi; Alanazi, Mohana; Alsairy, Norah; Althbiti, Ashrf; Akhmadjonov, Rakhmonjon; Mahariq, Ibrahim; Fouad, Yasser

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  This study proposes an integrated renewable energy-driven cogeneration system designed to deliver multiple outputs, including liquefied hydrogen, electricity, heating, cooling, and desalinated water. The configuration combines solar and wind resources with cascaded organic Rankine cycles, a proton exchange membrane electrolyzer, absorption-based cooling with hydrogen liquefaction and desalination units. Multi-stage thermal coupling within the Rankine subsystems improves recovery of energy across different temperature levels, while low-grade heat is utilized for cooling without additional power demand. Excess renewable electricity is converted into hydrogen to improve system flexibility and mitigate intermittency issues. Performance is assessed using combined thermodynamic, economic, and environmental metrics, including net present value and payback time. A machine-learning-assisted optimization framework is applied to efficiently obtain optimal operating conditions targeting high exergy efficiency and reduced cost. Optimal results indicate hydrogen production of 15.61 kg/h, exergy efficiency of 29.92%, and a payback period of 3.89 years.

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  Artificial Intelligence and Statistical Analysis -Driven Prediction and Optimization of Tensile Properties in Biomass/Resin Epoxy Composites

  (Elsevier Editora Ltda, 2026) Moudjari, Maroua; Hafez, Ramy M.; Alhelali, Omar Ahmed; Talbi, Nabil; Kezzar, Mohamed; Rashid, Farhan Lafta; Rafik Sari, Mohamed; Al-Zuheiri, Aya M.; Alawaideh, Yazen M.; Popa, Ioan-Lucian; Mahariq, Ibrahim

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  This study explores the tensile behavior of epoxy composites reinforced with agricultural waste—walnut, almond, and pistachio shell particles—at weight fractions of 10%, 15%, and 25%. Composites were fabricated via hand lay-up, and mechanical properties were evaluated according to ASTM D3039 standards. Scanning Electron Microscopy (SEM) analysis revealed uniform particle dispersion and strong matrix-filler adhesion in walnut composites, while almond and pistachio composites exhibited particle agglomeration and microvoids at higher loadings. Tensile testing showed that walnut-filled composites achieved the highest strength (105.12 MPa) and ductility (strain ~4.9%), whereas almond and pistachio fillers reached peak strength at intermediate loadings but suffered reduced elongation due to interfacial defects. Artificial Neural Networks (ANNs) accurately predicted tensile stress, strain, and young's modulus, with regression coefficients close to 0.99, while Response Surface Methodology (RSM) identified the optimal combination of filler type, weight fraction, and cross-sectional area for maximizing tensile performance. These results demonstrate that both filler selection and loading critically influence composite properties, and that AI-driven modeling provides a reliable tool for predicting and optimizing the mechanical behavior of biomass-reinforced epoxy composites.

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