Aerodynamic optimization of Offshore Wind Farms (OWF) is challenged by nonlinear wake interactions, turbulence transport, and stochastic Marine Climate Conditions (MCC). High-fidelity Computational Fluid Dynamics (CFD) models capture these dynamics accurately but impose prohibitive computational costs for large-scale optimization. Analytical wake models offer computational efficiency but oversimplify complex turbulence interactions. This study presents a hybrid Artificial Intelligence (AI)-CFD that integrates Deep Neural Network (DNN) surrogates with selective CFD validation to enable efficient, robust optimization under MCC. The model employs unsteady Reynolds-averaged Navier-Stokes simulations with actuator-line turbine representation to generate training data, which are used to train an ensemble surrogate model incorporating dimensionally reduced climate states. Uncertainty-driven adaptive sampling triggers CFD validation for high-uncertainty configurations, maintaining physical fidelity while accelerating optimization. A multi-objective evolutionary algorithm (NSGA-II) optimizes turbine layout, yaw angles, and pitch controls to balance power generation, wake losses, and structural loading. Validation on an 80-turbine, 320 MW wind farm in Dhanushkodi, Tamil Nadu, sea proves 7.8% power improvement, 23.5% wake-loss reduction, and 11.2% network load decrease compared to the baseline, with 7.2× computational speedup vs. CFD-only optimization. Sensitivity analyses confirm robustness across wind speeds (6–12 m·s⁻¹), turbulence intensities (5–15%), and inflow directions (0–30°). The model establishes a scalable methodology for optimizing OWF under realistic MCC.

With the rapid growth of data centers driven by information technology development, a significant amount of waste heat generated by cooling systems is currently not effectively utilized and is often discarded. Recovering this waste heat is therefore of great importance for improving overall energy efficiency and promoting low-carbon operation. In this study, we simulate a cold plate liquid cooling system integrated with a waste heat recovery unit that utilizes different natural cooling sources, taking into account real-time fluctuations in thermal load demand. The results show that, compared with conventional liquid-cooling systems without heat recovery, the proposed configurations reduce cooling system power consumption by approximately 20–30% across all operating conditions. The minimum partial Power Usage Effectiveness (pPUE) reaches 1.13, indicating a highly efficient cooling subsystem. Moreover, up to 23% of the energy consumed in the data center can be effectively reused when temperature constraints of the heat user are considered. From an economic perspective, selling the recovered heat for domestic hot water production can reduce the annual operating cost of the liquid-cooling system by approximately ¥200,000, resulting in a short payback period. From an environmental perspective, the waste heat recovery system significantly supports low-carbon heating, reducing carbon emissions by about 110 tCO₂ per year compared with conventional gas boilers and air-source heat pumps. Overall, this study demonstrates that liquid-cooled data centers can serve as reliable and economically attractive heat sources under realistic dynamic operating conditions, providing practical guidance for large-scale waste heat recovery deployment.

To gain a deeper understanding of the thermal reaction behavior and thermal safety characteristics of lithium-ion battery electrolytes under different atmospheric conditions, this study systematically investigates the thermal decomposition process of 1 mol·L⁻¹ LiPF₆ dissolved in three typical solvent systems—EC/EMC, EC/EMC/DMC, and EC/EMC/DEC—using a high-precision microcalorimeter (C600). The analysis is performed under two typical atmospheric conditions: closed adiabatic and nitrogen flow. The heat flow and differential heat flow curves are quantitatively analyzed, and the exothermic onset temperature, thermal intensity, and reaction complexity are compared by integrating the cumulative heat release enthalpy. Furthermore, the Coats–Redfern model is used to extract kinetic parameters, perform global and α-segment fitting, and analyze the trend of reaction path changes. The results show that nitrogen flow significantly inhibits chain-side reactions and improves thermal stability, while under closed conditions, overlapping exothermic peaks and multi-stage releases are more likely to occur. Kinetic analysis reveals that the EC/EMC system exhibits a sharp increase in activation energy during the high conversion stage, suggesting a higher potential for thermal runaway, whereas the EC/EMC/DMC system has a single reaction mechanism and higher activation energy, indicating better thermal safety.

 Geothermal energy is a renewable resource that can drive green industrial growth and holds significant potential to enhance the competitiveness of Micro, Small, and Medium Enterprises (MSMEs) in Indonesia. This study aims to determine which MSME sectors can potentially utilize geothermal energy directly to strengthen their long-term competitiveness. Using the Value, Rarity, Imitability, and Organization (VRIO) framework within the Resource-Based View (RBV), this research adopts a qualitative case study approach in Garut Regency. Data were obtained through 1) field observations of factories (leather tanning, dodol, vetiver oil, and batik), and 2) semi-structured interviews and expert assessment involving 17 participants representing MSME owners, government agencies, academics, and a geothermal energy company. The findings indicate that MSMEs in the leather tanning, dodol production, and vetiver oil sectors demonstrate relatively higher potential for competitive advantage and innovation capability compared to those in the batik sector. These advantages stem from valuable and unique local resources and more established organizational supports. However, limitations of support from the government and association, and the risk of product imitation by other regions, remain key challenges. Overall, the study provides empirical insights into the perceived link between geothermal utilization and MSME competitiveness, offering strategic implications for policymakers to strengthen organizational capacity and promote sustainable energy adoption.

Phase-change materials (PCMs) provide high-density Solar Thermal Energy Storage (STES) for solar applications but suffer from low thermal conductivity, excessive subcooling, and cycling degradation. This study systematically compares five nanostructure classes—graphene nanoplatelets (GNP), multi-walled carbon nanotubes (MWCNT), metallic nanoparticles (Cu, Ag), and metal oxides (Al₂O₃, TiO₂)—incorporated into paraffin and sodium nitrate PCMs to address these limitations. Nanostructures were characterized using XRD, FTIR, BET, SEM, and TEM to establish morphology-performance relationships. BET surface area (320.5 m²/g for GNPs, 265.4 m²/g for MWCNTs) correlated strongly with thermal conductivity enhancement (R² = 0.87, p < 0.001), confirming that high-aspect-ratio structures enable percolation network formation. At 3 wt% loading—identified as the optimal concentration through percolation analysis—carbon-based composites achieved 150% (GNPs) and 131% (MWCNTs) conductivity gains at 25 ℃. DSC analysis revealed 60% subcooling suppression with GNPs, reducing crystallization lag from 5.5 ℃ to 2.2 ℃ through heterogeneous nucleation. Charging-discharging experiments verified 30–34% reductions in thermal response time, with temperature uniformity improving by 67%. Statistical analysis using one-way ANOVA with Tukey's HSD test (p < 0.05) confirmed significant performance hierarchies: carbon-based > metallic > metal oxides across all metrics. Extended cycling tests (1000 melt-freeze cycles) validated superior durability, with carbon-enhanced paraffin and oxide-enhanced sodium nitrate retaining >93% of their latent heat capacity, compared with <83% for pristine PCMs. Post-cycling analysis confirmed the maintenance of nanoparticle dispersion and chemical stability. Comparison with recent literature validates that this work advances the field by systematic multi-additive evaluation, extended durability validation (2–3 times longer than typical studies), and dual-PCM coverage spanning 50–350 ℃. The quantified conductivity-loading relationships, percolation thresholds, and 1000-cycle performance data provide engineering guidelines for STES across residential to industrial temperature ranges.

Power energy sector is the largest anthropogenic source of greenhouse gas emissions, including CO₂. Oxy-fuel energy complexes (OFCs) are one of the most effective ways to reduce CO₂ emissions. In OFC’s combustion chamber gaseous fuel is burned in a mixture of O₂ and CO₂ at supercritical pressures up to 300 atm. However, in open sources there are currently no recommendations for designing such combustion chambers, including no recommendations on the choice of a kinetic mechanism for numerical simulation of combustion. In this paper, the kinetic combustion mechanisms GRI 3.0, UoS sCO₂ 2.0, OXY-NG, and Skeletal were compared using Chemkin 18.2 for oxy-fuel combustion of methane by laminar burning velocity under normal conditions. It is shown that OXY-NG can accurately simulate the laminar burning velocity during oxy-fuel combustion of methane under normal conditions in a wide range of mixture compositions by (α) 0.8–1.4 and (γ_CO2) 0.65–0.78. For this reason, OXY-NG mechanism is recommended for spatial numerical simulation of oxy-fuel combustion.

This study presents the design and experimental evaluation of an electromagnetic energy-harvesting speed hump (EHSH), developed to capture vehicular kinetic energy and convert it into usable electrical power. The system incorporates a rack-and-pinion mechanism coupled with a flywheel and permanent-magnet generator to ensure efficient torque transfer and energy storage. Experimental trials were performed with vehicles weighing 1100 kg, 1500 kg, and 2300 kg, operated at speeds ranging from 3 to 12 km/h. The resulting power outputs were recorded in terms of rotational speed, voltage, current, and harvested power, with comparative analysis between front- and rear-axle loading conditions. The results show that higher vehicle weights and speeds significantly increase energy output, with rear wheels generating slightly higher values than front wheels. Recent literature highlights that EHSH systems can achieve average outputs between 9–20 W in field applications and up to 85% conversion efficiency with optimized permanent-magnet linear generators. The findings of this work confirm the potential of EHSHs as sustainable urban infrastructure solutions, while also identifying challenges of fluctuating performance under diverse traffic conditions. This research contributes to ongoing efforts toward integrating renewable energy systems into road safety devices and smart city applications.

This study evaluates five prominent deep learning models—CNN-LSTM, Bidirectional LSTM, GRU, Transformer, and the proposed Deep Autoencoder-Transformer for the task of energy demand forecasting. Accurate prediction of energy demand is essential for optimizing consumption and maintaining power grid stability amidst increasing complexity and multivariate data characteristics. While previous research has predominantly assessed more traditional models such as LSTM and GRU, this research fills an important gap by thoroughly comparing these with the Transformer and a novel hybrid autoencoder-Transformer model. The models were systematically trained on multivariate inputs after comprehensive preprocessing and evaluated using statistical metrics including MAE, RMSE, MAPE, and coefficient of determination (R²). The findings demonstrate that the Deep Autoencoder-Transformer model outperforms all other architectures, achieving the lowest error rates (MAE = 8.5, RMSE = 10.75, MAPE = 3.46%) and highest explanatory power (R² = 0.991). The Transformer also achieves strong performance (MAE = 10.14, R² = 0.988), reflecting its ability to model long-term dependencies effectively. GRU and Bidirectional LSTM models follow, balancing accuracy and computational efficiency, while CNN-LSTM, despite its combined spatial and temporal feature extraction abilities, shows comparatively lower precision likely due to architectural limitations with long-range temporal modeling. This study highlights the superior capability of attention-based Transformer architectures, especially when combined with deep autoencoding, to capture complex temporal patterns in multivariate energy data. It offers a scalable and systematic framework for benchmarking deep learning models applicable to energy demand forecasting. These insights are valuable to energy system operators and policymakers for selecting appropriate machine learning models, with the hybrid Deep Autoencoder-Transformer emerging as a promising solution for more accurate, long-horizon, multi-step forecasting in intelligent energy systems.

Ignorance of the sustainability of coffee systems compromises the continuity of productive activities by weakening their economic viability, environmental integrity and social cohesion over time, which is why it is essential to carry out diagnoses. This study aimed to assess the sustainability level of coffee farms associated with the Cooperativa Agraria Cafetalera La Prosperidad de Chirinos. From January to March 2024, data were collected from 60 farms out of a population of 788. The analysis was based on nine criteria: six environmental (soil quality, crop health, solid waste and effluent management, integrated pest and disease management, ecological knowledge, and agricultural system), two economic (agricultural economy and food sovereignty), and one social (social aspects). To identify groups of farmers with homogeneous characteristics, a cluster analysis was performed and the level of sustainability of each group was determined by calculating overall averages, represented through Amoeba charts. Results identified two farm types farms in group 1 showed less sustainability than group 2, mainly due to unfavorable conditions related to soil quality. Consequently, it is recommended to to implement cover crops, live barriers, infiltration ditches, contour planting, and productive diversification for food security are recommended. This study provides a scientific diagnosis of sustainability levels on coffee farms and offers practical options for improving sustainability.

This review critically examines the ballistic performance and impact resistance of composite fabrics, with a particular focus on their role in next-generation protective systems. While previous work has focused on general ballistic materials, relatively few studies have provided a systematic and integrated synthesis that links material selection, structural design parameters, and recent nanotechnology improvements. This work addresses this gap by offering a comprehensive classification of composite fabric configurations, considering key factors such as fiber types, fabric architecture, thickness, stacking sequence, and layer orientation. The influence of these parameters on deformation behavior, failure mechanisms, and energy absorption efficiency is discussed in detail. It also evaluates standardized testing methods, current reference benchmarks, and limitations of current testing protocols. Recent advances, including high-strength fibers, hybrid architectures, and multifunctional material interactions, are analyzed to highlight emerging trends and unresolved challenges. By organizingrecent progress within a unified analytical framework, this review offers researchers, engineers, and practitioners actionable insights for the design and optimization of advanced ballistic impact materials. Furthermore, it outlines key research gaps and future directions, particularly in the areas of multifunctional integration, lightweight design, and improved predictive modeling for high-strain-rate impact conditions.