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The rising demand for energy resources and industrial goods presents significant challenges to sustainable development. Sunflower oil, commonly utilized in the food sector, biofuels, and various industrial applications, is notably affected by this demand. In Bulgaria, it serves as a primary source of vegetable fats, ranking second to butter in daily consumption. The aim of this study is to evaluate and propose methods to improve energy and resource efficiency in sunflower oil production in Bulgaria. The analysis is based on data from an energy audit conducted in 2023 at an industrial sunflower oil production facility. Reconstruction and modernization initiatives, which included the installation of high-performance, energy-efficient equipment, led to a 34% increase in energy efficiency. The findings highlight the importance of adjusting the technological parameters such as temperature, pressure, grinding level, and pressing time to reduce energy use and operational costs. Additionally, resource efficiency is improved through more effective raw material utilization and waste reduction. These strategies not only enhance the economic and environmental performance of sunflower oil production but also support sustainable development and competitiveness within the industry. The improvement reduces hexane use by approximately 2%, resulting in energy savings of 12–15 kWh/t of processed seeds and a reduction in CO₂ emissions by 3–4 kg/t, thereby improving the environmental profile of sunflower oil production. Full article

Decarbonization of the Greek economy requires significant investments in clean technologies. This will boost demand for goods and services and will create multiplier effects on output value added and employment, though reliance on imported technologies might increase the trade deficit. This study employs input–output analysis to estimate the direct, indirect, and multiplier effects of green transition investments on Greek output, value added, employment, and imports across five-year intervals from 2025 to 2050. Two scenarios are considered: the former is based on the National Energy and Climate Plan (NECP), driven by a large-scale exploitation of RES and technologies promoting electrification of final demand, while the latter (developed in the context of the CLEVER project) prioritizes energy sufficiency and efficiency interventions to reduce final energy demand. In the NECP scenario, GDP increases by 3–10% (relative to 2023), and employment increases by 4–11%. The CLEVER scenario yields smaller direct effects—owing to lower investment levels—but larger induced impacts, since energy savings boost household disposable income. The consideration of three sub-scenarios adopting different levels of import-substitution rates in key manufacturing sectors exhibits pronounced divergence, indicating that targeted industrial policies can significantly amplify the domestic economic benefits of the green transition. Full article

Skin aging is closely related to mitochondrial dysfunction and cell cycle abnormalities, and developing intervention strategies targeting mitochondrial quality control is an important direction for anti-aging research. In this study, we investigated the anti-aging mechanism of Camellia japonica flower (CJF) extract and its active ingredient hyperoside based on a doxorubicin (DOX)-induced endogenous senescence model in human skin fibroblasts (HSFs). LC-MS proteomics analysis revealed that CJF extract and hyperoside specifically activated the FUNDC1-mediated mitochondrial autophagy pathway, significantly ameliorated the DOX-induced decrease in mitochondrial membrane potential and the accumulation of reactive oxygen species (ROS), and alleviated the cellular S-phase blockade and reversed the high expression of senescence-associated β-galactosidase (SA-β-gal). Further studies showed that the two cleared damaged mitochondria by enhancing mitochondrial autophagy and restoring cellular energy metabolism homeostasis while promoting type III collagen and elastin synthesis and repairing the expression of Claudin 1 related to skin barrier function. For the first time, the present study reveals the molecular mechanism of CJF extract in delaying skin aging by regulating the FUNDC1-dependent mitochondrial autophagy pathway, which provides a theoretical basis and a candidate strategy for developing novel anti-aging agents targeting mitochondrial quality control. Full article

The microstructure, phase composition, and high-temperature oxidation resistance of Fe-free AlCoCrNiNb_0.2 and AlCoCr_0.5NiNb_0.2 high-entropy alloys (HEAs) were investigated. In the as-cast HEAs, niobium was found to mainly release as a Laves phase in the interdendritic region, and its solubility in the dendrites of the BCC solid solution was about 2 at.%. Both samples exhibited parabolic behavior during 100 h oxidation at 1000 °C and 1100 °C. The AlCoCrNiNb_0.2 alloy demonstrated higher resistance to high-temperature oxidation compared to AlCoCr_0.5NiNb_0.2. The specific weight changes after 100 h of isothermal holding at 1000 °C and 1100 °C were 0.65 mg/cm² and 1.31 mg/cm², respectively, which are superior compared to the Fe-containing HEAs. Cr was revealed to play an important role in the oxidation behavior of the HEAs, decreasing the parabolic oxidation rate constant and increasing the activation energy of the oxidation process in the alloys. Full article

LPG fuel supply systems are increasingly important for improving energy efficiency and reducing carbon emissions in the shipping industry. The primary objective of this research is to investigate the heat transfer phenomena to enhance the thermal performance of double-pipe heat exchangers (DPHEs) in LPG fuel supply systems. This study investigates the heat transfer performance of a glycol–steam double-pipe heat exchanger (DPHE) within an LPG fuel supply system under varying operating conditions. A computational model and methodology were developed and validated by comparing the numerical results with experimental data obtained from commissioning tests. Additionally, the effects of turbulence models and parametric variations were evaluated by analyzing the glycol–water mixing ratio and flow direction—both of which are critical operational parameters for DPHE systems. Numerical validation against the commissioning data showed a deviation of ±2% under parallel-flow conditions, confirming the reliability of the proposed model. With respect to the glycol–water mixing ratio and flow configuration, thermal conductance (UA) decreased by approximately 11% in parallel flow and 13% in counter flow for every 20% increase in glycol concentration. Furthermore, parallel flow exhibited approximately 0.6% higher outlet temperatures than counter flow, indicating superior heat transfer efficiency under parallel-flow conditions. Finally, the heat transfer behavior of the DPHE was further examined by considering the effects of geometric characteristics, pipe material, and fluid properties. This study offers significant contributions to the engineering design of double-pipe heat exchanger systems for LPG fuel supply applications. Full article

Millisecond lasers, with their high processing efficiency and large power, are widely used in manufacturing fields such as aerospace. This study aims to investigate the effects of different processing parameters on the micro-hole processing of 316 heat-resistant steel using millisecond lasers. Through the control variable method, the study examines the impact of pulse energy, pulse count, and pulse width on the quality of micro-holes, including the entrance diameter, exit diameter, and taper. Furthermore, combined with orthogonal experiments and COMSOL Multiphysics 6.2 simulations, the study explores the influence of pulse width on the formation of blind holes. The experimental results show that when the pulse energy is 2.2 J, the taper is minimal (2.2°), while the taper reaches its peak (2.4°) at 2.4 J pulse energy. As the pulse count increases to 55–60 pulses, the exit diameter stabilizes, and the taper decreases to 1.8°. Blind holes begin to form when the pulse width exceeds 1.2 ms. When the pulse width is 1.2 ms, pulse energy is 2.4 J, and pulse count is 50, the entrance diameter of the blind hole reaches its maximum, indicating that longer pulse widths result in more significant energy reflection and thermal accumulation effects. COMSOL simulations reveal that high-energy pulses cause intense melt ejection, while longer pulse widths exacerbate thermal accumulation at the micro-hole entrance, leading to blind hole formation. This study provides important process references for laser processing of through-holes and blind holes in heat-resistant steel. Full article

Natural gas plays a critical role in integrated energy systems. In this context, the present study proposes an optimization model for the electricity–gas coupling system, grounded in the theory of interdependent networks. By integrating network topology parameters with real-time operational metrics, the model substantially enhances system robustness and adaptability. To quantify nodal vulnerability and importance, the study introduces two novel evaluation indicators: the Electric Potential–Closeness Fusion Indicator (EPFI) for power networks and the Pressure Difference–Closeness Comprehensive Indicator (PDCI) for natural gas systems. Leveraging these indicators, three coupling paradigms—assortative, disassortative, and random—are systematically constructed and analyzed. System resilience is assessed through simulation experiments incorporating three attack strategies: degree-based, betweenness centrality-based, and random node removal. Evaluation metrics include network efficiency and the variation in the size of the largest connected subgraph under different coupling configurations. The proposed framework is validated using a hybrid case study that combines the IEEE 118-node electricity network with a 20-node Belgian natural gas system, operating under a unidirectional gas-to-electricity energy flow model. Results confirm that the disassortative coupling configuration, based on EPFI and PDCI indicators, exhibits superior resistance to network perturbations, thereby affirming the effectiveness of the model in improving the robustness of integrated energy systems. Full article

With the global low-carbon energy transition, accurate prediction of thermal and physical parameters of deep rock masses is critical for geothermal resource development. To address the insufficient generalization ability of machine learning models caused by scarce measured data on granite thermal conductivity, this study focused on granites from the Gonghe Basin and Songliao Basin in Qinghai Province. A data augmentation strategy combining cubic spline interpolation and Gaussian noise injection (with noise intensity set to 10% of the original data feature range) was proposed, expanding the original 47 samples to 150. Thermal conductivity prediction models were constructed using Support Vector Machine (SVM), Random Forest (RF), and Backpropagation Neural Network(BPNN). Results showed that data augmentation significantly improved model performance: the RF model exhibited the best improvement, with its coefficient of determination R² increasing from 0.7489 to 0.9765, Root Mean Square Error (RMSE) decreasing from 0.1870 to 0.1271, and Mean Absolute Error (MAE) reducing from 0.1453 to 0.0993. The BPNN and SVM models also improved, with R² reaching 0.9365 and 0.8743, respectively, on the enhanced dataset. Feature importance analysis revealed porosity (with a coefficient of variation of 0.88, much higher than the longitudinal wave velocity’s 0.27) and density as key factors, with significantly higher contributions than longitudinal wave velocity. This study provides quantitative evidence for data augmentation and machine learning in predicting rock thermophysical parameters, promoting intelligent geothermal resource development. Full article

A just and sustainable energy transition in historically coal-dependent regions like Kentucky requires more than the adoption of new technologies and market-based solutions. This study uses a stated preferences approach to evaluate public support for various attributes of energy transition programs, revealing broad backing for moving away from coal, as indicated by a negative willingness to pay (WTP) for the status quo (–USD 4.63). Key findings show strong bipartisan support for solar energy, with Democrats showing the highest WTP at USD 8.29, followed closely by Independents/Others at USD 8.22, and Republicans at USD 8.08. Wind energy also garnered support, particularly among Republicans (USD 4.04), who may view it as more industry-compatible and less ideologically polarizing. Job creation was a dominant priority across political affiliations, especially for Independents (USD 9.07), indicating a preference for tangible, near-term economic benefits. Similarly, preserving cultural values tied to coal received support among Independents/Others (USD 4.98), emphasizing the importance of place-based identity in shaping preferences. In contrast, social support programs (e.g., job retraining) and certain post-mining land uses (e.g., recreation and conservation) were less favored, possibly due to their abstract nature, delayed benefits, and political framing. Findings from Kentucky offer insights for other coal-reliant states like Wyoming, West Virginia, Pennsylvania, Indiana, and Illinois. Ultimately, equitable transitions must integrate local voices, address cultural and economic realities, and ensure community-driven planning and investment. Full article

The social acceptance of Renewable Energy Sources (RESs) is a decisive factor in the successful implementation of clean energy projects. This study explores the attitudes, demographic profiles, and common misconceptions of citizens in the Region of Epirus, Greece, toward photovoltaic and wind energy installations. Special attention is given to the role of education, age, and access to information—as well as spatial factors such as visual exposure—in shaping public perceptions and influencing acceptance of RES deployment. A structured questionnaire was administered to 320 participants across urban and rural areas, with subdivision between regions with and without visual exposure to RES infrastructure. Findings indicate that urban residents exhibit greater acceptance of RES, while rural inhabitants—especially those in proximity to installations—express skepticism, often grounded in esthetic concerns or perceived procedural injustice. Misinformation and lack of knowledge dominate in areas without visual contact. Statistical analysis confirms that younger and more educated participants are more supportive and environmentally aware. The study highlights the importance of targeted educational interventions, transparent consultation, and spatially sensitive communication strategies in fostering constructive engagement with renewable energy projects. The case of Epirus underscores the need for inclusive, place-based policies to bridge the social acceptance gap and support the national energy transition. Full article

In the context of the global response to climate change and the active promotion of energy transformation, a number of low-carbon policies coupled with the development of synergies to help power system transformation is an important initiative. However, the insufficient articulation of the green power market, tradable green certificate (TGC) market, and carbon emission trading (CET) mechanism, and the ambiguous policy boundaries affect the trading decisions made by its market participants. Therefore, this paper systematically analyses the composition of the main players in the electricity-CET-TGC markets and their relationship with each other, and designs the synergistic mechanism of the electricity-CET-TGC markets, based on which, it constructs the optimal profit model of the thermal power plant operators, renewable energy manufacturers, power grid enterprises, power users and load aggregators under the electricity-CET-TGC markets synergy, and analyses the behavioural decision-making of the main players in the electricity-CET-TGC markets as well as the electric power system to optimise the trading strategy of each player. The results of the study show that: (1) The synergistic mechanism of electricity-CET-TGC markets can increase the proportion of green power grid-connected in the new type of power system. (2) In the selection of different environmental rights and benefits products, the direct participation of green power in the market-oriented trading is the main way, followed by applying for conversion of green power into China certified emission reduction (CCER). (3) The development of independent energy storage technology can produce greater economic and environmental benefits. This study provides policy support to promote the synergistic development of the electricity-CET-TGC markets and assist the low-carbon transformation of the power industry. Full article

Short fiber-reinforced polymer (SFRP) has been extensively applied in structural engineering due to its exceptional specific strength and superior mechanical properties. Its mechanical behavior under medium strain rate conditions has become a key focus of ongoing research. A comprehensive understanding of the response characteristics and underlying mechanisms under such conditions is of critical importance for both theoretical development and practical engineering applications. This study proposes an innovative three-dimensional (3D) multiscale constitutive model that comprehensively integrates mesoscopic fiber–matrix interface effects and pore characteristics. To systematically investigate the dynamic response and damage evolution of SFRP under medium strain rate conditions, 3D-printed SFRP porous structures with volume fractions of 25%, 35%, and 45% are designed and subjected to drop hammer impact experiments combined with multiscale numerical simulations. The experimental and simulation results demonstrate that, for specimens with a 25% volume fraction, the strain rate strengthening effect is the primary contributor to the increase in peak stress. In contrast, for specimens with a 45% volume fraction, the interaction between damage evolution and strain rate strengthening leads to a more complex stress–strain response. The specific energy absorption (SEA) of 25% volume fraction specimens increases markedly with increasing strain rate. However, for specimens with 35% and 45% volume fractions, the competition between these two mechanisms results in non-monotonic variations in energy absorption efficiency (EAE). The dominant failure mode under impact loading is shear-dominated compression, with damage evolution becoming increasingly complex as the fiber volume fraction increases. Furthermore, the damage characteristics transition from fiber pullout and matrix folding at lower volume fractions to the coexistence of brittle and ductile behaviors at higher volume fractions. The numerical simulations exhibit strong agreement with the experimental data. Multi-directional cross-sectional analysis further indicates that the initiation and propagation of shear bands are the principal drivers of structural instability. This study offers a robust theoretical foundation for the impact-resistant design and dynamic performance optimization of 3D-printed short fiber-reinforced polymer (SFRP) porous structures. Full article

For a long time, the explanation of the various determinants of oil price fluctuations and their impact on economic activity has been based on the supply and demand mechanism. However, with various volatile changes in the international situation in recent years, such as threats to public health and an increase in regional conflicts, special attention has been paid to the geopolitical context as an additional driver of oil price fluctuations. This study examines the relationship between oil price changes and GDP growth and other macroeconomic variables from the perspective of the vulnerability of oil-importing and oil-exporting countries to unexpected oil price shocks, driven by tense geopolitical events, in three European countries (Norway, Germany, and Poland). We apply the Structural Vector Autoregressive (SVAR) model and orthogonalized impulse response functions, based on quarterly data, in regard to two samples: the first spans 1995Q1–2019Q4 (pre-2020 sample), with relatively gradual changes in oil prices, and the second spans 1995Q1–2024Q2 (whole sample), with sudden fluctuations in oil prices due to geopolitical developments. A key finding of this research is that vulnerability to unpredictable oil price shocks related to geopolitical tensions is higher than in regard to expected gradual changes in oil prices, both in oil-importing and oil-exporting countries. Different causality patterns and stronger responses in regard to GDP growth during the period, including in regard to tense geopolitical events in comparison to the pre-2020 sample, lead to the belief that economies are not more resilient to oil price shocks as has been suggested by some studies, which referred to periods that were not driven by geopolitical events. Our research also suggests that countries implementing policies to reduce oil dependency and promote investment in alternative energy sources are better equipped to mitigate the adverse effects of oil price shocks. Full article

As smart homes and Internet of Things (IoT) ecosystems continue to expand, the need for energy-efficient and low-latency communication has become increasingly critical. One of the key challenges in these systems is minimizing delay time while ensuring reliable and efficient communication between devices. This study focuses on optimizing delay time in ZigBee sensor networks used in smart-home systems. A Smart–Adaptive Communication Distribution Algorithm is proposed, which dynamically adjusts the communication between network nodes based on real-time network conditions. Experimental measurements were conducted under various scenarios to evaluate the performance of the proposed algorithm, with a particular focus on reducing delay and enhancing overall network efficiency. The results demonstrate that the proposed algorithm significantly reduces delay times compared to traditional methods, making it a promising solution for delay-sensitive IoT applications. Furthermore, the findings highlight the importance of adaptive communication strategies in improving the performance of ZigBee-based sensor networks. Full article

Accurate forecasting of energy consumption in buildings is essential for achieving energy efficiency and reducing carbon emissions. However, many existing models rely on limited input variables and overlook the complex influence of indoor environmental quality (IEQ). In this study, we assess the performance of hybrid machine learning ensembles for predicting hourly energy demand in a smart office environment using high-frequency IEQ sensor data. Environmental variables including carbon dioxide concentration (CO₂), particulate matter (PM_2.5), total volatile organic compounds (TVOCs), noise levels, humidity, and temperature were recorded over a four-month period. We evaluated two ensemble configurations combining support vector regression (SVR) with either Random Forest or LightGBM as base learners and Ridge regression as a meta-learner, alongside single-model baselines such as SVR and artificial neural networks (ANN). The SVR combined with Random Forest and Ridge regression demonstrated the highest predictive performance, achieving a mean absolute error (MAE) of 1.20, a mean absolute percentage error (MAPE) of 8.92%, and a coefficient of determination (R²) of 0.82. Feature importance analysis using SHAP values, together with non-parametric statistical testing, identified TVOCs, humidity, and PM_2.5 as the most influential predictors of energy use. These findings highlight the value of integrating high-resolution IEQ data into predictive frameworks and demonstrate that such data can significantly improve forecasting accuracy. This effect is attributed to the direct link between these IEQ variables and the activation of energy-intensive systems; fluctuations in humidity drive HVAC energy use for dehumidification, while elevated pollutant levels (TVOCs, PM_2.5) trigger increased ventilation to maintain indoor air quality, thus raising the total energy load. Full article