Search Results (18,093)

Against the backdrop of an urgent energy crisis, solar energy has attracted sufficient attention as one of the most inexhaustible and friendly types of environmental energy. Faced with long service and harsh environment, the poor performance ratios of photovoltaic arrays and safety hazards are frequently boosted worldwide. In particular, the hot spot effect plays a vital role in weakening the power generation performance and reduces the lifetime of photovoltaic (PV) modules. Here, our research reports a spatial–temporal hot spot management system integrated with fiber Bragg grating (FBG) temperature sensor arrays and cooling hydrogels. Through finite element simulations and indoor experiments in laboratory conditions, a superior cooling effect of hydrogels and photoelectric conversion efficiency improvement have been demonstrated. On this basis, field tests were carried out in which the FBG arrays detected the surface temperature of the PV module first, and then a classifier based on an optimized artificial neural network (ANN) recognized hot spots with an accuracy of 99.1%. The implementation of cooling hydrogels as a feedback mechanism achieved a 7.7 °C reduction in temperature, resulting in a 5.6% enhancement in power generation efficiency. The proposed strategy offers valuable insights for conducting predictive maintenance of PV power plants in the case of hot spots. Full article

To promote a more decentralized energy system, the European Commission introduced the concept of Renewable Energy Communities (RECs). Meanwhile, the increasing penetration of Electric Vehicles (EVs) may significantly increase peak power demand and consumption ramps when charging sessions are left uncontrolled. However, by integrating smart charging strategies, such as Vehicle-to-Grid (V2G), EV storage can actively support the energy balance within RECs. In this context, this work proposes a comprehensive and scalable model for leveraging smart charging capabilities in RECs. This approach focuses on an external cooperative framework to optimize incentive acquisition and reduce dependence on Medium Voltage (MV) grid substations. It adopts a hybrid strategy, combining Mixed-Integer Linear Programming (MILP) to solve the day-ahead global optimization problem with local rule-based controllers to manage power deviations. Simulation results for a six-month case study, using historical demand data and synthetic charging sessions generated from real-world events, demonstrate that V2G integration leads to a better alignment of overall power consumption with zonal pricing, smoother load curves with a 15.5% reduction in consumption ramps, and enhanced cooperation with a 90% increase in shared power redistributed inside the REC. Full article

This paper investigates weak fault feature extraction in AUV thrusters under multi-input signal conditions. Conventional methods often rely on insufficient input signals, leading to a non-monotonic mapping between fault features and fault severity. This, in turn, makes accurate fault severity identification infeasible. To overcome this limitation, this paper increases the number of input signals by utilizing all available measurable signals. To address the problems arising from the expanded signal set, a signal denoising method that combines Feature Mode Decomposition and wavelet denoising is proposed. Furthermore, a signal enhancement technique that integrates energy operators and the Modified Bayes method. Additionally, distinct technical approaches for noise reduction and enhancement are specifically designed for different input signals. Unlike conventional methods that extract features directly from raw input signals, for fault feature extraction and fusion, this study transforms the signals into the time, frequency, and time–frequency domains, extracting diverse fault features across these domains. A sensitivity factor selection method is then employed to identify the sensitive features. These selected features are subsequently fused using Dempster–Shafer evidence theory to construct the final fault feature. Finally, fault severity identification is carried out using the classical grey relational analysis. Pool experiments using the “Beaver II” AUV prototype validate the effectiveness of the proposed method. Full article

Driven by the need for sustainable and efficient energy systems, the optimal management of distributed generation, including photovoltaic systems and battery energy storage systems within prosumer households, is of crucial importance. This requires a comprehensive cost–benefit analysis to assess their viability. In this study, an optimization model formulated as a mixed-integer linear programming problem is proposed to evaluate the integration of battery storage systems for 10 prosumers on the radial feeder in Croatia and to quantify the benefits both from the prosumers’ perspective and that of the reduction in grid losses. The results show significant annual cost reductions for prosumers, totaling EUR 1798.78 for the observed feeder, with some achieving a net profit. Grid losses are significantly reduced by 1172.52 kWh, resulting in an annual saving of EUR 216.25 for the distribution system operator. However, under the current Croatian market conditions, the integration of battery storage systems is not profitable over the entire lifetime due to the high initial investment costs of EUR 720/kWh. The break-even analysis reveals that investment cost needs to decrease by 52.78%, or an inflation rate of 4.87% is required, to reach prosumer profitability. This highlights the current financial barriers to the widespread adoption of battery storage systems and emphasizes the need for significant cost reductions or targeted incentives. Full article

To address compacted soils with high power consumption and waterlogging risks in rice–rapeseed rotation areas of the Yangtze River, this study designed a ditching machine combining a stepped cutter head and trapezoidal cleaning blade, where the mechanical synergy between components minimizes energy loss during soil-cutting and -throwing processes. We mathematically modeled soil cutting–throwing dynamics and blade traction forces, integrating soil rheological properties to refine parameter interactions. Discrete Element Method (DEM) simulations and single-factor experiments analyzed impacts of the inner/outer blade widths, blade group distance, and blade opening on power consumption. Results indicated that increasing the inner/outer blade widths (200–300 mm) by expanding the direct cutting area significantly reduced the cutter torque by 32% and traction resistance by 48.6% from reduced soil-blockage drag; larger blade group distance (0–300 mm) initially decreased but later increased power consumption due to soil backflow interference, with peak efficiency at 200 mm spacing; the optimal blade opening (586 mm) minimized the soil accumulation-induced power loss, validated by DEM trajectory analysis showing continuous soil flow. Box–Behnken experiments and genetic algorithm optimization determined the optimal parameters: inner blade width: 200 mm; outer blade width: 300 mm; blade group distance: 200 mm; and blade opening: 586 mm, yielding a simulated power consumption of 27.07 kW. Field tests under typical 18.7% soil moisture conditions confirmed a <10% error between simulated and actual power consumption (28.73 kW), with a 17.3 ± 0.5% reduction versus controls. Stability coefficients for the ditch depth, top/bottom widths exceeded 90%, and the backfill rate was 4.5 ± 0.3%, ensuring effective drainage for rapeseed cultivation. This provides practical theoretical and technical support for efficient ditching equipment in rice–rapeseed rotations, enabling resource-saving design for clay loam soils. Full article

The core perturbome is defined as a central response to multiple disturbances, functioning as a complex molecular network to overcome the disruption of homeostasis under stress conditions, thereby promoting tolerance and survival under stress conditions. Based on the biological and clinical relevance of Escherichia coli and Staphylococcus aureus, we characterized their molecular responses to multiple perturbations. Gene expression data from E. coli (8815 target genes—based on a pangenome—across 132 samples) and S. aureus (3312 target genes across 156 samples) were used. Accordingly, this study aimed to identify and describe the functionality of the core perturbome of these two prokaryotic models using a machine learning approach. For this purpose, feature selection and classification algorithms (KNN, RF and SVM) were implemented to identify a subset of genes as core molecular signatures, distinguishing control and perturbation conditions. After verifying effective dimensional reduction (with median accuracies of 82.6% and 85.1% for E. coli and S. aureus, respectively), a model of molecular interactions and functional enrichment analyses was performed to characterize the selected genes. The core perturbome was composed of 55 genes (including nine hubs) for E. coli and 46 (eight hubs) for S. aureus. Well-defined interactomes were predicted for each model, which are jointly associated with enriched pathways, including energy and macromolecule metabolism, DNA/RNA and protein synthesis and degradation, transcription regulation, virulence factors, and other signaling processes. Taken together, these results may support the identification of potential therapeutic targets and biomarkers of stress responses in future studies. Full article

Atmospheric aerosols significantly impact solar photovoltaic (PV) energy generation through their effects on surface solar radiation. This study quantifies the impact of PM_2.5 pollution on PV power output using observational data from 10 stations across Hebei Province, China (2018–2019). Our analysis reveals that elevated PM_2.5 concentrations substantially attenuate solar irradiance, resulting in PV power losses reaching up to a 48.2% reduction in PV power output during severe pollution episodes. To capture these complex aerosol–radiation–PV interactions, we developed and compared the following six machine learning models: Support Vector Regression, Random Forest, Decision Tree, K-Nearest Neighbors, AdaBoost, and Backpropagation Neural Network. The inclusion of PM_2.5 as a predictor variable systematically enhanced model performance across all algorithms. To further optimize prediction accuracy, we implemented a stacking ensemble framework that integrates multiple base learners through meta-learning. The optimal stacking configuration achieved superior performance (MAE = 0.479 MW, indicating an average prediction error of 479 kilowatts; R² = 0.967, reflecting that 96.7% of the variance in power output is explained by the model), demonstrating robust predictive capability under diverse atmospheric conditions. These findings underscore the importance of aerosol–radiation interactions in PV forecasting and provide crucial insights for grid management in pollution-affected regions. Full article

Macrobrachium nipponense represents a commercial decapod species that predominantly inhabits freshwater ecosystems or environments with low salinity. However, the species exhibits normal survival and reproductive capacity in natural aquatic habitats with salinity levels up to 10 parts per thousand (ppt). The present study aimed to elucidate the molecular mechanisms underlying salinity acclimation in M. nipponense by investigating alterations in oxidative stress, morphological adaptations, and hepatopancreatic gene expression profiles following exposure to a salinity level of 10 ppt. The present study demonstrates that glutathione peroxidase and Na⁺/K⁺-ATPase play critical roles in mitigating oxidative stress induced by elevated salinity in M. nipponense. Furthermore, histological analysis revealed distinct pathological alterations in the hepatopancreas of M. nipponense following 7-day salinity exposure, including basement-membrane disruption, luminal expansion, vacuolization, and a marked reduction in storage cells. Transcriptomic profiling of M. nipponense hepatopancreas suggested coordinated activation of both immune (lysosome and protein processing in endoplasmic reticulum pathways) and energy (pyruvate metabolism, glycolysis/gluconeogenesis, and citrate cycle) metabolic processes during salinity acclimation in M. nipponense. Quantitative real-time PCR validation confirmed the reliability of RNA-seq data. This study provides molecular insights into the salinity adaptation mechanisms in M. nipponense, offering potential applications for improving cultivation practices in brackish water environments. Full article

Background/Objectives: Virtual non-contrast (VNC) images derived from dual-energy CTA (DE-CTA) could potentially replace true non-contrast (TNC) scans while reducing radiation exposure. This study evaluated the image quality of VNC compared to TNC for assessing native arteries and bypass grafts in patients with peripheral arterial disease (PAD). Methods: We retrospectively analyzed 175 patients (111 men, 64 women, mean age: 69.3 ± 9.5 years) with PAD who underwent lower extremity DE-CTA. Mean attenuation and image noise values of TNC and VNC images were measured in native arteries and bypass grafts at six arterial levels, from the aorta to the popliteal arteries, using circular regions of interest (ROI). Signal-to-noise ratios (SNRs) and contrast-to-noise ratios (CNRs) were calculated. Three independent radiologists evaluated the subjective image quality of VNC images compared to baseline TNC scans for overall quality (4-point Likert scale), and for residual contrast medium (CM), calcium subtractions, and bypass graft visualization (3-point Likert scales). Radiation dose parameters (DLP, CTDIvol) were recorded to estimate effective dose values (ED) and the potential radiation dose reduction. Differences between TNC and VNC measurements and radiation dose parameters were compared using a paired t-test. Interobserver agreement was assessed with Gwet’s AC2. Results: VNC attenuation and noise values were significantly lower across all native arterial levels (p < 0.05, mean difference: 4.7 HU–10.8 HU) and generally lower at all bypass regions (mean difference: 2.2 HU–13.8 HU). Mean image quality scores were 3.03 (overall quality), 2.99 (residual contrast), 2.04 (subtracted calcifications), and 3.0 (graft visualization). Inter-reader agreement was excellent for each assessment (AC2 ≥ 0.81). The estimated radiation dose reduction was 36.8% (p < 0.0001). Conclusions: VNC reconstructions demonstrated comparable image quality to TNC in a PAD assessment and offer substantial radiation dose reduction, supporting their potential as a promising alternative in clinical practice. Further prospective studies and optimization of reconstruction algorithms remain essential to confirm diagnostic accuracy and address remaining technical limitations. Full article

This paper proposes the Open Competency Optimization (OCO) approach, based on adaptive combinatorial operators, to solve the Dynamic Green Traveling Salesman Problem (DG-TSP), which extends the classical TSP by incorporating dynamic travel conditions, realistic road gradients, and energy consumption considerations. The objective is to minimize fuel consumption and emissions by reducing the total tour length under varying conditions. Unlike conventional metaheuristics based on real-coded representations, our method directly operates on combinatorial structures, ensuring efficient adaptation without costly transformations. Embedded within a dynamic metaheuristic framework, our operators continuously refine the routing decisions in response to environmental and demand changes. Experimental assessments conducted in practical contexts reveal that our algorithm attains a tour length of 21,059, which is indicative of a 36.16% reduction in fuel consumption relative to Ant Colony Optimization (ACO) (32,994), a 4.06% decrease when compared to Grey Wolf Optimizer (GWO) (21,949), a 2.95% reduction in relation to Particle Swarm Optimization (PSO) (21,701), and a 0.90% decline when juxtaposed with Genetic Algorithm (GA) (21,251). In terms of overall offline performance, our approach achieves the best score (21,290.9), significantly outperforming ACO (36,957.6), GWO (122,881.04), GA (59,296.5), and PSO (36,744.29), confirming both solution quality and stability over time. These findings underscore the resilience and scalability of the proposed approach for sustainable logistics, presenting a pragmatic resolution to enhance transportation operations within dynamic and ecologically sensitive environments. Full article

Our contemporary society is powered by fossil fuels, which results in environmental catastrophes. The combustion of these materials results in the release of CO₂, which accelerates the progression of climate change and its catastrophic consequences. The environmental repercussions of fossil fuel extraction have been highlighted through research into alternative energy sources. This inquiry uses the Tapio-Z decoupling approach to assess energy inputs and emissions. Furthermore, the fuzzy logic model is used to inspect the economic growth of the USA and China, as well as the impact of environmental factors, energy sources, and utilization, through decoupling effects from 1994 to 2023. The findings are substantiated by the individual perspectives of the environmental factors regarding decoupling, which ultimately lead to the acquisition of valuable results. We anticipate a substantial reduction in the total volume of CO₂ emissions in both the USA and China. Compared to China, the USA shows a significant increase in CO₂ emissions due to its reliance on fossil fuels. It is evident that a comprehensive transition to renewable resources and a broad range of technology is required to mitigate CO₂ emissions in high-energy zones. In their pursuit of sustainability, these two nations are making remarkable strides. The percentage change in CO₂ emissions indicates that effective changes in economic growth, energy input, and energy utilization, particularly sustainable energy, transmute energy output, as does the sustained implementation of robust environmental protection policies. The percentage change in CO₂ emissions indicates a remarkable transformation in energy input, energy consumption, and economic growth. This transition has been most visible in the areas of energy transformation, sustainability, and the maintenance of strong environmental protection measures. Full article

Rheumatoid arthritis (RA) is a chronic inflammatory disease frequently associated with alterations in body composition, including reduced lean mass and increased fat mass. These alterations are thought to be driven by persistent systemic inflammation, which may be influenced by inflammatory activity and by therapeutic interventions. Objectives: This pilot study aimed to provide preliminary data on changes in body composition and inflammatory activity in biologic-naive patients with active RA during the initial 6 months of TNF inhibitor treatment, and to compare baseline body composition with healthy controls. We conducted a single-center, observational, 24-week pilot study of 70 biologic-naive RA patients with moderate-to-severe disease activity and 70 matched healthy controls. Lean mass, fat mass, and lean mass index (LMI) were measured using dual-energy X-ray absorptiometry at baseline for both groups, and after 6 months only in the RA group. Clinical, laboratory, adipokines, and cytokine parameters were also recorded. At baseline, RA patients had lower lean mass and LMI than controls. Over 6 months, RA patients showed significant clinical and laboratory improvement, with a corresponding increase in lean mass and LMI. No statistically significant change was observed in fat mass. The increase in lean mass was paralleled by a reduction in inflammatory markers. The LMI was inversely associated with female sex (β = −0.562) and C-reactive protein (β = −0.432) and directly associated with body mass index (β = 0.570). Similar associations were observed for total lean mass and change in lean mass, as well as for DAS28 (β = −0.333). This pilot study provides preliminary evidence that TNF inhibitor therapy may be associated with increased lean mass and decreased inflammation in RA patients. Owing to the absence of a comparator RA group not receiving TNF inhibitors, these findings should be interpreted as hypothesis-generating. Full article

Background: Exercise-induced fatigue arises primarily from energy substrate depletion and the accumulation of metabolites such as lactate and ammonia, which impair performance and delay recovery. Emerging evidence implicates gut microbiota modulation—particularly via probiotics—as a means to optimize host energy metabolism and accelerate clearance of fatigue-associated by-products. Objective: This study aimed to determine whether live or heat-inactivated Lacticaseibacillus paracasei NB23 can enhance exercise endurance and attenuate fatigue biomarkers in a murine model. Methods: Forty male Institute of Cancer Research (ICR) mice were randomized into four groups (n = 10 each) receiving daily gavage for six weeks with vehicle, heat-killed NB23 (3 × 10¹⁰ cells/mouse/day), low-dose live NB23 (1 × 10¹⁰ CFU/mouse/day), or high-dose live NB23 (3 × 10¹⁰ CFU/mouse/day). Forelimb grip strength and weight-loaded swim-to-exhaustion tests assessed performance. Blood was collected post-exercise to measure serum lactate, ammonia, blood urea nitrogen (BUN), and creatine kinase (CK). Liver and muscle glycogen content was also quantified, and safety was confirmed by clinical-chemistry panels and histological examination. Results: NB23 treatment produced dose-dependent improvements in grip strength (p < 0.01) and swim endurance (p < 0.001). All NB23 groups exhibited significant reductions in post-exercise lactate (p < 0.0001), ammonia (p < 0.001), BUN (p < 0.001), and CK (p < 0.0001). Hepatic and muscle glycogen stores rose by 41–59% and 65–142%, respectively (p < 0.001). No changes in food or water intake, serum clinical-chemistry parameters, or tissue histology were observed. Conclusions: Our findings suggest that both live and heat-treated L. paracasei NB23 may contribute to improved endurance performance, increased energy reserves, and faster clearance of fatigue-related metabolites in our experimental model. However, these results should be interpreted cautiously given the exploratory nature and limitations of our study. Full article

Traditional forced-air cooling systems suffer from excessive energy consumption and noise pollution. This study proposes an innovative passive cooling strategy through developing aqueous radiative cooling coatings made from a combination of TiO₂-decorated carbon nanotube (TiO₂-CNT) hybrids and polyacrylic acid (PAA), designed to simultaneously enhance the heat dissipation and improve the mechanical strength of the coating films. Based on CNTs’ exceptional thermal conductivity and record-high infrared emissivity, bead-like TiO₂-CNT architectures have been prepared as the filler in PAA. The TiO₂ nanoparticles were in situ grown on CNTs, forming a rough surface that can produce asperity contacts and enhance the strength of the TiO₂-CNT/PAA composite. Moreover, this composite enhanced heat dissipation and achieved remarkable cooling efficiency at a small fraction of the filler (0.1 wt%). The optimized coating demonstrated a temperature reduction of 23.8 °C at an operation temperature of 180.7 °C, coupled with obvious mechanical reinforcement (tensile strength from 13.7 MPa of pure PAA to 17.1 MPa). This work achieves the combination of CNT and TiO₂ nanoparticles for strong radiative cooling coating, important for energy-efficient thermal management. Full article

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