Search Results (3,098)

The bidirectional power flows and time-varying characteristics generated by distributed photovoltaic integration into low-voltage distribution networks pose accuracy and fairness challenges to traditional line loss allocation methods. Existing methods, based on unidirectional power flow assumptions, are unable to quantify the true contributions of PV nodes and ignore the multi-dimensional value attributes of photovoltaics. Against this background, following the principle of “who caused the incremental part of loss, who is responsible for it“, this paper proposes a hierarchical line loss allocation model for low-voltage distribution networks with distributed photovoltaics. The first layer employs an enhanced marginal loss coefficient method to allocate the baseline line losses without PV integration to original distribution network users. The second layer utilizes spatiotemporal weighted Shapley values to quantify the marginal contributions of PV nodes to line loss variations, while establishing a multi-dimensional PV value correction system based on local consumption rate, spatiotemporal matching degree, and voltage support capability, and transforms the multi-dimensional PV values into economic incentive signals through an adaptive Softmax weighting algorithm. Finally, simulation analysis validates the effectiveness of the proposed line loss allocation method. Full article

Prunella vulgaris L. (PV) is a versatile plant with medicinal and culinary applications globally. Its mature fruit-spikes (red-brown) are the primary source of traditional medicine and herbal tea. However, large-scale cultivation and harvesting inevitably leads to the unintended inclusion of immature green fruit-spikes, which are considered substandard medicinal parts. To explore the medicinal and nutraceutical potential of green fruit-spikes, our study systematically compared green and red-brown samples. The distinctions between the two fruit-spikes were characterized by determination of total water-soluble extract content, comprehensive chemical difference analysis, and quantitation of six phenolic acids, including Danshensu, caffeic acid, protocatechuic acid, protocatechualdehyde, salviaflaside, and rosmarinic acid. Additionally, variations in antioxidant activity were evaluated by DPPH and ABTS assays. As a result, 106 compounds were identified from the PV samples. Green spikes exhibited higher total water extract yield and contents of the six phenolic acids than the red-brown ones. Moreover, green samples showed greater accumulations of phenolic acids, flavonoids, and triterpenes. Concomitantly, stronger antioxidant activity was displayed in green spikes in both assay models. Caffeic acid, danshensu, rosmarinic acid, and protocatechualdehyde were identified as major contributors by Pearson correlation analysis. Our findings reveal that green fruit-spikes possess advantages in accumulating specific chemical profiles and exhibiting antioxidant activity, highlighting their untapped pharmaceutical and nutraceutical potential. Full article

The rapid growth of Distributed Energy Resources (DERs) exerts significant pressure on distribution network margins, requiring predictive and safe coordination. This paper presents a closed-loop framework combining a topology-aware Spatio-Temporal Transformer (STT) for multi-horizon forecasting, a cooperative multi-agent reinforcement learning (MARL) controller under Centralized Training and Decentralized Execution (CTDE), and a real-time safety layer that enforces feeder limits via sensitivity-based quadratic programming. Evaluations on three SimBench feeders, with OLTC/capacitor hybrid control and a stress protocol amplifying peak demand and mid-day PV generation, show that the method reduces tail violations by 31% and 56% at the 99th percentile voltage deviation, and lowers branch overload rates by 71% and 90% compared to baselines. It mitigates tail violations and discrete switching while ensuring real-time feasibility and cost efficiency, outperforming rule-based, optimization, MPC, and learning baselines. Stress maps reveal robustness envelopes and identify MV–LV bottlenecks; ablation studies show that diffusion-based priors and coordination contribute to performance gains. The paper also provides convergence analysis and a suboptimality decomposition, offering a practical pathway to scalable, safe, and interpretable DER coordination. Full article

Bridges, as critical transportation infrastructure, are highly vulnerable to aerodynamic forces, particularly vortex-induced vibrations (VIV), which severely compromise their structural integrity and operational safety. These low-frequency, high-amplitude vibrations are a primary challenge to serviceability and fatigue life. Ensuring the resilience of these structures demands advanced understanding and robust mitigation strategies. This paper comprehensively addresses the multifaceted challenges of bridge aerodynamics, presenting an in-depth analysis of contemporary testing methodologies and innovative solutions. We critically examine traditional wind tunnel modeling, elucidating its advantages and inherent limitations, such as scale effects, Reynolds number dependence, and boundary interference, which can lead to inaccurate predictions of aerodynamic forces and vibration amplitudes. This scale discrepancy is critical, as demonstrated by peak pressure coefficients being underestimated by up to 64% in smaller-scale wind tunnel environments compared to high-Reynolds-number open-jet testing. To overcome these challenges, the paper details the efficacy of open-jet testing at facilities like the Windstorm Impact, Science, and Engineering (WISE) Laboratory, demonstrating its superior capability in replicating realistic atmospheric boundary layer flow conditions and enabling larger-scale, high-Reynolds-number testing for more accurate insights into bridge behavior under dynamic wind loads. Furthermore, we explore the design principles and applications of various aerodynamic mitigation devices, including handrails, windshields, guide vanes, and spoilers, which are essential for altering airflow patterns and suppressing vortex-induced vibrations. The paper critically investigates the innovative integration of green energy solutions, specifically solar panels, with bridge structures. This study presents the application of solar panel arrangements to provide both renewable energy production and verifiable aerodynamic mitigation. This strategic incorporation is shown not only to harness renewable energy but also to actively improve aerodynamic performance and mitigate wind-induced vibrations, thereby fostering both bridge safety and sustainable infrastructure development. Unlike previous studies focusing primarily on wind loads on PV arrays, this work demonstrates how the specific geometric integration of solar panels can serve as an active aerodynamic mitigation device for bridge decks. This dual functionality—harnessing renewable energy while simultaneously serving as a passive geometric countermeasure to vortex-induced vibrations—marks a novel advancement over single-purpose mitigation technologies. Through this interdisciplinary approach, the paper seeks to advance bridge engineering towards more resilient, efficient, and environmentally responsible solutions. Full article

The integration of electric vehicles (EVs) and fuel-cell electric vehicles (FCEVs) requires accessible and profitable facilities for fast charging. To promote fast-charging stations (FCSs), a systematic analysis that encompasses both planning and operation is required, including the incorporation of multi-energy resources and uncertainty. This paper presents an optimization framework that addresses a joint strategy for the configuration and operation of an EV/FCEV fast-charging station (FCS) integrated with distributed energy resources (DERs) and hydrogen systems. The framework incorporates uncertainties related to solar photovoltaic (PV) generation and demand for EVs/FCEVs. The proposed joint strategy comprises a four-phase decision-making framework. Phase 1 involves modeling EV/FECE demand, while Phase 2 focuses on determining an optimal long-term infrastructure configuration. Subsequently, in Phase 3, the operator optimizes daily power scheduling to maximize profit. A real-time uncertainty update is then executed in Phase 4 upon the realization of uncertainty. The proposed optimization framework, formulated as mixed-integer quadratic programming (MIQP), considers configuration investment, operational, maintenance, and penalty costs for excessive grid power usage. A heuristic algorithm is proposed to solve this problem. It yields good results with significantly less computational complexity. A case study shows that under the most adverse conditions, the proposed joint strategy increases the FCS owner’s profit by 3.32% compared with the deterministic benchmark. Full article

In 2019, bacterial canker caused by Pseudomonas syringae pv. actinidiae was first identified in Actinidia arguta. This disease has led to significant yield reduction, plant mortality, and substantial economic losses in A. arguta cultivation. Its emergence poses a novel challenge to the sustainable global production of kiwifruit. Currently available treatments for bacterial canker caused by P. syringae pv. actinidiae are scarce. Moreover, the environmental toxicity of copper-based compounds and emerging antibiotic resistance issues necessitate the development of eco-friendly control strategies. Disease management strategies based on biocontrol bacteria have shown broad application prospects. In this study, the isolate CHHM-1 with significant antagonistic activity against P. syringae pv. actinidiae was isolated from the rhizosphere soil of healthy A. arguta. It was identified as Pseudomonas koreensis through 16S rRNA gene and whole-genome sequencing. Genomic analysis revealed that the isolate CHHM-1 harbors various genes related to biocontrol, plant growth promotion, and antibiotic resistance, suggesting strong environmental adaptability and functional potential. Furthermore, the strain exhibited multiple plant growth-promoting traits, such as nitrogen fixation, phosphate solubilization, siderophore production, and synthesis of indole-3-acetic acid (IAA). In vitro antagonism assays confirmed the strong antagonistic activity of the isolate CHHM-1 against P. syringae pv. actinidiae. A dual-culture plate assay showed an average inhibition zone of 4.36 cm, while preventive application on plants significantly reduced lesion length to 1.3 mm (vs. 6.2 mm control) in shoots and lesion area to 10% (vs. 80% control) in leaf discs. Further antibacterial tests revealed that its inhibitory mechanism is attributed to secreted antimicrobial substances. These findings provide a promising candidate for developing novel biopesticides to combat P. syringae pv. actinidiae variants, reduce chemical dependency, and foster sustainable A. arguta production. Full article

This paper proposes a Light Gradient Boosting Machine (LightGBM) model for medium-term photovoltaic (PV) power forecasting by integrating meteorological features with historical generation data. This approach addresses prediction biases that often arise when relying solely on a single meteorological data source. Historical power output and meteorological variables (irradiance, temperature, humidity, etc.) were collected from a PV station and preprocessed through data cleaning, standardization, and temporal alignment to construct a multivariate prediction framework. A comprehensive feature set was then built, including meteorological, temporal, interaction, and lag features. Feature importance analysis and Recursive Feature Elimination (RFE) were employed for input optimization, while feature-layer concatenation was applied for data fusion. Finally, the LightGBM (Version 2.3.1) framework, combined with Bayesian optimization and time-series cross-validation, was used to enhance generalization and predictive robustness. Experimental results confirm that the model achieved an MAE of 37.49, RMSE of 64.67, and R² of 0.89. The model effectively captured high-dimensional nonlinear relationships, thereby improving the accuracy of medium-term photovoltaic forecasts and providing reliable decision support for power system scheduling and renewable energy integration. Full article

To address the insufficient consideration of system static voltage stability and PV–load coupling in distributed photovoltaic (PV) hosting capacity assessment, this study first investigates the impact of distributed PV integration on power system transient voltage stability based on a typical power supply system. Building on this analysis, we propose a Static Grid Stability Margin (SGSM) index. Subsequently, leveraging historical PV and load data, the copula function is introduced to establish a joint distribution function characterizing their correlation. Massive evaluation scenarios are generated through sampling, with robust clustering methods employed to form representative evaluation scenarios. Finally, a distributed PV bearing capacity assessment model is established with the objectives of maximizing PV bearing capacity, optimizing economic efficiency, and enhancing static voltage stability. Through simulation verification, the power system has a higher capacity for distributed PV when distributed PV is integrated into nodes with weak static voltage stability and a decentralized integration scheme is adopted. Full article

To reduce the amount of harmful gases produced by fossil fuels, more environmentally friendly and sustainable alternatives are being proposed around the world. As a result, technologies for manufacturing hydrogen fuel cells and producing green hydrogen are becoming more widespread, with an impact on energy production and environmental protection. In many countries around the world, and in Africa in particular, leaders, scientists, and populations are considering switching from fossil fuels to so-called green energies. Hydrogen is therefore an interesting alternative that deserves to be explored, especially since both rural and urban populations have shown an interest in using it in the near future, which would reduce pollution and the proliferation of greenhouse gases, thereby mitigating global warming. The aim of this paper is to determine the hybrid energy system best suited to addressing the energy problem in the study area, and then to make successive substitutions of different energy sources, starting with the most polluting, in order to assess the possibilities for transitioning the energy used in the area to green hydrogen. To this end, this study began with a technical and economic analysis which, based on climatic parameters, led to the proposal of a PV/DG-BATTery system configuration, with a Net Present Cost (NPC) of USD 19,267 and an average Cost Of Energy (COE) of USD 0.4, and with a high proportion of CO₂ emissions compared with the PV/H₂GEN-BATT and H₂GEN systems. The results of replacing fossil fuel generators with hydrogen generators are beneficial in terms of environmental protection and lead to a reduction in energy-related expenses of around 2.1 times the cost of diesel and a reduction in mass of around 2.7 times the mass of diesel. The integration of H₂GEN, at high duty percentages, increases the Cost Of Energy, whether in a hybrid PV/H₂GEN system or an H₂GEN system. This shows the interest in the study country in using favorable duty proportions to make the use of hydrogen profitable. Full article

Non-photosynthetic vegetation fractional cover (f_NPV) is a key indicator of vegetation decline and ecological health. Traditional inversion models assume identical spectral signatures for the same vegetation cover class across entire study areas. Spectral variations occur among regions due to divergent soil properties and vegetation types. To address this limitation, extensive ground sampling was conducted; ground observation data from multiple regions were utilized to establish localized spectral libraries, thereby enhancing spectral variability representation within the study area while concurrently optimizing vegetation indices across different sensor systems. The results reveal that, within the optimized spectral mixture analysis model, the coefficient of determination (R²) for f_NPV using the NPV soil separation index (NSSI) for Sentinel sensor is 0.6258, and that of f_PV using the modified soil adjusted vegetation index (MSAVI) is 0.8055. The MSAVI-NSSI achieved an R² of 0.7825 for f_NPV and 0.8725 for photosynthetic vegetation fractional cover (f_PV). Optimized vegetation indices also yielded favorable validation results. Landsat’s theoretical predictions improved by 0.1725, with validated results up by 0.1635. MODIS showed improvements of 0.1365 and 0.1923, respectively. This enhancement significantly improves the accuracy of NPV fractional cover identification, providing critical insights for vegetation ecological health assessment in arid and semi-arid regions under global warming. Furthermore, by optimizing the spectral constraint weights in remote sensing images, a solution is provided for the long-term monitoring of vegetation health status. Full article

This study examines the effects of hailstorms on photovoltaic (PV) modules, focussing on damage mechanisms, testing standards, numerical simulations, damage detection techniques, and mitigation strategies. A comprehensive review of the recent literature (2017–2025), experimental results, and case studies is complemented by advanced simulation methods such as finite element analysis (FEA) and smoothed particle hydrodynamics (SPH). The research emphasises the crucial role of protective glass thickness, cell type, number of busbars, and quality of lamination in improving hail resistance. While international standards such as IEC 61215 specify test protocols, actual hail events often exceed these conditions, leading to glass breakage, micro-cracks, and electrical faults. Numerical simulations confirm that thicker glass and optimised module designs significantly reduce damage and power loss. Detection methods, including visual inspection, thermal imaging, electroluminescence, and AI-driven imaging, enable rapid identification of both visible and hidden damage. The study also addresses the financial risks associated with hail damage and emphasises the importance of insurance and preventative measures. Recommendations include the use of certified, robust modules, protective covers, optimised installation angles, and regular inspections to mitigate the effects of hail. Future research should develop lightweight, impact-resistant materials, improve simulation modelling to better reflect real-world hail conditions, and improve AI-based damage detection in conjunction with drone inspections. This integrated approach aims to improve the durability and reliability of PV modules in hail-prone regions and support the sustainable use of solar energy amidst increasing climatic challenges. Full article

Airports are among the most energy-intensive infrastructures, and the decarbonization of ground operations is essential to achieving sustainable aviation goals. Vehicle-integrated photovoltaic (VIPV) offers a promising strategy to complement electrification by enabling on-board renewable generation. While previous studies have mainly focused on fixed PV installations such as rooftops or carports, the potential of VIPV in airports has largely been overlooked, and no structured methodology has been established to investigate it. This study addresses this gap by proposing a two-scenario framework for assessing VIPV performance. The first scenario, named the Generalized Approach, estimates annual energy production based on irradiance data, vehicle surface area, and driving-to-standby ratios. The second scenario, named the Data-Driven Approach, incorporates detailed GPS-based driving data to capture the dynamic effects of orientation, speed, and operating conditions. Applied to European and Middle Eastern airports, the framework showed that VIPV could cover 1700–5500 $\mathrm{k}\mathrm{m}/\mathrm{y}\mathrm{e}\mathrm{a}\mathrm{r}$ for buses, 650–5000 $\mathrm{k}\mathrm{m}/\mathrm{y}\mathrm{e}\mathrm{a}\mathrm{r}$ for minibuses, and 840–6180 $\mathrm{k}\mathrm{m}/\mathrm{y}\mathrm{e}\mathrm{a}\mathrm{r}$ for luggage tractors, with avoided emissions strongly influenced by local grid intensity. Grid parity analysis indicated favorable conditions in sunny, high-cost electricity markets. The framework is transferable to other VIPV applications and provides a practical tool for evaluating their technical, environmental, and economic potential. Full article

Background: Patent ductus arteriosus (PDA) in dogs causes persistent left-to-right shunting, leading to pulmonary overcirculation, left heart volume overload, and potential congestive heart failure. Accurate assessment of fluid imbalance is essential but challenging with conventional echocardiography or biomarkers. Phase angle (PhA), derived from bioelectrical impedance analysis (BIA), may serve as a non-invasive marker of extracellular fluid distribution and cellular integrity. Objectives: This study aimed to evaluate PhA as an indicator of thoracic fluid imbalance in dogs with PDAby analyzing its correlation with pulmonary velocity (PV) and end-diastolic volume (eV), as well as its responsiveness to surgical correction. In addition, we assessed the relationships between PhA and echocardiographic structural indices (LA/Ao, TDI Sep E/Em, TDI Lat E/Em) and examined the influence of the measurement region. Methods: PhA was measured at 5, 50, and 250 kHz in 30 PDA-affected and 15 healthy dogs, with electrode placement across thorax, trunk, and abdomen. Echocardiography evaluated PV, eV, and PDA-specific structural parameters. Results: Thoracic PhA at 5 kHz was significantly reduced in PDAdogs, strongly correlated with PV and moderately with eV. Postoperative measurements showed progressive PhA recovery. Only TDI Lat E/Em correlated with mid-frequency PhA, while other structural indices showed minimal association. Thoracic PhA was lower than trunk or abdominal values, indicating that thoracic measurements may better capture localized extracellular fluid changes in PDAcompared with other regions. Conclusion: Thoracic PhA at 5 kHz effectively reflects extracellular fluid changes in PDA, complements structural echocardiography, and tracks postoperative fluid normalization. Its non-invasive nature supports clinical utility for monitoring hemodynamic burden and therapeutic response. Full article

Background/Objectives: Pemphigus vulgaris (PV) and foliaceus (PF) are autoimmune blistering diseases mediated by IgG antibodies directed against desmogleins 1 and 3 and are still considered life-threatening disorders. In recent years, rituximab has been shown to be very effective, especially in PV and mainly in short follow-ups. The role of rituximab in achieving long-lasting complete clinical remission (cCR) in pemphigus still needs to be determined. Therefore, the aim of our study was to assess the efficacy, measured by achieving long-lasting cCR, and safety of rituximab in both PV and PF over an 8-year follow-up in light of several factors (body mass index—BMI, severity of disease—PDAI, age, gender, disease duration, COVID-19 period). Methods: In total, 28 patients with pemphigus were treated with rituximab and followed-up at one center. The entire analysis was performed using statistical methods. Results: Long-lasting cCR was achieved in 5 out of 6 patients (83%) with PF and 10 of 22 (45.5%) patients with PV. Univariate and multivariate analysis disclosed that studied factors did not statistically correlated with achieving long-lasting cCR. Among studied patients, few developed side effects, mainly urinary tract infection; one patient had sepsis, and one patient died. Conclusions: This study has demonstrated that rituximab is highly effective in PF and quite effective in PV over an 8-year follow-up in relation to independently studied factors. Moreover, the COVID-19 pandemic was not a negative factor influencing cCR achievement since 82% of patients treated with rituximab during that time still achieved cCR. Full article

In recent years, with the development of distributed photovoltaic (PV) systems, their impact on power grids has become increasingly significant. However, the complexity of meteorological variations makes the prediction of distributed PV power challenging and often ineffective. This study proposes a short-term power forecasting method for distributed photovoltaics that can identify seasonal characteristics matching weather types, enabling a deeper analysis of complex meteorological changes. First, historical power data is decomposed seasonally using the adaptive noise complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN). Next, each component is reconstructed based on a characteristic similarity approach, and a two-stage feature selection process is applied to identify the most relevant features for reconstruction, addressing the issue of nonlinear variable selection. A CNN-LSTM-KAN model with multi-dimensional spatial representation is then proposed to model different weather types obtained by the K-shape clustering method, enabling the segmentation of weather processes. Finally, the proposed method is applied to a case study of distributed PV users in a certain province for short-term power prediction. The results indicate that, compared to traditional methods, the average RMSE decreases by 8.93%, the average MAE decreases by 4.82%, and the R² increases by 9.17%, demonstrating the effectiveness of the proposed method. Full article