Search Results (1,477)

Elevated particulate matter (PM) concentrations pose severe health risks, necessitating green infrastructure mitigation. While deposition is well documented, wind-induced remobilization remains insufficiently quantified. This study establishes a size-fractionated (PM_1–2.5 and PM_2.5–10) wind-induced resuspension and net removal values for six Central European shrub and climbing species (Parthenocissus quinquefolia, Hedera helix, Viburnum opulus, Viburnum lantana, Ligustrum ovalifolium, and Cornus mas) under controlled laboratory conditions. Following standardized aerosol chamber loading, leaves were subjected to constant, laminar airflow velocity of 3 m/s. Numerical quantification of particle counts per unit area (cm²) was performed via scanning electron microscopy with backscattered electron signal processing. Results demonstrate significant interspecific variations. Parthenocissus quinquefolia was most efficient, retaining the highest particle counts (121.6 × 10³ particles/cm² for PM_2.5–10) and achieving net removal rates of 46.3% and 60.5% for PM_1–2.5 and PM_2.5–10, respectively, relative to initial deposition. Cornus mas exhibited the lowest net removal efficiency for coarse particles (21.2% for PM_2.5–10), while Hedera helix showed the highest fractional resuspension rates (k = 1.93 × 10⁻⁴ ∙ s⁻¹ and 2.01 × 10⁻⁴ ∙ s⁻¹, respectively). These species-specific traits are vital for optimizing urban green infrastructure. Ultimately, these findings provide actionable recommendations for targeted plant selection to maximize urban air purification. Full article

Open-cathode Proton Exchange Membrane Fuel Cells (PEMFCs) are a promising technology for increasing the endurance of small Unmanned Aerial Vehicles (UAVs), ground robots, e-bikes, and light electric vehicles. However, their performance under realistic operating conditions is strongly influenced by rapid variations in load, temperature, and ambient pressure, which are often neglected in design-oriented or quasi-steady-state analyses. This study experimentally investigates a 1 kW open-cathode PEMFC system, including its balance of plant and a passive supercapacitor buffer, under a representative UAV flight power profile. Steady-state and dynamic tests were conducted to assess polarization characteristics, thermal behavior, parasitic power consumption, and hydrogen utilization. Results revealed significant thermal inertia and hysteresis effects during load transients, causing voltage deviations from steady-state performance and stabilization times exceeding 90 s. The supercapacitor effectively reduced stack current ramp rates, although some high-frequency oscillations remained. Under flight-representative conditions, the system achieved stable operation with average voltaic efficiency ranging from 55.3% to 60.7% and net efficiency ranging from 50.2% to 54.2%. Auxiliary components had a measurable impact on overall performance: cooling fans accounted for 2–6% of stack power during steady operation and approximately 2.5% of total mission energy, while hydrogen purge losses can significantly reduce vehicle endurance. The findings demonstrate the importance of energy-based performance assessment, including auxiliary loads and purge losses, to obtain realistic estimates of efficiency and endurance in dynamic PEMFC-powered applications. Full article

Renewable energy curtailment has emerged as a growing challenge on the Korean mainland grid as photovoltaic (PV) and wind power capacity continues to expand toward national carbon neutrality targets. Plus demand response (Plus DR), in which electricity consumers increase consumption during curtailment periods, has been introduced as a demand-side mitigation measure. Buildings represent a potential resource for Plus DR participation. However, existing studies have primarily focused on load-reduction DR, and Plus DR availability by building use type under curtailment conditions has not been systematically quantified. This study estimates Plus DR availability of building loads by use type—department store, hotel, general commercial, public facility, apartment, and school—based on representative building load profiles, PV generation data, and 2025 curtailment occurrence data from the Korean mainland grid. Curtailment events were concentrated in the 10:00–16:00 window with peak frequency at 12:00 (80 events). The combined Plus DR availability across the six use types averaged 290.3 kW during curtailment hours, peaking at 300.9 kW at 14:00. The estimated Plus DR availability operated primarily through the load-increase pathway (additional grid consumption) rather than the surplus absorption pathway (reduced PV export). Surplus generation was observed only in the school at 13:00 (0.77 kW). These results provide a quantitative basis for identifying suitable building types and curtailment-responsive time windows for building-based Plus DR program design on the Korean mainland, and may serve as a reference for mainland DR market development. Full article

Concrete cracks are ubiquitous in practical engineering structures and continuously affect structural safety and durability. Crack images provide important visual evidence of damage evolution; however, crack images alone are insufficient to determine the residual load-bearing capacity of concrete members. Although the development of deep learning algorithms has significantly improved the automatic detection of concrete surface cracks, most existing methods remain limited to the extraction of crack geometric features and lack a direct connection with mechanical performance. To explore the relationship between image-based crack geometry and mechanical response, this study combines U-Net-based crack segmentation, OpenCV-based crack geometry extraction, and phase-field fracture simulation to establish a preliminary visual–mechanical framework for plain concrete beams. In this framework, surface crack images are first segmented using a U-Net model, and crack length, average width, and propagation path are extracted from the predicted binary masks. The extracted crack length is then used as the primary variable to match the observed crack state with the phase-field crack evolution sequence. Once the corresponding simulation stage is identified, the associated load level and residual load-bearing capacity can be obtained from the simulated load–crack mouth opening displacement (Load–CMOD) response. Through a mixed-mode I–II fracture test, the crack geometric features extracted by deep learning are compared with the phase-field simulation results. The results show that the error in crack length is within 2.5%. Meanwhile, the relative error between the simulated peak load and the experimental value was 1.57%, which preliminarily verified the correlation between image-based crack information and the load-bearing capacity of plain concrete beams. The method is further applied to a Mode I fracture test without recorded load-bearing capacity data. By mapping the crack length identified from the image, namely 36.89 mm, to the phase-field evolution sequence, the load-bearing capacity of the member at this stage is estimated to be 74.4% of the peak load. The results indicate that the crack geometry extracted from images can be correlated with phase-field crack evolution, thereby supporting preliminary residual load-bearing capacity assessment of plain concrete beams. However, the proposed framework should be regarded as a case-level feasibility study rather than a general structural assessment method. Before broader engineering application, further validation using synchronized crack image sequences, crack mouth opening displacement (CMOD) measurements, and load records is required. Full article

Groundwater quality assessment in urban and peri-urban environments is often constrained by incomplete monitoring records, irregular sampling frequencies, and heterogeneous environmental datasets. The primary objective of this study is to predict the Water Quality Index (WQI) in the Attica River Basin, Greece, using advanced machine learning (ML) techniques. A groundwater quality dataset comprising 958 observations from 80 monitoring stations was analyzed using six physicochemical parameters, namely electrical conductivity, ammonium, nitrate, nitrite, chloride, and sulphate. Three modeling approaches, namely TabNet (with Winsorization), SVM, and Gradient Boosting Machines (GBM), were implemented to estimate groundwater quality conditions. To address the challenge of missing data, Multiple Imputation by Chained Equations (MICE) with Predictive Mean Matching (PMM) was implemented and systematically compared against conventional imputation approaches, including smoothed averages, interpolation, and forward-fill methods. The novelty of this study lies in the integration of open-access groundwater chemistry data, advanced multivariate imputation (MICE-PMM), and attention-based deep learning (TabNet) for groundwater quality prediction in a Mediterranean peri-urban area under data-scarce conditions. Using a multi-year groundwater monitoring dataset, the results indicate that the integrated MICE-PMM and TabNet framework achieved the highest predictive performance, with R² = 0.91, NSE = 0.91, RMSE = 52.21, and MAE = 25.68. Feature importance and sensitivity analyses identified nitrate as the dominant driver of WQI variability, highlighting the strong influence of anthropogenic nutrient loading on groundwater quality. Overall, the proposed framework provides a transferable, data-driven approach for groundwater quality prediction, environmental monitoring, and groundwater resource management in urban and peri-urban aquifer systems characterized by incomplete environmental datasets. Full article

Against the backdrop of the global aviation industry’s commitment to achieving the “Net Zero Carbon Emissions by 2050” goal, the issue of superimposed peak loads on distribution networks—arising from the large-scale transition from fossil-fueled to electric Ground Service Equipment (GSE) at civil airports—has become increasingly prominent, emerging as a critical constraint on green airport development. Focusing on the high-value airside area, this paper presents the first systematic review of how Vehicle-to-Grid (V2G) technology can transform electric Ground Service Equipment (e-GSE) from mere “charging loads” into “dispatchable energy storage resources.” The study proposes that, through bidirectional DC charging/discharging and intelligent aggregation technologies, e-GSE fleets operating on predictable schedules can be integrated as flexible regulation units within airport microgrids. To realize this pathway, the study comprehensively examines the core technological framework, encompassing wide-power-range bidirectional charging infrastructure, grid-forming power conversion topologies, standardized communication and grid interconnection interfaces, flight-schedule-based potential assessment and dispatch algorithms, and photovoltaic storage–charging hybrid system integration schemes. The review demonstrates that this technology can not only enhance grid resilience and promote renewable energy accommodation through peak shaving, valley filling, and ancillary services but also yields significant economic benefits. Finally, the study identifies the technical, standardization, and business model barriers hindering large-scale deployment, thereby providing a theoretical reference and a technology roadmap for the energy system planning and construction of future “zero-carbon smart airports”. Full article

This study presents a novel iterative dual-loop methodology for the technical sizing of DC-coupled PV-BESS systems. The method was implemented for a commercial broiler farm characterized by a highly variable electricity demand profile (annual consumption: 7.6 MWh; coefficient of variation: 53%). The methodology introduces two original energy utilization indicators—the photovoltaic-to-converter matching factor (W_{PV_S}) and the photovoltaic-to-BESS matching factor (W_{PV_B})—enabling purely technical optimization independent of economic conditions. Minimization of the radius of curvature of the WPVB characteristic curve is applied as a rigorous mathematical criterion for determining the optimal BESS capacity. Simulation results indicate that the optimal configuration consists of a 9.7 kWp photovoltaic system, a 7 kW DC converter, and a 15 kWh battery storage system. The integration of an optimally sized energy storage system increased the self-consumption coverage ratio from 38% to 59% and improved the photovoltaic energy utilization factor from 35% to 54%. Additional economic analysis demonstrates that the PV-only subsystem achieves a simple payback period ranging from 8 to 18 years, depending on the selected pricing scenario. Consequently, the technically optimal configuration identified using the proposed methodology represents a practically feasible investment for broiler production facilities operating under Polish net-billing conditions. The proposed methodology provides a reproducible, economically independent framework for the design of DC-coupled PV-BESS systems in agricultural prosumer facilities, addressing a critical gap in the optimization literature and offering practical sizing guidelines applicable to similarly high-variability load profiles. Full article

The long-term deformation and stability of silty sand roadbeds subjected to repeated freeze–thaw cycles and traffic loading remain ongoing engineering concerns in seasonally frozen regions. To investigate the evolution and influencing factors of accumulative axial plastic deformation of silty sand under freeze–thaw cycles, this study focused on silty sand from a roadbed construction site in Inner Mongolia, China, a typical seasonally frozen region. Dynamic triaxial tests were conducted under loading stresses of 60–100 kPa, confining pressures of 20–60 kPa, water contents ranging from OMC to 1.2 OMC, and freeze–thaw cycles of 0–10. The results indicate that approximately 60–80% of the total accumulative axial plastic deformation occurs within the first 1000 loading cycles, after which the deformation growth rate gradually decreases. Increases in loading stress, water content, and freeze–thaw cycles promote deformation, whereas higher confining pressures suppress it. For example, increasing the confining pressure from 20 to 60 kPa reduced the final deformation from 0.16% to 0.07%, while increasing the number of freeze–thaw cycles from 0 to 10 increased the final deformation from 0.10% to 0.28%. Based on the experimental data, a new predictive model considering net stress, octahedral shear stress, water content ratio, and freeze–thaw cycles was developed. The model demonstrates high accuracy in predicting accumulative plastic deformation, with a coefficient of determination of 0.915, and is applicable to both plastically stable and weakly plastic creep conditions. This study provides a reference for the design, construction, and mitigation of subgrade damage in silty sand roadbeds in seasonally frozen regions. Full article

Background: Monitoring external load in team sports is essential for performance optimization, injury prevention, and individualized training prescription. Although Local Positioning Systems (LPS) are widely used for indoor athlete tracking, they require wearable devices and specialized infrastructure. Recent advances in artificial intelligence and computer vision allow markerless athlete tracking; however, their validity for basketball remains insufficiently explored. Objective: To evaluate the validity of a deep-learning multi-camera computer-vision system for quantifying external-load variables in basketball compared with a commercial LPS. Methods: The framework integrated fisheye video acquisition, player detection, and pose estimation using YOLOv11x-Pose and player re-identification through ResNet-50 and FAISS similarity search. Positional data were transformed into real-world court coordinates to derive distance, acceleration, deceleration, player load, and average speed metrics. Outputs were compared with measurements obtained from Kinexon LPS. Results: Strong correlations were observed for total distance (r = 0.92), acceleration counts (r = 0.90), deceleration counts (r = 0.92), and player load (r = 0.81), while average speed showed a moderate-to-strong correlation (r = 0.66). ICC and Bland–Altman analyses indicated agreement between systems. Conclusions: The proposed computer-vision system demonstrated high agreement with LPS, supporting its use as a valid, non-invasive, and scalable solution for external load monitoring in basketball. Full article

Hybrid power generation systems are an effective solution for matching energy production with electrical load demand. In this study, we examine the viability of a grid-connected hybrid PV/Wind system for meeting the electricity demand of the Lafarge cement factory in Al-Tafilah, Jordan, using HOMER Pro software. The results indicate that the optimal configuration consists of a $6.1 \mathrm{M}\mathrm{W}$ wind turbine and a $22.8 \mathrm{M}\mathrm{W}$ PV array, producing $71.94 \mathrm{G}\mathrm{W}\mathrm{h}$ annually, with wind and PV contributing $31.3\%$ and $68.7\%,$ respectively. The system achieves a 100% renewable fraction while maintaining a high level of reliability, with unmet load and capacity shortage limited to $0.057\%$ and $0.1\%,$ respectively. The economic evaluation reveals a levelized cost of energy (LCOE) of $0.13 \mathrm{U}\mathrm{S}\mathrm{D}/\mathrm{k}\mathrm{W}\mathrm{h}$ and a net present cost (NPC) of $\mathrm{U}\mathrm{S}\mathrm{D} 25.827 \mathrm{m}\mathrm{i}\mathrm{l}\mathrm{l}\mathrm{i}\mathrm{o}\mathrm{n}$, representing a $27.8\%$ reduction in LCOE compared to the national grid tariff. In this study, we present a novel large-scale PV/Wind system for the cement industry in Jordan, based on real data, with enhanced techno-economic performance. The innovation of this research lies in the development and optimization of a large-scale grid-connected hybrid PV/Wind system for the cement industry in Jordan, utilizing actual industrial load data and site-specific renewable energy resources. Unlike previous PV-dominated studies, the proposed system integrates a significant contribution of wind energy to improve system reliability and renewable energy penetration, reduce dependency on the national grid, and improve the overall techno-economic performance under actual industrial operating conditions. Full article

Background: Flattening of the medial longitudinal arch is traditionally attributed to excess body weight and Body Mass Index (BMI). However, controversy exists regarding whether adiposity or skeletal structure drives this biomechanical alteration, and which podometric index best detects it. Methods: A cross-sectional study evaluated 99 healthy university students (50 males, 49 females). Body composition was assessed via a four-component model. Plantar footprints were captured using 4K digital podoscopy and analyzed with five morphometric indices. Arch predictors were identified using multivariate regression models (Elastic Net regression) and Generalized Additive Models (GAMs). Results: Only Clarke’s Angle detected significant sexual dimorphism, showing structurally higher arches in females (50.28° ± 7.14) than in males (41.82° ± 11.20; p < 0.001). Multivariate analysis revealed bone mass as the dominant structural predictor, exerting a non-linear negative association with the arch profile, which stabilizes beyond 12 kg. BMI was not a significant predictor, whereas body fat percentage showed a modest positive association. Conclusions: Plantar arch morphology is strongly associated with skeletal load (anthropometrically estimated bone mass) rather than adiposity or BMI. Within this specific cohort, Clarke’s Angle emerged as a highly sensitive instrument for characterizing sexual dimorphism. Clinical assessments diagnosing functional flatfoot should prioritize underlying bone structure over BMI, particularly when evaluating a healthy and physically active university population. Future studies incorporating DXA or radiographic validation are needed to confirm these anthropometric findings. Full article

This paper presents a power-based dynamic programming (DP) method for day-ahead battery scheduling in a grid-connected photovoltaic (PV) microgrid under time-of-use (TOU) tariffs. The proposed formulation optimizes battery power directly, rather than SOC setpoints, so the dispatch is easier to apply in practical inverter control and remains computationally tractable over a 48 h horizon. The model includes battery degradation through a linear wear-cost term based on a 200 USD/kWh replacement cost, while also enforcing SOC and charging/discharging power limits. The case study uses a 250 kWh battery and evaluates two power limits, 0.1C and 0.2C, together with two degradation cases, 200 and 400 USD/kWh. The simulation considers two different operating days to test the controller under unequal renewable and demand conditions. Day 1 has stronger PV generation and lower load demand, whereas Day 2 has lower PV output and higher demand. Under the baseline 0.1C limit, DP reduces the net operating cost to 97.47 USD, compared with 122.95 USD for the TOU-aware rule-based benchmark. When the power limit increases to 0.2C, the net operating cost falls further to 78.35 USD because export revenue rises substantially. When the battery replacement cost doubles from 200 USD/kWh to 400 USD/kWh, the optimizer reduces cycling and the net operating cost increases to 129.21 USD. Overall, the results show that power-based DP provides a practical and transparent framework for balancing tariff arbitrage and battery preservation in grid-connected microgrids. Full article

The rapid growth of solar photovoltaic (PV) capacity is increasingly reshaping the operation of electricity systems, particularly in countries where renewable energy already represents a large share of generation. In Albania, where electricity production is strongly dominated by hydropower, increasing solar penetration is expected to affect short-term system behaviour, especially in terms of variability, surplus generation, and ramping dynamics. This study investigates PV integration at the system level using hourly electricity demand data for 2024 together with PV generation profiles scaled to different capacity scenarios. PV scenarios representing installed capacities of 150, 300, and 450 MWp, based on real PV deployment data, are analysed under varying levels of hydropower dominance. The analysis combines net-load modeling, ramping assessment, and a simplified flexibility-oriented mitigation approach to evaluate operational impacts under different hydropower conditions. The results indicate that increasing PV capacity significantly modifies the net-load profile. During summer periods, high solar generation substantially reduces midday net load, creating pronounced net-load valleys, whereas winter conditions remain more strongly influenced by electricity demand. As PV penetration increases, ramping intensity also increases. For example, extreme ramp values (Q99) rise from 80.87 MW/h at 300 MWp to 111.45 MW/h at 450 MWp, while the share of hours with ramp events exceeding 100 MW/h increases from 0.05% to 2.55%. The results of a conceptual flexibility approach that limits ramps to 60 MW/h show that extreme ramp events can be effectively mitigated, while moderate variability is largely unaffected. In summary, the results show that increasing solar PV penetration shifts the main operational challenge in Albania from energy balancing toward flexibility and variability management. The findings are particularly relevant for long-term system planning in hydropower-dominated systems and highlight the growing importance of flexibility measures and surplus management under high PV penetration. Full article

Addressing the challenge of power supply stability caused by the intermittent nature of photovoltaic power generation in off-grid microgrids, this study uses a commercial park in Wuhan as a case study and optimizes the capacity configuration of a photovoltaic–storage–hydrogen fuel cell hybrid microgrid system based on HOMER Pro software. First, a topology of the off-grid microgrid is constructed, comprising photovoltaic (PV), lithium-ion batteries, hydrogen fuel cells, and a diesel generator as backup. The power output characteristics, efficiency curves, and life-cycle cost models of each component are accurately established. On this basis, two typical operation strategies, namely Load Following (LF) and Cycle Charging (CC), are proposed and compared. The influence of different strategies on the optimal capacity configuration and operational economics is systematically analyzed, and the Cycle Charging strategy is identified as the optimal operation strategy for this scenario. Subsequently, a multi-scenario capacity optimization design is further conducted based on the optimal operation strategy. The minimization of net present cost (NPC) is taken as the primary objective, while multiple evaluation indicators such as renewable fraction (RF), levelized cost of electricity (LCOE), energy storage cycle life degradation, and system redundancy rate are comprehensively considered. The results show that, while ensuring 100% power supply reliability, the proposed model reduces the net present cost (NPC) by approximately 14.4% compared with the conventional PV-storage scheme. The renewable fraction (RF) reaches 95.8%, while the reliance on lithium-ion battery capacity is significantly reduced (battery capacity configuration decreased by 24.3%). This effectively extends the energy storage lifespan and enhances the overall economic and environmental benefits. The results provide a theoretical basis and technical reference for the planning and design of off-grid microgrids with high penetration of renewable energy. Full article

Main engine load varies continuously, whereas onboard carbon capture columns are installed with fixed capacities. For liquefied natural gas (LNG)-fueled ships, this mismatch between design and operation makes off-design robustness, rather than nominal-point performance, the governing sizing criterion. This study developed a variable-load design window for onboard monoethanolamine CO₂ capture and evaluated a dual-loop organic Rankine cycle (ORC) as a secondary thermal integration option. A verified process model was applied to a 5 × 5 design–operating matrix (D50–D90/O50–O90). The mismatch was strongly asymmetric. When operating load did not exceed design load, capture rate remained near 90%; under overload, absorber treated only the design-point-equivalent exhaust-gas flow, causing capture performance to deteriorate rapidly. The mean CO₂ avoided rate increased from 57.4% at D50 to 70.4% at D90, while absorber diameter increased from 3.23 to 4.06 m. D70 emerged as the balanced option for low- to medium-load services, D80 marked the transition before full robustness, and D90 was robustness-oriented for frequent high-load operation. The ORC recovered 104–185 kW net power and supplied 231–410 kW LNG-side heating. Results support capacity selection before ORC application; CO₂ liquefaction and storage, voyage-weighted validation, and shipboard ORC feasibility remain outside the present scope. Full article