Search Results (412)

Offshore wind can diversify the Brazilian electricity matrix, but foundation design on the Ceará continental shelf must account for carbonate sands whose stiffness, crushability and stress-dependent response differ from those of quartz sands. This study contribution is the use of carbonate-sand parameters calibrated from consolidated-drained triaxial tests on Ceará-shelf sediments and the direct comparison of two sands with contrasting carbonate contents. The measured responses calibrated the Hardening Soil model in PLAXIS 2D, and the resulting parameters drove PLAXIS 3D simulations of monopiles with different diameters, embedment lengths and tower heights under monotonic lateral loading. The more calcareous sand showed higher frictional strength but lower stiffness—as a result, it mobilised larger mudline displacements, making the Serviceability Limit State more restrictive than the Ultimate Limit State. Pile diameter controlled lateral capacity, whereas reduced L/D lowered system stiffness and serviceability performance. Within the monotonic, homogeneous-profile scope adopted here, M3 (D = 9 m, L = 36 m, L/D = 4) gave the most favourable response for the medium-rated turbine class. The results provide screening-level comparative evidence, not a design-ready proof of feasibility. Full article

An advanced power control and energy management strategy for an isolated hybrid microgrid system is presented in this paper. With the help of a battery energy storage system (BESS), the architecture combines a wind turbine (WT) emulator, photovoltaic (PV) arrays, and a variable-speed diesel generator (VSDG) emulated by a controlled DC source on a 1/22 reduced-scale laboratory platform. Three-level converters are used in a robust power control method to reduce the inherent intermittency of renewable sources and the stochastic nature of isolated loads. These converters are used to improve power quality, lower harmonic distortion, and control dynamic interactions between the sources in real time. The main goal is to minimize the VSDG contribution and estimated fuel consumption while optimizing renewable energy penetration, with fuel consumption reduction assessed through power dispatch analysis rather than direct measurement. Comprehensive simulations and real-time experimental prototyping using the dSPACE (CP1104) controller on a 1/22-scale platform were used to validate the proposed control strategy. Results from both simulations and experiments support the strategy’s viability and efficacy in preserving system stability and maximizing energy dispatch under various load profiles. Full article

Geographical traceability of rice is critical for authenticity identification and quality control, yet it poses considerable challenges for tracing origins in adjacent small-scale producing areas. To explore the causes of metabolic differences and geographical traceability potential of rice from adjacent small-scale producing areas, non-targeted metabolomics combined with multivariate statistical analysis was employed to systematically investigate the metabolic profiles of rice from Panjin (PJ), Donggang (DG) and Yingkou (YK) in Liaoning Province. The characteristic metabolic markers for each producing area were screened, and the effects of climatic and soil factors as well as their interactive effects on grain metabolite composition were elucidated. The results showed that the partial least squares-discriminant analysis (PLS-DA) model established based on differential metabolites achieved acceptable discrimination among rice samples from the three regions. With variable importance in projection (VIP) > 2.0 as the screening threshold, the core characteristic markers of each producing area were determined: PJ is LPC 17:2, LPA 18:3, D-(+)-Arabitol, DG is 2′-Deoxyadenosine, LDGTS 18:2, and YK is LPE 17:2; the markers are mainly primary metabolites, including lipids, sugar alcohols and nucleotides. Sunshine duration, air humidity, wind conditions and soil layer temperature were highly correlated climatic drivers responsible for metabolic differentiation, and characteristic metabolic markers from different producing areas exhibited distinct meteorological response patterns. Soil physicochemical properties and mineral elements significantly affected the differential accumulation of metabolites, among which soil Sr element and organic matter exhibited crucial indicative significance for metabolic variation of rice in adjacent regions. Multi-factor interaction analysis verified significant synergistic coupling effects between regional climate and soil environment. Meteorological factors, including sunshine, wind and soil temperature, together with soil chemical factors involving organic matter, pH, Sr, K and Ca, were identified as core driving factors for the spatial differentiation of region-specific rice metabolites. The present study provides theoretical support at the metabolic level for the construction of a small-scale rice geographical traceability system and the mechanism research on the regional quality formation of rice. Full article

U.S. renewable energy consumption has expanded substantially over the past five decades, but this transition cannot be adequately characterized by aggregate growth alone. This study developed an integrated empirical, forecasting, uncertainty, reconciliation, scenario, and planning framework for U.S. renewable energy consumption using a complete monthly multisector panel from January 1973 through December 2025. The analytic dataset contained 3180 sector–month observations across 636 monthly periods and five reporting sectors: Commercial, Electric Power, Industrial, Residential, and Transportation. The framework combined data harmonization, mutually exclusive source-family construction, long-run trend analysis, source-mix diversification metrics, structural-regime diagnostics, sector–source panel analysis, rolling-origin forecast benchmarking, probabilistic interval assessment, hierarchical reconciliation, future scenario analysis, and decision-focused planning evaluation. Annual reported total renewable energy consumption increased from 2475.547 trillion Btu in 1973 to 7050.214 trillion Btu in 2025, equivalent to approximately 2.476 quadrillion Btu and 7.050 quadrillion Btu, respectively. The results show that U.S. renewable energy growth was also a source-mix transformation: the portfolio became less concentrated as wind, solar, transportation biofuels, renewable diesel, waste, and other emerging sources gained importance alongside legacy wood and hydroelectric power. Sector–source heterogeneity was substantial, with Electric Power, Industrial, and Transportation showing distinct renewable-source profiles. Forecasting performance depended strongly on model family, horizon, validation window, target group, and evaluation lens. Strong statistical baselines and feature-based tree models remained competitive or superior to several deep learning architectures, while time-series foundation models provided useful modern comparators but required calibration and horizon-specific interpretation. All five selected foundation model comparators completed successfully. ChronosBolt was the fastest and strongest completed foundation model comparator, followed in runtime by TimesFM, Moirai/Uni2TS, TimeGPT, and LagLlama; however, foundation model forecasts remained too smooth for peak-sensitive planning and did not displace the strongest feature-based tree models in point-forecast benchmarking. Probabilistic diagnostics showed that nominal coverage alone was insufficient because interval width, Winkler score, CRPS, and visual inspection revealed target-specific miscalibration, underforecast bias, and weak peak coverage. Hierarchical and decision-focused evaluation changed the model-selection narrative: bottom-up and reconciled hierarchical forecasts produced stronger planning-loss and planning-value profiles than many nominally advanced alternatives, while selected tree-based models were particularly useful for preserving source-share allocation. Scenario analysis showed that solar acceleration increased projected totals but also increased concentration and coherence divergence, whereas diversification reduced concentration but required wider uncertainty buffers. Overall, U.S. renewable energy consumption should be analyzed as a dynamic, diversified, hierarchical, and planning-sensitive system. The proposed framework provides a reproducible basis for evaluating renewable energy growth, source-mix evolution, forecast reliability, uncertainty, source allocation, scenario trade-offs, and planning value beyond single-model forecasting claims. Full article

Accurate impedance modeling of wind turbines (WTs) is essential for assessing the small-signal stability of power systems with high penetration of renewable energy. Existing approaches face a fundamental trade-off: physics-based “white-box” models require proprietary manufacturer parameters that are rarely disclosed, while purely data-driven “black-box” models often lack physical interpretability and exhibit poor generalization under unseen operating conditions. To address this gap, this paper proposes a gray-box framework—the Physics-Informed Hybrid Model (PIHM)—that integrates a simplified physical impedance branch with a Bidirectional Long Short-Term Memory (Bi-LSTM) network in a novel parallel architecture. The physical branch, systematically parameterized via a constrained phase-error minimization method, captures the dominant baseline dynamics and decouples the learning task, allowing the Bi-LSTM to focus exclusively on the complex nonlinear residual. The framework is validated on a high-fidelity simulation platform of a doubly fed induction generator (DFIG) wind farm. Quantitative results demonstrate that the PIHM achieves an average coefficient of determination ($R^2$) of 0.989 and a mean squared error (MSE) of $1.24\times {10}^{-4}$ on unseen test data, while producing smooth, physically consistent impedance profiles that generalize across four distinct wind speed conditions. These results establish the PIHM as a reliable, parameter-free tool for impedance-based stability analysis of modern wind power systems. Full article

Newhall navel orange, a major citrus variety in China, shows considerable variation in fruit quality across production regions. To investigate the key factors driving the geographical variation, this study systematically compared the quality of Newhall navel oranges from 13 production areas and analyzed the relationships between volatile metabolites and climate variables. Our results revealed pronounced regional differences in both fruit physicochemical properties and volatile profiles. Total soluble solids, titratable acid content, and peel color parameters (L*, a*, b*) were identified as the core physicochemical indicators most strongly associated with quality variation. Using headspace solid-phase microextraction coupled with gas chromatography–mass spectrometry (HS-SPME-GC-MS), 106 volatile organic compounds (VOCs) were identified, of which 56 were selected as potential differential markers via partial least squares discriminant analysis (PLS-DA). Correlation analysis and partial least squares regression (PLSR) revealed that annual mean wind speed (AMWS), mean diurnal temperature variation at the expansion stage (MDTV-ES), and mean wind speed, total sunshine duration, mean diurnal temperature variation at the degreening stage (MWS-DS, TSD-DS, MDTV-DS) were important meteorological factors related to volatile metabolism. The study clarified the geographical variations in physicochemical characteristics and volatile profiles of Newhall navel oranges, as well as the key climatic factors linked to volatile metabolism, providing a crucial theoretical basis for site-specific cultivation planning, demarcation of high-quality production areas, and targeted quality regulation of citrus varieties. Full article

The rapid development of wind and solar energy necessitates a solution to the problem of the optimal spatial placement of wind farms (WFs) and solar farms (SFs) within electric power systems. The non-stationary generation schedules of WFs and SFs place increased demands on the flexibility of conventional generation, determined by the intensity of net load fluctuations. This paper proposes a methodology for the spatial optimization of WF and SF location, in which the optimization criteria include net load indicators (rate of net load change and net load increment), the base power of the RES system, and the economic criterion of maximum electricity generation. Unlike existing approaches, in which the geographical smoothing effect on power fluctuations is treated as an incidental outcome, the proposed methodology employs it as an explicit optimization criterion for RES placement. The algorithm provides for the preliminary ranking of candidate sites based on the maximum electricity generation criterion, followed by the redistribution of generating capacities among sites with an acceptable capacity factor in accordance with the selected optimization criterion. The methodology was tested on a model comprising six potential wind farm sites and two solar farm sites with a total installed capacity of 600 MW and a maximum power system load of 3000 MW. The obtained results show that the optimal redistribution of installed capacities among sites allows a 31.5% reduction in net load variability intensity to be achieved with an 11.6% reduction in electricity generation relative to the maximum possible. The study is based on idealized daily generation and consumption profiles and is theoretical in nature, proposing a pre-screening tool for RES siting that complements rather than replaces subsequent network-constrained planning studies, including power-flow analysis and grid verification, and establishes a methodological foundation for further development using real multi-year retrospective data. Full article

Coastal zones are dynamic interfaces where land, ocean, and atmosphere interact, making them sensitive indicators of environmental change. However, quantifying shoreline movement across long distances and over multi-year timescales remains challenging using traditional ground-based methods alone. We conducted an analysis of environmental factors and shoreline dynamics along a 58 km stretch of the arid Cabo Pulmo shoreline in Mexico from 2020 to 2026 using the CoastSat tool. The landscape is characterized by a diverse array of geographical features, including sandy beaches, granite cliffs, estuarine systems, and various anthropogenic structures. Results indicated a sea-level rise of 2 mm/year over the last 27 years, which is consistent with the reported range for the Pacific (1.8 to 3.8 mm/year). Notably, we observed an increasing trend of Category 4 and 5 hurricanes in the Mexican Pacific, with an average of 1 additional hurricane per decade (1950–2023). A total of 457 Sentinel-2 satellite images were used for automated analysis using the CoastSat platform, all of which were acquired under tidal conditions not exceeding 1 m. Our findings indicate that the granite cliffs show no detectable horizontal changes in the satellite images; however, their minimal vertical erosion contributes sediment to adjacent beaches. The most significant shoreline erosion was observed north of a marina breakwater, measuring −19.7 m, attributed to the disruption of littoral transport toward the southeast. In contrast, sandy beaches located in front of streams and estuaries—characterized by a lack of infrastructure (houses and breakwaters) and gentle slopes of 2° to 4°—demonstrated positive accretion of up to 5.9 m. According to the autoregressive distributed lag model, wave energy and hurricane-driven wind gusts are the primary agents of shoreline retreat, displacing sediment seaward to the continental shelf. Sea level rise exacerbates this retreat, while rainfall plays a minor but contributing role by transporting sediment during hurricanes in this arid region. This study highlights the effectiveness of CoastSat as a neural network-based tool for analyzing shoreline changes; however, we faced certain limitations, such as the absence of in situ beach profiles due to restricted access. Full article

Proton exchange membrane fuel cell–battery hybrid power systems provide an effective solution to overcome the limited endurance of battery-powered multirotor unmanned aerial vehicles. However, the highly transient power demands of quadcopter platforms, combined with balance-of-plant losses and operational constraints, create significant challenges for reliable energy management. This study proposes a degradation-aware stress-mitigation model predictive control-based energy management framework to maximize mission endurance under realistic conditions. A control-oriented, physics-consistent model is developed using manufacturer polarization data from a 500 W Aerostak proton exchange membrane fuel cell. The model captures polarization behavior, balance-of-plant loads, battery dynamics, and direct current-bus power balance. The model predictive control strategy optimally allocates power by maintaining direct current-bus stability, regulating battery state-of-charge within safe limits, and constraining fuel cell power ramp rates to mitigate degradation. High-fidelity simulations are conducted under stochastic wind disturbances and mission-dependent load profiles, including takeoff, climb, cruise, and maneuvering phases. The results show continuous power delivery without unmet load demand. The hybrid system achieves a flight endurance of 220–224 min, consuming a total of 89.99 g of hydrogen at an average rate of 0.398–0.412 g/min, indicating a notable reduction under the considered operating conditions. Additionally, long-term analysis indicates that over 97% of initial endurance is preserved after 100 cycles, demonstrating robustness against fuel cell aging. An analytical real-time feasibility assessment further indicates that the control-oriented formulation is compatible with the computational resources of typical unmanned aerial vehicle-class onboard processors, while the integration of adaptive and robust predictive control techniques is identified as a direction for future work. Full article

Electricity purchase price variation can influence the economic feasibility of residential distributed generation, particularly in regulated markets where grid electricity prices and export compensation conditions affect investment decisions. This study evaluates the impact of flat electricity purchase price scenarios on the techno-economic viability of residential grid-connected energy systems in Santo Domingo Tehuantepec, Oaxaca, Mexico, using HOMER Pro. The analysis considers PV, wind generation, diesel generation, converter, and grid connection as candidate components, while evaluating three residential demand profiles of 11.26, 30.00, and 83.30 kWh/day and 10 electricity purchase price scenarios ranging from 3.45 to 5.00 MXN/kWh. The objective is to identify the electricity purchase price values at which the optimal architecture changes from conventional grid-only supply to PV/converter/grid adoption under the evaluated case study assumptions. The results show that grid-only supply remains the least-cost option from 3.45 to 4.20 MXN/kWh for all demand profiles. At 4.25 MXN/kWh, HOMER Pro selects PV/converter/grid configurations for the medium- and high-demand profiles, while the low-demand profile remains grid-only. At 4.30 MXN/kWh, PV/converter/grid also becomes optimal for the low-demand profile. At 5.00 MXN/kWh, ROI reaches 11.0% for the three residential demand profiles, while payback decreases to 6.5 years for the low- and medium-demand profiles and 6.4 years for the high-demand profile. The wind turbine and diesel generator were not selected in the optimal configurations, despite being included as candidate technologies. These findings provide a practical case study indicator of the electricity purchase price levels at which residential PV-grid adoption becomes economically competitive under flat purchase price scenarios and zero export compensation. Full article

Currently, the large-scale integration of renewable energy sources (RESs), such as wind turbines and photovoltaic array, is profoundly altering the dynamic behavior of power systems. In particular, the reduction in system inertia makes transient stability more critical and increases the sensitivity of the network to disturbances. The originality of this work lies in the systematic analysis of the nonlinear dynamics of power systems by thoroughly examining the impact of RESintegration on system stability, particularly through frequency response and voltage profile. In this context, a methodology for the evaluation and optimization of power system stability was proposed, based on two key indicators: the Critical Clearing Time (CCT) and the Rate of Change of Frequency (RoCoF). The IEEE 39-bus test system was used as a benchmark to simulate different scenarios. Three-phase faults are applied to determine the corresponding CCT values and to assess the system’s ability to regain a stable operating state after a severe disturbance. In addition, RoCoF variations are analyzed to quantify the impact of RES penetration on the frequency stability of the network. The obtained results show that a high penetration of renewable energy sources tends to reduce the CCT and increase the RoCoF, indicating a reduction in the dynamic robustness of the system. These observations are confirmed through comparative simulations performed with and without renewable energy integration. In conclusion, this study highlights the importance of optimal placement of renewable generation units, as well as the use of the CCT and RoCoF indices as effective diagnostic and optimization tools for modern power systems characterized by a high penetration of renewable energy sources. Full article

This study investigates the distribution and concentration of microplastics (MPs) across the littoral profile of a beach, from dune base to offshore sector, including an estuarine channel and Sabellaria alveolata bioconstructions. The research was conducted at Pino di Lenne beach (Taranto, Ionian Sea), a wave-dominated, microtidal littoral system representing a unique natural laboratory with minimal anthropogenic pressure. An eco-friendly extraction protocol was used, combining methods that were already known in the literature. Olive oil proved highly effective in isolating a wide range of MP densities from sediment samples. Statistical analysis identified key accumulation zones, with the highest mean concentrations found in the submerged sandbar (2435 MPs/kg), Sabellaria bioconstructions (2324 MPs/kg), and the base of the dune (2065 MPs/kg). Fibres were the predominant morphology across all sub-environments. Distribution is interpreted as controlled by hydrodynamic processes and biological activity. The submerged beach drives MP transport, with the sandbar and shoreface acting as dynamic sinks. Sabellaria bioconstructions function as biological trap, actively incorporating MPs into their tubular structures. The dune base acts as a sink for wind-blown and storm-deposited plastics. These sub-environments function as critical littoral traps for MPs, essential for developing targeted monitoring and remediation strategies in similar coastal systems. Full article

The dynamics of landfalling tropical cyclones are not yet fully understood. In this study, ground-based observations were conducted using L-band radar, lidar and radiosonde during tropical cyclone Wutip. The L-band radar winds were corrected using lidar wind, and an objective analysis wind was derived from both the L-band radar and lidar data. Furthermore, we analyzed radiosonde profiles from a nearby station. The relative humidity was found to be higher in the lower boundary layer. Using the air temperature and humidity data, we computed the buoyancy frequency, and the Richardson number, which indicates shear instability in air turbulence. Within the altitude range of 15 km, the lower boundary layer exhibited a relatively low Richardson number. The integrated multi-source observations captured vertical profiles of wind, air temperature and relative humidity, and further revealed vertical wind shear, atmospheric stratification and associated shear instability throughout the passage of tropical cyclone Wutip. Full article

Large language models (LLMs) offer new possibilities for natural-language interaction with geospatial analysis systems, but their use in remote sensing instrument data analysis remains limited by weak execution control, poor reproducibility, and limited integration with domain-specific computation. The paper presents an agent for Incoherent Doppler wind LiDAR (ICDL) data analysis, named ICDL-Agent, a tool-augmented LLM framework for remote sensing instrument workflows. The system maps conversational user requests to executable analysis pipelines for wind retrieval, uncertainty estimation, visualization, and higher-level diagnostics through structured planning over a registry of domain-specific tools. To improve execution reliability, the system combines schema-constrained workflow generation, shared-state reuse of intermediate scientific products, and validation with bounded repair. In addition to supporting routine LiDAR processing, the framework can generate new tools when required and adapt to related analytical tasks through domain-aware guidance and procedural documentation. We evaluate the system on multiple atmospheric wind-observation datasets in China and show that it faithfully reproduces the refined Doppler wind-retrieval pipeline, achieving representative R²/MAE values of 0.52/3.73 m/s against ERA5 and 0.80/2.31 m/s against radiosonde observations, while supporting downstream analyses such as profile comparison, climatological interpretation, and gravity-wave diagnostics. More broadly, this study demonstrates how constrained LLM orchestration can support LiDAR researchers, remote-sensing instrument teams, and geospatial analysts seeking transparent, reproducible, and automated scientific data-processing workflows. Full article

Spinacia oleracea L. cultivation in South Asia is severely compromised by leaf spot disease caused by fungal plant pathogens, resulting in significant yield and quality losses. In this study, we report the first molecularly confirmed case of an Alternaria burnsii leaf spot on S. oleracea in Pakistan. Symptomatic S. oleracea leaves exhibiting necrotic lesions with concentric rings were collected during a field survey across Bahawalpur district, Punjab, Pakistan in 2024. After isolation, purification and morphological identification it was identified that it belongs to the Alternaria genus. For the confirmation of species, molecular identification was performed; using the ITS and GAPDH primer revealed that the fungal plant pathogen causing leaf spot of S. oleracea is A. burnsii which was also confirmed by phylogenetic analysis. Koch’s postulates were carried out to confirm pathogenicity on detached leaf assays. To assess the virulence of A. burnsii enzymatic analysis was performed. Notably, enzymatic virulence profiling demonstrated a markedly increased production of polygalacturonase (PG: 16.0 ± 0.8 AU), pectin lyase (PNL: 12.0 ± 0.6 AU) and cellulase (CL: 14.0 ± 0.7 AU) relative to controls (all p < 0.001; LSD = 0.16), with PG having the greatest relative increase. This report expands the known host range for A. burnsii and highlights its two-fold threat: as a bioaerosol disseminable by wind and an enzymatic pathogen. These findings highlight the urgent need for integrated disease management strategies for suppressing leaf spot disease in S. oleracea agroecosystems. Full article