Search Results (21,366)

This study presents a dataset of meteorological and soil-hydrological instrumental observations collected at three agrometeorological stations in the East Kazakhstan Region during the growing seasons of 2022–2025. The dataset includes time series from automatic weather stations: WS “OCES-1” (Solnechnoe village) provides hourly data over four years (2022–2025; 14,614 records; 65 variables), while WS “OCES-2” (Lugovoe village; 203,279 records) and WS “Altyn Kazan” (Sulusary village; 207,115 records) provide minute-resolution data for 2025 (49 variables each). Measured parameters at 200 cm height include air temperature and humidity, atmospheric pressure, precipitation, wind speed and direction; soil measurements down to 100 cm depth include temperature and moisture. Also, field-based express measurements of volumetric soil moisture within a 1 m profile (every 10 cm) were collected during three campaigns (May–August 2025), resulting in a total of 253 measurements. The stations are located across steppe and forest-steppe landscapes of the transboundary Altai–Sayan mountain region on active agricultural lands under diverse soil–climatic conditions. Climate types correspond to Dfb and Dfa per the Köppen–Geiger classification. Soils are classified under WRB as Chernozems and Calcic Chernozems. The dataset is published in CSV format on Zenodo under a CC-BY 4.0 license. Full article

Takeover safety remains a critical human-factors issue in conditionally automated driving because drivers must resume manual control under limited time and varying traffic conditions. This study examined how auditory alert urgency influences takeover safety under different weather and scenario conditions using a controlled driving-simulator experiment. Forty female licensed drivers completed a 3 × 2 × 2 within-subject design in OpenDS v.4.5 4.5, with three alert urgency levels, two weather conditions, and two representative takeover scenarios. Driving behavior and safety indicators were treated as the primary outcomes, while limited supplementary measures were retained as supporting evidence. Across the matched takeover tasks, alert urgency and scenario condition were associated with differences in takeover-related behavior and safety outcomes, whereas weather effects were more evident in subjective difficulty appraisal than in the main objective indicators. High-urgency alerts were perceived as the most urgent and the most helpful, whereas medium-urgency alerts showed the highest overall acceptability. Rainy weather and cut-in scenarios were consistently perceived as more demanding. Limited supplementary evidence provided selective support for interpreting the observed primary outcome patterns. These findings provide controlled simulator-based evidence for context-sensitive auditory warning design and takeover-support evaluation under combined environmental and scenario demands. Full article

Climate change has increased the frequency of extreme weather events, posing a major threat to the sustainable development of agriculture and farmers’ welfare. Based on provincial meteorological data and China Family Panel Studies (CFPS) data from 2014 to 2022, this study systematically investigates the impact of climate shocks on farmers’ welfare, heterogeneity characteristics, and the buffering role of off-farm employment, using a two-way fixed-effect model. The results show that climate shocks significantly reduce farmers’ welfare, with greater welfare losses in northern regions, major grain-producing areas, and plain areas. Extreme low temperatures, extreme high temperatures, and drought are the three dominant climate hazards. In response to climate shocks, off-farm employment effectively buffers welfare losses. This study clarifies the logic of changes in farmers’ welfare and livelihood adaptation mechanisms under climate change, providing micro-empirical support for improving differentiated climate adaptation policies, strengthening agricultural risk management systems, enhancing agricultural system resilience, and promoting high-quality and sustainable agricultural development. However, constrained by the matching precision between micro-level data and meteorological indicators, future research should further refine the measurement of climate shock exposure at the individual farmer level. Full article

The risk of wildfires is increasing due to high temperatures and dry weather conditions caused by climate change. Outbreaks and spread of wildfires are usually conditioned by weather, topography, and fuel characteristics. In the Republic of Korea (hereafter, the ROK), most wildfires are caused by anthropogenic factors rather than natural ones. However, the current forest fire forecasting system being operated in the ROK does not account for anthropogenic factors. To analyze the impact of human and physical factors on wildfire occurrence, a binary logistic regression model was constructed using data from the Gangwon and Gyeongbuk provinces from January 2022 to August 2025. The dependent variable was defined as the occurrence of a wildfire, while the independent variables comprised meteorological, seasonal, stand, and anthropogenic factors. To address multicollinearity, variables with high correlation coefficients were excluded from the independent variables, which were selected by three estimating approaches, including logistic regression and two machine learning techniques (namely, Random Forest and XGBoost). With machine learning, the variables with high feature importance were identified. The explanatory power of the logistic regression analysis with independent variables selected by the machine learning models was about 1.3 times higher than that of the model using variables adjusted solely for multicollinearity. The results of logistic regression analysis revealed that weather and coniferous forests are the most important factors fostering wildfires, while the mean stand age was the most significant factor in hindering wildfires. Among the anthropogenic factors, forest road density acted as a suppressor of wildfire spread rather than a promoter of occurrence. Conversely, trail density tends to increase the risk of wildfire occurrence. Among forest management activities, plantation forests may increase the risk of forest fires, although this remains uncertain. These findings suggest that preventing wildfires requires a paradigm shift in forest resource management policies, including extending forest rotation ages and converting coniferous forests to broadleaf forests. Meanwhile, it also indicates the need to restrict the expansion of hiking trails and improve regulations regarding hiker access and behavior to prevent wildfires. Full article

Solar chimneys represent an effective passive ventilation technology capable of improving indoor thermal comfort while reducing building energy consumption. In this study, the thermal and fluid dynamic performance of a solar chimney integrated into a residential building located in Bordj Bou Arréridj (Eastern Algeria) was investigated through a comprehensive numerical, predictive, and optimization framework. A transient mathematical model was developed to evaluate the influence of key geometric parameters, including chimney width and inlet opening width, as well as environmental factors such as solar radiation intensity and wind speed, on the system performance. The generated simulation database was subsequently employed to develop and compare four machine learning models, namely, Artificial Neural Networks with Bayesian Regularization (ANN-BR), Deep Neural Networks optimized by Improved Grey Wolf Optimization (DNN-IGWO), k-Nearest Neighbors (KNN), and Extreme Gradient Boosting (XGBoost), for predicting eight output parameters including glazing temperature, fluid temperature, absorber temperature, outlet temperature, thermal efficiency, air change rate (ACH), mass flow rate, and outlet velocity. The results demonstrated that increasing chimney and inlet widths significantly enhances ventilation performance by increasing airflow rate and ACH. Weather conditions and wind speed were also found to strongly affect thermal efficiency and buoyancy-driven airflow. Among the predictive models, XGBoost and DNN-IGWO exhibited the highest predictive accuracy, achieving coefficients of determination ($R^2$) close to unity and very low prediction errors for all output variables, confirming their robustness and generalization capability. The proposed methodology provides a reliable tool for rapid performance prediction and design optimization of solar chimney systems under different climatic and operating conditions, thereby supporting the development of energy-efficient passive ventilation strategies for residential buildings. Full article

Accurate substation-level Short-Term Load Forecasting (STLF) is essential for secure day-ahead power-system operation, yet localized demand is often affected by meteorological variation and discrete calendar shifts such as statutory holidays and makeup workdays. At this spatial scale, end-to-end forecasting models may over-smooth abrupt local changes and fail to represent peaks and valleys accurately. To address this issue, this study proposes a Calendar-Aware Frequency-Decoupled Framework (CA-FDF) for 24 h ahead substation load forecasting. The load series is decomposed by the Discrete Wavelet Transform (DWT), and the low-frequency component is tracked by a regime-aware Recursive Least Squares (RLS) filter. The residuals are then refined through explicit calendar-state encoding and day-ahead weather forecasts. A Multi-Layer Perceptron (MLP) learns latent weather representations, while SHapley Additive exPlanations (SHAP) interpret calendar- and weather-related effects. Experiments on hourly operational data from 29 anonymized substations in East China show that CA-FDF achieves a Mean Absolute Percentage Error (MAPE) of 1.92% and outperforms representative baselines under the same day-ahead setting. The results indicate that frequency-decoupled residual refinement improves localized load forecasting, with calendar-aware correction contributing the largest gain. Full article

This paper presents a cooperative trajectory planning method for multiple aircraft avoiding thunderstorms, formulated within a game-theoretic optimal control framework. We model the multi-aircraft system as a non-cooperative game and employ an Iterative Best Response (IBR) algorithm to decompose the coupled planning problem into a series of single-agent, nonlinear optimal control subproblems. Each subproblem is solved using the CasADi framework, enabling the continuous and simultaneous optimization of both aircraft velocity and heading. This approach directly generates smooth, dynamically feasible 4D trajectories that satisfy strict on-time arrival constraints at each waypoint, addressing a key limitation of many existing methods. Our simulations show that the framework not only ensures safe separation from thunderstorms and other aircraft but also effectively manages arrival times, with errors on the order of seconds. These results demonstrate the method’s capability to produce safe, efficient, and punctual trajectories for complex multi-aircraft encounters in dynamic weather. Full article

This study evaluated the effects of tillage and sowing systems on faba bean productivity and subsequent wheat yield under variable weather conditions in western Poland. A field experiment conducted in 2017–2019 compared four systems: conventional tillage with row sowing (CRS), conventional tillage with strip-drill sowing (SD-C), reduced tillage with strip-drill sowing (SD-R), and zero tillage with strip-drill sowing (SD-Z). Weather conditions varied markedly between years and were the main factor influencing yield formation. Faba bean seed yield declined from 6.3 t ha⁻¹ in 2017 to 1.0 t ha⁻¹ in 2019 due to reduced pod and seed numbers. Yield was strongly correlated with seeds per plant (r = 0.95), pods per plant (r = 0.86), and rainfall (r = 0.91). Strip-drill systems generally produced higher seed and protein yields than CRS, particularly under favorable moisture conditions, while protein content remained relatively stable. The establishment system of the preceding faba bean crop also affected subsequent wheat yield, with higher yields observed after strip-drill systems. Overall, weather conditions, especially water availability, were the primary drivers of productivity, whereas strip-drill systems improved crop performance and rotational benefits under variable climatic conditions. Full article

This study investigated the behavioural responses of the arthropods Folsomia candida (springtails) and Porcellionides pruinosus (woodlice) to leachates released from additive-free plastic polymers. Avoidance behaviour was evaluated to assess potential reductions in soil habitat function, while aggregation status was investigated to highlight possible functional impairments in the woodlice population. Leachates from pristine and artificially aged low-density polyethylene (LDPE) and polypropylene (PP) microplastics were tested at three different concentrations, ranging from environmentally relevant levels to a worst-case scenario of soil contamination. The distinct physicochemical structures of LDPE and PP led to different release compounds. The results revealed no statistically significant avoidance responses in arthropods for either treatment. Unlike PP, LDPE induced a statistically significant impairment of gregarious behaviour at the highest tested concentration (150 mg/kg d.w.). Furthermore, pristine LDPE induced more pronounced disaggregation than the aged one, suggesting that weathering may modulate behavioural responses depending on polymer type and endpoint. Therefore, it is recommended that high levels of plastic leachates can have an adverse effect on soil arthropods and that the aggregation behaviour of woodlice may be a more informative and sensitive biological endpoint than avoidance alone. Full article

Infrared and visible image fusion is critical for unmanned aerial vehicle (UAV) wilderness search and rescue. By integrating thermal radiation of the targets and texture details of the scenario, it enables accurate search for the wounded and comprehensive perception of disaster areas, thereby significantly improving emergency rescue efficiency. To alleviate data scarcity, we construct UAV-MSR, an infrared-visible dataset for casualty search, comprising 3889 paired images captured under diverse weather, illumination, and scenarios. Existing Transformer-based fusion methods mainly focus on high-intensity pixels while inadequately modeling low-intensity complementary features, resulting in blurred details and degraded target contrast in fused images. To this end, we propose a novel differential cross-attention Transformer network to address the issue of complementary information loss. Specifically, the encoder integrates convolution operations for local detail extraction and self-attention mechanisms for global context modeling. Then, we design a differential cross-attention guided feature fusion module to enhance the representation and preservation of detailed complementary features. Furthermore, a pixel loss function with a segmentation strategy is employed to improve the saliency of the target, enabling the fused image to facilitate subsequent target detection tasks. Experimental results and ablation studies demonstrate that the proposed method achieves notable performance and generalization ability. In summary, this work delivers a multimodal dataset and an efficient infrared-visible image fusion network to enable comprehensive perception for UAVs in wilderness search and rescue scenarios. Full article

Microbial degradation of organic matter is a key driver of iodine enrichment in groundwater. Using stable carbon isotopes (δ¹³C-DIC and δ¹³C-DOC), this study investigates the role of microbial processes and organic matter biodegradation in the formation of high-iodine groundwater downstream of the Kuitun River, China. The groundwater is weakly alkaline and reducing, with Cl⁻ and Na⁺ as the dominant ions, and is mainly slightly saline. I⁻ concentrations range from 51.66 to 552.79 µg/L (mean 177.68 µg/L), with 61.54% of samples classified as high-iodine water. Dissolved inorganic carbon (DIC, 22.97–100.85 mg/L, dominated by HCO₃⁻) originates primarily from microbial degradation of organic matter and silicate weathering. Dissolved organic carbon (DOC, 2.01–4.22 mg/L) is mainly derived from C3 plants. In this reducing, organic-rich aquifer, microbial decomposition of organic matter and reductive dissolution of iron minerals are the primary hydrobiogeochemical processes that release solid-phase iodine into groundwater. The high-iodine groundwater in the study area follows a burial–dissolution genesis model. Full article

The Global Navigation Satellite System Reflectometry (GNSS-R) technique provides global ocean surface wind observations unaffected by rainfall with high spatiotemporal resolution. The Fengyun-3E (FY-3E) mission, as the first operational GNSS-R satellite in China, offers low-latency data suitable for numerical weather prediction (NWP). However, the dense along-track sampling of GNSS-R winds poses challenges for observation error specification in data assimilation. In this study, FY-3E GNSS-R winds are assimilated into the Weather Research and Forecasting (WRF) model to investigate the impacts of different observation error configurations. Both static and dynamic error specifications, with and without data thinning, are evaluated through a sensitivity experiment and subsequent Observing System Experiments (OSEs). The results indicate that using a static observation error of 6 m/s without data thinning achieves the best performance. Under this configuration, GNSS-R winds influence atmospheric analyses from the surface up to approximately 700 hPa in a single assimilation case, while cycling experiments further extend the impact vertically and spatially. These findings highlight the importance of appropriate observation error specification for dense GNSS-R data and provide a practical reference for their assimilation in WRF, with potential applicability to other NWP systems. Full article

Assessing drought risk under evolving climate conditions is critical for adaptation planning, yet it remains challenged by projection uncertainty and methodological complexity. This study presents a global probabilistic framework for estimating self-calibrating Palmer Drought Severity Index (scPDSI) drought return periods by integrating observational climate data with statistical modeling. We combined the scPDSI with a stochastic weather generator and generalized extreme value (GEV) analysis to evaluate drought duration extremes at a 2.5° × 2.5° global resolution. The weather generator creates 1000 synthetic time series per grid cell to enable probabilistic assessment, reproducing observed variability, persistence, and long-term trends. To project future risk, we derived return periods by scaling synthetic series using regional temperature change factors from a multi-model CMIP6 ensemble. Results indicate broad agreement with climate model ensembles but highlight regions where nonlinear dynamics drive divergences. We explicitly address critical methodological limitations raised in the recent literature, including the use of scPDSI as a sole indicator, the assumption of stationary variance in the stochastic generator, and the statistical challenges of modeling discrete drought durations with GEV distributions. This framework offers a spatially explicit, observationally grounded tool for decision-makers, while underscoring the necessity of multi-index validation in future global assessments. Full article

A multi-analytical investigation was carried out to elucidate the deterioration processes affecting the stone materials of the Arca di Cansignorio della Scala in Verona (Italy) and to characterize the surviving traces of its original polychrome and gilded decoration. The study combined macroscopic mapping, stratigraphic sampling, optical microscopy (OM), environmental scanning electron microscopy coupled with energy-dispersive X ray spectroscopy (ESEM-EDS), X-ray diffraction (XRD), Fourier-transform infrared spectroscopy (FTIR), and ion chromatography (IC). The monument, predominantly carved from Candoglia marble, exhibits three principal weathering patterns: (i) rain washed areas affected by marble decohesion, (ii) grey deposits corresponding to dirt accumulation areas; and (iii) sulphation-induced black crusts developed in dirt wetting areas. In addition, severe mechanical deterioration was found to be associated with early twentieth-century structural consolidation interventions involving embedded iron bars, whose corrosion-driven volumetric expansion generated vertical cracking. Stratigraphic and microanalytical investigations revealed the presence of original azurite-based polychromy, proteinaceous and lipidic binding media, lead white preparatory layers, and multiple applications of gold leaf. The analytical results highlight the complex interplay between environmental exposure, atmospheric pollution, the incompatibility of materials introduced during past restorations campaigns. Furthermore, they contribute to a better understanding of the composition, execution techniques and preservation state of the surviving decorative layers, providing a scientific basis for future conservation strategies. Full article

Accurate wind estimation is essential for wind resource assessment. In this study, using scanning lidar measurements and high-resolution WRF simulations from two nearshore areas in Japan, we developed two extensions of the Temporal Correction (TC) method, which corrects wind fields generated by the Weather Research and Forecasting (WRF) model using on-site measurements. First, when using a single measurement point for correction, we derived two empirical formulas to predict appropriate correction coefficients based on reference–target correlation coefficients of wind speed obtained from WRF simulations and developed a method (TC-pred) using these formulas. TC-pred was shown to have higher wind speed estimation accuracy and a broader range of applicability than the conventional TC method. Next, we extended the TC-pred method to allow the use of multiple measurement points as references by introducing a weighting formula for each reference point. Wind speed estimation accuracy improved as the number of reference points increased, primarily because the probability of including reference points with high reference–target correlation coefficients increased. This suggests that it is effective for the suppression of wind estimation uncertainty to determine measurement layout such that the correlation coefficient between at least one reference point and each target point in the target area exceeds a certain value. Full article