Search Results (3,623)

Background: Soybean (Glycine max (L.) Merr.), a nutrient-rich leguminous crop high in protein, lipids, and minerals, is extensively cultivated worldwide. The chemical composition of soybean seeds depends not only on the genetic characteristics of the cultivar but also on environmental conditions and agricultural practices. In recent years, biostimulants have gained increasing importance in crop production due to their ability to enhance physiological processes in plants and potentially influence nutrient accumulation. This study aimed to investigate how cultivar and biostimulant type influence the chemical composition of soybean seeds under varying weather conditions in Central Europe. Methods: A three-year field experiment (2017–2019) was conducted in eastern Poland (Central Europe) using a split-plot design. The experimental factors included three non-GMO soybean cultivars (Abelina, Merlin, and SG Anser) and two foliar biostimulants (Asahi SL and Improver). In addition to classical ANOVA, the multivariate analysis of the impact of the investigated factors included principal component analysis (PCA). Results: The applied factors significantly affected seed contents of fat, protein, dry matter, ash, fibre, and macronutrients (N, P, K). Cv. Merlin had the highest fat (22.65%) and fibre content (9.33%), while Abelina showed the highest protein (37.06%) and dry matter content (94.42%). Biostimulant application increased the accumulation of several seed components. Asahi SL significantly enhanced fat content (by 0.69%), protein content (by over 1.5%), and dry matter content (by nearly 0.2%) compared to the control. Improver was more effective in increasing nitrogen (by 0.24%), phosphorus (by 0.5%), and potassium (by 0.15%) contents. Weather conditions throughout the growing seasons significantly altered the impact of the biostimulants. The PCA analysis revealed distinct relationships among the chemical properties of seeds, meteorological factors, and the applied biostimulants. Full article

Digital livestock farming faces a critical deployment challenge: bridging the gap between cutting-edge AI algorithms and practical implementation in resource-constrained agricultural environments. While deep learning models demonstrate exceptional accuracy in laboratory settings, their translation to operational farm systems remains limited by computational constraints, connectivity issues, and user accessibility barriers. Dairy DigiD addresses these challenges through a novel edge-cloud AI framework integrating YOLOv11 object detection with DenseNet121 physiological classification for cattle monitoring. The system employs YOLOv11-nano architecture optimized through INT8 quantization (achieving 73% model compression with <1% accuracy degradation) and TensorRT acceleration, enabling 24 FPS real-time inference on NVIDIA Jetson edge devices while maintaining 94.2% classification accuracy. Our key innovation lies in intelligent confidence-based offloading: routine detections execute locally at the edge, while ambiguous cases trigger cloud processing for enhanced accuracy. An entropy-based active learning pipeline using Roboflow reduces the annotation overhead by 65% while preserving 97% of the model performance. The Gradio interface democratizes system access, reducing technician training requirements by 84%. Comprehensive validation across ten commercial dairy farms in Atlantic Canada demonstrates robust performance under diverse environmental conditions (seasonal, lighting, weather variations). The framework achieves mAP@50 of 0.947 with balanced precision-recall across four physiological classes, while consuming 18% less energy than baseline implementations through attention-based optimization. Rather than proposing novel algorithms, this work contributes a systems-level integration methodology that transforms research-grade AI into deployable agricultural solutions. Our open-source framework provides a replicable blueprint for precision livestock farming adoption, addressing practical barriers that have historically limited AI deployment in agricultural settings. Full article

In high-altitude corrosive environments, weathering steel is widely applied due to its excellent corrosion resistance. However, the welded joint regions, where the chemical composition and microstructure undergo changes, are susceptible to the corrosion-induced degradation of mechanical properties. This study investigates the corrosion–mechanical synergistic degradation behavior of a 16 mm thick Q500 qENH base metal and its V-type and Y-type welded joint specimens. Periodic immersion corrosion tests were conducted to simulate plateau atmospheric conditions, followed by mechanical performance evaluations. Corrosion metrics—including corrosion rate, cross-sectional loss, penetration depth, and corrosion progression speed—were analyzed in relation to mechanical indicators such as the fracture location, yield load, ultimate load, yield strength, and tensile strength at varying exposure durations. The results indicate that the corrosion process exhibits distinct layering, with a two-stage characteristic of rapid initial corrosion followed by slower progression. Welded joints consistently exhibit higher corrosion rates than the base metal, with the rate difference evolving nonlinearly in an “increase–decrease–stabilization” trend. After corrosion, the mechanical performance degradation of welded joint specimens is more severe than that of base metal specimens. Full article

The Tarim Large Igneous Province (TLIP) in NW China hosts abundant Fe–Ti–V oxide deposits associated with mafic–ultramafic intrusions. In the Xiahenan area, on the western margin of the TLIP, a distinct magnetic anomaly is linked to widespread surface accumulations of black sand. However, the genesis and origin of these black sand grains remain unclear. Based on mineral assemblages, this study classifies the grains of the black sand into three types: (i) plagioclase (An_10–90)–ilmenite–olivine–magnetite assemblage (Sand I), (ii) plagioclase (An_0–10)-fine-grained magnetite assemblage (Sand II), and (iii) hornblende–magnetite highly complex assemblage (Sand III). Mineral geochemical studies demonstrate that magnetite in Sand I and Sand II is of magmatic origin, with protolith being basaltic magma. Magnetite in Sand III was eroded from veins formed by hydrothermal processes at 300–500 °C. Ilmenite in Sand I contains a high FeTiO₃ component, representing basaltic ilmenite. Olivine in Sand I has a low Fo content (43.86–47.27), belonging to hortonolite olivine. Research indicates that Sand I and Sand II share similar mineral assemblages and mineral geochemical characteristics with basalts in the Xiahenan area, suggesting they are weathering products of Xiahenan basalts or their cognate magmas. In contrast, the veined magnetite of Sand III formed during post-magmatic hydrothermal events. Full article

Non-destructive tests are especially useful for the assessment of building stones and their deterioration in built cultural heritage. Gamma-ray spectrometry is a non-destructive test that has not been applied extensively in these types of constructions. Therefore, the purpose of this study is to show the results of gamma-ray spectrometry for limestone characterization and deterioration assessment. This study was conducted in the North Temple of the Archaeological Zone of Chichen Itza and several outcrops in the area. Gamma-ray spectrometry data were corrected for attenuation caused by the moisture content in rocks to calculate the real radioelements concentrations using linear regression, with interpretation based on their mobility resulting from chemical weathering processes. The results obtained with gamma-ray spectrometry were corroborated by laboratory analyses, demonstrating that stones from the North Temple are more weathered than rocks from the outcrops, and that some limestones have clasts derived from terrigenous sources, causing them to show slightly higher radiation, which can be distinguished easily with gamma-ray spectrometry, even when lithology cannot be recognized in plain sight. Gamma-ray spectrometry proved to be useful for limestone characterization, and data obtained can be correlated with parameters from other analyses. Full article

This study investigates the psychological effects of climate change on gold mining communities in Zimbabwe. This research employs comprehensive interviews with miners, health professionals, and community leaders, who were selected using purposive sampling, to examine the emotional responses, such as eco-grief, anxiety, and helplessness, resulting from environmental degradation. Utilizing thematic analysis, we delineate core psychological themes and propose integrated policy solutions. This study identifies a gap in the existing literature regarding climate and mental health by investigating a vulnerable population in sub-Saharan Africa that has been inadequately studied. Many participants voiced grave concerns about their surroundings and how they impact their cognitive abilities, which calls for the creation of comprehensive laws that consider the effects of both weather-related and mental health conditions. Further research should concentrate on intervention studies to improve the efficacy of strong intellectual fitness support tailored to the challenging conditions encountered by mining communities, as well as longitudinal studies to determine the long-term mental effects of weather alternatives. A few of the recommendations include making sure that underrepresented viewpoints are considered at some stage of the decision-making process and boosting network resilience via information sharing and education. This study promotes a holistic strategy that combines health fitness treatments with environmental sustainability initiatives to guarantee a more resilient and healthy future for Zimbabwe’s mining communities. Full article

Given the growing number of residential photovoltaic installations and the challenges of self-consumption, accurate short-term PV production forecasting can become a key tool in supporting energy management. This issue is particularly significant in systems without energy storage, where excess production is fed back into the grid, reducing the profitability of prosumer investments. This paper presents an approach to forecasting short-term energy production in residential photovoltaic installations, based on real meteorological data and the use of machine learning methods. The analysis is based on measurement data from a functioning PV installation and a local weather station. This study compares three models: classical linear regression, Random Forest and the XGBoost algorithm. The method of data preparation, the model training process and the assessment of their effectiveness based on real energy production measurements are presented. This paper also includes a practical calculation example and an analysis of selected days in order to compare the forecast results with the actual production. Of the three models compared, the highest accuracy was achieved for XGBoost, with an MAE = 1.25 kWh, RMSE = 1.93 kWh, and coefficient of determination R² = 0.94. Compared to linear regression, this means a 66% reduction in MAE and a 41% reduction in the Random Forest model, confirming the practical usefulness of this method in a real-world environment. The proposed approach can be used in energy management systems in residential buildings, without the need to use energy storage, and can support the development of a more conscious use of energy resources on a local scale. Full article

Coral reefs are among the most important marine habitats but face significant threats from anthropogenic sources, including climate change. This paper reviews and compares the modern Great Barrier Reef Province and the 360-million-year-old Devonian Great Barrier Reef of western Australia. Despite occurring at times with different climates, biota (both marine and terrestrial), weathering processes and marine chemistry, similar reefs were constructed under certain circumstances. Major differences in global temperature, marine carbonate saturation, sea level behavior and reef community constituents were evaluated. The comparison highlights the integration of, and interdependencies within, reef communities and the need for both carbonate producers and significant binders, whether skeletal or microbial, to construct a reef in a high-energy setting. Devonian communities with abundant corals and skeletal sponges were incapable of making modern reef types without competent binders to unify framework into rigid substrate. The current strong focus on corals and bleaching in modern reef conservation may be obscuring the equally significant issue of ocean acidification, which impacts on equally crucial framework unification, i.e., hard binding by coralline algae and microbialites and early cementation. The comparison also supports the idea that ‘empty bucket’ carbonate platform morphologies require increased accommodation from high-amplitude icehouse sea level oscillations. Full article

The increasing frequency and intensity of extreme weather events pose significant challenges to mountain communities, particularly in terms of flash flood risks. This study presents a framework for stress testing and evaluating flood resilience in mountain communities through the integration of high-resolution InfoWorks ICM two-dimensional hydrodynamic modeling and systematic resilience assessment. The framework makes three key innovations: (1) multi-scale temporal stress scenarios combining short-duration extreme events (1–2 h) with long-duration persistent events (24 h) and historical extremes; (2) integrated infrastructure–drainage stress analysis that explicitly models roads’ dual role as critical infrastructure and emergency drainage channels; and (3) dynamic resilience quantification under multiple stressors across 15 systematically designed stress conditions. Using Western Beijing as a case study, the model is validated, achieving Nash–Sutcliffe efficiency values exceeding 0.9, demonstrating its robust capability in simulating complex mountainous terrain flood processes. Through systematic analysis of fifteen rainfall scenarios designed based on Chicago rainfall patterns and historical events (including the July 2023 Haihe River basin flood), encompassing various intensities (30–200 mm/h), durations (1 h, 2 h, 24 h), and return periods (10, 50, 100 years), the key findings include the following: (1) A rainfall intensity of 60 mm/h represents a crucial threshold for system performance, beyond which significant impacts on community infrastructure emerge, with built-up areas experiencing inundation depths of 0.27–0.4 m that exceed safe passage limits. (2) Road networks become primary drainage channels during intense precipitation, with velocities exceeding 5 m/s in village roads and exceeding 5 m/s in country road sections, creating significant hazard potential. (3) Four major risk spots were identified with distinct waterlogging patterns, characterized by maximum depths ranging from 0.8 to 2.0 m and recovery periods varying from 2 to 12 hours depending on the topographic confluence effects and drainage efficiency. (4) The system demonstrates strong recovery capability, achieving >90% recovery within 3–6 hours for short-duration events, while showing vulnerability to extreme scenarios, with performance declining to 0.75–0.80, highlighting the coupling effects between water depth and flow velocity in steep terrain. This research provides quantitative insights for flood risk management and for enhancing community resilience in mountainous regions, offering valuable guidance for infrastructure improvement, emergency response optimization, and sustainable community development. This study primarily focuses on physical resilience aspects, with socioeconomic and institutional dimensions representing important directions for future research. Full article

Rare earth elements (REYs) are crucial components of billions of products worldwide. Transitioning from foreign to domestic REY sources requires utilizing both primary (i.e., carbonatites, alkaline igneous rocks, pegmatites, skarn deposits) and secondary (unconventional) sources (i.e., ion-adsorption clays, placer deposits, weathered rock, black and/or oil shales). Coal and coal-bearing strata, promising secondary REY resources, are the focus of this study. Understanding REY mineral associations in unconventional resources is essential to quantifying resource volume and identifying viable mineral separation and processing techniques. Highly REY-enriched (>750 ppm) coal or mudstone samples from the Uinta Region, Utah, USA, were selected for scanning electron microscopy (SEM) analysis. Energy dispersive X-ray spectroscopy (EDS)-determined REY enrichment occurs in: (1) a silt-size fraction (5–30 μm) of monazite and xenotime REY-enriched grains, (2) a clay-size fraction (2–5 μm) of monazite REY-enriched grains dispersed in the clay-rich matrix, and (3) organically confined REY domains < 2 μm. Findings suggest possible REY enrichment from multiple sources, including: (1) detrital silt-size grains, (2) volcanic ash fall, largely in clay-size grains, and (3) organic REY uptake in the peat swamp depositional environment. Full article

The Qinghai-Tibet Plateau, known as the “third pole” of the Earth with an average elevation of approximately 4500 m, offers a unique natural laboratory for probing the dynamic behavior of the global electric circuit. In this study, we conduct a comprehensive analysis of near-surface vertical atmospheric electric field (AEF) measurements collected at the Gar Station (80.1° E, 32.5° N; 4259 m a.s.l.) on the western Tibetan Plateau, spanning the period from November 2021 to December 2024. Fair-weather conditions are imposed. The annual mean AEF at Gar is ∼0.331 kV/m, significantly higher than values observed at lowland and plain sites, indicating a pronounced enhancement in atmospheric electricity associated with high-altitude conditions. Moreover, the AEF exhibits marked seasonal variability, peaking in December (∼0.411–0.559 kV/m) and valleying around July–August (∼0.150–0.242 kV/m), yielding an overall amplitude of approximately 0.3 kV/m. We speculate that this seasonal pattern is primarily driven by variations in aerosol concentration. During winter, increased aerosol loading from residential heating and vehicle emissions due to incomplete combustion reduces atmospheric conductivity by depleting free ions and decreasing ion mobility, thereby enhancing the near-surface AEF. In contrast, lower aerosol concentrations in summer lead to weaker AEF. This seasonal decline in aerosol levels is likely facilitated by stronger winds and more frequent rainfall in summer, which enhance aerosol dispersion and wet scavenging, whereas weaker winds and limited precipitation in winter favor near-surface aerosol accumulation. On diurnal timescales, the Gar AEF curve deviates significantly from the classical Carnegie curve, showing a distinct double-peak and double-trough structure, with maxima at ∼03:00 and 14:00 UT and minima near 00:00 and 10:00 UT. This deviation may partly reflect local influences related to sunrise and sunset. This study presents the longest ground-based AEF observations over the Qinghai–Tibet Plateau, providing a unique reference for future studies on altitude-dependent AEF variations and their coupling with space weather and climate processes. Full article

Localizing ground-penetrating radar (LGPR) benefits from deep subsurface coupling, ensuring robustness against surface variations and adverse weather. While LGPR is widely recognized as the complement of existing vehicle localization methods, its reliance on prior maps introduces significant challenges. Channel misalignment during traversal positioning and time-dimension distortion caused by non-uniform platform motion degrade matching accuracy. Furthermore, rain and snow conditions induce subsurface water-content variations that distort ground-penetrating radar (GPR) echoes, further complicating the localization process. To address these issues, we propose a weather-resilient adaptive spatio-temporal mask alignment algorithm for LGPR. The method employs adaptive alignment and dynamic time warping (DTW) strategies to sequentially resolve channel and time-dimension misalignments in GPR sequences, followed by calibration of GPR query sequences. Moreover, a multi-level discrete wavelet transform (MDWT) module enhances low-frequency GPR features while adaptive alignment along the channel dimension refines the signals and significantly improves localization accuracy under rain or snow. Additionally, a local matching DTW algorithm is introduced to perform robust temporal image-sequence alignment. Extensive experiments were conducted on both public LGPR datasets: GROUNDED and self-collected data covering five challenging scenarios. The results demonstrate superior localization accuracy and robustness compared to existing methods. Full article

Climate change threatens the sustainability of historic buildings with increasing extreme weather events, making energy resilience critical. However, studies on energy resilience often lack forward-looking, holistic approaches. This study aims to develop a conceptual framework that includes how Artificial Intelligence (AI) technologies can support energy resilience in historical buildings with data-driven prediction and analysis to increase energy resilience against climate change. This study applied a methodology with four-stage qualitative research techniques, including a systematic literature review (PRISMA method), content analysis, AI integration, and conceptual framework development processes, in the intersections of historical building, energy resilience, and climate change. The findings reveal a significant research gap in the predictive analysis of the resilience of historic buildings and the integration of AI-based tools in the context of climate change. The proposed framework outlines a multi-layered system that includes data collection, performance analysis, scenario-based prediction, and AI-assisted decision-making, aiming to enhance the resilience of the building (including building envelope, thermal, and lifecycle analysis). Consequently, this study provides a theoretical and methodological perspective and proposes a scientifically based and applicable roadmap. It also highlights the potential of AI as a bridge between energy resilience and historical buildings in the face of a rapidly changing climate. Full article

Spectral models, due to their stability and efficiency, have become one of the most popular approaches for implementing numerical weather prediction systems. Given the complexity of these models, they often require the use of multi-node computing resources for parallel processing to meet the stringent real-time requirements. However, as the number of nodes increases, the efficiency of inter-node communication becomes a critical bottleneck. In the case of the Yin-He Global Spectral Model (YHGSM), developed by the National University of Defense Technology, communication overhead is very high during the Fourier transform section, which consists of the transform itself and the subsequent transposition from the z-μ decomposition to the z-m decomposition. To address this challenge, we introduce an optimized scheme that overlaps communication with computation. By grouping corresponding communication and computation tasks, this approach leverages non-blocking communication techniques within MPI, combined with the use of asynchronous communication progress threads. Our experimental results demonstrate that this scheme can reduce execution time by up to 30% compared to the non-overlapped version, thereby significantly hiding communication overhead and enhancing the efficiency of YHGSM. Full article

The complex lagoon system of Carmen, Pajonal, and Machona in the Southern Gulf of Mexico is characterized by highly active sedimentary dynamics. To reproduce the sedimentary dynamics processes, the MOHID model, coupled with the SWAN wave model, was applied to different scenarios through a climatic analysis of winds. Historical wind data indicate that the region has experienced a significant shift in the principal wind component over the last two decades. Furthermore, hurricanes have impacted the lagoon system on multiple occasions in recent decades. Five numerical experiments were conducted, considering both historical and present-day wind conditions, the impact of Hurricane Larry, and engineering works such as breakwaters, to better understand the sedimentary dynamics of the lagoon system. Model results revealed intense and variable sediment transport depending on the intensity and direction of the prevailing winds, waves, extreme weather events, and breakwater locations. Full article