Search Results (3,219)

Electrical sealing covers are widely used in various industrial equipment, where the quality of their metal-painted surfaces directly affects product appearance and long-term reliability. Micro-defects such as pores, particles, scratches, and uneven paint coatings can compromise protective performance during manufacturing. In the rapidly growing new energy vehicle (NEV) industry, battery charging-port sealing covers are critical components, requiring precise defect detection due to exposure to harsh environments, like extreme weather and dust-laden conditions. Even minor defects can lead to water ingress or foreign matter accumulation, affecting vehicle performance and user safety. Conventional manual or rule-based inspection methods are inefficient, and the existing deep learning models struggle with detecting minor and subtle defects. To address these challenges, this study proposes EIM-YOLO, an improved object detection framework for the automated detection of metal-painted surface defects on electrical sealing covers. We propose a novel lightweight convolutional module named C3PUltraConv, which reduces model parameters by 3.1% while improving mAP₅₀ by 1% and recall by 3.2%. The backbone integrates RFAConv for enhanced feature perception, and the neck architecture uses an optimized BiFPN-concat structure with adaptive weight learning for better multi-scale feature fusion. Experimental validation on a real-world industrial dataset collected using industrial cameras shows that EIM-YOLO achieves a precision of 71% (an improvement of 3.4%), with mAP₅₀ reaching 64.8% (a growth of 2.6%), and mAP_50–95 improving by 1.2%. Maintaining real-time detection capability, EIM-YOLO significantly outperforms the existing baseline models, offering a more accurate solution for automated quality control in NEV manufacturing. Full article

In arid and semi-arid agricultural regions, the increasing frequency of extreme climatic events—particularly high temperatures and drought—has severely disrupted crop growth dynamics, leading to significant yield uncertainty and potential threats to the growing global food demand. Optimizing irrigation strategies by integrating dynamic crop growth monitoring and accurate yield estimation is essential for mitigating the adverse effects of extreme weather and promoting sustainable agricultural development. Therefore, this study conducted two consecutive years of field experiments in cotton fields to evaluate the effects of irrigation interval and drip irrigation frequency on cotton growth dynamics and yield, and to develop an optimized irrigation schedule based on the AquaCrop model assimilated with Particle Swarm Optimization (AquaCrop-PSO). The sensitivity analysis identified the canopy growth coefficient (CGC), maximum canopy cover (CCX), and canopy cover at 90% emergence (CCS) as the most influential parameters for canopy cover (CC) simulation, while the crop coefficient at full canopy (KCTRX), water productivity (WP), and CGC were most sensitive for aboveground biomass (AGB) simulation. Ridge regression models integrating multiple vegetation indices outperformed single-index models in estimating CC and AGB across different growth stages, achieving R² values of 0.73 and 0.87, respectively. Assimilating both CC and AGB as dual-state variables significantly improved the model’s predictive accuracy for cotton yield, with R² values of 0.96 and 0.95 in 2023 and 2024, respectively. Scenario simulations revealed that the optimal irrigation quotas for dry, normal, and wet years were 520 mm, 420 mm, and 420 mm, respectively, with a consistent irrigation interval of five days. This study provides theoretical insights and practical guidance for irrigation scheduling, yield prediction, and smart irrigation management in drip-irrigated cotton fields in Xinjiang, China. Full article

This study investigates the influence of rear setback geometry and façade design parameters on microclimatic conditions, indoor thermal comfort, and energy performance in multi-story residential buildings in hot arid climates, addressing the growing need for climate-responsive design in regions with extreme temperatures and high solar radiation. Despite increasing interest in sustainable strategies, the combined effects of urban geometry and building envelope design remain underexplored in these environments. A coupled simulation framework was developed, integrating ENVI-met for outdoor microclimate modeling with Design Builder and EnergyPlus for dynamic building performance analysis. A total of 270 simulation scenarios were examined, combining three rear setback aspect ratios (1.5, 1.87, and 2.25), three window-to-wall ratios (10%, 20%, and 30%), three glazing types (single-, double-, and triple-pane), and two wall insulation states, using customized weather files derived from microclimate simulations. Global sensitivity analysis using rank regression and multivariate adaptive regression splines identified the glazing type as the most influential parameter (sensitivity index ≈ 0.99), especially for upper floors. At the same time, higher aspect ratios reduced peak Physiological Equivalent Temperature (PET) by up to 5 °C and decreased upper-floor cooling loads by 37%, albeit with a 9.3% increase in ground-floor cooling demand. Larger window-to-wall ratios lowered lighting energy consumption by up to 35% but had minimal impact on cooling loads, whereas wall insulation reduced annual cooling demand by up to 29,441 kWh. The results emphasize that integrating urban morphology with optimized façade components, particularly high-performance glazing and suitable aspect ratios, can significantly improve thermal comfort and reduce cooling energy consumption in hot arid residential contexts. Full article

Wind-induced erosion and extreme weather events pose growing risks to the structural integrity of masonry heritage buildings. However, current mitigation approaches often overlook ecological sustainability. This study investigates the wind-regulating effects of vegetation surrounding the Zhen Guo Tower, a 400-year-old masonry structure in Weihui, Henan Province, China. Using computational fluid dynamics (CFD) simulations, we first assess the protective performance of the existing vegetation layout and then develop and evaluate an optimized plant configuration. The results show that the proposed multilayered vegetation arrangement effectively reduces wind speeds by up to 13.57 m/s under extreme wind conditions, particularly within the 5–15 m height range. Wind protection efficiency improved by 28–68% compared to the baseline. This study demonstrates a replicable and ecologically integrated strategy for mitigating wind hazards in masonry heritage sites through vegetation-based interventions. Full article

As one of the world’s leading grain producers, Brazil stands out in soybean and corn production. Accurate estimation of key crop phenological stages is essential for agricultural decision-making, especially considering Brazil’s vast territory, climatic diversity, and increasing frequency of extreme weather events. This study investigated the applicability of the NDVI, EVI, WDRVI, and NDWI, derived from Harmonized Landsat Sentinel-2, to identify crop sowing and harvest dates at the field scale. We extracted the vegetative peak from each vegetation index time series and identified the left and right inflection points around the peak to delineate the crop season. A double-logistic function and a derivative approach were applied to identify the Start of Season, Peak of Season, and End of Season. For both soybeans and corn, the RMSE ranged from 5 to 8 days for sowing dates, while for harvest dates it ranged from 6 to 15 days for corn. Despite these differences, all vegetation indices exhibited robust performance, with Spearman correlation values between 0.56 and 0.84. Our findings indicate that the use of different indices does not have a significant impact on the results, as long as the adjustment of temporal parameters for the phenological metrics is appropriate for each index. Full article

This study aims to develop, implement, and evaluate an Early Warning System (EWS) to alert communities and government agencies in KwaZulu-Natal, South Africa, about extreme weather events (EWEs) and related disease outbreaks. The project focuses on eThekwini and Ugu municipalities, using a participatory, co-creation approach with communities and health providers. A systematic review will be undertaken to understand the impact of climate change on disease outbreaks and design an EWS that integrates data from rural and urban healthcare and environmental contexts. It will assess disease burden at primary healthcare clinics, examine health needs and community experiences during EWEs, and evaluate health system resilience. The project will also evaluate the design, development, and performance of the EWS intervention, including its implementation costs. Ethical approval will be sought, and informed consent obtained from participants. Based on the findings, recommendations will be made to the Department of Health to enhance early warning systems and health system resilience in response to EWEs and disease outbreaks. Full article

Hydrological drought in Central Europe is becoming an increasingly serious threat to agriculture, industry, and people due to climate change and the rising frequency and intensity of extreme weather events. The main aim of the paper was to assess the spatial variability of streamflow drought in Poland. The spatial analysis was conducted using daily streamflow series from 340 gauging stations for the period 1973–2022. Hydrological drought was defined as a period with a streamflow lower than Q_90%. The results show that, on average, hydrological droughts occur 52 times per year at a given gauging station. Drought duration and volume depend on the gauge elevation. At higher-altitude stations, shorter and smaller-volume droughts are most commonly observed. The longest droughts are recorded in Northern Poland, particularly in the Lakeland regions, which is a serious problem mainly for the agriculture sector. Hydrological droughts in Poland most frequently begin in summer and end in late summer or early autumn. Analyses showed that hydrological drought has a strong spatial distribution, and it is possible to identify five main regions with homogeneous drought duration and volume. Trend analysis of the annual number of low-flow days indicates no statistically significant trend at 46% of stations, while 54% exhibit statistically significant increases, with marked regional variability. The highest number of stations with statistically significant decreasing trends occurs in the Southern and Eastern Baltic Lake District and in the Central Poland Lowlands and Highlands with Polesie. The study highlights the necessity of enhancing water retention, particularly in the central, lowland regions of Poland. Full article

Climate change causes major agricultural losses, driven both by the rise of plant diseases and by extreme weather events such as droughts and floods. Increased precipitation can lead to waterlogging of important crops. The roots of plants submerged in water have limited access to oxygen, which leads to hypoxia, which, in turn, reduces plant resistance to other factors, e.g., plant pathogens. On the other hand, beneficial microorganisms can help plants oppose abiotic stress, e.g., by producing plant hormones or osmoprotectants such as trehalose, to increase plant tolerance to drought. It turns out that plant-beneficial microorganisms can also increase plant resistance to waterlogging. This can be achieved by various mechanisms that involve the production of 1-aminocyclopropane-1-carboxylate (ACC) deaminase, which reduces the amount of ethylene accumulated in the submerged roots. This can stimulate the production of reactive oxygen species scavengers that protect plants from the oxidative stress caused by less efficient anaerobic metabolism, produce plant hormones that help plants to better adapt to low-oxygen conditions, and shape the plant microbiome, supporting plant growth in waterlogging conditions. This review outlines plant responses to waterlogging and discusses examples of microorganisms that improve plant tolerance, focusing on their underlying mechanisms. Full article

Architecture can play a pivotal role in addressing the climate crisis by embedding sustainable design principles that reduce environmental impact and enhance resilience. Beyond ecological considerations, architectural interventions are crucial in developing structures capable of withstanding extreme weather events—and thereby mitigating the displacement of vulnerable populations. This study emphasizes the importance of tailoring architectural responses to the specific environmental challenges and evolving needs of rural communities. Drawing on the Perceived Values and Climate Change Resilience Dataset collected in Siaya County, Kenya, the research explores local perceptions of climate change and how these shape housing priorities. Among 300 respondents, 83% express concern about climate change, identifying drought as the most pressing environmental threat. The evolving desire for housing solutions that respond to specific needs highlights the need for more secure housing. This specifically calls for improvements in watertightness, pest resistance (especially against termites), and overall structural durability, as well as reducing maintenance effort, enabling houses to be enlarged, and improving their aesthetics. These findings provide critical insights into how rural populations in western Kenya are experiencing and responding to climate-related stressors. By foregrounding community perspectives, the study informs the development of adaptive, resilient, and contextually appropriate architectural solutions. It contributes to broader discourses on climate adaptation, vernacular design, and inclusive development strategies in Sub-Saharan Africa, reinforcing the imperative to align architectural innovation with both environmental imperatives and cultural realities. Full article

This study investigated the concentrations of metals in stormwater runoff collected during two extreme flooding events on the American University of Sharjah (AUS) campus in the United Arab Emirates (UAE). Given the increasing frequency of intense rainfall in arid regions, stormwater contamination represents a growing environmental and public health concern. Stormwater samples were analyzed using inductively coupled plasma optical emission spectrometry (ICP-OES) to quantify metal concentrations. The results showed that iron (0.049–2.080 mg/L), aluminum (0.097–2.020 mg/L), and potassium (0.614–3.860 mg/L) were the most abundant metals detected. Lower concentrations were observed for manganese (0.000–0.058 mg/L), barium (0.000–0.073 mg/L), chromium (0.000–0.013 mg/L), nickel (0.000–0.038 mg/L), and vanadium (0.000–0.004 mg/L). These findings underscore the critical need for effective stormwater management in arid regions, where climate change is expected to increase the frequency and intensity of extreme weather events. Improved drainage systems and long-term monitoring are essential to mitigate the environmental and public health risks posed by stormwater contamination in rapidly urbanizing areas. 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

Climate change has led to increased extreme weather events, such as severe droughts and intense rainfall, with regions in Portugal, like Alentejo and Algarve, being particularly affected. Understanding the influence of water availability in the concentration of phenolic compounds in autochthonous varieties could be an important tool to know how these varieties adapt to water scarcity. This work has been carried out with the aim to analyze the profile of phenolic compounds by HPLC-DAD in four Portuguese grape varieties (Tinta Gorda, Tinta Miúda, Tinta Caiada, and Moreto), cultivated under three irrigation regimes (water comfort, moderate water deficit, and rainfed). The results reveal that Tinta Gorda, Tinta Miúda, and Tinta Caiada varieties exhibit the higher concentrations of phenolic compounds under rainfed conditions. Among these, Tinta Miúda and Tinta Caiada stand out as the most promising varieties in terms of adaptability to water scarcity. Full article

Flood impacts are intensifying due to the increasing frequency and severity of factors such as severe weather events, climate change, and unplanned urbanization. This study focuses on Briar Creek in Charlotte, North Carolina, an area historically affected by flooding. Three machine learning algorithms —bagging (random forest), extreme gradient boosting (XGBoost), and logistic regression—were used to develop a flood susceptibility model that incorporates topographical, hydrological, and meteorological variables. Key predictors included slope, aspect, curvature, flow velocity, flow concentration, discharge, and 8 years of rainfall data. A flood inventory of 750 data points was compiled from historic flood records. The dataset was divided into training (70%) and testing (30%) subsets, and model performance was evaluated using accuracy metrics, confusion matrices, and classification reports. The results indicate that logistic regression outperformed both XGBoost and bagging in terms of predictive accuracy. According to the logistic regression model, the study area was classified into five flood risk zones: 5.55% as very high risk, 8.66% as high risk, 12.04% as moderate risk, 21.56% as low risk, and 52.20% as very low risk. The resulting flood susceptibility map constitutes a valuable tool for emergency preparedness and infrastructure planning in high-risk zones. Full article

It has always been a research topic for some meteorologists to design a new and reasonable calculation scheme of the intensity of the Tibetan Plateau (TP) summer monsoon (TPSM). Existing indices are defined based on dynamic factors. However, the intensity of the TPSM can also be influenced by thermal factors. We therefore propose defining a TPMI in terms of horizontal temperature advection within the main body of the TP. This provides a new index that directly quantifies the extent to which the thermal forcing in the TP region regulates the monsoon system. The new index emphasizes the importance of the atmospheric asymmetry structure in measuring TPSM strength, represents the variability of the TPSM circulation system, effectively reflects the meteorological elements, and accurately represents the climate variation. Tropospheric temperature (TT) and TPSM are linked by the new index. These significant centers of correlation are characterized by alternating positive and negative phases along the Eastern European Plain, across the Turan Plain, and into southwestern and northeastern China. The correlation coefficients are found to be significantly out of phase between high and low altitudes in the vertical direction. This research broadens our minds and helps us to develop a new approach to measuring TPSM strength. It can also predict extreme weather events in advance based on TPMI changes, providing a scientific basis for disaster warnings and the management of agriculture and water resources. Full article

Soil salinity stress, intensified by extreme weather patterns, significantly threatens global watermelon [Citrullus lanatus (Thunb.) Matsum & Nakai] production. Watermelon, a moderately salt-sensitive crop, exhibits reduced germination, stunted growth, and impaired fruit yield and quality under saline conditions. As freshwater resources decline and agriculture’s dependency on irrigation leads to soil salinization, we need sustainable mitigation strategies for food security. Recent advances highlight the potential of using salt-tolerant rootstocks and breeding salt-resistant watermelon varieties as long-term genetic solutions for salinity. Conversely, agronomic interventions such as drip irrigation and soil amendments provide practical, short-term strategies to mitigate the impact of salt stress. Biostimulants represent another tool that imparts salinity tolerance in watermelon. Plant growth-promoting microbes (PGPMs) have emerged as promising biological tools to enhance watermelon tolerance to salt stress. PGPMs are an emerging tool for mitigating salinity stress; however, their potential in watermelon has not been fully explored. Nanobiochar and nanoparticles are another unexplored tool for addressing salinity stress. This review highlights the intricate relationship between soil salinity and watermelon production in a unique manner. It explores the various mitigation strategies, emphasizing the potential of PGPM as eco-friendly bio-inoculants for sustainable watermelon management in salt-affected soils. Full article