Search Results (162)

On heavily traveled highway corridors, traffic congestion, lane merges, toll facilities, and complex interchanges frequently trigger sudden and aggressive deceleration, commonly referred to as harsh braking (HB). Such maneuvers reflect near-miss driving conditions that may precede crashes. Traditional traffic safety analyses rely primarily on historical crash records, a reactive approach that limits agencies’ ability to identify and address emerging risks in a timely manner. Because HB events are continuously captured by connected-vehicle telematics, they provide an opportunity to evaluate roadway safety risk more proactively. This study investigates the applicability of harsh braking events as a surrogate indicator of crash risk on New Jersey interstate highways. The analysis uses more than 8.5 million connected-vehicle telemetry records from Drivewyze and approximately 45,000 police-reported crashes collected between July and December 2024. HB events were identified using a deceleration threshold of 6 ft/s² (approximately 0.2 g) and spatially matched to one-mile highway segments along with crash data. Spatial analysis shows that both HB events and crashes are highly concentrated along major corridors, including I-95, I-80, I-78, and I-287, with notable clustering near toll plazas and complex interchange areas. Temporal patterns indicate that harsh braking activity increases substantially during late fall, likely reflecting seasonal congestion and adverse weather conditions. To quantify the relationship between HB events and crash frequency, Negative Binomial (NB) and Zero-Inflated Negative Binomial (ZINB) regression models were estimated at the segment level. Results reveal a positive and statistically significant association between HB events and crash counts. In the preferred ZINB model, each additional HB event is associated with approximately a one percent increase in expected crash frequency. While the effect of individual events is small, repeated harsh braking activity corresponds to a meaningful increase in crash risk; for example, an increase of 10 HB events corresponds to an expected crash frequency of about 10% higher. Overall, the findings suggest that connected-vehicle HB data can complement traditional crash records by providing early indications of elevated risk. Incorporating HB monitoring into highway safety programs may support proactive identification of hazardous locations and more timely deployment of targeted countermeasures. Full article

Climate change (CC) is widely recognized as a major human concern, affecting society across all aspects and activities. Among various economic sectors, aviation is one of the most affected due to its exposure to adverse weather events. Consequently, adaptation and mitigation actions are becoming increasingly important to reduce the negative effects of CC-driven extreme weather events on aviation operations. In this study, we analyzed 30 years of historical aerodrome meteorological routine reports (METARs) from several major Italian airports to assess multi-decadal changes in aviation weather-related hazards, based on observational evidence such as convection, visibility, and snow and freezing precipitation. Furthermore, we examined the ERA5 reanalysis dataset to assess potential anomalies in the synoptic circulation over the Euro-Mediterranean region that may drive fluctuations in local airport climatology. Our results reveal relevant trends for the considered aviation-related weather hazards, while also indicating meaningful links to variations in local and synoptic patterns. The observed increases in 500 hPa geopotential height, 850 hPa temperature, and convective available potential energy (CAPE) lead to changes in the climatology of the airports considered, including a general enhancement of thermoconvective phenomena, a reduction in events associated with synoptic-scale disturbances, an overall decrease in snowfall, and contrasting trends in fog occurrence depending on local factors. Full article

Sweet cherry (Prunus avium L.) is a highly appreciated fruit species for consumption but susceptible to climate change-induced weather, such as heavy rainfall, which catastrophically compromises yield and commercial fruit quality. Brassinosteroids (BRs) represent a novel biologically safe class of hormones that have been shown to increase plant resilience against these adversities and enhance crop yield and fruit quality in some fruit species. The main aim of this study was to evaluate the potential efficacy of the preharvest foliar spray treatments with 24-epibrassinolide (24-BL) at 0.01, 0.1 and 1 µM on crop yield, cracking incidence and fruit quality of ‘Sunburst’ and ‘Skeena’ sweet cherry cultivars, during two seasons with different weather conditions (2022 and 2023). Results revealed that 24-BL treatments improved fruit growth, fruit weight, and increased commercial crop yield, especially at 0.1 µM during the first season. Notably, in 2023, when extreme rainfall occurred, 24-BL at 0.01 and 0.1 µM significantly decreased cracking incidence by up to 50% for ‘Skeena’. Additionally, firmness, red colour and bioactive compounds, such as total phenolics and total anthocyanins, were also found at higher levels in fruits from 24-BL-treated trees compared to controls, in both cultivars and years. In conclusion, the foliar spray application of 24-BL at 0.01 µM and, especially at 0.1 µM, can be a useful and eco-friendly tool to reduce cracking incidence, improve crop yield and enhance sweet cherry quality traits regardless of environmental negative events, such as heavy rainfall. Importantly, the enhancement of bioactive compounds would promote additional antioxidant properties and enhance health benefits to consumers. Full article

Typhoon avoidance is critical for ship maneuvering safety under extreme meteo-ocean conditions. This study proposes a data-driven framework that converts AIS trajectories into interpretable course deviation and speed change responses for navigational decision support. After AIS cleaning, temporal resampling, and matching with gridded wind, wave, and current fields, rule-based sliding-window and regression procedures, informed by experienced captains and company staff, automatically generate proxy labels for deviation and speed reduction. Samples are stratified by vessel size to reflect differences in inertia and maneuverability, and XGBoost classifiers are trained with simple resampling to mitigate class imbalance. The framework is demonstrated on a single-event case study of Typhoon Yagi in the South China Sea, covering 8609 vessels and reconstructed sailing fragments. On the test set, the deviation model achieves 89.8% accuracy and high recall for deviation cases, while the speed change model reaches 82% balanced accuracy under the proxy-label setting. Results suggest a scale-dependent response: smaller vessels exhibit more frequent course deviation, whereas larger vessels more often reduce speed under severe wind-wave loading. The framework offers a proof-of-concept approach to derive behavior-based indicators from AIS and environmental data and may support situational assessment under adverse weather. Full article

Ensuring railway safety requires reliable monitoring of trains in critical safety areas, such as station throat zones and railway crossings. Compared with cameras, roadside LiDAR can more reliably capture the geometry of trains under low-light, high-speed, and adverse weather conditions. However, industrial LiDAR solutions still primarily use the background comparison technique, which compares each sample against a pre-recorded clean map and then applies a size-based filter. Such approaches are highly sensitive to point cloud background changes arising from varying LiDAR installation distances, train speeds, and surface materials, often resulting in fragmented clustering and missed detections. In this paper, train detection is reformulated as a point-level semantic segmentation problem. A lightweight 3D segmentation network that directly predicts train points from raw data is designed, and clustering-based post-processing is applied to generate train-level events in real time. Experiments on real railway data under various operating conditions show that the proposed method achieves higher detection accuracy and greater robustness than traditional compare-based methods and representative deep learning benchmark methods, and is therefore suitable for practical railway safety monitoring. Full article

Traffic accident detection and object detection have become key areas of research due to their direct impact on safety, traffic congestion mitigation, and intelligent traffic planning. This study presents a structured analysis of classical detection methods and artificial intelligence-based techniques, highlighting their methodologies, objectives, and performance results. The study categorizes existing research into threshold-based approaches, statistical approaches, image processing, rule-based approaches, and machine learning approaches, with further emphasis on predictive modeling, graph-based approaches, and optimization approaches. Considerable emphasis is placed on identifying systems that are capable of operating under adverse weather conditions such as fog, rain, and snow. These scenarios significantly affect detection accuracy. Although several authors incorporate environmental resilience into their models, most studies still evaluate performance under ideal conditions, revealing a critical gap in research. This analysis highlights the need to develop robust detection mechanisms that can adapt to real-world variability and environmental disturbances. Findings show that AI-based methods significantly outperform classical approaches in terms of adaptability and scalability, but their dependence on training data limits their performance in adverse conditions. The study concludes with recommendations for future work to prioritize multimodal sensing, generalization across weather conditions, and integration of environmental intelligence to ensure reliable real-time detection of traffic events under all operating conditions. Full article

The outcomes of the Winter Storm URI in Houston (February 2021) and its impact on awnings highlighted how climate change has altered the load combinations considered in design codes such as ASCE 7-16, introducing new uncertainties due to freezing storm events. Previously unused load categories are now presenting significant challenges, as designers assumed sufficient safety factors would prevent failures. This research investigates the consequences of the storm and offers guidelines for conservative awning design in zero ground snow load zones, emphasizing wind load as the primary design load in regions with no active snow zone. Additionally, an attempt has been made in this research to examine the importance of anchor reliability in concrete structures, particularly under environmental stress such as winter storms. Factors like improper installation, edge distance, and embedment depth significantly affect anchor performance, potentially leading to premature failure modes like concrete breakout, pullout, or rusting from water accumulation. Through field investigations and theoretical analyses, the research evaluates the axial load capacity of anchors, taking into account edge distance, embedment depth, and environmental factors like ice accumulation. The study stresses the need for proper anchor geometry, drainage, and reinforcement to ensure structural safety. By following the proposed recommendations, engineers can mitigate adverse effects and enhance the durability and safety of concrete structures, even under extreme weather conditions. Full article

Against the backdrop of global climate change and the increasing frequency of extreme weather events, a central scientific question has emerged: how do plants adapt to such “pulsed” stressors? While traditional research has focused on immediate physiological responses and long-term genetic adaptation, this review introduces “ecological memory” as a novel integrative framework. It emphasizes the ability of plants to actively “record” past stress experiences through epigenetic mechanisms, thereby enhancing their adaptability to future adversities. This article systematically elucidates the molecular basis whereby abiotic stressors induce specific epigenetic modifications (e.g., DNA methylation and histone modifications) to form memories. It further discusses how such memories mediate physiological integration mechanisms, such as acclimation and priming-induced resistance at the individual level, and highlights potential pathways for transgenerational epigenetic memory transmission, which may accelerate population-level adaptive evolution. Finally, we evaluate the applications of the ecological memory concept in predicting species distribution, enhancing ecosystem resilience, and guiding the design of “climate smart” crops, aiming to shift the research paradigm from static tolerance studies to dynamic memory and adaptation frameworks. Full article

To promptly address the disruptions caused by various sudden weather events to the normal operations of the quay apron, this study focuses on the optimization of integrated berth and quay crane (QC) scheduling under the impact of adverse weather. It emphasizes two key influences of adverse weather: port closures and the uncertainty in vessel handling times induced by weather conditions. A decision mechanism is designed, and strategies such as vessel dispatch, cargo omission, and backhaul are incorporated. Meanwhile, constraints including the prohibition of QC crossover and the spatio-temporal limitations on vessel berthing are taken into account. With the optimization objective of minimizing the total scheduling cost, a mixed-integer programming (MIP) model is constructed. A variable neighborhood search (VNS) algorithm is developed for solving the model, which proposes multi-layer encoding and a corresponding hybrid initialization strategy. Finally, comparative experiments are conducted to verify the effectiveness of the model and the rationality of the algorithm. Sensitivity analysis is also performed on the duration of port closures and QC handling efficiency. The research results can provide decision support for ports in formulating response strategies against adverse weather. Full article

Climate change-induced extreme weather events increasingly threaten public health, with a particularly acute impact on the mental well-being of urban populations. This study evaluates regional disparities in mental health outcomes associated with climate-induced extreme weather in urban environments, where social and infrastructural vulnerabilities exacerbate environmental stressors. We synthesized data from cohort and cross-sectional studies using both traditional frequentist and Bayesian meta-analytic frameworks to assess the mental health sequelae of extreme weather events (e.g., heatwaves, floods, droughts, and storms). The traditional meta-analysis indicated a significant increase in the odds of adverse mental health outcomes (OR = 1.32, 95% CI: 1.07–1.57). However, this global estimate was characterized by extreme heterogeneity (I² = 95.8%), indicating that the risk is not uniform but highly context-dependent. Subgroup analyses revealed that this risk is concentrated in specific regions; the strongest associations were observed in Africa (OR = 2.23) and Europe (OR = 2.26). Conversely, the Bayesian analysis yielded a conservative estimate, suggesting a slight reduction in odds (mean OR = 0.92, 95% CrI: 0.87–0.98). This divergence is driven by the Bayesian model’s shrinkage of high-magnitude outliers toward the high-precision data observed in resilient, high-income settings (e.g., USA). Given the extreme heterogeneity observed (I² = 95.8%), we caution against interpreting either pooled estimate as a universal effect size. Instead, the regional subgroup findings—particularly the consistently elevated risks in Africa and Europe—offer more stable and policy-relevant conclusions. These findings emphasize urgent, context-specific interventions in urban areas facing compounded climate social risks. Full article

Aflatoxins, mycotoxins produced by Aspergillus flavus and Aspergillus parasiticus, were discovered sixty-five years ago and remain a significant public health threat, particularly amid increasing instances of extreme weather events. Of the four principal forms of aflatoxins found in foods (B₁, B₂, G₁, and G₂), aflatoxin B₁ is the most potent carcinogen. Aflatoxins commonly contaminate a variety of foodstuffs, with maize being among the most susceptible. Chronic exposure to aflatoxins has been linked to liver cancer, childhood stunting, gallbladder cancer, and other adverse health effects. Due to public health concerns related to the consumption of aflatoxin-contaminated foods, most countries have established regulatory limits. Here, we present estimated aflatoxin exposure per day derived from human biomarker data across many studies around the world spanning more than forty years. We specifically focus on the impact of dietary aflatoxin in northern Latin America, where assessment of the total problem remains limited. These findings suggest a multipronged toolkit could mitigate aflatoxin exposure in the region, which would help to decrease the health burden. Full article

Effective disaster management depends on rapidly understanding earthquake damage, yet traditional methods struggle to operate at scale and rely on expert inspections that become difficult when access is limited or time is critical. Satellite-based damage detection also faces limitations, particularly under adverse weather conditions and delays associated with satellite overpass schedules. This study introduces a machine learning-based approach to assess post-earthquake building damage using real observations collected after the event. The aim is to develop fast and reliable estimation techniques that can be deployed immediately after the mainshock by integrating structural, seismic, and geographic data. Three machine learning models—Random Forest, Histogram Gradient Boosting, and Bagging Classifier—are evaluated across both reinforced concrete and masonry buildings and across multiple spatial levels, including building, district, and city scales. Damage is categorized using practical three-class (traffic light) and detailed four-class systems. The models generally perform better in simpler classifications, with the Bagging Classifier offering the most consistent results across different scales. Although detecting severely damaged buildings remains challenging in some cases, the three-class system proves especially effective for supporting rapid decision-making during emergency response. Overall, this study demonstrates how machine learning can provide faster, scalable, and practical earthquake damage assessments that benefit emergency teams and urban planners. Full article

Grasshoppers and their allies (Orthoptera) are numerous and diverse insects globally, while being significant components of biodiversity and nutrient cycling. They are variously responsive to environmental change but are paradoxical, as some species are major pests while others are threatened or even extinct. Most orthopteran species are somewhere in between, with their assemblage composition shifting in response to changing conditions and according to the response traits of the constituent species. With global concern over the impact of conventional agriculture on biodiversity, there is currently a strong transition to agroecology. The agroecological approach is two-fold: to set aside land and to better manage the overall landscape. Both approaches aim to boost the numbers and diversity of most orthopterans, while reducing the impact of the pest species using biologically based pesticides instead of chemical pesticides as part of an integrated pest management program. Set-aside land is present at various spatial scales for conservation action, involving a diversity of practical approaches. Management depends on understanding orthopteran responses to change, and harnessing the positive responses using, for example, improved grazing, fire management, and vegetation diversification for maximizing habitat heterogeneity. These initiatives also recognize the additional interactive effect of climate change and extreme weather events. Importantly, improvement of orthopteran abundance and diversity is an integral component of overall biodiversity conservation. New technologies, both aerial and genomic, are advancing the field of orthopteran biology and ecology greatly. We review advances being made in the field that hold the most promise and suggest ways forward based on three themes: appreciating orthopteran value, recognizing the adverse drivers of orthopteran abundance and diversity, and better design and management of agroecosystems. Full article

Rising temperatures, extreme precipitation events such as excessive or insufficient rainfall, increasing levels of carbon dioxide, and associated climatic factors will persistently impact crop growth and agricultural production. The warming temperatures have reduced the agricultural crop yields. Rice (Oryza sativa L.) is the major food crop, which is particularly susceptible to the effects of climate change. It is very important to accurately evaluate the impacts of climate change on rice growth and rice yield. In this study, the rice growth during 1981–2018 (baseline period) and 2041–2100 (future period) were separately simulated and compared within the CERES-Rice model (v4.6) using high-quality weather data, soil, and field experimental data at six agro-meteorological stations in Hainan Province. For the climate data of the future period, the SSP1-2.6, SSP3-7.0, and SSP5-8.5 scenarios were applied, with carbon dioxide (CO₂) fertilization effects considered. The adaptation strategies such as adjusting planting dates and switching rice cultivars were also assessed. The simulation results indicated that the early rice yields in the 2050s, 2070s, and 2090s were projected to decrease by 6.2%, 11.8%, and 20.0% when the CO₂ fertilization effect was not considered, compared with the results of the baseline period, respectively, while late rice yields would decline by 9.9%, 23.4%, and 36.3% correspondingly. When accounting for the CO₂ fertilization effect, the yields of early rice and late rice in the 2090s increased 16.9% and 6.2%, respectively. Regarding adaptation measures, adjusting planting dates and switching rice cultivars could increase early rice yields by 22.7% and 43.3%, respectively, while increasing late rice yields by 20.2% and 34.2% correspondingly. This study holds substantial scientific importance for elucidating the mechanistic pathways through which climate change influences rice productivity in tropical agro-ecosystems, and provides a critical foundation for formulating evidence-based adaptation strategies to mitigate climate-related risks in a timely manner. Cultivar substitution and temporal shifts in planting dates constituted two adaptation strategies for attenuating the adverse impacts of anthropogenic climate change on rice. Full article

Every year, thunderstorms initiating in the eastern Pyrenees cause a wide range of adverse phenomena, not only in the mountainous areas but also in the surrounding regions. Events such as heavy rainfall leading to flash floods, large or giant hail, and strong winds are common in this area. These phenomena cause significant damage and have major impacts on the population. We used remote sensing data, specifically weather radar, to identify areas that are more prone to convection initiation. This initial analysis covers the period from 2022 to 2024 and is intended to serve as the foundation for a more extensive study. The aim of this study is to characterize the diurnal convection cycle over the Pyrenees. Additionally, we plan to develop a technique that can be applied to other mountainous regions where similar data are available. The steps are as follows: (1) identifying events with precipitation over the area; (2) selecting cases associated with diurnal convection; (3) applying algorithms to determine the tracks of convective cells; and finally, (4) selecting the initial points of these trajectories. The result is a map highlighting these “hotspot” areas, which will allow us to incorporate other variables in the future, both meteorological and non-meteorological, to identify the main factors influencing the characteristics of each event. Full article