Occurrence Probability Of Events Research Articles

Multiscale Nonlinear Response of Extreme Meteorological–Hydrothermal Events in the Upper Reaches of the Yangtze River

ABSTRACTUnder the backdrop of climate warming, the outbreak of short‐term extreme heat events can easily lead to irreversible changes in aquatic ecosystems. Delving into their intrinsic driving mechanisms and nonlinear characteristics is key to preventing natural disasters. This study, focusing on the upper Yangtze River as the research area, constructs a joint copula function model to analyze the joint occurrence probability and return period of extreme meteorological heat events. Through the bivariate cross–wavelet transform method, the study explores the multiscale dynamic response relationships and phase characteristics of extreme meteorological–hydrothermal events in the upper Yangtze River. Furthermore, a joint multifractal model of nonlinear responses for extreme meteorological–hydrothermal events was established. The results indicate that extreme high‐heat meteorological events in the upper Yangtze River tend to occur more frequently and severely, with duration–kurtosis events likely to coincide within a 2‐year return period, as well as high‐intensity low‐frequency high‐heat duration–severity events occurring simultaneously. Overall, before 2005, extreme high‐hydrothermal events exhibited phase characteristics lagging behind meteorological changes, which then shifted from lag to lead. Under three scenarios of change, meteorological–hydrothermal events exhibit a clear nonlinear response relationship. Apart from duration, severity and kurtosis all show significant nonlinear relationships.

Improved decision-making through life event prediction: A case study in the financial services industry

Life event prediction is an important tool for customer relationship management (CRM), because life events shift customers’ preferences towards different products and services. Existing life event research mainly uses cross-sectional data, whereas in the CRM field, incorporating longitudinal data is increasingly common. Because longitudinal data can capture the dynamics of customer behavior, opportunities arise to benchmark the power of longitudinal customer data for predictions of cross-sectional versus longitudinal life events. Therefore, this study compares statistical and machine learning (SaML) classifiers, such as logistic regression, random forest, and XGBoost, with long- and short-term memory networks (LSTM), using data represented in both cross-sectional and longitudinal setups for life event prediction. Through a real-life longitudinal customer data set from a European bank, the authors represent the longitudinal data in a cross-sectional data format, using featurization in the form of aggregation. The available data cover 42 end-of-month snapshots for 760,438 unique customers. For marketing decision-making literature, this article (1) introduces three novel life events (i.e., primary, secondary, and rental residence purchases) to life event predictions; (2) offers guidance for how to leverage longitudinal customer data, according to the comparison of various featurization approaches and benchmarking SaML classifiers against LSTM; and (3) clarifies the importance of features and timing for improving marketing decision-making dynamically. The results show that aggregating features over time is preferable as a featurization approach for cross-sectional modeling using SaML classifiers. Furthermore, LSTM can capture behavioral changes over time, unlike SaML classifiers. It also performs significantly better than SaML classifiers on the area under curve and F1 metrics. Insights into the uses of integrated gradients reveal that feature importance changes over time. An integrated gradients method can assist decision-makers in their efforts to plan effective communication with customers in advance, such as by allocating more resources to customers who exhibit high probabilities of a particular life event occurrence.

Methodological approaches to assessing the risks of water resources depletion in the context of climate change (using plain territories of Ukraine as an example)

The relevance of the work is determined by the necessity of quantitative assessment of the risks of water resources changes occurring in the early 21st century due to the greenhouse effect and corresponding climate changes. To evaluate climate risks the authors used a probabilistic approach which involves multiplying the indicator of water resource reduction by the probability of such event occurrence. Reduction of water resources caused by warming characterizes their "depletion". The study utilized the results of calculations of the average long-term annual "climate" runoff, determined through the "climate-runoff" model based on meteorological data of the averaged model trajectory of the EURO-CORDEX project for the climate change scenarios RCP4.5 and RCP8.5. The calculation period extends from 2021 to 2050, while the baseline period continues from the beginning of observations until 1989. The comparison of the calculated climatic runoff and the baseline runoff was performed across meteorological stations. The assessment of the changes was carried out by calculating the relative deviation between the baseline and calculated runoff values. The probability of the "depletion" stage for water resources was determined as the ratio of the number of meteorological stations (grid nodes) where changes reached certain magnitudes to the total number of stations considered. The threshold indicators for the degree of depletion of water resources included changes exceeding 10% (statistically significant changes in water resources), 50% (water resources destruction), and 70% (irreversible destruction of water resources). The objective of the study consists in developing criteria for calculating climate risks and establishing the risks of formation of a tense, critical, and catastrophic state of water resources, taking Ukraine as an example. A detailed examination of the intervals of changes in water resources showed that the highest risk of their depletion over the period from 2020 to 2050 will be observed in the range from -20 to -40% (tense state) for the RCP4.5 scenario and from -30 to -60% (tense state) for the RCP8.5 scenario. It was established that the climate risks of depletion and irreversible depletion of water resources for the territory of Ukraine from 2020 to 2050 remain quite small, which is confirmed by the low probability of their occurrence. However, the risk values increase by 1.5 to 2.0 times for the RCP8.5 scenario compared to the RCP4.5 scenario. Additionally, for calculating the climate risks, it was proposed to use a relative area subject to depletion as an indirect indicator of losses (a larger area implies higher costs of restoration). This approach allowed for having more differentiated risk assessments. The developed methodology for assessing the climate risks of water resources depletion in the context of global warming can be applied for various models and countries.

An evaluation method of construction reliability of cable system of cable-stayed bridge based on Bayesian network

PurposeAs an important load-bearing component of cable-stayed bridge, the cable-stayed cable is an important load-bearing link for the bridge superstructure and the load transferred directly to the bridge tower. In order to better manage the risk of the cable system in the construction process, the purpose of this paper is to study a new method of dynamic risk analysis of the cable system of the suspended multi-tower cable-stayed bridge based on the Bayesian network.Design/methodology/approachFirst of all, this paper focuses on the whole process of the construction of the cable system, analyzes the construction characteristics of each process, identifies the safety risk factors in the construction process of the cable system, and determines the causal relationship between the risk factors. Secondly, the prior probability distribution of risk factors is determined by the expert investigation method, and the risk matrix method is used to evaluate the safety risk of cable failure quantitatively. The function expression of risk matrix is established by combining the probability of risk event occurrence and loss level. After that, the topology structure of Bayesian network is established, risk factors and probability parameters are incorporated into the network and then the Bayesian principle is applied to update the posterior probability of risk events according to the new information in the construction process. Finally, the construction reliability evaluation of PAIRA bridge main bridge cable system in Bangladesh is taken as an example to verify the effectiveness and accuracy of the new method.FindingsThe feasibility of using Bayesian network to dynamically assess the safety risk of PAIRA bridge in Bangladesh is verified by the construction reliability evaluation of the main bridge cable system. The research results show that the probability of the accident resulting from the insufficient safety of the cable components of the main bridge of PAIRA bridge is 0.02, which belongs to a very small range. According to the analysis of the risk grade matrix, the risk grade is Ⅱ, which belongs to the acceptable risk range. In addition, according to the reverse reasoning of the Bayesian model, when the serious failure of the cable system is certain to occur, the node with the greatest impact is B3 (cable break) and its probability of occurrence is 82%, that is, cable break is an important reason for the serious failure of the cable system. The factor that has the greatest influence on B3 node is C6 (cable quality), and its probability is 34%, that is, cable quality is not satisfied is the main reason for cable fracture. In the same way, it can be obtained that the D9 (steel wire fracture inside the cable) event of the next level is the biggest incentive of C6 event, its occurrence probability is 32% and E7 (steel strand strength is not up to standard) event is the biggest incentive of D9 event, its occurrence probability is 13%. At the same time, the sensitivity analysis also confirmed that B3, C6, D9 and E7 risk factors were the main causes of risk occurrence.Originality/valueThis paper proposes a Bayesian network-based construction reliability assessment method for cable-stayed bridge cable system. The core purpose of this method is to achieve comprehensive and accurate management and control of the risks in the construction process of the cable system, so as to improve the service life of the cable while strengthening the overall reliability of the structure. Compared with the existing evaluation methods, the proposed method has higher reliability and accuracy. This method can effectively assess the risk of the cable system in the construction process, and is innovative in the field of risk assessment of the cable system of cable-stayed bridge construction, enriching the scientific research achievements in this field, and providing strong support for the construction risk control of the cable system of cable-stayed bridge.

MEMPSEP‐I. Forecasting the Probability of Solar Energetic Particle Event Occurrence Using a Multivariate Ensemble of Convolutional Neural Networks

AbstractThe Sun continuously affects the interplanetary environment through a host of interconnected and dynamic physical processes. Solar flares, Coronal Mass Ejections (CMEs), and Solar Energetic Particles (SEPs) are among the key drivers of space weather in the near‐Earth environment and beyond. While some CMEs and flares are associated with intense SEPs, some show little to no SEP association. To date, robust long‐term (hours‐days) forecasting of SEP occurrence and associated properties (e.g., onset, peak intensities) does not effectively exist and the search for such development continues. Through an Operations‐2‐Research support, we developed a self‐contained model that utilizes a comprehensive data set and provides a probabilistic forecast for SEP event occurrence and its properties. The model is named Multivariate Ensemble of Models for Probabilistic Forecast of Solar Energetic Particles (MEMPSEP). MEMPSEP workhorse is an ensemble of Convolutional Neural Networks that ingests a comprehensive data set (MEMPSEP‐III by Moreland et al. (2024, https://doi.org/10.1029/2023SW003765)) of full‐disc magnetogram‐sequences and in situ data from different sources to forecast the occurrence (MEMPSEP‐I—this work) and properties (MEMPSEP‐II by Dayeh et al. (2024, https://doi.org/10.1029/2023SW003697)) of a SEP event. This work focuses on estimating true SEP occurrence probabilities achieving a 2.5% improvement in reliability and a Brier score of 0.14. The outcome provides flexibility for the end‐users to determine their own acceptable level of risk, rather than imposing a detection threshold that optimizes an arbitrary binary classification metric. Furthermore, the model‐ensemble, trained to utilize the large class‐imbalance between events and non‐events, provides a clear measure of uncertainty in our forecast.

Attribution of the unprecedented summer 2022 compound marine and terrestrial heatwave in the Northwest Pacific

In boreal summer (July–August) 2022, an unprecedented marine heatwave (MHW) occurred in the northwest Pacific Ocean (NWP), while a record-breaking terrestrial heatwave (THW) hit the Yangtze River Basin (YRB). The temperature anomalies caused by this compound MHW-THW event exceeded climatology by 2.5 standard deviations (SDs), breaking the historical record for nearly 100 years, with severe impacts on the ecosystems and social economy. To investigate the underlying causes, we explored the potential roles of anthropogenic forcing, atmospheric circulation, and ‘triple-dip’ La Niña on this compound event using the Coupled Model Intercomparison Project Phase 6 (CMIP6) multi-model simulations. Results indicate that the 2022-like compound MHW-THW event was extremely unlikely to happen without anthropogenic warming, and that such extreme heatwaves were governed by the climatic mean temperature rather than changes in temperature variability. Notably, the anticyclone circulation patterns and associated high-pressure systems (i.e. western North Pacific subtropical high (WNPSH) and South Asian high (SAH)) increase the probability of a 2022-like MHW-THW event by 3.7 times. However, the La Niña phase has no significant effect on the occurrence probability of such events. We further estimate that the 2022-like MHW-THW event will become 7.5 and 11.4 times more likely under the SSP3-7.0 scenario by the middle and end of the 21st century, respectively. This study demonstrates the contribution of anthropogenic climate change and natural variability to the occurrence of compound MHW-THW events and highlights the urgent need to build mitigation strategies for compound MHW-THW events.

High-risk event prone driver identification considering driving behavior temporal covariate shift

Drivers who perform frequent high-risk events (e.g., hard braking maneuvers) pose a significant threat to traffic safety. Existing studies commonly estimated high-risk event occurrence probabilities based upon the assumption that data collected from different time periods are independent and identically distributed (referred to as i.i.d. assumption). Such approach ignored the issue of driving behavior temporal covariate shift, where the distributions of driving behavior factors vary over time. To fill the gap, this study targets at obtaining time-invariant driving behavior features and establishing their relationships with high-risk event occurrence probability. Specifically, a generalized modeling framework consisting of distribution characterization (DC) and distribution matching (DM) modules was proposed. The DC module split the whole dataset into several segments with the largest distribution gaps, while the DM module identified time-invariant driving behavior features through learning common knowledge among different segments. Then, gated recurrent unit (GRU) was employed to conduct time-invariant driving behavior feature mining for high-risk event occurrence probability estimation. Moreover, modified loss functions were introduced for imbalanced data learning caused by the rarity of high-risk events. The empirical analyses were conducted utilizing online ride-hailing services data. Experiment results showed that the proposed generalized modeling framework provided a 7.2% higher average precision compared to the traditional i.i.d. assumption based approach. The modified loss functions further improved the model performance by 3.8%. Finally, benefits for the driver management program improvement have been explored by a case study, demonstrating a 33.34% enhancement in the identification precision of high-risk event prone drivers.

Review of Power System Resilience Concept, Assessment, and Enhancement Measures

Power systems are generally designed to be reliable when faced with low-impact, high-probability, and expected power outages. By contrast, the probability of extreme event (extreme weather or natural disasters) occurrence is low, but may seriously affect the power system, from long outage times to damage to major equipment such as substations, transmission lines, and power plants. As, in the short term, it is extremely difficult to completely avoid the damage caused by extreme events, it is important to enhance the resilience of power systems. This study has provided a comprehensive review of power system resilience by discussing its concepts, assessment, and enhancement measures. This article summarized possible impacts and quantitative indicators of various types of disasters on power grids, presented the concept of power system resilience, and analyzed the main characteristics that a resilient system should possess. Moreover, this article further distinguished the differences between the resilience, flexibility, and survivability of a power system. More importantly, this paper has proposed a novel framework and the corresponding metric for assessing resilience, which makes the evaluation of system resilience more accurate. Finally, this paper discussed various measures to enhance power system resilience and outlined potential challenges for future research.

A kinematic method for generating earthquake sequences

Computational earthquake sequence models provide generative estimates of the time, location, and size of synthetic seismic events that can be compared with observed earthquake histories and assessed as rupture forecasts. Here we describe a three-dimensional probabilistic earthquake sequence model that produces slip event time series constrained across geometrically complex non-planar fault systems. This model is kinematic in nature, integrating the time evolution of geometric moment accumulation and release with empirical earthquake scaling laws. The temporal probability of event occurrence is determined from the time history of geometric moment integrated with short-term Omori-style rate decay following each earthquake achieving long-term time-averaged moment balance. Similarly, the net geometric moment monotonically controls the probability of event localization, and seismic events release geometric moment with spatially heterogeneous slip on three-dimensional non-planar fault surfaces. We use this model to generate a synthetic earthquake sequence on the Nankai subduction zone over a 1,250-year-long interval, including 700+ MW=5.5−8.5 coseismic events, with decadal-to-centennial scale quiescent intervals quasi-periodic great earthquake clusters followed by aftershock sequences.

Rapid attribution of the record-breaking heatwave event in North China in June 2023 and future risks

A record-breaking heatwave event occurred in North China from 22 to 24 June 2023, with temperatures >40 °C at many meteorological stations. This marked the first time that Beijing had reached or exceeded 40 °C for three consecutive days. However, the extent to which such exceptional heatwave events are related to anthropogenic climate change remains unclear. It is also unclear how frequent and intense such strong heatwave events will be in the future. We carried out a rapid attribution analysis to address these questions. Our findings show that the return period of this three-day heatwave event in North China is about 111 years (24.3, +∞) at the 2023 climate state. Both the empirical and coupled model approaches consistently showed that the intensity of 2023-like three-day heatwave events has significantly increased by at least 1.0 °C (range 0.8 °C–1.3 °C) due to anthropogenic climate change. Future projections indicate that 2023-like events in North China are likely to occur at least 1.6 (range 1.3–2.1) times throughout the remainder of this century and be 0.5 °C (range 0.2 °C–0.8 °C) more intense than those under the 2023 climate even if carbon neutrality is achieved based on the very low CO2 emissions scenario simulations. For the intermediate emissions scenario, the occurrence probability of 2023-like events in the North China region by the end of this century will be 5.5 (range 4.9–6.3) times those under the 2023 climate, with an intensity 2.9 °C (range 2.4 °C–3.1 °C) higher than those under the 2023 climate. These findings highlight the need for adaptation measures to address the occurrence of 2023-like three-day heatwaves in North China in June even if carbon neutrality is achieved.

Joint Models for Longitudinal and Time-to-event Data with JM R Package

Even if the value of the risk factor evolves continuously in the extended Cox model, the inference may be somewhat biased because it employs a discrete approximation method. As a result, if the risk factor’s value not only fluctuates constantly but also contains measurement error, a model that substitutes the average value rather than the measured value of the risk factor might be considered. Such a model is known as a joint model. After introducing the most widely used the present-value model among joint models, this model was extended to various types of data. In addition, several residuals for model diagnosis were introduced, methods for predicting the probability of event occurrence and the value of the longitudinal risk factor by application of the estimation model were introduced, and an index that can evaluate how well the longitudinal risk factors divide patients into high-risk and low-risk groups was introduced. Finally, all the statistical inference methods introduced in this study were implemented using the JM R package and the source codes were supplied.

A class-specific effect of dysmyelination on the excitability of hippocampal interneurons.

The role of myelination for axonal conduction is well-established in projection neurons but little is known about its significance in GABAergic interneurons. Myelination is discontinuous along interneuron axons and the mechanisms controlling myelin patterning and segregation of ion channels at the nodes of Ranvier have not been elucidated. Protein 4.1B is implicated in the organization of the nodes of Ranvier as a linker between paranodal and juxtaparanodal membrane proteins to the spectrin cytoskeleton. In the present study, 4.1B KO mice are used as a genetic model to analyze the functional role of myelin in Lhx6-positive parvalbumin (PV) and somatostatin (SST) neurons, two major classes of GABAergic neurons in the hippocampus. We show that 4.1B-deficiency induces disruption of juxtaparanodal K+ channel clustering and mislocalization of nodal or heminodal Na+ channels. Strikingly, 4.1B-deficiency causes loss of myelin in GABAergic axons in the hippocampus. In particular, stratum oriens SST cells display severe axonal dysmyelination and a reduced excitability. This reduced excitability is associated with a decrease in occurrence probability of small amplitude synaptic inhibitory events on pyramidal cells. In contrast, stratum pyramidale fast-spiking PV cells do not appear affected. In conclusion, our results indicate a class-specific effect of dysmyelination on the excitability of hippocampal interneurons associated with a functional alteration of inhibitory drive.

Human influences on spatially compounding flooding and heatwave events in China and future increasing risks

Attribution of high-impact weather events to anthropogenic climate change is important for disentangling long-term trends from natural variability and estimating potential future impacts. Up to this point, most attribution studies have focused on univariate drivers, despite the fact that many impacts are related to multiple compounding weather and climate drivers. For instance, co-occurring climate extremes in neighbouring regions can lead to very large combined impacts. Yet, attribution of spatially compounding events with different hazards poses a great challenge. Here, we present a comprehensive framework for compound event attribution to disentangle the effects of natural variability and anthropogenic climate change on the event. Taking the 2020 spatially compounding heavy precipitation and heatwave event in China as a showcase, we find that the respective dynamic and thermodynamic contributions to the intensity of this event are 51% (35–67%) and 39% (18–59%), and anthropogenic climate change has increased the occurrence probability of similar events at least 10-fold. We estimate that compared to the current climate, such events will become 10 times and 14 times more likely until the middle and end of the 21st century, respectively, under a high-emissions scenario. This increase in likelihood can be substantially reduced (to seven times more likely) under a low-emissions scenario. Our study demonstrates the effect of anthropogenic climate change on high-impact compound extreme events and highlights the urgent need to reduce greenhouse gas emissions.

The extreme heat wave of late July/early August 2021 in Greece under the context of the direct effect of anthropogenic greenhouse gases

AbstractGreece is characterized by a significant warming trend in recent decades, accompanied by increasing frequency, intensity, and duration of heat waves (HWs). A particularly devastating HW that affected the country was the late July/early August 2021 event (JA2021HW), which lasted for 9 days (July 28–August 5). Focusing on the hottest day of the event (August 3), the main characteristics of JA2021HW are presented in the current study, using model reanalysis data and up to 11‐year observations derived from the dense network of ground‐based weather stations operated by the Meteo Unit at the National Observatory of Athens (NOA). This analysis highlights the severity of JA2021HW, especially in the central and southernmost regions of Greece. Most importantly, the impact of the direct effect of anthropogenic greenhouse gases to the examined extreme event, in terms of intensity and probability of occurrence, is examined by employing a regional 31‐member ensemble (ENS) modeling approach based on Weather Research and Forecasting (WRF) model, which is operationally used by NOA/Meteo. Firstly, WRF is validated under 7‐day lead‐time ENS simulations with current‐state greenhouse gas (GHG) concentrations (GHG_2021 ENS), showing a robust model performance in replicating the JA2021HW's magnitude on August 3rd. Then, 7‐day lead‐time ENS simulations with the GHG concentrations reduced to the pre‐industrial (1854) levels (GHG_1854 ENS) are performed and compared to the GHG_2021 ENS experiment. The results reveal a contribution of the immediate anthropogenic warming due to the increased GHG concentrations to the JA2021HW intensity in West and South continental and insular Greece, which can be important in the framework of the human health impacts of extreme temperatures. For the event's occurrence probability, no robust evidence of any change could be derived. These statements are partially constrained by the fact that only the direct GHG effect on the timescale of a few days was examined.

Assessing the groundwater deficit for agriculture requirements under precipitation change while achieving food and water security in the North China Plain

Groundwater resources are closely related to climate change. Evaluating the groundwater deficit and its variation characteristics under climate change is necessary to understand the risk associated with enhanced groundwater recovery solutions and to provide supporting information for choosing the best technology and risk-reducing measures to implement. In this study, by using the CROPWAT model and copula theory, (1) we developed a groundwater deficit index (Gsci) for agricultural requirements that assesses the risk to groundwater resources imposed by precipitation changes on the North China Plain (NCP), and the four grades of Gsci were high deficit, moderate deficit, low deficit, and no deficit. (2) We estimated Gsci on the NCP for the period 1971–2020 by applying the climatic, soil, crop phenological, and hydrogeology data to the developed index. (3) The occurrence probability of each Gsci grade was evaluated. In the years with abundant precipitation, the occurrence probabilities of no-deficit events were 1, 0, 0, 0, 0.36, 0.56, and 0 on the Yanshan Plain, Taihang-Hebei Plain, Taihang-Henan Plain, Central-Hebei Plain, Central-Shandong Plain, Central-Henan Plain, and Coastal Plain, respectively. (4) The effective precipitation threshold for the occurrence of each Gsci grade was calculated, and the effective precipitation thresholds for the occurrence of no-deficit events were 495 mm, 595 mm, 525 mm, 400 mm, 455 mm, and 555 mm on the Taihang-Hebei Plain, Taihang-Henan Plain, Central-Hebei Plain, Central-Henan Plain, Central-Shandong Plain, and Coastal Plain, respectively. Our findings are useful for assessing the impacts of precipitation changes on groundwater resources and provide a further basis for the design of groundwater resource management strategies with respect to climate change, especially in water‐limited arid agricultural regions.

Wall heat flux in supersonic turbulent expansion flow with shock impingement

We perform direct numerical simulations to investigate the characteristics of wall heat flux (WHF) in the interaction of an oblique shock wave at an angle of 33.2° and free-stream Mach number M ∞ = 2.25 impinging on supersonic turbulent expansion corners with deflection angles of 0o (flat plate), 6o and 12o. The effect of the expansion on the WHF characteristics is analysed by comparing it to the interaction with the flat plate under the same flow conditions and a fixed shock impingement point. In the post-expansion region, the decreased mean WHF is found to collapse onto the flat plate case when scaled with the mean wall pressure. The statistical properties of the WHF fluctuations, including probability density function, frequency spectra, and space–time correlations, are comparatively analysed. The expansion causes an increase in the occurrence probability of negative extreme events, an enhancement of high-frequency energy, and an inhibition of intermediate-frequency energy. The increased expansion angle also results in a faster recovery of characteristic spanwise length scales and an increase in convection velocity. We use the mean WHF decomposition method in conjunction with bidimensional empirical mode decomposition to quantitatively analyse the impact of expansion on scale contributions. It is demonstrated that the presence of the expansion corner has no significant impact on the decomposed results, but it significantly reduces the contribution associated with outer large-scale structures.

Attribution of the Record‐Breaking Extreme Precipitation Events in July 2021 Over Central and Eastern China to Anthropogenic Climate Change

AbstractIn July 2021, Typhoon In‐Fa produced record‐breaking extreme precipitation events (hereafter referred to as the 2021 EPEs) in central and eastern China, and caused serious socioeconomic losses and casualties. However, it is still unknown whether the 2021 EPEs can be attributed to anthropogenic climate change (ACC) and how the occurrence probabilities of precipitation events of a similar magnitude might evolve in the future. The 2021 EPEs in central (eastern) China occurred in the context of no linear trend (a significantly increasing trend at a rate of 4.44%/decade) in the region‐averaged Rx5day (summer maximum 5‐day accumulated precipitation) percentage precipitation anomaly (PPA), indicating that global warming might have no impact on the 2021 EPE in central China but might have impacted the 2021 EPE in eastern China by increasing the long‐term trend of EPEs. Using the scaled generalized extreme value distribution, we detected a slightly negative (significantly positive) association of the Rx5day PPA time series in central (eastern) China with the global mean temperature anomaly, suggesting that global warming might have no (a detectable) contribution to the changes in occurrence probability of precipitation extremes like the 2021 EPEs in central (eastern) China. Historical attributions (1961–2020) showed that the likelihood of the 2021 EPE in central/eastern China decreased/increased by approximately +47% (−23% to +89%)/+55% (−45% to +201%) due to ACC. By the end of the 21st century, the likelihood of precipitation extremes similar to the 2021 EPE in central/eastern China under SSP585 is 14 (9–19)/15 (9–20) times higher than under historical climate conditions.

Improving a process of managing dynamic occupational risks

Purpose. To improve the process of managing dynamic occupational risks, which considers changes in time in hazardous factors of the organization’s environment in the occupational safety and health management system. Methodology. To improve the process of managing occupational risks, we have applied a well-known “Bow-Tie” model (ISO 31010:2018). The model allows assessing occupational risks as the product of the probability of hazardous event occurrence and severity of the consequences, taking into account the influence of hazardous external and internal factors, hazardous actions or dangerous inactions, which, according to the requirements of Clause 4.1 of the ISO 45001:2018 standard, are interconnected and subject to the influence of time. Findings. A model of the connection of hazardous factors of the internal and external environment of an organization, related to their negative influence on the growing probability of hazardous event (incident) occurrence and a degree of severity in time, has been developed. The process of managing occupational risks is proposed, taking into account changes in the time of exposure to hazardous factors, which will allow determining the acceptability or unacceptability of the occupational risk in time. The analysis of changes in occupational risks is proposed to be considered in the following time intervals (specifically in those where there is a corresponding change in risk factors): time of the day, day of the week, month of the year, quarter, half year, year, years etc. All the proposed professional risks were divided into two groups of professional risks considering the changes in their levels in time: static and dynamic ones. To calculate the occupational risk level, it is also proposed to determine all combinations of hazardous factors that can occur simultaneously in time within the corresponding intervals of the time under analysis. Originality. It has been determined that identification of the acceptable level of an occupational risk in the maximum combination of all hazardous factors acting simultaneously at a certain point in time will lead to the fact that all other combinations of hazardous factors will also have an acceptable level of occupational risk. This provision follows from the fact that the level of occupational risk from a smaller number of hazardous factors will not exceed the indicator of occupational risk from the exposure to a larger number of hazardous factors in time. Practical value. The forms for dynamic occupational risk assessment have been developed; a matrix has been proposed for determining the number of combinations of hazardous factors acting simultaneously in time.

Strong surface winds in Storm Eunice. Part 1: storm overview and indications of sting jet activity from observations and model data

Strong surface winds in Storm <i>Eunice</i>. Part 1: storm overview and indications of sting jet activity from observations and model data

Prediction of Shooting Events in Soccer Videos Using Complete Bipartite Graphs and Players' Spatial-Temporal Relations.

In soccer, quantitatively evaluating the performance of players and teams is essential to improve tactical coaching and players' decision-making abilities. To achieve this, some methods use predicted probabilities of shoot event occurrences to quantify player performances, but conventional shoot prediction models have not performed well and have failed to consider the reliability of the event probability. This paper proposes a novel method that effectively utilizes players' spatio-temporal relations and prediction uncertainty to predict shoot event occurrences with greater accuracy and robustness. Specifically, we represent players' relations as a complete bipartite graph, which effectively incorporates soccer domain knowledge, and capture latent features by applying a graph convolutional recurrent neural network (GCRNN) to the constructed graph. Our model utilizes a Bayesian neural network to predict the probability of shoot event occurrence, considering spatio-temporal relations between players and prediction uncertainty. In our experiments, we confirmed that the proposed method outperformed several other methods in terms of prediction performance, and we found that considering players' distances significantly affects the prediction accuracy.