Risk Warning Research Articles

Research on the Regional Transport Development Index and Its Application in Decision Making and Sustainable Development of Transport Services: A Case Study in Yunnan Province, China

Transport and macroeconomic development are closely linked. Under the comprehensive management system for China’s rail, road, water, and air transport, there is an urgent need for a comprehensive, timely, and accurate index that reflects the relationship between transport and the macroeconomy, to help the government analyze the current situation of transport, judge the macroeconomic and sustainable transport trends, and make scientific decisions, so as to achieve the carbon emission peak and carbon neutrality goals. This paper innovatively proposed the Transport Development Index (TDI), a new evaluation system with 38 indicators, covering the infrastructure, development scale, efficiency, sustainability, and safety and profitability of the four modes of transport: road, rail, water, and air. The analytic hierarchy process and the entropy weight method were used to determine the weights for the indicators. Yunnan was selected as a case study to calculate the TDI values for 2016 to 2021 and analyze the transport service performance for each year. The study results show that the TDI can be used for analyzing the regional transport services and economic operation status, proving the positive effect of transport development on the macroeconomy or warning of possible risks, and facilitating scientific decision making for sustainable transport development.

Spatial monitoring of meteorological drought characteristics based on the NASA POWER precipitation product over various regions of Iran.

Drought directly impacts the human economy and society, so a proper understanding of its spatiotemporal characteristics in different time scales and return periods can be effective in its evaluation and risk warning. In this research, the spatiotemporal variation of drought characteristics in 70 investigated stations in Iran during 1981-2020 was examined, evaluated, and compared. The Standardized Precipitation Index (SPI) and Standardized Precipitation Evapotranspiration Index (SPEI) have been used on time scales of 1, 3, 6, 9, 12, and 24months to calculate the meteorological drought. Drought characteristics have been calculated through the run theory method, and the correlation between these characteristics has been checked. Statistical distribution functions have been used to calculate drought characteristics for the 10-, 20-, 50-, and 100-year return periods. Results show that the duration, severity, and peak of the drought in rainy areas increase as the return period increases. The drought features obtained from the SPI and SPEI show that the average value of severity obtained based on the SPI (43.5) is higher than that of the SPEI (40.9) while the average values of the peak are 3.9 and 2.6 for SPI and SPEI, respectively. Extreme drought was identified in 1990 in all regions of Iran. The highest severity in the current study is from 1999 to 2003. At the end of this period, Iran faced wet years. These results are evident on all time scales. The results obtained in this study can identify drought-prone regions and the beneficial use of water resources in the region.

Prediction model for the evolution of hydrogen concentration under leakage in hydrogen refueling station using deep neural networks

The widespread risks of leakages in the hydrogen industry chain require a method that can quickly predict the consequences of accidents, especially in the hydrogen refueling station (HRS). This paper presents a surrogate model based on physics-informed neural network (PINN) that can predict the distribution of hydrogen concentration after a leakage. The proposed Physics-informed Convolutional Long Short-Term Memory Network (PI-ConvLSTM) model improves the concentration prediction results at the gas cloud boundary by adding a physical constraint term to the loss function of the ConvLSTM model. The concentration distributions after hydrogen leakage at HRS simulated by FLACS are used as the training samples, and the concentration data are converted into grayscale maps for training. The hydrogen concentration prediction method with the proposed surrogate model as the core achieves fast prediction of the gas cloud concentration distribution with acceptable accuracy. It is observed that the method can greatly reduce the prediction time of the consequences of hydrogen leak accidents with the surrogate model already trained. It can provide real-time risk warning and consequence prediction for hydrogen refueling station leakage accidents.

Early warning of enterprise financial risk based on improved BP neural network model in low-carbon economy

The concept of low-carbon economic development has led to changes in the business environment and financial environment of enterprises, leading to increased financial risks faced by enterprises. How to help enterprises better warn, prevent and control financial risks from the perspective of low-carbon economy has become a hot issue worth studying. Based on this, this paper is based on the perspective of low carbon economy, on the basis of analyzing the financing risk, investment risk, capital operation risk and growth risk faced by enterprises under the requirements of low carbon economy development. A set of financial risk management framework with clear hierarchy and strict vertical logic has been constructed. Ten financial early-warning indicators are constructed from four aspects. The risk prediction model of the indicator system is established using the research method of BPNN (Back Propagation Neural Network). The model is trained and simulated through the MATLAB neural network toolbox. After 10 indicators passed Bartlett’s correlation test, the BPNN financial early warning model was programmed using MATLAB software. The accuracy rate was 84.3%. The neural network training results show that when the layer node is 8, the best correct recognition rate can be obtained. Incorporate “low carbon” into the financial risk early warning indicator system that meets the requirements of low carbon economic development in the design of enterprise financial risk early warning indicators. This paper is expected to provide reference and reference for low-carbon economy enterprises to deal with financial risks under the new situation.

Simulation Study on Risk Warning and Control Mechanism and Transmission Path of Major Disasters and Accidents in Coal Mine Based on Method of Structural Equation

Simulation Study on Risk Warning and Control Mechanism and Transmission Path of Major Disasters and Accidents in Coal Mine Based on Method of Structural Equation

Retracted: Early Warning of Basketball Injury Risk Based on Attribute Reduction Algorithm.

[This retracts the article DOI: 10.1155/2022/6981246.].

Severe Situation of Human Impact on Climate Change, Impact on Infectious Diseases and Solutions

The activities of the human activities, especially since the industrial revolution caused the greenhouse effect and the severe climate change situation, leading to a variety of suffering such as natural disasters, the collapse of the food system and extinction, also infectious diseases and mental diseases and so on. These grim situation makes most countries reach a consensus of net zero discharge and limiting the temperature rise to 1.5° C. To cope with and adapt to climate change threat in the future, we should carry out cross-regional and multidisciplinary cooperation as much as possible, develop high-tech products for early warning of climate health risks, carry out a number of climate health monitoring projects, strengthen the monitoring and early warning capacity of climate change risks, and work together to maintain and promote a good climate for the earth and human health.

Physicians' legal duty to disclose more cost-effective treatment options: an examination of Australian civil law applied to personal importation.

ObjectiveA significant proportion of Australians defer or do not fill prescriptions they require due to cost. This article explores whether, and under what circumstances, physicians have a duty to assist these patients by disclosing how they can access more affordable medicines via personal importation.MethodsThis study involved a critical examination of Australian statutory and case law pertaining to physicians' duty to disclose material information to identify key principles applicable to the context of cost-motivated personal importation.ResultsThere are several legal principles that suggest that physicians have a duty to advise patients of options for accessing more affordable medicines, including via personal importation. These include a duty to warn of inherent and non-inherent risks, a duty to disclose treatments that offer clear advantages, and a duty to facilitate access to the means for achieving patients' health goals. However, it is unclear whether, and on what grounds, responsibility for harm arising from a patient's inability to afford prescribed medicines should be attributed to the prescribing physician. Arguments supporting attribution of such a responsibility are proposed to motivate further legal, policy and ethical debate.ConclusionsPhysicians have a duty to take reasonable steps to mitigate foreseeable harm to their patients, however the law is silent on whether this duty extends to taking steps to help patients access medicines that they can afford. This investigation provides a framework to guide the development of sound policy and law on informed financial consent and economically motivated prescribing.

Catching Spores: Linking Epidemiology, Pathogen Biology, and Physics to Ground-Based Airborne Inoculum Monitoring.

Monitoring airborne inoculum is gaining interest as a potential means of giving growers an earlier warning of disease risk in a management unit or region. This information is sought by growers to aid in adapting to changes in the management tools at their disposal and the market-driven need to reduce the use of fungicides and cost of production. To effectively use inoculum monitoring as a decision aid, there is an increasing need to understand the physics of particle transport in managed and natural plant canopies to effectively deploy and use near-ground aerial inoculum data. This understanding, combined with the nuances of pathogen-specific biology and disease epidemiology, can serve as a guide to designing improved monitoring approaches. The complexity of any pathosystem and local environment are such that there is not a generalized approach to near-ground air sampler placement, but there is a conceptual framework to arrive at a "semi-optimal" solution based on available resources. This review is intended as a brief synopsis of the linkages among pathogen biology, disease epidemiology, and the physics of the aerial dispersion of pathogen inoculum and what to consider when deciding where to locate ground-based air samplers. We leverage prior work in developing airborne monitoring tools for hops, grapes, spinach, and turf, and research into the fluid mechanics governing particle transport in sparse canopies and urban and forest environments. We present simulation studies to demonstrate how particles move in the complex environments of agricultural fields and to illustrate the limited sampling area of common air samplers.

Early warning of financial statement data leakage risk based on grey fuzzy comprehensive evaluation

Early warning of financial statement data leakage risk based on grey fuzzy comprehensive evaluation

Artificial intelligence disruption and its impacts on future employment in Africa - A case of the banking and financial sector in ghana

Artificial Intelligence is a transformative technology with the potential to both displace and create jobs across industries. Experts believe it will greatly benefit humanity by reducing tedious tasks and advancing technology. The World Economic Forum predicts that by 2025, 50% of tasks will be automated, compared to the current 29%, and around 75% of companies plan to adopt AI, with 50% expecting it to drive job growth, according to their 2023 report. Fast-growing roles stem from technology, digitalization, and sustainability. By 2025, AI will replace 75 million jobs but generate 133 million new ones, resulting in a net increase of 58 million jobs globally. However, certain industries will face significant displacement, and AI's impact on unemployment rates will differ across countries, regions, and industries. AI will likely displace jobs in manufacturing but boost employment in healthcare and education. However, experts warn of AI's risks for job market in Africa, citing automation replacing repetitive tasks like data entry and customer service. Adapting skills to the changing job landscape is crucial, but it comes with added costs for both individuals and organizations. AI advancements are set to automate routine jobs, potentially causing employment shifts in Africa, similar to global trends. New opportunities in AI, data science, and tech may emerge, but the impact hinges on AI adoption speed, infrastructure, and policies. Challenges like skills gaps, data ecosystems, ethics, policies, infrastructure, and user attitudes hinder AI adoption, affecting industries, as seen in Ghana. To boost AI adoption in Africa, building strong ecosystems involving policymakers, universities, companies, startups, and partnerships is crucial. Failure to address these challenges will hinder Ghana and Africa's progress, causing them to fall behind globally. This paper highlights the hurdles faced by Ghana and African nations in AI adoption, emphasizing job displacement and unemployment effects on job seekers. Its aim is to equip policymakers and stakeholders with insights into AI's disruptive nature, aiding in the creation of sustainable policies for the industry. The study started with a review of AI disruption's impact on future jobs in Africa using secondary sources on evolving AI tech. It also involved gathering firsthand data via interviews in Ghana to understand challenges in AI adoption, especially among industry professionals.

Early warning of financial statement data leakage risk based on grey fuzzy comprehensive evaluation

Early warning of financial statement data leakage risk based on grey fuzzy comprehensive evaluation

Retracted: Identification and Early Warning of Financial Fraud Risk Based on Bidirectional Long‐Short Term Memory Model

Retracted: Identification and Early Warning of Financial Fraud Risk Based on Bidirectional Long‐Short Term Memory Model

Risk assessment and warning system for strategic biological resources in China

Risk assessment and warning system for strategic biological resources in China

Extremal Analysis of Flooding Risk and Its Catastrophe Bond Pricing

Catastrophic losses induced by natural disasters are receiving growing attention because of the severe increases in their magnitude and frequency. We first investigated the extreme tail behavior of flood-caused economic losses and maximum point precipitation based on the peaks-over-threshold method and point process (PP) model and its extreme tail dependence. We found that both maximum point precipitation and direct economic losses are well-modeled by the PP approach with certain tail dependence. These findings were further utilized to design a layered compensation insurance scheme using estimated value-at-risk (VaR) and conditional VaR (CVaR) among all stakeholders. To diversify the higher level of losses due to extreme precipitation, we designed a coupon paying catastrophe bond triggered by hierarchical maximum point precipitation level, based on the mild assumption on the independence between flood-caused risk and financial risk. The pricing sensitivity was quantitatively analyzed in terms of the tail risk of the flood disaster and the distortion magnitude and the market risk in Wang’s transform. Our trigger process was carefully designed using a compound Poisson process, modeling both the frequency and the layered intensity of flood disasters. Lastly, regulations and practical suggestions are provided regarding the flood risk prevention and warning.

Characteristics of Patients Treated with JAK Inhibitors in Rheumatoid Arthritis before versus after VTE Risk Warnings

Baricitinib (BARI) or Tofacitinib (TOFA) were the first Janus Kinase Inhibitors (JAKi) to be marketed in rheumatoid arthritis (RA). Concerns regarding venous thromboembolism (VTE) risk have emerged during the past years. The aim of the study was to compare the baseline characteristics of patients initiating BARI or TOFA in RA before versus after European Medicine Agency (EMA)'s VTE warnings and to compare real-world persistence with these two drugs. In this multicentric cohort study, RA patients initiating BARI or TOFA were included from October 2017, date of BARI marketing authorization in France, to September 2020. Baseline characteristics regarding VTE risk were compared (before vs. after May 2019) by using pre-specified statistical tests. Comparison of persistence was assessed by using propensity-score methods. 232 patients were included; 155 with BARI and 77 with TOFA. Baseline characteristics of patients regarding VTE risk factors were not statistically different when Janus Kinase inhibitor (JAKi) was initiated before vs. after EMA's warnings although a trend towards a lower proportion of VTE history was observed. Five VTE events occurred, four with BARI, one with TOFA. Cumulative persistence rate at 2 years was similar between BARI and TOFA: HR 0.96; 95% Cl: 0.52 to 1.74; p = 0.89. Our study did not show a significant change in patients characteristics starting a JAKi after the EMA's warnings, probably due to a lack of power. Though, the lower proportion of VTE history in patients after May 2019 suggests that rheumatologists have taken into account the potential VTE risk. These results need to be confirmed by further evidence.

Suicidal Electrocution: A Case Report

Most common electricity-related deaths in forensic practice are associated with accidents in domestic or industrialenvironment. Despite the fact that electricity is a readily available suicidal mean in developed countries, suicidesby electrocution are relatively rare. Mostly suppose in individuals working in the electrotechnical industry or athome by “handymen”. An autopsy findings are usually uncharacteristic, accompanied by pulmonary edema,fluid blood, organ congestion (suffocation signs). The passage of electric current through the body is confirmed byspecific finding so-called current signs. In many cases, current marks are small, easily overlooked or unrecognizable,which significantly complicates the diagnosis and determination of the cause of death by a forensic pathologist.In some cases, it´s possible to determine the suicidal electrocution only by the investigated circumstances or incooperation with an expert in the field of electrical engineering. Authors warn of the potential risk that a personmay be part of an active electrical circuit at the time of the finding. Knowledge of the victim‘s psychiatric and socialhistory is an equally important part. The authors present the case of an 82-year-old man who, in a suicidal manner,constructed a suicide electric circuit in a social services home.

Cause Analysis and Preventive Measures of Guizhou D2809 Train Derailment Accident in Guizhou, China on 4 June 2022.

This paper summarizes the cause of the debris flow impact train accident by investigating the local geological condition, meteorological data and field investigation that happened in Guizhou, China on 4 June 2022. The result showed that the major reason is the continuous heavy rain in the surrounding area, which led to a small landslide at the upper right of the tunnel entrance. Besides, the construction of the Jianrong Expressway in the upper reaches increased the catchment area, which makes the water content of the upper soil increase while the shear strength decreases. Such large-scale catastrophic accidents significantly threaten the local environment and public safety. Therefore, it is urgent to pay special attention to the changes in geological conditions along the line, especially the adverse effects of construction, to improve the early risk warning and post-accident treatment ability.

Classification of Driver Distraction Risk Levels: Based on Driver’s Gaze and Secondary Driving Tasks

Driver distraction is one of the significant causes of traffic accidents. To improve the accuracy of accident occurrence prediction under driver distraction and to provide graded warnings, it is necessary to classify the level of driver distraction. Based on naturalistic driving study data, distraction risk levels are classified using the driver’s gaze and secondary driving tasks in this paper. The classification results are then combined with road environment factors for accident occurrence prediction. Two ways are suggested to classify driver distraction risk levels in this study: one is to divide it into three levels based on the driver’s gaze and the AttenD algorithm, and the other is to divide it into six levels based on secondary driving tasks and odds ratio. Random Forest, AdaBoost, and XGBoost are used to predict accident occurrence by combining the classification results, driver characteristics, and road environment factors. The results show that the classification of distraction risk levels helps improve the model prediction accuracy. The classification based on the driver’s gaze is better than that based on secondary driving tasks. The classification method can be applied to accident risk prediction and further driving risk warning.

Linking ecosystem service supply and demand to landscape ecological risk for adaptive management: The Qinghai-Tibet Plateau case

Clarifying the spatiotemporal variations in ecosystem service (ES) supply and demand helps to understand natural-social coupled systems, and comprehensive landscape ecological risk (LER) assessment is the basis for risk warning. However, it is still a huge challenge to incorporate ES supply and demand into ecological adaptive management. In this study, we defined and identified ES supply and demand risk (ESSDR), and integrated it into LER assessment to develop a comprehensive ecological risk framework. Using InVEST model and multi-source data, this study explicitly quantified the spatiotemporal variations of ESSDR of soil retention (ESSDRI_SR), carbon sequestration (ESSDRI_CS), water yield (ESSDRI_WY), LER of Qinghai Province in the Qinghai-Tibet Plateau during 2010–2020. The results indicated that all ESSDRs and LER showed spatial heterogeneity. Among the ESSDR areas, the low risk areas accounted for the highest proportion, with ESSDRI_CS, ESSDRI_SR and ESSDRI_WY accounting for 4.83%, 14.84% and 12.45%, respectively. The area of very high and high LER decreased by 1.5% and 5.45% from 2010 to 2020, reaching 19.05% and 22.74%, respectively. The comprehensive ecological risk assessment showed that over 60% of Qinghai is designated as having ecological risks. However, the region with the most risk co-occurrence (risk group 4) accounted for 0.11% of Qinghai’s area. At last, adaptive suggestions were proposed for risk management and ecological conservation. This research provides and illustrates an innovative method for comprehensive ecological risk assessment, which could substantially enhance the scientific foundation on which ecological risk assessment is based and policy-making that follow compared to traditional LER framework.