Understanding the potential key risk factors associated with teen driver crashes in the United States: a literature review

This article is a compilation of teen driver crash contributing factors typically extractable from the crash data collection system in the United States.Tremendous research effort has been undertaken over the decades to comprehend teen driver crash risks,as teen drivers continue to be over-involved in crashes even when accounting for the driving exposure.This article presents the contexts of crash factors related to operating conditions,roadway,vehicle,and driver and their unique influences on teen driver crashes in terms of estimated risk,prevalence,and estimated likelihood mainly from descriptive and analytical studies.The key variables are selected based on the number of studies that considered each risk factor for analysis.The understanding of crash factors could be translated into graduated driver licensing and other teen driver safety programs.While the discussions were grounded in crash studies carried out in the United States,the insights gleaned from these studies hold the potential to offer valuable guidance to other countries.For example,the insights and discussions can serve as a catalyst for the development and improvement of driver education programs tailored to address the specific requirements and difficulties confronted by their teenage drivers.

Carbon emission trading system and stock price crash risk of heavily polluting listed companies in China:based on analyst coverage mechanism

This study reveals the inconsistencies between the negative externalities of carbon emissions and the recognition condition of accounting statements.Hence,the study identifies that heavily polluting enterprises in China have severe off-balance sheet carbon reduction risks before implementing the carbon emission trading system(CETS).Through the staggered difference-in-difference(DID)model and the propen-sity score matching-DID model,the impact of CETS on reducing the risk of stock price crashes is examined using data from China’s A-share heavily polluting listed companies from 2007 to 2019.The results of this study are as follows:(1)CETS can significantly reduce the risk of stock price crashes for heavily polluting companies in the pilot areas.Specifically,CETS reduces the skewness(negative conditional skewness)and down-to-up volatility of the firm-specific weekly returns by 8.7%and 7.6%,respectively.(2)Heterogeneity analysis further shows that the impacts of CETS on the risk of stock price crashes are more significant for heavily polluting enterprises with the bear market condition,short-sighted management,and intensive air pollution.(3)Mechanism tests show that CETS can reduce analysts’coverage of heavy polluters,reducing the risk of stock price crashes.This study reveals the role of CETS from the stock price crash risk perspective and helps to clarify the relationship between climatic risk and corporate financial risk.

IMPACT和CRASH模型对创伤性颅脑损伤患者预后评估价值的比较研究

背景国际颅脑损伤预后临床测试研究(IMPACT)和重型颅脑损伤后皮质类固醇的随机化研究(CRASH模型是国际上具有影响力的创伤性颅脑损伤(TBI)预后预测模型,需要继续开发,完善和持续的外部验证,以确保对不同环境的普适性。目的同时在中国TBI人群中进行验证IMPACT和CRASH模型的预后评估价值并进行比较。方法选取2017—2019年在郑州大学附属郑州中心医院内接受治疗的TBI患者210例为研究对象,收集纳入患者的基本信息。随访观察患者14 d存活情况和6个月格拉斯哥预后评分(GOS),随访截止时间为2021年6月,终止事件为中途失访。绘制受试者工作特征曲线(ROC曲线)评估IMPACT和CRASH模型对TBI患者预后的预测效能,计算ROC曲线下面积(AUC)。采用Brier评分评价模型的校准度。结果患者平均年龄(54.0±17.4)岁,分别绘制IMPACT模型与CRASH模型预测TBI患者预后的ROC曲线,结果显示IMPACT核心模型、CT模型、实验室模型预测TBI患者6个月GOS预后不良的AUC分别为0.807(95%CI=0.747~0.866,P<0.001)、0.843(95%CI=0.789~0.897,P<0.001)、0.845(95%CI=0.793~0.897,P<0.001),Brier评分分别为0.179、0.164、0.161;IMPACT核心模型、CT模型、实验室模型预测TBI患者6个月死亡的AUC分别为0.868(95%CI=0.816~0.919,P<0.001)、0.896(95%CI=0.851~0.941,P<0.001)、0.892(95%CI=0.850~0.935,P<0.001),Brier评分分别为0.151、0.144、0.136。CRASH基本模型、CT模型预测TBI患者6个月GOS预后不良的AUC分别为0.747(95%CI=0.682~0.813,P<0.001)、0.766(95%CI=0.703~0.829,P<0.001),Brier评分分别为0.306、0.308;CRASH基本模型、CT模型预测TBI患者14 d死亡的AUC分别为0.791(95%CI=0.723~0.860,P<0.001)、0.797(95%CI=0.728~0.865,P<0.001);Brier评分分别为0.348、0.383。结论对于TBI患者的预后,IMPACT模型整体较CRASH模型显示出较好的预测能力。

Study of the Discount on Private Placements and Risk of Stock Market Crash in Listed Companies

With the gradual completion of the split-share structure reform,private placement has gradually become the mainstream of refinancing. One of the points that the practical and theoretical circles are widely concerned about is that the private placement price is often higher than the market price at the time of the private placement. High discounts are often accompanied by the transmission of benefits,and the increase in insider information will lead to the risk of a stock market crash? This paper intends to use the data of A-share listed companies from 2006 to 2015 to empirically study the relationship between the discount on private placements and the risk of stock market crash. At the same time,this paper examines whether the degree of information asymmetry plays a regulatory role in the relationship between the discount on private placements and the risk of stock market crash. This paper provides a certain reference for the regulatory authorities to improve the relevant laws and regulations in the private placement,and to provide a certain reference for the protection of the interests of small and medium-sized investors.

Investor Attention,Analyst Optimism,and Stock Price Crash Risk

This paper used the A-shares listed companies in China as samples,constructed a comprehensive indicator of investor attention,and conducted an empirical analysis on the correlations among investor attention,analyst optimism,and stock price crash risk.The results indicated that investor attention aggravates the stock price crash risk and has a positive effect on analyst optimism.Meanwhile,the analyst optimism plays a mediating role in the positive correlation between investor attention and stock price crash risk.In addition to that,institutional investor attention also has direct and indirect effects on the crash risk.

Modeling Crash Risk at Rail-Highway Grade Crossings by Track Class

The Federal Railroad Administration (FRA)’s Web Based Accident Prediction System (WBAPS) is used by federal, state and local agencies to get a preliminary idea on safety at a rail-highway grade crossing. It is an interactive and user-friendly tool used to make funding decisions. WBAPS is almost three decades old and involves a three-step approach making it difficult to interpret the contribution of the variables included in the model. It also does not directly account for regional/local developments and technological advancements pertaining to signals and signs implemented at rail-highway grade crossings. Further, characteristics of a rail-highway grade crossing vary by track class which is not explicitly considered by WBAPS. This research, therefore, examines and develops a method and models to estimate crashes at rail-highway grade crossings by track class using regional/local level data. The method and models developed for each track class as well as considering all track classes together are based on data for the state of North Carolina. Linear, as well as count models based on Poisson and Negative Binomial (NB) distributions, was tested for applicability. Negative binomial models were found to be the best fit for the data used in this research. Models for each track class have better goodness of fit statistics compared to the model considering data for all track classes together. This is primarily because traffic, design, and operational characteristics at rail-highway grade crossings are different for each track class. The findings from statistical models in this research are supported by model validation.

Corporate pledgeable asset ownership and stock price crash risk

We investigate how a firm’s corporate pledgeable asset ownership(CPAO)affects the risk of future stock price crashes.Using pledgeable asset ownership and crash risk data for a large sample of U.S.firms,we provide novel empirical evidence that a firm’s risk of a future stock price crash decreases with an increase in its pledgeable assets.Our main findings are valid after conducting various robustness tests.Further channel tests reveal that firms with pledgeable assets increase their collateral value,thereby enhancing corporate transparency and limiting bad news hoarding,resulting in lower stock price crash risk.Overall,the results show that having more pledgeable assets enables easier access to external financing,making it less likely that managers will hoard bad news.

The Effect of Information Disclosure Quality on Stock Price Crash Risk： Evidence from Listed Companies in China

From most prior studies, the effect of information disclosure quality on stock price crash risk is negative. However, using a large sample of Chinese firms for the period of 2001-2012, we fmd a nonlinear U-shape link between information disclosure quality and crash risk. Using synchronicity risk indicator R2 as the proxy of information transparency, we find that opaque In＇ms, as well as excessive transparent firms, are both more prone to stock price crashes. We identify one possible mechanism through which excessive transparency increases crash risk： excessive volatility augments volatility feedback effect and overreaction effect and then increases crash risk.

Prediction of Point in Time with High Crash Risk by Integration of Bayesian Estimation of Drowsiness, Tracking Error, and Subjective Drowsiness

The aim of this study was to predict drivers＇ drowsy states with high risk of encountering a crash and prevent drivers from continuing to drive under such drowsy states with high risk of crash. While the participants were required to carry out a simulated driving task, EEG （Electro encephalography） （EEG-MPF and EEG-α/β）, ECG （Electrocradiogram） （RRV3）, t racking error, an d subjective rating on drowsiness were measured. On the basis of such measurements, an attempt was made to predict the point in time with high crash risk using Bayesian estimation of posterior probability of drowsiness, tracking error, and subjective drowsiness. As a result of applying the proposed method to the data of each participant, it was verified that the proposed method could predict the point in time with high crash risk before the point in time of crash.

Analyzing Crash-Prone Drivers in Multiple Crashes for Better Safety Educational and Enforcement Strategies

Crash-prone drivers should be effectively targeted for various safety education and regulation programs because their over-involvement in crashes presents a big adverse effect on highway safety. By analyzing seven years of crash data from Louisiana, this paper investigates crash-prone drivers’ characteristics and estimates their risk to have crashes in the seventh year based on these drivers’ crash history of the past six years. The analysis results show that quite a few drivers repeatedly had crashes;seven drivers had 13 crashes in seven years;and the maximum number of crashes occurring in a single year to a single driver is eight. The probability of having crash(es) in any given year is closely related to a driver’s crash history: less than 4% for drivers with no crash in the previous six years;and slightly higher than 30% for drivers with nine or more crashes in the previous six years. Based on the results, several suggestions are made on how to improve roadway safety through reducing crashes committed by drivers with much higher crash risk as identified by the analysis.

Preventing crash in stock market:The role of economic policy uncertainty during COVID‑19

This paper investigates the impact of economic policy uncertainty(EPU)on the crash risk of US stock market during the COVID-19 pandemic.To this end,we use the GARCHS(GARCH with skewness)model to estimate daily skewness as a proxy for the stock market crash risk.The empirical results show the significantly negative correlation between EPU and stock market crash risk,indicating the aggravation of EPU increase the crash risk.Moreover,the negative correlation gets stronger after the global COVID-19 outbreak,which shows the crash risk of the US stock market will be more affected by EPU during the epidemic.

Personal data security and stock crash risk:Evidence from China’s Cybersecurity Law

Using China’s Cybersecurity Law(CSL)as an exogenous shock,I examine how personal data security affects stock crash risk.I find that the stock crash risk of treatment firms(which collect personal data)significantly decreases after the CSL,and such decrease is larger when firms face greater personal data breach risk and have less transparent information environments before the CSL.Furthermore,treatment firms increase their investment in personal data protection after the CSL.Finally,enhanced personal data security increases firm value and promotes firms’social responsibility to stakeholders.Overall,I provide evidence of the importance of data security for the digital economy from the perspective of capital market stability,which may present implica-tions for data security policy worldwide.

Risk Assessment of Deep-Water Horizontal X-Tree Installation

Due to the high potential risk and many influencing factors of subsea horizontal X-tree installation,to guarantee the successful completion of sea trials of domestic subsea horizontal X-trees,this paper established a modular risk evaluation model based on a fuzzy fault tree.First,through the analysis of the main process oftree down and combining the Offshore&Onshore Reliability Data(OREDA)failure statistics and the operation procedure and the data provided by the job,the fault tree model of risk analysis of the tree down installation was established.Then,by introducing the natural language of expert comprehensive evaluation and combining fuzzy principles,quantitative analysis was carried out,and the fuzzy number was used to calculate the failure probability of a basic event and the occurrence probability of a top event.Finally,through a sensitivity analysis of basic events,the basic events of top events significantly affected were determined,and risk control and prevention measures for the corresponding high-risk factors were proposed for subsea horizontal X-tree down installation.

Machine Learning-Based Decision-Making Mechanism for Risk Assessment of Cardiovascular Disease

Cardiovascular disease(CVD)has gradually become one of the main causes of harm to the life and health of residents.Exploring the influencing factors and risk assessment methods of CVD has become a general trend.In this paper,a machine learning-based decision-making mechanism for risk assessment of CVD is designed.In this mechanism,the logistics regression analysismethod and factor analysismodel are used to select age,obesity degree,blood pressure,blood fat,blood sugar,smoking status,drinking status,and exercise status as the main pathogenic factors of CVD,and an index systemof risk assessment for CVD is established.Then,a two-stage model combining K-means cluster analysis and random forest(RF)is proposed to evaluate and predict the risk of CVD,and the predicted results are compared with the methods of Bayesian discrimination,K-means cluster analysis and RF.The results show that thepredictioneffect of theproposedtwo-stagemodel is better than that of the comparedmethods.Moreover,several suggestions for the government,the medical industry and the public are provided based on the research results.

Risk assessment of oil and gas investment environment in countries along the Belt and Road Initiative

With the implementation of the Belt and Road Initiative, China is deepening its cooperation in oil and gas resources with countries along the Initiative. In order to better mitigate risks and enhance the safety of investments, it is of significant importance to research the oil and gas investment environment in these countries for China's overseas investment macro-layout. This paper proposes an indicator system including 27 indicators from 6 dimensions. On this basis, game theory models combined with global entropy method and analytic hierarchy process are applied to determine the combined weights, and the TOPSIS-GRA model is utilized to assess the risks of oil and gas investment in 76 countries along the Initiative from 2014 to 2021. Finally, the GM(1,1) model is employed to predict risk values for 2022-2025. In conclusion, oil and gas resources and political factors have the greatest impact on investment environment risk, and 12 countries with greater investment potential are selected through cluster analysis in conjunction with the predicted results. The research findings may provide scientific decisionmaking recommendations for the Chinese government and oil enterprises to strengthen oil and gas investment cooperation with countries along the Belt and Road Initiative.

Assessing Future Flooding Risk in a Coastal Lagoon Using Hydrogeological Approaches and Analysis of the 2021 Flood Event: A Case Study of Tasi-Tolu Lagoon, Dili, Timor-Leste

This study aims to apply a hydrogeological approaches and analysis of the 2021 flood event of Tasi-Tolu Lagoon to achieve four specific goals. Firstly, the study seeks to determine the natural characteristics of the lagoon, which include factors such as size, depth, water quality, and ecosystem composition. Secondly, the influence of precipitation on the water volume in the lagoon will be examined. This analysis involves assessing historical rainfall patterns in the region, as well as the amount and frequency of precipitation during the 2021 flood event. Thirdly, the hydrogeologic and geologic conditions of the lagoon will be evaluated. This involves examining factors such as the type and structure of the soil and bedrock, the presence of aquifers or other underground water sources, and the movement of water through the surrounding landscape. Finally, the study seeks to assess the risk of future flooding in Tasi-Tolu Lagoon, based on the insights gained from the previous analyses. Overall, this study’s goal is to provide a comprehensive understanding of the hydrogeological factors that contribute to flooding in Tasi-Tolu Lagoon. This knowledge could be used to inform flood mitigation strategies or to improve our ability to predict and respond to future flooding events in the region.

Needs and Challenges of Nigeria Petroleum Industry in Assessing Process Safety Cumulative Risk

A cross-sectional exploratory assessment of the needs and challenges of petroleum industry in Nigeria, in assessing process safety cumulative risk for major accidents prevention was investigated. A purposive cum random sampling technique was used in this study, among selected petroleum companies operating in Nigeria. Survey questionnaires were received from 216 participants made up of asset integrity engineers/operators, process safety experts, production safety professionals in the petroleum industry in Nigeria. Data analyses were carried out to cover descriptive and inferential statistics. Overall, the study recognized that assessing process safety cumulative risk is not a simple process due largely to the changing nature of safety critical barriers degradation data. The study result showed four main challenges faced by petroleum industries in Nigeria, in assessing process safety cumulative risk: 1) the study showed that 94% of the respondents agreed that there is limited accessibility to safety critical barriers degradation data (little automation). Also 2) 94% of the respondents accounted for poor knowledge of process safety cumulative risk is and agreed it to be of low rating. The result further showed that 3) 90% of the respondents demonstrated that there are no guidance and procedures in assessing process safety cumulative risk and finally 4) 92% of the respondents reported that there is no real-time risk visualization model/ tool. Addressing these issues and challenges by the petroleum industries in the study area, will lead to successful assessment of process safety cumulative risk, thereby reducing the risk of major accidents.

A risk assessment method considering risk attributes and work safety informational needs and its application

The technological revolution has spawned a new generation of industrial systems,but it has also put forward higher requirements for safety management accuracy,timeliness,and systematicness.Risk assessment needs to evolve to address the existing and future challenges by considering the new demands and advancements in safety management.The study aims to propose a systematic and comprehensive risk assessment method to meet the needs of process system safety management.The methodology first incorporates possibility,severity,and dynamicity(PSD)to structure the“51X”evaluation indicator system,including the inherent,management,and disturbance risk factors.Subsequently,the four-tier(risk point-unit-enterprise-region)risk assessment(RA)mathematical model has been established to consider supervision needs.And in conclusion,the application of the PSD-RA method in ammonia refrigeration workshop cases and safety risk monitoring systems is presented to illustrate the feasibility and effectiveness of the proposed PSD-RA method in safety management.The findings show that the PSD-RA method can be well integrated with the needs of safety work informatization,which is also helpful for implementing the enterprise's safety work responsibility and the government's safety supervision responsibility.

Development of Localized Assessment of Municipal Wastewater Disposal Risks

A means to develop a comparative assessment of the risks of available wastewater effluent disposal options on a local scale needs to be developed to help local decision-makers make decisions on options such as direct or indirect potable reuse options. These options have garnered more interest as a result of water supply limitations in many urban areas. This risk assessment was developed from a risk assessment developed at the University of Miami in 2001 and Florida Atlantic University (FAU) in 2023. Direct potable reuse and injection wells were deemed to have the lowest risk in the most recent study by FAU. However, the injection well option may not be available everywhere. As a result, a more local means to assess exposure risk is needed. This paper outlines the process to evaluate the public health risks associated with available disposal alternatives which may be very limited in some areas. The development of exposure pathways can help local decision-makers define the challenges, and support later expert level analysis upon which public health decisions are based.

Distribution, health and ecological risk assessments of trace elements in Nigerian oil sands

The Nigerian oil sands represent the largest oil sand deposit in Africa, yet there is little published information on the distribution and potential health and ecological risks of trace elements in the oil resource. In the present study, we investigated the distribution pattern of 18trace elements(including biophile and chalcophile elements) as well as the estimated risks associated with exposure to these elements. The results of the study indicated that Fe was the most abundant element, with a mean concentration of 22,131 mg/kg while Br had the lowest mean concentration of 48 mg/kg. The high occurrence of Fe and Ti suggested a possible occurrence of ilmenite(Fe TiO_(3)) in the oil sands. Source apportionment using positive matrix factorization showed that the possible sources of detected elements in the oil sands were geogenic, metal production, and crustal. The contamination factor, geo-accumulation index, modified degree of contamination, pollution load index, and Nemerow pollution index indicated that the oil sands are heavily polluted by the elements. Health risk assessment showed that children were relatively more susceptible to the potentially toxic elements in the oil sands principally via ingestion exposure route(HQ > 1E-04). Cancer risks from inhalation are unlikely due to CR < 1E-06 but ingestion and dermal contact pose severe risks(CR > 1E-04). The high concentrations of the elements pose serious threats due to the potential for atmospheric transport, bioaccessibility, and bioavailability.