对象驱动的Linux内核crash分类技术研究

Crash(程序崩溃)分析是漏洞挖掘与利用的关键阶段,精准的crash分类是crash分析和漏洞利用的前提.针对现有的Linux内核crash存在大量重复的问题,本文提出一种对象驱动的Linux内核crash分类方法.该方法将内核crash与内核对象的关系建模为二部图结构,从而将crash分类问题转化为内核对象的相似性对比问题.首先,通过对crash执行后向污点分析提取crash相关的内核对象;其次,构造内核对象调用图计算内核与根本原因的相关性度量;最后,基于上述结果构造二部图实现crash相似性比较算法.基于上述方法,本文开发出了Linux内核crash分类的原型系统.通过在真实的数据集上进行实验,验证了系统的有效性和可用性,弥补了现有分类方法粒度较粗,存在误报较多的问题.

Injuries Associated with Auto-Tricycle Crashes in an African City: Incidence and Pattern

Purpose: The aim of this study was to determine the incidence and pattern of injuries resulting from auto-tricycle crashes among patients in a tertiary referral centre in Ghana. Methods: Data were retrospectively extracted from hospital records of patients who got involved in auto-tricycle crashes and presented to the Accident and Emergency Centre of the Komfo Anokye Teaching Hospital (KATH), over a one-year period using a structured questionnaire. The gathered data were then entered into an electronic database and then analysed with SPSS version 20.0. Results: The incidence of injury following auto-tricycle crashes over the one-year period was 5.9% (95% CI: 4.9% - 7.0%) with a case fatality rate (FR) of 3.8% (95% CI: 1.3% - 8.7%). All the mortalities resulted from head and neck injuries and none of the patients involved wore a crash helmet. Only 5% of those studied wore crash helmets and were all drivers. Closed fractures accounted for 58% of the injuries, followed by open fractures, 28%. The most commonly fractured bones were the tibia/fibula, followed by the femur and then radius/ulna. The most common mechanism of injury was auto-tricycle toppling over (29%). Passengers were the most injured (48%), followed by drivers (37%) and pedestrians (15%). Most (72%) injuries among participants involved a single body part. On the injury severity scale, most (61%) of patients had minor trauma and 38% had major trauma. Conclusion: Auto-tricycle crashes account for 5.9% of injuries at the study site with a case fatality rate of 3.8%. Passengers had a higher injury rate (48%) than drivers (37%). Fractures of the tibia/fibula were most commonly associated with auto-tricycle crashes. Injuries to the head and neck were responsible for the deaths in the study participants and non-use of a crash helmet was associated with mortalities.

A Multilayer Perceptron Artificial Neural Network Study of Fatal Road Traffic Crashes

This paper examines the relationship between fatal road traffic accidents and potential predictors using multilayer perceptron artificial neural network (MLANN) models. The initial analysis employed twelve potential predictors, including traffic volume, prevailing weather conditions, roadway characteristics and features, drivers’ age and gender, and number of lanes. Based on the output of the model and the variables’ importance factors, seven significant variables are identified and used for further analysis to improve the performance of models. The model is optimized by systematically changing the parameters, including the number of hidden layers and the activation function of both the hidden and output layers. The performances of the MLANN models are evaluated using the percentage of the achieved accuracy, R-squared, and Sum of Square Error (SSE) functions.

An In-Depth Analysis of Road Fatal Crash Patterns and Discussions in Ho Chi Minh City

Although there has been a slight decrease in road traffic crashes, fatalities, and injuries in recent years, HCMC (Ho Chi Minh City) will continue to encounter challenges in mitigating and preventing road crashes. This study analyzes road crash data from the past five years, obtained from the Road-Railway Police Bureau (PC08) and TSB (Traffic Safety Board) in HCMC. This analysis gives us valuable insights into road crash patterns, characteristics, and underlying causes. This comprehensive understanding serves as a scientific foundation for developing cohesive strategies and implementing targeted solutions to address road traffic safety issues more effectively in the future.

Modeling traffic barriers crash severity by considering the effect of traffic barrier dimensions

Traffic barriers are in widespread all around the USA as safety countermeasures for reducing the severity of run-off-road crashes. The effect of traffic barriers’ dimension had been ignored in past real-world crash studies due to the considerable cost and time needed for collecting field data. This paper presented two new analytical models to investigate the effect of different variables on the severity of crashes involving traffic barriers, and end treatments. For this reason, a field survey was conducted on over 1.3 million linear feet of traffic barriers (approximately 4,176 miles road) in Wyoming to measure traffic barriers’ geometric features like height, length, offset, and slope rate. The collected data included 55% of all non-interstate roads of Wyoming. Based on results, the crashes involving box beam barriers were less severe than the crashes involved with W-beam or concrete barriers. The traffic barriers with a height between 28 and 31 in. were found safer than the traffic barriers shorter than 28 in., while there was no significant difference between the traffic barriers taller than 31 in. to those shorter than 28 in. in terms of crash severity. The end treatments located nearer to the traffic lane had lower crash severity.

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.

Application of new generation low-strength hot stamping steel to improve the crash performance of BIW

With the improvement of safety performance,car parts have different requirements for material strength and energy absorption performance.The conventional 1500-MPa hot stamping steel cannot well meet the requirements.Considering the new generation 600-MPa hot stamping steel,this study investigates the applicable car parts and hot stamping process,then designs a new body-in-white(BIW)crash test for obtaining the crash performance of the new material.Through the actual part development and crash test,it is verified that the application of the new generation hot stamping steel can improve the crash performance of BIW.

Correlating Connected Vehicle Estimated Deceleration Events with Crash Incidents near Interstate Interchanges

Historical roadway safety analyses have used labor and time-intensive crash data collection procedures. However, crash reporting is often delayed and crash locations are reported with varying levels of spatial accuracy and detail. Recent advances in connected vehicle (CV) data provide an opportunity for stakeholders to proactively identify areas of safety concerns in near-real time with high spatial precision. Public and private sector stakeholders including automotive original equipment manufacturers (OEM) and insurance providers may independently define acceleration thresholds for reporting unsafe driver behavior. Although some OEMs have provided fixed threshold hard-braking event data for a number of years, this varies by OEM and there is no published literature on the best thresholds to use for identifying emerging safety issues. This research proposes a methodology to estimate deceleration events from raw CV trajectory data at varying thresholds that can be scaled to any CV. The estimated deceleration events and crash incident records around 629 interstate exits in Indiana were analyzed for a three-month period from March 1-May 31, 2023. Nearly 20 million estimated deceleration events and 4800 crash records were spatially joined to a 2-mile search radius around each exit ramp. Results showed that deceleration events between -0.5 g and -0.4 g had the highest correlation with an R2 of 0.69. This study also identifies the top 20 interstate exit locations with highest deceleration events. The framework presented in this study enables agencies and transportation professionals to perform safety evaluations on raw trajectory data without the need to integrate external data sources.

Traffic Flow Characteristics in Work Zone and Non-Work Zone Environment and Its Impact on Road Crashes at the Segment Level

This study investigates relationships between congestion and travel time performance metrics and crashes on road segments. The study focuses on work zone routes in Iowa, utilizing 2021 commercially-available probe vehicle data and crash data. Travel time performance metrics were derived from the probe vehicle data, and crash counts were obtained from the crash data. Additional variables included road characteristics (traffic volume, road type, segment length) and a categorical variable for the presence of a work zone. A mixed effect linear regression model was employed to identify relationships between road segment crash counts and the selected performance metrics. This was accomplished for two sets of models that include congestion performance measures at different defining threshold values, along with travel time performance measures. The study results indicate that the congestion indicators, certain travel time performance measures, and traffic counts were statistically significant and positively correlated with crash counts. Indicator variables for rural interstate locations and non-active work zones have a stronger influence on crash count than those for municipal interstate locations and active work zones. These findings can inform decision-makers on work zone safety strategies and crash mitigation planning, especially in high traffic volume areas prone to congestion and queues.

Application of LiDAR Data for Deep Learning Based Near Crash Prediction at Signalized Intersection

Near crash events are often regarded as an excellent surrogate measure for traffic safety research because they include abrupt changes in vehicle kinematics that can lead to deadly accident scenarios. In this paper, we introduced machine learning and deep learning algorithms for predicting near crash events using LiDAR data at a signalized intersection. To predict a near crash occurrence, we used essential vehicle kinematic variables such as lateral and longitudinal velocity, yaw, tracking status of LiDAR, etc. A deep learning hybrid model Convolutional Gated Recurrent Neural Network (CNN + GRU) was introduced, and comparative performances were evaluated with multiple machine learning classification models such as Logistic Regression, K Nearest Neighbor, Decision Tree, Random Forest, Adaptive Boost, and deep learning models like Long Short-Term Memory (LSTM). As vehicle kinematics changes occur after sudden brake, we considered average deceleration and kinematic energy drop as thresholds to identify near crashes after vehicle braking time . We looked at the next 3 seconds of this braking time as our prediction horizon. All models work best in the next 1-second prediction horizon to braking time. The results also reveal that our hybrid model gathers the greatest near crash information while working flawlessly. In comparison to existing models for near crash prediction, our hybrid Convolutional Gated Recurrent Neural Network model has 100% recall, 100% precision, and 100% F1-score: accurately capturing all near crashes. This prediction performance outperforms previous baseline models in forecasting near crash events and provides opportunities for improving traffic safety via Intelligent Transportation Systems (ITS).

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.

Estimating the severity levels of road traffic crashes in Bahrain with crash costs estimated with different approaches

An important issue in analyzing accident blackspots is the estimation of severity levels of different types of accidents.This study aims to estimate the severity level of accidents in Bahrain using crash costs.These crash costs were calculated by the Human Capital Approach(HCA)and total reported costs from the victims.The data was collected from the General Directorate of Traffic,insurance companies,Ministry of Works(MoW)and Ministry of Health.It was found,from the survey responses,that there was no significant effect of victim characteristics on the total cost of the accidents.The severity levels were found to be higher than those found in previous literature or adopted by local authorities which could be attributed to the economic conditions of Bahrain.Moreover,the weights found by both approaches were different from each other.Therefore,it is recommended to use the HCA approach due to its comprehensive calculations involving future costs.

基于PAM-CRASH的鸟撞飞机风挡动响应分析

结合显式动力分析有限元软件PAM-CRASH及其提供的SPH算法,建立了鸟撞飞机风挡数值分析模型。对某飞机风挡进行了动响应分析,计算了风挡中轴线上四点位移时间曲线;鸟撞击过程的仿真结果表明,SPH鸟体模型能有效模拟撞击时鸟体溅射成碎片的情形;建立了鸟撞击作用下风挡破坏判据,对风挡在试验条件下是否破坏进行了模拟计算。计算结果和试验结果吻合较好,表明本文建立的鸟撞飞机风挡数值模型是有效的。

基于Pc-crash的交通事故再现误差分析

对交通事故再现软件Pc-crash中的特征参数进行分析,设计具普遍意义的车-车斜撞模型,运用分析法、控制变量法先提取较主要的参数;再设定正交试验将这些较重要参数按权重大小排序.基于对特征参数的研究,探求这些重要参数对模拟结果影响的权重及产生影响的类型,以此寻求减小事故再现误差的方法,并用国内一起典型的车-两轮车交通事故验证结论的正确性,为提高Pc-crasch软件再现国内事故精度的研究建立基础.

Conditional autoregressive negative binomial model for analysis of crash count using Bayesian methods

In order to improve crash occurrence models to account for the influence of various contributing factors, a conditional autoregressive negative binomial （CAR-NB） model is employed to allow for overdispersion （tackled by the NB component）, unobserved heterogeneity and spatial autocorrelation （captured by the CAR process）, using Markov chain Monte Carlo methods and the Gibbs sampler. Statistical tests suggest that the CAR-NB model is preferred over the CAR-Poisson, NB, zero-inflated Poisson, zero-inflated NB models, due to its lower prediction errors and more robust parameter inference. The study results show that crash frequency and fatalities are positively associated with the number of lanes, curve length, annual average daily traffic （AADT） per lane, as well as rainfall. Speed limit and the distances to the nearest hospitals have negative associations with segment-based crash counts but positive associations with fatality counts, presumably as a result of worsened collision impacts at higher speed and time loss during transporting crash victims.

基于PC-Crash的车-人碰撞事故仿真与分析

基于多刚体动力学仿真分析软件PC-Crash,建立车-人碰撞事故的再现模型,分别对同一工况下汽车有无ABS刹车防抱死制动系统、不同碰撞初速度以及利用随机函数确定汽车与行人的随机参数,进行大量的数字化仿真试验,分析事故发生后人体头部重要器官与车体前部碰撞点以及损伤程度的分布规律。根据分析结果,从汽车生产厂商保护行人的角度改善车身布置结构,提出有价值的参考意见。

基于PC-Crash的车-人碰撞事故数字化仿真与分析

基于多刚体动力学仿真分析软件PC-Crash,建立车-人碰撞事故的数字化再现模型,分别就同一工况下汽车有无ABS(Anti-lock Braking System刹车防抱死系统)制动系统、不同碰撞初速度以及利用随机函数确定汽车与行人的随机参数,进行大量的仿真实验,分析碰撞事故发生后人体头部重要器官与车体前部碰撞点以及损伤程度的分布规律。根据分析结果,从汽车生产厂商保护行人的角度改善车身布置结构,提出有价值的参考意见。

遵循“Crash plan”查体顺序在多发性创伤临床教学中的应用

多发性创伤是骨科的常见急、危、重症,并发症多,病死率和致残率较高,更容易出现骨折漏诊,导致多发性创伤的早期诊断存在较多的困难。常规系统的查体方法虽然全面,但对于实习学生而言在急重症患者面前难以有效掌握,所以急需一些实用、快速、简便、易于记忆的骨科查体方法。而"Crash plan"(打碎计划)可以作为一种快速、易学的查体系统,有助于实习同学快速、全面掌握临床查体要点,从一定程度上防止多发性创伤漏诊的发生几率。通过临床实践,发现"Crash plan"查体方法能够很好地促进实习同学对骨科系统查体的快速掌握,做到学习有兴趣,查体快速不易遗漏,对促进实习同学角色转变具有很好的引导作用。

Construction of crash prediction model of freeway basic segment based on interactive influence of explanatory variables

In order to improve the prediction precision of the safety performance function （SPF） of freeway basic segments, design and crash data of 640 segments are collected from different institutions. Three negative binomial （NB） regression models and three generalized negative binomial （GNB） regression models are built to prove that the interactive influence of explanatory variables plays an important role in fitting goodness. The effective use of the GNB model in analyzing the interactive influence of explanatory variables and predicting freeway basic segments is demonstrated. Among six models, the two models （one is the NB model and the other is the GNB model. ） which consider the interactive influence of the annual average daily traffic （AADT） and length are more reasonable for predicting results. Furthermore, a comprehensive study is carried out to prove that when considering the interactive influence, the NB and GNB models have almost the same fitting performance in estimating the crashes, among which the GNB model is slightly better for prediction performance.

Linux Crash Dump的设计与实现

本文介绍了Linux Crash Dump的设计与实现方法。Linux Crash Dump提供了一种保存系统在发生Crash时内存映象的能力。通过对Dump结果的分析,可以有效地帮助诊断系统出错的原因。本文介绍了如何进行Crash Dump,以及Dump的数据的组织和保存方法等。