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.

Frontal Sinus Fractures: Management at the Yalgado Ouedraogo University Hospital Centre (Burkina Faso)

Introduction: Frontal sinus fractures are potentially serious. They are defined as a solution of continuity, open or closed, of one or both bone tables of the frontal sinus. This study aims to report on the management of them at the Yalgado OUEDRAOGO University Hospital Centre. Methodology: It is a descriptive cross-sectional study with retrospective collection from January 01, 2016 to December 31, 2018. Patients with frontal sinus fractures were managed at the Yalgado OUEDRAOGO University Hospital Centre through CT-scan proof. Results: Over three years, a total of 102 cases of frontal sinus fractures were collected with 29.9 years as average age. There were 96 men. Workers in the informal sector and pupils/students represented 58.90% of patients. The residence of the patients was urban in 68.80% of cases and rural in 31.40%. Road traffic accidents (RTAs) happened in 90.20%, and involved 2-wheelers in 98.20%. None of these drivers was wearing a helmet. The type III frontal fracture of Ioannides et al. represented 51.9% of cases. In 89.21% of cases, other facial and/or cranioencephalic injuries were compounded to frontal sinus fractures. No surgical management was observed in 82 (80.39%) patients and surgical management in 20 (19.61%) patients. The outcome was favourable, but sequelae and/or complications were noted in 10 patients who had surgery and 30 patients who did not. Conclusion: These results enforce helmet wearing for all riders of two-wheeled machines. In addition, vaccinations to prevent meningitis in frontal sinus fractures with dural breach should be systematic.

基于PC-Crash软件的多车碰撞事故再现研究

近三年来,全国交通事故的年均发生数量在20万次以上,多车碰撞事故的数量也逐年攀升。文章根据车辆碰撞时间的先后顺序将一则复杂的交通碰撞事故,等效成多个简化的碰撞事故,且每个碰撞过程只包含两个事故参与方。通过对事故现场中驾驶员进行损伤验证,以及事故车辆进行痕迹勘验,分析人体损伤程度、各个车辆的碰撞形式和碰撞前的运动速度,运用PC-Crash软件进行事故仿真并验证其真实性。结果表明,仿真得到的人体损伤情况与实际情况相符;事故参与方的最终停止位置、碰撞速度与实际勘验数据相比,误差均小于10%,说明仿真结果与实际勘验的结果相吻合。此方法对复杂的交通碰撞事故能够进行模拟,并且仿真具有一定的真实性,为交通管理部门进行事故责任认定提供了理论支撑。

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.

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。

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.

基于PC-Crash的载人两轮车事故再现仿真

为开发车-载人两轮车事故再现仿真案例,提出事故现场重建、事故参与者建模、仿真再现及结果分析的事故再现流程。通过1例车-载人两轮车事故演示与验证所提流程,发现通过该流程既能解释路面痕迹,又能解释车体变形及人体损伤痕迹,通过简单且可控的方式确保仿真痕迹与事故痕迹最大限度的一致,保证再现结果的客观、可靠。该研究对相关人员开展车-载人两轮车事故的再现及仿真分析工作具有借鉴作用。

Frontal Crash Analysis of a Fully Detailed Car Model Based on Finite Element Method

This paper sets up a highly detailed finite element model of a car for frontal crashworthiness applications, and then explains the characteristics of it. The geometry model is preprocessed by Hypermesh software. The finite element method solver program selected for the simulation is LS-DYNA. After the crash simulation is carefully analyzed, the frontal crash experiment is aimed to validate the finite element model. The simulation results are basically in agreement with the experimental results. The validation of the finite element model is crucial for the further research in optimization of the automotive structure or lightweighting of the vehicle.

Development of an occupant restraint system model and parametric study on equivalent crash pulse in vehicle frontal offset crash

Studies were conducted to evaluate driver injury metrics with varying crash pulse in offset crash. First, a vehicle finite element （ FE ） model and an occupant restraint system （ORS） model were developed and validated against tests; then, the crash pulse collected from the test vehicle was equivalent to a dual-trapezoid shape pulse which will be quantitatively described by six parameters and was put into the ORS model; finally, parametric studies were conducted to analyze the sensitivi- ties of parameters of equivalent crash pulse on head resultant acceleration, head injury criteria （HIC）, neck axial force and chest deformation. Results showed that the second peak value of the crash pulse was statistically significant on all these injury criteria （P = 0. 001, 0. 000, 0. 000, 0. 000 re- spectively）, the first peak level had a negative significantly effect on all the criteria aforementioned except the chest deformation （P = 0. 011, 0. 038, and 0. 033 respectively）, and the interaction of the time-points of first and second peak values had a significant influence on head resultant acceleration （P = 0. 03 ）. A higher first peak value and a lower second peak value of the crash pulse could bring deeply lower injury metrics.

Application of Improved RSM in the Optimization of Automotive Frontal Crashworthiness

Based on the vehicle front crash finite element analysis, it shows that there is a large acceleration, so it needs further optimization. In order to improve the performance of vehicle collision, eight parts were selected which have large impact for the result, its thickness as design variables to the right of the B-pillar acceleration peak of optimization goal;17 sample points were selected by Latin hypercube sampling method. Many structure parameters are optimized using sequential quadratic program (SQP) based on the surrogate model. The results show that the improved RSM has high accuracy;the right B-pillar acceleration reduced approximately 22.8%, reached the expected objective and was more conducive to the occupant safety.

Forecast Error and Predictability for the Warm-sector and the Frontal Rainstorm in South China

In south China, warm-sector rainstorms are significantly different from the traditional frontal rainstorms due to complex mechanism, which brings great challenges to their forecast. In this study, based on ensemble forecasting, the high-resolution mesoscale numerical forecast model WRF was used to investigate the effect of initial errors on a warmsector rainstorm and a frontal rainstorm under the same circulation in south China, respectively. We analyzed the sensitivity of forecast errors to the initial errors and their evolution characteristics for the warm-sector and the frontal rainstorm. Additionally, the difference of the predictability was compared via adjusting the initial values of the GOOD member and the BAD member. Compared with the frontal rainstorm, the warm-sector rainstorm was more sensitive to initial error, which increased faster in the warm-sector. Furthermore, the magnitude of error in the warm-sector rainstorm was obviously larger than that of the frontal rainstorm, while the spatial scale of the error was smaller. Similarly, both types of the rainstorm were limited by practical predictability and inherent predictability, while the nonlinear increase characteristics occurred to be more distinct in the warm-sector rainstorm, resulting in the lower inherent predictability.The comparison between the warm-sector rainstorm and the frontal rainstorm revealed that the forecast field was closer to the real situation derived from more accurate initial errors, but only the increase rate in the frontal rainstorm was restrained evidently.

Functional role of frontal electroencephalogram alpha asymmetry in the resting state in patients with depression:A review

Depression is a psychological disorder that affects the general public worldwide.It is particularly important to make an objective and accurate diagnosis of depression,and the measurement methods of brain activity have gradually received increasing attention.Resting electroencephalogram(EEG)alpha asymmetry in patients with depression shows changes in activation of the alpha frequency band of the left and right frontal cortices.In this paper,we review the findings of the relationship between frontal EEG alpha asymmetry in the resting state and depression.Based on worldwide studies,we found the following:(1)Compared with individuals without depression,those with depression showed greater right frontal EEG alpha asymmetry in the resting state.However,the pattern of frontal EEG alpha asymmetry in the resting state in depressive individuals seemed to disappear with age;(2)Compared with individuals without maternal depression,those with maternal depression showed greater right frontal EEG alpha asymmetry in the resting state,which indicated that genetic or experience-based influences have an impact on frontal EEG alpha asymmetry at rest;and(3)Frontal EEG alpha asymmetry in the resting state was stable,and little or no change occurred after antidepressant treatment.Finally,we concluded that the contrasting results may be due to differences in methodology,clinical characteristics,and participant characteristics.

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.

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.

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).

IMPACT RESPONSE OF LOWER-LIMBS OF VEHICLE OCCUPANT IN FRONTAL CRASHES CONSIDERING INTRUSION

The lower extremity forces developed during vehicle accidents with significant floor pan intrusion are considered, and the forces generated directly by the intrusion are focused on. The analysis is based on the modification and use of SUPERCRASH a multibody crash computer simulator. For 48 km/h frontal collisions, the results show that there is marked asymmetry on the forces exerted on the left and right legs, arising due to the asymmetrical restraint of the 3 point seat belts. The results also show, as expected, that floor pan intrusion reduction will greatly reduce the forces on the lower extremities.

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.

Comparison of Microphysical Characteristics of Warm-sector,Frontal and Shear-line Heavy Rainfall During the Pre-summer Rainy Season in South China

Warm-sector heavy rainfall(WR),shear-line heavy rainfall(SR),and frontal heavy rainfall(FR)are three types of rainfall that frequently occur during the pre-summer rainy season in south China.In this research,we investigated the differences in microphysical characteristics of heavy rainfall events during the period of 10-15 May 2022 based on the combined observations from 11 S-band polarimetric radars in south China.The conclusions are as follows:(1)WR has the highest radar echo top height,the strongest radar echo at all altitudes,the highest lightning density,and the most active ice-phase process,which suggests that the convection is the most vigorous in the WR,moderate in the FR,and the weakest in the SR.(2)Three types of rainfall are all marine-type precipitation,the massweighted mean diameter(Dm,mm)and the intercept parameter(Nw,mm^(-1) m^(-3))of the raindrops in the WR are the largest.(3)The WR possesses the highest proportion of graupel compared with the FR and SR,and stronger updrafts and more abundant water vapor supply may lead to larger raindrops during the melting and collision-coalescence processes.(4)Over all the heights,liquid and ice water content in the WR are higher than those in the SR and FR,the ratio of ice to liquid water content in the WR is as high as 27%when ZH exceeds 50 dBZ,definitely higher than that in the SR and FR,indicating that the active ice-phase process existing in the WR is conducive to the formation of heavy rainfall.

Recognition for Frontal Emergency Stops Dangerous Activity Using Nano IoT Sensor and Transfer Learning

Currently,it is difficult to extract the depth feature of the frontal emergency stops dangerous activity signal,which leads to a decline in the accuracy and efficiency of the frontal emergency stops the dangerous activ-ity.Therefore,a recognition for frontal emergency stops dangerous activity algorithm based on Nano Internet of Things Sensor(NIoTS)and transfer learning is proposed.First,the NIoTS is installed in the athlete’s leg muscles to collect activity signals.Second,the noise component in the activity signal is removed using the de-noising method based on mathematical morphology.Finally,the depth feature of the activity signal is extracted through the deep transfer learning model,and the Euclidean distance between the extracted feature and the depth feature of the frontal emergency stops dangerous activity signal is compared.If the European distance is small,it can be judged as the frontal emergency stops dangerous activity,and the frontal emergency stops dangerous activity recognition is realized.The results show that the average time delay of activity signal acquisition of the algorithm is low,the signal-to-noise ratio of the action signal is high,and the activity signal mean square error is low.The variance of the frontal emergency stops dangerous activity recognition does not exceed 0.5.The difference between the appearance time of the dangerous activity and the recognition time of the algorithm is 0.15 s,it can accurately and quickly recognize the frontal emergency stops the dangerous activity.