Time Of Crash Research Articles

Schizophrenia, antipsychotic treatment adherence and driver responsibility for motor vehicle crash: a population-based retrospective study in British Columbia, Canada

ObjectiveTo examine the relationship between schizophrenia, antipsychotic medication adherence and driver responsibility for motor vehicle crash.DesignRetrospective observational cohort study using 20 years of population-based administrative health and driving data.SettingBritish Columbia,...

Enhancement of Biological Nitrogen Removal System Resilience to Chronic Exposure of Zinc Oxide Nanoparticles by Quorum Sensing Modulation: Physiochemical, Microbial, and Metabolic Insights

The effects of three typical N-acyl-homoserine lactones (AHLs) on the tolerance of biological nitrogen removal (BNR) system to chronic exposure of zinc oxide nanoparticles (NPs) were investigated. C4-HSL successfully delayed the crash time of nitrogen removal performances in the NP-stressed system, while C6-HSL and C10-HSL maintained total nitrogen removal efficiencies throughout the 90-day NP exposure. All three AHLs increased NPs’ contents captured in extracellular polymeric substances, alleviating membrane damage and preserving floc structure. The activities of tricarboxylic acid cycle-related enzymes and the relative abundances of BNR-related functional genes and genera were significantly enhanced. Besides, C6-HSL and C10-HSL augmented antioxidant enzyme activities and the abundances of functional genes and metabolites related to antioxidation, flagellar assembly, and chemotaxis, which synergistically reduced the reactive oxygen species’ excessive accumulation. The tested AHLs effectively enhanced BNR systems’ tolerance to chronic NP exposure, providing inspiration for quorum sensing applications in emerging contaminant removal.

Numerical modelling of sawteeth and sawtooth-free regime

To better understand the sawteeth physics and the sawtooth-free regime associated with the hybrid scenario in tokamak experiments, numerical calculations up to quasi-steady state have been carried out for realistic middle-size tokamak plasma parameters, including the bootstrap current perturbation and basing on both the single- and two-fluid equations with the large aspect ratio approximation. Two types of the sawtooth crash are found in multiple sawteeth simulations: (1) For a low equilibrium bootstrap current fraction, the crash is caused by the internal kink mode, as expected; (2) When the bootstrap current density fraction is larger than 10% in the core region, however, the crash is caused by the non-ideal double kink mode, in contrary to the conventional understanding. In this case, a non-monotonic radial profile of the safety factor q with two q=1 surfaces emerges before the crash, caused by the bootstrap current density and plasma resistivity perturbations, although the original equilibrium has only single q = 1 surface. In both types of sawtooth crashes, the crash time in two-fluid simulations is tens of microseconds, as observed in experiments. Furthermore, for a relatively low ion density and finite bootstrap current density fraction, a transition from the sawtooth to the sawtooth-free regime is found, in which flat q profiles with the q value being about unity in the central region, similar to that observed in hybrid scenario experiments, are maintained by the dynamo effect. To enter into the sawtooth-free regime in two-fluid simulations, a much larger Alfvén velocity than that in single-fluid simulations is required due to the diamagnetic drift.

STRATEGIC OVERTIME IMPLEMENTATION: ASSESSING CRASH TIME, CRASH COST, AND COST SLOPE IN THE CONSTRUCTION OF KADIRI UNIVERSITY'S 4-STORY BUILDING

The lecture building is an important building which is the center of academic activities where educational activities are carried out. Currently, the construction of a four-story lecture building at Kadiri University is experiencing significant project delays with a deviation of 5.64% from the plan. Crash analysis can be carried out to speed up the duration of the project so that it can be completed on time with additional working hours. This research aims to determine crash time, crash costs, and cost slope for the application of overtime on the 4-story building construction project at Kadiri University. A quantitative and case study approach was used with data collected through observation, interviews, and analysis of project documents. Critical Path Method (CPM) analysis is carried out using Microsoft Project software to identify work that is on the critical path. Crashing is done by calculating crash time, crash cost, and cost slope. The research results show that applying 1 hour of overtime can speed up the project by 12 working days, with an additional cost of IDR 24,488,758. Cost slope analysis shows additional overtime costs of IDR 12,244,379. This research shows that the application of measurable overtime and crashing analysis can effectively overcome project delays in the context of building educational facilities.

Optimal Portfolios for Large Investors in Housing Markets Under Stress Scenarios: A Worst-Case Approach

AbstractThe study focuses on constructing a mathematical housing market threatened by a major catastrophic event or crash. It incorporates the worst-case scenario portfolio optimization problem as introduced in Korn and Wilmott (Int J Theor Appl Finance 5(02):171–187, 2002) into housing markets. The standard stochastic models for housing markets assume a geometric Brownian motion and neglect sudden housing price falls during crash times. However, the size, timing, and frequency of crashes have to be included in such models. By incorporating the worst-case portfolio optimization problem into housing markets, this study introduces a methodology to construct portfolios for large investors that are robust and resilient to extreme housing market conditions. The worst-case portfolio optimization approach can be used in housing markets to incorporate stress scenarios, minimize potential losses, utilize mathematical techniques, and manage housing investment risk effectively. This study provides valuable insights for large investors seeking to construct housing portfolios prioritizing downside protection and minimizing losses in extreme housing market conditions. Utilizing numerical illustrations, it provides insights into portfolio construction, demonstrating the effectiveness of adjusting portfolios to mitigate downside risks during housing market crises. The results highlight dynamic portfolio management’s significance in safeguarding wealth when housing prices undergo significant fluctuations.

Young novice drivers’ road crash injuries and contributing factors: A crash data investigation

Objective Collisions are a significant cause of injury and fatality among young novice drivers. Using real crash data, this study further explores the multifaceted and complex nature of young novice drivers’ crash injury risk by synthesizing different driver attributes and crash scenarios in order to update and validate previous research findings and provide more feasible recommendations for preventive measures. Methods Detailed data on traffic crash of young novice drivers were extracted from the National Automobile Accident In-Depth Investigation System (NAIS) in China, and a mixed research methodology using a Random Forest and multinomial logit modeling framework was used in order to explore and study the important influences on traffic crash injuries of young novice drivers in Songjiang District, Shanghai, during the period from 2018 to 2022. Results The results of the study showed that human, vehicle, road and environmental characteristics contributed 36.83%, 22.65%, 17.07% and 23.45% respectively to the prediction of crash injury level of novice drivers. Among the various single factors, driver negligence was the most important factor affecting the crash injury level of novice drivers. Age of the vehicle, crash location, road signal condition and time of crash all had a significant effect on the crash injury level of young novice drivers (95% of the confidence level). Conclusions The study comprehensively analyzed young novice driver crash data to reveal the crash injury risk and its severity faced by young novice drivers in different contexts, and suggested targeted safety improvements. There are similarities and differences with the results of previous studies, in which there are new contributions to understanding the driving risks of young novice drivers in daytime and nighttime.

Core electron temperature turbulence and transport during sawtooth oscillations in the DIII-D tokamak

Sawteeth are one of the concerning instabilities in ITER and future burning plasma experiments. Sawtooth dynamics and its interaction with broadband plasma turbulence has been a challenge for predictive simulations of core transport in future fusion devices. This study provides new observations of core turbulence behavior during sawtooth oscillations in DIII-D hydrogen L-mode neutral beam injection heated plasmas in an inner wall limited configuration. A strong correlation of electron temperature and density turbulence levels with the sawtooth oscillation phase has been observed at locations inside the T e inversion radius and/or safety factor q = 1 magnetic surface. The T e turbulence amplitude in the core during the sawtooth ramp exhibits a critical T e gradient behavior inside but not near the T e inversion radius/q = 1 magnetic surface. The most unstable mode calculated from the trapped gyro-landau fluid turbulence simulations reveal a change from low-k ion-type to low-k electron-type modes from pre- to post- sawtooth crash time periods.

Underwater acoustic analysis reveals unique pressure signals associated with aircraft crashes in the sea: revisiting MH370

Data analysis from the hydroacoustic stations of the Comprehensive Nuclear-Test-Ban Treaty Organization has unveiled distinctive pressure signals linked to aircraft crashes of varying sizes in the ocean. Notably, these signals were detected at distances ranging from two to five thousand kilometres, highlighting the efficacy of underwater acoustic technology in event identification and classification in marine environments. In this study, we investigate the plausibility of an aircraft, such as Malaysian Airlines Flight 370 (MH370), crashing into the sea leaving a discernible pressure signal at distant hydrophones. Consequently, we focus on recordings obtained from the hydroacoustic monitoring stations located at Cape Leeuwin and Diego Garcia, within a few minutes of the last satellite ping on the 7th arc, associated with the assumed crash time and location. Among the available data, only one relevant signal has emerged as a potential candidate, albeit recorded at a single station out of the two stations available. To ensure a comprehensive analysis, we also examine the time frame and location of the airplane along its initial route. Though no corresponding signal was observed. Nevertheless, the findings in this study narrow down the range of possibilities and present a novel scientific approach to investigate such incidents. These findings contribute to our understanding of acoustic signals associated with aircraft crashes at sea. They emphasise the potential for hydrophones to detect events even when the signal travels long distances through land. Ultimately, this research offers recommendations for conducting on-site experiments involving controlled explosions with energy levels similar to the impact of MH370 along the 7th arc. The aim is to encourage pertinent authorities to implement actions that could reveal insights into the destiny of MH370 specifically. Additionally, this initiative seeks to establish a comprehensive framework for addressing comparable incidents in the broader ocean context.

The self-contagion effect of a stock crash on the market response of the auditor’s other client

ABSTRACT This study aims to find out how information regarding a firm’s stock crash affects the stock price of other firms with the same auditor using 3,916 corporations listed in Korean stock market. The result shows that at the time of a stock crash within the industry, information regarding the crash is contagious via the auditor, as the market leverages the information audited for the crashed firm to additionally evaluate the auditor’s other clients within the industry. Moreover, the contagion effect of the crash within the same auditor to other client firms was found to not be related to the information quality of the client firm. When taking out information quality, however, a high positive discretional accruals quality is observed, and the greater the contagion effect of the crash. The results recognize and reflect the future risk of a stock crash for firms with a high amount of positive discretionary accruals due to the opportunistic incentives of managers in the market as well as market participants’ use of crash information audited by the same accounting firm. These results demonstrate that the market appreciates the audit effect on the crash and uses it for actual decision-making.

Optimizing Project Scheduling Using Linear Programming Approach: A Case Study of Heating Ventilation & Air Conditioning Mechanical Installation

In a construction environment, project scheduling is an essential tool to measure the success of all projects. This research paper will use project crashing of Activity to decrease the project completion schedule through the CPM method of time-cost trade-off and to minimize project cost. It includes linear programming with the aid of Microsoft Excel Solver to determine the result. And at the end of the research paper, it will show the impact of crashing the activities for the HVAC mechanical installation in a project for both normal and crash time, which displays that the selected Activity that had been crashed generated an increase of 9.8 % total cost from the total project cost.

Modeling spatiotemporal heterogeneity in interval-censored traffic incident time to normal flow by leveraging crowdsourced data: A geographically and temporally weighted proportional hazard analysis

Non-recurrent traffic congestion arising from traffic incidents is unpredictable but should be addressed efficiently to mitigate its adverse impacts on safety and travel time reliability. Numerous studies have been conducted about incident clearance time, while the recovery time, due to the limitations of data collection, is often inadvertently neglected in assessing incident-induced duration (i.e., the time from incident occurrence to the normal flow of traffic). Overlooking the recovery time is likely to underestimate the total incident-induced impact. Furthermore, the spatiotemporal heterogeneity of observed factors is not adequately captured in incident duration models. To address these gaps, this study specifically investigated traffic crashes as they reflect safety issues and are the primary cause of non-recurrent congestion. The emerging crowdsourced traffic reports were harnessed to estimate crash recovery time, which can complement the blind zone of fixed detectors. A geographically and temporally weighted proportional hazard (GWTPH) model was developed to untangle factors associated with the interval-censored crash duration. The results show that the GWTPH model outperforms the global model in goodness-of-fit. Many factors present a spatiotemporally heterogeneous effect. For example, the global model merely revealed that deploying dynamic message signs (DMS) shortened the crash time to normal. Notably, the GWTPH model highlights an average reduction of 32.8% with a standard deviation of 31% in time to normal. The study's findings and application of new spatiotemporal techniques are valuable for practitioners to localize strategies for incident management. For instance, deploying DMS can be very helpful in corridors when incidents happen, especially during peak hours.

Drivers’ liability-based comparative severity analysis of motorized two-wheeler crashes

Objective This study aims to investigate comparative severity analysis of motorized two-wheeler (MTW) crashes based on drivers’ liability using police-reported a crash data base. Methods Using crash data from 2016 to 2020, this study examines and analyses the key factors affecting the severity of MTW injuries in Dehradun. For analysis, the ordinal logistic approach is used because severity levels are attributed with natural ordering. Differentiating from past studies, this research distinguished between collisions in which MTW crashes were considered as first party (crash in which MTW rider is accountable) and second person (crash in which MTW rider is a victim). Results Result suggests that age, pillion passenger, type of collision, road network, and impacting vehicle increase the seriousness of a crash in both cases. However, crash day, crash time, and light condition were found to be significant in the case of second-party crashes. Similarly, crash seriousness tends to decrease in first-party crashes, whereas it increases in the context of second-party crashes. Conclusion The statistical results were correlated with past studies to provide proper justification in order to provide a better understanding of small-displacement MTW fatal accidents in developing countries. Additionally, this research aids in the development of mitigation strategies and future research directions to improve the safety of MTW users.

Developing a spatio-temporal interactions model for car crashes using a novel data-driven AHP-TOPSIS

We have proposed a data-driven method for spatio-temporal analysis of car crashes based on the Multi Criteria Decision Making (MCDM) procedure in the Zanjan city, NW Iran which are recorded in the period 2019–2020. A combination of Analytic Hierarchy Process (AHP) and Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) is used in this paper to accurately identify the spatio-temporal interactions exist in car crashes. A data-driven AHP-TOPSIS procedure is arranged based on assigning proper weights to the time series related to the peak of accidents. On the other hand, for spatial analysis, the Kernel Density Estimation method was used to create the continuous-value maps of different traffic accidents and then classified using Natural Breaks Classifier. In fact, the proposed methodology can be used to identify car crash hotspots by considering spatio-temporal interactions as well as addressing exaggerated weightings arising from knowledge-driven modeling. By using the spatio-temporal interaction maps in which the location and time of crashes are considered, simultaneously, it is possible to provide a new scientific strategy for identifying car crash hotspots which can lead to better traffic management, improved allocation of resources, and enhanced prevention regulations.

Enhancing bicyclist survival time in fatal crashes: Investigating the impact of faster crash notification time through explainable machine learning

Bicyclists are recognized as vulnerable road users, with the escalating fatalities posing a safety concern. While fatal crashes involving bicyclists are often assumed to be similar, there is a crucial distinction between instant death and death occurring several days later, with the former being substantially more severe. This study delves into the analysis of bicyclists’ time-to-death, spanning from immediate fatalities to deaths within 30 days, using data from the Fatality Analysis Reporting System from 2015 to 2019. Employing the Haddon Matrix approach, the variables are categorized into pre-crash, during-crash, and postcrash phases. This study considers crash notification time as the key postcrash measure. An explainable XGBoost model is developed using the SHAP technique to investigate the associations between variables and bicyclist time-to-death. The results show that substantial delays in crash notification time considerably reduce bicyclists’ time-to-death and increase the likelihood of early death. Specifically, hit-and-run crashes, crashes in rural areas, and crashes during late-night hours exhibit notably longer crash notification times compared to non-hit-and-run crashes, urban areas, and other hours, respectively. In such cases, when no witnesses or survivors can notify emergency responders, on-road vehicle technologies like the advanced automatic collision notification system can promptly inform responders, reducing notification delays.

Breathing New Life into an Old Tree: Resolving Logging Dilemma of B + -tree on Modern Computational Storage Drives

Having dominated databases and various data management systems for decades, B + -tree is infamously subject to a logging dilemma: One could improve B + -tree speed performance by equipping it with a larger log, which nevertheless will degrade its crash recovery speed. Such a logging dilemma is particularly prominent in the presence of modern workloads that involve intensive small writes. In this paper, we propose a novel solution, called per-page logging based B + -tree, which leverages the emerging computational storage drive (CSD) with built-in transparent compression to fundamentally resolve the logging dilemma. Our key idea is to divide the large single log into many small (e.g., 4KB), highly compressible per-page logs , each being statically bounded with a B + -tree page. All per-page logs together form a very large over-provisioned log space for B + -tree to improve its operational speed performance. Meanwhile, during crash recovery, B + -tree does not need to scan any per-page logs, leading to a recovery latency independent from the total log size. We have developed and open-sourced a fully functional prototype. Our evaluation results show that, under small-write intensive workloads, our design solution can improve B + -tree operational throughput by up to 625.6% and maintain a crash recovery time of as low as 19.2 ms, while incurring a minimal storage overhead of only 0.5-1.6%.

Epidemiology Of Motorcycle-Related Maxillofacial Injuries Presenting To The 37 Military Hospital, Ghana

Objective: The study examined the epidemiology of motorcycle-related maxillofacial injuries to provide data to guide the prevention and management of maxillofacial trauma. Methodology: The research was a one-year prospective cross-sectional study of patients presenting with motorcycle crash-related maxillofacial injuries. The variables recorded include the demographic data, type of motorcycle collision, day and time of motorcycle crash, location of the crash, role of injured patient, maxillofacial and concomitant injuries sustained. Descriptive statistics were used to ascertain the epidemiology of maxillofacial injuries sustained. Results: A total of 148 patients aged between 15 and 65 years were involved in the study. There was a high male dominance (96%) among patients who sustained maxillofacial injuries secondary to motorcycle-related road traffic crashes (RTC). The median age of the participants was 30.9 +/- 9.1 years. Most (55.6%) of the motorcycle crashes resulted from collisions with cars. Maxillofacial injuries were mostly soft tissue injuries with the midface sustaining the most. Skeletal injuries often occurred in the mandible with parasymphyseal fractures constituting the majority of the mandibular fractures. Conclusion: There is a male predominance in patients with motorcycle-related maxillofacial injuries with a peak age of 21-30 years. Victims of motorcycles crashes often sustain soft tissue injuries with aesthetic and functional implications. Motorcycle crashes contribute significantly to the public health burden of RTC, especially in developing countries like Ghana. Healthcare professionals ought to be conversant with the epidemiology of maxillofacial injuries in motorcycle crash victims to ensure prompt and comprehensive management.

Traffic Safety Risk Assessment for Selected Roads in Al-Qadisiyah Province

Road risk assessments use the same methodology as regular risk assessments and describe all of the dangers that are connected to drivers who use their vehicles for daily-related objectives. These risks include the distance traveled, the duration of the trip, the planned breaks, the driver’s posture, the sorts of routes, and the available driving options. The manuscript explored traffic crashes, the geographical distribution of crashes, and the road characteristics to identify the riskiest roads in Al-Qadisiyah Province. Data was collected from Al-Diwaniyah Teaching Hospital, and the data was divided into internal regions with 5996 crashes and external regions with about 6867 crashes. After compiling, indexing, and dividing the data, it was found that the critical crash times are at the beginning and end of the official working hours and at the time of rest after working hours. Likewise, the highest number of injuries are related to motorcyclists with 2447 crashes. It was also found that people (22-35) years old have the largest number of crashes followed by people 18 years old. After calculating the total number of accidents and the rate of accidents for all the roads selected for the study, the results indicated that the Diwaniyah-Daghara road had the highest number of accidents than the rest of the selected roads, as the number of accidents was 921 accidents, followed by the second place in terms of the number of accidents, the Diwaniyah-Sunya road, and after calculating the amount of danger for all roads ( There are 7 external roads and 3 internal roads). It is followed in second place by the Diwaniyah-Shafi’iyyah road with a crash severity of 15.05, and in third place by the Diwaniya-Daghara road with a severity of 13.65, whereas, Al-Askari road was ranked third with the number of crashes of 671, and now it ranks fifth with a severity of 8.31.

Modelling crash severity outcomes for low speed urban roads using back propagation – Artificial neural network (BP – ANN) – A case study in Indian context

This work analyses influence of road, weather and crash-specific factors on crash severity outcomes for low-speed urban midblock sections and intersections, for day and night time, using Backpropagation–Artificial Neural Network (BP–ANN). Five-year crash data (2015–2019) from 82Km urban road network of Patna, India was used for the study. The road factors include pavement width, distress condition, marking; shoulder type, condition; road section type as mid-block, intersection and intersection control. Weather factors include season of crash, fog or rain at crash time. Crash factor include collision partner, type and crash time. The most appropriate BP–ANN model architecture was estimated using Misclassification-Rate. It was observed that midblock segments witness higher severities during daytime, whereas intersections witness higher severities during night. Controlled intersections are safer compared to un-controlled intersections. Pavement distress greatly increase the chance of higher severities. Narrow roads record greater severities during day due to lack of surveillance.

Near-term impact of COVID-19 pandemic on seniors’ crash size and severity

Recent research revealed that COVID-19 pandemic was associated with noticeable changes in travel demand, traffic volumes, and traffic safety measures. Despite the reduction of traffic volumes across the US, several recent studies indicated that crash rates increased across different states during COVID-19 pandemic. Although some recent studies have focused on examining the changes in traffic conditions and crash rates before and during the pandemic, not enough research has been conducted to identify risk factors to crash severity. Even the limited research addressing the contributing factors to crash severity were focused on the pool category of drivers and no insight is available regarding older drivers, one of the most vulnerable groups to traffic collision and coronavirus. Moreover, these studies investigated the early impact of the COVID-19 pandemic mostly using up to three months of data. However, near-term and long-term effects of the COVID-19 pandemic are still unknown on traffic collisions. Therefore, this study aims to contribute to the literature by studying the near-term impact of the COVID-19 pandemic on crash size and severity among older drivers. To this end, a relatively large sample of crash data with senior drivers at fault was obtained and analyzed. To identify the main contributing factors affecting crash outcomes, Exploratory Factor Analysis was conducted on a high-dimension data set to identify potential latent factors which were validated through Confirmatory Factor Analysis. After that, Structural Equation Modeling technique was performed to examine the associations among the identified independent latent factors and the dependent variable. Additionally, SEM model identified the impact of the COVID-19 pandemic on seniors’ crash severity. The findings reveal that several latent variables were the significant predictors of crash severity of older drivers including “Driving maneuver & crash location”, “Road features and traffic control devices”, “Driver condition & behavior”, “Road geometric characteristics”, “Crash time and lighting”, and “Road class” latent factors. The binary variable of “Pandemic” was found to be as highly significant as the last four latent factors mentioned above. This means not only were older drivers more likely to be involved in higher crash size with higher severity level during the pandemic period, but also “Pandemic” was a risk factor to seniors as much as “Driver condition & behavior”, “Road geometric characteristics”, “Crash time & lighting”, and “Road class” factors. The results of this study provide useful insights that may improve road safety among senior drivers during pandemic periods like COVID-19.

Investigating the Probability of Potential Actions Performed by Drivers Experiencing Daytime Sun Glare-Induced Blindness

There has been extensive visual-disability-focused safety research resulting from various factors/conditions that have a significant impact on a driver’s ability to maneuver safely. One such critical condition among the major environmental issues that contribute to traffic crashes is daytime sun glare-induced temporary visual impairment. Nevertheless, the contribution of relevant factors to the choice of potential actions at the time of such crashes has not been widely studied. Approximately 3,000 crashes per year in the United States are attributed to sun glare, which may indicate that the driver had no control over the situation. Thus, this study investigated several factors to identify crashes that occurred, with acceptable certainty, under the direct influence of sun glare over 4 years (2014 to 2017) in Florida—“the Sunshine State.” A multinomial logistic regression model was developed to formulate relationships between crash-related contributing factors and to evaluate the likelihood of alternative actions being adopted by drivers experiencing sun glare. The results indicated that running red lights/stop signs was the most likely action one might take while experiencing this adverse condition, particularly on local roadways. A further consequence of sun glare is that drivers tend to follow the car in front too closely along segments with higher annual average daily traffic. The findings could help safety officials support the use of emerging intelligent transportation system technologies (i.e., automated traffic signal performance measures and cooperative intersection collision avoidance systems) to alert drivers of the possibility of a temporary vision impairment exactly when high-glare conditions exist.