Quantum speed limits set an upper bound to the rate at which a quantum system can evolve. Adopting a phase-space approach, we explore quantum speed limits across the quantum-to-classical transition ...and identify equivalent bounds in the classical world. As a result, and contrary to common belief, we show that speed limits exist for both quantum and classical systems. As in the quantum domain, classical speed limits are set by a given norm of the generator of time evolution.
Over the last decade, the electric vehicle (EV) has significantly changed the car industry globally, driven by the fast development of Li-ion battery technology. However, the fire risk and hazard ...associated with this type of high-energy battery has become a major safety concern for EVs. This review focuses on the latest fire-safety issues of EVs related to thermal runaway and fire in Li-ion batteries. Thermal runaway or fire can occur as a result of extreme abuse conditions that may be the result of the faulty operation or traffic accidents. Failure of the battery may then be accompanied by the release of toxic gas, fire, jet flames, and explosion. This paper is devoted to reviewing the battery fire in battery EVs, hybrid EVs, and electric buses to provide a qualitative understanding of the fire risk and hazards associated with battery powered EVs. In addition, important battery fire characteristics involved in various EV fire scenarios, obtained through testing, are analysed. The tested peak heat release rate (PHHR in MW) varies with the energy capacity of LIBs (
E
B
in Wh) crossing different scales as
P
H
R
R
=
2
E
B
0.6
. For the full-scale EV fire test, limited data have revealed that the heat release and hazard of an EV fire are comparable to that of a fossil-fuelled vehicle fire. Once the onboard battery involved in fire, there is a greater difficulty in suppressing EV fires, because the burning battery pack inside is inaccessible to externally applied suppressant and can re-ignite without sufficient cooling. As a result, an excessive amount of suppression agent is needed to cool the battery, extinguish the fire, and prevent reignition. By addressing these concerns, this review aims to aid researchers and industries working with batteries, EVs and fire safety engineering, to encourage active research collaborations, and attract future research and development on improving the overall safety of future EVs. Only then will society achieve the same comfort level for EVs as they have for conventional vehicles.
Road telematics and driver assistance systems offer a real opportunity to aid mobility and road safety. However, they also raise numerous questions. Problems related to the design and evaluation of ...intelligent driver support systems (IDSSs) and social perspectives related to their large scale introduction may only be fully addressed from a multi-disciplinary viewpoint. People from both engineering and social sciences, should be involved and this book provides such knowledge from both a human and social factors perspective.
Abstract
Background
Injuries caused by RTA are classified under the International Classification of Diseases-10 as ‘S00-T99’ and represent imbalanced samples with a mortality rate of only 1.2% among ...all RTA victims. To predict the characteristics of external causes of road traffic accident (RTA) injuries and mortality, we compared performances based on differences in the correction and classification techniques for imbalanced samples.
Methods
The present study extracted and utilized data spanning over a 5-year period (2013–2017) from the Korean National Hospital Discharge In-depth Injury Survey (KNHDS), a national level survey conducted by the Korea Disease Control and Prevention Agency, A total of eight variables were used in the prediction, including patient, accident, and injury/disease characteristics. As the data was imbalanced, a sample consisting of only severe injuries was constructed and compared against the total sample. Considering the characteristics of the samples, preprocessing was performed in the study. The samples were standardized first, considering that they contained many variables with different units. Among the ensemble techniques for classification, the present study utilized Random Forest, Extra-Trees, and XGBoost. Four different over- and under-sampling techniques were used to compare the performance of algorithms using “accuracy”, “precision”, “recall”, “F1”, and “MCC”.
Results
The results showed that among the prediction techniques, XGBoost had the best performance. While the synthetic minority oversampling technique (SMOTE), a type of over-sampling, also demonstrated a certain level of performance, under-sampling was the most superior. Overall, prediction by the XGBoost model with samples using SMOTE produced the best results.
Conclusion
This study presented the results of an empirical comparison of the validity of sampling techniques and classification algorithms that affect the accuracy of imbalanced samples by combining two techniques. The findings could be used as reference data in classification analyses of imbalanced data in the medical field.
Celotno besedilo
Dostopno za:
DOBA, IZUM, KILJ, NUK, PILJ, PNG, SAZU, SIK, UILJ, UKNU, UL, UM, UPUK
It is well known that traffic injuries still represent one of the main causes of death and that high blood alcohol concentrations while driving significantly increase the occurrence of accidents. ...However, only limited literature on the correlation between chronic alcohol abuse and accident risk is available. The aim of the present study was to investigate the hypothesis of an association between elevated concentrations of carbohydrate deficient transferrin (CDT) and the occurrence of alcohol-related traffic accidents.
The analytical determinations of BAC and CDT were performed following certified methods in HS-GC-FID and HPLC, respectively. For BAC, 0.50 g/L was used as cut-off, whereas 2.0% was used for CDT, according to the standardisation proposed by IFCC. A total of 929 drivers, tested for BAC at the time of hospital admission after a traffic accident, were classified into two groups: InjDr 1 (BAC ≤ 0.50 g/L) and InjDr 2 (BAC>0.50 g/L); all drivers were also tested for CDT.
InjDr 1 included 674 individuals, only 2.5% showing a CDT above the cutoff, whereas InjDr 2 group consisted of 255 subjects, 28.6% testing positive for CDT (Odds Ratio 15.5). When subdividing the InjDr group into increasing classes of CDT, a steady increase in the percentage of BAC-positive drivers was appreciated. Moreover, average BAC was found to parallel each class of CDT.
The reported data strongly support the use of CDT as a biomarker of increased risk of alcohol-related traffic accidents in the procedures of re-granting of the driving license upon confiscation for “drink driving”.
•Alcohol-related road traffic accidents are still one of the major causes of death.•Chronic alcohol abuse is scarcely investigated in this context.•The association between elevated CDT concentrations and such accidents was studied.•This work provides information that may significantly help improve traffic safety.•The data may suggest the opportunity of a re-evaluation of current CDT cut-offs.
The study presented an approach that integrated kernel density estimation (KDE) algorithm and spatial autocorrelation analysis, which helped to determine traffic accident (TA) hotspot locations and ...simultaneously evaluate the statistical significance of the hotspot clusters. Firstly, hotspots were identified by applying a GIS-based KDE algorithm. Secondly, the hotspot clusters were evaluated in terms of statistical significance by applying the Moran's I statistic indices. Finally, hotspots were arranged according to their significance. TA data in the (2015-2017) period in Hanoi, Vietnam were applied to test this approach. Importantly, the study proposed a validation process for the results by applying the Gi* statistics method to validate the (H-H) clusters and the EPDO method to validate the ranking of hotspots. The results showed that this integration overcame the drawbacks of the KDE method. The statistical test process of clusters helped to hinder the occurrence of too many clusters that were determined by the KDE method because they were not really dangerous. This approach was helpful and precise in identifying TA hotspots with the statistical meaning. These outcomes will not only enable traffic authorities to comprehensively understand the reasons for each accident but also to help them manage and deal with hazardous areas according to the prior order in case of limited expenses and allocate traffic safety sources accordingly.
The World Health Organization (WHO) reported 1.25 million deaths yearly due to road traffic accidents worldwide and the number has been continuously increasing over the last few years. Nearly fifth ...of these accidents are caused by distracted drivers. Existing work of distracted driver detection is concerned with a small set of distractions (mostly, cell phone usage). Unreliable ad hoc methods are often used. In this paper, we present the first publicly available dataset for driver distraction identification with more distraction postures than existing alternatives. In addition, we propose a reliable deep learning-based solution that achieves a 90% accuracy. The system consists of a genetically weighted ensemble of convolutional neural networks; we show that a weighted ensemble of classifiers using a genetic algorithm yields a better classification confidence. We also study the effect of different visual elements in distraction detection by means of face and hand localizations, and skin segmentation. Finally, we present a thinned version of our ensemble that could achieve 84.64% classification accuracy and operate in a real-time environment.
Traditional accident prediction models have been mostly designed with statistical analysis that finds and analyzes the causal relationships between traffic accidents and a variety of human, road ...geometry, and environmental factors. However, these statistical methods have limitations in that they are based on assumptions about data distribution and function type. Therefore, this study suggests an accident prediction model using deep learning. This newly suggested risk prediction model is for predicting risk by reflecting static features of the road, such its length and the speed limit on it, and dynamic features of the road, such as traffic volume when driving on it, and the altitude and azimuth of the sun. For this purpose, 4470 accident cases, collected over 5 months from August to December 2018 in Seoul—the most complex, high-traffic, and accident-prone city in Korea—were analyzed. As a result of testing the model using such data, it was found to have an accuracy of 75% and recall of 81%. Based on testing results for the suggested risk prediction model, a system was developed to guide not only accident-prone regions predicted using statistical data but to also guide a risk level for the road. This level of risk is estimated based upon each given situation, so it can change even for the same road. This guide system can be used to provide a level of risk for each road segment and region but also to improve roads with recommendations, such as installation of safety features. In addition, it could support a mobile system that provides a driver with the optimized driving path for safety.
(1) Background: Research on patterns of risky driving behaviors (RDBs) in adolescents is scarce. This study aims to identify distinctive patterns of RDBs and to explore their characteristics in a ...representative sample of adolescents. (2) Methods: this is a cross-sectional study of a representative sample of Tuscany Region students aged 14–19 years (n = 2162). The prevalence of 11 RDBs was assessed and a cluster analysis was conducted to identify patterns of RDBs. ANOVA, post hoc pairwise comparisons and multivariate logistic regression models were used to characterize cluster membership. (3) Results: four distinct clusters of drivers were identified based on patterns of RDBs; in particular, two clusters—the Reckless Drivers (11.2%) and the Careless Drivers (21.5%)—showed high-risk patterns of engagement in RDBs. These high-risk clusters exhibited the weakest social bonds, the highest psychological distress, the most frequent participation in health compromising and risky behaviors, and the highest risk of a road traffic accident. (4) Conclusion: findings suggest that it is possible to identify typical profiles of RDBs in adolescents and that risky driving profiles are positively interrelated with other risky behaviors. This clustering suggests the need to develop multicomponent prevention strategies rather than addressing specific RDBs in isolation.
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