Crash/Near-Crash: Impact of Secondary Tasks and Real-Time Detection of Distracted Driving

The main goal of this dissertation is to investigate the problem of distracted driving from two different perspectives. First, the identification of possible sources of distraction and their associated crash/near-crash risk. That can assist government officials toward more informed decision-making process, allowing for optimized allocation of available resources to reduce roadway crashes and improve traffic safety. Second, actively counteracting the distracted driving phenomenon by quantitative evaluation of eye glance patterns. This dissertation research consists of two different parts. The first part provides an in-depth analysis for the increased crash/near-crash risk associated with different secondary task activities using the largest real-world naturalistic driving dataset (SHRP2 Naturalistic Driving Study). Several statistical and data mining techniques are developed to analyze the distracted driving and crash risk. More specifically, two different models were employed to quantify the increased risk associated with each secondary task: a baseline-category logit model, and a rule mining association model. The baseline-category logit model identified the increased risk in terms of odds ratios, while the A-priori association algorithm detected the associated risks in terms of rules. Each rule was then evaluated based on the lift index. The two models succeeded in ranking all the secondary task activities according to the associated increased crash/near-crash risk efficiently. To actively counteract to the distracted driving phenomenon, a new approach was developed to analyze eye glance patterns and quantify distracted driving behavior under safety and non-Safety Critical Events (SCEs). This approach is then applied to the Naturalistic Engagement in Secondary Tasks (NEST) dataset to investigate how drivers allocate their attention while driving, especially while distracted. The analysis revealed that distracted driving behavior can be well characterized using two new distraction risk indicators. Additional statistical analyses showed that the two indicators increase significantly for SCE compared to normal driving events. Consequently, an artificial neural network (ANN) model was developed to test the SCEs predictability power when accounting for the two new indicators. The ANN model was able to predict the SCEs with an overall accuracy of 96.1%. This outcome can help build reliable algorithms for in-vehicle driving assistance systems to alert drivers before SCEs

Impact of Acceleration Aggressiveness on Fuel Consumption Using Comprehensive Power Based Fuel Consumption Model

Changes in vehicle fuel-consumption and emission rates are associated with changes in vehicle cruise speeds and acceleration. Higher levels of speed is believed to be one of the most prevalent factors contributing to fuel consumption. As a result, the relationship between fuel consumption and driving speed behaviour has been the subject of investigation by several research. The main objective of this paper is to investigate the fuel consumption during different acceleration degrees namely: aggressive, normal and mild. The test vehicle was examined on a 2 km section of Cairo - El Ain El Sokhna Road. The three levels of acceleration were determined based on pre-developed drive scenarios. In addition, fuel consumption was estimated based on a Virginia Tech Power Based Fuel Consumption Model (VT-CPFM). This model is a simple and rapid method for investigating fuel consumption rates. The study demonstrated that the fuel consumed to accelerate an initially stationary vehicle was not related to the target speed as to driving behaviour. It was also observed that the fuel consumed per maneuvers decreased as the degree of aggressiveness increased due to the fact that the vehicle time spent during acceleration was less.Keywords: Fuel consumption, VT-CPFM, Acceleration levels

Fungal systematics and evolution : FUSE 6

Fungal Systematics and Evolution (FUSE) is one of the journal series to address the “fusion” between morphological data and molecular phylogenetic data and to describe new fungal taxa and interesting observations. This paper is the 6th contribution in the FUSE series—presenting one new genus, twelve new species, twelve new country records, and three new combinations. The new genus is: Pseudozeugandromyces (Laboulbeniomycetes, Laboulbeniales). The new species are: Albatrellopsis flettioides from Pakistan, Aureoboletus garciae from Mexico, Entomophila canadense from Canada, E. frigidum from Sweden, E. porphyroleucum from Vietnam, Erythrophylloporus flammans from Vietnam, Marasmiellus boreoorientalis from Kamchatka Peninsula in the Russian Far East, Marasmiellus longistipes from Pakistan, Pseudozeugandromyces tachypori on Tachyporus pusillus (Coleoptera, Staphylinidae) from Belgium, Robillarda sohagensis from Egypt, Trechispora hondurensis from Honduras, and Tricholoma kenanii from Turkey. The new records are: Arthrorhynchus eucampsipodae on Eucampsipoda africanum (Diptera, Nycteribiidae) from Rwanda and South Africa, and on Nycteribia vexata (Diptera, Nycteribiidae) from Bulgaria; A. nycteribiae on Eucampsipoda africanum from South Africa, on Penicillidia conspicua (Diptera, Nycteribiidae) from Bulgaria (the first undoubtful country record), and on Penicillidia pachymela from Tanzania; Calvatia lilacina from Pakistan; Entoloma shangdongense from Pakistan; Erysiphe quercicola on Ziziphus jujuba (Rosales, Rhamnaceae) and E. urticae on Urtica dioica (Rosales, Urticaceae) from Pakistan; Fanniomyces ceratophorus on Fannia canicularis (Diptera, Faniidae) from the Netherlands; Marasmiellus biformis and M. subnuda from Pakistan; Morchella anatolica from Turkey; Ophiocordyceps ditmarii on Vespula vulgaris (Hymenoptera, Vespidae) from Austria; and Parvacoccum pini on Pinus cembra (Pinales, Pinaceae) from Austria. The new combinations are: Appendiculina gregaria, A. scaptomyzae, and Marasmiellus rodhallii. Analysis of an LSU dataset of Arthrorhynchus including isolates of A. eucampsipodae from Eucampsipoda africanum and Nycteribia spp. hosts, revealed that this taxon is a complex of multiple species segregated by host genus. Analysis of an SSU–LSU dataset of Laboulbeniomycetes sequences revealed support for the recognition of four monophyletic genera within Stigmatomyces sensu lato: Appendiculina, Fanniomyces, Gloeandromyces, and Stigmatomyces sensu stricto. Finally, phylogenetic analyses of Rhytismataceae based on ITS–LSU ribosomal DNA resulted in a close relationship of Parvacoccum pini with Coccomyces strobi.http://www.sydowia.at/index.htmpm2021Medical Virolog

Analysis of Queue Estimation Process at Signalized Intersections Under Low Connected Vehicle Penetration Rates

This study investigates the factors affecting estimation accuracy of queue length at signalized intersections under low penetration of connected vehicles. A shockwave-based algorithm is proposed to estimate the maximum queue length and residual queue on a cycle-by-cycle basis. Simulation data collected from three consecutive signalized intersections were used to extract trajectories of CVs under five different market penetration rates and two different traffic conditions (under-saturated and moderate). The results confirm that the queue length estimation process is probabilistic and affected by the stochastic changes in traffic conditions. This probabilistic nature is defined by a queue formation coverage index (QI) that proved to significantly affect the queue length estimation accuracy. Overall, the results show that the queue estimates accuracy is acceptable when a QI value of at least 50% is achieved. In such limited data environments, the QI showed the potential to help as an assessment tool to evaluate the obtained queue estimates.The accepted manuscript in pdf format is listed with the files at the bottom of this page. The presentation of the authors' names and (or) special characters in the title of the manuscript may differ slightly between what is listed on this page and what is listed in the pdf file of the accepted manuscript; that in the pdf file of the accepted manuscript is what was submitted by the author

An eXtreme Gradient Boosting Method for Identifying the Factors Contributing to Crash/Near-Crash Events: A Naturalistic Driving Study

Despite the research efforts for reducing traffic accidents, the number of global annual vehicle accidents is still on the rise. This continues to motivate researchers to examine the factors contributing to Crash and Near-Crash events (CNC). Recently, many studies attempted to identify the associated crash factors using Naturalistic Driving Study (NDS-SHRP2) data. Despite the many classifiers developed in the literature, the high dimensionality and multicollinearity within the NDS-SHRP2 data limit the accuracy and reliability of the developed models. This study develops an eXtreme Gradient Boosting (XGB) classifier, robust to multicollinearity, using the NDS-SHRP2 dataset for identifying the factors contributing to CNC events. The performance of the XGB classifier is evaluated against three other advanced machine-learning algorithms. Results indicate that the XGB model outperformed the other models with a detection accuracy of 85% and identified the “Driver Behaviour” and “Intersection Influence” as the most contributing factors to CNC detection.The accepted manuscript in pdf format is listed with the files at the bottom of this page. The presentation of the authors' names and (or) special characters in the title of the manuscript may differ slightly between what is listed on this page and what is listed in the pdf file of the accepted manuscript; that in the pdf file of the accepted manuscript is what was submitted by the author

A distraction index for quantification of driver eye glance behavior: A study using SHRP2 NEST database

Distracted driving behavior and driving inattention are two leading causes of roadway crashes. The state-of-the-art safety research made several attempts to understand and quantify distracted driving and driver inattention. While each attempt had its limitation, there was a consensus on the relevance of eye glance behavior as a promising parameter in understanding distracted driving. In this study, a renewal cycle approach is implemented to provide deeper insights into how drivers allocate their attention while driving. This approach is then applied to the Naturalistic Engagement in Secondary Tasks (NEST) dataset to analyze drivers’ eye glance patterns and determine the relationship between their visual behavior and engagement in different types of secondary tasks (activities performed while driving). The analysis revealed that distracted driving behavior could be well characterized by two new measures: the number of renewal cycles per event (NRC) and a distraction level index (DI). Consequently, mixed-effects modeling is implemented to test the effectiveness of the two measures to differentiate crash/near-crash events from non-crash events. The analysis showed that the two measures increase significantly for crash/near-crash events compared to non-crash driving events with p-values less than 0.0001. The findings of this paper are promising to the quantification of the risk associated with distraction related visual behavior. The finding can also help build reliable algorithms for in-vehicle driving assistance systems to alert drivers before crash/near-crash events

Regulation of glutamate dehydrogenase by reversible ADP-ribosylation in mitochondria

Mitochondrial ADP-ribosylation leads to modification of two proteins of ∼26 and 53 kDa. The nature of these proteins and, hence, the physiological consequences of their modification have remained unknown. Here, a 55 kDa protein, glutamate dehydrogenase (GDH), was established as a specific acceptor for enzymatic, cysteine-specific ADP-ribosylation in mitochondria. The modified protein was isolated from the mitochondrial preparation and identified as GDH by N-terminal sequencing and mass spectrometric analyses of tryptic digests. Incubation of human hepatoma cells with [(14)C]adenine demonstrated the occurrence of the modification in vivo. Purified GDH was ADP-ribosylated in a cysteine residue in the presence of the mitochondrial activity that transferred the ADP-ribose from NAD(+) onto the acceptor site. ADP- ribosylation of GDH led to substantial inhibition of its catalytic activity. The stoichiometry between incorporated ADP-ribose and GDH subunits suggests that modification of one subunit per catalytically active homohexamer causes the inactivation of the enzyme. Isolated, ADP-ribosylated GDH was reactivated by an Mg(2+)-dependent mitochondrial ADP-ribosylcysteine hydrolase. GDH, a highly regulated enzyme, is the first mitochondrial protein identified whose activity may be modulated by ADP-ribosylation

Influence of Si content on phase stability and mechanical properties of TiAlSiN films grown by AlSi-HiPIMS/Ti-DCMS co-sputtering

Ti1-x(AlySi1-y)xN coatings covering a wide compositional range, 0.38 &lt; x &lt; 0.76 and 0.68 ≤ y ≤ 1.00, are deposited to investigate the influence of Al+/Si+ ion irradiation on microstructural and mechanical properties. The samples are grown in Ar/N2 atmosphere by the hybrid high-power impulse and dc magnetron co-sputtering (HiPIMS/DCMS) method with substrate bias synchronized to the Al+/Si+-rich portion of the HiPIMS pulses. Two Ti targets are operated in DCMS mode, while one AlSi target is operated in HiPIMS mode. Four different AlSi target compositions are used: Al1.0Si0.0, Al0.9Si0.1, Al0.8Si0.2, and Al0.6Si0.4. X-ray diffractometry reveals that films without Si (i.e., y = 1.0) have high Al solubility in NaCl-structure, c-TiAlN, up to x ≤ 0.67 no w-AlN is detected. Once Si (y &lt; 1.0) is introduced the Al solubility limit decreases, but remains higher than other PVD techniques, along with grain refinement and the formation of a SiNz rich tissues phase, as shown by transmission electron microscopy. The nanoindentation hardness is high (~ 30 GPa) for all films that do not contain the w-AlN phase. All the coatings have compressive stresses lower than -3 GPa. Interestingly, a range of films with different compositions displayed both high hardness (~ 30 GPa) and low residual stress (σ &lt; 0.5 GPa). Such an unique combination of properties highlights the benefits of using HiPIMS/DCMS configuration with metal-ion-synchronized substrate bias, which utilizes the Al+/Si+ supplantation effect and minimizes the Ar+ incorporation.Funding agencies: VINNOVA (FunMat-II project grant no. 2016-05156), the Swedish Research Council (grants no 2017-03813 and 2017-06701), the Swedish government strategic research area grant AFM – SFO MatLiU (2009-00971), the Swedish Research Council VR-RFI (#2017-00646_9) and the Swedish Foundation for Strategic Research (contract RIF14-0053)</p