Driver Detection Research Articles

Different trait arrangements can blur the significance of ecological drivers of community assembly of mosses from rocky outcrops

Rocky outcrops are distinctive formations in arid and semiarid zones, characterized by the presence of plant species with functional traits related to water deficit and high temperature tolerance. These areas are also characterized by high habitat heterogeneity. We tested three hypotheses related to organization of moss communities in rocky outcrops: 1) trait convergence related to habitat filtering; 2) trait divergence related to habitat heterogeneity; and 3) random pattern of traits related to dispersal limitation. We developed null models to test trait convergence and divergence and related these patterns to habitat filtering and heterogeneity, respectively. Additionally, we also tested for different plant strategies to cope with harsh environments and we related the size of moss' spore to the number of rocky outcrops the species occupy. In spite of the convergence in some traits, this pattern was not related to annual precipitation and temperature seasonality. Moreover, the random trait organization on some outcrops was not related to dispersal limitations of the bryophytes’ species. Each community, on the other hand, had different functional compositions, indicating that mosses species have different trait arrangements to cope with environmental constraint, blurring the detection of ecological drivers of community assembly in bryophytes from rocky outcrops.

Distracted Driver Detection: Deep Learning vs Handcrafted Features

According to the National Highway Traffic Safety Administration, one in ten fatal crashes and two in ten injury crashes were reported as distracted driver accidents in the United State during 2014. In an attempt to mitigate these alarming statistics, this paper explores using a dashboard camera along with computer vision and machine learning to automatically detect distracted drivers. We consider a dataset that incorporates drivers engaging in seven different distracting behaviors using left and/or right hands. Traditional handcrafted features paired with a Support Vector Machine classifier are contrasted with deep Convolutional Neural Networks. The traditional features include a blend of Histogram of Oriented Gradients and Scale-Invariant Feature Transform descriptors used to create Bags of Words. The deep convolutional methods use transfer learning on AlexNet, VGG-16, and ResNet-152. The results yield 85% accuracy with ResNet and 82.5% accuracy with VGG-16, which outperformed AlexNet by almost 10%. Replacing the fully connected layers by a Support Vector Machine classifier did not improve the classification accuracy. The traditional features yielded much lower accuracy than the deep convolutional networks.

P1.02-028 Detection of Oncogenic Drivers in Pleural Effusions and Archived FNA Smears of Pulmonary Adenocarcinoma

Cytological materials are widely used in diagnosis and staging of lung cancer due to advanced stage of disease at the time of presentation. Mutational oncogenic drivers in pulmonary adenocarcinoma (ADC) include EGFR, Kras and Her-2/neu. We utilized archived FNA smears and pleural effusion samples of ADC for detection of oncogenic molecular drivers. Pleural fluids (36) and May Grunwald stained FNA smears (18) were used for mutation analysis. Sanger sequencing and ARMS and Scorpions PCR performed. Each tumor sample was evaluated for all classes of genomic alterations, including base-pair substitutions, insertions/deletions, as well as intronic/CDS polymorphisms. There were 31 males and 23 females, aged 18 to 82 years with mean age 65.5 years. A total of 9 patients (16.6%) were found positive for EGFR mutations (Table 1, Figure 1). EGFR exon 18 intronic poylmorphism was seen in 4 cases and EGFR exon 20 showed intronic and CDS polymorphism in most cases. However, none of the cases were found to be positive for EGFR, exon 18, 20, Kras and Her-2/neu mutation.View Large Image Figure ViewerDownload Hi-res image Download (PPT) These findings verify the feasibility of analysis of oncogenic drivers in cytological specimens in advanced ADC. Stained aspiration smears can be used after establishing diagnosis and checking adequacy of the specimen.

Non-intrusive Detection and Prediction of Driver’s Fatigue Using Optimized Yawning Technique

There are hoards evidences that driver fatigue can lead to vehicle accidents so pre alert is must require for such kind of delinquent. This paper taxonomy gives an algorithm for detection of yawing state of a driver driving a vehicle .Whether it is open or closed. The method presented is based upon the non-intrusive method as the camera is used for capturing the image of the driver. A new method is proposed which gives a fast response about yawing. The algorithm presented works upon image processing concept. Owing to its efficiency the same can be implemented in real-time. The results are analyzed and the efficiency obtained for open mouth detection is 98%.

Study of detection of driver’s sleepiness using wearable NIRS

Study of detection of driver’s sleepiness using wearable NIRS

Detection of the Coupling between Vegetation Leaf Area and Climate in a Multifunctional Watershed, Northwestern China

Accurate detection and quantification of vegetation dynamics and drivers of observed climatic and anthropogenic change in space and time is fundamental for our understanding of the atmosphere–biosphere interactions at local and global scales. This case study examined the coupled spatial patterns of vegetation dynamics and climatic variabilities during the past three decades in the Upper Heihe River Basin (UHRB), a complex multiple use watershed in arid northwestern China. We apply empirical orthogonal function (EOF) and singular value decomposition (SVD) analysis to isolate and identify the spatial patterns of satellite-derived leaf area index (LAI) and their close relationship with the variability of an aridity index (AI = Precipitation/Potential Evapotranspiration). Results show that UHRB has become increasingly warm and wet during the past three decades. In general, the rise of air temperature and precipitation had a positive impact on mean LAI at the annual scale. At the monthly scale, LAI variations had a lagged response to climate. Two major coupled spatial change patterns explained 29% and 41% of the LAI dynamics during 1983–2000 and 2001–2010, respectively. The strongest connections between climate and LAI were found in the southwest part of the basin prior to 2000, but they shifted towards the north central area afterwards, suggesting that the sensitivity of LAI to climate varied over time, and that human disturbances might play an important role in altering LAI patterns. At the basin level, the positive effects of regional climate warming and precipitation increase as well as local ecological restoration efforts overwhelmed the negative effects of overgrazing. The study results offer insights about the coupled effects of climatic variability and grazing on ecosystem structure and functions at a watershed scale. Findings from this study are useful for land managers and policy makers to make better decisions in response to climate change in the study region.

Examination of driver detection of roadside traffic signs and advertisements using eye tracking

Research shows that driver factors, particularly driver distraction, are the most common cause of traffic accidents. Among various visual distractions, objects such as advertisements that are commonly prevailing elements at the roadside, represent an important external distractor that may affect driving performance. Research findings on the influence that roadside signs or advertisements have on driver’s attention focus are not consistent. Therefore, with the application of eye-tracking technology, this research was designed to test several assumptions regarding drivers’ detection and perception of roadside elements. Seventeen volunteer participants, 19–76years old, performed ten kilometres of urban driving, in which they were visually challenged with 56 traffic signs and 31 advertisements. It was found that drivers’ age is not associated with the number of roadside objects detected. Those drivers who detected more traffic signs were also more attentive to visual advertisements. Furthermore, a positive and large effect size between the number of detected street-level and raised-level advertisements was also found.

A novel dendrochronological approach reveals drivers of carbon sequestration in tree species of riparian forests across spatiotemporal scales

Aboveground carbon (C) sequestration in trees is important in global C dynamics, but reliable techniques for its modeling in highly productive and heterogeneous ecosystems are limited. We applied an extended dendrochronological approach to disentangle the functioning of drivers from the atmosphere (temperature, precipitation), the lithosphere (sedimentation rate), the hydrosphere (groundwater table, river water level fluctuation), the biosphere (tree characteristics), and the anthroposphere (dike construction). Carbon sequestration in aboveground biomass of riparian Quercus robur L. and Fraxinus excelsior L. was modeled (1) over time using boosted regression tree analysis (BRT) on cross-datable trees characterized by equal annual growth ring patterns and (2) across space using a subsequent classification and regression tree analysis (CART) on cross-datable and not cross-datable trees. While C sequestration of cross-datable Q. robur responded to precipitation and temperature, cross-datable F. excelsior also responded to a low Danube river water level. However, CART revealed that C sequestration over time is governed by tree height and parameters that vary over space (magnitude of fluctuation in the groundwater table, vertical distance to mean river water level, and longitudinal distance to upstream end of the study area). Thus, a uniform response to climatic drivers of aboveground C sequestration in Q. robur was only detectable in trees of an intermediate height class and in taller trees (>21.8m) on sites where the groundwater table fluctuated little (≤0.9m). The detection of climatic drivers and the river water level in F. excelsior depended on sites at lower altitudes above the mean river water level (≤2.7m) and along a less dynamic downstream section of the study area. Our approach indicates unexploited opportunities of understanding the interplay of different environmental drivers in aboveground C sequestration. Results may support species-specific and locally adapted forest management plans to increase carbon dioxide sequestration from the atmosphere in trees.

Abstract 1510: Subtype-specific cancer driver gene detection improves sensitivity to detect drivers

Abstract Identifying cancer driver mutations is a crucial step toward understanding the underlying mechanisms of oncogenesis. However, driver gene detection is complicated, considering the inherent complexity and heterogeneity of cancer.The past decade has seen the large-scale application of next-generation sequencing technologies (NGS) in cancer genomics, and many methods have been published utilizing NGS to detect novel oncogenic drivers. However, the low concordance across methods raises the concern about false positives and negatives in those findings. In particular, proper modeling of the background mutation profile (known to be affected by multiple factors) is critical to improving precision and recall in driver detection. Though intra-sample heterogeneity in the background mutation profile is considered to some extent in multiple methods, most computational driver detection methods assume a homogeneous mutational landscape across cancer samples. With more and more subtypes being discovered in various cancers, the assumption is largely untenable. In this study, we present a driver gene detection framework taking into account the heterogeneous mutational context in a cancer cohort. Our approach improves sensitivity to detect drivers by first pre-selecting a more uniform sample subset to apply driver detection algorithms. We combine this method with enhancements to an existing ensemble approach that combines methods with different assumptions about the characteristics of driver mutations (recurrence across samples, functional impact bias and positional clustering). We apply this combined approach to ∼750 breast invasive carcinoma samples from The Cancer Genome Atlas. In this dataset, genes identified by our approach show higher enrichment in known cancer driver genes compared to other methods, and the subtype-driven method identifies additional potential drivers that are missing in overall analysis. Citation Format: Yao Fu, Aparna Chhibber, Narges Bani-Asadi, Hugo YK Lam. Subtype-specific cancer driver gene detection improves sensitivity to detect drivers. [abstract]. In: Proceedings of the 107th Annual Meeting of the American Association for Cancer Research; 2016 Apr 16-20; New Orleans, LA. Philadelphia (PA): AACR; Cancer Res 2016;76(14 Suppl):Abstract nr 1510.

Abstract 4342: Ultra-deep next generation sequencing (NGS) of plasma cell-free DNA (cfDNA) from patients with advanced lung cancers: results from the Actionable Genome Consortium

Abstract Introduction: Noninvasive genotyping using plasma cfDNA from cancer patients has the potential to obviate the need for some biopsies while also characterizing disease heterogeneity. This study was undertaken to develop an ultra-deep plasma NGS panel for patients with non-small cell lung cancers (NSCLC). Methods / Results: Plasma was prospectively collected from 51 patients with advanced, progressive NSCLC and a known oncogenic driver from prior tumor genotyping. We performed ultra-deep NGS on extracted cfDNA using a customized Illumina library preparation, hybrid capture panel covering 37 lung cancer related genes (complete exons and partial introns), and ultra-deep sequencing (HiSeq4000). Mean sequencing depth was ∼50,000X (150 million, 150bp reads per sample). After specialized consensus-based error correction for low allele frequency (AF) genomic alterations, the median unique DNA molecules per position were ∼3,500. The mean sequence error rate was reduced by 20-fold to 0.002%, enabling the confident call of a driver mutation as low as 0.03%. In a subset of cases, paired plasma droplet digital PCR (ddPCR) was performed for common EGFR and KRAS mutations using a validated assay. Blinded to tumor genotype, plasma NGS detected SNVs (EGFR, KRAS, BRAF), indels (EGFR, ERBB2), and fusions (ALK, ROS1) as well as significant copy number gains (CNG) (ERBB2, MET). Sensitivity of cfDNA for the detection of known oncogenic drivers was 88% (45/51). A single false positive driver mutation was identified in a case with a known EGFR mutation in tumor; plasma NGS found both EGFR exon 19 del (0.88% AF) and KRAS G12D (2.65% AF), and plasma ddPCR confirmed the presence of both mutations (2.2% and 2.0% AF). Evaluation for an occult second primary is ongoing. In 22 EGFR, ALK, or ROS1 cases with acquired resistance to targeted therapy, plasma genotyping detected a range of potential resistance mechanisms: EGFR T790M and C797S, ALK F1174C, ERBB2 CNG, MET CNG. In 16 cases with paired resistance biopsies, concordance for EGFR T790M status was 94% (15/16). 18 cases with known EGFR or KRAS mutations underwent paired ddPCR. In 14 cases the driver mutation was detected using both assays with high concordance of the%AF (r = 0.91). The remaining 4 cases were negative with ddPCR but 3 were positive with NGS at low AF (0.04%, 0.08%, and 0.29%), and the specificity for each driver was 100%. Conclusions: Ultra-deep plasma NGS can detect a wide range of oncogenic drivers in NSCLC and may be more sensitive than established ddPCR assays. In the setting of acquired resistance to targeted therapy, plasma NGS reliably captured EGFR T790M and additional somatic alterations as potential resistance mechanisms. Citation Format: Bob T. Li, Filip Janku, Pasi A. Janne, Gordon B. Mills, Kiran Madwani, Ryan S. Alden, Cloud P. Paweletz, Marc Ladanyi, Alex Aravanis, Byoungsok Jung, Sante Gnerre, Amy J. Sehnert, David B. Solit, Gregory J. Riely, Geoffrey R. Oxnard. Ultra-deep next generation sequencing (NGS) of plasma cell-free DNA (cfDNA) from patients with advanced lung cancers: results from the Actionable Genome Consortium. [abstract]. In: Proceedings of the 107th Annual Meeting of the American Association for Cancer Research; 2016 Apr 16-20; New Orleans, LA. Philadelphia (PA): AACR; Cancer Res 2016;76(14 Suppl):Abstract nr 4342.

Privacy-preserving electronic road pricing system for low emission zones with dynamic pricing

The high levels of pollution and traffic congestion present in almost all major cities around the world have brought solutions such as the deployment of electronic toll systems in some of these cities. The main purpose of those electronic toll systems is to restrict the access of vehicles to certain city areas, named low emission zones. Since its adoption, this solution has proven to be quite promising. However, current proposals are still far from being ideal. More specifically, current schemes still introduce a significant error percentage in the detection of fraudulent drivers. Moreover, they usually require toll systems to be equipped with cameras that take pictures of all the vehicles that pass through the control points. This behavior may represent a serious privacy threat for the drivers. In this article, a new electronic toll system is proposed. The aim of the new proposal is to detect fraud while preserving drivers privacy. More concretely, it provides a non-probabilistic fraud control, and the control points only take pictures of the vehicles that misbehave. Last but not least, the proposed system applies an enhanced dynamic pricing that can help the authorities to better distribute traffic over the road network. Copyright © 2016 John Wiley & Sons, Ltd.

Impaired driving enforcement practices among state and local law enforcement agencies in the United States

IntroductionAlcohol-impaired driving (DUI) persists as a substantial problem, yet detailed data on DUI enforcement practices are rarely collected. The present study surveyed state and local law enforcement agencies about their DUI enforcement activities. MethodTelephone interviews were conducted with law enforcement liaisons in state highway safety offices. Officers from a nationally representative sample of municipal, county, and state law enforcement agencies were also interviewed about their agency's DUI enforcement activities, including the types of enforcement, frequency of use, and whether activities were publicized. Response rates were 100% among law enforcement liaisons, 86% among county agencies, 93% among municipal agencies, and 98% among state agencies. ResultsBased on the highway safety office survey, 38 states conducted sobriety checkpoints in 2011. Nationally, 58% of law enforcement agencies reported that they conducted or helped conduct sobriety checkpoints during 2011–12, with 14% of all agencies conducting them monthly or more frequently. The vast majority (87%) of agencies reported conducting dedicated DUI patrols. However, dedicated DUI patrols were less likely to be publicized than checkpoints. Less than a quarter of agencies reported using passive alcohol sensors to improve detection of alcohol-impaired drivers. ConclusionsResults show that 38 states conducted sobriety checkpoints in 2011, little changed from a previous survey in 2000. Despite evidence of effectiveness, many agencies do not conduct frequent, publicized DUI enforcement or use passive alcohol sensors. Practical applicationsThe survey suggests that there are several areas in which impaired driving enforcement could be improved: increasing the frequency of special enforcement, such as sobriety checkpoints and/or dedicated patrols; publicizing these efforts to maximize deterrent effects; and using passive alcohol sensors to improve detection of alcohol-impaired drivers.

Time-based low emission zones preserving drivers’ privacy

Nowadays, big cities try to fight high levels of pollution and traffic jams by limiting the access of vehicles to centric zones or Low-Emission Zones (LEZ). One of the most important drawbacks of LEZs is the citizens’ risk to be tracked by means of their interactions with the Electronic Road Pricing (ERP) system in use; another relevant problem is the significant error percentage on the detection of fraudulent drivers. The former shortcoming clearly affects the peace of mind of users regarding their privacy. The latter represents an important disadvantage for any entity willing to employ resources on managing a LEZ. In this article, a new ERP system specifically designed for cities with Low-Emission Zones is proposed. The new scheme is user-centric by design, in the sense that preserving the privacy of honest drivers is a fundamental objective. Regarding fraudulent drivers, the proposed system is able to detect them and revoke their anonymity.

Effect of intelligent transport systems on traffic safety

One of the biggest problems of constantly growing transport sector is traffic safety. Innovative technologies are an effective tool to deal with the following issue. In order to ensure efficient and coordinated development of intelligent transport systems, it is essential to know their individual performance and impact on traffic safety. This article gives the summary of the effect of intelligent transport systems on road traffic safety. The aim of the research is to highlight intelligent transport systems and intelligent transport systems applications which have the greatest positive impact on traffic safety. To achieve this aim, meta-analysis of previous studies and analysis of experts interviews was carried out. The research consider the following thirteen driver support and control systems regarding their potentials to reduce the number of fatalities: average speed control system, stationary speed cameras, “Alcolock” ‒ application of drunk driver detection, “eCall” system, variable speed limit sign, red light violation detection cameras, forbidden maneuver detection system, vehicle identification system, variable message boards, intelligent road studs, incident detection system, weighin- motion system and seat belt control system.

Automatic Social Behaviour Detection of Driver in Context Environment of Tier 2 City

Automatic Social Behaviour Detection of Driver in Context Environment of Tier 2 City

Hypovigilant System: An Approach for Lethargy Detection

There is much attentional impairment while driving that affect driver’s reaction. Among which driving while lethargic is one of the major causes behind road accidents, and exposes the driver to a large extent of crash risk compared to driving while alert. Therefore, the use of an assistive system that monitor a driver’s level of vigilance and alert the driver in case of lethargy can be significant in the prevention of accidents. This paper introduces a new approach towards detection of driver's lethargy based on yawning measurement and head movement. This involves several steps which includes the real time detection and tracking of head movement, the detection of yawning based on measuring the amount of changes in the mouth contour area. Test results express that the proposed system is able to measure the aforesaid parameters and detect driver’s lethargy.

Propensity Score Matching and Its Application to Risk Drivers Detection in Financial Setting

Propensity Score Matching and Its Application to Risk Drivers Detection in Financial Setting

Nintedanib in NSCLC: evidence to date and place in therapy.

The treatment of advanced non-small cell lung cancer (NSCLC) is currently driven by the detection of targetable oncogenic drivers, i.e. epidermal growth factor receptor, echinoderm microtubule-associated protein-like 4-anaplastic lymphoma kinase, etc. Those patients who are wildtype for known and valuable oncogenes can receive standard chemotherapy as first-line treatment, with the possibility of adding bevacizumab. With regard to second-line treatment, nintedanib can improve the efficacy of docetaxel. Nintedanib is a tyrosine kinase inhibitor targeting three angiogenesis-related transmembrane receptors. The usefulness of nintedanib as an anticancer agent for NSCLC has been proved by both preclinical and clinical phase I and II trials; however, its approval for the use in clinical practice has been possible because of the positive results of the LUME-Lung 1 trial (nintedanib + docetaxel versus docetaxel alone) in terms of progression-free survival and overall survival, and a manageable tolerability profile. Therefore, the good results seen in the clinical trials with nintedanib in the second-line setting for NSCLC patients with adenocarcinoma subtype are encouraging enough to recommend it in clinical practice.

Frequent severe natural disturbances and non-equilibrium landscape dynamics shaped the mountain spruce forest in central Europe

Disturbances shape forest structure and composition, but the temporal dynamics of disturbance patterns, their influence on dynamics of forest structural complexity, and the potential impacts of ongoing climate changes are not fully understood. We addressed these issues by focusing on (1) long-term, landscape level retrospective analysis of disturbance dynamics of mountain Norway spruce (Picea abies) forest; (2) testing for the prevailing disturbance agent; and (3) the detection of disturbance drivers, particularly site conditions, using a dendrochronological approach.We had a unique opportunity to sample a large area of natural forest (in otherwise altered region) in Bohemian Forest, that was recently affected by extensive and severe wind and bark beetle disturbances raising management concerns. Tree-ring series of a minimum of 35 trees on each of the 26 study plots were analysed for signals of past disturbances that were indicated either by release from suppression (rapid growth increase) or rapid early growth rate (gap origin).A broad range of disturbance severities were detected across the landscape, but severe disturbances (>50% trees responded), with a short rotation period of 174years, best characterize the area. Reconstructed disturbances were associated with historically documented windstorms and bark beetle outbreaks. They were distributed across the landscape and affected its large portions with spatially variable severity. Over the last five centuries, disturbances peaked in the 1820s, but were rare after 1880. This non-equilibrium dynamics resulted into mature landscape structure at the end of 20th century and contributed to the large extent of disturbances in the last two decades. Disturbances were more frequent on wind-exposed locations such as mountain ridges and flatter terrain.Frequent disturbances did not allow the forest to develop into an old-growth phase; rather disturbance events of variable severities created structures important for biodiversity and could also preserve key early-seral phases in the landscape. We anticipate that potential future increase of disturbance intensities will not lead to comparable regime shifts in forests shaped by high-severity disturbances that kill largely mature trees (e.g., wind and bark beetle), because such disturbances are followed by resistant forest structure. Conservation areas should be large enough to allow for a wide range of disturbances. Emphasizing ecological functions needs understanding of biological legacies of natural disturbances and managers could partly rely on natural events to restore natural features into cultivated forests.

Stress Events Detection of Driver by Wearable Glove System

This paper is focused to develop a wearable glove system to detect driver stress events in real time. The driver’s stress is estimated by the use of physiological signals and steering wheel motion analysis. The steering wheel motion is analyzed by driver’s hand moving characteristic. Principally, the sensors on the glove gathered the photoplethysmogram signal via fingertip, and hand motion signal via inertial motion unit. The sensor module readings are transmitted to an end terminal application via a Bluetooth low energy transmission module to compute the driver stress index. The studies are carried out in a simulated driving, which is composed of three distinct driving scenarios to study the subjects’ behaviors that correlate with stress. Twenty-eight subjects are requested to perform three different driving sessions with random scenarios generated while performing various driving maneuvers to assess the dynamic of mental workloads. The stress assessments of driving test subjects are self-reported at pre- and post-stimulus as well as observed through facial expression recorded throughout the whole experiments. Moreover, this paper also aimed to investigate the correlation of stress events with different driving tasks. Stress index is computed by a support vector machine pattern classifier with extracted features from sensors reading. Notably, stress index differences were found among three driving scenarios and driving maneuvers. Results revealed the true accuracy of stress detection is greater than 95% in average.