Abstract

The exposome is a complement to the genome that includes non-genetic causes of disease. Multiple definitions are available, with salient points being global inclusion of exposures and behaviors, and cumulative integration of associated biologic responses. As such, the concept is both refreshingly simple and dauntingly complex. This article reviews high-resolution metabolomics (HRM) as an affordable approach to routinely analyze samples for a broad spectrum of environmental chemicals and biologic responses. HRM has been successfully used in multiple exposome research paradigms and is suitable to implement in a prototype universal exposure surveillance system. Development of such a structure for systematic monitoring of environmental exposures is an important step toward sequencing the exposome because it builds upon successes of exposure science, naturally connects external exposure to body burden and partitions the exposome into workable components. Practical results would be repositories of quantitative data on chemicals according to geography and biology. This would support new opportunities for environmental health analysis and predictive modeling. Complementary approaches to hasten development of exposome theory and associated biologic response networks could include experimental studies with model systems, analysis of archival samples from longitudinal studies with outcome data and study of relatively short-lived animals, such as household pets (dogs and cats) and non-human primates (common marmoset). International investment and cooperation to sequence the human exposome will advance scientific knowledge and also provide an important foundation to control adverse environmental exposures to sustain healthy living spaces and improve prediction and management of disease.

Abstract

Redwood is used as an impact energy-absorbing material in the plutonium air transportable (PAT) package. To function properly the redwood must retain its properties over a wide temperature range. Since data were not available, an experimental program was conducted on 3-inch cubes of redwood over the temperature range of -40 to 230/sup 0/F (-40 to 110/sup 0/C). The specific energy, average crushing stress, and percent compression at bottoming are presented for the 22 specimens tested. Average values show an approximately 10% decrease in the specific energy and average crushing stress for a temperature rise from 70 to 230/sup 0/F (21 to 110/sup 0/C); and an approximate 30% increase in these quantities for a decrease from 70 to -40/sup 0/F (21 to -40/sup 0/C). 10 figs. Document type: Report

Abstract

The anesthetic propofol and other exhaled organic compounds can be sampled in Tenax sorbent tubes and analyzed by gas chromatography coupled with mass spectrometry. The aim of this study was to evaluate the stability of propofol in Tenax sorbent tubes during overseas shipping. This is relevant for international pharmacokinetic studies on propofol in exhaled air. Tenax sorbent tube propofol samples with concentrations between 10 and 100 ng were prepared by liquid injection and with a calibration gas generator. For each preparation method, one reference set was analyzed immediately after preparation, a second set was stored at room temperature, and a third one was stored refrigerated. The fourth set was sent from Germany by airmail to USA and back. The shipped set of tubes was analyzed when it returned after 55 days elapsed. Then, the room temperature samples and the refrigerated stored samples were also analyzed. To evaluate the stability of propofol in the stored and shipped tubes, we calculated the recovery rates of each sample set. The mean recovery in the stored samples was 101.2% for the liquid preparation and 134.6% for the gaseous preparation at 4°C. At 22°C, the recovery was 96.1% for liquid preparation and 92.1% for gaseous preparation, whereas the shipped samples had a recovery of 85.3% and 111.3%. Thus, the deviation of the shipped samples is within a range of 15%, which is analytically acceptable. However, the individual values show significantly larger deviations of up to -32.1% (liquid) and 30.9% (gaseous). We conclude that storage of propofol on Tenax tubes at room temperature for 55 days is possible to obtain acceptable results. However, it appears that due to severe temperature and pressure variations air shipment of propofol samples in Tenax tubes without cooling shows severe deviations from the initial concentration. Although it was not tested in this study, we assume that refrigerated transport might be necessary to obtain comparable results as in the stored samples. Document type: Article

Abstract

Worldwide federation of national standards bodies (ISO members), whereby members (governmental and non-governmental organizations) have the right to be represented on such committees; approval of ISO procedures requires at least 75% approval by the members voting; standards are prepared by technical committees; appropriate standard to apply depends on activities of the company; → see therefore iso 9000, iso 9000-3, iso 9001, iso 9002, iso 9003, iso 10011–2.

Abstract

High-precision and lane selective position estimation is of fundamental importance for prospective advanced driver assistance systems (ADAS) and automated driving functions, as well as for traffic information and management processes in intelligent transportation systems (ITS). User and vehicle positioning is usually based on Global Navigation Satellite System (GNSS), which, as stand-alone positioning, does not meet the necessary requirements in terms of accuracy. Furthermore, the rise of connected driving offers various possibilities to enhance GNSS positioning by applying cooperative positioning (CP) methods. Utilizing only low-cost sensors, especially in urban environments, GNSS CP faces several demanding challenges. Therefore, this contribution presents an empirical study on how Vehicle-to-Everything (V2X) technologies can aid GNSS position estimation in urban environments, with the focus being solely on positioning performance instead of multi-sensor data fusion. The performance of CP utilizing common positioning approaches as well as CP integration in state-of-the-art Vehicular Ad-hoc Networks (VANET) is displayed and discussed. Additionally, a measurement campaign, providing a representational foundation for validating multiple CP methods using only consumer level and low-cost GNSS receivers, as well as commercially available IEEE 802.11p V2X communication modules in a typical urban environment is presented. Evaluating the algorithm&rsquo s performance, it is shown that CP approaches are less accurate compared to single positioning in the given environment. In order to investigate error influences, a skyview modelling seeking to identify non-line-of-sight (NLoS) effects using a 3D building model was performed. We found the position estimates to be less accurate in areas which are affected by NLoS effects such as multipath reception. Due to covariance propagation, the accuracy of CP approaches is decreased, calling for strategies for multipath detection and mitigation. In summary, this contribution will provide insights on integration, implementation strategies and accuracy performances, as well as drawbacks for local area, low-cost GNSS CP in urban environments. Document type: Article

Abstract

Tailored routing and navigation services utilized by wheelchair users require certain information about sidewalk geometries and their attributes to execute efficiently. Except some minor regions/cities, such detailed information is not present in current versions of crowdsourced mapping databases including OpenStreetMap. CAP4Access European project aimed to use (and enrich) OpenStreetMap for making it fit to the purpose of wheelchair routing. In this respect, this study presents a modified methodology based on data mining techniques for constructing sidewalk geometries using multiple GPS traces collected by wheelchair users during an urban travel experiment. The derived sidewalk geometries can be used to enrich OpenStreetMap to support wheelchair routing. The proposed method was applied to a case study in Heidelberg, Germany. The constructed sidewalk geometries were compared to an official reference dataset (“ground truth dataset”). The case study shows that the constructed sidewalk network overlays with 96% of the official reference dataset. Furthermore, in terms of positional accuracy, a low Root Mean Square Error (RMSE) value (0.93 m) is achieved. The article presents our discussion on the results as well as the conclusion and future research directions. Document type: Article

Abstract

Presently, in the event of a failure in Automated Driving Systems, control architectures rely on hardware redundancies over software solutions to assure reliability or wait for human interaction in takeover requests to achieve a minimal risk condition. As user confidence and final acceptance of this novel technology are strongly related to enabling safe states, automated fall-back strategies must be assured as a response to failures while the system is performing a dynamic driving task. In this work, a fail-operational control architecture approach and dead-reckoning strategy in case of positioning failures are developed and presented. A fail-operational system is capable of detecting failures in the last available positioning source, warning the decision stage to set up a fall-back strategy and planning a new trajectory in real time. The surrounding objects and road borders are considered during the vehicle motion control after failure, to avoid collisions and lane-keeping purposes. A case study based on a realistic urban scenario is simulated for testing and system verification. It shows that the proposed approach always bears in mind both the passenger&rsquo s safety and comfort during the fall-back maneuvering execution. Document type: Article

Abstract

Perception is a vital part of driving. Every year, the loss in visibility due to snow, fog, and rain causes serious accidents worldwide. Therefore, it is important to be aware of the impact of weather conditions on perception performance while driving on highways and urban traffic in all weather conditions. The goal of this paper is to provide a survey of sensing technologies used to detect the surrounding environment and obstacles during driving maneuvers in different weather conditions. Firstly, some important historical milestones are presented. Secondly, the state-of-the-art automated driving applications (adaptive cruise control, pedestrian collision avoidance, etc.) are introduced with a focus on all-weather activity. Thirdly, the most involved sensor technologies (radar, lidar, ultrasonic, camera, and far-infrared) employed by automated driving applications are studied. Furthermore, the difference between the current and expected states of performance is determined by the use of spider charts. As a result, a fusion perspective is proposed that can fill gaps and increase the robustness of the perception system. Document type: Article

Abstract

Bike-sharing services are flourishing in Smart Cities worldwide. They provide a low-cost and environment-friendly transportation alternative and help reduce traffic congestion. However, these new services are still under development, and several challenges need to be solved. A major problem is the management of rebalancing trucks in order to ensure that bikes and stalls in the docking stations are always available when needed, despite the fluctuations in the service demand. In this work, we propose a dynamic rebalancing strategy that exploits historical data to predict the network conditions and promptly act in case of necessity. We use Birth-Death Processes to model the stations’ occupancy and decide when to redistribute bikes, and graph theory to select the rebalancing path and the stations involved. We validate the proposed framework on the data provided by New York City’s bike-sharing system. The numerical simulations show that a dynamic strategy able to adapt to the fluctuating nature of the network outperforms rebalancing schemes based on a static schedule. Document type: Article

Abstract

Autonomous vehicles (AVs) are already operating on the streets of many countries around the globe. Contemporary concerns about AVs do not relate to the implementation of fundamental technologies, as they are already in use, but are rather increasingly centered on the way that such technologies will affect emerging transportation systems, our social environment, and the people living inside it. Many concerns also focus on whether such systems should be fully automated or still be partially controlled by humans. This work aims to address the new reality that is formed in autonomous shuttles mobility infrastructures as a result of the absence of the bus driver and the increased threat from terrorism in European cities. Typically, drivers are trained to handle incidents of passengers’ abnormal behavior, incidents of petty crimes, and other abnormal events, according to standard procedures adopted by the transport operator. Surveillance using camera sensors as well as smart software in the bus will maximize the feeling and the actual level of security. In this paper, an online, end-to-end solution is introduced based on deep learning techniques for the timely, accurate, robust, and automatic detection of various petty crime types. The proposed system can identify abnormal passenger behavior such as vandalism and accidents but can also enhance passenger security via petty crimes detection such as aggression, bag-snatching, and vandalism. The solution achieves excellent results across different use cases and environmental conditions. Document type: Article