Motor vehicle crashes remain the leading cause of adolescent mortality and injury in the United States. For young drivers, crash risk peaks immediately after licensure and declines during the next two years, making the point of licensure an important safety intervention opportunity. Legislation in Ohio established a unique health-transportation partnership among the State of Ohio, Children's Hospital of Philadelphia, and Diagnostic Driving, Inc., to identify underprepared driver license applicants through a virtual driving assessment system. The system, a computer-based virtual driving test, exposes drivers to common serious crash scenarios to identify critical skill deficits and is delivered in testing centers immediately before the on-road examination. A pilot study of license applicants who completed it showed that the virtual driving assessment system accurately predicted which drivers would fail the on-road examination and provided automated feedback that informed drivers on their skill deficits. At this time, the partnership's work is informing policy changes around integrating the virtual driving assessment system into licensing and driver training with the aim of reducing crashes in the first months of independent driving. The system can be developed to identify deficits in safety-critical skills that lead to crashes in new drivers and to address challenges that the coronavirus disease 2019 pandemic has introduced to driver testing and training.
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http://dx.doi.org/10.1377/hlthaff.2020.00802 | DOI Listing |
Curr Med Chem
January 2025
Center for Natural and Human Sciences Federal University of ABC, Santo André, SP, Brazil.
The discovery of new drugs for neglected tropical diseases (NTDs) is challenging due to the complexity of parasite-host interactions, causing resistance and the scarcity of financial resources. However, computational techniques, particularly molecular docking, have made significant advancements. This approach allows for the virtual screening of large compound libraries against specific molecular targets in parasites, efficiently cost-effectively identifying potential drug candidates.
View Article and Find Full Text PDFSensors (Basel)
December 2024
School of Computing and Information Science, Anglia Ruskin University, Cambridge CB1 1PT, UK.
Improving the ability of autonomous vehicles to accurately identify and follow lanes in various contexts is crucial. This project aims to provide a novel framework for optimizing a self-driving vehicle that can detect lanes and steer accordingly. A virtual sandbox environment was developed in Unity3D that provides a semi-automated procedural road and driving generation framework for a variety of road scenarios.
View Article and Find Full Text PDFSensors (Basel)
December 2024
Department of Semiconductor Systems Engineering, Sejong University, Seoul 05006, Republic of Korea.
Recently, AI systems such as autonomous driving and smart homes have become integral to daily life. Intelligent multi-sensors, once limited to single data types, now process complex text and image data, demanding faster and more accurate processing. While integrating NPUs and sensors has improved processing speed and accuracy, challenges like low resource utilization and long memory latency remain.
View Article and Find Full Text PDFSensors (Basel)
December 2024
School of Vehicle and Transportation Engineering, Tsinghua University, Beijing 100083, China.
In response to the current situation of backward automation levels, heavy labor intensities, and high accident rates in the underground coal mine auxiliary transportation system, the mining trackless auxiliary transportation robot (MTATBOT) is presented in this paper. The MTATBOT is specially designed for long-range, space-constrained, and explosion-proof underground coal mine environments. With an onboard perception and autopilot system, the MTATBOT can perform automated and unmanned subterranean material transportation.
View Article and Find Full Text PDFBehav Sci (Basel)
November 2024
VA VISN 17 Center of Excellence for Research on Returning War Veterans and the Central Texas Veterans Healthcare System, 4800 Memorial Drive Building 93, Waco, TX 76711, USA.
This study examined the effectiveness of the virtual delivery of the Strength at Home (SAH) intervention program for intimate partner violence in a sample of 605 military veterans across 69 Veterans Affairs (VA) Medical Centers through a national implementation of the program. Outcome measures included physical IPV, psychological IPV, coercive control behaviors, post-traumatic stress disorder (PTSD) symptoms, and alcohol misuse. Significant pre-intervention to post-intervention reductions were found for all the outcomes, with similar effect size estimates relative to a prior investigation of in-person-delivered SAH through the same national VA implementation.
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