4 research outputs found

    AUTOtech.agil: architecture and technologies for orchestrating automotive agility

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    Future mobility will be electrified, connected and automated. This opens completely new possibilities for mobility concepts that have the chance to improve not only the quality of life but also road safety for everyone. To achieve this, a transformation of the transportation system as we know it today is necessary. The UNICARagil project, which ran from 2018 to 2023, has produced architectures for driverless vehicles that were demonstrated in four full-scale automated vehicle prototypes for different applications. The AUTOtech.agil project builds upon these results and extends the system boundaries from the vehicles to include the whole intelligent transport system (ITS) comprising, e.g., roadside units, coordinating instances and cloud backends. The consortium was extended mainly by industry partners, including OEMs and tier 1 suppliers with the goal to synchronize the concepts developed in the university-driven UNICARagil project with the automotive industry. Three significant use cases of future mobility motivate the consortium to develop a vision for a Cooperative Intelligent Transport System (C-ITS), in which entities are highly connected and continually learning. The proposed software ecosystem is the foundation for the complex software engineering task that is required to realize such a system. Embedded in this ecosystem, a modular kit of robust service-oriented modules along the effect chain of vehicle automation as well as cooperative and collective functions are developed. The modules shall be deployed in a service-oriented E/E platform. In AUTOtech.agil, standardized interfaces and development tools for such platforms are developed. Additionally, the project focuses on continuous uncertainty consideration expressed as quality vectors. A consistent safety and security concept shall pave the way for the homologation of the researched ITS

    SARS-CoV-2 vaccination modelling for safe surgery to save lives: data from an international prospective cohort study

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    Background: Preoperative SARS-CoV-2 vaccination could support safer elective surgery. Vaccine numbers are limited so this study aimed to inform their prioritization by modelling. Methods: The primary outcome was the number needed to vaccinate (NNV) to prevent one COVID-19-related death in 1 year. NNVs were based on postoperative SARS-CoV-2 rates and mortality in an international cohort study (surgical patients), and community SARS-CoV-2 incidence and case fatality data (general population). NNV estimates were stratified by age (18-49, 50-69, 70 or more years) and type of surgery. Best- and worst-case scenarios were used to describe uncertainty. Results: NNVs were more favourable in surgical patients than the general population. The most favourable NNVs were in patients aged 70 years or more needing cancer surgery (351; best case 196, worst case 816) or non-cancer surgery (733; best case 407, worst case 1664). Both exceeded the NNV in the general population (1840; best case 1196, worst case 3066). NNVs for surgical patients remained favourable at a range of SARS-CoV-2 incidence rates in sensitivity analysis modelling. Globally, prioritizing preoperative vaccination of patients needing elective surgery ahead of the general population could prevent an additional 58 687 (best case 115 007, worst case 20 177) COVID-19-related deaths in 1 year. Conclusion: As global roll out of SARS-CoV-2 vaccination proceeds, patients needing elective surgery should be prioritized ahead of the general population

    Effects of pre-operative isolation on postoperative pulmonary complications after elective surgery: an international prospective cohort study

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