1,721,370 research outputs found
Integrating traffic & driving behaviour in automotive research
No full textConnected and automated vehicles (CAVs) promise a number of benefits for the individual, the society and the economy, e.g. regarding road safety, social inclusion and transport efficiency. At the same time, it is of high relevance for the competitiveness of the European industry and it provides the potential for disruptive innovation in the transportation of people and goods and in the associated services.
While so far the safety and efficiency of road transport was organized with the driver and other road users being in charge to comply with traffic rules and traffic management, connected and automated road transport implies an essential paradigm shift from such approach; automated vehicles will directly receive stimuli from traffic. On the other hand, interaction design must solve potential loss of safety in mode transition situations up to SAE level 3 of CAVs and interaction between automated and human driving behaviour should be carefully considered. Moreover, the human role changes even more fundamentally from a driver to a user/passenger (SAE level 4 and 5).
All the previous said, traffic and driving behaviour have to be duly taken into account in the development and testing, as well in the validation of CAV solutions and in the definition of the Operational Design Domains (ODDs) associated to CAVs development and testing. Finally, in-vehicle systems need to collaborate with off-board perception systems through intelligent transportation systems (ITS), such as positioning, navigation and dynamic maps, as well as real-time traffic information, to enhance the in-vehicle perception system capabilities.
In order to properly deal with all the previous needs, a comprehensive simulation platform is going to be established at the University of Naples Federico II, aimed to help scientists and technicians in the development and testing of CAVs. The platform is based on a co-simulation approach, able to integrate different state-of-the art tools (coming from different domains) into one enhanced platform. The issues to be addressed and the architectural framework at the base of the integrated platform are here briefly presented
Smart roads e testing di veicoli autonomi
Full text linkOpportunità e criticità relative alla guida autonoma e connesso ed alla realizzazione delle Smart Roa
Elucidating the Relative and Absolute Configuration of Organic Compounds by Quantum Mechanical Approaches
No full textDifferent examples of the application of quantum mechanical (QM) methods combined with experimental approaches, such as NMR spectroscopy and electronic circular dichroism (ECD), are reported to highlight their successful application in the determination of the stereochemical arrangement of organic compounds. A first part is dedicated to various examples and methodological studies based on the comparison of experimental NMR parameters (13C and 1H NMR chemical shifts, homonuclear and heteronuclear J coupling constants) and their related values calculated at the QM theory level, with particular applications in the assignment of the relative configuration of organic compounds. A final section is focused on the determination of the absolute configuration by the comparison of experimental and predicted NMR parameters and ECD spectra
A comparison between two different computer-based simulation approaches to traffic analysis: static vs. dynamic
No full text
Addressing the Target Identification and Accelerating the Repositioning of Anti-Inflammatory/Anti-Cancer Organic Compounds by Computational Approaches
No full textThe use of computational chemistry techniques has led to notable advances in the structural and pharmacological investigation of organic compounds. The combination of quantum mechanical (QM) approaches with experimental methods (e. g., NMR spectroscopy) has contributed to the configurational and conformational structural assignment of the investigated items. Once this information has been obtained, in silico tools have been employed for assessing the pharmacological features of natural and synthetic molecules, especially those lacking precise information about their interacting macromolecules. With this aim, we have developed and implemented the Inverse Virtual Screening (IVS) computational methodology for addressing the target identification task. This minireview focuses on the key technical information and on successful examples about the convenient and fast use of such computational methods in the frame of the drug repositioning and the discovery of anti-inflammatory/anti-cancer agents
Going Beyond Counting First Authors in Author Co-citation Analysis
Full text linkThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
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