CarEs: An Emotional Model of a Car With the Stress Factor

Autonomous driving is a growing research field, that still has many challenges. The main challenges are related to decision‑making algorithms, human‑machine interaction, and acceptance of the technology. Also, the absence of human drivers in autonomous vehicles creates a gap between users and pedestrians interacting with the vehicle. This article aims to define vehicle awareness, which eases the collaboration with users to improve safety and have a more human‑like driving to increase technology acceptance. In addition, our approach can be extended to express vehicle social awareness towards pedestrians and road users. Our approach is based on affective computing. Affective computing is a tool to grant computers to genuinely become intelligent and interact better with humans. Moreover, one of its components is the generation of emotions, of which two of the most important elements are cognitive emotions and primary emotions. The article’s objective is to design the model of a primary emotion component, based on safety that can be personalized depending on the user’s driving style. This component is called the stress factor. The stress factor is correlated with the probability of an accident. The vehicle stress factors contain parameters that can be personalized as a function of a driving style. The stress factor is then attached to an existing cognitive emotion system (CarE) in the automotive domain which we called CarEs. The results of the system behavior showed promising results. The stress factor showed to be useful as a safety indicator. Also, the stress factor can be personalized with the vehicle operation state component. In conclusion, the new system known as CarEs generates vehicle awareness, by improving the vehicle’s collaboration with the driver. The collaboration has a positive impact on the vehicle’s safety and comfort, and people’s reliance on automated vehicles

A comparative analysis of foliar chemical composition and leaf construction costs of beech (Fagus sylvatica L.), sycamore maple (Acer pseudoplatanus L.) and ash (Fraxinus excelsior L.) saplings along a light gradient

• Construction cost (g glucose g−1), chemical composition and morphology of leaves of beech (Fagus sylvatica L.) and two co-occurring valuable broadleaved species (sycamore maple – Acer pseudoplatanus L. – and ash – Fraxinus excelsior L.) were investigated along a horizontal light gradient (3–60% of above canopy radiation) and from top to bottom within the crowns in a fairly even-aged mixed-species thicket established by natural regeneration beneath a patchy shelterwood canopy. • Construction cost and carbon concentration increased with irradiance in ash and sycamore maple and were independent of irradiance in beech. Leaf traits expressed on an area basis, like construction cost, nitrogen content and leaf mass (LMA) increased significantly with irradiance in all three species and decreased from top to bottom within crowns. • The shade tolerant beech invested more glucose to produce a unit foliar biomass, but less to build a unit foliar area due to lower LMA. Thereby beech was able to display a greater total leaf area, what at least in parts counterbalanced the lower values of Na as compared to ash and sycamore maple

Rapid Detection of Human Torque Teno Viruses Using High-Resolution Melting Analysis

Torque teno viruses (TTVs) are recently discovered DNA viruses, with heterogeneous genomes, highly prevalent in populations worldwide. The species that infect humans are Torque teno virus (TTV), Torque teno midi virus (TTMDV) and Torque teno mini virus (TTMV). High-resolution melting analysis (HRMA) is a sensitive and effective method for genotyping and mutation scanning. Up to now, HRMA has not been utilized for detection of TTVs

Comprehensive analysis of drugs to treat SARS-CoV-2 infection: Mechanistic insights into current COVID-19 therapies (Review)

The major impact produced by the severe acute respiratory syndrome coronavirus 2 (SARS-coV-2) focused many researchers attention to find treatments that can suppress transmission or ameliorate the disease. Despite the very fast and large flow of scientific data on possible treatment solutions, none have yet demonstrated unequivocal clinical utility against coronavirus disease 2019 (COVID-19). This work represents an exhaustive and critical review of all available data on potential treatments for COVId-19, highlighting their mechanistic characteristics and the strategy development rationale. Drug repurposing, also known as drug repositioning, and target based methods are the most used strategies to advance therapeutic solutions into clinical practice. current in silico, in vitro and in vivo evidence regarding proposed treatments are summarized providing strong support for future research efforts. © 2020 Spandidos Publications. All rights reserved

Development of a Virtual Simulation Environment and a Digital Twin of an Autonomous Driving Truck for a Distribution Center

This paper presents the development of a Virtual Simulation Environment (VSE) and a Digital Twin (DT) of an autonomously driving truck for a distribution center. While autonomous driving on public roads still faces various technical and legal challenges, within a distribution center, which is a confined area, some of these restrictions do not apply. Therefore, distribution centers can be the first environment where the autonomous driving of trucks is possible. A distribution center is a closed environment with no, or minimal generic traffic, where the trucks have relatively low speeds, short stopping distance and layout precisely known. Dedicated sensors locate the trucks. This paper addresses the mentioned aspects of driving in the distribution centers describing the necessary steps taken for the design, implementation, and testing of a VSE for a distribution center, and a DT of an autonomously driving truck. The development of the VSE is based on the integration of a SysML modeling tool – IBM Rhapsody, MATLAB Simulink, and Unity Game Engine using a Model-Based System Engineering approach. The paper also presents the test and the validation of a driving scenario used in a distribution center, using the TruckLab setup of the Eindhoven University of Technology, The Netherlands. The VSE and the DT showed considerable potential as testing and validation tools for automotive engineers, making it possible to define driving test scenarios for different types of tractor and trailer combinations