25 research outputs found
Nurses' Labour Supply Elasticities: The Importance of Accounting for Extensive Margins
Techno-economic and simulation study of a V2I-based cooperative manoeuvring case in a cross-border scenario
In this paper, we present a techno-economic analysis of a Cooperative, Connected and Automated Mobility (CCAM) use case in a specific cross-border environment, namely Cooperative Lane Merging (CLM). The latter is assumed to rely on Vehicle-to-Infrastructure (V2I) connectivity with respect to a set of inter-connected Road Side Units (RSUs). In order to feed the techno-economic framework with the required data in terms of road infrastructure, extensive system-level simulations have been performed using a connectivity oriented key performance indicator (KPI), while considering three different deployment scenarios and realistic road traffic densities. The proposed model identifies the minimum additional RSUs required to satisfy the CLM KPI with respect to the number of simultaneous connected cars. First results show the beneficial impact from densifying the road network infrastructure on the CLM service availability, especially under the highest road traffic conditions. In terms of Total Cost of Ownership (TCO), cost results of a set of scenarios considering the variation of both the number of connected cars and the RSUs to be deployed are discussed as well
Hydrogen and oxygen isotope compositions of eclogites from the Dabie Mountains and geodynamic implications
Evaluation of the predictive value of Gensini score on determination of severity of coronary artery disease in cases with left bundle branch block
Densities and viscosities of binary mixtures of tri-n-butyl phosphate + cyclohexane, + n-heptane at T = (288.15, 293.15, 298.15, 303.15, and 308.15) K
Densities and viscosities of binary mixtures of tri-n-butyl phosphate with cyclohexane and n-heptane have been measured over the entire range of compositions at T = (288.15, 293.15, 298.15, 303.15, and 308.15) K and atmospheric pressure. The viscosity data have been represented by the Grunberg−Nissan equation