Factors Affecting the Acceptance of Autonomous Vehicle Technology: A Multiple Regression Analysis

The acceptance of autonomous vehicle technology is a topic of growing interest and research. This study aims to identify the factors that influence the acceptance of autonomous vehicle technology using multiple regression analysis. The study collected data on safety concerns, cost, infrastructure, regulation, trust, human factors, technical limitations, and cultural factors from a sample of 500 individuals. The study found that all of these variables were significant predictors of the acceptance of autonomous vehicle technology in the multiple regression analysis. Specifically, safety concerns, cost, and trust were found to have the strongest impact on the acceptance of autonomous vehicle technology. These findings have important implications for policymakers, industry practitioners, and researchers interested in promoting the widespread acceptance of autonomous vehicle technology. By understanding the factors that influence acceptance, stakeholders can develop effective strategies to overcome barriers to acceptance and accelerate the transition to a future with autonomous vehicles. The findings of this study provide important insights into the complex interplay of factors that affect the acceptance of autonomous vehicle technology. The study contributes to the existing literature on this topic by using a comprehensive approach that considers a wide range of factors. The results suggest that promoting the safety and reliability of autonomous vehicles, addressing cost concerns, and building trust among the public are key priorities for stakeholders interested in accelerating the acceptance of this technology. Additionally, the study highlights the need for continued research and development to address technical limitations and overcome cultural barriers that may impede acceptance. Overall, this study underscores the importance of a multidisciplinary approach to understanding and promoting the acceptance of autonomous vehicle technology

Reduced order models based on local POD plus Galerkin projection

A method is presented to accelerate numerical simulations on parabolic problems using a numerical code and a Galerkin system (obtained via POD plus Galerkin projection) on a sequence of interspersed intervals. The lengths of these intervals are chosen according to several basic ideas that include an a priori estimate of the error of the Galerkin approximation. Several improvements are introduced that reduce computational complexity and deal with: (a) updating the POD manifold (instead of calculating it) at the end of each Galerkin interval; (b) using only a limited number of mesh points to calculate the right hand side of the Galerkin system; and (c) introducing a second error estimate based on a second Galerkin system to account for situations in which qualitative changes in the dynamics occur during the application of the Galerkin system. The resulting method, called local POD plus Galerkin projection method, turns out to be both robust and efficient. For illustration, we consider a time-dependent Fisher-like equation and a complex Ginzburg–Landau equation

POD\u2013Galerkin reduced order methods for combined Navier\u2013Stokes transport equations based on a hybrid FV-FE solver

The purpose of this work is to introduce a novel POD\u2013Galerkin strategy for the semi-implicit hybrid high order finite volume/finite element solver introduced in Berm\ufadez et al. (2014) and Busto et al. (2018). The interest is into the incompressible Navier\u2013Stokes equations coupled with an additional transport equation. The full order model employed in this article makes use of staggered meshes. This feature will be conveyed to the reduced order model leading to the definition of reduced basis spaces in both meshes. The reduced order model presented herein accounts for velocity, pressure, and a transport-related variable. The pressure term at both the full order and the reduced order level is reconstructed making use of a projection method. More precisely, a Poisson equation for pressure is considered within the reduced order model. Results are verified against three-dimensional manufactured test cases. Moreover a modified version of the classical cavity test benchmark including the transport of a species is analysed

A computationally efficient reduced order model to generate multi-parameter fluid-thermal databases

A reduced order model (ROM) is proposed to generate multi-parameter databases of some fluid-thermal problems, using a combination of proper orthogonal decomposition, a gradient-like method, and a continuation method. The resulting ROM greatly reduces the CPU time required by slower methods based on genetic algorithm formulations. As a byproduct, the number of required snapshots is also reduced, which yields an additional improvement of the computational efficiency. The work presented in this article aims to facilitate the use of ROMs in industrial environments, in which time is a very important asset. The methodology is illustrated with the non-isothermal flow past a backward-facing step in the laminar regime, which is a representative problem, related to the engineering design of micro-heat sinks

Proper Orthogonal Decomposition Closure Models For Turbulent Flows: A Numerical Comparison

This paper puts forth two new closure models for the proper orthogonal decomposition reduced-order modeling of structurally dominated turbulent flows: the dynamic subgrid-scale model and the variational multiscale model. These models, which are considered state-of-the-art in large eddy simulation, together with the mixing length and the Smagorinsky closure models, are tested in the numerical simulation of a 3D turbulent flow around a circular cylinder at Re = 1,000. Two criteria are used in judging the performance of the proper orthogonal decomposition reduced-order models: the kinetic energy spectrum and the time evolution of the POD coefficients. All the numerical results are benchmarked against a direct numerical simulation. Based on these numerical results, we conclude that the dynamic subgrid-scale and the variational multiscale models perform best.Comment: 28 pages, 6 figure

POD-Galerkin reduced order methods for CFD using Finite Volume Discretisation: vortex shedding around a circular cylinder

Vortex shedding around circular cylinders is a well known and studied phenomenon that appears in many engineering fields. A Reduced Order Model (ROM) of the incom- pressible flow around a circular cylinder is presented in this work. The ROM is built performing a Galerkin projection of the governing equations onto a lower dimensional space. The reduced basis space is generated using a Proper Orthogonal Decomposition (POD) approach. In particular the focus is into (i) the correct reproduction of the pres- sure field, that in case of the vortex shedding phenomenon, is of primary importance for the calculation of the drag and lift coefficients; (ii) the projection of the Governing equations (momentum equation and Poisson equation for pressure) performed onto dif- ferent reduced basis space for velocity and pressure, respectively; (iii) all the relevant modifications necessary to adapt standard finite element POD-Galerkin methods to a finite volume framework. The accuracy of the reduced order model is assessed against full order results

Adaptive POD-based low-dimensional modeling supported by residual estimates

An adaptive low-dimensional model is considered to simulate time-dependent dynamics in nonlinear dissipative systems governed by PDEs. The method combines an inexpensive POD-based Galerkin system with short runs of a standard numerical solver that provides the snapshots necessary to first construct and then update the POD modes. Switching between the numerical solver and the Galerkin system is decided ‘on the fly’ by monitoring (i) a truncation error estimate and (ii) a residual estimate. The latter estimate is used to control the mode truncation instability and highly improves former adaptive strategies that detected this instability by monitoring consistency with a second instrumental Galerkin system based on a larger number of POD modes. The most computationally expensive run of the numerical solver occurs at the outset, when the whole set of POD modes is calculated. This step is improved by using mode libraries, which may either be generic or result from former applications of the method. The outcome is a flexible, robust, computationally inexpensive procedure that adapts itself to the local dynamics by using the faster Galerkin system for the majority of the time and few, on demand, short runs of a numerical solver. The method is illustrated considering the complex Ginzburg Landau equation in one and two space dimensions

Health of men, women, and children in post-traffi cking services in Cambodia, Thailand, and Vietnam: an observational cross-sectional study

Background Traffi cking is a crime of global proportions involving extreme forms of exploitation and abuse. Yet little research has been done of the health risks and morbidity patterns for men, women, and children traffi cked for various forms of forced labour. Methods We carried out face-to-face interviews with a consecutive sample of individuals entering 15 post-traffi cking services in Cambodia, Thailand, and Vietnam. We asked participants about living and working conditions, experience of violence, and health outcomes. We measured symptoms of anxiety and depression with the Hopkins Symptoms Checklist and post-traumatic stress disorder with the Harvard Trauma Questionnaire, and used adjusted logistic regression models to estimate the eff ect of traffi cking on these mental health outcomes, controlling for age, sector of exploitation, and time in traffi cking. Findings We interviewed 1102 people, of whom 1015 reached work destinations. Participants worked in various sectors including sex work (329 [32%]), fi shing (275 [27%]), and factories (136 [13%]). 481 (48%) of 1015 experienced physical violence, sexual violence, or both, with 198 (35%) of 566 women and girls reporting sexual violence. 478 (47%) of 1015 participants were threatened and 198 (20%) were locked in a room. 685 (70%) of 985 who had data available worked 7 days per week and 296 (30%) of 989 worked at least 11 hours per day. 222 (22%) of 983 had a serious injury at work. 61·2% (95% CI 58·2–64·2) of participants reported symptom of depression, 42·8% (39·8–45·9) reported symptoms of anxiety, and 38·9% (36·0–42·0) reported symptoms of post-traumatic stress disorder. 5·2% (4·0–6·8) had attempted suicide in the past month. Participants who experienced extremely excessive overtime at work, restricted freedom, bad living conditions, threats, or severe violence were more likely to report symptoms of depression, anxiety, and post-traumatic stress disorder. Interpretation This is the fi rst health study of a large and diverse sample of men, women, and child survivors of traffi cking for various forms of exploitation. Violence and unsafe working conditions were common and psychological morbidity was associated with severity of abuse. Survivors of traffi cking need access to health care, especially mental health care

Finite volume POD-Galerkin stabilised reduced order methods for the parametrised incompressible Navier\u2013Stokes equations

In this work a stabilised and reduced Galerkin projection of the incompressible unsteady Navier\u2013Stokes equations for moderate Reynolds number is presented. The full-order model, on which the Galerkin projection is applied, is based on a finite volumes approximation. The reduced basis spaces are constructed with a POD approach. Two different pressure stabilisation strategies are proposed and compared: the former one is based on the supremizer enrichment of the velocity space, and the latter one is based on a pressure Poisson equation approach