University of Kentucky

About: University of Kentucky is a education organization based out in Lexington, Kentucky, United States. It is known for research contribution in the topics: Population & Poison control. The organization has 43933 authors who have published 92195 publications receiving 3256087 citations. The organization is also known as: UK.

An Efficient Identity-Based Conditional Privacy-Preserving Authentication Scheme for Vehicular Ad Hoc Networks

Abstract: By broadcasting messages about traffic status to vehicles wirelessly, a vehicular ad hoc network (VANET) can improve traffic safety and efficiency. To guarantee secure communication in VANETs, security and privacy issues must be addressed before their deployment. The conditional privacy-preserving authentication (CPPA) scheme is suitable for solving security and privacy-preserving problems in VANETs, because it supports both mutual authentication and privacy protection simultaneously. Many identity-based CPPA schemes for VANETs using bilinear pairings have been proposed over the last few years to enhance security or to improve performance. However, it is well known that the bilinear pairing operation is one of the most complex operations in modern cryptography. To achieve better performance and reduce computational complexity of information processing in VANET, the design of a CPPA scheme for the VANET environment that does not use bilinear paring becomes a challenge. To address this challenge, we propose a CPPA scheme for VANETs that does not use bilinear paring and we demonstrate that it could supports both the mutual authentication and the privacy protection simultaneously. Our proposed CPPA scheme retains most of the benefits obtained with the previously proposed CPPA schemes. Moreover, the proposed CPPA scheme yields a better performance in terms of computation cost and communication cost making it be suitable for use by the VANET safety-related applications.

Production of Hydrogen from Ethanol: Review of Reaction Mechanism and Catalyst Deactivation

Abstract: Mechanism and Catalyst Deactivation Lisiane V. Mattos,† Gary Jacobs,‡ Burtron H. Davis,‡ and Fab́io B. Noronha* †Departamento de Engenharia Química e de Petroĺeo, Universidade Federal Fluminense (UFF), Rua Passo da Pat́ria, 156-CEP 24210-240, Niteroí, RJ, Brazil ‡Center for Applied Energy Research, The University of Kentucky, 2540 Research Park Drive, Lexington, Kentucky 40511, United States Instituto Nacional de Tecnologia−INT, Av. Venezuela 82, CEP 20081-312, Rio de Janeiro, Brazil

Diagnostic categories or dimensions? A question for the Diagnostic And Statistical Manual Of Mental Disorders--fifth edition.

Abstract: The question of whether mental disorders are discrete clinical conditions or arbitrary distinctions along dimensions of functioning is a long-standing issue, but its importance is escalating with the growing recognition of the frustrations and limitations engendered by the categorical model. The authors provide an overview of some of the dilemmas of the categorical model, followed by a discussion of research that addresses whether mental disorders are accurately or optimally classified categorically or dimensionally. The authors' intention is to document the importance of this issue and to suggest that future editions of the Diagnostic and Statistical Manual of Mental Disorders give more recognition to dimensional models of classification. They conclude with a dimensional mental disorder classification that they suggest provides a useful model.

Stereo Matching with Color-Weighted Correlation, Hierarchical Belief Propagation, and Occlusion Handling

Abstract: In this paper, we formulate a stereo matching algorithm with careful handling of disparity, discontinuity, and occlusion. The algorithm works with a global matching stereo model based on an energy-minimization framework. The global energy contains two terms, the data term and the smoothness term. The data term is first approximated by a color-weighted correlation, then refined in occluded and low-texture areas in a repeated application of a hierarchical loopy belief propagation algorithm. The experimental results are evaluated on the Middlebury data sets, showing that our algorithm is the top performer among all the algorithms listed there.