Thermodynamic geometry of black holes in f(R) gravity

In this paper, we consider three types (static, static charged and rotating charged) of black holes in f(R) gravity. We study the thermodynamical behavior, stability conditions and phase transition of these black holes. It will be shown that, the number and type of phase transition points are related to different parameters, which shows the dependency of stability conditions to these parameters. Also, we extended our study to different thermodynamic geometry methods (Ruppeiner, Weinhold and GTD). Next, we investigate the compatibility of curvature scalar of geothermodynamic methods with phase transition points of the above balck holes. In addition, we point out the effect of different values of spacetime parameters on stability conditions of mentioned black holes.Comment: 45 figures,35 page

Data base management and quality control approaches: Users and developers

The widepsread use of Computer Integrated Manufacturing, the quest for Total Quality Management, and the increasing role of technology management have given an added importance to today\u27s application of DBMS and the accuracy of its data. Indeed, the empirical evidence has documented data inaccuracy as the most (difficult part of implementing information systems softwares. This paper explores the process of building a data base necessary to ensure the quality of the data for conversion into information. This article also illustrates the application of quality control techniques for maintaining data base quality

A Note on the PageRank of Undirected Graphs

The PageRank is a widely used scoring function of networks in general and of the World Wide Web graph in particular. The PageRank is defined for directed graphs, but in some special cases applications for undirected graphs occur. In the literature it is widely noted that the PageRank for undirected graphs are proportional to the degrees of the vertices of the graph. We prove that statement for a particular personalization vector in the definition of the PageRank, and we also show that in general, the PageRank of an undirected graph is not exactly proportional to the degree distribution of the graph: our main theorem gives an upper and a lower bound to the L_1 norm of the difference of the PageRank and the degree distribution vectors

Computational Modeling of Trust Factors Using Reinforcement Learning

As machine-learning algorithms continue to expand their scope and approach more ambiguous goals, they may be required to make decisions based on data that is often incomplete, imprecise, and uncertain. The capabilities of these models must, in turn, evolve to meet the increasingly complex challenges associated with the deployment and integration of intelligent systems into modern society. Historical variability in the performance of traditional machine-learning models in dynamic environments leads to ambiguity of trust in decisions made by such algorithms. Consequently, the objective of this work is to develop a novel computational model that effectively quantifies the reliability of autonomous decision-making algorithms. The approach relies on the implementation of a neural network based reinforcement learning paradigm known as adaptive critic design to model an adaptive decision making process that is regulated by a quantitative measure of risk associated with each possible decision. Specifically, this work expands on the risk-directed exploration strategies of reinforcement learning to obtain quantitative risk factors for an automated object recognition process in the presence of imprecise data. Accordingly, this work addresses the challenge of automated risk quantification based on the confidence of the decision model and the nature of given data. Additionally, further analysis into risk directed policy development for improved object recognition is presented

Development and Application of Tools for Genetic Analysis of Clonal Populations

Research on the population genetics of microbial organisms requires the use of specialized analyses designed for clonal organisms to avoid violating the assumptions of traditional population genetic models. The tools necessary for performing these analyses existed as a set of unrelated software with non-overlapping capabilities and did not cover all aspects of analysis. This meant that researchers not only had to reshape their data into different formats for each analysis, but they also had to switch computing platforms, thus creating a drain in time, and increasing the risk of propagating human error into the analysis. To address this problem, we created the software package poppr, written in the R statistical language, available on all computing platforms. This package is designed for analysis of clonal, partially clonal, and sexual populations, empowering researchers to perform their work in a reproducible manner. We additionally demonstrate the utility of poppr for both plant pathological and theoretical questions by using real-world and simulated data. In chapter 4, we apply these new tools to demonstrate evidence for at least two origins for the outbreak of the Sudden Oak Death pathogen, Phytophthora ramorum in Curry County, Oregon. In chapter 5, we use poppr to assess the power of the index of association with clone-correction, showing that clone-correction has the potential to reduce the power of detecting clonal reproduction. All of the software and analyses in this work were performed in an open and reproducible framework, serving as an example of the power of reproducible research in plant pathology