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A Basic LaTeX template for the NSF GRFP Research Plan document.

Abstract for the Austrian HPC Meeting 2017 - AHPC17

Template for CS 109 PSET at Stanford

Homework template for MATH 304 Spring 2017

Modified previously existing template for CS 330 Duke HW Latex-ing Includes following packages: fancyhdr % Required for custom headers lastpage % Required to determine the last page for the footer extramarks % Required for headers and footers \usepackage[usenames,dvipsnames]{color % Required for custom colors subcaption graphicx % Required to insert images listings % Required for insertion of code courier % Required for the courier font comment multirow amsmath \usepackage[table,xcdraw]{xcolor geometry pdflscape pdfpages booktabs geometry

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The following paper is a try out on the application of multivariate analysis (regression tree, principal component analysis, and cluster analysis) for classifying hot water chemistry. The number of sample analysed was 416 from all over Indonesia. Regression tree technique has failed to read the data structure due to multi-collinearity effect therefore PCA and cluster analysis were applied. We used open source R statistical packages to do the calculation. Such technique classifies hot water samples into three major clusters: cluster 1 (pure hot water), cluster 2 (mixing water), and cluster 3 (cold-meteoric water). Similar clustering were also detected in the PCA plot. The statistical is able to detect the close and open geothermal system based on data structure. This robust method should be applied to more geothermal system with larger dataset to see its performance.

Excerpt from main dissertation

Excerpt from main dissertation