Posters are a great way to showcase your work, whether at conferences, class presentations, or university open days. Formatting a poster correctly can be difficult but these templates and examples make it easy to create beautiful, eye-catching posters with key content clearly laid out. Each template provides placeholders for text, tables, figures and equations. Font size is usually set automatically, and it’s easy to switch between landscape or portrait, A0, A1, A2, A3 and A4 size posters.
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.
Prihadi Sumintadireja, Dasapta Erwin Irawan, Rina Herdianita, Yuano Rezky, Prana Ugiana Gio, Anggita Agustin, and Ali Lukman
Updates:
qr code on the left or on the right
color-coded depending on the type of poster
refactorisation for easier use
LaTeX Implementation of this video https://www.youtube.com/watch?v=1RwJbhkCA58&t=1s
Original powerpoint at https://osf.io/g6xsm/
Template contendo o modelo de POSTER para trabalhos acadêmicos desenvolvidos por alunos do curso de Engenharia de Computação do Centro Federal Celso Suckow da Fonseca (CEFET) campus Petrópolis.
MatPlotLib and Random Cheat Sheet
Edited by Michelle Cristina de Sousa Baltazar
http://matplotlib.org/api/pyplot_summary.html
http://matplotlib.org/users/pyplot_tutorial.html