Gallery — Poster

Information before unblinding regarding the success of confirmatory clinical trials is highly uncertain. Estimates of expected future power which purport to use this information for purposes of sample size adjustment after given interim points need to reflect this uncertainty. Estimates of future power at later interim points need to track the evolution of the clinical trial. We employ sequential models to describe this evolution. We show that current techniques using point estimates of auxiliary parameters for estimating expected power: (i) fail to describe the range of likely power obtained after the anticipated data are observed, (ii) fail to adjust to different kinds of thresholds, and (iii) fail to adjust to the changing patient population. Our algorithms address each of these shortcomings. We show that the uncertainty arising from clinical trials is characterized by filtering later auxiliary parameters through their earlier counterparts and employing the resulting posterior distribution to estimate power. We devise MCMC-based algorithms to implement sample size adjustments after the first interim point. Bayesian models are designed to implement these adjustments in settings where both hard and soft thresholds for distinguishing the presence of treatment effects are present. Sequential MCMC-based algorithms are devised to implement accurate sample size adjustments for multiple interim points. We apply these suggested algorithms to a depression trial for purposes of illustration.

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.

Based on the MUW Poster LaTeX Template and the Jacobs Portrait Poster Adaptado por Augusto Ferreira, Universidade Federal de Lavrasgstfrr@gmail.com

Modelo Latex para criação de pôsteres no formato 120x90cm para a Universidade Federal de Goiás - UFG. A LaTeX template to help formatting 120x90cm posters for Universidade Federal de Goiás - UFG.

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.

Modelo de poster utilizado pelo Mestrado Profissional em Instrumentação, Controle e Automação de Processos de Mineração para o SBAI 2017.

evaluacion de correlaciones para la determinacion del coeficiente de transferecia de calor en un solo tubo

El proyecto está basado en la construcción y diseño de un transmisor de frecuencia modulada de baja potencia, que consta de dos etapas, una la cual amplifica la señal que es una señal de audio sinusoidal por medio de una entrada Jack 3.5 que va directamente a un dispositivo electrónico ya sea un reproductor mp3 o también un celular o computador, y la segunda etapa se encarga de modular la señal y transmitirla, el circuito tiene una como fuente una pila de 9v y su diseño está basado en una forma en la que pueda ser portable y de fácil uso en general, utilizando los conocimientos vistos en todas las clases en especial la que tenían enfoque en transmisión de frecuencia modulada y también el comportamiento de los dispositivos electrónicos básicos en alta frecuencia, basados en esta teoría pudimos construir el transmisor.

Modelo de poster padrão do Programa de Pós-graduação em Computação Aplicada, Departamento de Computação e Matemática - DCM, FFCLRP, USP. Ribeirão Preto.