Articles — Posters

Exercício_Metododogia

Trabalho 4 - Metodologia Científica

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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.

Uno de los fenómenos más extendidamente estudiados y considerados en la Cosmología Observacional es el corrimiento al rojo. En particular, el corrimiento al rojo gravitacional es la tendencia de la luz proveniente de los cúmulos de galaxias (galaxy clusters en inglés) a correrse hacia el rojo en su espectro electromagnético que llega a la Tierra, debido a los pozos de potencial gravitacional que traspasa a la hora de desplazarse hasta nosotros. Se sabe que este corrimiento es proporcional a estas diferencias del potencial gravitacional entre una y otra región dentro de los cúmulos, y es más observado sobretodo en aglomeraciones más densas de estrellas; por lo cual está considerado como fenómeno de gran escala en cosmología. En años recientes (Wojtak, 2011) (Croft, 2013), se ha mostrado cómo la fenomenología del corrimiento al rojo gravitacional va de la mano con lo propuesto por la teoría de la Relatividad General de Einstein, ya con un siglo de antigüedad, y su vertiente contemporánea más exitosa dentro de la cosmología: el modelo ΛCDM. Teorías modificadas de la gravedad como f(R) y MOND-TeVeS se han prestado a comparaciones en los últimos años. Se describe pues aquí un poco de los resultados de estos análisis comparativos recientes, para corrimientos al rojo gravitacionales de cúmulos de galaxias.

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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.