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.
Improper oral care is often an unintentional lifestyle that can lead to a variety of health issues. Lacking access to oral health care services is a problem faced by many, which can lead to adverse economic and health consequences. Tooth decay is one of the earliest indicators of inadequate oral health care in children. By utilizing a preventative approach that o ers u- oride tablets to elementary children in uoride-de cient com- munities, the prevalence of oral cavities can be combated.
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.
Prihadi Sumintadireja, Dasapta Erwin Irawan, Rina Herdianita, Yuano Rezky, Prana Ugiana Gio, Anggita Agustin, and Ali Lukman