Predictive factors of success at the French National Ranking Examination (NRE) : a retrospective study of the student performance from a French medical school

Background The national ranking examination (NRE) marks the end of the second cycle (6th university year) of French medical studies and ranks students allowing them to choose their specialty and city of residency. We studied the potential predictive factors of success at the 2015 NRE by students attending a French School of Medicine. Methods From March 2016 to March 2017, a retrospective study of factors associated with the 2015 NRE success was conducted and enrolled 242 students who attended their sixth year at the school of medicine of Reims. Demographic and academic data collected by a home-made survey was studied using univariate and then multivariate analysis by generalized linear regression with a threshold of p <  0.05 deemed significant. Results The factors independently associated with a better ranking at the NRE were the motivation for the preparation of the NRE (gain of 3327 ± 527 places, p <  0.0001); to have participated in the NRE white test organized by la Revue du Praticien in November 2014 (gain of 869 ± 426 places, p <  0.04), to have participated in the NRE white test organized by la conférence Hippocrate in March 2015 (+ 613 places ±297, p <  0.04). The factors independently associated with poor NRE ranking were repeating the first year (loss of 1410 places ±286, p <  0.0001), repeating a year during university course (loss of 1092 places ±385, p <  0.005), attendance of hospital internships in 6th year (loss of 706 places ±298, p <  0.02). Conclusions The student motivation and their white tests completion were significantly associated with success at the NRE. Conversely, repeating a university year during their course and attendance of 6th year hospital internships were associated with a lower ranking

Sustainable Investing and the Cross-Section of Maximum Drawdown

We use supervised learning to identify factors that predict the cross-section of maximum drawdown for stocks in the US equity market. Our data run from January 1980 to June 2018 and our analysis includes ordinary least squares, penalized linear regressions, tree-based models, and neural networks. We find that the most important predictors tended to be consistent across models, and that non-linear models had better predictive power than linear models. Predictive power was higher in calm periods than stressed periods, and environmental, social, and governance indicators augmented predictive power for non-linear models

Reproductive Den Habitat Characterization of American Badgers (\u3cem\u3eTaxidea taxus\u3c/em\u3e) in Central California

The American badger (Taxidea taxus) is a species of special concern in California, and, as such, conservation measures are necessary. The goal of this study was to identify potential reproductive den habitat characteristics in order to more accurately predict critical reproductive habitat in central California grasslands. A paired study design was used to examine differences between reproductive and non-reproductive sites, and logistical regression was used to analyze the variables and produce two predictive models, one with biotic factors and one with abiotic factors. Badgers in central Californian grasslands appear to rely on both biotic and abiotic factors when selecting locations for reproductive den sites. Predictive biotic variables included amount of ground vegetation, presence of predators, presence of prey, and nearest shrub width. Predictive abiotic variables included distance to a drainage point and slopes at 10, 30, and 40 m from the den entrance. Integrating information from these models into conservation efforts will identify critical reproductive habitat and help form viable conservation strategies for the species

Success Factors in Peer-to-Business (P2B) Crowdlending: A Predictive Approach

Peer-to-Business (P2B) crowdlending is gaining importance among companies seeking funding. However, not all projects get the same take-up by the crowd. Thus, this study aims to determine the key factors that drive non-professional investors to choose a given loan in an online environment. To this purpose, we have analyzed 243 crowdlending campaigns on October.eu platform. We have obtained a series of variables from the analyzed loans using logistic regression. Results indicate that loan amount, loan term and overall credit rating are the key predictors of non-professional lender P2B crowdlending success. These findings may be useful for predicting whether the crowd will subscribe to a loan request or not. This information would help businesses to modify specific loan characteristics (if possible) to make their loans more attractive or could even lead companies to consider a different financial option. It could also help platforms select and adapt project parameters to secure their success

Pauses and the temporal structure of speech

Natural-sounding speech synthesis requires close control over the temporal structure of the speech flow. This includes a full predictive scheme for the durational structure and in particuliar the prolongation of final syllables of lexemes as well as for the pausal structure in the utterance. In this chapter, a description of the temporal structure and the summary of the numerous factors that modify it are presented. In the second part, predictive schemes for the temporal structure of speech ("performance structures") are introduced, and their potential for characterising the overall prosodic structure of speech is demonstrated

A logistic regression model for microalbuminuria prediction in overweight male population

Background: Obesity promotes progression to microalbuminuria and increases the risk of chronic kidney disease. Current protocols of screening microalbuminuria are not recommended for the overweight or obese.&#xd;&#xa;&#xd;&#xa;Design and Methods: A cross-sectional study was conducted. The relationship between metabolic risk factors and microalbuminuria was investigated. A regression model based on metabolic risk factors was developed and evaluated for predicting microalbuminuria in the overweight or obese.&#xd;&#xa;&#xd;&#xa;Results: The prevalence of MA reached up to 17.6% in Chinese overweight men. Obesity, hypertension, hyperglycemia and hyperuricemia were the important risk factors for microalbuminuria in the overweight. The area under ROC curves of the regression model based on the risk factors was 0.82 in predicting microalbuminuria, meanwhile, a decision threshold of 0.2 was found for predicting microalbuminuria with a sensitivity of 67.4% and specificity of 79.0%, and a global predictive value of 75.7%. A decision threshold of 0.1 was chosen for screening microalbuminuria with a sensitivity of 90.0% and specificity of 56.5%, and a global predictive value of 61.7%.&#xd;&#xa;&#xd;&#xa;Conclusions: The prediction model was an effective tool for screening microalbuminuria by using routine data among overweight populations

Extracting low-dimensional psychological representations from convolutional neural networks

Deep neural networks are increasingly being used in cognitive modeling as a means of deriving representations for complex stimuli such as images. While the predictive power of these networks is high, it is often not clear whether they also offer useful explanations of the task at hand. Convolutional neural network representations have been shown to be predictive of human similarity judgments for images after appropriate adaptation. However, these high-dimensional representations are difficult to interpret. Here we present a method for reducing these representations to a low-dimensional space which is still predictive of similarity judgments. We show that these low-dimensional representations also provide insightful explanations of factors underlying human similarity judgments.Comment: Accepted to CogSci 202

Predictive Factors Associated with Solar Energy Development in Laikipia District Central Kenya

The abundance of sunlight and the availability affordable solar technologies in many areas far from grid-based electricity has sparked the development of renewable energy technologies (RETs) which tap solar radiation energy to provide electricity. A study on solar photovoltaics (SPVs) use and utilization took place in the Wiyumiririe Location of Kenya. A purposive randomized convenience sample of 246 households was selected and landowner interviews conducted guided by a questionnaire, followed by field surveys and observations. Although solar energy contributed less than a quarter of total household energy needs, residents specifically associated it with specific developmental initiatives. Correlation and logistic regression model analyses showed that solar power development was closely associated (and thus can be predicted) from five main independent variables. The findings of the study allowed the development of a probabilistic model general enough to be applicable elsewhere in the development of alternative energy resources particularly those based on solar input