Abstract

Soursop (Annona muricata L.) germplasm, from the central region of the Ecuadorian littoral, has diverse phenotypic characteristics, which have been little studied. The main objective of this research was to characterize in situ the morphological variability of 60 accessions of soursop. The morphological traits were evaluated using 20 quantitative and 10 qualitative variables of the plant and the fruit. Multivariate statistical methods were used as principal components, hierarchical conglomerate aggregation, discriminant and correlation analysis (Pearson) were performed. Descriptive analysis such as central tendency and dispersion (coefficient of variation, range, mean and standard deviation) were determined for the quantitative data; while frequencies were calculated for qualitative data. In terms of morphological traits, 74 % of the total variability was explained by the fourth component. Three conglomerates of similarity were formed, in which the height, diameter of canopy and fruit number per tree, were the characters of greater contribution for its conformation.

Abstract

This paper analyzes the relation between commuting time and health in the United Kingdom. I focus on four different types of health outcomes: subjective health measures, objective health measures, health behavior, and health care utilization. Fixed effect models are estimated with British Household Panel Survey data. I find that whereas objective health and health behavior are barely affected by commuting time, subjective health measures are clearly lower for people who commute longer. A longer commuting time is, moreover, related to more visits to the general practitioner. Effects turn out to be more pronounced for women and for commuters driving a car. For women, commuting time is also negatively related to regular exercise and positively to calling in sick.

Abstract

International audience; Human Activity Recognition (HAR) is a well-studied scientific area that has gained much traction with the rise of Internet of Things (IoT). Despite the interest in HAR for a wide spectrum of domains (technological, medical, etc.) only a few works exist, which study the variability in IoT data. To correctly perceive this variability, it is essential to dynamically model the evolving context of daily-life activities. Additionally, it is required to reduce the calculation cost of HAR, which is crucial for security and real-time applications. For the purpose of dynamically modeling, three context-aware approaches are formalized along with a context-free baseline. This study demonstrates improvements in terms of both of accuracy and calculation cost by considering variability in IoT data; our experimental study on real datasets reduced calculation cost by 20% while increasing accuracy by 20%.