Sensory analysis via multi-block multivariate additive PLS splines

In the last decade, much effort has been spent on modelling dependence between sensory variables and chemical–physical ones, especially when observed at different occasions/spaces/times or if collected from several groups (blocks) of variables. In this paper, we propose a nonlinear generalization of multi-block partial least squares with the inclusion of variable interactions. We show the performance of the method on a known data set.     

MANAGING TWO ROLES

The main reasons for students combining term-time employment with university life are seen as financial, although additional benefits and costs are associated with ‘earning and learning’. Most studies focus on the negative impact of combining these roles, suggesting that students’ term-time employment leads to poorer adjustment to university life in terms of academic performance, social inclusion and reduced psychological well-being. Methodological problems with previous research have made it difficult to draw firm and generalizable conclusions regarding the interrelationship between university life and term-time employment roles. The current study adopts a pluralistic approach to study the links between psychological well-being, satisfaction with academic performance and social integration and students’ term-time and vacation employment. The first phase, reported here, consisted of qualitative research. Nine focus groups were conducted with undergraduate students at Stirling University, Scotland. These data were examined using a content analysis approach. Both positive and negative aspects of combining work and studying were discussed within the focus groups. This provides a balanced picture of combining the two roles. Results are discussed in relation to the models of complementarity, spillover and separation.     

Barcoding,linear and nonlinear analysis of full-day leg movements in infants with typical development and infants at risk of developmental disabilities: Cross-sectional study

Traditional methods do not capture the multidimensional domains and dynamic nature of infant behavioral patterns. We aim to compare full-day, in-home leg movement data between infants with typical development (TD) and infants at risk of developmental disabilities (AR) using barcoding and nonlinear analysis. Eleven infants with TD (2–10 months) and nine infants AR (adjusted age: 2–14 months) wore a sensor on each ankle for 7 days. We calculated the standard deviation for linear variability and sample entropy (SampEn) of leg acceleration and angular velocity for nonlinear variability. Movements were also categorized into 16 barcoding states, and we calculated the SampEn and proportions of the barcoding. All variables were compared between the two groups using independent-samples t-test or Mann-Whitney U test. The AR group had larger linear variability compared to the TD group. SampEn was lower in the AR group compared to TD group for both acceleration and angular velocity. Two barcoding states’ proportions were significantly different between the two groups. The results showed that nonlinear analysis and barcoding could be used to identify the difference of dynamic multidimensional movement patterns between infants AR and infants with TD. This information may help early diagnosis of developmental disabilities in the future.