The Hierarchical Age-Period-Cohort model: Why does it find the results that it finds?

It is claimed the hierarchical-age–period–cohort (HAPC) model solves the age–period–cohort (APC) identification problem. However, this is debateable; simulations show situations where the model produces incorrect results, countered by proponents of the model arguing those simulations are not relevant to real-life scenarios. This paper moves beyond questioning whether the HAPC model works, to why it produces the results it does. We argue HAPC estimates are the result not of the distinctive substantive APC processes occurring in the dataset, but are primarily an artefact of the data structure—that is, the way the data has been collected. Were the data collected differently, the results produced would be different. This is illustrated both with simulations and real data, the latter by taking a variety of samples from the National Health Interview Survey (NHIS) data used by Reither et al. (Soc Sci Med 69(10):1439–1448, 2009) in their HAPC study of obesity. When a sample based on a small range of cohorts is taken, such that the period range is much greater than the cohort range, the results produced are very different to those produced when cohort groups span a much wider range than periods, as is structurally the case with repeated cross-sectional data. The paper also addresses the latest defence of the HAPC model by its proponents (Reither et al. in Soc Sci Med 145:125–128, 2015a). The results lend further support to the view that the HAPC model is not able to accurately discern APC effects, and should be used with caution when there appear to be period or cohort near-linear trends

Etude du fonctionnement de systemes agropedologiques en milieu equatorial (Amazonie bresilienne) et de leurs transformations sous l'effet de la deforestation et de la mise en valeur

Available at INIST (FR), Document Supply Service, under shelf-number : AR 14809 / INIST-CNRS - Institut de l'Information Scientifique et TechniqueSIGLEFRFranc

Indoor Environmental Quality: Lighting and Acoustics

Buildings are characterized by a mixture of environmental factors—(hygro)thermal, olfactory, luminous, and acoustic—that are commonly encapsulated under the banner of indoor environmental qualities (IEQ). Although a substantial amount of research has been done on the effects of IEQ on comfort and satisfaction of building users, the complex physical, physiological, and psychological processes underlying human responses to environmental stimuli are yet to be fully characterized. Among environmental forces, light and sound contribute significantly, individually and combined, towards the regulation of physio-psychological well-being, being most influential to perception and behavior. This article presents the current state-of-the-art of knowledge in lighting and acoustics, describing the challenges that need to be tackled for a more comprehensive understanding of the influence of the luminous and aural environments on the comfort, performance, and health of building users