Perceptions of high involvement work practices, person-organization fit and burnout: a time lagged study of health care employees

Previous research demonstrates that high-involvement work practices (HIWPs) may be associated with burnout (emotional exhaustion and depersonalization); however, to date, the process through which HIWPs influence burnout is not clear. This article examined the impact of HIWPs on long-term burnout (emotional exhaustion and depersonalization) by considering the mediating role of person-organization fit (P-O fit) in this relationship. The study used a time-lagged design and was conducted in a Canadian general hospital among health care personnel. Findings from structural equation modeling (N = 185) revealed that perceived HIWPs were positively associated with P-O fit. There was no direct effect of HIWPs on burnout; rather, P-O fit fully mediated the relationship between employee perceptions of HIWPs and burnout. This study fills a void in the HR and burnout literature by demonstrating the role that P-O fit has in explaining how HIWPs alleviate emotional exhaustion and depersonalization. © 2016 Wiley Periodicals, Inc.

Recurrent Convolutional Network for Video-based Person Re-Identification

In this paper we propose a novel recurrent neural networkarchitecture for video-based person re-identification.Given the video sequence of a person, features are extracted from each frame using a convolutional neural network that incorporates a recurrent final layer, which allows information to flow between time-steps. The features from all time steps are then combined using temporal pooling to give an overall appearance feature for the complete sequence. The convolutional network, recurrent layer, and temporal pooling layer, are jointly trained to act as a feature extractor for video-based re-identification using a Siamese network architecture.Our approach makes use of colour and optical flow information in order to capture appearance and motion information which is useful for video re-identification. Experiments are conduced on the iLIDS-VID and PRID-2011 datasets to show that this approach outperforms existing methods of video-based re-identification.

https://github.com/niallmcl/Recurrent-Convolutional-Video-ReID
Project Source Code

Introducing the CTA concept

The Cherenkov Telescope Array (CTA) is a new observatory for very high-energy (VHE) gamma rays. CTA has ambitions science goals, for which it is necessary to achieve full-sky coverage, to improve the sensitivity by about an order of magnitude, to span about four decades of energy, from a few tens of GeV to above 100 TeV with enhanced angular and energy resolutions over existing VHE gamma-ray observatories. An international collaboration has formed with more than 1000 members from 27 countries in Europe, Asia, Africa and North and South America. In 2010 the CTA Consortium completed a Design Study and started a three-year Preparatory Phase which leads to production readiness of CTA in 2014. In this paper we introduce the science goals and the concept of CTA, and provide an overview of the project. © 2013 Elsevier B.V. All rights reserved.