Abstract
The automatic differentiation of retinal vessels into arteries and veins (A/V) is a highly relevant task within the field of retinal image analysis. however, due to limitations of retinal image acquisition devices, specialists can find it impossible to label certain vessels in eye fundus images. In this paper, we introduce a method that takes into account such uncertainty by design. For this, we formulate the A/V classification task as a four-class segmentation problem, and a Convolutional Neural Network is trained to classify pixels into background, A/V, or uncertain classes. The resulting technique can directly provide pixelwise uncertainty estimates. In addition, instead of depending on a previously available vessel segmentation, the method automatically segments the vessel tree. Experimental results show a performance comparable or superior to several recent A/V classification approaches. In addition, the proposed technique also attains state-of-the-art performance when evaluated for the task of vessel segmentation, generalizing to data that, was not used during training, even with considerable differences in terms of appearance and resolution.

Abstract
A new wearable data capture system for gait analysis is being developed. It consists of a pantyhose with embedded conductive yarns interconnecting customized sensing electronic devices that capture inertial and electromyographic signals and send aggregated information to a personal computer through a wireless link. The use of conductive yarns to build the myoelectric electrodes and the interconnections of the wired sensors network as well as the topology and functionality of the sensor modules are presented. © The Institution of Engineering and Technology 2017.

Abstract

Abstract
This study investigated stress, coping, and work engagement among Portuguese police officers while undergoing academy training and then 1 year later, when on duty. It was hypothesized that stress appraisal and coping preferences predicted engagement. Additionally, in order to test a full cross-lagged prediction model, it was hypothesized that stress, coping, and engagement in recruits predicted these variables later when working as police officers. Structural equation modeling was used to test the research hypotheses. Results suggest that coping and stress appraisals do not seem to be strong predictors of work engagement among recruits and police officers on duty. With the exception of self-blame, that seems to be a strong predictor of work engagement among police officers on duty. These results highlight the need to investigate other potential variables such as working conditions that may better explain work engagement. Considering the positive influence of engagement on health, wellbeing, and performance of police recruits and officers future applied and theoretical implications are discussed.

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Abstract