Prognostic models should properly take into account the effects of operating conditions on the degradation process and on the signal measurements used for monitoring. In this paper, we develop a particle filter-based (PF) prognostic model for the estimation of the remaining useful life (RUL) of aluminum electrolytic capacitors used in electrical automotive drives, whose operation is characterized by continuously varying conditions. The capacitor degradation process, which remarkably depends on the temperature experienced by the component, is typically monitored by observing the capacitor equivalent series resistance (ESR). However, the ESR measurement is influenced by the temperature at which the measurement is performed, which changes depending on the operating conditions. To address this problem, we introduce a novel degradation indicator independent from the measurement temperature. Such indicator can, then, be used for the prediction of the capacitor degradation and its RUL. For this, we develop a particle filter prognostic model, whose performance is verified on data collected in simulated and experimental degradation tests

The use of Echo State Networks (ESNs) for the prediction of the Remaining Useful Life (RUL) of industrial components, i.e. the time left before the equipment will stop fulfilling its functions, is attractive because of their capability of handling the system dynamic behavior, the measurement noise, and the stochasticity of the degradation process. In particular, in this paper we originally resort to an ensemble of ESNs, for enhancing the performances of individual ESNs and providing also an estimation of the uncertainty affecting the RUL prediction. The main methodological novelties in our use of ESNs for RUL prediction are: i) the use of the individual ESN memory capacity within the dynamic procedure for aggregating of the ESNs outcomes; ii) the use of an additional ESN for estimating the RUL uncertainty, within the Mean Variance Estimation (MVE) approach. With these novelties, the developed approach outperforms a static ensemble and a standard MVE approach for uncertainty estimation in tests performed on a synthetic and two industrial datasets.

PDF became a very common format for exchanging printable documents. Further, it can be easily generated from the major documents formats, which make a huge number of PDF documents available over the net. However its use is limited to displaying and printing, which considerably reduces the search and retrieval capabilities. For this reason, additional tools have recently appeared that allow to extract the textual content. However their practical use is limited in the sense that the text's reading order is not necessary preserved, especially when handling multi-column documents, or in presence of complex layout. Our thesis is that those tools do not consider the hidden layout and logical structures of documents, which could greatly improve their results.We propose a novel approach to overcome the document content extraction, by merging a) low-level extraction methods applied on PDF files with b) layout analysis performed on a synthetically generated TIFF image. The paper describes the various steps necessary to achieve this task. Finally, we present a first experiment on the restitution of the newspapers' reading order which shows encouraging results.

The paper presents an extension to the Excentric Labeling, a labeling technique to dynamically show labels around a movable lens. Each labels refers to one object within the lens and is connected to it through a line. The original implementation has several known limitations and potential improvements that we address in this work, like: high density areas, uneven density distributions, and summary statistics. We describe the implemented extensions and present a think-aloud user study. The study shows that users can naturally understand and easily operate the majority of the implemented function but label scrolling, which requires additional research. From the study we also gained unanticipated requirements and interesting directions for further research.

Recent lines of research suggest that repeated executive control of motor responses to food items modifies their perceived value and in turn their consumption. Cognitive interventions involving the practice of motor control and attentional tasks have thus been advanced as potential approach to improve eating habits. Yet, their efficacy remains debated, notably due to a lack of proper control for the effects of expectations. We examined whether a one-month intervention combining the practice of Go/NoGo and Cue approach training modified the perceived palatability of food items (i.e. decrease in unhealthy and increase in healthy food items' palatability ratings), and in turn participants’ weights. We assessed our hypotheses with a parallel, double-blind, randomized controlled trial. Motivation and adherence to the intervention were maximized by a professional-level gamification of the training tasks. The control intervention differed from the experimental intervention only in the biasing of the stimulus–response mapping rules, enabling to balance expectations between the two groups and thus to conclude on the causal influence of motoric control on items valuation. We found a larger decrease of the unhealthy items' palatability ratings in the experimental (20.6%) than control group (13.1%). However, we did not find any increase of the healthy items’ ratings or weight loss. Overall, the present registered report confirms that the repeated inhibition of motor responses to food cues, together with the development of attentional biases away from these cues, reduces their perceived value.