It is well established that emotions are organized around two motivational systems: the defensive and the appetitive. Individual differences are relevant factors in emotional reactions, making them more flexible and less stereotyped. There is evidence that health professionals have lower emotional reactivity when viewing scenes of situations involving pain. The objective of this study was to investigate whether the rating of pictures of surgical procedure depends on their personal/occupational relevance. Fifty-two female Nursing (health discipline) and forty-eight Social Work (social science discipline) students participated in the experiment, which consisted of the presentation of 105 images of different categories (e.g., neutral, food), including 25 images of surgical procedure. Volunteers judged each picture according to its valence (pleasantness) and arousal using the Self-Assessment Manikin scale (dimensional approach). Additionally, the participants chose the word that best described what they felt while viewing each image (discrete emotion perspective). The average valence score for surgical procedure pictures for the Nursing group (M = 4.57; SD = 1.02) was higher than the score for the Social Work group (M = 3.31; SD = 1.05), indicating that Nursing students classified those images as less unpleasant than the Social Work students did. Additionally, the majority of Nursing students (65.4%) chose “neutral” as the word that best described what they felt while viewing the pictures. In the Social Work group, disgust (54.2%) was the emotion that was most frequently chosen. The evaluation of emotional stimuli differed according to the groups' personal/occupational relevance: Nursing students judged pictures of surgical procedure as less unpleasant than the Social Work students did, possibly reflecting an emotional regulation skill or some type of habituation that is critically relevant to their future professional work.

The present study investigated the influences of coping styles on posttraumatic stress symptoms (PTSS) among a sample of non-clinical college students who were exposed to traumatic events. Ninety-nine college students participated in the study. However, the sample used in the analyses consisted of only 37 participants who fulfilled the DSM-IV criterion A for Posttraumatic Stress Disorder (PTSD) diagnosis. The PTSD Checklist-Civilian Version (PCL-C) and the Brief COPE were used to assess the participants' PTSS and habitual use of coping strategies, respectively. Bayesian and frequentist correlations showed that emotion-focused coping style was negatively associated with PTSS, while dysfunctional coping style was positively related to PTSS. In the subsequent linear regression on both statistical framework, dysfunctional coping was the only consistent variable predicting more PTSD symptoms. The findings presented here show that lower use of adaptive coping (emotion-focused) and higher use of dysfunctional coping styles on a daily basis are associated to PTSS severity in a non-clinical sample of college students. According to the Bayesian approach, which permits more generalization of data, dysfunctional coping style is determinant to higher levels of PTSS. These findings add new data to the body of research that highlight the critical role of distinct coping strategies in the severity of PTSS.

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Goal: To evaluate the performance of a set of forecasting methods in the prediction of future values on a dataset of time series collected from sensors installed in an industrial gas turbine. Design / Methodology / Approach: Forecasting methods tested include the use of multivariate and univariate neural networks (FNN and LSTM), exponential smoothing and ARIMA models. Results: Results show that the use of ARIMA models to forecast on the dataset is the best default method to apply, and is the only forecasting method that consistently beats a simple naïve no-change model. Limitation of the investigation: There was a focus on evaluating neural networks. This limited resources available to evaluate other forecasting methods. There is no guarantee that it would not be possible to find neural networks capable of yielding better forecasts than the ones achieved by the best performing methods in this research. Practical implications: The broadest possible implications of the results are that the best default method to forecast industrial machinery time series is the use of ARIMA models. Additionally, neural networks are not capable of beating methods well stablished within the forecasting community, namely ARIMA models. Originality / Value: To the best of the authors' knowledge, there is a scarce amount of published evaluations of multiple forecasting methods on data from real machines. This knowledge is useful for the understanding of the best forecasting methods available for the estimation of machine's RUL using sensor time series.

In pervasive healthcare monitoring, activity recognition is critical information for adequate management of the patient. Despite the great number of studies on this topic, a contextually relevant parameter that has received less attention is intensity recognition. In the present study, we investigated the potential advantage of coupling activity and intensity, namely, Activity-Intensity, in accelerometer data to improve the description of daily activities of individuals. We further tested two alternatives for supervised classification. In the first alternative, the activity and intensity are inferred together by applying a single classifier algorithm. In the other alternative, the activity and intensity are classified separately. In both cases, the algorithms used for classification are k-Nearest Neighbors (KNN), Support Vector Machine (SVM), and Random Forest (RF). The results showed the viability of the classification with good accuracy for Activity-Intensity recognition. The best approach was KNN implemented in the single classifier alternative, which resulted in 79% of accuracy. Using two classifiers, the result was 97% accuracy for activity recognition (Random Forest), and 80% for intensity recognition (KNN), which resulted in 78% for activity-intensity coupled. These findings have potential applications to improve the contextualized evaluation of movement by health professionals in the form of a decision system with expert rules.