Abstract
Network human operators' decision-making during grid outages requires significant attention and the ability to perceive real-time feedback from multiple information sources to minimize the number of control actions required to restore service, while maintaining the system and people safety. Data-driven event and alarm management have the potential to reduce human operator cognitive burden. However, the high complexity of events, the data semantics, and the large variety of equipment and technologies are key barriers for the application of Artificial Intelligence (AI) to raw SCADA data. In this context, this paper proposes a methodology to convert a large volume of alarm events into data mining terminology, creating the conditions for the application of modern AI techniques to alarm data. Moreover, this work also proposes two novel data-driven applications based on SCADA data: (i) identification of anomalous behaviors regarding the performance of the protection relays of primary substations, during circuit breaker tripping alarms in High Voltage (HV) and Medium Voltage (MV) lines; (ii) unsupervised learning to cluster similar events in HV line panels, classify new event logs based on the obtained clusters and membership grade with a control parameter that helps to identify rare events. Important aspects associated with data handling and pre-processing are also covered. The results for real data from a Distribution System Operator (DSO) showed: (i) that the proposed method can detect unexpected relay pickup events, e.g., one substation with nearly 41% of the circuit breaker alarms had an 'atypical' event in their context (revealed an overlooked problem on the electrification of a protection relay); (ii) capability to automatically detect and group issues into specific clusters, e.g., SF6 low-pressure alarms and blocks with abnormal profiles caused by event time-delay problems.

Abstract
This paper aims at contributing to personalize anesthetic drug administration during surgery. This study devel-ops an online robust model to predict the maintenance dose of atracurium necessary for the resulting effect, i.e. neuromuscular blockade, to attain a target profile. The model is based on the patient's neuromuscular blockade (NMB) response to the initial bolus only, overcoming the need for information on the patient's weight, age, height and Lean Body Mass usually associated to pharmacokinetic and pharmacodynamic models. To achieve this, a statistical analysis of the response of the patient to the initial bolus is carried out and a set of variables is established as predictors of the maintenance dose. The prediction is accomplished using Classification and Regression Trees, CART, which is a supervised learning method. Simulated data from a stochastic model for the NMB induced by atracurium is used as training set. All the 5000 doses predicted by the model lead to NMB level between 5% and 10%, which supports the proposed predictive model since it is clinically required that the steady state NMB level lies between this two values. The methodology is applied both to simulated and to clinical data sets and is found appropriate for online dose prediction.

Abstract

Abstract
The COVID-19 pandemic has imposed challenges to health systems and institutions, which had to quickly create conditions to meet the growing health needs of the population. Thus, this study aimed to assess the impact of COVID-19 on professional nursing practice environments and to identify the variables that affected their quality. Quantitative, observational study, conducted in 16 Portuguese hospitals, with 1575 nurses. Data were collected using a questionnaire and participants responded to two different moments in time: the pre-pandemic period and after the fourth critical period of COVID-19. The pandemic had a positive impact on the Structure and Outcome components, and a negative trend in the Process component. The variables associated with the qualification of the components and their dimensions were predominantly: work context, the exercise of functions in areas of assistance to COVID-19 patients, length of professional experience and length of experience in the service. The investment in professional practice environments impacted the improvement of organizational factors, supporting the development of nurses' work towards the quality of care. However, it is necessary to invest in nurses' participation, involvement and professional qualifications, which are aspects strongly dependent on the institutions' management strategies.

Abstract

Abstract
(1) Background: Unadjusted lifestyles have been the main cause of risk for the loss of years of healthy life. However, currently valid and reliable instruments to assess the lifestyles of the elderly are quite long and difficult to interpret. For this reason, the objective of this study was to adapt and validate the 'Individual Lifestyle Profile' (ILP) scale in a sample of elderly people; (2) Methods: A methodological study was carried out and a sample of 300 older adults enrolled in a Health Unit located in the North of Portugal was used, who responded to the scale. We examined internal consistency, predictive validity, and discriminative ability; (3) Results: After the Exploratory Factorial analysis, a solution was found with four factors that explain a variance of 67.8%. The designation of the factors was changed from the original scale, with the exception of one dimension, and they were called Health Self-management, Social Participation and Group Interaction, Citizenship and Physical Activity. The total internal consistency (Cronbach's alpha) was 0.858, ranging from 0.666 to 0.860 in the mentioned factors; (4) Conclusions: The ILP scale proved to be easy to apply and presented a good reliability and validity index, based on internal consistency, AFE and AFC. The scale allows evaluating the lifestyle of older adults, and its use will be aimed at modifying behaviors associated with negative lifestyles of older adults and their individual needs.