Abstract
Blood banks face inventory management problems associated to demand uncertainty and high inventory levels. An efficient blood inventory management is related to the use of simple, transparent and easy-to-understand procedures by blood banks’ employees. However, the literature about good practices in blood bank inventory management is scarce, reinforcing new developments need on this subject to ensure a good availability of blood products and reducing wastage. This research presents a blood inventory management system implemented in software, DOAR, able to meet demand while minimizing blood bags wastage. DOAR is simple, user-friendly and able to optimize blood inventory and donations. The purpose of the software is to provide a link between the demand by blood components and collected blood bags.

Abstract
Forecasting heating and cooling energy demand in buildings plays a critical role in supporting building management and operation. Thus, analysing the energy consumption pattern of a building could help in the design of potential energy savings and also in operation fault detection, while contributing to provide proper indoor environmental conditions to the building's occupants.This paper aims at presenting the main results of a study consisting in forecasting the hourly heating and cooling demand of an office building located in Lisbon, Portugal, using machine learning models and analysing the influence of exogenous variables on those predictions. In order to forecast the heating and cooling demand of the considered building, some traditional models, such as linear and polynomial regression, were considered, as well as artificial neural networks and support vector regression, oriented to machine learning. The input parameters considered in the development of those models were the hourly heating and cooling energy historical records, the occupancy, solar gains through glazing and the outside dry-bulb temperature.The models developed were validated using the mean absolute error (MAE) and the root mean squared error (RMSE), used to compare the values obtained from machine learning models with data obtained through a building energy simulation performed on an adequately calibrated model.The proposed exploratory analysis is integrated in a research project focused on applying machine learning methodologies to support energy forecasting in buildings. Hence, the research line proposed in this article corresponds to a preliminary project task associated with feature selection/extraction and evaluation of potential use of machine learning methods. © 2020 IEEE.

Abstract
Background: Disease-related malnutrition is a significant problem in hospitalized patients, with high prevalence rates depending on the studied population. Internal Medicine wards are the backbone of the hospital setting. However, prevalence and determinants of malnutrition in these patients remain unclear. We aimed to determine the prevalence of malnutrition in Internal Medicine wards and to identify and characterize malnourished patients. Methods: A cross-sectional observational multicentre study was performed in Internal Medicine wards of 24 Portuguese hospitals during 2017. Demographics, hospital admissions during the previous year, type of admission, primary diagnosis, Charlson comorbidity index, and education level were registered. Malnutrition at admission was assessed using Patient-Generated Subjective Global Assessment (PG-SGA). Demographic characteristics were compared between well-nourished and malnourished patients. Logistic regression analysis was used to identify determinants of malnutrition. Results: 729 participants were included (mean age 74 years, 51% male). Main reason for admission was respiratory disease (32%). Mean Charlson comorbidity index was 5.8 +/- 2.8. Prevalence of malnutrition was 73% (56% moderate/suspected malnutrition and 17% severe malnutrition), and 54% had a critical need for multidisciplinary intervention (PG-SGA score >= 9). No education (odds ratio [OR] 1.88, 95% confidence interval [CI]: 1.16-3.04), hospital admissions during previous year (OR 1.53, 95%CI: 1.05-2.26), and multiple comorbidities (OR 1.22, 95%CI: 1.14-1.32) significantly increased the odds of being malnourished. Conclusions: Prevalence of malnutrition in the Internal Medicine population is very high, with the majority of patients having critical need for multidisciplinary intervention. Low education level, admissions during previous year, and multiple comorbidities increase the odds of being malnourished.

Abstract
The new 5G mobile communication system era brings a new set of communication devices that will appear on the market. These devices will generate data streams that require proper handling by machine algorithms. The processing of these data streams requires the design, development, and adaptation of appropriate machine learning algorithms. While stream processing algorithms include hyper-parameters for performance refinement, their tuning process is time-consuming and typically requires an expert to do the task. In this paper, we present an extension of the Self Parameter Tuning (SPT) optimization algorithm for data streams. We apply the Nelder-Mead algorithm to dynamically sized samples that converge to optimal settings in a double pass over data (during the exploration phase), using a relatively small number of data points. Additionally, the SPT automatically readjusts hyper-parameters when concept drift occurs. We did a set of experiments with well-known classification data sets and the results show that the proposed algorithm can outperform the results of previous hyper-parameter tuning efforts by human experts. The statistical results show that this extension is faster in terms of convergence and presents at least similar accuracy results when compared with the standard optimization techniques.

Abstract
A robótica assistiva está presente em diversas áreas de pesquisa do mundo atual. Trabalhos voltados para o aumento da produtividade e para o auxílio de pessoas com deficiência física são alguns exemplos de como a robótica pode facilitar e melhorar a qualidade de vida do ser humano. Com o desenvolvimento de aplicações remotas é possível controlar diferentes dispositivos sem a necessidade de estar presente no local de atuação. Este artigo tem como objetivo controlar um robô humanoide remotamente através do reconhecimento de sinais de eletromiografia, bem como localizá-lo em seu ambiente.

Abstract
Purpose The purpose of this paper is to study the nature and concept of emoticons/emojis. Instead of taking for granted that these user-generated formats are necessarily emotional, we empirically assessed in what extent are they and the specificity of each one. Drawing on congruent mood state, valence core and emotion appraisal theories we expected a compatible statistical association between positive/negative/neutral emotional valence expressions and emoticons of similar valence. The positive emoticons were consistently associated with positive valence posts. Added to that analysis, 21 emotional categories were identified in posts and correlated with eight emoticons. Design/methodology/approach Two studies were used to address this question. The first study defined emoticon concept and interpreted their meaning highlighting their communication goals and anticipated effects. The link between emojis and emoticons was also obtained. Some emoticons types present more ambiguity than others. In the second study, three years of real and private (Facebook) posts from 82 adolescents were content analyzed and coded. Findings Only the neutral emoticons always matched neutral emotional categories found in the written interaction. Although the emoticon valence and emotional category congruence pattern was the rule, we also detected a combination of different valence emoticons types and emotion categories valence expressions. Apparently the connection between emoticon and emotion are not so obviously straightforward as the literature used to assume. The created objects designed to communicate emotions (emoticons) have their specific corresponding logic with the emotional tone of the message. Originality/value Theoretically, we discussed the emotional content of emoticons/emojis. Although this king of signals have an Asian origin and later borrowed from the western countries, their ambiguity and differing specificity have never been analyzed.