Abstract
In a globalization context, underlined by the speed of technological transformation and increasingly competitive markets, the perspective of human capital, as an asset of strategic importance, stands out in differentiating human resource practices. Under this reality, the employer branding (EB) concept gains more and more importance as a strategic tool to attract, retain, and involve human capital, given that this has become a source of competitive advantage to companies. Within this context, this study aimed to evaluate the relationship between employer branding strategies implemented by organizations, as well as their impact on the employee's affective commitment, evident in certain organizational cultures, which are sustained over time. The methodological framework applied to this study is quantitative, and the data collection was carried out with the application of an employer branding and an affective commitment questionnaire. To achieve a good representation of the active population, the sample of the quantitative study was composed of 172 individuals, working in the public and private sectors in Portugal, exercising different positions in the different sectors of activity. Results obtained with these techniques indicate a high level of affective organizational commitment (AOC) of employees in the organizations surveyed, suggesting that affective commitment develops when the individual becomes involved and identifies with the organization.

Abstract
The importance of price forecasting has gained attention over the last few years, with the growth of aggregators and the general opening of the European electricity markets. Market participants manage a tradeoff between, bidding in a lower price market (day-ahead), but with typically higher volume, or aiming for a lower volume market but with potentially higher returns (balance energy market). Companies try to forecast the extremes of revenues or prices, in order to manage risk and opportunity, assigning their assets in an optimal way. It is thought that in general, electricity markets have quasi-deterministic principles, rather than being based on speculation, hence the desire to forecast the price based on variables that can describe the outcome of the market. Many studies address this problem from a statistical approach or by performing multiple-variable regressions, but they very often focus only on the time series analysis. In 2019, the Loss of Load Probability (LOLP) was made available in the UK for the first time. Taking this opportunity, this study focusses on five LOLP variables (with different time-ahead estimations) and other quasi-deterministic variables, to explain the price behavior of a multi-variable regression model. These include base production, system load, solar and wind generation, seasonality, day-ahead price and imbalance volume contributions. Three machine-learning algorithms were applied to test for performance, Gradient Boosting (GB), Random Forest (RF) and XGBoost. XGBoost presented higher performance and so it was chosen for the implementation of the real time forecast step. The model returns a Mean Absolute Error (MAE) of 7.89 £/MWh, a coefficient of determination (R2 score) of 76.8% and a Mean Squared Error (MSE) of 124.74. The variables that contribute the most to the model are the Net Imbalance Volume, the LOLP (aggregated), the month and the De-rated margins (aggregated) with 28.6%, 27.5%, 14.0%, and 8.9% of weight on feature importance respectively.

Abstract
In this paper, we present TIV.lib, an open-source library for the content-based tonal description of musical audio signals. Its main novelty relies on the perceptually-inspired Tonal Interval Vector space based on the Discrete Fourier transform, from which multiple instantaneous and global representations, descriptors and metrics are computed-e.g., harmonic change, dissonance, diatonicity, and musical key. The library is cross-platform, implemented in Python and the graphical programming language Pure Data, and can be used in both online and offline scenarios. Of note is its potential for enhanced Music Information Retrieval, where tonal descriptors sit at the core of numerous methods and applications.

Abstract
Over 2.5 million scientific articles are published annually, totaling 6,849.32 per day in 2015; in 2018 this value was increased to over 3 million articles, totaling 8.219,18 per day [1]. Thus, finding the most relevant Research Outputs (ROs), such as articles, theses, patents, among others, is increasingly difficult due, in part, to the existing interfaces returning massive lists of results. The project aims to develop and test a platform that incorporates social data for capturing various usage metrics to define a new metric that we call Social Scholarly Experience Metrics (SSEM) and a new visualization technique that, jointly, will support the fast access to find relevant ROs.

Abstract
Objectives Our research question was: what are the most frequent baseline clinical characteristics in adult patients with COVID-19? Our major aim was to identify common baseline clinical features that could help recognise adult patients at high risk of having COVID-19. Design We conducted a scoping review of all the evidence available at LitCovid, until 23 March 2020. Setting Studies conducted in any setting and any country were included. Participants Studies had to report the prevalence of sociodemographic characteristics, symptoms and comorbidities specifically in adults with a diagnosis of infection by SARS-CoV-2. Results In total, 1572 publications were published on LitCovid. We have included 56 articles in our analysis, with 89% conducted in China and 75% containing inpatients. Three studies were conducted in North America and one in Europe. Participants' age ranged from 28 to 70 years, with balanced gender distribution. The proportion of asymptomatic cases were from 2% to 79%. The most common reported symptoms were fever (4%-99%), cough (4%-92%), dyspnoea/shortness of breath (1%-90%), fatigue (4%-89%), myalgia (3%-65%) and pharyngalgia (2%-61%), while regarding comorbidities, we found cardiovascular disease (1%-40%), hypertension (0%-40%) and cerebrovascular disease (1%-40%). Such heterogeneity impaired the conduction of meta-analysis. Conclusions The infection by COVID-19 seems to affect people in a very diverse manner and with different characteristics. With the available data, it is not possible to clearly identify those at higher risk of being infected with this condition. Furthermore, the evidence from countries other than China is, at the moment, too scarce.

Abstract