Abstract
We study the equilibria of an Ordinary Differencial Equation (ODE) system where CD4+ effector or helper T cells and Regulatory T cells (Tregs) are present. T cells trigger an immune response in the presence of their specific antigen. Regulatory T cells (Tregs) play a role in limiting auto-immune diseases due to their immune-suppressive ability. Here, we present explicit exact formulas that give the relationship between the concentration of T cells, the concentration of Tregs, and the antigenic stimulation of T cells, when the system is at equilibria, stable or unstable. We found a parameter region of bistability, limited by two thresholds of antigenic stimulation of T cells (hysteresis). Moreover, there are values of the slope parameter of the tuning for which an isola-center bifurcation appears, and, for some other values, there is a transcritical bifurcation. We also present time evolutions of the ODE system.

Abstract
Wind power curtailment (WPC) occurs because of the non-correlation between wind power generation (WPG) and load, and also due to the fast sub-hourly variations of WPG. Recently, advances in energy storage technologies facilitate the use of bulk energy storage units (ESUs) to provide the ramping required to respond to fast sub-hourly variations of WPGs. To minimize the sub-hourly WPC probability, this paper addresses a generic continuous-time risk-based model for sub-hourly scheduling of energy generating units and bulk ESUs in the day-ahead unit commitment (UC) problem. Accordingly, the Bernstein polynomials are hosted to model the continuous-time risk-based UC problem with ESU constraints. Also, the proposed continuous-time risk-based model ensures that the generating units and ESUs track the sub-hourly variations of WPG, while the load and generation are balanced in each sub-hourly intervals. Finally, the performance of the proposed model is demonstrated by simulating the IEEE 24-bus Reliability and Modified IEEE 118-bus test systems.

Abstract
In this paper it is described a Pilot that took place at Emidio Garcia School from Braganca, Portugal. The presented Pilot is based on a Challenge based Learning Approach, being an activity of the RoboSTEAM - Integrating STEAM and Computational Thinking development by using robotics and physical devices - ERASMUS+ Project. In the presented educational experiment it were used physical devices, being chosen the mBot robot, programmed using Scratch. The presented challenges had as research question a global problem that was Wildfires. Student had to propose and to develop solutions based on the use of robots to prevent and fight wildfires. The students that participated in the experiment were secondary school students, from Spain and Portugal, with their background in technology and arts respectively. Previously to the experiment the involved students filled a STEM semantic Survey and during the experiments their performance was evaluated.

Abstract
This paper addresses the topic of emergency landing spot detection for Unmanned Aerial Vehicles (UAVs). During operation, the vehicle is susceptible to faults and must be able to predict the land spot able to ensure that the UAV will be able to land without damages and injuries to humans and structures. A method was developed, based on geometric features extracted from Light Detection And Ranging (LIDAR) data. A simulation environment was developed in order to validate the effectiveness and the robustness of the proposed method.

Abstract
At an estimated annual growth rate of 13.74%, the global health informatics market can reach $123 billion, by 2025, figures that exemplify the development trends of an ever-growing industry. The authors decided to collect data from the general public through an anonymous survey on the subject of health informatics. This survey was developed on Google Forms and later sent to multiple recipients by email and shared on social networks. A total of 165 people, aged 16 to 81 years old, participated in this survey. 98.8% of the respondents consider response time in health a determining factor. 97.6% of the survey participants consider that it is possible to make more accurate and viable clinical diagnoses using health informatics. Furthermore, according to our survey, people do not mind mortgaging their personal data (which is known from the outset for its incalculable value) because they know that, in return, they will benefit from better living conditions. Three experts were also interviewed and according to one of them, one of the biggest challenges in health informatics is “understanding and detecting diseases long before they happen”. This interviewee also stressed the importance of artificial intelligence “in helping people to improve their health through indicators that alert and recommend certain habits and influence the improvement of people’s quality of life”. Finally, the emphasis needs to be on eliminating health costs and facilitating life for people with chronic diseases. © The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG 2020.

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.