Agroforestry actions of the articulafito project - value chains in medicinal plants. (Special Issue: Saberes agroecologicos e sistemas alimentares na America Latina e Caribe.)

The article presents an account of the experience carried out during the COVID-19 syndemic, from the implementation actions of agroforestry systems aimed at productive inclusion, health and quality of life in the Koyakati Village - Mae Maria Indigenous Land -, in Bom Jesus do Tocantins-southeast of Para. These actions are part of the ArticulaFito project and result from a diagnosis of the national production base of medicinal plants and socio-biodiversity products, where the need for recovery and conservation of degraded areas was identified as a factor of fragility of productive relations, especially with regard to territories of populations and traditional communities, pressured by predatory production models that negatively impact the ways of life and health of these populations. The Value Links-B methodology was applied in the diagnosis with a view to drawing up an action plan aimed at the challenges diagnosed in the chain of Brazil nut (Bertholletia excelsa Bonpl.), a species categorized as threatened with extinction due to population decline. Then, it indicates the agroforestry systems, to guarantee the adequate management of the native agroextractive species and, in this way, to conserve biodiversity, as well as guarantee the access to the raw material. The implementation actions of the Agroecological Experimentation Unit (UEA) are described here, with the objective of strengthening production systems and, thus, generating employment and income, in order to improve health indicators in these territories.

Modeling the Geospatial Evolution of COVID-19 using Spatio-temporal Convolutional Sequence-to-sequence Neural Networks

Europe was hit hard by the COVID-19 pandemic and Portugal was severely affected, having suffered three waves in the first twelve months. Approximately between January 19th and February 5th 2021 Portugal was the country in the world with the largest incidence rate, with 14-day incidence rates per 100,000 inhabitants in excess of 1,000. Despite its importance, accurate prediction of the geospatial evolution of COVID-19 remains a challenge, since existing analytical methods fail to capture the complex dynamics that result from the contagion within a region and the spreading of the infection from infected neighboring regions. We use a previously developed methodology and official municipality level data from the Portuguese Directorate-General for Health (DGS), relative to the first twelve months of the pandemic, to compute an estimate of the incidence rate in each location of mainland Portugal. The resulting sequence of incidence rate maps was then used as a gold standard to test the effectiveness of different approaches in the prediction of the spatial-temporal evolution of the incidence rate. Four different methods were tested: a simple cell level autoregressive moving average (ARMA) model, a cell level vector autoregressive (VAR) model, a municipality-by-municipality compartmental SIRD model followed by direct block sequential simulation, and a new convolutional sequence-to-sequence neural network model based on the STConvS2S architecture. We conclude that the modified convolutional sequence-to-sequence neural network is the best performing method in this task, when compared with the ARMA, VAR, and SIRD models, as well as with the baseline ConvLSTM model.

Catering for Students' Well-being during COVID-19 Social Distancing: A Case Study from a University Campus

COVID-19 gave rise to discussions around designing for life during the pandemic, in particular related to health, leisure and education. In 2020, an online survey aimed at university students (N=225) pointed the authors to various challenges related to well-being in terms of studying, socializing, community, and safety during the COVID-19 pandemic. These results shaped the crowdsensing-enabled service design of a mobile application, Tecnico GO!, aimed at supporting students' well-being. Considering the constant changing context caused by the pandemic, we present a study conducted during the academic year 2021-2022 and if/how the App's features continue to respond to student's needs. The evaluation of the App focused on 12 semi-structured interviews and think-aloud protocols. Findings cluster around three themes: a) Supporting the study experience;b) Building a sense of community;c) Improving gamification for better participation. Discussion elaborates on the student's perceptions around well-being during pandemics. Students' insights of the App are overall positive and highlight that crowdsensing-enabled design does contribute to learning, community and safety, but the gamification as currently deployed does not. © 2022 ACM.

Promoting a safe return to university campuses during the COVID-19 pandemic: Crowdsensing room occupancy

We describe the work behind a privacy-preserving, crowdsensing approach that promotes social distancing upon the return of students to University. Our main motivation is enabling visualizations that predict room occupancy based on the number of connected devices to particular access points, via anonymous reports about these predictions, and via an unenforced booking system that allows users to communicate their intents about room use. © 2021 ACM.

Addressing the Challenges of COVID-19 Social Distancing Through Passive Wi-Fi and Ubiquitous Analytics: A Real World Deployment

During the COVID-19 pandemic, social distancing measures were employed to contain its spread. This paper describes the deployment and testing of a passive Wi-Fi scanning system to help people keep track of crowded spaces, hence comply with social distancing measures. The system is based on passive Wi-Fi sensing to detect human presence in 93 locations around a medium-sized European Touristic Island. This data is then used in website plugins and a mobile application to inform citizens and tourists about the locations’ crowdedness with real-time and historical data. To understand how people react to this type of information, we deployed online questionnaires in situ to collect user insights regarding the usefulness, safety, and privacy concerns. Results show that users considered the occupancy data reported by the system as positively related to their perception. Furthermore, the public display of this data made them feel safer while travelling and planning their commute. © 2021, IFIP International Federation for Information Processing.

A data visualization interactive exploration of human mobility data during the COVID-19 outbreak: a case study

In this paper, we present a real-world study where a community-based tracking infrastructure has been put to good use for understanding human mobility during the COVID-19 outbreak, in order to contrast its diffusion. In particular, the infrastructure, deployed in 81 points of interests (POIs) across the Madeira Islands (Portugal), can collect a massive amount of spatio-temporal data, that can be enriched with potentially independent data sources of additional values (such as the official number of people affected by the coronavirus disease), and crowdsourced data collected by citizens. These enriched hyper-local data can be manipulated to provide i) stakeholders with a visual tool to contrast COVID-19 diffusion through human mobility monitoring, and ii) citizens with an interactive tool to visualize, in real-time, how crowded is a POI and plan their daily activities, and contribute to the data acquisition. Here we present the deployed community-based infrastructure and the data visualization interactive web application, designed to extract meaningful information from human mobility data during the COVID-19 outbreak.