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- Deep Learning-Based Event Data Coding: A Joint Spatiotemporal and Polarity SolutionPublication . Seleem, Abdelrahman; Guarda, André F. R.; Rodrigues, Nuno M. M.; Pereira, FernandoNeuromorphic vision sensors, commonly referred to as event cameras, generate a massive number of pixel-level events, composed by spatiotemporal and polarity information, thus demanding highly efficient coding solutions. Existing solutions focus on lossless coding of event data, assuming that no distortion is acceptable for the target use cases, mostly including computer vision tasks such as classification and recognition. One promising coding approach exploits the similarity between event data and point clouds, both being sets of 3D points, thus allowing to use current point cloud coding solutions to code event data, typically adopting a two-point clouds representation, one for each event polarity. This paper proposes a novel lossy Deep Learning-based Joint Event data Coding (DL-JEC) solution, which adopts for the first time a single-point cloud representation, where the event polarity plays the role of a point cloud attribute, thus enabling to exploit the correlation between the geometry/spatiotemporal and polarity event information. Moreover, this paper also proposes novel adaptive voxel binarization strategies which may be used in DL-JEC, optimized for either quality-oriented or computer vision task-oriented purposes which allow to maximize the performance for the task at hand. DL-JEC can achieve significant compression performance gains when compared with relevant conventional and DL-based state-of-the-art event data coding solutions, notably the MPEG G-PCC and JPEG Pleno PCC standards. Furthermore, it is shown that it is possible to use lossy event data coding, with significantly reduced rate regarding lossless coding, without compromising the target computer vision task performance, notably event classification, thus changing the current event data coding paradigm.
- Health Literacy of the Polytechnic of Leiria StudentsPublication . Ascenso, Rita Margarida Teixeira; Dias, Sara Simões; Luis, Luis; Gonçalves, DulceHealth Literacy (HL) has several definitions and numerous HL assessment tools. Several systematic reviews on HL identified tools for HL assessment. Health Literacy Survey with 47 questions (HLS-EU-Q47) for Europe was adapted for 16 questions (HLS-EU-Q16), and for only 6 questions (HLS-EU-Q6). These are already in Portuguese and have been used to assess HL since 2017. The studies involved the Portuguese population, and recently, in 2021, the HL evaluation in university students identified limitations in HL. The HLS-EU-Q16_Pt used showed adequate internal consistency (Cronbach's alpha = 0.778, [0.737, 0.816]). Among 251 students from the Polytechnic of Leiria there was a statistically significant association of HL scores with the health area, and more evident when students had a previous degree in health.
- Functional Dependence in Brazilian Adults One Year After COVID-19 Infection: Prevalence and Risk Factors in a Cross-Sectional StudyPublication . Milan, Natália; Laranjeira, Carlos; Rossoni, Stéfane Lele; Ali, Amira Mohammed; Fekih-Romdhane, Feten; Baccon, Wanessa; Carreira, Lígia; Salci, Maria AparecidaOne of the challenges post-COVID-19 is reducing the negative impacts on quality of life, performance, and independence in activities of daily living. Assessing functional dependence in adults one year after acute infection can help to understand the long-term consequences, evaluate the impact on quality of life, plan rehabilitation and healthcare, identify the most vulnerable groups, measure the socioeconomic impact, and support public policies and clinical decisions. Objectives: The objectives of this study are as follows: (a) to assess the prevalence of functional dependence in Brazilian adults with COVID-19; (b) to analyze the association between the study variables; and (c) to determine the factors associated with functional dependence. Methods: This was an observational, cross-sectional study with 987 adults (18 to 59 years old) living in the State of Paraná (Brazil) hospitalized for COVID-19 between March and December 2020. Data were collected by telephone 12 months after the acute infection using an instrument to retrieve sociodemographic and health information, and a functional dependence scale to assess dependence before COVID-19 retrospectively (using participant recall information) and at the time of the interview. Data were analyzed using penalized logistic regression after imputing missing data. Data were analyzed using penalized logistic regression after imputing missing data. Results: Functional dependence after COVID-19 was 5.0% and was associated with low levels of education, not having a partner, living with someone, not owning a home, experiencing job changes, requiring care, obesity, smoking, multimorbidity, ICU admission in the acute phase, use of invasive ventilation, or having Long COVID. Individuals who required care or used invasive ventilation support were, respectively, 9.3 and 6.5 times more likely to develop dependence after COVID-19. Despite adjustment for multiple factors, the magnitude of the observed effects warrants cautious interpretation, as unmeasured or residual confounding effects may still be present. Sample recall bias due to collection after 12 months and the presence of the alpha variant without COVID-19 vaccination coverage may limit data generalization. Conclusions: The results highlight the need to emphasize the public health implications of identifying functional dependence. In this vein, it is necessary to implement preventive measures, identify and monitor more vulnerable groups, plan rehabilitation programs, and develop public health policies.