Multivariate Analysis of the Determinants of Total Mortality in the European Union with Focus on Fat Intake, Diabetes, Myocardial Infarction, Life Expectancy, and Preventable Mortality: A Panel Data Fixed-Effects Panel Data Model Approach.

Cardiovascular disease is the leading cause of death in the European Union (EU), and while the mortality rates of diabetes, myocardial infarction, and the total fat intake have been extensively studied, we believe that understanding the interaction between such closely correlated determinants is crucial to the development of effective health policies in the EU. Our paper's novelty is represented by the econometric modelling, and its ability to capture both temporal and unit variations. The research methodology consists of using a panel data model with fixed effects for the 27 EU member states over the period 2010-2021.

Rapidly adaptable automated interpretation of point-of-care COVID-19 diagnostics.

Background: Point-of-care diagnostic devices, such as lateral-flow assays, are becoming widely used by the public. However, efforts to ensure correct assay operation and result interpretation rely on hardware that cannot be easily scaled or image processing approaches requiring large training datasets, necessitating large numbers of tests and expert labeling with validated specimens for every new test kit format.

Methods: We developed a software architecture called AutoAdapt POC that integrates automated membrane extraction, self-supervised learning, and few-shot learning to automate the interpretation of POC diagnostic tests using smartphone cameras in a scalable manner.

A Review of Deep Learning-Based Methods for Pedestrian Trajectory Prediction.

Pedestrian trajectory prediction is one of the main concerns of computer vision problems in the automotive industry, especially in the field of advanced driver assistance systems. The ability to anticipate the next movements of pedestrians on the street is a key task in many areas, e.g.