In Soccer two teams are constantly interacting between each other, changing and adapting continuously accordingly to the rival’s actions and environmental changes. As such, soccer is an excellent example of a complex adaptative system which, thanks to the recent growth of available data, allows to make novel approaches to study and describe its dynamics.
Talent detection and selection in the developing process of professional players is a task that requires data analysis, creativity, innovation, planning and at least the assessment of the physical, technical, tactical, cognitive and social dimensions.
A graph with positive and negative signs on the edges is called a signed graph. A signed graph (network) is balanced if its set of vertices can be partitioned into two subsets such that each negative (positive) edge joins vertices belonging to different subsets (same subset) [1]. If a signed network satisfies the same condition when partitioned into k subsets, it is clusterable (k-balanced) [2]. Signed networks representing real data often do not satisfy these conditions [3]. This motivates analyzing them based on their distance to balance and clusterability.
In this study we present a new analytical model of a Parliament and investigate the beneficial effects of the selection of legislators by lot in order to reduce some of the drawbacks of modern representative democracies. Resorting to sortition for the selection of public officers used to be in the past a popular way of taming factionalism in public affairs. Factionalism is assumed to be detrimental since public officers tend to favour their own faction instead of pursuing the general interest.
With the ubiquity of smartphones and wearable accelerometer units, movement data has become easily procurable. The easy availability of such data makes it convenient to detect changes in physical activity patterns in real time. Such changes in physical activity levels are one of the most recognizable features of depression. In this work we conduct recurrence quantification analysis to explore how recurrences of patterns in physical activity data differs between depressed and healthy individuals, collected as part of the MOOVD project.
Background: Network theory of mental illness posits that causal interactions between symptoms give rise to mental health disorders. This is thought to occur because positive feedback loops between symptoms trigger cascades of further symptom activation. Increasing evidence suggests that depression network connectivity is therefore a risk factor for transitioning and sustaining a depressive state. However, much of the evidence comes from cross-sectional studies that estimate networks across groups, rather than within individuals.