Other applications of Complex Systems

Very  recently  we  have  applied q-generalized  statistical  mechanics  methods  to  high  energy scattering processes, which lie at the root of the production process of cosmic rays.

Recent developments in understanding the structure and dynamics of networks have transformed research in many fields, however, the Geosciences have not benefited much from this new conceptual framework. We focus our study on River deltas, which exhibit complex channel networks, whose connectivity constrains, if not drives, their morphology and evolution. Thus, understanding and quantifying properties of these complex patterns is an essential step to solve the inverse problem of inferring process from form and predict their response in a heavily anthropogenic impacted environment.

We explore urban mobility from a gendered perspective by analysing the mobility traces extracted from the Call Detail Records (CDRs) of a large cohort of about 400,000 anonymized mobile phone users in the greater metropolitan area of Santiago, Chile, over a period of 3 months. To investigate gender differences in mobility behavior, we analyze gender disaggregated mobility patterns of users by computing the number of unique locations visited by each user and their associated frequency of visits.

Scientific research is a complex endeavor and the right mix of skills and competences is necessary to effectively produce new knowledge. In this work we describe how we can use an unsupervised machine learning algorithm to obtain a vector representation of scientific topics which in turn allows us to create a mapping of the research space in Physics. The research space is then used to explain the evolution of scientific expertise of urban areas over time and across scientific fields.

Addressing the network reconstruction problem means facing the double challenge represented by the estimation of topology and the estimation of link weights. Among the methods proposed so far, some attribute weights to the binary configuration using a completely separate methodology, while others assume that the binary and weighted constraints jointly determine the final configuration in terms of both topology and weights.

The spread of infectious and bacterial diseases at the global scale is mediated by long-range human travel. Our ability to predict the impact of an outbreak on human health requires understanding the spatiotemporal signature of long range human mobility as well as the behavior of individuals on adopting policy driven or otherwise initiated mitigation strategies.

A representation of scalar time series, which has proved to be useful to derive insights about the underlying process generating the data, is to transform the time series into a complex network. This allows data analysis via the rigor of mathematical graph theory, and characterization through application of a range of complex network metrics. For most of these techniques, however, the size of the network (given by the number of nodes) will scale with the size (length) of the time series, enabling only a statistical description of the network properties.

The study of complex systems has gained great interest recently due to the abundance of big data and the advances of the methodology for their analysis. For complex dynamical systems, the observed variables can be large resulting in high-dimensional time series. To make sense of the dependencies among the observed variables and form complex networks, Granger causality has been the pioneering concept from which many inter-dependence (connectivity) measures have been originated.

The Police Reform Act of 2002 launched an initiative in the UK to augment the various ranks of police officers with Police Community Support Officers (PCSOs) to provide greater police presence on the streets and relieve more highly trained police officers for other duties. One way of deploying PCSOs is for local team briefings led by a Neighbourhood Policing Sergeant before they go out on foot patrol, telling them the areas that should be visited and any particular things that should be done, e.g. revisiting victims of crime, or patrol identified areas suffering from anti-social behavior.

In recent years, there has been substantial progress in measuring mixing patterns relevant for disease transmission with wearable sensors. The progressive miniaturization of the devices and their decreasing costs have allowed to scale-up large contact experiments up to hundreds of individuals across a variety of settings. Some examples include hospitals, schools, conferences and more recently, households. However, so far, the deployment of wearable proximity sensing technologies has been mostly limited to high developed countries in Europe or in the USA.