Category: Thesis

Rafal Piwowarczyk defended his Master Thesis. Congrats!

Rafal Piwowarczyk defended his Master thesis in Complex Adaptive Systems at Chalmers University of Technology on 19 February 2018

​Title: Influence of Delay on the Vicsek Model

The aim of this work is to show that sensorial delay influences the behaviour of self-propelling agents using self-aligning interactions. The model was based on the Vicsek model, which is a two-dimensional system of self-propelling particles that are able to detect and align with each other within a certain radius. We prove that the introduction of short delays favours cluster and swarm formation, while extending the delay to higher values or implementation of negative delays significantly harms this process. We introduce a global clustering parameter, which is based on the use of the Voronoi tessellation, which allows us to measure the emergence of clusters. The sensorial delay might play a crucial role in systems that exhibit swarming behaviours and it’s better understanding can result in the construction of key tools for the realisation and manipulation of complex networks of autonomous robots.

​Name of the master programme: MPCAS – Complex Adaptive Systems
Supervisor: Giovanni Volpe, Department of Physics, University of Gothenburg
Examiner: Giovanni Volpe, Department of Physics, University of Gothenburg
Opponent: Freddie Ogemark, MP Complex Adaptive Systems, Department of Physics, Chalmers University of Technology

Place: PJ, lecture hall, Fysik Origo, Fysik
Time: 19 February, 2018, 11:00

Oleksii Bielikh defended his Master Thesis. Congrats!

Oleksii Bielikh defended his Master thesis in Complex Adaptive Systems  at Chalmers University of Technology on 5 October 2017.

Thesis title: Generation of Random Graphs for Graph Theory Analysis Applied to the Study of Brain Connectivity

Thesis advisor: Giovanni Volpe

One of the current frontiers in neurosciences is to understand brain connectivity both in healthy subjects and patients. Recent studies suggest that brain connectivity measured with graph theory is a reliable candidate biomarker of neuronal dysfunction and disease spread in neurodegenerative disorders. Widespread abnormalities in the topology of the cerebral networks in patients correlate with a higher risk of developing dementia and worse prognosis.

In order to recognize such abnormalities, brain network graph measures should be compared with the corresponding measures calculated on random graphs with the same degree distribution. However, creating a random graph with prescribed degree sequence that has number of nodes of magnitude of 105 is a recognized problem. Existing algorithms have a variety of shortcomings, among which are slow run-time, non-uniformity of results and divergence of degree distribution with the target one.

The goal of this thesis is to explore the possibility of finding an algorithm that can be used with very large networks. Multiple common algorithms were tested to check their scaling with increasing number of nodes. The results are compared in order to find weaknesses and strengths of particular algorithms, and certain changes are offered that speed up their runtimes and/or correct for the downsides. The degree distributions of the resulting random graphs are compared to those of the target graphs, which are constructed in a way that mimics some of the most common characteristics of brain networks, namely small-worldness and scale-free topology, and it is discussed why some of the models are more appropriate than others in this case. Simulations prove that the majority of algorithms are vastly inefficient in creating random large graphs with necessary limitations on their topology, while some can be adapted to showcase to a certain extent promising results.

Simon Nilsson defended his Master Thesis. Congrats!

Simon Nilsson defended his Master thesis in Complex Adaptive Systems at Chalmers University of Technology on 14 June 2017.

Thesis title: Collective Dynamics in a Complex Environment

Thesis advisor: Giovanni Volpe

Collective behaviour is a phenomenon that often occurs in systems of many interacting individuals. Common macroscopic examples of collective behaviour are flocks of birds, swarms of insects and crowds of people. On the microscopic scale, it is often observed in so-called active systems, constituted by self-propelled particles, also known as active particles. Motile bacteria or synthetic microswim- mers are among the most commonly studied active particles.

The potential applications of collective behaviour and understanding thereof encompass multiple disciplines, ranging from robotics and medicine to algorithms, like ant colony optimization. However, the apparent complexity makes under- standing an intimidating task. Despite this, simple models have proven successful in capturing the defining characteristics of such systems.

This thesis examines a well-known model of active matter and expands it to incorporate necessary components to explore the effects a complex environment has on this pre-existing model. Additionally, a new model is proposed and explored in purely active systems as well as in complex environments. Simulations show that a phase transition between a gaseous state and the formation of metastable clusters occurs as the level of orientational noise decreases. Furthermore, they show that this model describes the formation of metastable channels in a crowded environment of passive particles.

Lovisa Hagstöm, Erik Holmberg, Eliza Nordén, Teodor Norrestad, Martin Selin & Lisa Sjöblom defended their Bachelor Thesis. Congrats!

Lovisa Hagstöm, Erik Holmberg, Eliza Nordén, Teodor Norrestad, Martin Selin & Lisa Sjöblom defended their Bachelor Thesis at Chambers University of Technology on 23 May 2017.

Title: Autonoma agenter i komplexa miljöer — En studie av tidsfördröjningens inverkan på kollektiva beteenden

Abstract: Interagerande autonoma agenter är ett högintressant och relativt outforskat område. Syftet med detta arbete är att utforska grundläggande metoder för att simulera aktiva agenter som påverkas av ett intensitetsfält med en fördröjning. Fördröjningen mellan agentens indata och dess reaktion på denna visar sig vara väsentlig vad gäller styrandet av dess beteende. Efter att de grundläggande metoderna är etablerade ämnar återstoden av arbetet att fördjupa sig i tre olika aspekter av autonoma agenter. Den rotationella diffusionskoefficienten, DR, visar sig vara en parameter som likt farten kan användas för att styra agenternas beteende. Dock syns inga kvalitativa skillnader i beteendet om inte en fördröjning införs. Med en positiv fördröjning söker sig agenterna till områden med stort DR och med en negativ söker de sig till områden med litet DR. Intressanta beteenden framkallas också genom att låta en aktiv agent röra sig i en propagerande vågpotential, både i en och två dimensioner. För det endimensionella vågfallet kan man med hjälp av fördröjningen styra om agenten färdas mot eller från vågkällan. Agenter som interagerar via tvådimensionella vågpulser kan manipuleras till att samlas eller sprida sig, beroende på fördröjningens karaktär. Slutligen utreds möjligheterna att använda autonoma aktiva agenter för att simulera rovdjur och bytesdjur. För att realisera detta används fördröjningen som styrande parameter. Utöver detta utvecklas en enkel evolutionsalgoritm där byten och rovdjur visar sig kunna anpassa sig efter varandra. Fördröjningar visar sig överlag vara ett kraftfullt verktyg för att påverka beteendet hos aktiva agenter med stor potential i framtida applikationer.

Supervisor: Giovanni Volpe, Department of Physics, University of Gothenburg
Examiner: Lena Falk, Department of Physics, University of Gothenburg

Erçağ Pinçe defended his PhD Thesis. Congrats!

Erçağ Pinçe defended his PhD thesis on 21 October 2016. Assist. Prof. Evren Doruk Engin (Ankara University), Assist. Prof. Giovanni Volpe (Bilkent University), Assist. Prof. Balázs Hétenyi (Bilkent University), Assoc. Prof. Fatih Ömer İlday (Bilkent University) and Prof. Alper Kiraz (Koç University) participated as thesis committee members.

Erçağ Pinçe investigated the role that spatial disorder can play to alter collective dynamics in a colloidal living active matter system where motile E. Coli bacteria are present. The results suggested that the level of heterogeneity present in the background changes the long-term spatial dynamics in a colloidal ensemble coupled to a bacterial bath. This work provided insights about statistical behavior and far-from-equilibrium interactions in an active matter system.

Thesis title: Manipulation and control of collective behavior in active matter systems

Thesis advisor: Giovanni Volpe

Thesis abstract: Active matter systems consist of active constituents that transform energy into directed motion in a non-equilibrium setting. The interaction of active agents with each other and with their environment results in collective motion and emergence of long-range ordering. Examples to such dynamic behaviors in living active matter systems are pattern formation in bacterial colonies, ocking of birds and clustering of pedestrian crowds. All these phenomena stem from far-from-equilibrium interactions. The governing dynamics of these phenomena are not yet fully understood and extensively studied. In this thesis, we studied the role that spatial disorder can play to alter collective dynamics in a colloidal living active matter system. We showed that the level of heterogeneity in the environment in uences the long-range order in a colloidal ensemble coupled to a bacterial bath where the non-equilibrium forces imposed by the bacteria become pivotal to control switching between gathering and dispersal of colloids. Apart from studying environmental factors in a complex active matter system, we also focused on a new class of active particles, \bionic microswimmers”, and their clustering behavior. We demonstrated that spherical bionic microswimmers which are fabricated by attaching motile E. coli bacteria on melamine particles can agglomerate in large colloidal structures. Finally, we observed the emergence of swimming clusters as a result of the collective motion of bionic microswimmers. Our results provide insights about statistical behavior and far-from-equilibrium interactions in an active matter system.