The understanding of human brain mechanisms has captured the imagination of scientists for ages. From the quantitative perspective, there is evidence that damages to brain structure affect brain function and, as a consequence, cognitive aspects. As there is evidence that brain structure might be affected by altered cognition. However, the complex interplay between the human brain and the mind remains still poorly understood. This fact has important clinical consequences, limiting applications devoted to the prevention and treatment of brain diseases. In the present thesis, we aim to enhance our understanding of human brain mechanisms by means of an integrated and data-driven approach, by adopting a systemic perspective and leveraging on tools from computational and network neuroscience. We successfully enhance the state of the art of computational neuroscience in several manners. Firstly, we inspect human cognition by focusing on the geometric exploration of concepts in the human mind to build new datadriven metrics to complement the neurological assessment and to confirm Alzheimer’s disease diagnosis. We formalize a new stochastic process, the potential-driven random walk, able to model the trade-off between exploitation and exploration of network structure, by accounting for local and global information, providing a flexible tool to span from random walk to shortestpath based navigation. Probing the interplay between brain structure and dynamics by means of its Von Neumann entropy, we develop a new framework for the multiscale analysis of the human connectome, which is effective for discerning between healthy conditions and Alzheimer’s disease. Finally, by integrating data from the human brain structural connectivity, its functional response errors as measured by Direct Electrical Stimulation and semantic selectivity, we propose a new procedure for mapping the human brain triadic nature, thus providing a model-oriented bridge between the human brain and mind. Besides shedding more light on human brain functioning, our findings offer original and promising clues to develop integrated biomarkers for Alzheimer’s disease detection, with the potential of extension for applications to other neurodegenerative diseases and psychiatric disorders.

Requirements engineering in a system-engineering project is a key factor in the success of a project. In the current state, stand-alone research has been conducted tackling this area, however, few studies addressed the requirements based on a probabilistic approach. In this thesis, a multi-scale probabilistic approach has been developed, named Bayesian Network, to evaluate the requirements engineering of a complex systems In order to pursue the aim of this paper, the FFG(X) navy ship is chosen to serve as a case study and to validate the proposed model. Results indicate the sub-requirements that highly affect the FFG capability/performance. These sub-requirements are: 1) guns, 2) ballistic missiles, 3) antisubmarine, and 4) radar.

We are recently witnessing an increase in large-scale micro/individual/- granular level behavioural data. Such data has been proven to have the capacity to aid the development of more accurate simulations that will ef- fectively predict the behaviours of complex systems. Despite this increase, the literature has failed to produce a structured modelling approach that will effectively take advantage of such granular data, in modelling com- plex systems that involve social phenomenons (i.e. social complex sys- tems). In this thesis, we intend to bridge this gap by answering the question of how novel structural frameworks, that systematically guides the use of micro-level behaviour and attribute data, directly extracted from the ba- sic entities within a social complex system can be created. These frame- works should involve the systematic processes of using such data to di- rectly model agent attributes, and to create agent behaviour rules, that will directly represent the unique micro entities from which the data was ex- tracted. The objective of the thesis is to define generic frameworks, that would create agent based micro simulations that would directly reflect the target complex system, so that alternative scenarios, that cannot be inves- tigated in the real system, and social policies that need to be investigated before being applied on the social system can be explored. In answering this question, we take advantage of the pros of other model- ing techniques such as micro simulation and agent based techniques in cre- ating models that have a micro-macro link, such that the micro behaviour that causes the macro emergence at the simulation’s global level can be easily investigated. which is a huge advantage in policy testing. We also utilized machine learning in the creation of behavioural rules.This created agent behaviours that were empirically defined. Therefore, this thesis also answers the question of how such structural framework will empirically create agent behaviour rules through machine learning algorithms. In this thesis we proposed two novel frameworks for the creation of more accurate simulations. The concepts within these frameworks were proved using case studies, in which these case studies where from different so- cial complex systems, so as to prove the generic nature of the proposed frameworks. In concluding of this thesis, it was obvious that the questions posed in the first chapter had been answered. The generic frameworks had been created, which bridged the existing gap in the creation of accurate mod- els from the presently available granular attribute and behavioral data, al- lowing the simulations created from these models accurately reflect their target social complex systems from which the data was extracted from.

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Complex socioenvironmental problems such as food, energy and water shortages, health impacts from environmental contamination and global climate change present significant challenges to the global community. Addressing these problems will require an interdisciplinary systems-thinking approach that coordinates problem-solving between practitioners of varied disciplines including engineers, physical scientists, economists and other social scientists. Civil and environmental engineers have distinct technical skills necessary to help address these challenges as part of coordinated multidisciplinary efforts towards the achievement of comprehensive and sustainable resolutions to these problems. Ensuring civil and environmental engineers are trained to think and work in this multidisciplinary exchange requires incorporation of systems-thinking into engineering academic curricula. Attempts have been made to incorporate these skill sets into civil and environmental engineering (CEE) coursework. These efforts, as well as evaluation of their effectiveness in training CEE students to think systemically, have lacked in coordination to integrate them as part of the overarching academic curricula. This research advances the current body of knowledge regarding incorporation of systems-thinking into CEE coursework by examining the impacts of system dynamics model based educational tools on systems-thinking learning outcomes of CEE students in a one-semester CEE elective course. The findings suggest that system dynamics modeling can be an effective tool in educating future systems thinkers in the CEE disciplines. / Doctor of Philosophy / Complex socioenvironmental problems such as food, energy and water shortages, health impacts from environmental contamination and global climate change present significant challenges to the global community. Addressing these problems will require an interdisciplinary systems-thinking approach that coordinates problem-solving between practitioners of varied disciplines including engineers, physical scientists, economists and other social scientists. Civil and environmental engineers have distinct technical skills necessary to help address these challenges as part of coordinated multidisciplinary efforts towards the achievement of comprehensive and sustainable resolutions to these problems. Ensuring civil and environmental engineers are trained to think and work in this multidisciplinary exchange requires incorporation of systems-thinking into engineering academic curricula. Attempts have been made to incorporate these skill sets into civil and environmental engineering (CEE) coursework. These efforts, as well as evaluation of their effectiveness in training CEE students to think systemically, have lacked in coordination to integrate them as part of the overarching academic curricula. This research advances the current body of knowledge regarding incorporation of systems-thinking into CEE coursework by examining the impacts of system dynamics model based educational tools on systems-thinking learning outcomes of CEE students in a one-semester CEE elective course. The findings suggest that system dynamics modeling can be an effective tool in educating future systems thinkers in the CEE disciplines.

Yes / The research presented in this paper addresses the challenge of developing functional models for complex systems that have multiple modes of operation or use cases. An industrial case study of an electric vehicle is used to illustrate the proposed methodology, which is based on a systematic modelling of functions through nested systems using the system state flow diagram (SSFD) method. The paper discusses the use of SSFD parameter based state definition to identify physical and logical conditions for joining function models, and the use of heuristics to construct complex function models.

This thesis explores the dynamical and structural properties of human mobility within urban environments through the lens of complex systems and network science. Beginning with an introduction to the relevance of studying cities and human mobility, we outline our aim to investigate the interplay between transportation network properties and collective human flows. The theoretical background introduces essential concepts from network science and statistical physics, focusing on their application to spatial and transportation networks as well as urban systems. The thesis is devoted to three specific investigations. Firstly, we analyze the role of multiple pathways in defining effective network distances and their utility in predicting human mobility at diffusive scales, particularly in assessing pandemic potentials such as COVID-19 variants. Secondly, we delve into the optimization of flow-weighted transportation networks, demonstrating how network topologies can emerge from optimization processes under various constraints. We focus on a case study on the Greater London Area highlighting the integration of spatial attractiveness and traffic congestion in simulating human mobility patterns. The thesis finally explores the dynamics of out-of-routine mobility by integrating individual and collective behaviors. Leveraging large-scale datasets from US cities, we improve next-location prediction models by combining insights from individual trajectories and collective mobility dynamics. This approach is further examined in the context of novel mobility patterns influenced by COVID-19 restrictions, emphasizing the statistical properties of collective mobility near urban points of interests. Through these investigations, this thesis contributes to understanding complex urban systems and lays foundations for predictive models that integrate theoretical insights with empirical data to enhance our understanding of human mobility dynamics.

The interest in complex systems has increased exponentially during the past years because it was found helpful in addressing many of today's challenges. The study of the brain, biology, earthquakes, markets and social sciences are only a few examples of the fields that have benefited from the investigation of complex systems. Internet, the increased mobility of people and the raising energy demand are among the factors that brought in contact complex systems that were isolated till a few years ago. A theory for the interaction between complex systems is becoming more and more urgent to help mankind in this transition. The present work builds upon the most recent results in this field by solving a theoretical problem that prevented previous work to be applied to important complex systems, like the brain. It also shows preliminary laboratory results of perturbation of in vitro neural networks that were done to test the theory. Finally, it gives a preview of the studies that are being done to create a theory that is even closer to the interaction between real complex systems.

Com a divulgação dos efeitos antrópicos sobre o clima nos últimos anos, nota-se um adensamento no debate acerca da incorporação da temática socioambiental na agenda corporativa. Embora as externalidades ambientais da atividade produtiva sejam conhecidas desde o início da Revolução Industrial, os processos de elaboração e implementação de estratégias empresariais de sustentabilidade ainda é algo em desenvolvimento. O presente trabalho visa analisar a dinâmica da difusão de estratégias de sustentabilidade em redes empresariais através de modelos de sistemas sociais complexos. Para tanto, são analisados alguns modelos consolidados na literatura, como o modelo de Ising (1925), Barabási-Albert (1999) e Ito e Kaneko (2002). Tal análise subsidia a criação de um modelo específico, cujos resultados de sua simulação são utilizados para gerar hipóteses que alicerceiam a elaboração de cenários prospectivos, pautando-se no referencial de Berger (1959) e Godet (2008). Por fim, tais cenários apresentam possíveis realidades futuras quanto à emergência de um setor produtivo mais sustentável, auxiliando no planejamento de empresas e governos. / With the disclosure of anthropogenic impacts on climate in recent years, there has been a growing debate about the incorporation of environmental issues on the corporate agenda. Although the environmental externalities of productive activity are known since the beginning of the Industrial Revolution, the processes of development and implementation of corporate sustainability strategies are still under development. The present work analyzes the dynamics of the diffusion of sustainability strategies on enterprise networks through models of complex social systems. Hence, we analyze three well known models: Ising (1925), Barabási-Albert (1999) and Ito and Kaneko (2002). This analysis underpins the creation of a specific model, which results are used to generate hypotheses that support the development of prospective scenarios, based on Berger (1959) and Godet (2008). Finally, these scenarios present possible future realities for the emergence of a sustainable productive sector, assisting in the planning of businesses and governments.

Com a divulgação dos efeitos antrópicos sobre o clima nos últimos anos, nota-se um adensamento no debate acerca da incorporação da temática socioambiental na agenda corporativa. Embora as externalidades ambientais da atividade produtiva sejam conhecidas desde o início da Revolução Industrial, os processos de elaboração e implementação de estratégias empresariais de sustentabilidade ainda é algo em desenvolvimento. O presente trabalho visa analisar a dinâmica da difusão de estratégias de sustentabilidade em redes empresariais através de modelos de sistemas sociais complexos. Para tanto, são analisados alguns modelos consolidados na literatura, como o modelo de Ising (1925), Barabási-Albert (1999) e Ito e Kaneko (2002). Tal análise subsidia a criação de um modelo específico, cujos resultados de sua simulação são utilizados para gerar hipóteses que alicerceiam a elaboração de cenários prospectivos, pautando-se no referencial de Berger (1959) e Godet (2008). Por fim, tais cenários apresentam possíveis realidades futuras quanto à emergência de um setor produtivo mais sustentável, auxiliando no planejamento de empresas e governos. / With the disclosure of anthropogenic impacts on climate in recent years, there has been a growing debate about the incorporation of environmental issues on the corporate agenda. Although the environmental externalities of productive activity are known since the beginning of the Industrial Revolution, the processes of development and implementation of corporate sustainability strategies are still under development. The present work analyzes the dynamics of the diffusion of sustainability strategies on enterprise networks through models of complex social systems. Hence, we analyze three well known models: Ising (1925), Barabási-Albert (1999) and Ito and Kaneko (2002). This analysis underpins the creation of a specific model, which results are used to generate hypotheses that support the development of prospective scenarios, based on Berger (1959) and Godet (2008). Finally, these scenarios present possible future realities for the emergence of a sustainable productive sector, assisting in the planning of businesses and governments.