European Gas Markets: Market Integration & Market Efficiency: A Network Perspective

In this thesis, I study the market integration of European Natural Gas markets through two papers, whilst the third paper considers the impact of News Sentiment on the pricing and trading of Clean Energy and Traditional Energy stocks. Specifically, the first paper studies the level of harmonisation of European Natural Gas prices, characterised by 12 European gas hubs. The key finding is that, under normal market conditions, European Natural Gas markets are becoming increasingly integrated, with few physical barriers to increased market integration. Conversely, the detection of non-physical barriers to trade suggests that the liberalisation and development of certain national gas markets is yet to be fully achieved, inferring that improvements in technical arrangements are required. The second paper provides a framework for forecasting the short term presence of phys- ical barriers to market integration of European Natural Gas markets. The identification of infrastructure congestion is an important prerequisite in enforcing price competition, and the implementation of an internal European gas market. In order to address this challenge, the underlying infrastructure network is learnt as a graph, and a deep learning framework, Graph Convolutional Long Short-Term Memory Neural Network (GC-LSTM), based on the topology of the infrastructure network, is applied to learn the interactions between different pipelines, and forecast gas flows throughout the network. Empirical results show that the GC-LSTM outperforms baseline methods in predicting gas pipeline flows. The third paper studies the impact of News sentiment on pricing and trading for European Clean Energy companies and Traditional Energy companies. Using daily news extracted from Bloomberg, we estimate Vector Autoregressive (VAR) models and evaluate the dynamic spillover effects between News sentiment, stock returns and trading volumes. We find that European Clean Energy firms and Traditional Energy firms share the same patterns; that News sentiment positively affects both stock returns and trading volumes, and in return, stock returns and trading volumes have a limited impact on News sentiment. Nevertheless, the spillovers are relatively moderate and asymmetric

Quantifizierung der Zuverlässigkeit und Komponentenbedeutung von Infrastrukturen unter Berücksichtigung von Naturkatastropheneinwirkung

The central topic is the quantification of the reliability of infrastructure networks subject to extreme wind loads. Random fields describe the wind distributions and calibrated fragility curves yield the failure probabilities of the components as a function of the wind speed. The network damage is simulated taking into account possible cascading component failures. Defined "Importance Measures" prioritize the components based on their impact on system reliability - the basis for system reliability improvement measures.Zentrales Thema ist die Quantifizierung der Zuverlässigkeit von Infrastrukturnetzen unter Einwirkung extremer Windlasten. Raumzeitliche Zufallsfelder beschreiben die Windverteilungen und spezifisch kalibrierte Fragilitätskurven ergeben die Versagenswahrscheinlichkeiten der Komponenten. Der Netzwerkschaden wird unter Berücksichtigung von kaskadierenden Komponentenausfällen simuliert. Eigens definierte „Importance Measures“ priorisieren die Komponenten nach der Stärke ihres Einflusses auf die Systemzuverlässigkeit - die Basis für Verbesserungen der Systemzuverlässigkeit

Smart Classifiers and Bayesian Inference for Evaluating River Sensitivity to Natural and Human Disturbances: A Data Science Approach

Excessive rates of channel adjustment and riverine sediment export represent societal challenges; impacts include: degraded water quality and ecological integrity, erosion hazards to infrastructure, and compromised public safety. The nonlinear nature of sediment erosion and deposition within a watershed and the variable patterns in riverine sediment export over a defined timeframe of interest are governed by many interrelated factors, including geology, climate and hydrology, vegetation, and land use. Human disturbances to the landscape and river networks have further altered these patterns of water and sediment routing. An enhanced understanding of river sediment sources and dynamics is important for stakeholders, and will become more critical under a nonstationary climate, as sediment yields are expected to increase in regions of the world that will experience increased frequency, persistence, and intensity of storm events. Practical tools are needed to predict sediment erosion, transport and deposition and to characterize sediment sources within a reasonable measure of uncertainty. Water resource scientists and engineers use multidimensional data sets of varying types and quality to answer management-related questions, and the temporal and spatial resolution of these data are growing exponentially with the advent of automated samplers and in situ sensors (i.e., “big data”). Data-driven statistics and classifiers have great utility for representing system complexity and can often be more readily implemented in an adaptive management context than process-based models. Parametric statistics are often of limited efficacy when applied to data of varying quality, mixed types (continuous, ordinal, nominal), censored or sparse data, or when model residuals do not conform to Gaussian distributions. Data-driven machine-learning algorithms and Bayesian statistics have advantages over Frequentist approaches for data reduction and visualization; they allow for non-normal distribution of residuals and greater robustness to outliers. This research applied machine-learning classifiers and Bayesian statistical techniques to multidimensional data sets to characterize sediment source and flux at basin, catchment, and reach scales. These data-driven tools enabled better understanding of: (1) basin-scale spatial variability in concentration-discharge patterns of instream suspended sediment and nutrients; (2) catchment-scale sourcing of suspended sediments; and (3) reach-scale sediment process domains. The developed tools have broad management application and provide insights into landscape drivers of channel dynamics and riverine solute and sediment export

Modelling and quantifying brain connectivity and dynamics with applications in aging and ADHD

Human brain is a complex organ and made up of integrative networks encompassing a large number of regions. These regions communicate with each other to share information involved in complex cognitive processes. Functional connectivity (FC) represents the level of synchronization between different brain regions/networks. Studying functional interactions of the brain creates a platform for understanding functional architecture of the brain as an integrative network and has implications for understanding human cognition. Furthermore, there is evidence that FC patterns are sensitive to different diseases. In addition, age is a significant determinant of intra-/inter-individual variability in the FC patterns. Therefore, key aims for the studies included in this thesis were to apply and develop novel resting-state FC methodologies, with applications in healthy aging and ADHD. Indeed, measures of the brain’s FC may serve as a useful tool to diagnose and predict the course of disease, and useful in developing individualized therapies. Age- or disease-related alterations in the FC could reflect a multitude of factors, including changes in structural connectivity. However, we still have limited knowledge of the emergence of brain dynamics from the underlying anatomy. The interplay between the brain’s structure and dynamics underlies all brain functions. Therefore, in the last study we focused on the systematic modeling of the brain network dynamics. Large-scale computational models are uniquely suited to address difficult questions related to the role of brain’s structural network in shaping functional interactions. In addition, computational modeling of the brain enables us to test different hypotheses without any experimental complication while it provides us with a platform for improving our understanding of different brain mechanisms. A new macroscopic computational model of the brain oscillations for resting-state fMRI was introduced in this thesis, which outperforms previous model in the same class. Then, the effects of malfunctions in different brain regions were simulated and subsequently predicted perturbation patterns were recruited for local vulnerability mapping as well as quantification of hazard rates induced after perturbing any brain regio

The modular structure of brain functional connectivity networks: a graph theoretical approach

Complex networks theory offers a framework for the analysis of brain functional connectivity as measured by magnetic resonance imaging. Within this approach the brain is represented as a graph comprising nodes connected by links, with nodes corresponding to brain regions and the links to measures of inter-regional interaction. A number of graph theoretical methods have been proposed to analyze the modular structure of these networks. The most widely used metric is Newman's Modularity, which identifies modules within which links are more abundant than expected on the basis of a random network. However, Modularity is limited in its ability to detect relatively small communities, a problem known as ``resolution limit''. As a consequence, unambiguously identifiable modules, like complete sub-graphs, may be unduly merged into larger communities when they are too small compared to the size of the network. This limit, first demonstrated for Newman's Modularity, is quite general and affects, to a different extent, all methods that seek to identify the community structure of a network through the optimization of a global quality function. Hence, the resolution limit may represent a critical shortcoming for the study of brain networks, and is likely to have affected many of the studies reported in the literature. This work pioneers the use of Surprise and Asymptotical Surprise, two quality functions rooted in probability theory that aims at overcoming the resolution limit for both binary and weighted networks. Hereby, heuristics for their optimization are developed and tested, showing that the resulting optimal partitioning can highlight anatomically and functionally plausible modules from brain connectivity datasets, on binary and weighted networks. This novel approach is applied to the partitioning of two different human brain networks that have been extensively characterized in the literature, to address the resolution-limit issue in the study of the brain modular structure. Surprise maximization in human resting state networks revealed the presence of a rich structure of modules with heterogeneous size distribution undetectable by current methods. Moreover, Surprise led to different, more accurate classification of the network's connector hubs, the elements that integrate the brain modules into a cohesive structure. In synthetic networks, Asymptotical Surprise showed high sensitivity and specificity in the detection of ground-truth structures, particularly in the presence of noise and variability such as those observed in experimental functional MRI data. Finally, the methodological advances hereby introduced are shown to be a helpful tool to better discern differences between the modular organization of functional connectivity of healthy subjects and schizophrenic patients. Importantly, these differences may point to new clinical hypotheses on the etiology of schizophrenia, and they would have gone unnoticed with resolution-limited methods. This may call for a revisitation of some of the current models of the modular organization of the healthy and diseased brain

Energy Efficient Cooperative Mobile Sensor Network

Ph.DDOCTOR OF PHILOSOPH

Micro-, Meso- and Macro-Connectomics of the Brain

Neurosciences, Neurolog

Novel non-linear approaches to understanding the dynamic brain: knowledge from rsfMRI and EEG studies

Advances in neuroimaging techniques have been critical to identifying new biomarkers for brain diseases. Resting State Functional Magnetic Resonance Imaging (rsfMRI) non-invasively quantifies the Blood Oxygen Level Dependent (BOLD) signal across brain regions with high spatial resolution; whilst temporal resolution of Electroencephalography (EEG) in measuring the brain's electrical response is unsurpassed. Most of the statistical and machine learning methods used to analyze rsfMRI and EEG data, are static and linear, fail to capture the dynamics and complexity of the brain, and are prone to residual noise. The general goals of this thesis dissertation are i) to provide methodological insight by proposing a statistical method namely point process analysis (PPA) and a machine learning (ML) multiband non-linear EEG method. These methods are especially useful to investigate the brain configuration of older participants and individuals with neurodegenerative diseases, and to predict age and sleep quality; and ii) to share biological insights about synchronization between brain regions (i.e., functional connectivity and dynamic functional connectivity) in different stages of mild cognitive impairment and in Alzheimer's disease. The findings, reported and discussed in this thesis, open a path for new research ideas such as applying PPA to EEG data, adjusting the non-linear ML algorithm to apply it to rsfMRI and use these methods to better understand other neurological diseases. Los avances en las técnicas de neuroimagen han sido fundamentales para identificar nuevos biomarcadores de enfermedades cerebrales. La resonancia magnética funcional en estado de reposo (rsfMRI) cuantifica de forma no invasiva la señal dependiente del nivel de oxígeno en sangre (BOLD) en todas las regiones del cerebro con una alta resolución espacial, mientras que la resolución temporal de la electroencefalografía (EEG) para medir la respuesta eléctrica del cerebro es insuperable. La mayoría de los métodos estadísticos y de aprendizaje automático utilizados para analizar datos de rsfMRI y EEG son estáticos y lineales, no captan el dinamismo y la complejidad del cerebro y son propensos al ruido residual. Los objetivos generales de esta tesis doctoral son i) proporcionar una visión metodológica proponiendo un método estadístico, llamado análisis por proceso de puntos (PPA), y un método de aprendizaje automático (ML) multibanda no lineal de EEG. Estos métodos son especialmente útiles para investigar la configuración cerebral de participantes de edad avanzada y de individuos con enfermedades neurodegenerativas, y para predecir la edad y la calidad del sueño; y ii) compartir conocimientos biológicos sobre la sincronización entre regiones cerebrales (es decir, la conectividad funcional y la conectividad funcional dinámica) en diferentes etapas del deterioro cognitivo leve y en la enfermedad de Alzheimer. Los hallazgos, comunicados y discutidos en esta tesis, abren un camino para nuevas ideas de investigación, como la aplicación de PPA a datos de EEG, el ajuste del algoritmo ML no lineal para aplicarlo a rsfMRI y el uso de estos métodos para comprender mejor otras enfermedades neurológicas

Multi-Robot Systems: Challenges, Trends and Applications

This book is a printed edition of the Special Issue entitled “Multi-Robot Systems: Challenges, Trends, and Applications” that was published in Applied Sciences. This Special Issue collected seventeen high-quality papers that discuss the main challenges of multi-robot systems, present the trends to address these issues, and report various relevant applications. Some of the topics addressed by these papers are robot swarms, mission planning, robot teaming, machine learning, immersive technologies, search and rescue, and social robotics