Vegetation dynamics in northern south America on different time scales

The overarching goal of this doctoral thesis was to understand the dynamics of vegetation activity occurring across time scales globally and in a regional context. To achieve this, I took advantage of open data sets, novel mathematical approaches for time series analyses, and state-of-the-art technology to effectively manipulate and analyze time series data. Specifically, I disentangled the longest records of vegetation greenness (>30 years) in tandem with climate variables at 0.05° for a global scale analysis (Chapter 3). Later, I focused my analysis on a particular region, northern South America (NSA), to evaluate vegetation activity at seasonal (Chapter 4) and interannual scales (Chapter 5) using moderate spatial resolution (0.0083°). Two main approaches were used in this research; time series decomposition through the Fast Fourier Transformation (FFT), and dimensionality reduction analysis through Principal Component Analysis (PCA). Overall, assessing vegetation-climate dynamics at different temporal scales facilitates the observation and understanding of processes that are often obscured by one or few dominant processes. On the one hand, the global analysis showed the dominant seasonality of vegetation and temperature in northern latitudes in comparison with the heterogeneous patterns of the tropics, and the remarkable longer-term oscillations in the southern hemisphere. On the other hand, the regional analysis showed the complex and diverse land-atmosphere interactions in NSA when assessing seasonality and interannual variability of vegetation activity associated with ENSO. In conclusion, disentangling these processes and assessing them separately allows one to formulate new hypotheses of mechanisms in ecosystem functioning, reveal hidden patterns of climate-vegetation interactions, and inform about vegetation dynamics relevant for ecosystem conservation and management

Metodologias para caracterização de tráfego em redes de comunicações

Tese de doutoramento em Metodologias para caracterização de tráfego em redes de comunicaçõesInternet Tra c, Internet Applications, Internet Attacks, Tra c Pro ling, Multi-Scale Analysis abstract Nowadays, the Internet can be seen as an ever-changing platform where new and di erent types of services and applications are constantly emerging. In fact, many of the existing dominant applications, such as social networks, have appeared recently, being rapidly adopted by the user community. All these new applications required the implementation of novel communication protocols that present di erent network requirements, according to the service they deploy. All this diversity and novelty has lead to an increasing need of accurately pro ling Internet users, by mapping their tra c to the originating application, in order to improve many network management tasks such as resources optimization, network performance, service personalization and security. However, accurately mapping tra c to its originating application is a di cult task due to the inherent complexity of existing network protocols and to several restrictions that prevent the analysis of the contents of the generated tra c. In fact, many technologies, such as tra c encryption, are widely deployed to assure and protect the con dentiality and integrity of communications over the Internet. On the other hand, many legal constraints also forbid the analysis of the clients' tra c in order to protect their con dentiality and privacy. Consequently, novel tra c discrimination methodologies are necessary for an accurate tra c classi cation and user pro ling. This thesis proposes several identi cation methodologies for an accurate Internet tra c pro ling while coping with the di erent mentioned restrictions and with the existing encryption techniques. By analyzing the several frequency components present in the captured tra c and inferring the presence of the di erent network and user related events, the proposed approaches are able to create a pro le for each one of the analyzed Internet applications. The use of several probabilistic models will allow the accurate association of the analyzed tra c to the corresponding application. Several enhancements will also be proposed in order to allow the identi cation of hidden illicit patterns and the real-time classi cation of captured tra c. In addition, a new network management paradigm for wired and wireless networks will be proposed. The analysis of the layer 2 tra c metrics and the di erent frequency components that are present in the captured tra c allows an e cient user pro ling in terms of the used web-application. Finally, some usage scenarios for these methodologies will be presented and discussed

Persistence in complex systems

Persistence is an important characteristic of many complex systems in nature, related to how long the system remains at a certain state before changing to a different one. The study of complex systems' persistence involves different definitions and uses different techniques, depending on whether short-term or long-term persistence is considered. In this paper we discuss the most important definitions, concepts, methods, literature and latest results on persistence in complex systems. Firstly, the most used definitions of persistence in short-term and long-term cases are presented. The most relevant methods to characterize persistence are then discussed in both cases. A complete literature review is also carried out. We also present and discuss some relevant results on persistence, and give empirical evidence of performance in different detailed case studies, for both short-term and long-term persistence. A perspective on the future of persistence concludes the work.This research has been partially supported by the project PID2020-115454GB-C21 of the Spanish Ministry of Science and Innovation (MICINN). This research has also been partially supported by Comunidad de Madrid, PROMINT-CM project (grant ref: P2018/EMT-4366). J. Del Ser would like to thank the Basque Government for its funding support through the EMAITEK and ELKARTEK programs (3KIA project, KK-2020/00049), as well as the consolidated research group MATHMODE (ref. T1294-19). GCV work is supported by the European Research Council (ERC) under the ERC-CoG-2014 SEDAL Consolidator grant (grant agreement 647423)

Persistence in complex systems

Persistence is an important characteristic of many complex systems in nature, related to how long the system remains at a certain state before changing to a different one. The study of complex systems’ persistence involves different definitions and uses different techniques, depending on whether short-term or long-term persistence is considered. In this paper we discuss the most important definitions, concepts, methods, literature and latest results on persistence in complex systems. Firstly, the most used definitions of persistence in short-term and long-term cases are presented. The most relevant methods to characterize persistence are then discussed in both cases. A complete literature review is also carried out. We also present and discuss some relevant results on persistence, and give empirical evidence of performance in different detailed case studies, for both short-term and long-term persistence. A perspective on the future of persistence concludes the work.This research has been partially supported by the project PID2020-115454GB-C21 of the Spanish Ministry of Science and Innovation (MICINN). This research has also been partially supported by Comunidad de Madrid, PROMINT-CM project (grant ref: P2018/EMT-4366). J. Del Ser would like to thank the Basque Government for its funding support through the EMAITEK and ELKARTEK programs (3KIA project, KK-2020/00049), as well as the consolidated research group MATHMODE (ref. T1294-19). GCV work is supported by the European Research Council (ERC) under the ERC-CoG-2014 SEDAL Consolidator grant (grant agreement 647423)

Rheology and Structure Formation in Complex Polymer Melts

Polymeric materials are ubiquitous in our modern lives. Their many applications in complex materials are accompanied by potentially huge benefits for technological advancement. These applications range from batteries, fuel cells, molecular sieves, tires, and microelectronic devices. The ability to self-assemble into nanostructures in combination with their viscoelastic properties make polymers attractive for this wide range of applications. I perform computer simulations gaining knowledge about their properties for applications and manufacturing, to improve the understanding of these materials. The simulation of multicomponent polymer melts poses an extreme computational challenge. The large spatial extent of defects in self-assembled structures or nonperiodic metastable phases, which are prone to finite size effects, require the study of large system sizes. Hence, I use a soft, coarse-grained polymer model reducing the degrees of freedom to gain insights into long time and length scales. Consistent implementations of these models that scale well on modern GPUs accelerated HPCs hardware enable investigations with up to billions of particles. Consequently, I can address challenges that were deemed intractable before. Firstly, I analyze metastable network phases as a function of the volume fraction, f, of diblock copolymers for polymeric battery electrolytes. One polymer block provides the mechanical stability while the other is ion conducting. The focus lies on the structure of the conducting phase. Due to the trapped metastable states, I investigate systems of extreme sizes with billions of particles circumventing finite size effects. In fact, I identify fractal structures on significant length scales inside the network phase, which influence the transport properties locally. As such, this work highlights the necessity of soft models and scaling implementations obtaining insights on engineering scales. Secondly, I will investigate the simulation of viscoelastic properties of polymeric materials with soft, coarse-grained models. It is particularly challenging to correctly capture the entangled dynamics. The noncrossability of polymer backbones introduces topological constraints on the motion of the chains. A soft, coarse-grained model does not capture this noncrossability automatically. Hence, I utilize a SLSP model to mimic the entanglements via dynamic bonds. With this model and a novel technique to average the stress auto-correlation function G(t), I perform a dynamic mechanical analysis of polymer melts and a cross-linked network. The obtained storage modulus G'(w) and loss modulus G''(w) meet the expectations for a comparison with experimental studies. A nonequilibrium study of diblock copolymers in shear flow completes this work. Shear flow is a powerful method to macroscopically order a metastable microstructure. In a symmetric diblock copolymer melt, the equilibrium microstructure is a lamellar phase. The first step determines the perpendicular orientation of the lamellae in shear flow as stable at all stresses according to the concept of the Rayleighian, R. Further, I study the transition between a grain in the unstable orientation next to a grain in the stable orientation. I identify two different transition pathways. At low applied stresses, the grain boundary of the stable grain grows into the unstable grain. At higher stresses, the unstable orientation is destabilized and forms an intermediate microemulsion-like phase with no local orientation. This intermediate phase turns subsequently into the stable orientation. Oscillatory shear at high frequencies delays the onset of this microemulsion pathway. In a collaboration with Matthias Heck and Manfred Wilhelm at KIT, these transitions have been studied in LAOS experiments as well