Emerging horizons for tick-borne pathogens: from the ‘one pathogen–one disease’ vision to the pathobiome paradigm

Ticks as vectors of several notorious zoonotic pathogens, represent an important and increasing threat for human, animal health in Europe. Recent application of new technology revealed the complexity of the tick microbiome that might impact upon its vectorial capacity. Appreciation of these complex systems is expanding our vision of tick-borne pathogens leading us to evolve a more integrated view that embraces the “pathobiome” representing the pathogenic agent integrated within its abiotic and biotic environments. In this review, we will explore how this new vision will revolutionize our understanding of tick-borne diseases. We will discuss the implications in terms of research approach for the future in order to efficiently prevent and control the threat posed by ticks.

Providing User Security Guarantees in Public Infrastructure Clouds

The infrastructure cloud (IaaS) service model offers improved resource flexibility and availability, where tenants - insulated from the minutiae of hardware maintenance - rent computing resources to deploy and operate complex systems. Large-scale services running on IaaS platforms demonstrate the viability of this model; nevertheless, many organizations operating on sensitive data avoid migrating operations to IaaS platforms due to security concerns. In this paper, we describe a framework for data and operation security in IaaS, consisting of protocols for a trusted launch of virtual machines and domain-based storage protection. We continue with an extensive theoretical analysis with proofs about protocol resistance against attacks in the defined threat model. The protocols allow trust to be established by remotely attesting host platform configuration prior to launching guest virtual machines and ensure confidentiality of data in remote storage, with encryption keys maintained outside of the IaaS domain. Presented experimental results demonstrate the validity and efficiency of the proposed protocols. The framework prototype was implemented on a test bed operating a public electronic health record system, showing that the proposed protocols can be integrated into existing cloud environments.

Relatório de estágio em gabinete de engenharia na componente de climatização

Trabalho Final de Mestrado para obtenção do grau de Mestre em Engenharia Mecânica com especialização em Energia, Climatização e Refrigeração

Analysis and visualization of seismic data using mutual information

Seismic data is difficult to analyze and classical mathematical tools reveal strong limitations in exposing hidden relationships between earthquakes. In this paper, we study earthquake phenomena in the perspective of complex systems. Global seismic data, covering the period from 1962 up to 2011 is analyzed. The events, characterized by their magnitude, geographic location and time of occurrence, are divided into groups, either according to the Flinn-Engdahl (F-E) seismic regions of Earth or using a rectangular grid based in latitude and longitude coordinates. Two methods of analysis are considered and compared in this study. In a first method, the distributions of magnitudes are approximated by Gutenberg-Richter (G-R) distributions and the parameters used to reveal the relationships among regions. In the second method, the mutual information is calculated and adopted as a measure of similarity between regions. In both cases, using clustering analysis, visualization maps are generated, providing an intuitive and useful representation of the complex relationships that are present among seismic data. Such relationships might not be perceived on classical geographic maps. Therefore, the generated charts are a valid alternative to other visualization tools, for understanding the global behavior of earthquakes.

Analysis of temperature time-series: embedding dynamics into the MDS method

Global warming and the associated climate changes are being the subject of intensive research due to their major impact on social, economic and health aspects of the human life. Surface temperature time-series characterise Earth as a slow dynamics spatiotemporal system, evidencing long memory behaviour, typical of fractional order systems. Such phenomena are difficult to model and analyse, demanding for alternative approaches. This paper studies the complex correlations between global temperature time-series using the Multidimensional scaling (MDS) approach. MDS provides a graphical representation of the pattern of climatic similarities between regions around the globe. The similarities are quantified through two mathematical indices that correlate the monthly average temperatures observed in meteorological stations, over a given period of time. Furthermore, time dynamics is analysed by performing the MDS analysis over slices sampling the time series. MDS generates maps describing the stations’ locus in the perspective that, if they are perceived to be similar to each other, then they are placed on the map forming clusters. We show that MDS provides an intuitive and useful visual representation of the complex relationships that are present among temperature time-series, which are not perceived on traditional geographic maps. Moreover, MDS avoids sensitivity to the irregular distribution density of the meteorological stations.

Self-similarity principle: the reduced description of randomness

A new general fitting method based on the Self-Similar (SS) organization of random sequences is presented. The proposed analytical function helps to fit the response of many complex systems when their recorded data form a self-similar curve. The verified SS principle opens new possibilities for the fitting of economical, meteorological and other complex data when the mathematical model is absent but the reduced description in terms of some universal set of the fitting parameters is necessary. This fitting function is verified on economical (price of a commodity versus time) and weather (the Earth’s mean temperature surface data versus time) and for these nontrivial cases it becomes possible to receive a very good fit of initial data set. The general conditions of application of this fitting method describing the response of many complex systems and the forecast possibilities are discussed.

Fractional dynamics of a system with particles subjected to impacts

This paper studies the dynamics of a system composed of a collection of particles that exhibit collisions between them. Several entropy measures and different impact conditions of the particles are tested. The results reveal a Power Law evolution both of the system energy and the entropy measures, typical in systems having fractional dynamics.

Multidimensional scaling visualization of earthquake phenomena

Earthquakes are associated with negative events, such as large number of casualties, destruction of buildings and infrastructures, or emergence of tsunamis. In this paper, we apply the Multidimensional Scaling (MDS) analysis to earthquake data. MDS is a set of techniques that produce spatial or geometric representations of complex objects, such that, objects perceived to be similar/distinct in some sense are placed nearby/distant on the MDS maps. The interpretation of the charts is based on the resulting clusters since MDS produces a different locus for each similarity measure. In this study, over three million seismic occurrences, covering the period from January 1, 1904 up to March 14, 2012 are analyzed. The events, characterized by their magnitude and spatiotemporal distributions, are divided into groups, either according to the Flinn–Engdahl seismic regions of Earth or using a rectangular grid based in latitude and longitude coordinates. Space-time and Space-frequency correlation indices are proposed to quantify the similarities among events. MDS has the advantage of avoiding sensitivity to the non-uniform spatial distribution of seismic data, resulting from poorly instrumented areas, and is well suited for accessing dynamics of complex systems. MDS maps are proven as an intuitive and useful visual representation of the complex relationships that are present among seismic events, which may not be perceived on traditional geographic maps. Therefore, MDS constitutes a valid alternative to classic visualization tools, for understanding the global behavior of earthquakes.

Dynamic Analysis and Pattern Visualization of Forest Fires

This paper analyses forest fires in the perspective of dynamical systems. Forest fires exhibit complex correlations in size, space and time, revealing features often present in complex systems, such as the absence of a characteristic length-scale, or the emergence of long range correlations and persistent memory. This study addresses a public domain forest fires catalogue, containing information of events for Portugal, during the period from 1980 up to 2012. The data is analysed in an annual basis, modelling the occurrences as sequences of Dirac impulses with amplitude proportional to the burnt area. First, we consider mutual information to correlate annual patterns. We use visualization trees, generated by hierarchical clustering algorithms, in order to compare and to extract relationships among the data. Second, we adopt the Multidimensional Scaling (MDS) visualization tool. MDS generates maps where each object corresponds to a point. Objects that are perceived to be similar to each other are placed on the map forming clusters. The results are analysed in order to extract relationships among the data and to identify forest fire patterns.

Projecto de sistema de fixação de punções para quinadoras hidráulicas

Mestrado em Engenharia Mecânica - Construções Mecânicas

Distributed Intelligent Management of Microgrids using a Multi-Agent Simulation Platform

Multi-agent approaches have been widely used to model complex systems of distributed nature with a large amount of interactions between the involved entities. Power systems are a reference case, mainly due to the increasing use of distributed energy sources, largely based on renewable sources, which have potentiated huge changes in the power systems’ sector. Dealing with such a large scale integration of intermittent generation sources led to the emergence of several new players, as well as the development of new paradigms, such as the microgrid concept, and the evolution of demand response programs, which potentiate the active participation of consumers. This paper presents a multi-agent based simulation platform which models a microgrid environment, considering several different types of simulated players. These players interact with real physical installations, creating a realistic simulation environment with results that can be observed directly in the reality. A case study is presented considering players’ responses to a demand response event, resulting in an intelligent increase of consumption in order to face the wind generation surplus.

Dynamic Analysis and Pattern Visualization of Forest Fires

This paper analyses forest fires in the perspective of dynamical systems. Forest fires exhibit complex correlations in size, space and time, revealing features often present in complex systems, such as the absence of a characteristic length-scale, or the emergence of long range correlations and persistent memory. This study addresses a public domain forest fires catalogue, containing information of events for Portugal, during the period from 1980 up to 2012. The data is analysed in an annual basis, modelling the occurrences as sequences of Dirac impulses with amplitude proportional to the burnt area. First, we consider mutual information to correlate annual patterns. We use visualization trees, generated by hierarchical clustering algorithms, in order to compare and to extract relationships among the data. Second, we adopt the Multidimensional Scaling (MDS) visualization tool. MDS generates maps where each object corresponds to a point. Objects that are perceived to be similar to each other are placed on the map forming clusters. The results are analysed in order to extract relationships among the data and to identify forest fire patterns.

The Persistence of Memory

This paper analyzes several natural and man-made complex phenomena in the perspective of dynamical systems. Such phenomena are often characterized by the absence of a characteristic length-scale, long range correlations and persistent memory, which are features also associated to fractional order systems. For each system, the output, interpreted as a manifestation of the system dynamics, is analyzed by means of the Fourier transform. The amplitude spectrum is approximated by a power law function and the parameters are interpreted as an underlying signature of the system dynamics. The complex systems under analysis are then compared in a global perspective in order to unveil and visualize hidden relationships among them.

Integer and Fractional Order Entropy Analysis of Earthquake Data-series

This paper studies the statistical distributions of worldwide earthquakes from year 1963 up to year 2012. A Cartesian grid, dividing Earth into geographic regions, is considered. Entropy and the Jensen–Shannon divergence are used to analyze and compare real-world data. Hierarchical clustering and multi-dimensional scaling techniques are adopted for data visualization. Entropy-based indices have the advantage of leading to a single parameter expressing the relationships between the seismic data. Classical and generalized (fractional) entropy and Jensen–Shannon divergence are tested. The generalized measures lead to a clear identification of patterns embedded in the data and contribute to better understand earthquake distributions.

Utilização de sensores de baixo custo na medição de monóxido de carbono no ar ambiente

Nos últimos anos tem-se assistido à introdução de novos dispositivos de medição da poluição do ar baseados na utilização de sensores de baixo custo. A utilização menos complexa destes sistemas, possibilita a obtenção de dados com elevada resolução temporal e espacial, abrindo novas oportunidades para diferentes metodologias de estudos de monitorização da poluição do ar. Apesar de apresentarem capacidades analíticas distantes dos métodos de referência, a utilização destes sensores tem sido sugerida e incentivada pela União Europeia no âmbito das medições indicativas previstas na Diretiva 2008/50/CE, com uma incerteza expandida máxima de 25%. O trabalho desenvolvido no âmbito da disciplina de Projeto consistiu na escolha, caracterização e utilização em medições reais de um sensor de qualidade do ar, integrado num equipamento protótipo desenvolvido com esse fim, visando obtenção uma estimativa da incerteza de medição associada à utilização deste dispositivo através da aplicação da metodologia de demonstração de equivalência de métodos de medição de qualidade do ar definida pela União Europeia. A pesquisa bibliográfica realizada permitiu constatar que o monóxido de carbono é neste momento o parâmetro de qualidade do ar que permite ser medido de forma mais exata através da utilização de sensores, nomeadamente o sensor eletroquímico da marca Alphasense, modelo COB4, amplamente utilizado em projetos de desenvolvimento neste cotexto de monitorização ambiental. O sensor foi integrado num sistema de medição com o objetivo de poder ser utlizado em condições de autonomia de fornecimento de energia elétrica, aquisição interna dos dados, tendo em consideração ser o mais pequeno possível e de baixo custo. Foi utlizado um sistema baseado na placa Arduino Uno com gravação de dados em cartão de memória SD, baterias e painel solar, permitindo para além do registo das tensões elétricas do sensor, a obtenção dos valores de temperatura, humidade relativa e pressão atmosférica, com um custo global a rondar os 300 euros. Numa primeira fase foram executados um conjunto de testes laboratoriais que permitiram a determinação de várias características de desempenho em dois sensores iguais: tempo de resposta, a equação modelo do sensor, avaliação da repetibilidade, desvio de curto e longo termo, interferência da temperatura e histerese. Os resultados demonstraram um comportamento dos sensores muito linear, com um tempo de resposta inferior a um minuto e com uma equação modelo do sensor dependente da variação da temperatura. A estimativa da incerteza expandida laboratorial ficou, para ambos os sensores, abaixo dos 10%. Após a realização de duas campanhas reais de medição de CO em que os valores foram muito baixos, foi realizada uma campanha de quinze dias num parque de estacionamento subterrâneo que permitiu a obtenção de concentrações suficientemente elevadas e a comparação dos resultados dos sensores com o método de referência em toda a gama de medição (0 a 12 mol.mol-1). Os valores de concentração obtidos pelos dois sensores demonstraram uma excelente correlação com o método de referência (r2≥0,998), obtendo-se resultados para a estimativa da incerteza expandida de campo inferiores aos obtidos para a incerteza laboratorial, cumprindo o objetivo de qualidade de dados definido para as medições indicativas de incerteza expandida máxima de 25%. Os resultados observados durante o trabalho realizado permitiram confirmar o bom desempenho que este tipo de sensor pode ter no âmbito de medições de poluição do ar com um caracter mais indicativo.