The amount of water flowing through streams and rivers changes through time. The seasonality and duration of these changes can have profound impacts on human freshwater availability, aquatic habitat, and biogeochemical cycling. Numerous factors are thought to influence streamflow regime, including drainage basin area, temperature, precipitation, and land cover. Few of these qualities have remained untouched, either directly or indirectly, by expanding human activities. Altered climate, sweeping changes to large portions of the earth's surface, and the construction of dams and other infrastructure have fundamentally altered streamflows worldwide. Understanding the nature of these changes, both globally and regionally in the Western United States, is the subject of this thesis. In chapter 1 we explore ideal metric spaces for describing streamflow regime. The representation of information in concise terms is usually preliminary to developing an understanding of any system, and streamflow regime, which has been described with over 600 unique variables, is no exception. We demonstrate the efficacy of dimensionality reduction techniques, as well as frequency decompositions, in succinctly capturing much of the information previously described with hundreds of variables. We use this succinct language to gain key insights into major drivers of streamflow regime and present a new hypothesis about the mechanisms mediating flow variability. In chapter 2, we use frequency decompositions and several machine learning approaches to characterize streamflow regimes around the world and to understand how they are changing through time. Finally, in chapter 3, we analyze the effect that wildfire has had on the timing, amount, and variability of flow in the western US in recent decades. The work presented here demonstrates the power that advances in data science, particularly in time series analysis methods and machine learning, can have when coupled with large datasets in revealing insights into global and regional phenomena in hydrology.

Condition based maintenance is becoming increasingly popular in many industrial contexts, offering substantial savings and minimising accidental damage. When applied to rotating machinery, its most common tool is vibration analysis, which relies on well-established mathematical models rooted in the theory of cyclo-non-stationary processes. However, the extraction of diagnostic information from the real world vibration signals is a delicate task requiring the application of sophisticated signal processing techniques, tailored for specific machines operating under restricted conditions. Such difficulty in the current state of the art of vibration analysis forces the industry to apply methods with reduced diagnostic capabilities but higher adaptability. However in doing so most of the potential of vibration analysis is lost and advanced techniques become of use only for academic endeavours. The aim of this document is to reduce the gap between industrial and academic applications of condition monitoring, offering ductile and automated tools which still show high detection capabilities. Three main lines of research are presented in this document. Firstly, the implementation of stochastic resonance in an electrical circuit to enhance directly the analog signal from an accelerometer, in order to lower the computational requirements in the next digital signal processing step. Secondly, the extension of already well-established digital signal processing techniques, cepstral prewhitening and spectral kurtosis, to a wider range of operating conditions, proving their effectiveness in the case of non-stationary speeds. Thirdly, the main contribution of the thesis: the introduction of two novel techniques capable of separating the vibrations of a defective component from the overall vibrations of the machine, by means of a threshold in the amplitude spectrum. After the separation, the cyclic content of the vibration signal is extracted and the thresholded signals provide an enhanced detection. The two proposed methods, phase editing and amplitude cyclic frequency decomposition, are both intuitive and of low computational complexity, but show the same capabilities as more sophisticated state of the art techniques. Furthermore, all these tools have been successfully tested on numerically simulated signals as well as on real vibration data from different machinery, lasting from laboratory test rigs to wind turbines drive-trains and aircraft engines. So in conclusion, the proposed techniques are a promising step toward the full exploitation of condition based maintenance in industrial contexts.

Arrays of fully hydrogel-supported, artificial hair cell (AHC) sensors based on bilayer membrane mechanotransduction are designed and characterized to determine sensitivity to multiple stimuli. The work draws upon key engineering design principles inspired by the characteristics of biological hair cells, primarily the use of slender hair-like structures as flow measurement elements. Many hair cell microelectromechanical (MEMS) devices to sense fluid flow have already been built based on this principle. However, recent developments in lipid bilayer applications, namely physically encapsulated bilayers and hydrogel interface bilayers, have facilitated the development of AHCs made primarily from biomolecular materials. The most current research in this field of "membrane based AHCs," shows promise, yet still lacks the modularity to create large sensor arrays similar to those in nature. This paper presents a novel bilayer based AHC platform, developed for array implementation by applying some of the core design principles of biological hair cells. These principles are translated into key design, fabrication and material considerations toward improved sensor sensitivity and modularity. Single hair cell responses to base excitation and short air pulses are to investigate the dynamic coupling between hair and bilayer membrane transducer. In addition, a spectral analysis of the AHC system under varying voltages and air flow velocities helps to build simple, predictive models for the sensitivity properties of the AHC. And finally, based on these results, we implement a spatial sensing strategy that involves mapping frequency content to stimulus location by "tuning" linear, three-unit arrays of AHCs. Individual AHC sensors characterization results demonstrate peak current outputs in the nanoamp range and measure flow velocities as high as 72 m/s. Characterization of the AHC response to base excitation and air pulses show that membrane current oscillates with the first three bending modes of the hair. Output magnitudes reflect of vibrations near the base of the hair. A 2 degree-of-freedom Rayleigh-Ritz approximation of the system dynamics yields estimates of 19 N/m and 0.0011 Nm/rad for the equivalent linear and torsional stiffness of the hair's hydrogel base, although double modes suggest non-symmetry in the gel's linear stiffness. The sensor output scales linearly with applied voltage (1.79 pA/V), avoiding a higher-order dependence on electrowetting effects. The free vibration amplitude of the sensor also increases in a linear fashion with applied airflow pressure (3.39 pA/m s??). Array sensing tests show that the bilayers' consistent spectral responses allow for an accurate localization of the airflow source. However, temporal variations in bilayer size affect sensitivity properties and make airflow magnitude estimation difficult. The overall successful implementation of the array sensing method validates the sensory capability of the bilayer based AHC. / Master of Science

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior / This work seeks to analyze at different frequencies, the transmission of volatility in the prices of crude oil produced by OPEC members and other producing and exporting countries that are not part of this organization and to analyze the presence of structural breaks in dynamic correlation between crude oil spot and future prices. The Wavelets methodology employed aims to decompose the series to verify its behavior at different frequencies, revealing additional information or confirming trends. To check the transmission process of the volatility it is proposed the application of Granger Causality Test. This made it possible to understand the functioning of this important market and answer the following question: How behaves the volatility of oil prices when analyzed considering several time horizons in an analysis in the frequency domain? The analysis of volatility transmission shows a strong integration of the international oil market, the correlation structural breaks tests results shows that structural break point is not static for any analysis, it moves, depending the frequency scale and the time window. / Este trabalho busca analisar em diferentes frequências, o sentido e a transmissão da volatilidade nos preços do petróleo bruto produzido pelos países membros da OPEP (Organização dos Países Exportadores de Petróleo) e dos demais países produtores e exportadores que não fazem parte desta organização; bem como analisar a presença de quebras estruturais na correlação dinâmica entre os preços à vista e futuro do petróleo. A metodologia de Wavelets empregada tem por objetivo decompor as séries estudas a fim de verificar seu comportamento em diferentes frequências, revelando informações adicionais ou confirmando tendências observadas. Para a verificação do processo de transmissão da volatilidade foi proposta a utilização do método de Causalidade de Granger. Desta forma foi possível compreender o funcionamento deste importante mercado e responder a seguinte questão: Como se comporta a volatilidade do preço do petróleo quando se analisam variados horizontes de tempo em uma análise no domínio da frequência? A análise da transmissão da volatilidade aponta para uma forte integração do mercado internacional do petróleo, enquanto o resultado da análise das quebras na correlação mostra que o ponto de quebra estrutural não é estático para toda e qualquer análise, ele se move, dependendo da frequência e do horizonte temporal.

1 Abstract We study economic policy uncertainty spillovers on a panel of ten countries between April 1998 to September 2019. The analysis is performed on the Economic Policy Uncertainty indices data. To measure the spillovers, we utilize forecast error variance decompositions of VAR model. We found that approximately half of the forecast variance can be explained by spillovers shocks across countries. Further, we disentangle the spillover measure to short-, mid- and long-term cycles using frequency domain. Our results suggest that most of the spillovers are caused by shocks into low frequencies, hence with long persistence. Employing quantile regression on equation-by-equation basis to estimate the VAR model, we find that idiosyncratic uncertainty shocks do not propagate strongly at the median but that powerful spillovers occur in the right tail of distribution. Additionally, we perform rolling window estimates of the spillovers. The results indicate strong variation in time, especially during major geopolitical events, such as Iraq War (2003), Global Financial Crisis (2007-09), European debt crisis (2010-12) or Brexit (2016).