The objective of this study was to investigate the spatial relationship between the most likely distribution of saturated hydraulic conductivity (K-s) and the observed pressure head (P) distribution within a hillslope. The cross-correlation analysis method was used to investigate the effects of the variance of lnK(s), spatial structure anisotropy of lnK(s), and vertical infiltration flux (q) on P at some selected locations within the hillslope. The cross-correlation analysis shows that, in the unsaturated region with a uniform flux boundary, the dominant correlation between P and Ks is negative and mainly occurs around the observation location of P. A relatively high P value is located in a relatively low Ks zone, while a relatively low P value is located in a relatively high Ks zone. Generally speaking, P is positively correlated with q/Ks at the same location in the unsaturated region. In the saturated region, the spatial distribution of K-s can significantly affect the position and shape of the phreatic surface. We therefore conclude that heterogeneity can cause some parts of the hillslope to be sensitive to external hydraulic stimuli (e.g., rainfall and reservoir level change), and other parts of the hillslope to be insensitive. This is crucial to explaining why slopes with similar geometries would show different responses to the same hydraulic stimuli, which is significant to hillslope stability analysis. (C) 2016 Hohai University. Production and hosting by Elsevier B.V.

This dissertation is composed of two parts, an integrative essay and a set of published papers. The essay and the collection of papers are placed in the context of development and application of time series econometric models in a temporal-axis from 1970s through 2005, with particular focus in the Marketing discipline. The main aim of the integrative essay is on modelling the effects of marketing actions on performance variables, such as sales and market share in competitive markets. Such research required the estimation of two kinds of time series econometric models: multivariate and multiple time series models. I use Autoregressive Integrated Moving Average (ARIMA) intervention models and the Pierce and Haugh statistical test to model the impact of a single marketing instrument, mainly price promotions, to measure own and cross-short term sales effects, and to study asymmetric marketing competition. I develop and apply Vector AutoRegressive (VAR) and Bayesian Vector AutoRegressive (BVAR) models to estimate dynamic relationships in the market and to forecast market share. Especially, BVAR models are advantageous because they contain all relevant dynamic and interactive effects. They accommodate not only classical competitive reaction effects, but also own and cross-market share brand feedback effects and internal decision rules and provided substantively useful insights into the dynamics of demand. The integrative essay is structured in four main parts. The introduction sets the basic ideas behind the published papers, with particular focus on the motivation of the essay, the types of competitive reaction effects analysed, an overview of the time series econometric models in marketing, a short discussion of the basic methodology used in the research and a brief description of the inter-relationships across the published papers and structure of the essay. The discussion is centred on how to model the effects of marketing actions at the selective demand or brand level and at the primary demand or product level. At the brand level I discuss the research contribution of my work on (i) modelling promotional short-term effects of price and non-price actions on sales and market share for consumer packaged goods, with no competition, (ii) how to measure own and cross short-term sales effects of advertising and price, in particular, cross-lead and lag effects, asymmetric sales behaviour and competition without retaliatory actions, in an automobile market, (iii) how to model the marketing-mix effectiveness at the short and long-term on market shares in a car market, (iv) what is the best method to forecast market share, and (v) the study of causal linkages at different time horizons between sales and marketing activity for a particular brand. At the product or commodity level, I propose a way to model the flows of tourists that come from different origins (countries) to the same country-destination as market segments defining the primary demand of a commodity - the product

Submitted by (ana.araujo@ufrpe.br) on 2016-07-05T15:38:42Z No. of bitstreams: 1 Erinaldo Leite Batista Almeida.pdf: 3620819 bytes, checksum: b2532ef7524f47d5417d01445fec797b (MD5) / Made available in DSpace on 2016-07-05T15:38:42Z (GMT). No. of bitstreams: 1 Erinaldo Leite Batista Almeida.pdf: 3620819 bytes, checksum: b2532ef7524f47d5417d01445fec797b (MD5) Previous issue date: 2009-08-10 / Financial markets are complex systems that contain large numbers of interacting units, including interactions among various units in the same market and interactions between units in different markets. Various methods of economics, statistics and econophysics have been developed to analyze financial temporal series (such as price returns, share volume, number of transactions), and serve to establish theoretical models for underlying stochastic processes. The availability of financial data on the internet and increasing computational power have enabled researchers to conduct a large number of empirical studies on financial markets. These studies have shown some universal properties: the risk function of price returns is scale invariant, with power-law behavior and similar value of exponent for different markets; the absolute values of returns (volatility) exhibit long-range power-law correlations. In this work, we use methods if econophysics to study the statistical properties of Brazilian financial markets. We analyze and compare scale properties of risk functions and correlations in temporal series of price returns of agricultural commodities and stocks of various companies traded at Bovespa. We analyze the daily prices of five commodities and twenty stocks traded in the period 2000-2008. For both commodities and stocks, the risk function of daily price returns shows powerlaw behavior with the exponent outside the Levy stable region. The values of exponents are higher for stocks than for commodities. We use Detrended Fluctuation Analysis (DFA) to study correlations in daily time series of absolute values of returns (volatility). This method was developed to quantify long range correlations in non-stationary temporal series.All analyzed series show persistent behavior, meaning that large (small) values are more likely to be followed with large (small) values. The value of the DFA exponent is higher for commodities than for stocks. We also use Detrended Cross Correlation Analysis (DCCA) to study cross-correlations between two series. The values of DCCA exponents are above 0.5 for all series, indicating the existence of long range cross-correlations. This means that each stock or commodity has long memory of its own previous values and of previous values of other stocks or commodities studied. These results are in agreement with results obtained for American financial markets. / Mercados financeiros são caracterizados por um grande número de unidades e interações complexas, incluindo as interações internas (entre diferentes elementos de um mercado) e fatores externos (influência de outros mercados). Vários métodos de economia, estatística e recentemente econofísica foram desenvolvidos para analisar as séries temporais de variáveis financeiras (retorno de preços de ações, mercadorias e taxas de cambio, índice de mercado, volume de negociação, etc.), com objetivo de estabelecer os modelos teóricos para processos estocásticos que estão em base desses fenômenos. A disponibilidade de dados financeiros de vários mercados e crescente poder computacional resultaram em um grande número de estudos empíricos cujos resultados mostraram algumas propriedades universais: a função risco de retornos de preços segue uma lei de potência com o valor de expoente similar para os vários mercados; os valores absolutos de retornos possuem correlações de longo alcance. Neste trabalho foram usados os métodos de econofísica para estudar as propriedades estatísticas do mercado financeiro brasileiro. Foram analisadas e comparadas as propriedades de escala de função risco e de correlações em séries temporais de retornos de preços de mercadorias agrícolas e preços de ações de várias empresas negociadas na Bolsa de Valores de São Paulo (BOVESPA). Foram analisados os preços diários de cinco mercadorias: açúcar, algodão, café, soja e boi, registrados em período 2000-2008. Para ações, analisamos as características seguintes: preços de abertura, fechamento, valores máximo e mínimo, volume e montante. Todas as séries são diárias, registradas no período de 2000-2008. São estudadas 20 empresas divididas em 4 grupos: bancos, energia, telecomunicações e siderurgia (5 empresas de cada grupo). Para todas as séries estudadas a função risco de retornos de preços segue uma lei de potência com os valores de expoente maiores para ações do que para mercadorias. As correlações são analisadas para os valores absolutos de retornos de preços (volatilidade). Foi usado o método Detrended Fluctuation Analysis (DFA), desenvolvido para quantificar as correlações de longo alcance em séries temporais não estacionárias. Todas as séries mostraram um comportamento persistente, significando que os valores grandes (pequenos) tem maior probabilidade de serem seguidos por valores grandes (pequenos). Os valores de expoente DFA são maiores para mercadorias do que para as ações. Foi utilizada uma generalização de DFA, Detrended Cross Correlation Analysis (DCCA) para analisar as correlações cruzadas entre duas séries. Os valores de expoente DCCA para todas as séries estudadas indicam a existência de correlações cruzadas de longo alcance significando que os valores de cada série possuem memória de longo alcance de seus valores anteriores e também de valores anteriores de outras série. Os resultados estão em acordo com os resultados obtidos para mercado americano.

A study was made in order to deduce whether certain chemical species, namely water (H2O) and carbon monoxide (CO), are present in the atmosphere of the exoplanet WASP-107 b, which lies about 200 light-years away from Earth in the constellation Virgo. The project was carried out at Uppsala University at the Department of Physics and Astronomy. This was done through the use of transmission spectroscopy and executed using a cross-correlation technique, one of the leading methods available today to extract exoplanetary atmospheric information. The data used was collected during a transit which occured in March 2022, originally gathered by the spectrograph CRIRES+ stationed at Very Large Telescope (VLT) in Paranal. WASP-107 b is a warm Jupiter-type planet, and since the aforementioned chemical species exhibit spectral lines mainly in the infrared (0.95-5.3 μm), it makes CRIRES+ a desirable instrument due to its specialization for working in the infrared. The data analysis was performed using several scripts built in Python with subsequent data-reduction methods. The data-reduction methods used for this purpose was the standard ESO CRIRES+ data reduction pipeline which includes removal of systemic sources of noise such as dead pixels and cosmic rays, and SysRem, which is an algorithm used to remove any trends with time and any constant features in time for each pixel time series. SysRem is currently one of the most efficient way available for doing so, and is commonly used in these types of studies. Several detection maps were then generated and studied in order to deduce whether a detection had been made or not. For this project, one exoplanet was examined and its atmosphere was probed for H2O and CO. The cross-correlation templates utilized were a combination of both species as well as one corresponding to only CO. The detection maps generated from the cross-correlation analysis initially suggested non-detections for all combinations of SysRem iterations and templates, except for two which presented features that might imply a detection but without any strong certainty. Those results indicate the possible existence of CO in the atmosphere of WASP-107 b, but further investigation is needed in order to determine their validity. / Denna rapport beskriver en studie som utförts för att undersöka ifall vissa kemiska arter, nämligen vatten (H2O) och kolmonoxid (CO), existerar i atmosfären kring exoplaneten WASP-107 b. Exoplaneten ligger cirka 200 ljusår bort från jorden i konstellationen Jungfrun. Arbetet utfördes på Uppsala universitet på institutionen för fysik och astronomi, eller Department of Physics and Astronomy. Detta gjordes huvudsakligen med hjälp av transmissionsspektroskopi och cross-correlation - en av de ledande metoderna idag för att analysera exoplanetära atmosfärer. Datan som använts för denna studie samlades in under en transit som skedde i mars 2022 med hjälp av spektrografen CRIRES+, stationerad vid Very Large Telescope (VLT) i Paranal. WASP-107 b klassas som en varm Jupiter, och eftersom de undersökta kemiska arterna huvudsakligen uppvisar spektrallinjer i det infraröda området (0.95-5.3 μm), är CRIRES+ ett sunt val då spektrografen är specialiserad på att undersöka infrarött ljus. Dataanalysen utfördes genom användningen av flertal script, byggda i Python med påföljande datareduktion. De datareduktionsmetoder som användes i detta syfte var ESO CRIRES+ standard data reduction pipeline, vilken inkluderar avlägsnandet av systematiska källor till brus såsom döda pixlar och den kosmiska bakgrundsstrålningen, och SysRem, vilket är en algoritm som används för att ta bort trender samt konstanta drag beroende på tid utmed varje pixelserie. I nuläget är SysRem en av de mer effektiva sätten att göra detta på, och är en vanlig metod i studier som denna. I detta projekt blev en exoplanet undersökt och dess atmosfär granskad för att se ifall H2O och CO förekommer i den. De cross-correlation templates som användes bestod av en som använde en kombination av båda kemiska arter, tillika en som endast detekterade CO. De detection maps som genererats från cross-correlation analysen föreslog först en ickedetektion för alla kombinationer av SysRem iterationer och templates, förutom två, vilka uppvisade signalement som möjligtvis indikerade en detektion, men utan särskild stark säkerhet. Dessa resultat föreslog en möjlig detektion av CO i atmosfären, men för att säkerställa detta krävs vidare undersökning.