Traffic safety is a major concern for the public, and it is an important component of the roadway management strategy. In order to improve highway safety, extensive efforts have been made by researchers, transportation engineers, Federal, State, and local government officials. With these consistent efforts, both fatality and injury rates from road traffic crashes in the United States have been steadily declining over the last six years (2006~2011). However, according to the National Highway Traffic Safety Administration (NHTSA, 2013), 33,561 people died in motor vehicle traffic crashes in the United States in 2012, compared to 32,479 in 2011, and it is the first increase in fatalities since 2005. Moreover, in 2012, an estimated 2.36 million people were injured in motor vehicle traffic crashes, compared to 2.22 million in 2011. Due to the demand of highway safety improvements through systematic analysis of specific roadway cross-section elements and treatments, the Highway Safety Manual (HSM) (AASHTO, 2010) was developed by the Transportation Research Board (TRB) to introduce a science-based technical approach for safety analysis. One of the main parts in the HSM, Part D, contains crash modification factors (CMFs) for various treatments on roadway segments and at intersections. A CMF is a factor that can estimate potential changes in crash frequency as a result of implementing a specific treatment (or countermeasure). CMFs in Part D have been developed using high-quality observational before-after studies that account for the regression to the mean threat. Observational before-after studies are the most common methods for evaluating safety effectiveness and calculating CMFs of specific roadway treatments. Moreover, cross-sectional method has commonly been used to derive CMFs since it is easier to collect the data compared to before-after methods. Although various CMFs have been calculated and introduced in the HSM, still there are critical limitations that are required to be investigated. First, the HSM provides various CMFs for single treatments, but not CMFs for multiple treatments to roadway segments. The HSM suggests that CMFs are multiplied to estimate the combined safety effects of single treatments. However, the HSM cautions that the multiplication of the CMFs may over- or under-estimate combined effects of multiple treatments. In this dissertation, several methodologies are proposed to estimate more reliable combined safety effects in both observational before-after studies and the cross-sectional method. Averaging two best combining methods is suggested to use to account for the effects of over- or under- estimation. Moreover, it is recommended to develop adjustment factor and function (i.e. weighting factor and function) to apply to estimate more accurate safety performance in assessing safety effects of multiple treatments. The multivariate adaptive regression splines (MARS) modeling is proposed to avoid the over-estimation problem through consideration of interaction impacts between variables in this dissertation. Second, the variation of CMFs with different roadway characteristics among treated sites over time is ignored because the CMF is a fixed value that represents the overall safety effect of the treatment for all treated sites for specific time periods. Recently, few studies developed crash modification functions (CMFunctions) to overcome this limitation. However, although previous studies assessed the effect of a specific single variable such as AADT on the CMFs, there is a lack of prior studies on the variation in the safety effects of treated sites with different multiple roadway characteristics over time. In this study, adopting various multivariate linear and nonlinear modeling techniques is suggested to develop CMFunctions. Multiple linear regression modeling can be utilized to consider different multiple roadway characteristics. To reflect nonlinearity of predictors, a regression model with nonlinearizing link function needs to be developed. The Bayesian approach can also be adopted due to its strength to avoid the problem of over fitting that occurs when the number of observations is limited and the number of variables is large. Moreover, two data mining techniques (i.e. gradient boosting and MARS) are suggested to use 1) to achieve better performance of CMFunctions with consideration of variable importance, and 2) to reflect both nonlinear trend of predictors and interaction impacts between variables at the same time. Third, the nonlinearity of variables in the cross-sectional method is not discussed in the HSM. Generally, the cross-sectional method is also known as safety performance functions (SPFs) and generalized linear model (GLM) is applied to estimate SPFs. However, the estimated CMFs from GLM cannot account for the nonlinear effect of the treatment since the coefficients in the GLM are assumed to be fixed. In this dissertation, applications of using generalized nonlinear model (GNM) and MARS in the cross-sectional method are proposed. In GNMs, the nonlinear effects of independent variables to crash analysis can be captured by the development of nonlinearizing link function. Moreover, the MARS accommodate nonlinearity of independent variables and interaction effects for complex data structures. In this dissertation, the CMFs and CMFunctions are estimated for various single and combination of treatments for different roadway types (e.g. rural two-lane, rural multi-lane roadways, urban arterials, freeways, etc.) as below: 1) Treatments for mainline of roadway: - adding a thru lane, conversion of 4-lane undivided roadways to 3-lane with two-way left turn lane (TWLTL) 2) Treatments for roadway shoulder: - installing shoulder rumble strips, widening shoulder width, adding bike lanes, changing bike lane width, installing roadside barriers 3) Treatments related to roadside features: - decrease density of driveways, decrease density of roadside poles, increase distance to roadside poles, increase distance to trees Expected contributions of this study are to 1) suggest approaches to estimate more reliable safety effects of multiple treatments, 2) propose methodologies to develop CMFunctions to assess the variation of CMFs with different characteristics among treated sites, and 3) recommend applications of using GNM and MARS to simultaneously consider the interaction impact of more than one variables and nonlinearity of predictors. Finally, potential relevant applications beyond the scope of this research but worth investigation in the future are discussed in this dissertation.

This dissertation addresses three problems of applied statistics involving discrete time series and network data. The three problems are (1) finding and analyzing community structure in directed networks, (2) capturing changes in dynamic count-valued time series of COVID-19 daily deaths, and (3) inferring the edges of an implicit network given noisy observations of a multivariate point process on its nodes. We use tools of spectral clustering, state-space models, Bayesian hierarchical modeling and variational inference to address these problems. Each chapter presents and discusses statistical methods for the given problem. We apply the methods to simulated and real data to both validate them and demonstrate their limitations. In chapter 1 we consider a directed spectral method for community detection that utilizes a graph Laplacian defined for non-symmetric adjacency matrices. We give the theoretical motivation behind this directed graph Laplacian, and demonstrate its connection to an objective function that reflects a notion of how communities of nodes in directed networks should behave. Applying the method to directed networks, we compare the results to an approach using a symmetrized version of the adjacency matrices. A simulation study with a directed stochastic block model shows that directed spectral clustering can succeed where the symmetrized approach fails. And we find interesting and informative differences between the two approaches in the application to Congressional cosponsorship data. n chapter 2 we propose a generalized non-linear state-space model for count-valued time series of COVID-19 fatalities. To capture the dynamic changes in daily COVID-19 death counts, we specify a latent state process that involves second order differencing and an AR(1)-ARCH(1) model. These modeling choices are motivated by the application and validated by model assessment. We consider and fit a progression of Bayesian hierarchical models under this general framework. Using COVID-19 daily death counts from New York City's five boroughs, we evaluate and compare the considered models through predictive model assessment. Our findings justify the elements included in the proposed model. The proposed model is further applied to time series of COVID-19 deaths from the four most populous counties in Texas. These model fits illuminate dynamics associated with multiple dynamic phases and show the applicability of the framework to localities beyond New York City. In Chapter 3 we consider the task of inferring the connections between noisy observations of events. In our model-based approach, we consider a generative process incorporating latent dynamics that are directed by past events and the unobserved network structure. This process is based on a leaky integrate-and-fire (LIF) model from neuroscience for aggregating input and triggering events (spikes) in neural populations. Given observation data we estimate the model parameters with a novel variational Bayesian approach, specifying a highly structured and parsimonious approximation for the conditional posterior distribution of the process's latent dynamics. This approach allows for fully interpretable inference of both the model parameters of interest and the variational parameters. Moreover, it is computationally efficient in scenarios when the observed event times are not too sparse. We apply our methods in a simulation study and to recorded neural activity in the dorsomedial frontal cortex (DMFC) of a rhesus macaque. We assess our results based on ground truth, model diagnostics, and spike prediction for held-out nodes.

The overarching goal of this thesis is to use distance covariance based methods to extend asymptotic results from the i.i.d. case to general time series settings. Accounting for dependence may make already difficult statistical inference all the more challenging. The distance covariance is an increasingly popular measure of dependence between random vectors that goes beyond linear dependence as described by correlation. It is defined by a squared integral norm of the difference between the joint and marginal characteristic functions with respect to a specific weight function. Distance covariance has the advantage of being able to detect dependence even for uncorrelated data. The energy distance is a closely related quantity that measures distance between distributions of random vectors. These statistics can be used to establish asymptotic limit theory for stationary ergodic time series. The asymptotic results are driven by the limit theory for the empirical characteristic functions. In this thesis we apply the distance covariance to three problems in time series modeling: (i) Independent Component Analysis (ICA), (ii) multivariate time series clustering, and (iii) goodness-of-fit using residuals from a fitted model. The underlying statistical procedures for each topic uses the distance covariance function as a measure of dependence. The distance covariance arises in various ways in each of these topics; one as a measure of independence among the components of a vector, second as a measure of similarity of joint distributions and, third for assessing serial dependence among the fitted residuals. In each of these cases, limit theory is established for the corresponding empirical distance covariance statistics when the data comes from a stationary ergodic time series. For Topic (i) we consider an ICA framework, which is a popular tool used for blind source separation and has found application in fields such as financial time series, signal processing, feature extraction, and brain imaging. The Structural Vector Autogregression (SVAR) model is often the basic model used for modeling macro time series. The residuals in such a model are given by e_t = A S_t, the classical ICA model. In certain applications, one of the components of S_t has infinite variance. This differs from the standard ICA model. Furthermore the e_t's are not observed directly but are only estimated from the SVAR modeling. Many of the ICA procedures require the existence of a finite second or even fourth moment. We derive consistency when using the distance covariance for measuring independence of residuals under the infinite variance case.Extensions to the ICA model with noise, which has a direct application to SVAR models when testing independence of residuals based on their estimated counterparts is also considered. In Topic (ii) we propose a novel methodology for clustering multivariate time series data using energy distance. Specifically, a dissimilarity matrix is formed using the energy distance statistic to measure separation between the finite dimensional distributions for the component time series. Once the pairwise dissimilarity matrix is calculated, a hierarchical clustering method is then applied to obtain the dendrogram. This procedure is completely nonparametric as the dissimilarities between stationary distributions are directly calculated without making any model assumptions. In order to justify this procedure, asymptotic properties of the energy distance estimates are derived for general stationary and ergodic time series. Topic (iii) considers the fundamental and often final step in time series modeling, assessing the quality of fit of a proposed model to the data. Since the underlying distribution of the innovations that generate a model is often not prescribed, goodness-of-fit tests typically take the form of testing the fitted residuals for serial independence. However, these fitted residuals are inherently dependent since they are based on the same parameter estimates and thus standard tests of serial independence, such as those based on the autocorrelation function (ACF) or distance correlation function (ADCF) of the fitted residuals need to be adjusted. We apply sample splitting in the time series setting to perform tests of serial dependence of fitted residuals using the sample ACF and ADCF. Here the first f_n of the n data points in the time series are used to estimate the parameters of the model. Tests for serial independence are then based on all the n residuals. With f_n = n/2 the ACF and ADCF tests of serial independence tests often have the same limit distributions as though the underlying residuals are indeed i.i.d. That is, if the first half of the data is used to estimate the parameters and the estimated residuals are computed for the entire data set based on these parameter estimates, then the ACF and ADCF can have the same limit distributions as though the residuals were i.i.d. This procedure ameliorates the need for adjustment in the construction of confidence bounds for both the ACF and ADCF, based on the fitted residuals, in goodness-of-fit testing. We also show that if f_n < n/2 then the asymptotic distribution of the tests stochastically dominate the corresponding asymptotic distributions for the true i.i.d. noise; the stochastic order gets reversed under f_n > n/2.

We study the convergence of a nonlinear approximation method introduced in the engineering literature for the numerical solution of a high-dimensional Fokker--Planck equation featuring in Navier--Stokes--Fokker--Planck systems that arise in kinetic models of dilute polymers. To do so, we build on the analysis carried out recently by Le~Bris, Leli\`evre and Maday (Const. Approx. 30: 621--651, 2009) in the case of Poisson's equation on a rectangular domain in $\mathbb{R}^2$, subject to a homogeneous Dirichlet boundary condition, where they exploited the connection of the approximation method with the greedy algorithms from nonlinear approximation theory explored, for example, by DeVore and Temlyakov (Adv. Comput. Math. 5:173--187, 1996). We extend the convergence analysis of the pure greedy and orthogonal greedy algorithms considered by Le~Bris, Leli\`evre and Maday to the technically more complicated situation of the elliptic Fokker--Planck equation, where the role of the Laplace operator is played out by a high-dimensional Ornstein--Uhlenbeck operator with unbounded drift, of the kind that appears in Fokker--Planck equations that arise in bead-spring chain type kinetic polymer models with finitely extensible nonlinear elastic potentials, posed on a high-dimensional Cartesian product configuration space $\mathsf{D} = D_1 \times \dotsm \times D_N$ contained in $\mathbb{R}^{N d}$, where each set $D_i$, $i=1, \dotsc, N$, is a bounded open ball in $\mathbb{R}^d$, $d = 2, 3$. We exploit detailed information on the spectral properties and elliptic regularity of the Ornstein--Uhlenbeck operator to give conditions on the true solution of the Fokker--Planck equation which guarantee certain rates of convergence of the greedy algorithms. We extend the analysis to discretized versions of the greedy algorithms.

INTRODUÇÃO: Muitos dos comportamentos de saúde envolvidos no aparecimento das doenças crônicas não comunicáveis são originados na infância sob influência dos pais. A mãe é a pessoa mais envolvida na educação e nos cuidados de saúde da criança. O estilo de vida (EdV) é um determinante social da saúde. Nunca se compreendeu características de EdV materno associadas com aspectos da nutrição infantil. OBJETIVO: Verificar a associação dos EdV materno comportamental e não comportamental com aspectos nutricionais do pré-escolar. MÉTODOS: Entre janeiro a dezembro de 2010 realizou-se um estudo transversal com 255 pares de mães-pré-escolares moradores em cinco subdistritos na Região Sudoeste do município de São Paulo. Selecionou-se uma amostra probabilística aleatória estratificada proporcional, com dois estratos, sendo primeiro sorteadas as escolas e depois as crianças. Da mãe, foram coletadas informações sobre a idade, classe econômica, escolaridade, estado civil e EdV não comportamental e comportamental. Da criança, foram coletadas informações de idade, peso e estatura para avaliação do estado nutricional, circunferência da cintura, comportamento sedentário, consumo alimentar através de um questionário de frequência alimentar para a faixa etária estudada. Foi realizada análise fatorial por extração de fatores comuns e de componentes principais dos questionários de EdV não comportamental e de frequência alimentar, respectivamente, com subsequente agrupamento das mães nos domínios de EdV e das crianças nos fatores extraídos. As associações foram calculadas por meio do teste do qui-quadrado e por regressão logística. RESULTADOS: as mães foram agrupadas em três clusteres de EdV: socioconsciente, autoatualizada e consumista. As crianças foram agrupadas em dois clusteres de alimentação: minimamente processado e ultraprocessado. As crianças com alimentação \'minimamente processada\' eram filhas de mães com estilo de vida não comportamental mais \'socioconsciente\', enquanto que as crianças com alimentação \'ultraprocessada\' eram filhas de mães com estilo de vida não comportamental mais do tipo \'consumista\'. Nenhuma associação foi encontrada entre as características nutricionais do pré-escolar e os tipos de estilo de vida materno comportamental. As crianças \'não eutróficas\' e com \'adiposidade central presente\' tiveram uma chance 99% e 92% maiores, respectivamente, de serem filhas de mães com \'alto\' escore no domínio \'moderno\'. As crianças com \'comportamento sedentário presente\' e alimentação \'ultraprocessada\' tiveram uma chance 113% e 84% maior, respectivamente, de serem filhas de mães pertencentes ao cluster \'consumista\'. CONCLUSÃO: o EdV materno não comportamental está associado a características nutricionais do pré-escolar, com as mães com estilo de vida do tipo \'consumista\' tendo influências negativas na nutrição infantil / BACKGROUNG: Many of the health behaviors involved in the emergence of non - communicable chronic diseases are originated in childhood under their parents influence. The mother is the most involved person in education and on the child health care. Lifestyle (LS) is a social health determinant and never before was understood maternal LS on aspects of childhood nutrition. OBJECTIVE: Verify the association on maternal behavior and non- behavioral LS with nutritional aspects in preschool. METHODS: From January 2010 to December 2010, it was performed a cross sectional study with 255 of preschool mothers peers resident on five different sub-districts in São Paulo Southwest. A stratified random sample was selected on two layers, being first the drawn schools and then, the children. From the mother, was caught the age, economical status, education, marital status, behavioral and non- behavioral LS. From the child, the age, weight and height to nutritional status, waist circumference, sedentary behavior, food intake, through an alimentation frequency questionnaire to the studied age group were all taken information about. An extraction analysis factor was performed in order to common factors and principal components on non-behavioral LS and food frequency, respectively, with a subsequent group of mothers on the domain of LS and the children on extracted factors. The association was calculated using the chi-square test and logistic regression. RESULTS: All the mothers were grouped into three clusters LS: socio conscious, self-actualization and consumeristic. The children were grouped into two feeding clusters: minimally and upper processed. Children with minimally processed food were born from mothers with more socio conscious non- behavioral LS, while children with upper processed food were born from mothers with more consumeristic non-behavioral LS. No other association was found between the nutritional preschool characteristics and the kinds of behavioral maternal LS. Not eutrophic and central adiposity present, children had 99% and 92% higher chance, respectively, to be children from mothers with a high score in the modern domain. Children with sedentary behavior and upper processed alimentation had 113% and 84% higher chance, respectively, to be children from mothers belonging to the consumeristic cluster. CONCLUSION: Non- behavioral maternal LS is associated to nutritional preschool characteristics being the mothers living a consumeristic lifestyle tending to have negative influences on child nutrition

INTRODUÇÃO: Muitos dos comportamentos de saúde envolvidos no aparecimento das doenças crônicas não comunicáveis são originados na infância sob influência dos pais. A mãe é a pessoa mais envolvida na educação e nos cuidados de saúde da criança. O estilo de vida (EdV) é um determinante social da saúde. Nunca se compreendeu características de EdV materno associadas com aspectos da nutrição infantil. OBJETIVO: Verificar a associação dos EdV materno comportamental e não comportamental com aspectos nutricionais do pré-escolar. MÉTODOS: Entre janeiro a dezembro de 2010 realizou-se um estudo transversal com 255 pares de mães-pré-escolares moradores em cinco subdistritos na Região Sudoeste do município de São Paulo. Selecionou-se uma amostra probabilística aleatória estratificada proporcional, com dois estratos, sendo primeiro sorteadas as escolas e depois as crianças. Da mãe, foram coletadas informações sobre a idade, classe econômica, escolaridade, estado civil e EdV não comportamental e comportamental. Da criança, foram coletadas informações de idade, peso e estatura para avaliação do estado nutricional, circunferência da cintura, comportamento sedentário, consumo alimentar através de um questionário de frequência alimentar para a faixa etária estudada. Foi realizada análise fatorial por extração de fatores comuns e de componentes principais dos questionários de EdV não comportamental e de frequência alimentar, respectivamente, com subsequente agrupamento das mães nos domínios de EdV e das crianças nos fatores extraídos. As associações foram calculadas por meio do teste do qui-quadrado e por regressão logística. RESULTADOS: as mães foram agrupadas em três clusteres de EdV: socioconsciente, autoatualizada e consumista. As crianças foram agrupadas em dois clusteres de alimentação: minimamente processado e ultraprocessado. As crianças com alimentação \'minimamente processada\' eram filhas de mães com estilo de vida não comportamental mais \'socioconsciente\', enquanto que as crianças com alimentação \'ultraprocessada\' eram filhas de mães com estilo de vida não comportamental mais do tipo \'consumista\'. Nenhuma associação foi encontrada entre as características nutricionais do pré-escolar e os tipos de estilo de vida materno comportamental. As crianças \'não eutróficas\' e com \'adiposidade central presente\' tiveram uma chance 99% e 92% maiores, respectivamente, de serem filhas de mães com \'alto\' escore no domínio \'moderno\'. As crianças com \'comportamento sedentário presente\' e alimentação \'ultraprocessada\' tiveram uma chance 113% e 84% maior, respectivamente, de serem filhas de mães pertencentes ao cluster \'consumista\'. CONCLUSÃO: o EdV materno não comportamental está associado a características nutricionais do pré-escolar, com as mães com estilo de vida do tipo \'consumista\' tendo influências negativas na nutrição infantil / BACKGROUNG: Many of the health behaviors involved in the emergence of non - communicable chronic diseases are originated in childhood under their parents influence. The mother is the most involved person in education and on the child health care. Lifestyle (LS) is a social health determinant and never before was understood maternal LS on aspects of childhood nutrition. OBJECTIVE: Verify the association on maternal behavior and non- behavioral LS with nutritional aspects in preschool. METHODS: From January 2010 to December 2010, it was performed a cross sectional study with 255 of preschool mothers peers resident on five different sub-districts in São Paulo Southwest. A stratified random sample was selected on two layers, being first the drawn schools and then, the children. From the mother, was caught the age, economical status, education, marital status, behavioral and non- behavioral LS. From the child, the age, weight and height to nutritional status, waist circumference, sedentary behavior, food intake, through an alimentation frequency questionnaire to the studied age group were all taken information about. An extraction analysis factor was performed in order to common factors and principal components on non-behavioral LS and food frequency, respectively, with a subsequent group of mothers on the domain of LS and the children on extracted factors. The association was calculated using the chi-square test and logistic regression. RESULTS: All the mothers were grouped into three clusters LS: socio conscious, self-actualization and consumeristic. The children were grouped into two feeding clusters: minimally and upper processed. Children with minimally processed food were born from mothers with more socio conscious non- behavioral LS, while children with upper processed food were born from mothers with more consumeristic non-behavioral LS. No other association was found between the nutritional preschool characteristics and the kinds of behavioral maternal LS. Not eutrophic and central adiposity present, children had 99% and 92% higher chance, respectively, to be children from mothers with a high score in the modern domain. Children with sedentary behavior and upper processed alimentation had 113% and 84% higher chance, respectively, to be children from mothers belonging to the consumeristic cluster. CONCLUSION: Non- behavioral maternal LS is associated to nutritional preschool characteristics being the mothers living a consumeristic lifestyle tending to have negative influences on child nutrition

Reliability prediction of components operating in complex systems has historically been conducted in a statistically isolated manner. Current physics-based, i.e. mechanistic, component reliability approaches focus more on component-specific attributes and mathematical algorithms and not enough on the influence of the system. The result is that significant error can be introduced into the component reliability assessment process. The objective of this study is the development of a framework that infuses the influence of the system into the process of conducting mechanistic-based component reliability assessments. The formulated framework consists of six primary steps. The first three steps, identification, decomposition, and synthesis, are qualitative in nature and employ system reliability and safety engineering principles for an appropriate starting point for the component reliability assessment. The most unique steps of the framework are the steps used to quantify the system-driven local parameter space and a subsequent step using this information to guide the reduction of the component parameter space. The local statistical space quantification step is accomplished using two newly developed multivariate probability tools: Multi-Response First Order Second Moment and Taylor-Based Inverse Transformation. Where existing joint probability models require preliminary statistical information of the responses, these models combine statistical information of the input parameters with an efficient sampling of the response analyses to produce the multi-response joint probability distribution. Parameter space reduction is accomplished using Approximate Canonical Correlation Analysis (ACCA) employed as a multi-response screening technique. The novelty of this approach is that each individual local parameter and even subsets of parameters representing entire contributing analyses can now be rank ordered with respect to their contribution to not just one response, but the entire vector of component responses simultaneously. The final step of the framework is the actual probabilistic assessment of the component. Variations of this final step are given to allow for the utilization of existing probabilistic methods such as response surface Monte Carlo and Fast Probability Integration. The framework developed in this study is implemented to conduct the finite-element based reliability prediction of a gas turbine airfoil involving several failure responses. The framework, as implemented resulted in a considerable improvement to the accuracy of the part reliability assessment and an increased statistical understanding of the component failure behavior.

This diploma thesis researches coffee authenticity problematice, mainly focusing on the authenticity of geographic origin. In the theoretical part of this work, botanical classification is described as well as production technology and processes. The work also includes chemical composition of coffee, describing the major components and changes during production phases. It describes major production areas of the world, in terms of general description and brief history. Problematics with coffee fraud and its identification are also described. Theoretical part also includes general geological description of 17 studied coffee growing regions. Experimental part is devoted to trace amount analysis of selected elements and volatile compounds. The element analysis was conducted using mass spectrometry or optical emission spectrometry, volatile compounds were determined using gas chromatography combined with mass spectrometry detection. Results were statistically described and analyzed, resulting in several discrimination models based on geographic origin.

Whereas traditional data warehouse systems assume that data is complete or has been carefully preprocessed, increasingly more data is imprecise, incomplete, and inconsistent. This is especially true in the context of big data, where massive amount of data arrives continuously in real-time from vast data sources. Nevertheless, modern data analysis involves sophisticated statistical algorithm that go well beyond traditional BI and, additionally, is increasingly performed by non-expert users. Both trends require transparent data mining techniques that efficiently handle missing data and present a complete view of the database to the user. Time series forecasting estimates future, not yet available, data of a time series and represents one way of dealing with missing data. Moreover, it enables queries that retrieve a view of the database at any point in time - past, present, and future. This article presents an overview of forecasting techniques in database management systems. After discussing possible application areas for time series forecasting, we give a short mathematical background of the main forecasting concepts. We then outline various general strategies of integrating time series forecasting inside a database and discuss some individual techniques from the database community. We conclude this article by introducing a novel forecasting-enabled database management architecture that natively and transparently integrates forecast models.

Membrane protein mechanotransduction is the altered function of an integral membrane protein in response to mechanical force. Such mechanosensors are found in all kingdoms of life, and increasing numbers of membrane proteins have been found to exhibit mechanosensitivity. How they mechanotransduce is an active research area and the topic of this thesis. The methodology employed is classical molecular dynamics (MD) simulations. MD systems are complex, and two programs were developed to reduce this apparent complexity in terms of both visual abstraction and statistical analysis. Bendix detects and visualises helices as cylinders that follow the helix axis, and quantifies helix distortion. The functionality of Bendix is demonstrated on the symporter Mhp1, where a state is identified that had hitherto only been proposed. InterQuant tracks, categorises and orders proximity between parts of an MD system. Results from multiple systems are statistically interrogated for reproducibility and significant differences at the resolution of protein chains, residues or atoms. Using these tools, the interaction between membrane and the Escherichia coli mechanosensitive channel of small conductance, MscS, is investigated. Results are presented for crystal structures captured in different states, one of which features electron density proposed to be lipid. MD results supports this hypothesis, and identify differential lipid interaction between closed and open states. It is concluded that propensity for lipid to leave for membrane bulk drives MscS state stability. In a subsequent study, MscS is opened by membrane surface tension for the first time in an MD setup. The gating mechanism of MscS is explored in terms of both membrane and protein deformation in response to membrane stretch. Using novel tension methodology and the longest MD simulations of MscS performed to date, a molecular basis for the Dashpot gating mechanism is proposed. Lipid emerges as an active structural element with the capacity to augment protein structure in the protein structure-function paradigm.