Carbon-capture and sequestration (CCS) in geologic reservoirs is one strategy for reducing anthropogenic CO2 emissions from large-scale point source emitters. Recent developments have shown that basalt reservoirs are highly effective for permanent mineral trapping on the basis of CO2-water-rock interactions, which result in the formation of carbonate minerals. However, the injection of super-critical CO2 into the subsurface causes a disturbance in the pressure, temperature, and chemical systems within the target reservoir. How the ambient conditions change in response to a CO2 injection ultimately affects the transport and fate of the injected CO2. Understanding the behavior and transport of CO2 within a geologic reservoir is a difficult problem that is only exacerbated by heterogeneities within the reservoir; for example, permeability can be highly heterogeneous and exhibits significant control on the movement of CO2. This work is focused on constraining the permeability uncertainty within a flood basalt reservoir, specifically the Columbia River Basalt Group (CRBG). In order to do so, this dissertation is a culmination of four projects: (1) a geostatistical analysis resulting in a spatial correlation model of regional scale permeability within the CRBG, (2) a Monte Carlo-type modeling studying investigating the effects that permeability uncertainty has on the injectivity and storativity of the CRBG as a storage reservoir, (3) a modeling study utilizing 1-, 2-, and 3-D numerical models to investigate how the thermal signature of the CO2-water system evolves during a CO2 injection, and (4) a Monte Carlo-type modeling study focused on the integrity of the CRBG as a CO2 storage reservoir through a probabilistic assessment of static threshold criteria. / Doctor of Philosophy / The process of capturing CO2 from point-source emitters, such as power plants and injecting that CO2 into a geologic formation is one way to reduce anthropogenic CO2 emissions. Recent field studies have shown that basalt reservoirs may be very effective at permanently storing the injected CO2 making them a secure geologic formation to store the CO2. However, basalt reservoirs can be highly fractured, which causes the properties of the reservoir (e.g. permeability, porosity, etc.) to be nonuniform. Having nonuniform reservoir properties creates uncertainty when planning a large-scale CO2 injection. This research is focused on understanding and constraining the uncertainty of nonuniform reservoir properties associated with a large-scale CO2 injection. The work presented utilizes a geostatistical analysis of permeability to inform a variety of numerical models to study how nonuniform reservoir properties affect CO2 injection rate, how much CO2 can be stored, how the pressure and temperature of the reservoir changes, and how secure the storage reservoir is during a CO2 injection.

We sampled forest stands in upland forests of the Southeastern US along a chronosequence of a replicated successional forest sere (1, 6, 10, 25, and 80 years) to elucidate the temporal changes in soil spatial heterogeneity. Samples were collected from loblolly pine plantations representing reorganization through aggradation phases of succession, and from one set of oak-hickory stands to signify the steady-state phase of the model. These trends are characterized and compared to a conceptual model of pattern dynamics. Variability in soil properties (NO3, NH4, pH, Total N, Total C) and forest floor litter at scales relevant to individual plants was quantified using univariate and geostatistical procedures. Global variation (using both coefficient of variation and standard deviation), patch size and proportion of spatially structured variation were examined for individual variables at each successional stage. These patterns were also averaged to produce a generalized model of spatial heterogeneity change during succession. Individual variables often showed differing patterns. However, when patterns from individual variables were averaged, overall patterns emerged. Early in succession global variability was largest and patch sizes were smallest. As succession progressed, trends in the data showed that global variability decreased and patch sizes increased to the middle stage of succession. Both of these trends fit our conceptual model of pattern dynamics. However, the slopes in these trends were not significant at alpha=0.05. / Master of Science

Doctor of Philosophy / Department of Statistics / Gary Gadbury / Researches in the fields in science and statistics often go beyond the two-variable cause-and-effect relationship, and also try to understand what connects the causal relationship and what changes the magnitude or direction of the causal relationship between two variables, predictor(T) and outcome (Y). A mediator (Z) is a third variable that links a cause and an effect, whereby T causes the Z and Z causes Y. In general, a given variable may be said to function as a mediator to the extent that it accounts for the relation between the predictor and the outcome (Baron and Kenny, 1986). The initial question regards the appropriate characterization of a mediation effect. Most studies, when comparing one or more treatments focus on an average mediating effect. This average mediating effect can be misleading when the mediating effects vary from subject to subject in the population. The primary focus of this research is to investigate individual mediating effects in a population, and to define a variance of these individual mediating effects. A concept called subject-mediator (treatment) interaction is presented and its role in evaluating a mediator’s behavior on a population of units is studied. This is done using a framework sometimes called a counterfactual model. Some common experimental designs that provide different knowledge about this interaction term are studied. The subgroup analysis is the most common analytic approach for examining heterogeneity of mediating effects. In mediation analysis, situations can arise where Z and Y cannot both be measured on an individual unit. We refer to such data as terminal measures data. We show a design where a mediating effect cannot be estimated in terminal measures data and another one where it can be, with an assumption. The assumption is linked to the idea of pseudo-replication. These ideas are discussed and a simulation study illustrates the issues involved when analyzing terminal measures data. We know of no methods that are currently available that specifically address terminal measures data.

The benthic animals have important roles in benthic communities. Indicator organisms can tell things about a lakes status. Many of them are sensitive to acidification but there are also tolerant taxa. Some prefer eutrophic environments. The benthic fauna can therefore decide a lakes status since some species prefer different environments. The focus of this study was to compare benthic fauna within the littoral in the lakes Sidsjön and Vintertjärnen. They are situated a little south of Sundsvall, central Sweden and belong to the water system of Selångersån. The comparison was done at eight random locals in each lake at two occasions, during early and late summer to analyze if the benthic fauna differed between the two lakes and if it changed through the season. Five of the locals in each lake were protected and had soft bottoms and the remaining were exposed locals with hard bottom. Sidsjön is bigger than Vintertjärnen and has more variated bottom types so the biodiversity should be higher in Sidsjön. I also expect that the number of taxa should decrease during the later occasion due to hatchings. Three statistical analyses were done to see if these hypotheses were right or if they should be rejected. The two first analyses were ANOVA analyses, two-way with replication. The first ANOVA was for number of taxa and the second was for number of animals. The third analysis was a DCA analysis with the variables lakes, bottom types and emergent plants. ASPT index and Eq was also calculated and pH was measured during autumn for the lakes. The conclusion is that the there is no difference between the lakes but a significant effect when it comes to seasons according to the first ANOVA analysis. The second ANOVA analysis show that the lakes are not differing, that no factor is significant and there are no interactions. According to the DCA analysis there are differences between lakes and occasions when it comes to the variables. / <p>Datum för godkännande 2016-03-31</p>

This thesis sets out to investigate the Alzheimer's disease (AD) heterogeneity in an unsupervised framework. Different subtypes of AD were identified in the past from a number of studies. The major objective of the thesis is to apply clustering methods that are specialized in coping with high dimensional data sets, in a sample of AD patients. The evaluation of these clustering methods and the interpretation of the clustered groups from a statistical and a medical point of view, are some of the additional objectives. The data consist of 271 MRI images of AD patients from the AddNeuroMed and the ADNI cohorts. The raw MRI's have been preprocessed with the software Freesurfer and 82 cortical and subcortical volumes have been extracted for the needs of the analysis. The effect of different strategies in the initialization of a modified Gaussian Mixed Model (GMM) (Bouveyron et al, 2007) has been studied. Additionally, the GMM and a Bayesian clustering method proposed by Nia (2009) have been compared with respect to their performances in various distance based evaluation criteria. The later method resulted in the most compact and isolated clusters. The optimal numbers of clusters was evaluated with the Hopkins statistic and 6 clusters were decided while 2 observations formed an outlier cluster. Different patterns of atrophy were discovered in the 6 clusters. One cluster presented atrophy in the medial temporal area only (n=37,~13.65%). Another cluster resented atrophy in the lateral and medial temporal lobe and parts of the parietal lobe (n=39,~14.4%). A third cluster presented atrophy in temporoparietal areas but also in the frontal lobe (n=74,~27.3%). The remaining three clusters presented diffuse atrophy in nearly all the association cortices with some variation in the patterns (n1=40,~14.7%,n2=58,~21.4,n3=21,7.7%). The 6 subtypes also differed in their demographical, clinical and pathological features.

No description available.

Within the next decade India is expected to surpass China as the world's most populous country due to still higher fertility and a younger population. Around 2025 each country will be home to around 1.5 billion people. India is demographically very heterogeneous with some rural illiterate populations still having more than four children on average while educated urban women have fewer than 1.5 children and with great differences between states. We show that the population outlook greatly depends on the degree to which this heterogeneity is explicitly incorporated into the population projection model used. The conventional projection model, considering only the age and sex structures of the population at the national level, results in a lower projected population than the same model applied at the level of states because over time the high-fertility states gain more weight, thus applying the higher rates to more people. The opposite outcome results from an explicit consideration of education differentials because over time the proportion of more educated women with lower fertility increases, thus leading to lower predicted growth than in the conventional model. To comprehensively address this issue, we develop a five-dimensional model of India's population by state, rural/urban place of residence, age, sex, and level of education and show the impacts of different degrees of aggregation. We also provide human capital scenarios for all Indian states that suggest that India will rapidly catch up with other more developed countries in Asia if the recent pace of education expansion is maintained.

The moose population in Sweden is one of the highest in the world. The population have positive impacts on humans but also cause problems such as browsing damages and collisions with traffic. To manage this population in an adaptive and ecological sound way, the managers need to have as much information as possible. The aim of this study was to improve the description of Sweden’s moose management areas regarding available forage for moose. This is done by describing the heterogeneity of the landscape within the moose management areas, which is done by using different landscape indexes. The heterogeneity indexes is then used to analyze if correlation exists between the variation of the landscape and firstly data that describes the available food, secondly data that indicates the moose population density. Multiple regression analysis were done in order to find a model with the indexes that best explains the variation in available forage and moose population indicators.  The results in this study showed a positive correlation between number of moose shot per 1000 hectares and patch richness awhile a negative correlation was found between number of moose shot per 1000 hectares and edge density. Results also showed a negative correlation between both edge density and the patch richness in the landscape with the presence of rowan, sallow, aspen and oak (RASE). Further developments of indexes that describe landscape heterogeneity is needed, but this study may indicate that knowledge of the variation in the landscape might provide useful information on the prerequisites for the Swedish moose population.

Cette thèse contribue à la littérature théorique et empirique concernant l’hétérogénéité des entreprises et le commerce international. La partie théorique analyse les conséquences de la libéralisation du commerce lorsque les entreprises sont hétérogènes et les pays asymétriques. La partie empirique discute le sens de causalité de la relation entre la performance des entreprises et leur statut international. Les entreprises sont-elles plus performantes parce qu’elles exportent et/ou importent ? Ou sont-ce les entreprises les plus performantes qui s’auto-sélectionnent sur le marché international ? Les deux hypothèses ne s’excluent pas mutuellement et ce travail les accrédite toutes deux. / This thesis contributes to both theoretical and empirical aspects of the literature on firm heterogeneity in international trade. On the theoretical side, I provide insights of the consequences of trade liberalisation when firms are heterogeneous and countries are asymmetric. On the empirical side, I discuss the causality of the relationship between performances and trading status of firms. Do more productive firms self-select into international markets? Do firms become more productive because they enter international markets? These hypotheses are not mutually exclusive and my work provides support for both of them.

Ascending thoracic aortic aneurysms (ATAAs) are focal dilatations in the aorta that are prone to rupture or dissect. Currently, the clinically used indicator of the rupture risk is the diameter. However, it has been demonstrated that the diameter alone may not properly predict the risk. To evaluate the rupture risk, one must look into the local mechanical conditions at the rupture site and understand how rupture is triggered in the tissue which is a layered fibrous media. A challenge facing experimental studies of ATAA rupture is that the ATAA tissue is highly heterogeneous; experimental protocols that operate under the premise of tissue homogeneity will have difficulty delineating the heterogeneous properties. In general, rupture initiates at the location where the micro-structure starts to break down and consequently, it is more meaningful to investigate the local conditions at the rupture site. In this work, a combined experimental and computational method was developed and employed to characterize wall stress, strain, and property distributions in harvested ATAA samples to a sub-millimeter resolution. The results show that all tested samples exhibit a significant degree of heterogeneous in their mechanical properties. Large inter-subject variability is also observed. A heterogeneous anisotropic finite strain hyperelastic model was introduced to describe the tissue; the distributions of the material parameters were identified. The elastic energy stored in the tissue was computed. It was found that the tissue fractures preferentially in the direction of the highest stiffness, generating orifices that are locally transverse to the peak stiffness direction. The rupture appears to initiate at the position absorbed of the highest energy. Machine learning was used to classify the curves at rupture and non-rupture locations. Features including material properties and curve geometric characteristics were used. The work showed that the rupture and non-rupture states can indeed be classified using pre-rupture response features. Support vector machine(SVM) and random forest algorithm was employed to provide insight on the importance of the features. Inspired by the importance scores provided by random forest, the rupture groups were interrogated and some strong correlations between the strength and the response features were revealed. In particular, it was found that the strength correlates strongly with the tension at the point where the curvature of the total tension strain curve attains maximum, which occurs early in the response.