The accomplished case studies focus on the influence of land-use on the distributions of latent heat-fluxes and cloud-water. The numerical case studies were performed with the threedimensional non-hydrostatic Mesoscale-Model GESIMA for different land-use distributions applying always the same initial conditions of a cloudy day in spring with a geostrophic wind of 8 m/s from the west. The cloud-water distributions at different times and at different levels, their temporal development, the daily sums of the domain-averaged latent heat-fluxes and cloud-water mixing ratios were investigated. Even simple initial conditions (no orography, stable atmosphere) and simple pattern in the land-use distributions emphasize that the influence of surface heterogeneity on meteorological processes cannot be neglected. As shown in this case study, land-use distribution influences the distribution and the amount of cloud-water as weil as the latent heat-flux. On the whole, all these processes are very complex and non-linear. / Die durchgeführten Sensitivitätsstudien konzentrieren sich auf den Einfluß der Landnutzungsverteilung auf die Flüsse latenter Wärme und das Wolkenwasser. Die numerischen Untersuchungen wurden mit dem dreidimensionalen nicht-hydrostatischen Mesoskalen-Modell GESIMA für verschiedene Landnutzungsmuster unter immer den gleichen meteorologischen Anfangsbedingungen für einen bewölkten Frühlingstag mit einem geostrophischen Wind von 8 m/s durchgeführt. Die Wolkenwasserverteilung zu bestimmten Zeiten und in bestimmten Niveaus, die zeitliche Entwicklung der Wolkenwasserverteilung, die Tagessummen der Gebietsmittelwerte der Flüsse latenter Wärme und des Wolkenwassers werden untersucht. Auch einfache Randbedingungen (keine Orographie, stabile, atmosphärische Bedingungen) und einfache Landnutzungsverteilungsmuster machen deutlich, daß der Einfluß der Heterogenität der Unterlage auf meteorologische Prozesse nicht zu vernachlässigen ist. Sie kann entscheidend die Verteilungen der Flüsse latenter Wärme und des Wolkenwassers beeinflussen. Die damit verbundenen Prozesse sind äußerst komplex und nicht linear.

Bayesian inference is particularly challenging on hierarchical statistical models as computational complexity becomes a significant issue. Sampling-based methods like the popular Markov Chain Monte Carlo (MCMC) can provide accurate solutions, but they likely suffer a high computational burden. An attractive alternative is the Integrated Nested Laplace Approximations (INLA) approach, which is faster when applied to the broad class of Latent Gaussian Models (LGMs). The method computes fast and empirically accurate deterministic posterior marginal approximations of the model's unknown parameters. In the first part of this thesis, we discuss how to extend the software's applicability to a joint posterior inference by constructing a new class of joint posterior approximations, which also add marginal corrections for location and skewness. As these approximations result from a combination of a Gaussian Copula and internally pre-computed accurate Gaussian Approximations, we name this class Skew Gaussian Copula (SGC). By computing moments and correlation structure of a mixture representation of these distributions, we achieve new fast and accurate deterministic approximations for linear combinations in a subset of the model's latent field. The same mixture approximates a full joint posterior density through a Monte Carlo sampling on the hyperparameter set. We set highly skewed examples based on Poisson and Binomial hierarchical models and verify these new approximations using INLA and MCMC. The new skewness correction from the Skew Gaussian Copula is more consistent with the outcomes provided by the default INLA strategies. In the last part, we propose an extension of the parametric fit employed by the Simplified Laplace Approximation strategy in INLA when approximating posterior marginals. By default, the strategy matches log derivatives from a third-order Taylor expansion of each Laplace Approximation marginal with those derived from Skew Normal distributions. We consider a fourth-order term and adapt an Extended Skew Normal distribution to produce a more accurate approximation fit when skewness is large. We set similarly skewed data simulations with Poisson and Binomial likelihoods and show that the posterior marginal results from the new extended strategy are more accurate and coherent with the MCMC ones than its original version.

In automatically categorizing massive corpora of text, various topic models have been applied with good success. Much work has been done on applying machine learning and NLP methods on Internet media, such as Twitter, to survey online discussion. However, less focus has been placed on studying how geographical locations discussed in online fora evolve over time, and even less on associating such location trends with topics. Can online discussions be geographically tracked over time? This thesis attempts to answer this question by evaluating a geo-aware Streaming Latent Dirichlet Allocation (SLDA) implementation which can recognize location terms in text. We show how the model can predict time-dependent locations of the 2016 American primaries by automatic discovery of election topics in various Twitter corpora, and deduce locations over time.

Providing high quality of topics inference in today's large and dynamic corpora, such as Twitter, is a challenging task. This is especially challenging taking into account that the content in this environment contains short texts and many abbreviations. This project proposes an improvement of a popular online topics modelling algorithm for Latent Dirichlet Allocation (LDA), by incorporating supervision to make it suitable for Twitter context. This improvement is motivated by the need for a single algorithm that achieves both objectives: analyzing huge amounts of documents, including new documents arriving in a stream, and, at the same time, achieving high quality of topics’ detection in special case environments, such as Twitter. The proposed algorithm is a combination of an online algorithm for LDA and a supervised variant of LDA - labeled LDA. The performance and quality of the proposed algorithm is compared with these two algorithms. The results demonstrate that the proposed algorithm has shown better performance and quality when compared to the supervised variant of LDA, and it achieved better results in terms of quality in comparison to the online algorithm. These improvements make our algorithm an attractive option when applied to dynamic environments, like Twitter. An environment for analyzing and labelling data is designed to prepare the dataset before executing the experiments. Possible application areas for the proposed algorithm are tweets recommendation and trends detection. / Tillhandahålla högkvalitativa ämnen slutsats i dagens stora och dynamiska korpusar, såsom Twitter, är en utmanande uppgift. Detta är särskilt utmanande med tanke på att innehållet i den här miljön innehåller korta texter och många förkortningar. Projektet föreslår en förbättring med en populär online ämnen modellering algoritm för Latent Dirichlet Tilldelning (LDA), genom att införliva tillsyn för att göra den lämplig för Twitter sammanhang. Denna förbättring motiveras av behovet av en enda algoritm som uppnår båda målen: analysera stora mängder av dokument, inklusive nya dokument som anländer i en bäck, och samtidigt uppnå hög kvalitet på ämnen "upptäckt i speciella fall miljöer, till exempel som Twitter. Den föreslagna algoritmen är en kombination av en online-algoritm för LDA och en övervakad variant av LDA - Labeled LDA. Prestanda och kvalitet av den föreslagna algoritmen jämförs med dessa två algoritmer. Resultaten visar att den föreslagna algoritmen har visat bättre prestanda och kvalitet i jämförelse med den övervakade varianten av LDA, och det uppnådde bättre resultat i fråga om kvalitet i jämförelse med den online-algoritmen. Dessa förbättringar gör vår algoritm till ett attraktivt alternativ när de tillämpas på dynamiska miljöer, som Twitter. En miljö för att analysera och märkning uppgifter är utformad för att förbereda dataset innan du utför experimenten. Möjliga användningsområden för den föreslagna algoritmen är tweets rekommendation och trender upptäckt.

Nowadays continuous increase of energy consumption increases the importance of replacing fossil fuels with renewable energy sources so the CO2 emissions can be reduced. To use the energy in a more efficient way is also favorable for this purpose. Thermal Energy Storage (TES) is a technology that can make use of waste heat, which means that it can help energy systems to reduce the CO2 emissions and improve the overall efficiency. In this technology an appropriate material is chosen to store the thermal energy so it can be stored for later use. The energy can be stored as sensible heat and latent heat. To achieve a high energy storage density it is convenient to use latent heat based TES. The materials used in this kind of storage system are called Phase Change Materials (PCM) and it is its ability of absorbing and releasing thermal energy during the phase change process that becomes very useful. In this thesis a simulation model for a system of thermal energy transportation has been developed. The background comes from district heating systems ability of using surplus heat from industrials and large scale power plants. The idea is to implement transportation of heat by trucks closer to the demand instead of distributing heat through very long pipes. The heat is then charged into containers that are integrated with PCM and heat exchangers. A mathematical model has been created in Matlab to simulate the system dynamics of the logistics of the thermal energy transport system. The model considers three main parameters: percentage content of PCM in the containers, annual heat demand and transport distance. How the system is affected when these three parameters varies is important to visualize. The simulation model is very useful for investigation of the economic and environmental capability of the proposed thermal energy transportation system. Simulations for different scenarios show some expected results. But there are also some findings that are more interesting, for instance how the variation of content of PCM gives irregular variation of how many truck the system requires, and its impact on the economic aspect. Results also show that cost for transporting the heat per unit of thermal energy can be much high for a small demands compared to larger demands.

When conducting research, it is valuable to find high-ranked papers closely related to the specific research area, without spending too much time reading insignificant papers. To make this process more effective an automated process to extract topics from documents would be useful, and this is possible using topic modeling. Topic modeling can also be used to provide topic trends, where a topic is first mentioned, and who the original author was. In this paper, over 5000 articles are scraped from four different top-ranked internet security conferences, using a web scraper built in Python. From the articles, fourteen topics are extracted, using the topic modeling library Gensim and LDA Mallet, and the topics are visualized in graphs to find trends about which topics are emerging and fading away over twenty years. The result found in this research is that topic modeling is a powerful tool to extract topics, and when put into a time perspective, it is possible to identify topic trends, which can be explained when put into a bigger context.

Thesis advisor: Jacqueline V. Lerner / Young people identifying as Black, Latino/a/x, Hispanic, Asian, and other races and ethnicities that are minoritized and marginalized have constrained opportunities for positive development in the United States due to oppression grounded in white supremacy (NASEM, 2019). Importantly, youth of color engage in critical consciousness: interrogating and dismantling systems of oppression (Freire, 1970/2016). My aim was to illuminate the variation within youth of color in their development of critical consciousness, and to consider the implications for their overall development as viewed from a positive youth development perspective (Lerner et al., 2015). Associations between patterns of critical consciousness development and two variables measuring youths’ perceptions of their school context were examined. Using latent profile transition analysis, I explored variation among a sample of youth of color (n = 335) in cognitive, socioemotional, and behavioral processes of critical consciousness (Diemer et al., 2016; Watts et al., 2011) over a short longitudinal period. The mean age was fourteen at time 1 (which took place in 2016) and fifteen at time 2. Group-differential patterns in critical consciousness development were related to contribution—supporting the development of self and giving back to community; engagement in risk and problem behaviors; and emotional problems. Associations between patterns of critical consciousness development and (1) classroom discussions about social justice and (2) open classroom climate were estimated. Multiple patterns of engagement with critical consciousness were identified. Some youth shifted in their patterns of critical consciousness over time. Many participants reported a pattern of low engagement in multiple components of critical consciousness across both time points; higher classroom discussions about social justice were associated with a lower likelihood of youth following this pattern. These youth concurrently reported low contribution. Young people who sustained high levels across all dimensions of critical consciousness had high levels of emotional problems and risk and problem behaviors. Findings indicate broad involvement in critical consciousness can be associated with negative outcomes. Nevertheless, young people who were participating less in critical consciousness may struggle to promote positive development within themselves and their contexts through contribution. Implications for supporting the thriving of youth of color are discussed. / Thesis (PhD) — Boston College, 2021. / Submitted to: Boston College. Lynch School of Education. / Discipline: Counseling, Developmental and Educational Psychology.

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Objectives: To evaluate the effects of an appearance-focused intervention to reduce the risk of skin cancer by decreasing indoor tanning, examine potential heterogeneity in tanning across this time, and correlate the subtypes with predictors collected at baseline. Design: Randomized controlled trial with 379 female college students measured at 6 monthly time points. Main Outcome Measure: Self-reported indoor tanning frequency. Results: The intervention was effective at decreasing tanning over the period between the fall and spring. Longitudinal latent class analysis found 3 patterns of tann1ers among the treatment individuals: abstainers, moderate tanners, and heavy tanners. These classes appeared in both the treatment and control conditions, and the intervention had a harm reduction effect by reducing levels of exposure within the moderate and heavy tanner classes. Participant age and self-reported tanning patterns were found to be predictive of class membership. Conclusions: This research suggests that brief intervention approaches can be effective at reducing risk for skin cancer and illustrates several ways in which these protective effects can be enhanced.

Cognitive studies reveal that less-than-expert clinicians are less able to recognize meaningful patterns of data in clinical narratives. Accordingly, psychiatric residents early in training fail to attend to information that is relevant to diagnosis and the assessment of dangerousness. This manuscript presents cognitively motivated methodology for the simulation of expert ability to organize relevant findings supporting intermediate diagnostic hypotheses. Latent Semantic Analysis is used to generate a semantic space from which meaningful associations between psychiatric terms are derived. Diagnostically meaningful clusters are modeled as geometric structures within this space and compared to elements of psychiatric narrative text using semantic distance measures. A learning algorithm is defined that alters components of these geometric structures in response to labeled training data. Extraction and classification of relevant text segments is evaluated against expert annotation, with system-rater agreement approximating rater-rater agreement. A range of biomedical informatics applications for these methods are suggested.