A comparative study of two different approaches in wind energy simulations has been made where the aim was to investigate the performance of two commercially available tools. The study includes the linear model by WAsP and the computational fluid dynamic model of WindSim (also featuring an additional forest module). The case studied is a small wind farm located in the inland of Sweden featuring a fairly complex and forested terrain. The results showed similar estimations from both tools and in some cases an advantage for WindSim. The site terrain is however deemed not complex enough to manifest the potential benefits of using the CFD model. It can be concluded that estimating the energy output in this kind of terrain is done satisfyingly with both tools. WindSim does however show a significant improvement in consistency when estimating the energy output from different measurement heights when using the forest module compared to only using the standardized roughness length.

Tardive dyskinesia (TD) is a serious side effect of certain antipsychotic medications that are used to treat schizophrenia (SCZ) and other mental illnesses. The methylation status of the insulin receptor substrate 1 (IRS1) gene is reportedly associated with SCZ; however, no study, to the best of the authors' knowledge, has focused on the quantitative DNA methylation levels of the IRS1 gene using pyrosequencing in SCZ with or without TD. The present study aimed to quantify DNA methylation levels of 4 CpG sites in the IRS1 gene using a Chinese sample including SCZ patients with TD and without TD (NTD) and healthy controls (HCs). The general linear model (GLM) was used to detect DNA methylation levels among the 3 proposed groups (TD vs. NTD vs. HC). Mean DNA methylation levels of 4 CpG sites demonstrated normal distribution. Pearson's correlation analysis did not reveal any significant correlations between the DNA methylation levels of the 4 CpG sites and the severity of SCZ. GLM revealed significant differences between the 3 groups for CpG site 1 and the average of the 4 CpG sites (P=0.0001 and P=0.0126, respectively). Furthermore, the TD, NTD and TD + NTD groups demonstrated lower methylation levels in CpG site 1 (P=0.0003, P<0.0001 and P<0.0001, respectively) and the average of 4 CpG sites (P=0.0176, P=0.0063 and P=0.003, respectively) compared with the HC group. The results revealed that both NTD and TD patients had significantly decreased DNA methylation levels compared with healthy controls, which indicated a significant association between the DNA methylation levels of the IRS1 gene with SCZ and TD.

Leaf morphology of Quercus (oak) species is highly variable and complicated confounded with phenotypic plasticity and fluctuating asymmetry (FA). However, the study of variation is mostly limited to leaf morphology. This study was extended to plasticity and FA variations in Q. alba (white oak), Q. palustris (pin oak), and Q. velutina (black oak). It was hypothesized that light exposure, individual trees, leaf position, and other leaf traits will influence variation in these species. Leaves were sampled from trees of these species and their morphological traits were measured. Absolute asymmetry of leaf width and area were determined and plasticity of each species was calculated. The data were analyzed using nested ANOVA with General Linear Model. Leaf morphology, plasticity and FA varied across the species and light exposure was the main source of variation. Individual trees and several leaf covariate traits also influenced leaf morphological and FA variations in all species.

When the big data time comes, people also need to keep pace with the times to seek and develop tools that can deal with the vast amount of information. In this project, lassois applied to build parametric models of electricity prices based on different affecting factors. Thereafter, the models are used to predict the electricity prices 8 days forward for three different time periods. We compare their prediction performances in terms of normalized mean square error (NMSE) and identify dominant factors of the electricity prices in different time periods using lasso. The results show that a model that spans over a 24 hourlong period gives the lowest NMSE, followed by one spanning over a two hour long period where the electricity prices are leading up to a peak value. The model that obtains the highestNMSE is from a two hour long period, where the electricity prices have a peak value. Besides, we also analyze potential reasons for the results. / När big data-tiden kommer måste även människor hålla jämna steg med tiderna för att söka och utveckla verktyg som kan hantera den stora mängden information. I detta projekt används lasso för att bygga parametriska modeller av elpriser baserade på olika påverkansfaktorer. Därefter används modellerna för att förutsäga elpriserna 8 dagar framåt för tre olika tidsperioder. Vi jämför deras prediktionsprestanda i termer av normaliserat medelkvadratfel (NMSE) och identifierar dominerande faktorer för elpriserna under olika tidsperioder med hjälp av lasso. Resultaten visar att en modell som sträcker sig över en 24 timmar lång period ger lägst NMSE värde, följt av en som sträcker sig över en två timmar lång period där elpriserna leder fram till ett toppvärde. Modellen som får högst NMSE är från en två timmar lång period, där elpriserna har ett toppvärde. Dessutom analyserar vi också potentiella orsaker till resultaten. / Kandidatexjobb i elektroteknik 2022, KTH, Stockholm

[pt] A importância de sistemas que monitorem continuamente as resposta dos consumidores à propaganda é notadamente reconhecida pela comunidade de pesquisa de mercado. A coleta sistemática deste tipo de informação é importante porque através desta, pode-se revisar campanhas anteriores, corrigir tendências detectadas em pré-testes e melhor orientar as tomadas de decisão nos setores de propaganda. O presente trabalho contém um modelo para tentar medir esta resposta baseada em Modelos Lineares Dinâmicos Generalizados. / [en] Analysis of consumer markets define and attempt to measure many variables in studies of the effectiveness of adversitising. The awareness in a consumer population of a particular advertising is one such quantity, the subject of the above-referenced studies. We define and give the implementation of model based in dynamic Generalised Linear Models which is used to measure this quantity.

Cancer is a complex disease and presents one of the greatest challenges in modern medicine. Despite remarkable advances in treatment of several cancer types, relapse and resistance to therapy remain recurring outcomes in patients, which underscores a need for personalized treatment approaches. These complications have been related to the high genetic diversity observed within tumors, termed intratumor heterogeneity (ITH). While specific mutational profiles have been associated with the development of heterogeneous tumors, the relationship between ITH and phenotype could unveil features that undergo selection and convey fitness. Features presented in the transcriptome, as markers of heterogeneity, might therefore be valuable biomarkers. In this project, these features are explored by assuming a linear relationship between genetic ITH measures and gene expression data from The Cancer Genome Atlas samples. By first reducing the number of variables among the transcriptome to the differentially expressed genes between low and high ITH samples, the association between specific gene expression profiles and ITH is sought with a linear model. By using two different methods for estimating ITH, called Expands and PhyloWGS, the association was modeled with each method. Interestingly, the model based on Expands captured the elevated expression of a chaperone gene DNAJC18 as being consistently associated with lower ITH in four cancer types. On the other hand, models based on PhyloWGS presented lower predictive power. These results demonstrate that the transcriptome can be used to predict genetic ITH, although this depends on the method used for characterizing ITH. / Cancer är en komplex sjukdom och en av de största utmaningarna i dagens medicin. Trots stora framsteg i behandlingen av flera cancerformer är återfall och terapiresistens återkommande problem vilket talar starkt för behov av individualiserad behandling. Dessa komplikationer har relaterats till den höga genetiska variabiliteten som observeras inom tumörer, även kallad intratumoral heterogenitet (ITH). Undersökning av relationen mellan ITH och fenotypisk data kan ta fram markörer som är involverade i cancerutvecklingen som bidragare till heterogenitet. Genom att modellera associationen mellan transcriptomen och ITH kan man även hitta kliniskt relevanta biomarkörer. I detta projekt undersöks relationen mellan genutryck och ITH genom att applicera linjär regression. Genom att först reducera antalet variabler i transkriptomen till de diferentiellt utryckta gener, används linjära modellen för att ta fram specifika gener vars utryck kan relateras till ändringar i ITH uppskattad för The Cancer Genome Atlas prover. ITH uppskattas med två algoritmiska metoder, kallade Expands och PhyloWGS. Resultaten visade att förhöjd uttryck an genen DNAJC18 är associerad med lägre ITH uppskattad med Expands bland fyra cancer typer. Trots detta visade inte genutryck och ITH uppskattat med PhyloWGS lika starkt linjärt samband.

This study examined the use of the set, area, and linear models of fraction representation to enhance elementary students' conceptual understanding of fractions. Students' preferences regarding the set, area, and linear models of fractions during independent work was also investigated. This study took place in a 5th grade class consisting of 21 students in a suburban public elementary school. Students participated in classroom activities which required them to use manipulatives to represent fractions using the set, area, and linear models. Students also had experiences using the models to investigate equivalent fractions, compare fractions, and perform operations. Students maintained journals throughout the study, completed a pre and post assessment, participated in class discussions, and participated in individual interviews concerning their fraction model preference. Analysis of the data revealed an increase in conceptual understanding. The data concerning student preferences were inconsistent, as students' choices during independent work did not always reflect the preferences indicated in the interviews.

The first semester of practicum is a difficult time for counseling students as they learn to integrate knowledge and theory into clinical practice, often evoking high levels of anxiety (Barbee, Scherer, & Combs, 2003; Ronnestad & Skovholt, 1993) and limiting counselor selfefficacy (Bernard & Goodyear, 2009; Melchert et al., 1996). Practicum is the first opportunity counselors-in-training have to apply theoretical knowledge in a professional setting, use new clinical skills, and test how well they fit into the field of counseling (O‟Connell & Smith, 2005). Additionally, if counselor educators do not fully understand the process counselors in training develop counselor self-efficacy, they may be overlooking opportunities to educate a new generation of counselors or using their time, energy and resources in areas that may not be the most efficient in counselor development. The purpose of this study was to examine the effect of an embedded, rich-media distributed learning environment added to practicum had on the development of counselor self-efficacy, reduction of anxiety and effect on treatment outcomes for counselors in training in their first semester of practicum. This study found the use of distributed learning to extend education beyond the classroom significantly and positively affected the development of counselor selfefficacy, had mixed statistical results on the reduction of anxiety and did not have an affect on treatment outcome. Furthermore, the study used hierarchical linear modeling to see if the characteristics of individual practicums affected the three main constructs, the results did not find a significant effect from the groups. iv The results of the study produced several implications for counseling. First, if counselor educators help counselors in training become more aware of counselor self-efficacy, the students can better understand how the construct affects their anxiety, their comfort with expanding or improving their clinical skills and the approach they take to a client, session or treatment plan. A second implication is that using an embedded, rich-media learning environment may help the counselors in training to develop their clinical skills. The results of this study imply that utilizing technology and discussions beyond the classroom is beneficial for (a) increasing the students‟ counselor self-efficacy, (b) normalizing the emotions the students may experience and (c) improving the methods for development through vicarious learning. Also, as technology continues to evolve and as education continues to adapt by integrating technology into the classrooms, counselor educators should begin exploring how to best use technology to teach students during practicum. Traditionally, based on the nature of counseling, practicum has been an interpersonal experience, but the results of the current study imply the methods of extending learning beyond the traditional class time is beneficial. Finally, as counselor educators strive to increase students‟ counselor self-efficacy early in practicum, in an environment that contains anxiety and self-doubt (Bernard & Goodyear, 2009; Cashwell & Dooley, 2001) using vicarious learning through video and online discussions can assist in accomplishing the goal.

Nowadays, outlier detection has attracted growing interest. Unlike typical outlier detection problems, relational outlier detection focuses on detecting abnormal patterns in datasets that contain relational implications within each data point. Furthermore, different from the traditional outlier detection that focuses on only numerical data, modern outlier detection models must be able to handle data in various types and structures. Detecting relational outliers should consider (1) Dependencies among different data types, (2) Data types that are not continuous or do not have ordinal characteristics, such as binary, categorical or multi-label, and (3) Special structures in the data. This thesis focuses on the development of relational outlier detection methods and real-world applications in datasets that contain non-numerical, mixed-type, and special structure data in three tasks, namely (1) outlier detection in mixed-type data, (2) categorical outlier detection in music genre data, and (3) outlier detection in categorized time series data. For the first task, existing solutions for mixed-type data mostly focus on computational efficiency, and their strategies are mostly heuristic driven, lacking a statistical foundation. The proposed contributions of our work include: (1) Constructing a novel unsupervised framework based on a robust generalized linear model (GLM), (2) Developing a model that is capable of capturing large variances of outliers and dependencies among mixed-type observations, and designing an approach for approximating the analytically intractable Bayesian inference, and (3) Conducting extensive experiments to validate effectiveness and efficiency. For the second task, we extended and applied the modeling strategy to a real-world problem. The existing solutions to the specific task are mostly supervised, and the traditional outlier detection methods only focus on detecting outliers by the data distributions, ignoring the input-output relation between the genres and the extracted features. The proposed contributions of our work for this task include: (1) Proposing an unsupervised outlier detection framework for music genre data, (2) Extending the GLM based model in the first task to handle categorical responses and developing an approach to approximate the analytically intractable Bayesian inference, and (3) Conducting experiments to demonstrate that the proposed method outperforms the benchmark methods. For the third task, we focused on improving the outlier detection performance in the second task by proposing a novel framework and expanded the research scope to general categorized time-series data. Existing studies have suggested a large number of methods for automatic time series classification. However, there is a lack of research focusing on detecting outliers from manually categorized time series. The proposed contributions of our work for this task include: (1) Proposing a novel semi-supervised robust outlier detection framework for categorized time-series datasets, (2) Further extending the new framework to an active learning system that takes user insights into account, and (3) Conducting a comprehensive set of experiments to demonstrate the performance of the proposed method in real-world applications. / Doctor of Philosophy / In recent years, outlier detection has been one of the most important topics in the data mining and machine learning research domain. Unlike typical outlier detection problems, relational outlier detection focuses on detecting abnormal patterns in datasets that contain relational implications within each data point. Detecting relational outliers should consider (1) Dependencies among different data types, (2) Data types that are not continuous or do not have ordinal characteristics, such as binary, categorical or multi-label, and (3) Special structures in the data. This thesis focuses on the development of relational outlier detection methods and real-world applications in datasets that contain non-numerical, mixed-type, and special structure data in three tasks, namely (1) outlier detection in mixed-type data, (2) categorical outlier detection in music genre data, and (3) outlier detection in categorized time series data. The first task aims on constructing a novel unsupervised framework, developing a model that is capable of capturing the normal pattern and the effects, and designing an approach for model fitting. In the second task, we further extended and applied the modeling strategy to a real-world problem in the music technology domain. For the third task, we expanded the research scope from the previous task to general categorized time-series data, and focused on improving the outlier detection performance by proposing a novel semi-supervised framework.

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