In this educational study, the research problem was that each semester a variable number of community college students are unable to complete an introductory computer applications course at a community college in the state of Mississippi with a successful course letter grade. Course failure, or non-success, at the collegiate level is a negative event for students and is a factor that leads to high attrition but does not always receive much research (Haynes Stewart et al., 2011). The purpose of this study was to see if a relationship existed between ACT scores (i.e., English, reading, mathematics, science reasoning, and composite) and student success in a computer applications course at a Mississippi community college. The first research question examined to see if the ACT composite test score was a statistically significant predictor of success in a computer applications course at a Mississippi community college. The second research question studied the ACT sub scores in English, reading, mathematics, and science reasoning to see if they were statistically significant predictors of success in the same course. Demographics of the sample were gathered from a multi-campus Mississippi community college along with the ACT scores and final grade in the computer applications class of the sample. Descriptive statistics were run and reported on the demographic data while bivariate and multivariate logistic regressions were utilized to examine predictability of the ACT scores in relation to course final grade. The time period covered by this study was from fall 2010 through spring 2012 excluding the summer semesters. The study showed that while the ACT scores were excellent predictors of inclusion in the success category, the ACT scores did a very poor job of predicting non-success. The study concluded with a summary of the findings as well as limitations of the study. Also discussed were recommendations for practitioners and policy makers to include making the information available to students, teachers, advisors, and administration as an advisement tool when deciding to take the computer applications class. As well, recommendations for future research include treating withdrawals separately, examining multiple schools for differences, and increasing internal validity.

Genetic and Hormonal Regulation of Children's Growth MUDr. Jan Vosáhlo Abstract Growth in childhood is a complex process of changing the body, which can be disrupted by various illnesses including endocrine disorders, particularly growth hormone deficiency. Tumors or other processes affecting hypothalamic-pituitary area can be a postnatal cause of GHD; prenatal causes include 1) developmental disorders of the pituitary as part of complex syndromes, 2) developmental disorders of the pituitary due to defects in regulatory genes and 3) defects in genes involved in the synthesis and secretion of GH. The first topic of the thesis was septo-optic dysplasia - a complex syndrome involving optic nerve hypoplasia, structural brain abnormalities and pituitary dysfunctions. We extensively described phenotype in 11 Czech patients; we observed both complete SOD and incomplete forms variously combining two of the three main components of the syndrome. The cohort then became a part of an international study of 68 patients, in which we studied the phenotype in dependence on the brain morphology. We found correlation between the severity of clinical symptoms and the degree of septum pellucidum abnormities and also a correlation between hippocampus and falx abnormities and neurological symptoms. As the second topic we studied...

This thesis is concerned with the problem of predicting the remaining part of the trajectory of a golf ball as it travels through the air where only the three-dimensional position of the ball is captured. The approach taken to solve this problem relied on recurrent neural networks in the form of the long short-term memory networks (LSTM). The motivation behind this choice was that this type of networks had led to state-of-the-art performance for similar problems such as predicting the trajectory of pedestrians. The results show that using LSTMs led to an average reduction of 36.6 % of the error in the predicted impact position of the ball, compared to previous methods based on numerical simulations of a physical model, when the model was evaluated on the same driving range that it was trained on. Evaluating the model on a different driving range than it was trained on leads to improvements in general, but not for all driving ranges, in particular when the ball was captured at a different frequency compared to the data that the model was trained on. This problem was solved to some extent by retraining the model with small amounts of data on the new driving range. / Detta examensarbete har studerat problemet att förutspå den fullständiga bollbanan för en golfboll när den flyger i luften där endast den tredimensionella positionen av bollen observerades. Den typ av metod som användes för att lösa problemet använde sig av recurrent neural networks, i form av long short-term memory nätverk (LSTM). Motivationen bakom detta var att denna typ av nätverk hade lett till goda resultatet för liknande problem. Resultatet visar att använda sig av LSTM nätverk leder i genomsnitt till en 36.6 % förminskning av felet i den förutspådda nedslagsplatsen för bollen jämfört mot tidigare metoder som använder sig av numeriska simuleringar av en fysikalisk modell, om modellen användes på samma golfbana som den tränades på. Att använda en modell som var tränad på en annan golfbana leder till förbättringar i allmänhet, men inte om modellen användes på en golfbana där bollen fångades in med en annan frekvens. Detta problem löstes till en viss mån genom att träna om modellen med lite data från den nya golfbanan.

In the past decade, characterizing spiking neuron models has been extensively researched as an essential issue in computational neuroscience. In this thesis, we examine the estimation problem of two different neuron models. In Chapter 2, We propose a modified Izhikevich model with an adaptive threshold. In our two-stage estimation approach, a linear least squares method and a linear model of the threshold are derived to predict the location of neuronal spikes. However, desired results are not obtained and the predicted model is unsuccessful in duplicating the spike locations. Chapter 3 is focused on the parameter estimation problem of a multi-timescale adaptive threshold (MAT) neuronal model. Using the dynamics of a non-resetting leaky integrator equipped with an adaptive threshold, a constrained iterative linear least squares method is implemented to fit the model to the reference data. Through manipulation of the system dynamics, the threshold voltage can be obtained as a realizable model that is linear in the unknown parameters. This linearly parametrized realizable model is then utilized inside a prediction error based framework to identify the threshold parameters with the purpose of predicting single neuron precise firing times. This estimation scheme is evaluated using both synthetic data obtained from an exact model as well as the experimental data obtained from in vitro rat somatosensory cortical neurons. Results show the ability of this approach to fit the MAT model to different types of reference data.

Companies in the spare part industry can implement a variety of different pricing techniques, which have traditionally been done through personnel know-how and industry conventions. One such technique is the use of price indices to track sales performance. This thesis investigates if machine learning or time series analysis can predict revenue using price and price indices in a data-driven manner which can potentially validate current pricing strategies or serve as a basis for sales teams pricing decisions. Price indices used were the Fisher Index and the Törnqvist Index. The data came from a spare parts supplier and consisted of daily transactions. Two target variables were tested: revenue as a continuous and categorical variable. The continuous target variable represented revenue the following day, while the categorical variable represented either an increase or decrease the following day. Models tested were OLS, XGBoost, ARIMAX and LSTM for the continuous case and Logistic Regression and XGBoost in the categorical case on several different feature sets. In the continuous case, ARIMAX outperformed the other models, but the best model was produced by the feature set not containing any indices. In the categorical case on a feature set containing price indices, XGBoost yielded an accuracy of 68% in classifying revenue increases or decreases. This study suggest that price indices contain some information about whether a revenue movement is going to happen, but not the magnitude of it. / Företag som säljer reservdelar kan implementera olika prissättningsstrategier. Dessa har traditionellt baserats på personalkunnande och branschkonventioner. En strategi som tillämpas är prisindex för att följa upp försäljning. Detta examensarbete undersöker om maskininlärning eller tidsserier kan prediktera omsättning med hjälp av pris- och prisindex på ett datadrivet sätt som kan potentiellt validera nuvarande strategier eller agera underlag för prissättningsbeslut. Prisindex som användes var Fisherindex och Törnqvistindex. Datan kom från en reservdelsleverantör och bestod av dagliga transaktioner. Två beroende variabler testades: omsättning som en kontinuerlig och omsättning som kategorisk variabel. Den kontinuerliga variabeln representerade omsättning nästa dag, medan den kategoriska variabeln representerade utfallet ökning eller minskning av omsättning nästa dag. Modellerna som tränades var OLS, XGBoost, ARIMAX och LSTM i det kontinuerliga fallet och Logistisk Regression och XGBoost i det kategoriska fallet. De tränades på flera uppsättningar av oberoende variabler. I det kontinuerliga fallet presterade ARIMAX bäst, men den bästa modellen tränades på en uppsättning oberoende variabler som inte innehöll några index. I det kategoriska fallet gav XGBoost en noggrannhet på 68% vid klassificering av omsättningsökningar eller minskningar. Detta på en uppsättning oberoende variabler som innehöll prisindex. Resultaten antyder att prisindex innehåller viss information om huruvida en omsättningsrörelse kommer att ske, men inte storleken på den.

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Predictive statistical modeling shows promise in accurately predicting academic performance for students enrolled in online programs. This approach has proven effective in accurately identifying students who are at-risk enabling instructors to provide instructional intervention. While the potential benefits of statistical modeling is significant, implementations have proven to be complex, costly, and difficult to maintain. To address these issues, the purpose of this study is to develop a fully integrated, automated predictive modeling system (PMS) that is flexible, easy to use, and portable to identify students who are potentially at-risk for not succeeding in a course they are currently enrolled in. Dynamic and static variables from a student system (edX) will be analyzed to predict academic performance of an individual student or entire class. The PMS model framework will include development of an open-source Web application, application programming interface (API), and SQL reporting services (SSRS). The model is based on knowledge discovery database (KDD) approach utilizing inductive logic programming language (ILP) to analyze student data. This alternative approach for predicting academic performance has several unique advantages over current predictive modeling techniques in use and is a promising new direction in educational research.

本研究旨在探討國民中、小學生后設認知的發展及其與閱讀理解之關係，並進而驗證 后設認知的閱讀策略教學之有效性。期藉此研究發現，提供國民中、小學教師閱讀教 學及學習輔導之參考，俾提高教學之效果，並增益學習的效率。 為使研究結果具有代表性，本研究不惜動用大量經費、人力及時間，以從事調查研究 ，並進行教學實驗研究工作。 有關後設認知與閱讀理解之調查，取樣對象遍及台灣地區，分北、中、南三區，每一 地區隨機抽取四個縣市，每一縣市各隨機抽選一所國民中學和一所國民小學；研究對 象從國小三年級至國中三年級，每一年級再分層隨機抽選男生10位、女生10位。 有關教學實驗之研究，則選取高雄市一所國小和一所國中，由國小三年級至國中二年 級，每一年級分別組成一組實驗組和一組控制組，進行教學實驗。 本研究首先採用調查法，實地實施測驗並進行一對一的晤談錄音，搜集有關的資料， 再運用統計技術加以分析，比較不同年級、不同心理特質的男女學生後設認知之差異 情形，並探究不同後設認知能力的男女學生其閱讀理解的表現情形。其次，進行後設 認知的閱讀策略教學；實驗設計為實驗組、控制組前測後測設計。採用Palincsar 和 Brown(1984) 的「相互教學法」 (reciprical teaching)，選取適合各年級水準的閱 讀資料二十篇，進行包含了綜合閱讀策略與後設認知技能的四種活動；摘錄重點(su- mmarizing)、自我發問 (self-questioning) 、澄清疑慮(clarifying)、和預測後果 (predicting)。最後評量實驗結果，以驗證後設認知的可教性及其教學成效。 研究結果主要發現如下： 一、國民中、小學生後設認知的差異，隨年級、性別之不同而有差異： (一)國民中、小學生後設認知知識之差異，因年級、性別之不同在而有差異。年級方 面, 以國小六年級為最好，其次為國二和國三，而以國小三年級最差。性別方面，女 生