The impact of school factors on academic achievement has become an important focus for school social work and revealed the need for a comprehensive school social work model that allows for the identification of critical areas to apply social work services. This study was designed to develop and test a more comprehensive school social work model. Specifically, the relationship between cumulative grade point average (GPA) and the cumulative risk index (CRI) and an additive risk index (ARI) were tested and a comparison of the two models was presented. Over 20,000 abstracts were reviewed in order to create a list of factors which have been shown in previous research to impact academic achievement. These factors were divided into the broad domains of personal factors, family factors, peer factors, school factors, and neighborhood or community factors. Factors that were placed under the school domain were tested and those factors which met all three criteria were included in the overall model. Consistent with previous research, both the CRI and ARI were shown to be related to cumulative GPA. As the number of risk factors increased, GPA decreased. After a discussion of the results, a case was made for the use of an additive risk index approach fitting more with the current state of social work. In addition, selecting cutoff points for determining risk and non-risk students was accomplished using an ROC analysis. Finally, implications for school social work practice on the macro-, meso-, and micro- levels were discussed.

The purpose of this analysis is to evaluate the usefulness of Fourier Transform Infrared (FTIR) spectroscopy in the detection of Herpes Simplex Virus 1 (hsv1) infection at an early stage. The raw absorption values were standardized to eliminate inter-sampling error. Wilcoxon-Mann-Whitney (WMW) statistic's Z score was calculated to select significant spectral regions. Partial least squares modeling was performed because of multicollinearity. Kolmogorov-Smirnov statistic showed models for healthy tissues from different time groups were not from same distribution. The additional 24 hour dataset was evaluated using the following methods. Variables were selected by WMW Z score. Difference of Composites statistic, DC, was created as a disease indicator and evaluated using area under the ROC curve, specificities, and confidence intervals using bootstrap algorithm. The specificity of DC was high, however the confidence intervals were large. Future studies are required with larger sample sizes to test this statistic's usefulness.

Objektų klasterizavimas – tai viena iš duomenų gavybos (angl. data mining) sričių. Šių algoritmų pagrindinis privalumas – gebėjimas atpažinti grupavimo struktūrą be jokios išankstinės informacijos. Magistriniame darbe yra pristatomas vaizdų klasterizavimo algoritmas, naudojantis savaime susitvarkančius neuroninius tinklus (angl. Self-Organizing Map). Darbe analizuojami vaizdų apdorojimo, ypatingųjų taškų radimo bei palyginimo metodai. Nustatyta, kad SIFT (angl. Scale Invariant Feature Transform) ypatingųjų taškų radimas bei aprašymas veikia patikimiausiai, todėl būtent SIFT taškiniai požymiai yra naudojami klasterizavime. Darbe taip pat analizuojamas atstumo tarp paveikslėlių radimo algoritmas, tiriami skirtingi jo parametrai. Algoritmų palyginimui yra naudojamos ROC (angl. Receiver Operating Characteristic) kreivės ir EER (angl. Equal Error Rate) rodiklis. Vaizdų klasterizavimui yra naudojamas ESOM (Emergent Self-Organizing Map) neuroninis tinklas, jis vizualizuojamas U-Matrix (angl. Unified distance Matrix) pagalba ir tinklo neuronai skirstomi į klasterius vandenskyros algoritmu su skirtingu aukščio parinkimu. Magistriniame darbe demonstruojami klasterizavimo rezultatai su pavyzdinėmis paveikslėlių duomenų bazėmis bei realiais gyvenimiškais vaizdais. / Clustering algorithms – a field of data mining – aims at finding a grouping structure in the input data without any a-priori information. The master thesis is dedicated for image processing and clustering algorithms. There are point-feature detection, description and comparison methods analyzed in this paper. The SIFT (Scale Invariant Feature Transform) by D. Lowe has been shown to behave better than the other ones; hence it has been used for image to image distance calculation and undirectly in clustering phase. Finding distances between images is not a trivial task and it also has been analysed in this thesis. Several methods have been compared using ROC (Receiver Operating Curve) and EER measurements. Image clustering process is described as: (1) training of ESOM (Emergent Self-Organizing Map), (2) its visualization in U-Matrix, (3) neuron clustering using waterflood algorithm, and (4) image grouping according to their best-matching unit neurons. The paper demonstrates the image clustering algorithm on public object image databases and real life images from the Internet as well.

In this thesis, comparative experienced similar historical events the countries in East Asia, Korea and Taiwan. Research focus is democratic development and media’s role in Korea and Taiwan. This research theme is composition of communication studies and political science. Firstly, I reviewed basic concept of media and democracy’s correlation, and media’s role in democratic countries, Secondly, purchased Korea and Taiwan’s democratization process on view of comparative political science. And I followed media’s role on democratization process. Research’s basic point of view is comparative study, and also used literature analysis method. The purpose of this study is review Korea and Taiwan’s journey of democratization, and through the past experience what was the role of the media.

In this thesis, an investigation was performed to find ways of differencing between firesand vehicles at waste stations in hope of removing vehicles as a source of error duringearly fire detection. The existing system makes use of a heat camera, which rotates in 48different angles (also known as zones) in a fixed position. If the heat is above a certainvalue within a zone the system sounds the fire alarm.The rotation of the camera results in an unwanted displacement between two successiveframes within the same zone. By use of image registration, this displacement wasremoved. After the registration of an image, segmentation was performed where coldobjects are eliminated as an error source. Lastly, an analysis was performed upon thewarm objects.At the end, it was proven that the image registration had been a successful improvementof the existing system. It was also shown that vehicles can, to some extent, beeliminated as an error source. / I denna uppsats görs en undersökning av sätt att urskilja mellan bränder och fordon vid avfallsbunkrar, i hopp om att ta bortfordon som felkälla under tidig branddetektion. Dagens system använder sig av en värmekamera som roterar i 48 vinklar(även kallade zoner) från en fix position och larmar då det blir för varmt i någon zon.Roteringen av kameran medför en icke önskvärd förskjutning mellan två efterföljande bilder inom samma zon. Processenbildregistrering används för att eliminera denna förskjutning. Efter registreringen utförs en segmentering där kalla objekt tasbort som felkälla. När detta är utfört görs en analys av de varma objekten med en mängd mätningar.I slutet bevisas att registreringen har fungerat mycket väl, likaså att det går till viss del att eliminera fordon som felkällaunder tidig brandetektion.

The main objective of our research was to develop a new bankruptcy prediction model for the Czech economy. For that purpose we used the logistic regression and 150,000 financial statements collected for the 2002-2016 period. We defined 41 explanatory variables (25 financial ratios and 16 dummy variables) and used Bayesian model averaging to select the best set of explanatory variables. The resulting model has been estimated for three prediction horizons: one, two, and three years before bankruptcy, so that we could assess the changes in the importance of explanatory variables and models' prediction accuracy. To deal with high skew in our dataset due to small number of bankrupt firms, we applied over- and under- sampling methods on the train sample (80% of data). These methods proved to enhance our classifier's accuracy for all specifications and periods. The accuracy of our models has been evaluated by Receiver operating characteristics curves, Sensitivity-Specificity curves, and Precision-Recall curves. In comparison with models examined on similar data, our model performed very well. In addition, we have selected the most powerful predictors for short- and long-term horizons, which is potentially of high relevance for practice. JEL Classification C11, C51, C53, G33, M21 Keywords Bankruptcy...

Philosophiae Doctor - PhD / Expressed sequence tag database is a rich and fast growing source of data for gene expression analysis and drug discovery. Clustering of raw EST data is a necessary step for further analysis and one of the most challenging problems of modem computational biology. There are a few systems, designed for this purpose and a few more are currently under development. These systems are reviewed in the "Literature and software review". Different strategies of supervised and unsupervised clustering are discussed, as well as sequence comparison techniques, such as based on alignment or oligonucleotide compositions. Analysis of potential bottlenecks and estimation of computation complexity of EST clustering is done in Chapter 2. This chapter also states the goals for the research and justifies the need for new algorithm that has to be fast, but still sensitive to relatively short (40 bp) regions of local similarity. A new sequence comparison algorithm is developed and described in Chapter 3. This algorithm has a linear computation complexity and sufficient sensitivity to detect short regions of local similarity between nucleotide sequences. The algorithm utilizes an asymmetric approach, when one of the compared sequences is presented in a form of oligonucleotide table, while the second sequence is in standard, linear form. A short window is moved along the linear sequence and all overlapping oligonucleotides of a constant length in the frame are compared for the oligonucleotide table. The result of comparison of two sequences is a single figure, which can be compared to a threshold. For each measure of sequence similarity a probability of false positive and false negative can be estimated. The algorithm was set up and implemented to recognize matching ESTs with overlapping regions of 40bp with 95% identity, which is better than resolution ability of contemporary EST clustering tools This algorithm was used as a sequence comparison engine for two EST clustering programs, described in Chapter 4. These programs implement two different strategies: stringent and loose clustering. Both are tested on small, but realistic benchmark data sets and show the results, similar to one of the best existing clustering programs, 02_cluster, but with a significant advantage in speed and sensitivity to small overlapping regions of ESTs. On three different CPUs the new algorithm run at least two times faster, leaving less singletons and producing bigger clusters. With parallel optimization this algorithm is capable of clustering millions of ESTs on relatively inexpensive computers. The loose clustering variant is a highly portable application, relying on third-party software for cluster assembly. It was built to the same specifications as 02_ cluster and can be immediately included into the STACKPack package for EST clustering. The stringent clustering program produces already assembled clusters and can apprehend alternatively processed variants during the clustering process.

Breast cancer is the second leading cause of cancer deaths among women in the United States and microcalcifications clusters are one of the most important indicators of breast disease. Computer methodologies help in the detection and differentiation between benign and malignant lesions and have the potential to improve radiologists' performance and breast cancer diagnosis significantly. A Computer-Aided Diagnosis (CAD-Dx) algorithm has been previously developed to assist radiologists in the diagnosis of mammographic clusters of calcifications with the modules: (a) detection of all calcification-like areas, (b) false-positive reduction and segmentation of the detected calcifications, (c) selection of morphological and distributional features and (d) classification of the clusters. Classification was based on an artificial neural network (ANN) with 14 input features and assigned a likelihood of malignancy to each cluster. The purpose of this work was threefold: (a) optimize the existing algorithm and test on a large database, (b) rank classification features and select the best feature set, and (c) determine the impact of single and two-view feature estimation on classification and feature ranking. Classification performance was evaluated with the NevProp4 artificial neural network trained with the leave-one-out resampling technique. Sequential forward selection was used for feature selection and ranking. Mammograms from 136 patients, containing single or two views of a breast with calcification cluster were digitized at 60 microns and 16 bits per pixel. 260 regions of interest (ROI's) centered on calcification cluster were defined to build the single-view dataset. 100 of the 136 patients had a two-view mammogram which yielded 202 ROI's that formed the two-view dataset. Classification and feature selection were evaluated with both these datasets. To decide on the optimal features for two-view feature estimation several combinations of CC and MLO view features were attempted. On the single-view dataset the classifier achieved an AZ =0.8891 with 88% sensitivity and 77% specificity at an operating point of 0.4; 12 features were selected as the most important. With the two-view dataset, the classifier achieved a higher performance with an AZ =0.9580 and sensitivity and specificity of 98% and 80% respectively at an operating point of 0.4; 10 features were selected as the most important.

This thesis presents the area under the ROC (Receiver Operating Characteristics) curve, or abbreviated AUC, as an alternate measure for evaluating the predictive performance of ANNs (Artificial Neural Networks) classifiers. Conventionally, neural networks are trained to have total error converge to zero which may give rise to over-fitting problems. To ensure that they do not over fit the training data and then fail to generalize well in new data, it appears effective to stop training as early as possible once getting AUC sufficiently large via integrating ROC/AUC analysis into the training process. In order to reduce learning costs involving the imbalanced data set of the uneven class distribution, random sampling and k-means clustering are implemented to draw a smaller subset of representatives from the original training data set. Finally, the confidence interval for the AUC is estimated in a non-parametric approach.

Objectives: The Distress Thermometer (DT) is a validated and widely used screening tool to identify clinically relevant distress in cancer patients. It is unclear, to which extend subjectively perceived distress measured by the DT is related to objective burden (mental disorder). We therefore examine the co-occurrence of a mental disorder for different DT thresholds and explore the diagnostic properties of the DT in detecting a mental disorder. Methods: In this multicenter cross-sectional study, we included 4,020 patients with mixed cancer diagnoses. After selection of relevant cases, weighting procedure and imputation of missing data we evaluated the data of N = 3,212 patients. We used the DT to assess perceived distress and the standardized Composite International Diagnostic Interview for Oncology (CIDI-O) to assess the 4-week prevalence of mental disorders. The association between distress and any mental disorder (MD) is calculated using Pearson correlations. Relative risks for MD in patients with/without distress and the co-occurrence of distress and MD were calculated with Poisson regression. To assess the operating characteristics between distress and MD, we present the area under the curve (AUC). Results: 22.9% of the participants had a cut-off DT level of 5 and were affected by MD. Each level of distress co-occurs with MD. The proportion of patients diagnosed with MD was not greater than the proportion of patients without MD until distress levels of DT = 6 were reached. The correlation between DT and MD was r = 0.27. The ROCanalysis shows the area under curve (AUC) = 0.67, which is classified as unsatisfactory. With increasing distress severity, patients are not more likely to have a mental disorder. Conclusion: Our results suggests viewing and treating cancer-related distress as a relatively distinct psychological entity. Cancer-related distress may be associated with an increased risk for a mental disorder and vice versa, but the overlap of both concepts is very moderate.