Economy is not always in perfect equilibrium when it runs in capital market system. Sometimes crisis attacks the development of economy. Economy crisis is a sign which will appear in capital imbalance of economy. Inside economy, capital distribution in three composers--consumption, investment, and government is the key to analyze economy crisis. Between economy, war, global-industrialization, and capinvesting are triggers to worldwide economy crises. The transaction costs of capital market were sharply negative-accumulated and the transaction efficiency was zero and under during the process of economy crisis. Take Asian financial turmoil in 1997 for example, currency distress and banking system disruption were the signs of crisis-hit economy resulting in consumption and investment contraction. And there were adverse real effects on aggregate output and employment in 1998.

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Allelic expression imbalance, or AEI, is the term given to differences in the expression levels of the two alleles of a gene. AEI has been previously identified in a number of species using various techniques. Here, the genome-wide extent of allelic expression imbalance in the pathogenic yeast species, Candida albicans, was examined through use of RNA sequencing in combination with a novel computational pipeline based around the diploid reference genome. Techniques for validating these results were investigated, and the difficulties surrounding specificity and quantification are discussed. As C. albicans is a highly heterozygous species, it was hypothesised that polymorphisms within alleles lead to differences in allele expression, which are further linked to differences in allele function. The functional consequences of AEI were therefore interrogated through investigation of Gene Ontology, identification of condition specific responses in AEI, and targeted construction and phenotypic screening of heterozygous knockout strains. Together, these results strongly suggest that divergence in allele expression is not linked to differences in allele function. Investigations of the possible control mechanisms behind the differences in allele expression were considered, with a focus upon structural factors such as chromosomal location, GC content, allele length and codon usage. However, issues with establishing causality are present, and difficulties lie in distinguishing between functional differences and consequences of bias in sequencing technologies. This piece of research has advanced the understanding of gene expression mechanisms within a medically important pathogen, paving the way for further investigations into the functional consequences of allelic expression imbalance in Candida albicans.

Web page classification is the process of assigning predefined categories to web pages. Empirical evaluations of classifiers such as Support Vector Machines (SVMs), k-Nearest Neighbor (k-NN), and Naïve Bayes (NB), have shown that these algorithms are effective in classifying small segments of web directories. The effectiveness of these algorithms, however, has not been thoroughly investigated on large-scale web page classification of such popular web directories as Yahoo! and LookSmart. Such web directories have hundreds of thousands of categories, deep hierarchies, spindle category and document distributions over the hierarchies, and skewed category distribution over the documents. These statistical properties indicate class imbalance and rarity within the dataset. In hierarchical datasets similar to web directories, expanding the content of each category using the web pages of the child categories helps to decrease the degree of rarity. This process, however, results in the localized overabundance of positive instances especially in the upper level categories of the hierarchy. The class imbalance, rarity and the localized overabundance of positive instances make applying classification algorithms to web directories very difficult and the problem has not been thoroughly studied. To our knowledge, the maximum number of categories ever previously classified on web taxonomies is 246,279 categories of Yahoo! directory using hierarchical SVMs leading to a Macro-F1 of 12% only. We designed a unified framework for the content based classification of imbalanced hierarchical datasets. The complete Yahoo! web directory of 639,671 categories and 4,140,629 web pages is used to setup the experiments. In a hierarchical dataset, the prior probability distribution of the subcategories indicates the presence or absence of class imbalance, rarity and the overabundance of positive instances within the dataset. Based on the prior probability distribution and associated machine learning issues, we partitioned the subcategories of Yahoo! web directory into five mutually exclusive groups. The effectiveness of different data level, algorithmic and architectural solutions to the associated machine learning issues is explored. Later, the best performing classification technologies for a particular prior probability distribution have been identified and integrated into the Yahoo! Web directory classification model. The methodology is evaluated using a DMOZ subset of 17,217 categories and 130,594 web pages and we statistically proved that the methodology of this research works equally well on large and small dataset. The average classifier performance in terms of macro-averaged F1-Measure achieved in this research for Yahoo! web directory and DMOZ subset is 81.02% and 84.85% respectively.

Följande studie avser att undersöka relationen mellan engagemang, belöning och välbefinnande på arbetsplatsen. Siegrist (1996) förklarar sambandet genom Effort-Reward Imbalance model (ERI), något som denna studie kombinerar med Dieners (1986) teori om subjektivt välbefinnande. 156 personer deltog i enkätstudien, där 97 var kvinnor. Enkäterna var utformade med påståenden där deltagarna fick skatta sina svar. Datan analyserades genom en multipel regressionsanalys, korrelationer och t-test. I den multipla regressionen framkom det att hög insats/ låg belöning och överengagemang inte var signifikanta prediktorer utav variansen i subjektivt välbefinnande. Genom korrelationer kunde man utläsa att tre hypoteser visade signifikanta resultat, medan en hypotes inte visade något signifikant resultat genom stora t-test i avseende på huruvida kvinnor skattar högre nivå av överengagemang än män. Studien bidrog till en ökad förståelse för obalansen mellan hög insats/ låg belöning och överengagemang och dess betydelse för individens subjektiva välbefinnande.

Data mining is a multi-disciplinary field of science and technology widely used in developing predictive models and data visualization in various domains. Although there are numerous data mining algorithms and techniques across multiple fields, it appears that there is no consensus on the suitability of a particular model, or the ways to address data preprocessing issues. Moreover, the effectiveness of data mining techniques depends on the evolving nature of data. In this study, we focus on the suitability and robustness of various data mining models for analyzing real financial data to identify financial restatements. From data mining perspective, it is quite interesting to study financial restatements for the following reasons: (i) the restatement data is highly imbalanced that requires adequate attention in model building, (ii) there are many financial and non-financial attributes that may affect financial restatement predictive models. This requires careful implementation of data mining techniques to develop parsimonious models, and (iii) the class imbalance issue becomes more complex in a dataset that includes both intentional and unintentional restatement instances. Most of the previous studies focus on fraudulent (or intentional) restatements and the literature has largely ignored unintentional restatements. Intentional (i.e. fraudulent) restatements instances are rare and likely to have more distinct features compared to non-restatement cases. However, unintentional cases are comparatively more prevalent and likely to have fewer distinct features that separate them from non-restatement cases. A dataset containing unintentional restatement cases is likely to have more class overlapping issues that may impact the effectiveness of predictive models. In this study, we developed predictive models based on all restatement cases (both intentional and unintentional restatements) using a real, comprehensive and novel dataset which includes 116 attributes and approximately 1,000 restatement and 19,517 non-restatement instances over a period of 2009 to 2014. To the best of our knowledge, no other study has developed predictive models for financial restatements using post-financial crisis events. In order to avoid redundant attributes, we use three feature selection techniques: Correlation based feature subset selection (CfsSubsetEval), Information gain attribute evaluation (InfoGainEval), Stepwise forward selection (FwSelect) and generate three datasets with reduced attributes. Our restatement dataset is highly skewed and highly biased towards non-restatement (majority) class. We applied various algorithms (e.g. random undersampling (RUS), Cluster based undersampling (CUS) (Sobhani et al., 2014), random oversampling (ROS), Synthetic minority oversampling technique (SMOTE) (Chawla et al., 2002), Adaptive synthetic sampling (ADASYN) (He et al., 2008), and Tomek links with SMOTE) to address class imbalance in the financial restatement dataset. We perform classification employing six different choices of classifiers, Decision three (DT), Artificial neural network (ANN), Naïve Bayes (NB), Random forest (RF), Bayesian belief network (BBN) and Support vector machine (SVM) using 10-fold cross validation and test the efficiency of various predictive models using minority class recall value, minority class F-measure and G-mean. We also experiment different ensemble methods (bagging and boosting) with the base classifiers and employ other meta-learning algorithms (stacking and cost-sensitive learning) to improve model performance. While applying cluster-based undersampling technique, we find that various classifiers (e.g. SVM, BBN) show a high success rate in terms of minority class recall value. For example, SVM classifier shows a minority recall value of 96% which is quite encouraging. However, the ability of these classifiers to detect majority class instances is dismal. We find that some variations of synthetic oversampling such as ‘Tomek Link + SMOTE’ and ‘ADASYN’ show promising results in terms of both minority recall value and G-mean. Using InfoGainEval feature selection method, RF classifier shows minority recall values of 92.6% for ‘Tomek Link + SMOTE’ and 88.9% for ‘ADASYN’ techniques, respectively. The corresponding G-mean values are 95.2% and 94.2% for these two oversampling techniques, which show that RF classifier is quite effective in predicting both minority and majority classes. We find further improvement in results for RF classifier with cost-sensitive learning algorithm using ‘Tomek Link + SMOTE’ oversampling technique. Subsequently, we develop some decision rules to detect restatement firms based on a subset of important attributes. To the best of our knowledge, only Kim et al. (2016) perform a data mining study using only pre-financial crisis restatement data. Kim et al. (2016) employed a matching sample based undersampling technique and used logistic regression, SVM and BBN classifiers to develop financial restatement predictive models. The study’s highest reported G-mean is 70%. Our results with clustering based undersampling are similar to the performance measures reported by Kim et al. (2016). However, our synthetic oversampling based results show a better predictive ability. The RF classifier shows a very high degree of predictive capability for minority class instances (97.4%) and a very high G-mean value (95.3%) with cost-sensitive learning. Yet, we recognize that Kim et al. (2016) use a different restatement dataset (with pre-crisis restatement cases) and hence a direct comparison of results may not be fully justified. Our study makes contributions to the data mining literature by (i) presenting predictive models for financial restatements with a comprehensive dataset, (ii) focussing on various datamining techniques and presenting a comparative analysis, and (iii) addressing class imbalance issue by identifying most effective technique. To the best of our knowledge, we used the most comprehensive dataset to develop our predictive models for identifying financial restatement.

This work deals with properties and physical phenomena occurring in semiconductor materials. In details are described generation - recombination processes in a state of thermodynamic disequilibrium. The output of work is a software application simulating waveforms of energy levels in the band's own models and impurity semiconductors, depending on the type of semiconductor, impurities concentration and temperature. Finally, the processed virtual lab experiment deliverable in the educational process.

Polycystic Ovarian Syndrome is the one of the leading causes of infertility among women who are of child-bearing age. The syndrome’s vast range of phenotypes has made it challenging for researchers to not only consistently diagnose but also discover a cure. Currently, there are several proposed treatments being looked into, however, much of the research focuses on employing promising biomarkers, micro ribonucleic acids (miRNAs), that can potentially aid in diagnosis. The four prominent locations of research for these biomarkers include: ovarian tissues specifically looking into granulosa cells (GC), adipose tissue, follicular fluid, and the serum. My goal is to determine which of these areas holds the most promise to diagnose this syndrome in the years to come. This study reviewed a large collection of the current polycystic ovarian syndrome literature evaluating both reported miRNAs and how viable those would be as potential biomarkers to use for the future. The data showed that a majority of these promising biomarkers were found in granulosa cells, adipose tissue, and follicular fluid. Although there were miRNAs that were deemed promising in the serum, research is still far from conclusive in using these miRNAs as biomarkers for diagnosis of polycystic ovarian syndrome. By comparing the miRNAs selected from each type of location, I was able to conclude that miR-21, miR-93, miR-223, and miR-let-7b hold the most promise for the potential to become biomarkers for polycystic ovarian syndrome in the near future. Currently, there is a lot of research particularly surrounding these miRNAs and how they were shown to have been expressed in statistically significant levels among women with the syndrome. However, because of their complexity, miRNAs do not regulate one single pathway, it is hard to describe a mechanism that explains the pathophysiology of the syndrome. I believe we are still far away from successfully zooming in on one biomarker. By determining the most potential biomarker(s), we can focus resources and efforts towards finding a better diagnostic tool for this syndrome.

Polymorphisms in the gene encoding SORL1, involved in cellular trafficking of APP, have been implicated in late-onset Alzheimer's disease, by a mechanism thought to affect mRNA expression. To search for regulatory polymorphisms, we have measured allele-specific mRNA expression of SORL1 in human autopsy tissues from the prefrontal cortex of 26 Alzheimer's patients, and 51 controls, using two synonymous marker SNPs (rs3824968 in exon 34 (11 heterozygous AD subjects and 16 controls), and rs12364988 in exon 6 (8 heterozygous AD subjects)). Significant allelic expression imbalance (AEI), indicative of the presence of cis-acting regulatory factors, was detected in a single control subject, while allelic ratios were near unity for all other subjects. We genotyped 7 SNPs in two haplotype blocks that had previously been implicated in Alzheimer's disease. Since each of these SNPs was heterozygous in several subjects lacking AEI, this study fails to support a regulatory role for SORL1 polymorphisms in mRNA expression.

Ultraprecision, single point diamond turning (SPDT) is a tool based machining technology that allows the ability to produce high quality surface finishes on the order of nanometers while meeting tight form tolerances on the order of micrometers. It is generally agreed that surface finish in SPDT is primarily affected by four factors: Tool edge quality, relative vibration between the tool and workpiece, material properties and microstructure, and tool geometry (nose radius and machining parameters) machining. To the author’s knowledge, no work has been done to combine all the factors to study their effect on surface generation in SPDT. This is important given that the factors are highly interdependent. Two diamond tools with nose radius of 12mm were used; however, one of them was chemically honed. Results suggest that the honed tool provides a much better surface finish with a significantly reduced amount of running-in stage tool wear. The cutting edge radius of the diamond tools was measured using a novel 3D confocal laser microscope to analyze the chemical honing process and to measure tool wear. The presence of built-up edge (BUE) is more prominent on the honed tool earlier in its life which results in unpredictable surface roughness to appear sooner than on the regular tool. To understand the dynamics of the machine, a redesign of the tool holder bracket was done to increase stiffness. Modal tests were then performed on it to verify performance improvement. With an understanding of the vibration and its effect on the cutting force, a 400Hz disturbance frequency was detected in the cutting forces. From a 3D scan of the surface, a total of 24 undulations on the surface of the part were observed when the spindle speed was set to 1000RPM The machine was instrumented and a rotordynamic investigation was carried out to determine the cause and nature of the vibration in an effort to reduce it and in so doing improve surface form accuracy. / Thesis / Master of Applied Science (MASc)