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With the increasing number of transistors in the circuit, the time it requires to label the circuit as defect-free also increases. Test application time plays a major role in increasing yield in manufacturing. This thesis presents an approach to generate a test set to detect manufacturing faults considering the test application time. The test set is constructed using an initial compact test set and by utilizing the output signature of the test vectors and increasing the overlap with a succeeding test vector. The novelty of the approach is the consideration of the essential faults for the test generation of the optimal test vector and the distribution of those faults among other test vectors if such a test vector is not possible. The test generation of the optimal test vector is done using structural and SAT-based approach. The generated test set retains the fault coverage without any additional hardware overhead. The experimental results on the ISCAS89 benchmark circuit show significant reductions in the test application time.

This investigation identified children whose noncompliant behavior was maintained by negative reinforcement through brief functional analysis conditions and then evaluated the effectiveness of a contraindicated treatment (i.e., time-out package) for decreasing the noncompliant behavior to developmentally appropriate levels. The current results provided initial supporting data that time-out, in conjunction with other treatment variables, can be an effective strategy in reducing escape maintained noncompliance. Brief experimental analysis conditions consistent with the methodology demonstrated by Northup and colleagues (1991), were utilized to identify escape as the maintaining variable for noncompliance. Following identification of the function of the behavior, baseline data were collected. Across all participants, the average percentage of intervals of noncompliance during baseline was above 60% of observed intervals, which has been defined as maladaptive (Forehand, 1977) and may be indicative of future externalizing behavior problems, as well as the inability to acquire appropriate academic and social skills (Forehand et al., 1978; Rhode et al., 1993). The time-out package consisting of time in, effective instructions, time-out, and escape extinction was then implemented. Inspection of the data revealed that all participants exhibited clinically significantly lower percentage of intervals of noncompliance during intervention phases that were developmentally acceptable. Follow up data revealed that developmentally acceptable levels of noncompliance were maintained at one month. Overall, these data support the findings that the treatment package was effective in reducing the percentage of intervals of noncompliance for each participant, thus, demonstrating the effectiveness of this time-out procedure (i.e., a contraindicated treatment) in reducing the occurrence of an escape-maintained behavior.

DNA microarray technology makes it possible to analyze the expression levels of many thousands of genes simultaneously. One of the goals of microarray data analysis is to understand the multiple biological roles of genes and their interactions in complex biological processes. Genes with similar expression patterns are likely to share similar functions or biological processes. Therefore, analysis of changes in gene expression of a certain biological processes over time is of particular interest. Unsupervised clustering methods provide an efficient way of finding overall patterns and tendencies by clustering microarray gene expression data. The genes in the same cluster are regulated in a similar manner based on the assumption above. But traditional unsupervised clustering methods usually end up with clusters of genes with similar expression patterns but without interpretations describing the clusters in terms of gene functions or processes involved. In this project, some statistical techniques are applied to analyze the data set from microarray experiments of sporulation in yeast. These techniques include LOWESS data normalization, which is intended to remove the systematic variations from the data; a partitional clustering method, K-means, is used with initial centroids obtained from hierarchical clustering method of DIANA; the "gap statistic" technique is implemented to estimate the "optimal" number of clusters in the data set; and finally multiple hypothesis testing is used to determine whether biologically related genes are statistically over-represented in the gene clusters using the web query tool FatiGO. These methods are combined with graphical representation of cluster profile shape and colour maps of up and down regulation via heat maps. Application of these methods to a yeast sporulation time-course data set [Chu 𝘦𝘵 𝘢𝘭. 1998] demonstrates the utility of cluster analysis to such data sets and provides an automated method for including biological information about gene function and characteristics. / Thesis / Master of Science (MSc)

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What are the layers of interaction between building and user? Weight? Time? Material? Space? How do these four elements affect the perception of an architectural figure? / Master of Architecture