This thesis, studies automated methods to achieve web service advertisement and discovery, and presents efficient search and matching techniques based on OWL-S. In the proposed system, the service discovery and matchmaking is performed via a centralized peer-to-peer web service repository. The repository has the ability to run on a software cloud, which improves the availability and scalability of the service discovery. The service advertisement is done semi-automatically on the client side, with an automatic WSDL to OWL-S conversion, and manual service description annotation. An OWL-S based unified ontology -Suggested Upper Merged Ontology- is used during annotation, to enhance semantic matching abilities of the system. The service advertisement and availability are continuously monitored on the client side to improve the accuracy of the query results. User-agents generate query specification using the system ontology, to provide semantic unification between the client and the system during service discovery. Query matching is performed via complex Hilbert Spaces composed of conceptual planes and categorical similarities for each web service. User preferences following the service queries are monitored and used to improve the service match scores in the long run.

Adhoc networks do not depend on any fixed infrastructure. The most outstanding features of adhoc networks are non-centralized structure and dynamic topology change due to high mobility. Since mentioned dynamics of mobile adhoc networks complicate reaching the resources in the network, service discovery is significantly an important part of constructing stand-alone and self-configurable mobile adhoc networks. The heterogeneity of the devices and limited resources such as battery are also load up more difficulty to service discovery. Due to the volatile nature of the adhoc networks, service discovery algorithms proposed for mobile and adhoc networks suffer from some problems. Scalability becomes a problem when the service discovery is based on flooding messages over the network. Furthermore, the high traffic which occurs due to the message exchange between network nodes makes the communication almost impossible. Partitioning a network into sub-networks is an efficient way of handling scalability problem. In this thesis, a mobility based service discovery algorithm for clustered MANET is presented. The algorithm has two main parts. First one is for partitioning the MANET into sub-networks, named &ldquo / clustering&rdquo / . Second part is composed of an efficient discovery of services on overall network. Clustering algorithm used in this study is enhanced version of DMAC (Distributed Mobility Adaptive Clustering, which is one of the golden algorithms of the wireless network clustering area). To be fast and flexible in service discovery layer, a simple and fastresponding algorithm is implemented. Integration of two algorithms enables devices to be mobile in the network

A Study of the Effect of Cognitive Styles Learning Approaches on Identifying English Clause Tasks Lieu, Pin-huei Abstract The main purpose of this study intended to discuss the effect of Field Independent subjects(FIs), Field Dependent subjects (FDs) of junior high school using Discovery, Rule Learning approaches on identifying English clauses task. The questions explored here were: 1.How did FIs and FDs differently perform on identifying English clauses tasks. 2.How did Discovery and Rule learning approaches differently perform on identifying English clauses tasks. 3.How did FIs / FDs and Discovery/Rule learning approaches create interactive effect on identifying English clauses tasks. The study used experimental research method. The subjects were 90 third grade students of junior high school. According to the scores of Embedded Figures Test students were divided into FI and FD. Then depending on the scores of the prior test on identifying English clauses task, FIs and FDs match with two group to accept Discovery and Rule learning approaches , and each one was composed of 10 students. The instruments was ¡§ Embedded Figures Test¡¨ , ¡§self-made that clauses test ,¡¨and the information acquired was dealt with statistical testing through 2*2 ANOVA .The results indicated as followings. 1.An interactive effect of cognitive style and learning approaches were found through ANOVA. FIs using Discovery learning performed better than using Rule learning ,and FDs using Rule learning performed better using Discovery learning .In sum ,FIs appropriately use Discovery learning approach and FDs appropriately use Rule learning approach on identifying English clauses task. 2.Cognitive style lives up significantly different level .FIs performed better than those of FDs. 3.No overall difference were found between Discovery and Rule learning approach. Finally the study discussed the above results in more detail ,and provided suggestions and references of research concerning teaching of English clauses .

Traditional relational database model does not have enough capability to cope with a great deal of data in finite time. To address these requirements, data warehouses and online analytical processing (OLAP) have emerged. Data warehouses improve the productivity of corporate decision makers through consolidation, conversion, transformation, and integration of operational data, and supports online analytical processing (OLAP). The data warehouse design is a complex and knowledge intensive process. It needs to consider not only the structure of the underlying operational databases (source-driven), but also the information requirements of decision makers (user-driven). Past research focused predominately on supporting the source-driven data warehouse design process, but paid less attention to supporting the user-driven data warehouse design process. Thus, the goal of this research is to propose a user-driven data warehouse design support system based on the knowledge discovery approach. Specifically, a Data Warehouse Design Support System was proposed and the generalization hierarchy and generalized star schemas were used as the data warehouse design knowledge. The technique for learning these design knowledge and reasoning upon them were developed. An empirical evaluation study was conducted to validate the effectiveness on the proposed techniques in supporting data warehouse design process. The result of empirical evaluation showed that this technique was useful to support data warehouse design especially on reducing the missing design and enhancing the potentially useful design.

In instances of mass fatality, such as plane crashes, natural disasters, or terrorist attacks, investigators may encounter hundreds or thousands of DNA specimens representing victims. For example, during the January 2010 Haiti earthquake, entire communities were destroyed, resulting in the loss of thousands of lives. With such a large number of victims the discovery of family pedigrees is possible, but often requires the manual application of analytical methods, which are tedious, time-consuming, and expensive. The method presented in this thesis allows for automated pedigree discovery by extending Link Discovery Tool (LDT), a graph visualization tool designed for discovering linkages in large criminal networks. The proposed algorithm takes advantage of spatial clustering of graphs of DNA specimens to discover pedigree structures in large collections of specimens, saving both time and money in the identification process.

Thesis (LLD)--University of Pretoria, 2009. / Summaries in English and Afrikaans. Includes bibliographical references.

The aim of the present study is to identify the di®erentially expressed genes be- tween two di®erent conditions and apply it in predicting the class of new samples using the microarray data. Microarray data analysis poses many challenges to the statis- ticians because of its high dimensionality and small sample size, dubbed as "small n large p problem". Microarray data has been extensively studied by many statisticians and geneticists. Generally, it is said to follow a normal distribution with equal vari- ances in two conditions, but it is not true in general. Since the number of replications is very small, the sample estimates of variances are not appropriate for the testing. Therefore, we have to consider the Bayesian approach to approximate the variances in two conditions. Because the number of genes to be tested is usually large and the test is to be repeated thousands of times, there is a multiplicity problem. To remove the defect arising from multiple comparison, we use the False Discovery Rate (FDR) correction. Applying the hypothesis test repeatedly gene by gene for several thousands of genes, there is a great chance of selecting false genes as di®erentially expressed, even though the signi¯cance level is set very small. For the test to be reliable, the probability of selecting true positive should be high. To control the false positive rate, we have applied the FDR correction, in which the p -values for each of the gene is compared with its corresponding threshold. A gene is, then, said to be di®erentially expressed if the p-value is less than the threshold. We have developed a new method of selecting informative genes based on the Bayesian Version of Behrens-Fisher distribution which assumes the unequal variances in two conditions. Since the assumption of equal variances fail in most of the situation and the equal variance is a special case of unequal variance, we have tried to solve the problem of ¯nding di®erentially expressed genes in the unequal variance cases. We have found that the developed method selects the actual expressed genes in the simulated data and compared this method with the recent methods such as Fox and Dimmic’s t-test method, Tusher and Tibshirani’s SAM method among others. The next step of this research is to check whether the genes selected by the pro- posed Behrens -Fisher method is useful for the classi¯cation of samples. Using the genes selected by the proposed method that combines the Behrens Fisher gene se- lection method with some other statistical learning methods, we have found better classi¯cation result. The reason behind it is the capability of selecting the genes based on the knowledge of prior and data. In the case of microarray data due to the small sample size and the large number of variables, the variances obtained by the sample is not reliable in the sense that it is not positive de¯nite and not invertible. So, we have derived the Bayesian version of the Behrens Fisher distribution to remove that insu±ciency. The e±ciency of this established method has been demonstrated by ap- plying them in three real microarray data and calculating the misclassi¯cation error rates on the corresponding test sets. Moreover, we have compared our result with some of the other popular methods, such as Nearest Shrunken Centroid and Support Vector Machines method, found in the literature. We have studied the classi¯cation performance of di®erent classi¯ers before and after taking the correlation between the genes. The classi¯cation performance of the classi¯er has been signi¯cantly improved once the correlation was accounted. The classi¯cation performance of di®erent classi¯ers have been measured by the misclas- si¯cation rates and the confusion matrix. The another problem in the multiple testing of large number of hypothesis is the correlation among the test statistics. we have taken the correlation between the test statistics into account. If there were no correlation, then it will not a®ect the shape of the normalized histogram of the test statistics. As shown by Efron, the degree of the correlation among the test statistics either widens or shrinks the tail of the histogram of the test statistics. Thus the usual rejection region as obtained by the signi¯cance level is not su±cient. The rejection region should be rede¯ned accordingly and depends on the degree of correlation. The e®ect of the correlation in selecting the appropriate rejection region have also been studied.

The intraverbal is argued to be the most socially significant verbal operant and yet it is the least studied. Heal and Hanley (2011) suggest that different teaching strategies will lead to different rates of acquisition and child-preference with the tacting operant. This study continued this research into the realm of intraverbals, with focus on whether the embedded teaching strategy could be punishing on play or engaging in learning opportunities. The teaching strategies of discovery teaching, embedded prompting, and direct teaching were compared to see which strategy correlated with higher rates of acquisition and higher child preference. The study utilized a multi-element design by rapidly alternating teaching strategies while evaluating rate of acquisition and number of learning opportunities within the teaching strategies. Child preference was also demonstrated through card selection of associated teaching strategies in a concurrent chains agreement design. The teaching strategies differed in the amount of teacher directedness and taught intraverbal "Wh" questions. It was found through this study that embedded prompting did not punish play or the engagement in learning opportunities. The three participants preferred the three strategies differently and all participants were responding correctly the highest percentage of the time during the direct teaching contingencies by the end of the teaching sessions.

Pharmaceutical biotechnology has been emerging as a defined, increasingly important area of science dedicated to the discovery and delivery of drugs and therapies for the treatment of various human diseases. In contrast to the advancement in pharmaceutical biotechnology, current drug discovery efforts are facing unprecedented challenges. Difficulties in identifying novel drug targets and developing effective and safe drugs are closely related to the complexity of the network of interacting human proteins. Protein-protein interactions mediate virtually all cellular processes. Therefore both identification and understanding of protein-protein interactions are essential to the process of deciphering disease mechanisms and developing treatments. Unfortunately, our current knowledge and understanding of the human interactome is largely incomplete. Most of the unknown protein-protein interactions are expected to be weak and/or transient, hence are not easily identified. These unknown or uncharacterized interactions could affect the efficacy and toxicity of drug candidates, contributing to the high rate of failure. In an attempt to facilitate the ongoing efforts in drug discovery, we describe herein a series of novel methods and their applications addressing the broad topic of protein-protein interactions. We have developed a highly efficient site-specific protein cross-linking technology mediated by the genetically incorporated non-canonical amino acid L-DOPA to facilitate the identification and characterization of weak protein-protein interactions. We also established a protocol to incorporate L-DOPA into proteins in mammalian cells to enable in vivo site-specific protein cross-kinking. We then applied the DOPA-mediated cross-linking methodology to design a protein probe which can potentially serve as a diagnostic tool or a modulator of protein-protein interactions in vivo. To deliver such engineered proteins or other bioanalytical reagents into single live cells, we established a laser-assisted cellular nano-surgery protocol which would enable detailed observations of cell-to-cell variability and communication. Finally we investigated a possible experimental scheme to genetically evolve a fluorescent peptide, which has tremendous potential as a tool in cellular imaging and dynamic observation of protein-protein interactions in vivo. We aim to contribute to the discovery and development of new drugs and eventually to the overall health of our society by adding the technology above to the array of currently available bioanalytical tools. / text

eEF-2K, also known as CaM kinase-III, is an atypical protein kinase which negatively regulates the global rate of protein synthesis through the phosphorylation and inactivation of its substrate eEF-2. Recently eEF-2K has been validated as a novel target for anti-cancer therapy. However, a detailed understanding of the role of eEF-2K in cancer biology is unavailable. Mechanistic studies can often provide an understanding of enzyme function. Therefore, we determined the kinetic mechanism of eEF-2K using a peptide substrate (Acetyl-RKKYKFNEDTERRRFL-amide). We found that eEF-2K adopts a ternary-complex, steady state ordered mechanism, with ATP binding required before the peptide substrate. A good cellular inhibitor is required for elucidating the role of eEF-2K in cancer biology. To date, NH125 is the only inhibitor used to investigate the activity of eEF-2K in cells. Although it is reported as a specific inhibitor of eEF-2K, its exact mode of action has not been reported. Through in-vitro assays and cellular studies, we found that NH125 is a non-specific inhibitor of eEF-2K that blocks eEF-2 phosphorylation in cells. There is a great demand for specific inhibitors of eEF-2K. We developed a fluorescence high throughput assay system for eEF-2K. The assay utilizes the peptide substrate labeled with a Sox moiety whose phosphorylation can be monitored at 485 nm in the presence of magnesium. We also validated the assay in a screen of 30,000 compounds in 384 well plates. We found the assay to be robust and identified a relatively specific inhibitor of eEF-2K and determined its mechanism of action. We found it behaved as a slowly reversible inhibitor of eEF-2K with a two step inhibition mechanism - fast initial binding at the enzyme active site, followed by a slower inactivation step. We propose that the nitrile group on the compound binds to the active site thiol in the enzyme covalently forming a reversible thioimidate adduct to inactivate the enzyme. / text