Thesis (Ph. D.)--University of Connecticut, 1994. / eContent provider-neutral record in process. Description based on print version record. Includes bibliographical references (leaves 293-309).

Thesis (Ph. D.)--Florida State University, 1995. / Typescript. A study on NBINET (National Bibliographic Information Network) in Taiwan. Includes bibliographical references.

Thesis (Ph. D.)--Texas Woman's University, 1990. / Vita. Includes bibliographical references (leaves 163-176).

Thesis (Ph. D.)--University of Wisconsin-Madison, 1989. / Includes bibliographical references (leaves 171-179).

Thesis (Ph. D.)--University of North Texas, 1992. / Includes bibliographical references (leaves 226-235).

Thesis (Ph.D.)--University of Illinois at Urbana-Champaign, 2006. / Source: Dissertation Abstracts International, Volume: 67-11, Section: A, page: 4023. Adviser: Christine Jenkins. Includes bibliographical references (leaves 207-229) Available on microfilm from Pro Quest Information and Learning.

Thesis (Ph.D.)--University of Illinois at Urbana-Champaign, 2006. / Source: Dissertation Abstracts International, Volume: 67-07, Section: A, page: 2457. Adviser: Chip Bruce. Includes bibliographical references (leaves 342-354) Available on microfilm from Pro Quest Information and Learning.

Oral administration is the most common route for administration of drugs. With the growing cost of drug discovery, the development of Quantitative Structure-Activity Relationships (QSAR) as computational methods to predict oral absorption is highly desirable for cost effective reasons. The aim of this research was to develop QSAR models that are highly accurate and interpretable for the prediction of oral absorption. In this investigation the problems addressed were datasets with unbalanced class distributions, feature selection and the effects of solubility and permeability towards oral absorption prediction. Firstly, oral absorption models were obtained by overcoming the problem of unbalanced class distributions in datasets using two techniques, under-sampling of compounds belonging to the majority class and the use of different misclassification costs for different types of misclassifications. Using these methods, models with higher accuracy were produced using regression and linear/non-linear classification techniques. Secondly, the use of several pre-processing feature selection methods in tandem with decision tree classification analysis – including misclassification costs – were found to produce models with better interpretability and higher predictive accuracy. These methods were successful to select the most important molecular descriptors and to overcome the problem of unbalanced classes. Thirdly, the roles of solubility and permeability in oral absorption were also investigated. This involved expansion of oral absorption datasets and collection of in vitro and aqueous solubility data. This work found that the inclusion of predicted and experimental solubility in permeability models can improve model accuracy. However, the impact of solubility on oral absorption prediction was not as influential as expected. Finally, predictive models of permeability and solubility were built to predict a provisional Biopharmaceutic Classification System (BCS) class using two multi-label classification techniques, binary relevance and classifier chain. The classifier chain method was shown to have higher predictive accuracy by using predicted solubility as a molecular descriptor for permeability models, and hence better final provisional BCS prediction. Overall, this research has resulted in predictive and interpretable models that could be useful in a drug discovery context.

Maintaining a fair balance between individuals’ interests and the public interest is arguably believed to be the most effective approach in serving the ultimate objective of copyright: to promote social, economic and cultural development for the benefit of both rightsholders and the public at large. Through this balanced approach, creativity can be maximised and thrive better than by tilting towards one or the other. In copyright law, such balance is reflected by the way in which the short-term grant of exclusive rights respects the long-term public interest represented by limitations and exceptions to copyright. Despite an unclear and non-uniform definition and scope, the importance of the public interest has been implicitly and explicitly recognised through disparate forms of safeguards in different jurisdictions. However, the last few decades have seen a rapid development of information technologies which, in turn, has contributed to an unparalleled legislative drive at international level towards overprotecting the interests of rightsholders. This has then left the public interest under-protected and now constitutes an imbalance of copyright. This thesis therefore examines legislative intervention into the international copyright regime in an attempt to ensure that the public interest is uniformly and mandatorily safeguarded at international level. In particular, it strives to establish an overarching public interest defence capable of protecting certain aspects of public values embedded in copyright works. In achieving this, the thesis examines the scope of the defence, what it should entail, and what aspects are to be taken into account in the course of formulating and giving effect to the defence. The substantive chapters investigate the public policy grounds, the right to freedom of expression and the international three-step test, i.e. the roles they play in shaping the latitude and operations of the defence, respectively. Finally, the thesis also evaluates different ways in which the defence can be incorporated into the international copyright regime in order to effectively counterbalance the rightsholder-centric tendency and restore the balance of copyright.

High-impact academic papers are not necessarily the most cited. For example, Einstein's 'Special Relativity' paper from 1905 received (and continues to receive) fewer citations from other papers than his 'Brownian Motion" paper of the same year, despite the former radically changing the course of an entire scientific discipline to a much greater extent. Similarly, 'impact' metrics using citation count alone are, it is argued, not adequate for determining the scientific influence of papers, authors or small groups of authors. Although valid, they remain controversial when used to determine influence of larger groups or journals. While the term 'impact' has become closely linked to a journal's citation-based Journal Impact Factor score, this thesis uses the term 'influence' to describe the wider effectiveness of research, combining citation and metadata analysis to allow richer calculations to be performed over large-scale document networks. As a result, more qualitative influence ratings can be determined and a broader outlook on scientific disciplines can be produced. These ratings are best applied using an ontology-based data source, allowing more efficient inference than under a traditional RDBMS system, and allowing easier integration between heterogeneous data sources. These metrics, termed 'Semantic Bibliometrics' or 'Semiometrics', can be applied at a variety of levels of granularity, allowing a compositional framework for impact and influence analysis. This thesis describes the process of data preparation, systems architecture, metric value and data integration for such a system, introducing novel approaches at all four stages, thereby creating a working semiometrics system for determining influence at different semantic levels of granularity.