Indiana University-Purdue University Indianapolis (IUPUI) / This study is a comparative examination of factors related to a state’s likelihood of granting its local governments broad home rule authority or broad local governing discretion. Six factors are assessed: percentage of a state’s residents living in urban areas, state’s heterogeneity, per capita income, region, age of state, and dominant political culture. Binary, logistic regression analysis is used to determine logistic odds of a state’s granting structural and broad functional home rule authority given the posited factors. Results showed no significant relationships between variables.

Thesis (M.A.)--Indiana University, 2009. / Title from screen (viewed on January 26, 2010). Department of Political Science, Indiana University-Purdue University Indianapolis (IUPUI). Advisor(s): Margaret R. Ferguson, Ramla M. Bandele, Johnny Goldfinger. Includes vitae. File lacks bibliographical references listed in Table of Contents.

The Vellore mutiny of July 1806 occupies a rather enigmatic position in the history of British rule in India. It was a brief but extremely bloody episode. For a short time it appeared to threaten the military predominance of the East India Company in Bouth India, yet the threat died away quickly. This thesis attempts to resolve some of the mysteries which have subsequently surrounded the mutiny. Chapter I deals with the event itself. A detailed description is given of the events of 10 July 1806, drawing on first-hand accounts from a wide range of sources. The recapture of the fort by the British dragoons and the bloodshed which ensued is also discussed. Chapter 2 deals with the military background to the mutiny, citing previous examples of breaches of allegiance to the Company army by its sepoys and dealing in detail with the rejection by thf' sepoys at Vellore of a new pattern of turban in May 1806, three months prior to the mutiny itself. Chapter 3 examines the proceedings and findings of the three enquiries into the mutiny which were held by the authorities in Ivladras. Possible explanations for the different conclusions reached by these enquiries are discussed. Chapter 4 analyses the strength of the arguments which sought to place the blame for the mutiny either on the sons of Tipu Sultan, imprisoned at Vellore, or on the introduction of new dress regulations into the army. Evidence is adduced to argue that the underlying cause of the mutiny lay in the overall conditions of service of the Indian troops. Chapters 5 and 6 study the effects of the Vellore mutiny on the hadras government. The bitter division between the civil and military authorities over the causes of the mutiny is examined, as is the personal confrontation between the Governor, Lord William Bentinck, and the Commander-in-Chief, Sir John Cradock. In Chapter 7 the wide ranging effects and consequences of the mutiny are highlighted. Not only did the mutiny cause tremendous friction within the Madras government, it also deeply divided the Court of Directors in London and brought the debate on the ethics of missionary activity in India to the forefront of public attention o Chapter 8 looks at the way in which the Vellore mutiny was interpreted by some of its contemporaries both in India and in Britain and traces the way in which much of the public conception of the mutiny came to be based on gossip and rumour rather than on fact. In Chapter 9, attention is given to the historiography of the mutiny and it is argued that gossip and rumour also became built in to historical accounts of the mutiny. The effect of the Indian Mutiny of 1857 on interpretation of the Vellore mutiny is examined, along with the most recent contributions to its historiography.

No description available.

No description available.

No description available.

No description available.

No description available.

No description available.

This research identifies and investigates major issues in inducing accurate and comprehensible fuzzy rules from datasets. A review of the current literature on fuzzy rulebase induction uncovers two significant issues: A. There is a tradeoff between inducing accurate fuzzy rules and inducing comprehensible fuzzy rules; and, B. A common strategy for the induction of fuzzy rulebases, that of iterative rule learning where the rules are generated one by one and independently of each other, may not be an optimal one. FRANTIC, a system that provides a framework for exploring the claims above is developed. At the core lies a mechanism for creating individual fuzzy rules. This is based on a significantly modified social insect-inspired heuristic for combinatorial optimisation -- Ant Colony Optimisation. The rule discovery mechanism is utilised in two very different strategies for the induction of a complete fuzzy rulebase: 1. The first follows the common iterative rule learning approach for the induction of crisp and fuzzy rules; 2. The second has been designed during this research explicitly for the induction of a fuzzy rulebase, and generates all rules in parallel. Both strategies have been tested on a number of classification problems, including medical diagnosis and industrial plant fault detection, and compared against other crisp or fuzzy induction algorithms that use more well-established approaches. The results challenge statement A above, by presenting evidence to show that one criterion need not be met at the expense of the other. This research also uncovers the cost that is paid -- that of computational expenditure -- and makes concrete suggestions on how this may be resolved. With regards to statement B, until now little or no evidence has been put forward to support or disprove the claim. The results of this research indicate that definite advantages are offered by the second simultaneous strategy, that are not offered by the iterative one. These benefits include improved accuracy over a wide range of values for several key system parameters. However, both approaches also fare well when compared to other learning algorithms. This latter fact is due to the rule discovery mechanism itself -- the adapted Ant Colony Optimisation algorithm -- which affords several additional advantages. These include a simple mechanism within the rule construction process that enables it to cope with datasets that have an imbalanced distribution between the classes, and another for controlling the amount of fit to the training data. In addition, several system parameters have been designed to be semi-autonomous so as to avoid unnecessary user intervention, and in future work the social insect metaphor may be exploited and extended further to enable it to deal with industrial-strength data mining issues involving large volumes of data, and distributed and/or heterogeneous databases.