Doctor of Philosophy / Department of Sociology, Anthropology, and Social Work / Laszlo Kulcsar / This dissertation examines the discourse of biofuels development in Kansas as promoted by rural growth machines. Corn-based ethanol production capacity and use in the United States has grown exponentially between 2000 and 2009, culminating with the 2007 Energy Independence and Security Act’s 36 billion gallon Renewable Fuels Standard 2. At the national level, biofuels development is promoted by the media as important to national goals such as energy/national security, economic growth, and environmental improvement. Examination of the biofuels discourse employed content analysis of newspaper articles as well as in-depth individual interviews and focus groups. The analysis revealed that rural growth machines created an ethanol discourse similar to the one promoted at national level, but with an almost exclusive emphasis on the economic development frame. The rural growth machine’s ideological hegemony promoting ethanol development in the region was maintained through their power of creating and disseminating information. For the issue of biofuels development in Kansas, the analyzed newspapers played both conduit and contributor roles, as newspaper coverage strongly supported the interests of growth machines when the subject was local economic growth opportunities. Members of the rural growth machines set an exclusive and one-sided discourse to legitimate their pro-growth activities and to portray the ethanol development projects as corresponding with the wider good of these communities. Because of dwindling demographic and economic bases as well as scarce natural resources, local political and economic elites approached the issue of growth form a standpoint of hegemony. They promoted growth to carry out their own political and economic agenda while there was a strong desire among the residents for almost any type of economic development. This might explain the weak opposition to the actions of the growth machine in these rural settings.

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LD2668 .T4 1941 T71 / Master of Science

Call number: LD2668 .T4 1963 M49 / Master of Science

This paper presents a study on classifying receipts and invoices using Machine Learning. Furthermore, Naïve Bayes Algorithm and the advantages of using it will be discussed.  With information gathered from theory and previous research, I will show how to classify images into a receipt or an invoice. Also, it includes pre-processing images using a variety of pre-processing methods and text extraction using Optical Character Recognition (OCR). Moreover, the necessity of pre-processing images to reach a higher accuracy will be discussed. A result shows a comparison between Tesseract OCR engine and FineReader OCR engine. After embracing much knowledge from theory and discussion, the results showed that combining FineReader OCR engine and Machine Learning is increasing the accuracy of the image classification.

Morphology and syntax have both received attention in statistical machine translation research, but they are usually treated independently and the historical emphasis on translation into English has meant that many morphosyntactic issues remain under-researched. Languages with richer morphologies pose additional problems and conventional approaches tend to perform poorly when either source or target language has rich morphology. In both computational and theoretical linguistics, feature structures together with the associated operation of unification have proven a powerful tool for modelling many morphosyntactic aspects of natural language. In this thesis, we propose a framework that extends a state-of-the-art syntax-based model with a feature structure lexicon and unification-based constraints on the target-side of the synchronous grammar. Whilst our framework is language-independent, we focus on problems in the translation of English to German, a language pair that has a high degree of syntactic reordering and rich target-side morphology. We first apply our approach to modelling agreement and case government phenomena. We use the lexicon to link surface form words with grammatical feature values, such as case, gender, and number, and we use constraints to enforce feature value identity for the words in agreement and government relations. We demonstrate improvements in translation quality of up to 0.5 BLEU over a strong baseline model. We then examine verbal complex production, another aspect of translation that requires the coordination of linguistic features over multiple words, often with long-range discontinuities. We develop a feature structure representation of verbal complex types, using constraint failure as an indicator of translation error and use this to automatically identify and quantify errors that occur in our baseline system. A manual analysis and classification of errors informs an extended version of the model that incorporates information derived from a parse of the source. We identify clause spans and use model features to encourage the generation of complete verbal complex types. We are able to improve accuracy as measured using precision and recall against values extracted from the reference test sets. Our framework allows for the incorporation of rich linguistic information and we present sketches of further applications that could be explored in future work.

Inspired by the performance and evolutionarily-optimised natural molecular machines that carry out all the essential tasks contributing to the molecular basis of life, chemists aim towards fabricating synthetic molecular machines that mimic biological nanodevices. The use of rotaxanes as a prototype for molecular machines has emerged as a result of their ability to undergo translational motion between two or more co-conformations. Although biological machines are capable of complex and intricate functions, their inherent stability and operational conditions are restricted to in vivo. Synthetic systems offer a limitless number of building blocks and a range of interactions to be manipulated. Transition metal-ligand interactions are utilised as one strategy to control the directional movement of submolecular components in artificial machines due to their well-defined geometric requirements and significant strength. This thesis presents new externally addressable and switchable molecular elements for transition metal complexed-molecular machines involving an acid-base switch. The proton input that induces changes to cyclometallated platinum complexes can be exploited to control exchange between different coordination modes. The development of the pH-switchable metal-ligand motif for the stimuli-responsive platinum-complexed molecular shuttle has also been explored. The metal-directed self-assembly of tubular complexes were studied in order to develop self-assembled rotaxanes. A series of metal building blocks was explored to extend the scope for a tube self-assembly.

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In this paper, we try to compare the performance of two feature dimension reduction methods, the LASSO and PCA. Both simulation study and empirical study show that the LASSO is superior to PCA when selecting significant variables. We apply Logistics Regression (LR), Artificial Neural Network (ANN), Support Vector Machine (SVM), Decision Tree (DT) and their corresponding ensemble machines constructed by bagging and adaptive boosting (adaboost) in our study. Three experiments are conducted to explore the impact of class-unbalanced data set on all models. Empirical study indicates that when the percentage of performing loans exceeds 83.3%, the training models shall be carefully applied. When we have class-balanced data set, ensemble machines indeed have a better performance over single machines. The weaker the single machine, the more obvious the improvement we can observe.

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