Made available in DSpace on 2014-12-17T15:04:50Z (GMT). No. of bitstreams: 1 OdeniseMB.pdf: 523472 bytes, checksum: 4c5d9b7ec084791f932f3572d11986e4 (MD5) Previous issue date: 2008-02-26 / Coordena??o de Aperfei?oamento de Pessoal de N?vel Superior / This study reflects on some procedural aspects about the development of mathematics learning from the experience with investigative activities concerning the resolution of second degree equation, which was tested a proposal for education, supported the use of texts in history of mathematics. The survey was conducted in two stages, taking the first-served basis for the second, which was carried out with a study group remainder of the first experiment. The intention was to investigate how the group participant, known as the study group, involved in the implementation of activities of research in mathematics, supported the use of the history of mathematics. Based on the results achieved during the study, it was possible to understand that the activities of research enable the development of students, range of learning mathematics and the development of skills and expertise for research as a vehicle for construction of their mathematical knowledge. This approach proposed research into the classroom is important, both for prospective teachers of mathematics and for students from elementary school, bringing a new phase for mathematical education that will come to schools / O presente estudo reflete sobre alguns aspectos processuais acerca do desenvolvimento da aprendizagem matem?tica a partir da experi?ncia com atividades investigativas, acerca da resolu??o de equa??o do 2? grau, na qual foi testada uma proposta de ensino, apoiada no uso de textos em hist?ria da matem?tica. A pesquisa foi realizada em duas etapas, tendo a primeira servido de base para a segunda, a qual foi realizada com um grupo de estudo remanescente da primeira experi?ncia. A inten??o foi investigar como o grupo participante, denominado como grupo de estudo, envolveu-se na realiza??o de atividades de investiga??o em matem?tica, apoiada no uso da hist?ria da matem?tica. Com base nos resultados alcan?ados no decorrer do estudo, foi poss?vel compreender que as atividades de investiga??o possibilitam o desenvolvimento dos alunos, alcance de aprendizagem matem?tica e o desenvolvimento de habilidades e compet?ncias para a investiga??o como ve?culo de constru??o do seu conhecimento matem?tico. Essa proposta de abordagem investigativa para a sala de aula ? importante, tanto para futuros professores de matem?tica quanto para estudantes de ensino fundamental e implicar? numa nova fase para a educa??o matem?tica que chegar? ?s escolas

Indiana University-Purdue University Indianapolis (IUPUI) / In recent years, there have been a lot of technology innovations to automate the day to day processes done by every person. These days the automobile manufacturers introduce new features in their cars, in order to improve customer experience, like Adaptive cruise control, Parallel park assist, etc. The objective of this thesis is to model an automated parallel parking system and to simulate the system behavior, by taking into account the high level events which happen when a car is parallel parked. The tool used in this thesis to model and simulate the system is Hybrid Petri net (HPN), which is versatile to model the real life systems. Chapter 1 deals with a brief introduction of the related work in Hybrid Petri net modeling of real life systems, automatic parallel parking systems and how the concept for modeling the parallel parking system was developed. Chapter 2 deals with the general introduction about Discrete, Continuous and Hybrid Petri nets and their dynamics which are essential for understanding this thesis. Chapter 3 deals with the development of the model and the various stages in the model development. Errors encountered in each stage is briefly discussed and the improvements are discussed in the next stage of development. This chapter concludes with the final integrated model and operation of the model. Chapter 4 deals with the discussion of results obtained when the model is tested in MATLAB and SIMHPN (which is a Matlab embedded simulation program). The results are compared, the system behavior is observed and the purpose of the thesis is justified. In Chapter 5, a conclusion is provided to summarize the entire thesis.

The Edinburgh Mathematical Society started out in 1883 as a society with a large proportion of teachers. Today, the member base is mainly academical and there are only a few teachers left. This thesis explores how and when this change came about, and discusses what this meant for the Society. It argues that the exit of the teachers is related to the rising standard of mathematics, but even more to a change in the Society’s printing policy in the 1920s, that turned the Society’s Proceedings into a pure research publication and led to the death of the ‘teacher journal’, the Mathematical Notes. The thesis also argues that this change, drastic as it may seem, does not represent a change in the Society’s nature. For this aim, the role of the teachers within the Society has been studied and compared to that of the academics, from 1883 to 1946. The mathematical contribution of the teachers to the Proceedings is studied in some detail, in particular the papers by John Watt Butters. A paper in the Mathematical Notes by A. C. Aitken on the Bell numbers is considered in connection with a series of letters on the same topic from 1938–39. These letters, written by Aitken, Sir D’Arcy Thompson, another EMS member, and the Cambridge mathematician G. T. Bennett, explores the relation between the three and gives valuable insight into the status of the Notes. Finally, the role of the first women in the Society is studied. The first woman joined without any official university education, but had received the necessary mathematical background from her studies under the Edinburgh Association for the University Education of Women. The final chapter is largely an assessment of this Association’s mathematical classes.

Indiana University-Purdue University Indianapolis (IUPUI) / Proteins are nano-machines that work inside every living organism. Functional disruption of one or several proteins is the cause for many diseases. However, the functions for most proteins are yet to be annotated because inexpensive sequencing techniques dramatically speed up discovery of new protein sequences (265 million and counting) and experimental examinations of every protein in all its possible functional categories are simply impractical. Thus, it is necessary to develop computational function-prediction tools that complement and guide experimental studies. In this study, we developed a series of predictors for highly accurate prediction of proteins with DNA-binding, RNA-binding and carbohydrate-binding capability. These predictors are a template-based technique that combines sequence and structural information with predicted binding affinity. Both sequence and structure-based approaches were developed. Results indicate the importance of binding affinity prediction for improving sensitivity and precision of function prediction. Application of these methods to the human genome and structure genome targets demonstrated its usefulness in annotating proteins of unknown functions and discovering moon-lighting proteins with DNA,RNA, or carbohydrate binding function. In addition, we also investigated disruption of protein functions by naturally occurring genetic variations due to insertions and deletions (INDELS). We found that protein structures are the most critical features in recognising disease-causing non-frame shifting INDELs. The predictors for function predictions are available at http://sparks-lab.org/spot, and the predictor for classification of non-frame shifting INDELs is available at http://sparks-lab.org/ddig.

Indiana University-Purdue University Indianapolis (IUPUI) / Traditional treatment strategy development for diseases involves the identification of target proteins related to disease states, and the interference of these proteins with drug molecules. Computational drug discovery and virtual screening from thousands of chemical compounds have accelerated this process. The thesis presents a comprehensive framework of computational drug discovery using system biology approaches. The thesis mainly consists of two parts: disease biomarker identification and disease treatment discoveries. The first part of the thesis focuses on the research in biomarker identification for human diseases in the post-genomic era with an emphasis in system biology approaches such as using the protein interaction networks. There are two major types of biomarkers: Diagnostic Biomarker is expected to detect a given type of disease in an individual with both high sensitivity and specificity; Predictive Biomarker serves to predict drug response before treatment is started. Both are essential before we even start seeking any treatment for the patients. In this part, we first studied how the coverage of the disease genes, the protein interaction quality, and gene ranking strategies can affect the identification of disease genes. Second, we addressed the challenge of constructing a central database to collect the system level data such as protein interaction, pathway, etc. Finally, we built case studies for biomarker identification for using dabetes as a case study. The second part of the thesis mainly addresses how to find treatments after disease identification. It specifically focuses on computational drug repositioning due to its low lost, few translational issues and other benefits. First, we described how to implement literature mining approaches to build the disease-protein-drug connectivity map and demonstrated its superior performances compared to other existing applications. Second, we presented a valuable drug-protein directionality database which filled the research gap of lacking alternatives for the experimental CMAP in computational drug discovery field. We also extended the correlation based ranking algorithms by including the underlying topology among proteins. Finally, we demonstrated how to study drug repositioning beyond genomic level and from one dimension to two dimensions with clinical side effect as prediction features.

Multiple-outcome longitudinal data are abundant in clinical investigations. For example, infections with different pathogenic organisms are often tested concurrently, and assessments are usually taken repeatedly over time. It is therefore natural to consider a multivariate modeling approach to accommodate the underlying interrelationship among the multiple longitudinally measured outcomes. This dissertation proposes a multivariate semiparametric modeling framework for such data. Relevant estimation and inference procedures as well as model selection tools are discussed within this modeling framework. The first part of this research focuses on the analytical issues concerning binary data. The second part extends the binary model to a more general situation for data from the exponential family of distributions. The proposed model accounts for the correlations across the outcomes as well as the temporal dependency among the repeated measures of each outcome within an individual. An important feature of the proposed model is the addition of a bivariate smooth function for the depiction of concurrent nonlinear and possibly interacting influences of two independent variables on each outcome. For model implementation, a general approach for parameter estimation is developed by using the maximum penalized likelihood method. For statistical inference, a likelihood-based resampling procedure is proposed to compare the bivariate nonlinear effect surfaces across the outcomes. The final part of the dissertation presents a variable selection tool to facilitate model development in practical data analysis. Using the adaptive least absolute shrinkage and selection operator (LASSO) penalty, the variable selection tool simultaneously identifies important fixed effects and random effects, determines the correlation structure of the outcomes, and selects the interaction effects in the bivariate smooth functions. Model selection and estimation are performed through a two-stage procedure based on an expectation-maximization (EM) algorithm. Simulation studies are conducted to evaluate the performance of the proposed methods. The utility of the methods is demonstrated through several clinical applications.