Selection of steels for industrial purposes usually means choosing a type of steel to make a part or a product. The steel that is chosen must meet all the designer requirements. A quantitative selection procedure has been used to analyze the large amount of data involved in this selection process so that a complete and systematic evaluation can be made. The designer is responsible for the selection of steel, and this selection requires the designer to find data and information on the mechanical properties required, and also learns ways to improve these properties through different heat treatment processes. When a large number of steels and a large number of specified mechanical properties are being evaluated for selection, the weighed properties method can require a large number of tedious and time-consuming calculations. In such cases a computer program could greatly facilitate the selection process. This thesis reports the selection of steels for gears, shafts, fasteners and springs where the steps involved in the weighted properties method which is written in the form of a simple computer program to select steels from a data bank. This program also includes the digital logic method to help in determining weighing factors. The steels are ranked according to standard designation; BS, AISI, and DIN. It has been found that alloyed steels hardened and tempered at 2050C are most suitable for gears, shafts, fasteners, and springs when higher mechanical properties required, and carbon and low alloyed steels when cost is the main consideration.

Biological network data is plentiful with practically every experimental methodology giving 'network views' into cellular function and behavior. Bioinformatic screens that yield network data include, for example, genome-wide deletion screens, protein-protein interaction assays, RNA interference experiments, and methods to probe metabolic pathways. Efficient and comprehensive computational approaches are required to model these screens and gain insight into the nature of biological networks. This thesis presents three new algorithms to model and mine network datasets. First, we present an algorithm that models genome-wide perturbation screens by deriving relations between phenotypes and subsequently using these relations in a local manner to derive genephenotype relationships. We show how this algorithm outperforms all previously described algorithms for gene-phenotype modeling. We also present theoretical insight into the convergence and accuracy properties of this approach. Second, we define a new data mining problem–constrained minimal separator mining—and propose algorithms as well as applications to modeling gene perturbation screens by viewing the perturbed genes as a graph separator. Both of these data mining applications are evaluated on network datasets from S. cerevisiae and C. elegans. Finally, we present an approach to model the relationship between metabolic pathways and operon structure in prokaryotic genomes. In this approach, we present a new pattern class—biclusters over domains with supplied partial orders—and present algorithms for systematically detecting such biclusters. Together, our data mining algorithms provide a comprehensive arsenal of techniques for modeling gene perturbation screens and metabolic pathways. / Ph. D.

Having a sight impairment should not limit one’s opportunity to be socially included and obtain the many benefits of being in a green space. It is a challenge for landscape architects to ensure that every green space is sensibly planned and designed to provide benefits to all users, including the visually impaired. However, to date, little research has explored the extent to which this group of people use their local green space and how the attributes of green space help to maintain or increase their sense of emotional well-being, especially when their vision loss occurs later in life. This study has drawn on a sample of visually impaired people with central vision loss caused by late onset Age-related Macular Degeneration (AMD) from across Scotland to address this research gap. Subjects ranged from being partially sighted to severely sight impaired or blind. It employed a mixed method research strategy with a quantitative method as the main approach, supplemented by qualitative methods and triangulation. The study began with focus group discussions aimed at identifying those green space attributes that this group of people deemed important, as a basis for developing a choice-based conjoint (CBC) questionnaire survey. The survey data were analysed using conjoint analysis software (Sawtooth Software version 8.3) with a Hierarchical Bayesian (HB) method to evaluate the relative importance of green space attributes to the study participants. The purpose of this method was to demonstrate the different priorities placed by people with visual impairment on the physical, social, sensory and accessibility attributes of the green space. This work was followed by a series of walk-along and home interviews to gain an in-depth understanding of how the attributes that emerged as most important from the conjoint survey helped the participants to obtain a restoration of their emotional well-being through being in green spaces. The conjoint analysis results demonstrated that the relative importance of green space attributes differs by gender, visual condition and the emotional state caused by sight loss. The qualitative findings suggest that green space can act as a medium to promote emotional restoration by offering a compatible environment that motivates individuals to undertake the kind of outdoor physical and social activities that reduce social isolation. Taken together, the two most influential factors in relative importance and emotional restoration were individual affordance and social company. The value of this research lies in identifying the landscape design attributes that are of the greatest importance to people with AMD. Such findings could help policymakers and landscape architects to provide better design solutions to include this group of people. They may also prove valuable as part of a new approach to enable people to deal with the emotional issues surrounding their late-onset visual impairment.

The purpose of the study was to identify the effects of the influence of cooperating teachers on how student teachers prioritize particular goals of education. This was accomplished by administering a predetermined list of eighteen educational goals to a group of student teachers prior and subsequent to their quarter-long student teaching experience, and to their respective cooperating teachers during their student teaching quarter. The list enabled the teacher groups to rank the goals in order of priority. In this way the cooperating teachers' goal rankings were compared to both the student teachers' pre and post student teaching goal rankings. Twenty-three student teachers and their respective cooperating teachers in secondary and special education served as subjects. The Phi Delta Kappa Goal Setting Instrument was used as the goals list in the study. The questions explored were: 1) are there differences between the relative importance as signed to selected educational goals by student teachers prior to the student teaching experience and the relative importance assigned to the same goals by the cooperating teachers; 2) are there differences in the relative importance assigned to selected educational goals by student teachers before their student teaching experience as compared to their assigned rankings after their student teaching experience; and 3) is there a relationship between any changes in the relative importance assigned by the student teachers prior and subsequent to the student teaching experience and the relative importance assigned by the cooperating teachers. To test the hypotheses under investigation, eighteen one-way analyses of variance with repeated measures were computed. Significant F ratios were found for two of the eighteen goals; the remaining F ratios were not statistically significant. The results suggest some tentative support for student teachers' goal prioritizations of two goals changing after the student teaching experience. Some tentative support was also suggested on these two goals for the student teachers' goal prioritizations changing after the student teaching experience to become more similar to the cooperating teacher' s goal prioritizations. However, the lack of significant change in sixteen of the eighteen goals more strongly suggested that the influence of a) the experience of the student teaching activity and b) the cooperating teachers' own goals prioritization biases upon the student teachers did not markedly affect student teacher goal prioritization behavior. The possibilities that the teacher groups had initial general agreement on goal priorities, that the goals may represent stable educational values, and that instrumentation concerns may have affected the results were then discussed.

The aim of this thesis is to propose a decision support tool for contractors before the bidding stage to quantify the probability of schedule delay in construction projects by using fuzzy logic incorporated with relative importance index (RII) method. Eighty three (83) different schedule delay factors were identified through detailed literature review and interview with experts from a leading Turkish construction company, then categorized into nine (9) groups and visualized by utilizing Ishikawa (Fish Bone) Diagrams. The relative importances of schedule delay factors were quantified by relative importance index (RII) method and the ranking of the factors and groups were demonstrated according to their importance level on schedule delay. A schedule delay assessment model was proposed by using Fuzzy Theory in order to determine a realistic time contingency by taking into account of delay factors characterized in construction projects. The assessment model was developed by using Fuzzy Logic Toolbox of the MATLAB Program Software. Proposed methodology was tested in a real case study and probability of schedule delay was evaluated by the assessment model after the required inputs were inserted to software. According to the case study results, the most contributing factors and groups (that need attention) to the probability of schedule delays were discussed. The assessment model results were found to be conceivably acceptable and adequate for the purpose of this thesis.

Background and Objectives: Understanding patient and public values and preferences is essential to healthcare and policy decision making. Discrete choice experiments (DCEs) are a common tool used to capture and quantify these preferences. Recent technological advances allow for a variety of approaches to create and analyze DCEs. However, there is no optimal DCE design, nor analysis method. Our objectives were to (i) survey DCE simulation studies to determine what design features affect statistical efficiency, and assess their reporting, (ii) further investigate these findings with a de novo simulation study, and (iii) explore the sensitivity of individuals’ preference of attributes to several methods of analysis. Methods: We conducted a systematic survey of simulation studies within the health literature, created a DCE simulation study of 3204 designs, and performed two empirical comparison studies. In one empirical comparison study, we determined addiction agency employees’ preferences on knowledge translation attributes using four models, and in the second, we determined elementary school children’s choice of bullying prevention programs using nine models. Results and Conclusions: In our evaluation of DCE designs, we identified six design features that impact the statistical efficiency of a DCE, several of which were further investigated in our simulation study. The reporting quality of these studies requires improvement to ensure that appropriate inferences can be made, and that they are reproducible. In our empirical comparison of statistical models to explore the sensitivity of individuals preferences of attributes, we found similar rankings in the relative importance measures of attributes’ mean part-worth utility estimates, which differed when using latent class models. Understanding the impact of design features on statistical efficiency are useful for designing optimal DCEs. Incorporating heterogeneity in the analysis of DCEs may be important to make appropriate inferences about individuals’ preferences of attributes within a population. / Thesis / Doctor of Philosophy (PhD) / This thesis focuses on the design and analysis of preference surveys, which are referred to as discrete choice experiments. These surveys are used to capture and quantify individuals’ preferences on various characteristics describing a product or service. They are applied in various health settings to better understand a population. For example, clinicians may want to further understand a patient population’s preferences in regards to multiple treatment alternatives. Currently, there is no optimal approach for designing or analyzing preference surveys. We investigated what factors help improve the design of a preference survey by exploring the literature and conducting our own simulation study. We also investigated how sensitive the results of a preference survey were based on the statistical model used. Overall, we found that (i) increasing the amount of information presented and reducing the number of variables to explore will maximize the statistical optimality of the survey; and (ii) analyzing the data with different statistical models will yield similar results in the ranking of individuals’ preferences of the variables explored.

Turnover is one of the most important phenomena for management scholars and practitioners. Yet, researchers and practitioners are often frustrated by their inability to accurately predict why individuals leave their jobs. This should be worrisome given that total replacement costs can exceed 100% of an employee’s salary (Cascio, 2006) and can represent up to 40% of a firm’s pre-tax income (Allen, 2008). Motivated by these concerns, the purpose of this study was to assess the predictive validity of commonly-investigated correlates and, by extension, conceptualizations of employee turnover using a large-scale database of scientific findings. Results indicate that job satisfaction, organizational commitment, and embeddedness (e.g., person-job fit, person-organization fit) may be the most valid proximal predictors of turnover intention. Results for a tripartite analysis of the potential empirical redundancy between job satisfaction and organizational commitment when predicting turnover intention align well with previous research on this topic and generally suggest that the two constructs may be empirically indistinguishable in the turnover context. Taken together, this study has important implications for the turnover and sensitivity analysis literatures. With regard to the sensitivity analysis literature, this study demonstrates the application of a sensitivity analysis for relative importance weights in the meta-analytic context. This new method takes into account variance around the meta-analytic mean effect size estimate when imputing relative importance weights and may be adapted to other correlation matrix-based techniques (i.e., structural equation modeling) that are often used to test theory.

Cownose rays are benthic, suction feeders whose foraging activities have been implicated in severe damage to commercial shellfish industries and seagrass habitat. With jaws highly modified for durophagy, it has been assumed that they are crushing specialists, feeding primarily upon hard molluscan prey. In addition, R. bonasus are believed to be highly migratory, transient residents of coastal inshore waters. However, minimal quantitative data exist regarding R. bonasus feeding or movement patterns in the Gulf of Mexico. Stomach contents from 50 cownose rays caught within the Charlotte Harbor estuary between July 2003 and July 2004 were analyzed using the index of relative importance (IRI). Crustaceans, polychaetes, and bivalves were the dominant groups present, with bivalves representing the smallest proportion of the three dominant groups. High dietary overlap was observed between sexes, size groups and seasons. Shoalmates exhibited significantly more similar diets to each other than to members of other shoals. Although currently believed to be a hard prey specialist, these results suggest the cownose ray may behave as an opportunistic generalist, consuming any readily available prey. Between July 2003 and November 2004, 21 cownose rays were tagged and tracked within Charlotte Harbor using passive acoustic telemetry. Residence time ranged between 1-102 days. No significant relationship was detected between activity patterns and tidal stage or time of day. Minimum convex polygons (MCP) and kernel utilization distributions (KUD) were calculated to demonstrate the extent of an animals home range and core areas of use. Daily MCPs ranged between 0.01 and 25.8 km2, and total MCPs ranged between 0.81 and 71.78 km2. Total 95% KUDs ranged between 0.18 and 62.44 km2, while total 50% KUDs were significantly smaller, ranging from 0.09 to 9.68 km2.

Le risque attribuable est un outil épidémiologique apparu dans les années 1950 aujourd’hui encore assez peu utilisé. Il permet d’estimer la proportion de cas d’une maladie potentiellement évitable par suppression ou réduction de l’exposition d’une population à un facteur de risque. Son principal intérêt réside dans la prise en compte concomitante de l’ampleur d’effet du facteur de risque et de la distribution de ce facteur au sein de la population. Après une présentation des caractéristiques essentielles du risque attribuable et des principes de son estimation à partir d’une étude cas-témoins, nous proposons un cadre conceptuel qui permet d’estimer l’impact d’une intervention de santé publique dans une nouvelle population dont l’exposition à certains facteurs de risque diffère de celle observée dans la population d’étude. Une décomposition du risque attribuable permet alors de prendre en compte l’action combinée, ou synergie, des facteurs de risque dans la survenue de la maladie. Parce qu’il donne une dimension populationnelle à l’estimation de l’effet d’une variable, le risque attribuable est particulièrement intéressant pour quantifier l’importance relative des différentes variables explicatives d’un modèle de régression. La question de l’importance relative des biomarqueurs classiques et de ceux issus des technologies à haut débit dans les modèles diagnostiques est actuellement centrale pour établir les apports respectifs de ces deux niveaux d’information. À partir de simulations, nous montrons comment l’apport des nouvelles technologies, quantifié en termes de risque attribuable, peut être faussé par l’utilisation de méthodologies inadaptées / The attributable risk is an epidemiologic tool that dates back to the fifties but is still relatively seldom used. It estimates the proportion of cases of a given disease that could be avoided if the exposure to a specific risk factor was removed or reduced. Its major interest is that it combines the magnitude of the effect of the risk factor to the distribution of this factor within the population. After a review of the attributable risk main features and the principles of its estimation from case-control studies data, we propose a conceptual framework that allows estimating the impact of a public health intervention in a new population with different exposure to certain risk factors than those observed in the study population. To reach this goal, we used a splitting of the attributable risk that takes into account the combined action –or synergy– of the risk factors on the occurrence of the disease. Because the attributable risk allows estimating the effect of a variable at the population level, it is particularly interesting to quantify the relative importance of the covariates of a regression model. In diagnostic models, the estimation of the relative importance of classic biomarkers and biomarkers obtained from high-throughput technologies is currently crucial in establishing the contribution of each of these two levels of information. Using simulations we have demonstrated the way the role of high-throughput-technologies –quantified in terms of attributable risk– may be wrongly assessed through the use of unsuitable methodology

Made available in DSpace on 2015-03-26T13:32:08Z (GMT). No. of bitstreams: 1 texto completo.pdf: 801719 bytes, checksum: 87934fb7e8c6635218cccfd6cf57b830 (MD5) Previous issue date: 2010-02-11 / Coordenação de Aperfeiçoamento de Pessoal de Nível Superior / Conjoint Analysis (CA) and the Constant Sum (CS) approach were both applied in a consumer preference study in order to estimate Relative Importance (RI%) of attributes. The main goal of this study was to verify if the CS approach can be used to evaluate estimates of RI% obtained from CA. A sample of 192 consumers, residents of Viçosa city, Minas Gerais State, evaluated 8 alternative packages of a milk based drink, formed by the combination of 3 factors with two levels each: Size (200mL and 1000mL), name (coffee and chocolate flavor and chocolate and coffee flavor) and information decaffeinated (with and without). We applied Descriptive (Box and Whisker plots, descriptive measures and dispersion plots) and inferential (paired and independent two sample t tests, general linear hypothesis tests) statistical analysis were applied using estimates of RI obtained from both methods and we concluded that CS does not provide a good evaluation for estimates obtained from CA, although this method can be used as a prior study in order to select factors for a CA study, specially when there exists many candidate factors. / A proposta deste trabalho foi comparar os métodos Soma Constante (SC) e Análise Conjunta de Fatores (ANCF) para estimar a importância relativa ((IR%)) dos fatores em estudos da preferência do consumidor. O objetivo principal foi verificar se o método SC pode ser utilizado para avaliar as estimativas de IR (%) obtidas pela ANCF. Foi realizado um estudo para avaliar a intenção de compra de uma bebida láctea sabor café com base na embalagem. Utilizou-se uma amostra de 192 julgadores residentes na cidade de Viçosa. Os fatores e respectivos níveis avaliados foram: tamanho da embalagem (200 mL e 1000 mL), nome do produto (café e chocolate e chocolate e café) e informação descafeinado (com e sem). Com as estimativas de IR (%) fornecidas pelos julgadores obtidas pelos dois métodos foram realizadas análises descritivas: gráficos Box e Whisker, histogramas e tabelas com medidas descritivas; e também análises inferenciais: teste t para dados pareados e para amostras independentes e testes de hipóteses lineares gerais. Optou-se por realizar as análises com e sem a exclusão de julgadores que não se adequaram ao modelo da ANCF, com base na não significância (valor p > 0,20) da Análise de Variância (ANOVA) realizada para os três fatores com os dados fornecidos por cada julgador. Após a realização das análises estatísticas concluiu-se que o método SC não se mostrou apropriado para avaliar os valores de importância relativa dos fatores estimados na ANCF. Devido à facilidade de aplicação, o método SC pode ser utilizado como um pré-estudo na seleção fatores para serem submetidos à ANCF, principalmente quando existem muitos fatores avaliados no estudo.