Esta tese tem como objetivo a modelagem da degradação de correias em transportadores utilizados em mineração, como função do tempo e de outras covariáveis independentes que fazem parte do processo de mineração e que influenciam no desgaste das mesmas. Para a realização do trabalho, utilizou-se um método dividido em duas etapas: (i) abordagem qualitativa (estudo teórico do tópico degradação de correias e coleta de dados através da técnica de Grupo Focado – GF), para definição de variáveis influentes no desgaste, e (ii) abordagem quantitativa, para obtenção do modelo de degradação das correias, sendo utilizada, no estudo em questão, uma regressão linear múltipla. Como resultado foi possível identificar através da literatura, assim como via GF, que as variáveis ciclo da correia, comprimento e largura da correia, queda do material, limpador de correias (raspadores), taxa de alimentação, granulometria, composto e velocidade da correia impactariam potencialmente na degradação de correias. Já com o uso da regressão múltipla, constatou-se que as mesmas realmente são significativas e influentes, corroborando os dados obtidos via GF. De posse dos modelos de degradação obtidos para cada correia, foi possível elaborar uma proposta de sistemática de gestão da degradação de correias, baseada na comparação da evolução do desgaste real com o previsto, de modo a se detectar possíveis desvios e permitir a elaboração de ações de correção, visando minimizar a degradação acelerada e maximizar a vida útil das correias. Conseguiu-se estimar um ganho financeiro potencial de cerca de R$ 1.132.000,00 por ano, a partir da comparação entre a vida útil calculada pelos modelos de degradação e a vida estimada pela área de manutenção do complexo. / This thesis aims at modelling of the conveyor’s belt degradation used in mining as a function of time and other independent covariate that are part of the mining process and have influence in their wearing. To carry out the research we implemented a method divided in two stages: (i) a qualitative approach (theoretical study of conveyor belts degradation and data collection through Focused Groups – FG) for definition of factors that are influential in the wearing of belts, and (ii) a quantitative approach for obtaining a belts’ degradation model through multiple linear regression. It was possible to identify in the literature and through FG that variables such as belt cycle, belt length and width, material fall, belt cleaner, feed rate, particle size, compound and belt speed could potentially impact on the degradation of belts. Using multiple regression such variables were found to be statistically significant, corroborating the data obtained from FG. With the degradation models obtained for each conveyor belt it was possible to propose a method for the maintenance management of conveyor belts. The method was based on the comparison of real wear versus predicted wear in order to detect possible deviations and to allow the development of correction actions that aim at minimizing accelerated degradation and maximizing the belt’s lifetime. A potential financial gain of approximately R$ 1.132.000,00 per year was estimated comparing the lifetime obtained using the degradation models and the life estimated by the maintenance area of the complex.

The promise of telemedicine is great as observed in developed countries. However, its adoption in developing countries has been very slow. The Kenyan government approved telemedicine (use of ICT to overcome geographical barriers and increase access to health care services) as a strategic approach of improving healthcare delivery especially in the marginalised areas of the country. However, the adoption of telemedicine is further hindered in developing countries by the fact that the cost of implementing telemedicine technology is deemed high and the resources needed are scarce especially in the public sector. Extant literature on healthcare technological innovation indicates that organisation collaboration can expedite the adoption of telemedicine especially in developing countries. Since it is highlighted that empirical evidence on how organisation collaboration can facilitate telemedicine deployment in developing countries is still lacking, this research aims to develop a model to examine the potential of various organisation factors in facilitating telemedicine deployment in developing countries. This study employed a conceptual research framework to examine organisation factors that may influence organisation collaboration in facilitating telemedicine deployment in developing countries. A questionnaire survey was conducted in 50 private and public hospitals located in Eastern Kenya. 177 valid questionnaires were received and analysed using SPSS software (version 20). The findings of this research revealed that Kenyan hospitals collaborate with other organisations mainly to lessen budget restrained suffered during technological innovation process. Further, it was revealed that organisation affiliation might enhance their ability to adopt telemedicine. Organisation affiliation was observed to significantly influence organisation resources, organisation’s innovation acceptance, organisation’s innovative capacities, organisation agility and collaborative innovation aspects. In addition, all the organisational model factors were supported and explained 46.5% of the variance in collaborative innovation internal outcomes and 53.2% of the variance in collaborative innovation external outcomes. However, personnel innovation acceptance made no significant effect on collaborative innovation outcomes.

En gren inom statistikteorin är den så kallade Regressionsanalysen där man studerar hur data från exempelvis ett stickprov kan anpassas till en graf. Skrivandet av denna uppsats har haft som syfte att studera några av de metoder som finns att tillgå vid bestämning av de ingående parametrarna i de enklare fallen av regression. Dessutom ges i de avslutande kapitlen exempel på den del inom regressionsanalysen som kallas Styckvis Linjär Regression eller Piecewise Linear Regression. / Presentationen är redan avklarad den 26 april 2010 kl. 11.30

Varying diameter ratios associated with smooth concentric tube-in-tube heat exchangers are known to have an effect on its convective heat transfer capabilities. Much literature exists for predicting the inner tube’s heat transfer coefficients, however, limited research has been conducted for the annulus and some of the existing correlations are known to have large errors. Linear and nonlinear regression models exist for determining the heat transfer coefficients, however, these are complex and time consuming methods and require much experimental data in order to obtain accurate solutions. A direct solution to obtain the heat transfer coefficients in the annulus is sought after. In this study a large dataset of experimental measurements on heat exchangers with annular diameter ratios of 0.483, 0.579, 0.593 and 0.712 was gathered. The annular diameter ratio is defined as the ratio of the outer diameter of the inner tube to the inner diameter of the outer tube. Using various methods, the data was processed to determine local and average Nusselt numbers in the turbulent flow regime. These methods included the modified Wilson plot technique, a nonlinear regression scheme, as well as the log mean temperature difference method. The inner tube Reynolds number exponent was assumed to be a constant 0.8 for both the modified Wilson plot and nonlinear regression methods. The logarithmic mean temperature difference method was used for both a mean analysis on the full length of the heat exchanger, and a local analysis on finite control volumes. Friction factors were calculated directly from measured pressure drops across the annuli. The heat exchangers were tested for both a heated and cooled annulus, and arranged in a horizontal counter-flow configuration with water as the working medium. Data was gathered for Reynolds numbers (based on the hydraulic diameter) varying from 10 000 to 28 000 for a heated annulus and 10 000 to 45 000 for a cooled annulus. Local inner wall temperatures which are generally difficult to determine, were measured with thermocouples embedded within the wall. Flow obstructions within the annuli were minimized, with only the support structures maintaining concentricity of the inner and outer tubes impeding flow. / Dissertation (MEng)--University of Pretoria, 2013. / gm2014 / Mechanical and Aeronautical Engineering / unrestricted

Philosophiae Doctor - PhD / One of the assumptions of conventional regression analysis is I that the parameters are constant over all observations. It has often been suggested that this may not be a valid assumption to make, particularly if the econometric model is to be used for economic forecasting0 Apart from this it is also found that econometric models, in particular, are used to investigate the underlying interrelationships of the system under consideration in order to understand and to explain relevant phenomena in structural analysis. The pre-requisite of such use of econometrics is that the regression parameters of the model is assumed to be constant over time or across different crosssectional units.

Multiple criteria systems are recommended as best practice to identify culturally, linguistically, economically diverse students for gifted services, in which schools often incorporate measures of creativity. However, the role of creativity in identification systems and its recruitment of diverse student populations is unclear. The Torrance Test of Creative Thinking (TTCT) is the most widely used norm-referenced creativity test in gifted identification. Although commonly used for identifying talent, little is known on the variability in composite scores on the TTCT-Figural and student demographics (i.e., race/ethnicity, sex, socioeconomic status, English language learning status). This study evaluated student demographic subgroup differences that exist after the initial phase of an identification process (i.e., universal screening, referrals) and examined the relationship among student demographics (i.e., race/ethnicity, free/reduced lunch status, English language learning status, sex), cognitive ability, academic achievement, and creativity, as measured by the TTCT-Figural Form A or B, to the probability of being identified for gifted programs. In a midsized school district in the state of Texas, findings indicate several demographic differences for students who were referred or universally screened across the measures of cognitive ability, academic achievement, and creativity. However, there were lower differences when using the TTCT-Figural. Results of a hierarchical generalized linear regression indicate underrepresented groups showed no difference in the probability of being identified after controlling for measures of cognitive ability, academic achievement, and creativity. Though, cognitive ability and academic achievement tests were more predictive of identification compared to the TTCT-Figural. Implications and recommendations for future research are discussed.

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College football stadiums host anywhere from 15,000 to 115,000 people each Saturday from late summer to early winter and leave fans exposed to ambient conditions. Amplified heat from stadium infrastructure substantially impact attendants’ thermal comfort. In order to assess personal heat exposure and mitigate exposure misclassification, temperature and relative humidity sensors (iButtons) were placed throughout Mississippi State University’s Davis Wade Stadium during the 2016 Football Season. iButton measurements established a micro-climate and compared its readings to the Soil Climate Analysis Network site 1.2 miles north of the stadium. The program RayMan Pro modeled a Physiological Equivalent Temperature (PET) micro-climate to create an individualized heat metric. The results of this study assess stadium occupants’ thermal comfort through Heat Index and PET. Heat-related health outcomes were examined regarding thermal comfort and the stadium micro-climate using data from the stadium’s EMS calls and First Aid stations during game days.

PROPHAZ is a computer program created for the analysis of survival data using the general proportional hazards model. It was designed specifically for the situation in which the underlying hazard function may be estimated from the mortality experience of a large reference population, but may be used for other problems as well. Input for the program includes the variables of interest as well as the information necessary for estimating the hazard function (demographic and mortality data). Regression coefficients for the variables of interest are obtained iteratively using the Newton-Raphson method. Utilizing large sample asymptotic theory, χ2 statistics are derived which may be used to test hypotheses of the form Cβ = 0. Input format is completely flexible for the variables of interest as well as the mortality data.

Stock market speculation is captivating to many people. Millions of people worldwide sell and buy stocks in the hope of turning a profit. By using machine learning could Random Forest or Linear Regression estimate which direction the trend of the stock market is heading, and would Random Forest outperform Linear Regression since it involves more complex methods. To explore the subject, several stocks from Nasdaq and the index of Swedish OMX are studied and used to evaluate the machine learning models. The data was modified to measure the change in percentage to accommodate the Random Forests inability to extrapolate. The return on investment in percentage was chosen as a dependent variable. Without a technical analysis both models performed poorly, but when RSI 14, EMA 10 and SMA 10 was added, both models proved significant, while Random Forest proved the superior of them both. Hyperparameter optimization was applied on Random Forest to evaluate if it was possible to prove it even more superior to Linear Regression, but alas, it only gave an improvement in half of the datasets, which made it inconclusive. This thesis adds to the already existing papers of predicting stock prices, but goes into exploring the difference between Random Forest and Linear Regression to see if there are any obvious differences in their ability to estimate the direction of a stock’s price in a near future.