To significantly improve the early detection of cervical precancers and cancers, LightTouch™ is under development by SpectRx Inc.. LightTouch™ identifies cancers and precancers quickly by using a spectrometer to analyze light reflected from the cervix. Data from the spectrometer is then used to create an image of the cervix that highlights the location and severity of disease. Our research is conducted to find the appropriate models that can be used to generate map-like image showing disease tissue from normal and further diagnose the cervical cancerous conditions. Through large work of explanatory variable search and reduction, logistic regression and Partial Least Square Regression successfully applied to our modeling process. These models were validated by 60/40 cross validation and 10 folder cross validation. Further examination of model performance, such as AUC, sensitivity and specificity, threshold had been conducted.

This study starts with the review of the literature in customer satisfaction, customer satisfaction methods and models. After selecting a proper customer satisfaction method and model, the study conducts a survey and a questionnaire among the customers and professionals in the modular kitchen sector. The aim of the study is to analyze the factors affecting customer satisfaction and finding out the ones related with the modular kitchen sector. After applying the survey, the relations between the inputs and outputs of the satisfaction are analyzed with the overall satisfaction itself. The strong and weak factors are determined and a proper CRM tool is build-up to realize a decision-support and forecast tool in the study, which can be seen as a beginning for the companies in the real sector in this business to build a much more detailed and ERP integrated software and to use them. The results of the survey are compared with the similar studies from the literature.

Neste trabalho, foram estudadas estratégias para caracterização de bebidas à base de soja (BBS), por meio de análises por espectroscopia no infravermelho médio com transformada de Fourier com acessório de reflexão total atenuada (FTIR-ATR). Foram utilizadas 20 amostras comerciais de BBS, de 7 diferentes sabores e 3 diferentes marcas. Os teores estudados nas BBS foram glicídios totais, glicídios redutores, glicídios não redutores, e proteínas totais. Os modelos de regressão multivariada foram construídos por mínimos quadrados parciais (PLS), empregando como seleção de variáveis os métodos de mínimos quadrados parciais por intervalo (iPLS) e mínimos quadrados parciais por sinergismo de intervalos (siPLS). As seleções de variáveis por siPLS apresentaram os melhores resultados para os modelos construídos. Entre as propriedades avaliadas, a de glicídios totais apresentou modelos com erros de calibração e previsão (RMSECV e RMSEP) baixos, e coeficientes de determinação (R2cv e R2prev) próximos de um. Para proteínas totais, os modelos apresentaram resultados promissores, pois também tiveram erros de calibração e previsão (RMSECV e RMSEP) baixos, e coeficientes de determinação (R2cv e R2prev) próximos de um, considerando-se que as amostras reais e não apresentavam uma variabilidade de concentração de proteínas ideal. Para as propriedades de glicídios redutores e glicídios não redutores, não foram obtidos bons resultados para os modelos de regressão. Desta forma, a metodologia proposta apresenta potencial em análises de rotinas para determinação simultânea de glicídios totais e proteínas, atendendo aos requisitos referente às informações nutricionais na rotulagem das BBS, somando-se às vantagens da espectroscopia no infravermelho, tais como rapidez na análise, elevada frequência analítica, pequena quantidade de amostra necessária, baixo custo, não ser destrutiva e ser ambientalmente amigável. / In this work, strategies were studied for the characterization of soy-based beverages (SBB), by means of Fourier transform infrared spectroscopy with attenuated total reflectance (FTIR-ATR). Twenty commercial samples of SBB were used, of 7 different flavors and 7 different brands. The contents studied in SBB were total sugar, reducing sugar, non-reducing sugars, and total proteins. The multivariate regression models were constructed by partial least squares (PLS), with evaluation of the methods by interval partial least squares (iPLS) and by sinergy interval partial least squares (siPLS), for selection of variables. The selections of variables per siPLS presented the best results for the constructed models. Among the evaluated properties, the total sugar content presented models with low calibration and prediction errors (RMSECV and RMSEP), and determination coefficients (R2cv and R2prev) close to one. For total proteins, the models presented promising results, as they also had low calibration and prediction errors (RMSECV and RMSEP), and determination coefficients (R2cv and R2prev) close to one, considering that the actual samples did not present an ideal protein concentration variability. For the properties of reducing sugars and non-reducing sugars, good results were not obtained for the regression models. In this way, the proposed methodology presents potential in routine analysis for simultaneous determination of total glycogen and protein, taking into account the requirements referring to the nutritional information in the SBB labeling, adding to the advantages of the infrared spectroscopy, such as speed in the analysis, high analytical frequency, small amount of sample required, low cost, non destructive and environmentally friendly.

Neste trabalho, foram estudadas estratégias para caracterização de bebidas à base de soja (BBS), por meio de análises por espectroscopia no infravermelho médio com transformada de Fourier com acessório de reflexão total atenuada (FTIR-ATR). Foram utilizadas 20 amostras comerciais de BBS, de 7 diferentes sabores e 3 diferentes marcas. Os teores estudados nas BBS foram glicídios totais, glicídios redutores, glicídios não redutores, e proteínas totais. Os modelos de regressão multivariada foram construídos por mínimos quadrados parciais (PLS), empregando como seleção de variáveis os métodos de mínimos quadrados parciais por intervalo (iPLS) e mínimos quadrados parciais por sinergismo de intervalos (siPLS). As seleções de variáveis por siPLS apresentaram os melhores resultados para os modelos construídos. Entre as propriedades avaliadas, a de glicídios totais apresentou modelos com erros de calibração e previsão (RMSECV e RMSEP) baixos, e coeficientes de determinação (R2cv e R2prev) próximos de um. Para proteínas totais, os modelos apresentaram resultados promissores, pois também tiveram erros de calibração e previsão (RMSECV e RMSEP) baixos, e coeficientes de determinação (R2cv e R2prev) próximos de um, considerando-se que as amostras reais e não apresentavam uma variabilidade de concentração de proteínas ideal. Para as propriedades de glicídios redutores e glicídios não redutores, não foram obtidos bons resultados para os modelos de regressão. Desta forma, a metodologia proposta apresenta potencial em análises de rotinas para determinação simultânea de glicídios totais e proteínas, atendendo aos requisitos referente às informações nutricionais na rotulagem das BBS, somando-se às vantagens da espectroscopia no infravermelho, tais como rapidez na análise, elevada frequência analítica, pequena quantidade de amostra necessária, baixo custo, não ser destrutiva e ser ambientalmente amigável. / In this work, strategies were studied for the characterization of soy-based beverages (SBB), by means of Fourier transform infrared spectroscopy with attenuated total reflectance (FTIR-ATR). Twenty commercial samples of SBB were used, of 7 different flavors and 7 different brands. The contents studied in SBB were total sugar, reducing sugar, non-reducing sugars, and total proteins. The multivariate regression models were constructed by partial least squares (PLS), with evaluation of the methods by interval partial least squares (iPLS) and by sinergy interval partial least squares (siPLS), for selection of variables. The selections of variables per siPLS presented the best results for the constructed models. Among the evaluated properties, the total sugar content presented models with low calibration and prediction errors (RMSECV and RMSEP), and determination coefficients (R2cv and R2prev) close to one. For total proteins, the models presented promising results, as they also had low calibration and prediction errors (RMSECV and RMSEP), and determination coefficients (R2cv and R2prev) close to one, considering that the actual samples did not present an ideal protein concentration variability. For the properties of reducing sugars and non-reducing sugars, good results were not obtained for the regression models. In this way, the proposed methodology presents potential in routine analysis for simultaneous determination of total glycogen and protein, taking into account the requirements referring to the nutritional information in the SBB labeling, adding to the advantages of the infrared spectroscopy, such as speed in the analysis, high analytical frequency, small amount of sample required, low cost, non destructive and environmentally friendly.

The Multicore Multithreaded Microprocessor maximizes parallelism on a chip for the optimal system performance, such that its popularity is growing rapidly in high-performance computing. It increases the complexity in resource distribution on a chip by leading it to two directions: isolation and unification. On one hand, multiple cores are implemented to deliver the computation and memory accessing resources to more than one thread at the same time. Nevertheless, it limits the threads’ access to resources in different cores, even if extensively demanded. On the other hand, simultaneous multithreaded architectures unify the domestic execu- tion resources together for concurrently running threads. In such an environment, threads are greatly affected by the inter-thread interference. Moreover, the impacts of the complicated distribution are enlarged by variation in workload behaviors. As a result, the microprocessor requires an adaptive management scheme to schedule threads throughout different cores and coordinate them within cores. In this study, an adaptive thread management scheme was proposed, integrating both hardware and software approaches. The instruction fetch policy at the hardware level took the responsibility by prioritizing domestic threads, while the Operating System scheduler at the software level was used to pair threads dynami- vi cally to multiple cores. The tie between them was the proposed online linear model, which was dynamically constructed for every thread based on data misses by the regression algorithm. Consequently, the hardware part of the proposed scheme proactively granted higher priority to the threads with less predicted long-latency loads, expecting they would better utilize the shared execution resources. Mean- while, the software part was invoked by such a model upon significant changes in the execution phases and paired threads with different demands to the same core to minimize competition on the chip. The proposed scheme was compared to its peer designs and overall 43% speedup was achieved by the integrated approach over the combination of two baseline policies in hardware and software, respectively. The overhead was examined carefully regarding power, area, storage and latency, as well as the relationship between the overhead and the performance.

Neste trabalho, foram estudadas estratégias para caracterização de bebidas à base de soja (BBS), por meio de análises por espectroscopia no infravermelho médio com transformada de Fourier com acessório de reflexão total atenuada (FTIR-ATR). Foram utilizadas 20 amostras comerciais de BBS, de 7 diferentes sabores e 3 diferentes marcas. Os teores estudados nas BBS foram glicídios totais, glicídios redutores, glicídios não redutores, e proteínas totais. Os modelos de regressão multivariada foram construídos por mínimos quadrados parciais (PLS), empregando como seleção de variáveis os métodos de mínimos quadrados parciais por intervalo (iPLS) e mínimos quadrados parciais por sinergismo de intervalos (siPLS). As seleções de variáveis por siPLS apresentaram os melhores resultados para os modelos construídos. Entre as propriedades avaliadas, a de glicídios totais apresentou modelos com erros de calibração e previsão (RMSECV e RMSEP) baixos, e coeficientes de determinação (R2cv e R2prev) próximos de um. Para proteínas totais, os modelos apresentaram resultados promissores, pois também tiveram erros de calibração e previsão (RMSECV e RMSEP) baixos, e coeficientes de determinação (R2cv e R2prev) próximos de um, considerando-se que as amostras reais e não apresentavam uma variabilidade de concentração de proteínas ideal. Para as propriedades de glicídios redutores e glicídios não redutores, não foram obtidos bons resultados para os modelos de regressão. Desta forma, a metodologia proposta apresenta potencial em análises de rotinas para determinação simultânea de glicídios totais e proteínas, atendendo aos requisitos referente às informações nutricionais na rotulagem das BBS, somando-se às vantagens da espectroscopia no infravermelho, tais como rapidez na análise, elevada frequência analítica, pequena quantidade de amostra necessária, baixo custo, não ser destrutiva e ser ambientalmente amigável. / In this work, strategies were studied for the characterization of soy-based beverages (SBB), by means of Fourier transform infrared spectroscopy with attenuated total reflectance (FTIR-ATR). Twenty commercial samples of SBB were used, of 7 different flavors and 7 different brands. The contents studied in SBB were total sugar, reducing sugar, non-reducing sugars, and total proteins. The multivariate regression models were constructed by partial least squares (PLS), with evaluation of the methods by interval partial least squares (iPLS) and by sinergy interval partial least squares (siPLS), for selection of variables. The selections of variables per siPLS presented the best results for the constructed models. Among the evaluated properties, the total sugar content presented models with low calibration and prediction errors (RMSECV and RMSEP), and determination coefficients (R2cv and R2prev) close to one. For total proteins, the models presented promising results, as they also had low calibration and prediction errors (RMSECV and RMSEP), and determination coefficients (R2cv and R2prev) close to one, considering that the actual samples did not present an ideal protein concentration variability. For the properties of reducing sugars and non-reducing sugars, good results were not obtained for the regression models. In this way, the proposed methodology presents potential in routine analysis for simultaneous determination of total glycogen and protein, taking into account the requirements referring to the nutritional information in the SBB labeling, adding to the advantages of the infrared spectroscopy, such as speed in the analysis, high analytical frequency, small amount of sample required, low cost, non destructive and environmentally friendly.

Near infrared spectroscopy (NIR) has become increasingly widespread throughout various fields as an alternative method for efficiently phenotyping crops and plants at rates unparalleled by conventional means. With growing reliability, the convergence of NIR spectroscopy and modern machine learning represent a promising methodology offering unprecedented access to rapid, high throughput phenotyping at negligible costs, representing prospects that excite agronomists and plant physiologists alike. However, as is true of all emergent methodologies, progressive refinement towards optimization exposes potential flaws and raises questions, one of which is the cornerstone of this study. Spectroscopic determination of plant functional traits utilizes plants' morphological and biochemical properties to make predictions, and has been validated at the community (inter-family) and individual crop (intraspecific) levels alike, yielding equally reliable predictions at both scales, yet what lies amid these poles on the spectrum of taxonomic scale remains unexplored territory. In this study, we replicated the protocol used in studies of the aforementioned taxonomic scale extremes and applied it to an intermediate scale. Interestingly, we found that predictive models built upon hyperspectral reflectance data collected across three genera of woody plants: Cornus, Rhododendron, and Salix, yielded inconsistent predictions of varying accuracy within and across taxa. Identifying the potential cause(s) underlying variability in predictive power at this intermediate taxonomic scale may reveal novel properties of the methodology, potentially permitting further optimization through careful consideration.

Soil fertility is an important component of forest ecosystem, yet evaluating soil fertility remains one of the least understood aspects of forest science. Phytocentric and geocenctric approaches were used to assess soil fertility in loblolly pine plantations throughout their geographic range in the United States. The model to assess soil fertility using a phytocentric approach was constructed using the relationship between site index and aboveground productivity. Geocentric models used physical and chemical properties of the A-horizon. Soil geocentric models were constructed using two modeling approaches. In the first approach, ordinary least squares methods of multiple regression were used to derive soil fertility estimated from site index using soil physical and chemical properties from the A-horizon. Ordinary least squares methods were found unsuitable due to multicollinearity among the soil variables. In the second approach, a multivariate modeling approach, partial least squares regression, was used to mitigate multicollinearity effects. The best model to quantify soil fertility using soil physical and chemical properties included N, Ca, Mg, C, and sand percentage as the significant predictors. The 3-PG process-based model was evaluated for simulating the response of loblolly pine to changes in soil fertility. Fertility rating (FR) is a parameter in 3-PG that scales soil fertility in the range of 0 to 1. FR values estimated from phytocentric and geocentric approaches were tested against observed production. The 3-PG model prediction of aboveground productivity described 89% percent of the variation in observed aboveground productivity using FR derived from site index and 84% percent of the vari- ation in observed aboveground productivity using FR derived from physical and chemical properties of the A-horizon. A response function to model dynamics of FR (∆FR) due to one time midrotatoin fertilization of N and P was developed using the Weibull function. The magnitude of ∆FR varied with intensity of N and time since application of fertilizer. The hypothesis that repeated fertilization with N and P eliminate major nutrient deficiency in the southeastern US was tested and a relationship between baseline fertility rating and fertilizer response was developed. An inverse relationship was observed between fertilizer response and baseline FR. / Ph. D.

Fourier Transform Infrared (FTIR) microscopy is a sensitive method for detecting difference in the morphology of biological cells. In this study FTIR spectra were obtained for uninfected cells, and cells infected with two different viruses. The spectra obtained are difficult to discriminate visually. Here we apply advanced statistical methods to the analysis of the spectra, to test if such spectra are useful for diagnosing viral infections in cells. Logistic Regression (LR) and Partial Least Squares Regression (PLSR) were used to build models which allow us to diagnose if spectral differences are related to infection state of the cells. A three-fold, balanced cross-validation method was applied to estimate the shrinkages of the area under the receiving operator characteristic curve (AUC), and specificities at sensitivities of 95%, 90% and 80%. AUC, sensitivity and specificity were used to gauge the goodness of the discrimination methods. Our statistical results shows that the spectra associated with different cellular states are very effectively discriminated. We also find that the overall performance of PLSR is better than that of LR, especially for new data validation. Our analysis supports the idea that FTIR microscopy is a useful tool for detection of viral infections in biological cells.

Fourier transform infrared (FTIR) spectroscopy, one method of electromagnetic radiation for detecting specific cellular molecular structure, can be used to discriminate different types of cells. The objective is to find the minimum time (choice among 2 hour, 4 hour and 6 hour) to record FTIR readings such that different viruses can be discriminated. A new method is adopted for the datasets. Briefly, inner differences are created as the control group, and Wilcoxon Signed Rank Test is used as the first selecting variable procedure in order to prepare the next stage of discrimination. In the second stage we propose either partial least squares (PLS) method or simply taking significant differences as the discriminator. Finally, k-fold cross-validation method is used to estimate the shrinkages of the goodness measures, such as sensitivity, specificity and area under the ROC curve (AUC). There is no doubt in our mind 6 hour is enough for discriminating mock from Hsv1, and Coxsackie viruses. Adeno virus is an exception.