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.

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.

Summary Fresh water availability and distribution have been declining over time due to population increase, climate change and variability, emerging new demands due to economic growth, and changing consumption patterns. Spatial and temporal changes in environmental changes, such as climate and land use/cover (LULC) dynamics have an enormous impact on water availability. Food and energy security, urbanization and industrial growth, as well as climate change (CC) will pose critical challenges on water resources. Climate variability and change may affect both the supply and demand sides of the balance, and thus add to the challenges. Land-cover changes are vastly prominent in the developing countries that are characterized by agriculture-based economies and rapidly increasing human population. The consequent changes in water availability and increase in per capita water demand will adversely affect the food, water and energy security of those countries. Therefore, evaluating the response of the catchment to environmental changes is crucial in the critical part of the basin for sustainable water resource management and development. In particular, assessing the contribution of individual LULC classes to changes in water balance components is vital for effective water and land resource management, and for mitigation of climate change impacts. The dynamic water balance of a catchment is analyzed by hydrological models that consider spatio-temporal catchment characteristics. As a result, hydrological models have become indispensable tools for the study of hydrological processes and the impacts of environmental stressors on the hydrologic system. Physically-based distributed hydrological models are able to explicitly account for the spatial variability of hydrological process, catchment characteristics such as climatic parameters, and land use/cover changes. For improved illustration of physical processes in space and time, the distributed hydrological models need serially complete and homogenized rainfall and temperature data. However, observed rainfall and temperature data are neither serially complete nor homogeneous, particularly in developing countries. Using inhomogeneous climatological data inputs to hydrological models affects the output magnitude of climate and land use/cover change impacts and, hence, climate change adaptation. The Nile River Basin, one of the transboundary river flows through 11 riparian states, serves the livelihoods of millions of people in the basin (nearly 20 per cent of the African population) and covers one-tenth of the land cover of Africa. The basin is characterized by high population growth and high temporal variability in the river flow and rainfall patterns. The Blue Nile river basin, which contributes 62% of the annual main Nile flow, has faced serious land degradation. This has led to increased soil erosion and loss of soil fertility. The most overwhelming challenge that the basin faces is food insecurity caused by subsistence farming and rain-fed agriculture (over 70% of the basin’s population), together with high rainfall variability. Drought and floods are also critical issues in the Blue Nile basin, with the potential for exacerbation by environmental changes. Understanding how LULC and climate changes influence basin hydrology will therefore enable decision makers to introduce policies aimed at reducing the detrimental effects of future environmental changes on water resources. Understanding types and impacts of major environmental stressors in representative and critical regions of the basin is crucial for developing of effective response strategies for sustainable land- and water-resource management in the Eastern Nile Basin in general, and at the Tana and Beles watersheds in particular. In this study, serially completed and homogenized rainfall and temperature dataset are maintained from 1980 to 2013 to fill-in the gap which characterized previous studies on trend analyses. The new hydroclimatic data revealed that the climate the study region has become wetter and warmer. The proportional contribution of main rainy season rainfall to annual total rainfall has increased. This might result in high runoff and ultimately flooding as well as erosion and sedimentation in the source region of the Blue Nile, and siltation in the downstream reservoirs unless soil and water conservation measures are taking place. In the Tana sub-basin, it is found that expansion of cultivation land and decline in woody shrub are the major contributors to the rise in surface run-off and to the decline in the groundwater component from 1986 to 2010. Similarly, decline of woodland and expansion of cultivation land are found to be the major contributors to the increase in surface run-off and water yield. They also contributed to the decrease in groundwater and actual evapotranspiration components in the Beles watershed. Increased run-off and reduced baseflow and actual evapotranspiration would have negative impacts on water resources, especially in relation to erosion and sedimentation in the upper Blue Nile River Basin. As a result, expansion of cultivation land and decline in woody shrub/woodland appear to be major environmental stressors affecting local water resources. GCMs simulated near-future annual total rainfall and average temperature were used to investigate the sensitivity of the catchment to near-future CC. The results showed an increase in streamflow in the annual and the main rainy season, but decrease in the dry period when compared to the baseline period. Catchment response for future LULC scenario showed opposite effect to that of near-future CC. The combined effects of climate change and LULC dynamics can be quite different from the effects resulting from LULC or CC alone. At the outlet of the Tana watershed, streamflow response is amplified under concurrent land cover and climate change scenarios compared to the baseline scenario; but the streamflow has an augmenting response at the outlet of the Beles watershed under future climate change and land use scenarios compared to that of current period. The important inference from these findings is that it could be possible to alleviate intense floods or droughts due to future climate change by planning LULC to achieve particular hydrological effects of land cover in the basin. Continuing expansion of cultivation land and decrease in natural vegetation, coupled with increased rainfall due to climate change, would result in high surface runoff in the main rainy season, which would subsequently increase flooding, erosion and sedimentation in already degraded lands. Sound mitigation measures should therefore be applied to reduce these adverse environmental consequences. On the other hand, the simulated climate and land-use change impacts on the Tana watershed hydrological regime might increase the availability of streamflow to be harnessed by water-storage structures. In conclusion, the present study has developed an innovative approach to identify the major environmental stressors of critical source region of the Blue Nile River in order to effectively managing the water resources and climate risk. Understanding the catchment responses to environmental changes improves sustainability of the water resources management particularly given that the hydropower and the irrigation schemes are recently established for energy and food security.:TABLE OF CONTENTS LIST OF ABBREVIATIONS LIST OF FIGURES LIST OF TABLES 1. General Introduction 2. The study area 3. Gap Filling and Homogenization of Climatological Datasets in the Headwater Region of the Upper Blue Nile Basin, Ethiopia Abstract 3.1. Introduction 3.1.1. Data 3.2. Methodology 3.2.1. Quality control and gap filling 3.2.2. Homogenization 3.3. Results and Discussion 3.3.1. Gap filling 3.3.2. Homogeneity 3.3.3. Verification of the homogenization 3.3.4. Impact of homogenization on the rainfall and temperature series 3.4. Conclusions Acknowledgements 4. Revisiting trend analysis of hydroclimatic data in the Upper Blue Nile basin based on homogenized data Abstract 4.1 Introduction 4.2 Data and Methodology 4. 2.1 Data 4. 2.2 Linear trend 4. 2.3 Trend magnitude 4.3 Results and Discussions 4.3.1. Linear mean climate trends 4.3.1.1. Rainfall 4.3.1.2. Maximum Temperature (Tmax) 4.3.1.3. Minimum Temperature (Tmin) 4.3.1.4. Mean temperature (Tmean) 4.3.1.5. Diurnal temperature range (DTR) 4.3.1.6. Streamflow 4.3.2. Effect of homogenization on Tmax, Tmin, Tmean and DTR linear trends 4.3.3. Linear extreme climate trends 4.3.1. Temperature 4.3.2. Precipitation 4.4 Conclusions Acknowledgements 5. Recent Changes in Land Use/Cover in the Headwater Region of the Upper Blue Nile Basin, Ethiopia 85 Abstract 5.1 Introduction 5.2 Materials and Methods 5.2.1 Data used and image pre-processing 5.2.2 Classification accuracy assessment 5.2.3 Extent and rate of change 5.2.4 Detecting the most systematic transitions (dominant signals of change) 5.4 Results and Discussion 5.4.1 Accuracy assessment 5.4.2 Extent and rate of LULC changes 5.4.3 Rate of land use and land cover change 5.4.4 Detection of most systematic transitions 5.5 Conclusions Acknowledgements 6. Hydrological Responses to Land use/cover Changes in the Tana and Beles Watersheds, the Upper Blue Nile, Ethiopia Abstract 6.1 Introduction 6.2 Method 6.2.1 Hydrological modeling 6.2.2 Partial least squares regression 6.3 Results and Discussion 6.3.1 Calibration and validation of SWAT 6.3.2 Impacts of LULC changes on hydrology at the basin scale 6.3.3 Contribution of changes in individual LULCs to hydrological components 6.4 Conclusions Acknowledgements 7. Combined Impact of Climate and Land Use Changes on Hydrology in the Tana and Beles Sub-Basins, Upper Blue Nile, Ethiopia Abstract 7.1 Introduction 7.2 Methodology 7.2.1 Simulation 7.2.2 Climate change scenarios 7.2.3 LULC change scenarios 7.3 Results and Discussion 7.3.1 Future versus current LULC impact on the basin hydrology 7.3.2 Future versus baseline climate 7.3.3 Impact of combined future climate and LULC changes on hydrology 7.4 Uncertainties and Limitations 7.5 Conclusions Acknowledgements 8. Overall Conclusions, Recommendations and Future Research Directions 8.1. Overall Conclusions 8.2 Recommendations and Directions for further research References