Thesis (M.S. in Operations Research) Naval Postgraduate School, March 1997. / Thesis advisor, Robert R. Read. AD-A329 431. Includes bibliographical references (p. 59). Also available online.

Doctor of Philosophy / Department of Human Nutrition / Edgar Chambers IV / Companies introduce new products with the goal of achieving success. However, many products fail. The overall objective of this research was to design processes for determining sensory and market characteristics of food products that could predict success. The first sub-objective was to determine if success could be predicted using information known before launch. The second sub-objective was to describe a process for determining specific sensory characteristics that promote success. Most methods chosen for this research are commonly used. However, previous research has identified a relationship between consumers liking and salivation, without defining a method. Thus, three salivation methods were selected for initial testing: spit, cotton rolls and sensory scale. These were tested on foods with different textures. Although all methods gave similar results, the spit method was chosen for further testing of flavor differences. Differences in salivation measurements were found for snacks where flavors were different but texture was unchanged. Next, flavored snack products from 15 countries were selected that were successful or had failed. Questionnaires were completed for each product and included questions related to authenticity, familiarity, current trends, packaging and marketplace issues such as product competition and pricing, all of which would be known before launch. A discriminant function was developed that correctly identified 75.8% of the successful flavored snack products as successful and 66.7% of the unsuccessful products as unsuccessful. Stepwise comparisons were used to determine that four variables are necessary to correctly categorize these products. The products then were clustered into three groups to select 34 products from 11 countries for further sensory testing. Information from extensive sensory descriptive methods were evaluated individually and in various combinations through stepwise regression and discriminant analysis. The final sensory model correctly predicted all successful and unsuccessful products, had an R-square of 0.84 and included nine regression factors: seven flavor attributes and two flavor attribute ratios. Many of the attributes were base flavor notes necessary for this flavored snack category. A process for selecting key attributes for success was described. For this snack category, creating products with flavors that interact well with base flavor notes can lead to a successful product.

This study surveyed floristic composition and distribution of weed vegetation in Mituo County. 206 relevés were surveyed according to relevé method. A total of 140 species belonging to 32 families of the vascular plants were recorded. The weed communities were classified with nonmetric multidimentional scaling, two-way indicator species analysis, tabular comparison method, fidelity and synoptic table analysis. Discriminate analysis was used to evaluate the distinctness of classification unitsal vegetation classification system was made using Braun-Blanquet approach of floristic-sociological classification in lower levels and physiognomic-sociological classification in higher levels. In floristic-sociological classification, assication is the basic unit, and it should be grouped into higher units (alliance) by floristic composition. The results showed 1 formation class, 2 formations in phsiogonomic units, and 4 alliances, 6 associations in floristic units: I. Lower montane-lowland weed vegetation formation A. Echinochloa colona alliance a. Echinochloa colona association b. Trianthemum portulacastrum association c. Panicum maximum association B. Dichanthium aristatum alliance d. Dichanthium aristatum association C. Eriochloa procera alliance e. Eriochloa procera association II. Sand dune vegetation formation D. Ipomoea pescaprae subsp. brasiliensis alliance f. Ipomoea pescaprae subsp. brasiliensis association

Due to the association of heart diseases and the patterns of the diastoles and systoles of heart in left ventricle, we analyze and classify the data gathered form Kaohsiung Veterans General Hospital by using the cardiology ultrasound images. We make use of the differences between the gray-scale values of diastoles and systoles in left ventricle to evaluate the function of heart. Following Chen (2011) and Kao (2011), we modified the way about the reduction and alignment of the image data. We also add some more subjects into the study. We treat images in two manners, saving the parts of concern. Since the ultrasound image after transformation to data form is expressed as a high-dimensional matrix, the principal component analysis is adapted to retain the important factors and reduce the dimensions. In this work, we compare the loadings calculated by the usual principal and sparse principal component analysis, then the factor scores are used to carry out the discriminant analysis and discuss the accuracy of classification. By the statistical methods in this work, the accuracy, sensitivity and specificity of the original classifications are over 80% and the cross validations are over 60%.

In order to reduce default risk, financial institutions have been investigating into credit ratings of companies, which they want to give credit to. This research tries to give a method for financial institutions to differentiate between default and normal company with financial ratios, which is already announced in their seasonal financial reports. The samples are abstracted from security markets, and restricted to building companies. With Discriminant analysis and Logistic regression models, financial institutions can estimate what company may become into default situation and others stay in good condition. According to this research, financial ratios that can be used to discriminate between default and normal companies are: net worth ratio and short-turn borrowing/liquid asset and asset turnover and gross profit margin. It can also be described with asset turnover and gross profit margin if default risk is been estimated.

The study intended to investigate the phylogeography of mid-altitude species, Japalura brevipes, which is the endemic to Taiwan. I used mitochondrial cytochrome b sequences and morphological characters to investigate the phylogeography. The molecular results showed that there are 16 haplotypes. The length of partial cytochrome b sequences is 617 bp including 84 informative sites, and the mean genetic distance is 5.4%. In phylogenetic analyses, three major clades were found in the phylogenetic trees. Moreover, these clades (north, central-north and south) corresponded to the geographic distribution. Analysis of population genetic structure revealed significant differentiation among populations, and most of the haplotypes were restricted to the locally. The morphological analyses showed that there is no difference between sexes at 18 characters, with the exception of the axilla-groin length and dorsal-crest scales. The Principal Component Analysis and Canonical Discriminate Analysis using 16 morphological characters showed the different results. Principal Component Analysis failed to separate populations. However, Canonical Discriminate Analysis could discriminate the populations of Guanwu, Nanheng, Sihyuantkou and Wuling Farm from others. Phylogeography of J. brevipes belonged to category 1 defined by Avise (2000). I infer refuge effect and dispersal ability hypotheses to explain the genetic structure of J. brevipes.

The design of a face recognition system ( FRS ) can been separated into two major modules ¡V face detection and face recognition. In the face detection part, we combine image pre-processing techniques with maximum-likelihood estimation to detect the nearest frontal face in a single image. Under limited restrictions, our detection method overcomes some of the challenging tasks, such as variability in scale, location, orientation, facial expression, occlusion ( glasses ), and lighting change. In the face recognition part, we use both Karhunen-Loeve transform and linear discrimant analysis ( LDA ) to perform feature extraction. In this feature extraction process, the features are calculated from the inner products of the original samples and the selected eigenvectors. In general, as the size of the face database is increased, the recognition time will be proportionally increased. To solve this problem, hard-limited Karhunen-Loeve transform ( HLKLT ) is applied to reduce the computation time in our FRS.

Se presenta una propuesta metodológica de clasificación de la degradación del suelo en ecosistemas semiáridos por pérdida de materia orgánica, basada en el tratamiento estadístico por análisis discriminante y factorial de los parámetros que habitualmente se determinan en los suelos y que influyen de manera decisiva en su degradación biológica y física. Se aplica el estudio a suelos calizos de siete zonas de la Región de Murcia, obteniéndose tres elementos discriminatorios: materia orgánica, textura y clase de productividad, siendo las dos primeras las de mayor peso específico. Con el análisis discriminante se establecen cuatro clases de degradación de suelos, en función del contenido en materia orgánica. A continuación, para evaluar el estado de degradación del suelo se recurre al análisis factorial de las variables estudiadas, presentando los suelos menos degradados un factor fertilidad positivo, mientras que los más degradados tienen un factor fertilidad negativo, tanto más bajo cuanto más intensa es la degradación. Como conclusión, se considera que el Calcisol pétrico, cultivado, procede de la degradación del Kastanosem pétrico, bajo vegetación natural, por degradación biológica de éstos, con la consiguiente disminución de materia orgánica con el transcurso del tiempo. Se calcula que dicha degradación viene operando desde hace unos 100 años. / We presented/displayed in this work a methodologic proposal of classification of the degradation of the ground in semi-arid ecosystems by loss of organic matter, cradle in the statistical treatment by discriminante and factorial analysis of the parameters that habitually determine in grounds and that they influence of decisive way in its biological and physical degradation. It is applied to the study to limestone grounds of seven zones of the Region of Murcia, obtaining itself three discriminatory elements: organic matter, texture and class of productivity, being two first those of greater specific weight. With the discriminante analysis four classes of ground degradation settle down, based on the content in organic matter, of greater to minor, assigning each sample to one of them. Class 1: Calcic Kastanosem, Leptosol and Cambisol. Class 2: Pétrico Kastanosem. Class 3: Pétrico and háplico Calcisol. Class 4: Regosol, Fluvisol and Antrosol. Next, to evaluate the state of degradation of the ground one resorts to the factorial analysis of the studied variables, presenting grounds less degraded a factor positive fertility, whereas the most degraded they have a negative factor fertility, lower as much at the most intense is the degradation. Like conclusion, it is considered that the pétrico Calcisol, cultivated, comes from the degradation of the pétrico Kastanosem, under natural vegetation, by biological degradation of these, with the consequent diminution of organic matter with the course of the time. One calculates that this degradation has been coming operating for about 100 years.

From the Helichrysum genus 600 species occur in Africa of which 244 species are found in South Africa. The most commonly used Helichrysum species for medicinal purposes are H. cymosum, H. odoratissimum, H. petiolare and H. nudifolium. The medicinal uses include the treatment of coughs, colds, fever, infection, headaches, menstrual pain and are very popular for wound dressing. Previous published research has shown that H. aureonitens has antiviral properties against Herpes simplex virus type 1 (HSV-1). In this study, further investigation into the Helichrysum species was undertaken, to establish the active constituents responsible for anti-HSV activity using a metabolomics approach. The cytotoxicity of 12 Helichrysum species was investigated and ranged from <3.125 μg/ml to 277.8 μg/ml on the vero cell line. The 12 Helichrysum species also showed various levels of antiviral activity against HSV, with both the water-methanol and chloroform extracts of H. adenocarpum subsp. adenocarpum being the most active extract at 25 μg/ml. In this study the activity of Helichrysum species against HIV-1 RT was also investigated. Helichrysum populifolium was the most active extract, inhibiting the HIV-1 RT enzyme by 63.78 % at 200 μg/ml. The bioactivity data and the spectral nuclear magnetic resonance (NMR) data of al the Helichrysum species from this study was analysed using the SIMCA-P software to discriminate between the different species on the basis of their bioactivity and chemical composition. The samples did not group well on Principal Component Analysis (PCA) but did separate well using the Orthogonal Projection to Latent Structure – Discriminate Analysis (OPLS-DA) on the basis of their activity and NMR spectra data. From the OPLS scoring plots analysis, contribution plots were created which indicated regions responsible for the difference between the species, with these regions being investigated to identify the bioactive constituents. It was thus possible to use metabolomics to discriminate between samples on the basis of their activity and show that it could probably be used in future as a tool to identify active ingredients in medicinal plants and accelerate drug discovery. Copyright / Dissertation (MSc)--University of Pretoria, 2009. / Plant Science / unrestricted

Nondestructive testing is used extensively throughout the industry for quality assessment and detection of defects in engineering materials. The range and variety of anomalies is enormous and critical assessment of their location and size is often complicated. Depending upon final operational considerations, some of these anomalies may be critical and their detection and classification is therefore of importance. Despite the several advantages of using Nondestructive testing for flaw detection, the conventional NDT techniques based on the heuristic experience-based pattern identification methods have many drawbacks in terms of cost, length and result in erratic analysis and thus lead to discrepancies in results. The use of several statistical and soft computing techniques in the evaluation and classification operations result in the development of an automatic decision support system for defect characterization that offers the possibility of an impartial standardized performance. The present work evaluates the application of both supervised and unsupervised classification techniques for flaw detection and classification in a semi-infinite half space. Finite element models to simulate the MASW test in the presence and absence of voids were developed using the commercial package LS-DYNA. To simulate anomalies, voids of different sizes were inserted on elastic medium. Features for the discrimination of received responses were extracted in time and frequency domains by applying suitable transformations. The compact feature vector is then classified by different techniques: supervised classification (backpropagation neural network, adaptive neuro-fuzzy inference system, k-nearest neighbor classifier, linear discriminate classifier) and unsupervised classification (fuzzy c-means clustering). The classification results show that the performance of k-nearest Neighbor Classifier proved superior when compared with the other techniques with an overall accuracy of 94% in detection of presence of voids and an accuracy of 81% in determining the size of the void in the medium. The assessment of the various classifiers’ performance proved to be valuable in comparing the different techniques and establishing the applicability of simplified classification methods such as k-NN in defect characterization. The obtained classification accuracies for the detection and classification of voids are very encouraging, showing the suitability of the proposed approach to the development of a decision support system for non-destructive testing of materials for defect characterization.