Combined selection in early generation testing of self-pollinated plants

Seven selection indexes based on the phenotypic value of the individual and the mean performance of its family were assessed for their application in breeding of self-pollinated plants. There is no clear superiority from one index to another although some show one or more negative aspects, such as favoring the selection of a top performing plant from an inferior family in detriment of an excellent plant from a superior family

Using the best linear predictor (BLP) in the selection between and among half-sib progenies of the CMS-39 maize population

Data of corn ear production (kg/ha) of 196 half-sib progenies (HSP) of the maize population CMS-39 obtained from experiments carried out in four environments were used to adapt and assess the BLP method (best linear predictor) in comparison with to the selection among and within half-sib progenies (SAWHSP). The 196 HSP of the CMS-39 population developed by the National Center for Maize and Sorghum Research (CNPMS-EMBRAPA) were related through their pedigree with the recombined progenies of the previous selection cycle. The two methodologies used for the selection of the twenty best half-sib progenies, BLP and SAWHSP, led to similar expected genetic gains. There was a tendency in the BLP methodology to select a greater number of related progenies because of the previous generation (pedigree) than the other method. This implies that greater care with the effective size of the population must be taken with this method. The SAWHSP methodology was efficient in isolating the additive genetic variance component from the phenotypic component. The pedigree system, although unnecessary for the routine use of the SAWHSP methodology, allowed the prediction of an increase in the inbreeding of the population in the long term SAWHSP selection when recombination is simultaneous to creation of new progenies.

Working fluid selection and design of small-scale waste heat recovery systems based on organic Rankine cycles

Demand for the use of energy systems, entailing high efficiency as well as availability to harness renewable energy sources, is a key issue in order to tackling the threat of global warming and saving natural resources. Organic Rankine cycle (ORC) technology has been identified as one of the most promising technologies in recovering low-grade heat sources and in harnessing renewable energy sources that cannot be efficiently utilized by means of more conventional power systems. The ORC is based on the working principle of Rankine process, but an organic working fluid is adopted in the cycle instead of steam. This thesis presents numerical and experimental results of the study on the design of small-scale ORCs. Two main applications were selected for the thesis: waste heat re- covery from small-scale diesel engines concentrating on the utilization of the exhaust gas heat and waste heat recovery in large industrial-scale engine power plants considering the utilization of both the high and low temperature heat sources. The main objective of this work was to identify suitable working fluid candidates and to study the process and turbine design methods that can be applied when power plants based on the use of non-conventional working fluids are considered. The computational work included the use of thermodynamic analysis methods and turbine design methods that were based on the use of highly accurate fluid properties. In addition, the design and loss mechanisms in supersonic ORC turbines were studied by means of computational fluid dynamics. The results indicated that the design of ORC is highly influenced by the selection of the working fluid and cycle operational conditions. The results for the turbine designs in- dicated that the working fluid selection should not be based only on the thermodynamic analysis, but requires also considerations on the turbine design. The turbines tend to be fast rotating, entailing small blade heights at the turbine rotor inlet and highly supersonic flow in the turbine flow passages, especially when power systems with low power outputs are designed. The results indicated that the ORC is a potential solution in utilizing waste heat streams both at high and low temperatures and both in micro and larger scale appli- cations.

Portfolio Selection via Composite Measure Analysis: Evidence from the U.S. Stock Market

The aim of this research is to examine the pricing anomalies existing in the U.S. market during 1986 to 2011. The sample of stocks is divided into decile portfolios based on seven individual valuation ratios (E/P, B/P, S/P, EBIT/EV, EVITDA/EV, D/P, and CE/P) and price momentum to investigate the efficiency of individual valuation ratio and their combinations as portfolio formation criteria. This is the first time in financial literature when CE/P is employed as a constituent of composite value measure. The combinations are based on median scaled composite value measures and TOPSIS method. During the sample period value portfolios significantly outperform both the market portfolio and comparable glamour portfolios. The results show the highest return for the value portfolio that was based on the combination of S/P & CE/P ratios. The outcome of this research will increase the understanding on the suitability of different methodologies for portfolio selection. It will help managers to take advantage of the results of different methodologies in order to gain returns above the market.

Green supplier selection via Multiple Criteria Data Envelopment Analysis

An appropriate supplier selection and its profound effects on increasing the competitive advantage of companies has been widely discussed in supply chain management (SCM) literature. By raising environmental awareness among companies and industries they attach more importance to sustainable and green activities in selection procedures of raw material providers. The current thesis benefits from data envelopment analysis (DEA) technique to evaluate the relative efficiency of suppliers in the presence of carbon dioxide (CO2) emission for green supplier selection. We incorporate the pollution of suppliers as an undesirable output into DEA. However, to do so, two conventional DEA model problems arise: the lack of the discrimination power among decision making units (DMUs) and flexibility of the inputs and outputs weights. To overcome these limitations, we use multiple criteria DEA (MCDEA) as one alternative. By applying MCDEA the number of suppliers which are identified as efficient will be decreased and will lead to a better ranking and selection of the suppliers. Besides, in order to compare the performance of the suppliers with an ideal supplier, a “virtual” best practice supplier is introduced. The presence of the ideal virtual supplier will also increase the discrimination power of the model for a better ranking of the suppliers. Therefore, a new MCDEA model is proposed to simultaneously handle undesirable outputs and virtual DMU. The developed model is applied for green supplier selection problem. A numerical example illustrates the applicability of the proposed model.

Adaptive Dynamics of Resource Specialization

Ecological specialization in resource utilization has various facades ranging from nutritional resources via host use of parasites or phytophagous insects to local adaptation in different habitats. Therefore, the evolution of specialization affects the evolution of most other traits, which makes it one of the core issues in the theory of evolution. Hence, the evolution of specialization has gained enormous amounts of research interest, starting already from Darwin’s Origin of species in 1859. Vast majority of the theoretical studies has, however, focused on the mathematically most simple case with well-mixed populations and equilibrium dynamics. This thesis explores the possibilities to extend the evolutionary analysis of resource usage to spatially heterogeneous metapopulation models and to models with non-equilibrium dynamics. These extensions are enabled by the recent advances in the field of adaptive dynamics, which allows for a mechanistic derivation of the invasion-fitness function based on the ecological dynamics. In the evolutionary analyses, special focus is set to the case with two substitutable renewable resources. In this case, the most striking questions are, whether a generalist species is able to coexist with the two specialist species, and can such trimorphic coexistence be attained through natural selection starting from a monomorphic population. This is shown possible both due to spatial heterogeneity and due to non-equilibrium dynamics. In addition, it is shown that chaotic dynamics may sometimes inflict evolutionary suicide or cyclic evolutionary dynamics. Moreover, the relations between various ecological parameters and evolutionary dynamics are investigated. Especially, the relation between specialization and dispersal propensity turns out to be counter-intuitively non-monotonous. This observation served as inspiration to the analysis of joint evolution of dispersal and specialization, which may provide the most natural explanation to the observed coexistence of specialist and generalist species.

Selection of patients for myocardial perfusion scintigraphy based on fuzzy sets theory applied to clinical-epidemiological data and treadmill test results

Coronary artery disease (CAD) is a worldwide leading cause of death. The standard method for evaluating critical partial occlusions is coronary arteriography, a catheterization technique which is invasive, time consuming, and costly. There are noninvasive approaches for the early detection of CAD. The basis for the noninvasive diagnosis of CAD has been laid in a sequential analysis of the risk factors, and the results of the treadmill test and myocardial perfusion scintigraphy (MPS). Many investigators have demonstrated that the diagnostic applications of MPS are appropriate for patients who have an intermediate likelihood of disease. Although this information is useful, it is only partially utilized in clinical practice due to the difficulty to properly classify the patients. Since the seminal work of Lotfi Zadeh, fuzzy logic has been applied in numerous areas. In the present study, we proposed and tested a model to select patients for MPS based on fuzzy sets theory. A group of 1053 patients was used to develop the model and another group of 1045 patients was used to test it. Receiver operating characteristic curves were used to compare the performance of the fuzzy model against expert physician opinions, and showed that the performance of the fuzzy model was equal or superior to that of the physicians. Therefore, we conclude that the fuzzy model could be a useful tool to assist the general practitioner in the selection of patients for MPS.

Service product internationalisation : market selection for new service products in a multinational financial service firm

The significance and impact of services in the modern global economy has become greater and there has been more demand for decades in the academic community of international business for further research into better understanding internationalisation of services. Theories based on the internationalisation of manufacturing firms have been long questioned for their applicability to services. This study aims at contributing to understanding internationalisation of services by examining how market selection decisions are made for new service products within the existing markets of a multinational financial service provider. The study focused on the factors influencing market selection and the study was conducted as a case study on a multinational financial service firm and two of its new service products. Two directors responsible for the development and internationalisation of the case service products were interviewed in guided semi-structured interviews based on themes adopted from the literature review and the outcome theoretical framework. The main empirical findings of the study suggest that the most significant factors influencing the market selection for new service products within a multinational financial service firm’s existing markets are: commitment to the new service products by both the management and the rest of the product related organisation; capability and competence by the local country organisations to adopt new services; market potential which combines market size, market structure and competitive environment; product fit to the market requirements; and enabling partnerships. Based on the empirical findings, this study suggests a framework of factors influencing market selection for new service products, and proposes further research issues and methods to test and extend the findings of this research.

Immunodominance: a new hypothesis to explain parasite escape and host/parasite equilibrium leading to the chronic phase of Chagas' disease?

Intense immune responses are observed during human or experimental infection with the digenetic protozoan parasite Trypanosoma cruzi. The reasons why such immune responses are unable to completely eliminate the parasites are unknown. The survival of the parasite leads to a parasite-host equilibrium found during the chronic phase of chagasic infection in most individuals. Parasite persistence is recognized as the most likely cause of the chagasic chronic pathologies. Therefore, a key question in Chagas' disease is to understand how this equilibrium is established and maintained for a long period. Understanding the basis for this equilibrium may lead to new approaches to interventions that could help millions of individuals at risk for infection or who are already infected with T. cruzi. Here, we propose that the phenomenon of immunodominance may be significant in terms of regulating the host-parasite equilibrium observed in Chagas' disease. T. cruzi infection restricts the repertoire of specific T cells generating, in some cases, an intense immunodominant phenotype and in others causing a dramatic interference in the response to distinct epitopes. This immune response is sufficiently strong to maintain the host alive during the acute phase carrying them to the chronic phase where transmission usually occurs. At the same time, immunodominance interferes with the development of a higher and broader immune response that could be able to completely eliminate the parasite. Based on this, we discuss how we can interfere with or take advantage of immunodominance in order to provide an immunotherapeutic alternative for chagasic individuals.

THE GENERALIZED MAXIMUM LIKELIHOOD METHOD APPLIED TO HIGH PRESSURE PHASE EQUILIBRIUM

The generalized maximum likelihood method was used to determine binary interaction parameters between carbon dioxide and components of orange essential oil. Vapor-liquid equilibrium was modeled with Peng-Robinson and Soave-Redlich-Kwong equations, using a methodology proposed in 1979 by Asselineau, Bogdanic and Vidal. Experimental vapor-liquid equilibrium data on binary mixtures formed with carbon dioxide and compounds usually found in orange essential oil were used to test the model. These systems were chosen to demonstrate that the maximum likelihood method produces binary interaction parameters for cubic equations of state capable of satisfactorily describing phase equilibrium, even for a binary such as ethanol/CO2. Results corroborate that the Peng-Robinson, as well as the Soave-Redlich-Kwong, equation can be used to describe phase equilibrium for the following systems: components of essential oil of orange/CO2.

HIGH PRESSURE PHASE EQUILIBRIUM: PREDICTION OF ESSENTIAL OIL SOLUBILITY

This work describes a method to predict the solubility of essential oils in supercritical carbon dioxide. The method is based on the formulation proposed in 1979 by Asselineau, Bogdanic and Vidal. The Peng-Robinson and Soave-Redlich-Kwong cubic equations of state were used with the van der Waals mixing rules with two interaction parameters. Method validation was accomplished calculating orange essential oil solubility in pressurized carbon dioxide. The solubility of orange essential oil in carbon dioxide calculated at 308.15 K for pressures of 50 to 70 bar varied from 1.7± 0.1 to 3.6± 0.1 mg/g. For same the range of conditions, experimental solubility varied from 1.7± 0.1 to 3.6± 0.1 mg/g. Predicted values were not very sensitive to initial oil composition.

Scalable Feature Selection Applications for Genome-Wide Association Studies of Complex Diseases

Personalized medicine will revolutionize our capabilities to combat disease. Working toward this goal, a fundamental task is the deciphering of geneticvariants that are predictive of complex diseases. Modern studies, in the formof genome-wide association studies (GWAS) have afforded researchers with the opportunity to reveal new genotype-phenotype relationships through the extensive scanning of genetic variants. These studies typically contain over half a million genetic features for thousands of individuals. Examining this with methods other than univariate statistics is a challenging task requiring advanced algorithms that are scalable to the genome-wide level. In the future, next-generation sequencing studies (NGS) will contain an even larger number of common and rare variants. Machine learning-based feature selection algorithms have been shown to have the ability to effectively create predictive models for various genotype-phenotype relationships. This work explores the problem of selecting genetic variant subsets that are the most predictive of complex disease phenotypes through various feature selection methodologies, including filter, wrapper and embedded algorithms. The examined machine learning algorithms were demonstrated to not only be effective at predicting the disease phenotypes, but also doing so efficiently through the use of computational shortcuts. While much of the work was able to be run on high-end desktops, some work was further extended so that it could be implemented on parallel computers helping to assure that they will also scale to the NGS data sets. Further, these studies analyzed the relationships between various feature selection methods and demonstrated the need for careful testing when selecting an algorithm. It was shown that there is no universally optimal algorithm for variant selection in GWAS, but rather methodologies need to be selected based on the desired outcome, such as the number of features to be included in the prediction model. It was also demonstrated that without proper model validation, for example using nested cross-validation, the models can result in overly-optimistic prediction accuracies and decreased generalization ability. It is through the implementation and application of machine learning methods that one can extract predictive genotype–phenotype relationships and biological insights from genetic data sets.

Moisture adsorption characteristics of ginger slices

The moisture adsorption characteristics of dried ginger slices was studied to determine the effect of storage conditions on moisture adsorption for the purpose of shelf life prediction, selection of appropriate packaging materials, evaluate the goodness-of-fit of sorption models, and determine the thermodynamics of moisture adsorption for application in drying. There was a highly significant effect (p < 0.05) of water activity (a w), temperature, and pre-treatment on the equilibrium moisture content (EMC) of the dried ginger slices. At constant a w, the EMC decreased as temperature increased. The EMC of all samples increased as the a w increased at constant temperature. The sorbed moisture of the unpeeled ginger slices was higher than the peeled while those of unblanched samples were higher than the blanched. Henderson equation allows more accurate predictions about the isotherms with the lowest %RMS, and therefore, it describes best the adsorption data followed by GAB, Oswin, and Halsey models in that order. The monolayer moisture generally decreased with temperature for all samples. The isosteric heat decreased with moisture content approaching the asymptotic value or the latent heat of vaporization of pure water (∆Hst = 0) while the entropy of sorption was observed to increase with moisture content.