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.

Peruskoulun yläkouluvalinnat Turussa. Perheiden yläkouluvalintojen rakentuminen äitien puheissa ja toimissa

Tutkimuksessa tarkastellaan peruskoulun yläkouluvalintoja Turussa. Tarkastelun keskiössä ovat vuonna 1997 syntyneiden turkulaislasten vanhempien yläkouluvalintaa koskeva yleinen sekä omaan lapseen kiinnittyvä puhe ja toimijuus paikallisessa institutionaalisessa kouluvalintatilassa sekä vanhempien lapsen koulutukseen ja kouluvalintaan liittämät perustelut, merkitykset, arvot ja arvostukset. Tämän lisäksi tutkimuksessa tarkastellaan puheesta ja toimista rakentuvia perheiden kouluvalintastrategioita, joita peilataan äitien koulutuksellisiin ja sosiaalisiin resursseihin sekä paikalliseen toimintapolitiikkaan. Tutkimus ei kerro ainoastaan paikallisessa kontekstissa tapahtuvista kouluvalinnoista, vaan laajemmin yhteiskunnassa vallitsevista hierarkioista ja arvoista sekä koulutukseen ja sosioekonomiseen asemaan linkittyvistä normatiivisista toimintatavoista. Tutkimuksessa käytetään haastattelu- ja kyselyaineistoja. Aineistot kerättiin osana kahta laajempaa Suomen Akatemian rahoittamaa Helsingin ja Turun yliopistojen kanssa yhteistyössä tehtyä tutkimusprojektia Vanhemmat ja kouluvalinta – Perheiden koulutusstrategiat, eriarvoistuminen ja paikalliset koulupolitiikat suomalaisessa peruskoulussa (VAKOVA) 2009–2012 sekä Parents and School Choice. Family Strategies, Segregation and School Policies in Chilean and Finnish Basic Schooling (PASC) 2010–2013. Tutkimusaineistot koostuvat 87 turkulaisäidin haastattelusta ja kyselyaineistosta. Kyselyaineiston analyysissä on käytetty kuvailevia tilastollisia menetelmiä, ja sitä käytetään ensisijaisesti taustoittamaan haastatteluaineistoa. Haastatteluaineiston analyysi perustuu pääasiallisesti teema-analyysiin, mutta toimija-asema-analyysin osalta myös diskursiiviseen lähestymistapaan. Haastatteluaineiston pohjalta esiin nousseiden lasten koulutusta ja kouluvalintoja koskevien kuvausten perusteella perheiden yläkouluvalinnat jaettiin kolmeen erityyppiseen valintastrategiaan: perinteiseen lähikouluvalintastrategiaan (n=41), ambivalenttiseen kouluvalintastrategiaan (n=23) ja päämäärätietoiseen kouluvalintastrategiaan (n=23). Jokainen kolmesta strategiasta piti sisällään kahdenlaista toimijuutta kouluvalintakentällä. Ryhmittely kouluvalintastrategioittain ja toimija-asemittain perustui äitien puhetapaan kouluvalinnoista ja yleisemmin koulutukseen liitetyistä merkityksistä ja arvoista sekä konkreettiseen toimintaan kouluvalinnan suhteen. Lähikouluvalintastrategiaa suosivien jälkeläiset siirtyivät koulunsa yleisluokalle. Perheet toimivat valintakentällä kaupungin rajaavan toimintapolitiikan ohjaamina, jolloin kouluvalinta näytti passiiviselta. Osoitteenmukaiseen kouluun siirtymistä perusteltiin praktisilla syillä; koulumatkan pituudella, kulkuyhteyksillä ja lapsen kaverisuhteilla. Hyvinvointivaltion edellytykseksi nähtiin kaikille taattu samanvertainen koulutus ja edelleen luotettiin perinteistä peruskoulua määrittävään mahdollisuuksien tasa-arvoon. Koulutuksen yhdeksi tärkeäksi tehtäväksi nähtiin lapsen kasvattaminen hyvinvoivaksi ja onnelliseksi. Vanhempien toiminta oli perinteisen kouluvalintastrategian mukaista. Ambivalenttista kouluvalintastrategiaa käyttävistä perheistä toiminta kouluvalintakentällä oli kahtalaista. Äidit joko harkitsivat kouluvalintoja tai vertailivat kouluja ja niihin pääsymahdollisuuksia realistisesti tasapainoillen ohjaavan ja mahdollistavan toimintapolitiikan välimaastossa. Tärkeintä oli olla tietoinen kaupungin kouluvalintapolitiikasta sekä siitä, että valinnoilla voi olla merkitystä jälkikasvun koulupolulle. Eri vaihtoehtojen punnitsemisen jälkeen päädyttiin useimmin lähikoulun painotettuun opetukseen. Lapsen peruskoulutusta haluttiin rikastaa painotetulla opetuksella ja hänen toivottiin pääsevän motivoituneeseen ja oppimismyönteiseen koululuokkaan. Valintoja tehtiin paikallisen toimintapolitiikan puitteissa lapsen parasta toivoen. Koulutuksen tehtäväksi nähtiin lapsen intellektuaalinen kasvu kiedottuna koulutuksen tuottamaan hyvinvointiin ja onnellisuuteen. Perheiden valintastrategiaksi muodostui ambivalenttinen strategia motivoituneen oppimisympäristön löytämiseksi. Päämäärätietoista kouluvalintastrategiaa käyttävät vanhemmat hyödynsivät aktiivisesti erilaisia reittejä tiettyihin yläkouluihin pääsemiseksi. Ennakoivien perheiden lapset olivat opiskelleet sellaisessa alakoulussa, joka ei kuulunut yläkoulun oppilasalueelle, mutta takasi lapselle reitin suosittuun yläkouluun. Määrätietoisten perheissä havahduttiin valintoihin puolestaan yläkouluun siirryttäessä, jolloin koulupaikkaa haettiin sopivimman painotetun opetuksen ja koulun maineen mukaan pois lähiyläkoulusta. Lähikoulu -periaate koettiin epäoikeudenmukaiseksi, sillä lapsella tulee olla oikeus toteuttaa omia kykyjään ja lahjakkuuttaan valikoidussa oppilasryhmässä ja perheillä mahdollisuus valita lapsen koulu. Paikallinen toimintapolitiikka ei näyttänyt rajaavan vanhempien kouluvalintoja. Koulutuksen tarkoitukseksi nähtiin intellektuaalinen kasvu ja akateemissivistävä tehtävä. Päämäärätietoisen kouluvalintavalintastrategian tavoitteena oli perheelle sopivan habituksen takaaminen. Paikallinen toimintapolitiikka mahdollisti vanhempien erilaisten kouluvalintastrategioiden rakentumisen ohjaten ensisijaisesti lähiyläkouluun, mutta samalla mahdollistaen koulun valinnan toissijaisen haun kriteerein. Kouluvalintastrategioihin ja toimintatapaan kouluvalintakentällä kytkeytyi vanhempien koulutukseen liittämät arvot sekä kulttuuriset ja sosiaaliset resurssit ja se, miten niitä käytettiin.

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.

Plant-Herbivore Interaction in a Fragmented Landscape: Local Adaptation and Inbreeding

Reciprocal selection between interacting species is a major driver of biodiversity at both the genetic and the species level. This reciprocal selection, or coevolution, has led to the diversification of two highly diverse and abundant groups of organisms, flowering plants and their insect herbivores. In heterogeneous environments, the outcome of coevolved species interactions is influenced by the surrounding community and/or the abiotic environment. The process of adaptation allows species to adapt to their local conditions and to local populations of interacting species. However, adaptation can be disrupted or slowed down by an absence of genetic variation or by increased inbreeding, together with the following inbreeding depression, both of which are common in small and isolated populations that occur in fragmented environments. I studied the interaction between a long-lived plant Vincetoxicum hirundinaria and its specialist herbivore Abrostola asclepiadis in the southwestern archipelago of Finland. I focused on mutual local adaptation of plants and herbivores, which is a demonstration of reciprocal selection between species, a prerequisite for coevolution. I then proceeded to investigate the processes that could potentially hamper local adaptation, or species interaction in general, when the population size is small. I did this by examining how inbreeding of both plants and herbivores affects traits that are important for interaction, as well as among-population variation in the effects of inbreeding. In addition to bi-parental inbreeding, in plants inbreeding can arise from self-fertilization which has important implications for mating system evolution. I found that local adaptation of the plant to its herbivores varied among populations. Local adaptation of the herbivore varied among populations and years, being weaker in populations that were most connected. Inbreeding caused inbreeding depression in both plants and herbivores. In some populations inbreeding depression in herbivore biomass was stronger in herbivores feeding on inbred plants than in those feeding on outbred ones. For plants it was the other way around: inbreeding depression in anti-herbivore resistance decreased when the herbivores were inbred. Underlying some of the among-population variation in the effects of inbreeding is variation in plant phenolic compounds. However, variation in the modification of phenolic compounds in the digestive tract of the herbivore did not explain the inbreeding depression in herbivore biomass. Finally, adult herbivores had a preference for outbred host plants for egg deposition, and herbivore inbreeding had a positive effect on egg survival when the eggs were exposed to predators and parasitoids. These results suggest that plants and herbivores indeed exert reciprocal selection, as demonstrated by the significant local adaptation of V. hirundinaria and A. asclepiadis to one another. The most significant cause of disruption of the local adaptation of herbivore populations was population connectivity, and thus probably gene flow. In plants local adaptation tended to increase with increasing genetic variation. Whether or not inbreeding depression occurred varied according to the life-history stage of the herbivore and/or the plant trait in question. In addition, the effects of inbreeding strongly depended on the population. Taken together, inbreeding modified plant-herbivore interactions at several different levels, and can thus affect the strength of reciprocal selection between species. Thus inbreeding has the potential to affect the outcome of coevolution.

A glycoprotein E gene-deleted bovine herpesvirus 1 as a candidate vaccine strain

A bovine herpesvirus 1 (BoHV-1) defective in glycoprotein E (gE) was constructed from a Brazilian genital BoHV-1 isolate, by replacing the full gE coding region with the green fluorescent protein (GFP) gene for selection. Upon co-transfection of MDBK cells with genomic viral DNA plus the GFP-bearing gE-deletion plasmid, three fluorescent recombinant clones were obtained out of approximately 5000 viral plaques. Deletion of the gE gene and the presence of the GFP marker in the genome of recombinant viruses were confirmed by PCR. Despite forming smaller plaques, the BoHV-1△gE recombinants replicated in MDBK cells with similar kinetics and to similar titers to that of the parental virus (SV56/90), demonstrating that the gE deletion had no deleterious effects on replication efficacy in vitro. Thirteen calves inoculated intramuscularly with BoHV-1△gE developed virus neutralizing antibodies at day 42 post-infection (titers from 2 to 16), demonstrating the ability of the recombinant to replicate and to induce a serological response in vivo. Furthermore, the serological response induced by recombinant BoHV-1△gE could be differentiated from that induced by wild-type BoHV-1 by the use of an anti-gE antibody ELISA kit. Taken together, these results indicated the potential application of recombinant BoHV-1 △gE in vaccine formulations to prevent the losses caused by BoHV-1 infections while allowing for differentiation of vaccinated from naturally infected animals.

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.