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.

Association between EcoRI fragment-length polymorphism of the immunoglobulin lambda variable 8 (IGLV8) gene family with rheumatoid arthritis and systemic lupus erythematosus

The human immunoglobulin lambda variable 8 (IGLV8) subgroup is a gene family containing three members, one of them included in a monomorphic 3.7-kb EcoRI genomic fragment located at the major lambda variable locus on chromosome 22q11.1 (gene IGLV8a, EMBL accession No. Z73650) at 100% frequency in the normal urban population. The second is a polymorphic RFLP allele included in a 6.0-kb EcoRI fragment at 10% frequency, and the third is located in a monomorphic 8.0-kb EcoRI fragment at 100% frequency, the last being translocated to chromosome 8q11.2 and considered to be an orphan gene. Our Southern blot-EcoRI-RFLP studies in normal individuals and in patients with rheumatoid arthritis (RA) or with systemic lupus erythematosus (SLE), using a specific probe for the IGLV8 gene family (probe pVL8, EMBL accession No. X75424), have revealed the two monomorphic genomic fragments containing the IGLV8 genes, i.e., the 3.7-kb fragment from chromosome 22q11.1 and the 8.0-kb fragment from 8q11.2, both occurring at 100% frequency (103 normal individuals, 48 RA and 28 SLE patients analyzed), but absence of the 6.0-kb IGLV8 polymorphic RFLP allele in all RA or SLE patients. As expected, the frequency of the 6.0-kb allele among the normal individuals was 10%. These findings suggest an association between the absence of the 6.0-kb EcoRI fragment and rheumatoid arthritis and systemic lupus erythematosus.

Lebesgue and Sobolev spaces with variable exponents

Kirjallisuusarvostelu

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.

Pipeline for macro- and microarray analyses

The pipeline for macro- and microarray analyses (PMmA) is a set of scripts with a web interface developed to analyze DNA array data generated by array image quantification software. PMmA is designed for use with single- or double-color array data and to work as a pipeline in five classes (data format, normalization, data analysis, clustering, and array maps). It can also be used as a plugin in the BioArray Software Environment, an open-source database for array analysis, or used in a local version of the web service. All scripts in PMmA were developed in the PERL programming language and statistical analysis functions were implemented in the R statistical language. Consequently, our package is a platform-independent software. Our algorithms can correctly select almost 90% of the differentially expressed genes, showing a superior performance compared to other methods of analysis. The pipeline software has been applied to 1536 expressed sequence tags macroarray public data of sugarcane exposed to cold for 3 to 48 h. PMmA identified thirty cold-responsive genes previously unidentified in this public dataset. Fourteen genes were up-regulated, two had a variable expression and the other fourteen were down-regulated in the treatments. These new findings certainly were a consequence of using a superior statistical analysis approach, since the original study did not take into account the dependence of data variability on the average signal intensity of each gene. The web interface, supplementary information, and the package source code are available, free, to non-commercial users at http://ipe.cbmeg.unicamp.br/pub/PMmA.

Variable positive end-expiratory pressure can maintain oxygenation in experimental acute respiratory distress syndrome induced by oleic acid in dogs

The use of positive end-expiratory pressure (PEEP) or lung recruitment maneuvers (RM) to improve oxygenation in acute respiratory distress syndrome (ARDS) is used but it may reduce cardiac output (CO). Intermittent PEEP may avoid these complications. Our objective was to determine if variable PEEP compared with constant PEEP is capable of maintaining arterial oxygenation and minimizing hemodynamic alterations with or without RM. Eighteen dogs with ARDS induced by oleic acid were randomized into three equal groups: group 1, low variable PEEP; group 2, high variable PEEP, and group 3, RM + high variable PEEP. All groups were submitted to constant PEEP, followed by variable PEEP (PEEP was increased from 5 to 10 cmH2O in group 1, and from 5 to 18 cmH2O in the other two groups). PaO2 was higher in group 3 (356.2 ± 65.4 mmHg) than in group 1 (92.7 ± 29.7 mmHg) and group 2 (228.5 ± 72.4 mmHg), P < 0.05. PaO2 was maintained during variable PEEP except in group 2 (318.5 ± 82.9 at constant PEEP to 228.5 ± 72.4 at variable PEEP). There was a reduction in CO in group 3 after RM (3.9 ± 1.1 before to 2.7 ± 0.5 L·min-1·(m2)-1 after; P < 0.05), but there was not any difference between constant and variable PEEP periods (2.7 ± 0.5 and 2.4 ± 0.7 L·min-1·(m2)-1; P > 0.05. Variable PEEP is able to maintain PaO2 when performed in combination with RM in dogs with ARDS. After RM, CO was reduced and there was no relevant difference between the variable and constant PEEP periods.

A conjugate of an anti-midkine single-chain variable fragment to doxorubicin inhibits tumor growth

Doxorubicin (DOX) was conjugated to a single-chain variable fragment (scFv) against human midkine (MK), and the conjugate (scFv-DOX) was used to target the chemotherapeutic agent to a mouse solid tumor model in which the tumor cells expressed high levels of human MK. The His-tagged recombinant scFv was expressed in bacteria, purified by metal affinity chromatography, and then conjugated to DOX using oxidative dextran (Dex) as a linker. The molecular formula of this immunoconjugate was scFv(Dex)1.3(DOX)20. In vitro apoptosis assays showed that the scFv-DOX conjugate was more cytotoxic against MK-transfected human adenocarcinoma cells (BGC823-MK) than untransfected cells (55.3 ± 2.4 vs 22.4 ± 3.8%) for three independent experiments. Nude mice bearing BGC823-MK solid tumors received scFv-DOX or equivalent doses of scFv + DOX for 2 weeks and tumor growth was more effectively inhibited by the scFv-DOX conjugate than by scFv + DOX (51.83% inhibition vs 40.81%). Histological analysis of the tumor tissues revealed that the highest levels of DOX accumulated in tumors from mice treated with scFv-DOX and this resulted in more extensive tumor cell death than in animals treated with the equivalent dose of scFv + DOX. These results show that the scFv-DOX conjugate effectively inhibited tumor growth in vivo and suggest that antigen-specific scFv may be competent drug-carriers.

Expression, production, and renaturation of a functional single-chain variable antibody fragment (scFv) against human ICAM-1

Intercellular adhesion molecule-1 (ICAM-1) is an important factor in the progression of inflammatory responses in vivo. To develop a new anti-inflammatory drug to block the biological activity of ICAM-1, we produced a monoclonal antibody (Ka=4.19×10−8 M) against human ICAM-1. The anti-ICAM-1 single-chain variable antibody fragment (scFv) was expressed at a high level as inclusion bodies in Escherichia coli. We refolded the scFv (Ka=2.35×10−7 M) by ion-exchange chromatography, dialysis, and dilution. The results showed that column chromatography refolding by high-performance Q Sepharose had remarkable advantages over conventional dilution and dialysis methods. Furthermore, the anti-ICAM-1 scFv yield of about 60 mg/L was higher with this method. The purity of the final product was greater than 90%, as shown by denaturing gel electrophoresis. Enzyme-linked immunosorbent assay, cell culture, and animal experiments were used to assess the immunological properties and biological activities of the renatured scFv.

Comparison of Model-Based Flow Rate Estimation Methods in Frequency-Converter-Driven Pumps and Fans

Fluid handling systems such as pump and fan systems are found to have a significant potential for energy efficiency improvements. To deliver the energy saving potential, there is a need for easily implementable methods to monitor the system output. This is because information is needed to identify inefficient operation of the fluid handling system and to control the output of the pumping system according to process needs. Model-based pump or fan monitoring methods implemented in variable speed drives have proven to be able to give information on the system output without additional metering; however, the current model-based methods may not be usable or sufficiently accurate in the whole operation range of the fluid handling device. To apply model-based system monitoring in a wider selection of systems and to improve the accuracy of the monitoring, this paper proposes a new method for pump and fan output monitoring with variable-speed drives. The method uses a combination of already known operating point estimation methods. Laboratory measurements are used to verify the benefits and applicability of the improved estimation method, and the new method is compared with five previously introduced model-based estimation methods. According to the laboratory measurements, the new estimation method is the most accurate and reliable of the model-based estimation methods.

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.

Doubly fed wind turbine performance in variable grid conditions

Wind turbines based on doubly fed induction generators (DFIG) become the most popular solution in high power wind generation industry. While this topology provides great performance with the reduced power rating of power converter, it has more complicated structure in comparison with full-rated topologies, and therefore leads to complexity of control algorithms and electromechanical processes in the system. The purpose of presented study is to present a proper vector control scheme for the DFIG and overall control for the WT to investigate its behavior at different wind speeds and in different grid voltage conditions: voltage sags, magnitude and frequency variations. The key principles of variable-speed wind turbine were implemented in simulation model and demonstrated during the study. Then, based on developed control scheme and mathematical model, the set of simulation is made to analyze reactive power capabilities of the DFIG wind turbine. Further, the rating of rotor-side converter is modified to not only generate active rated active power, but also to fulfill Grid Codes. Results of modelling and analyzing of the DFIG WT behavior under different speeds and different voltage conditions are presented in the work.