173 research outputs found
Three Essays on Transaction Frequency, Financial Stress and Investment Ownership
No full textFinancial well-being is defined by an individual's ability to meet current and future financial obligations, feel confident about their financial future, and make choices that contribute to a fulfilling life. Financial well-being is also associated with mental health. This dissertation comprises three essays dedicated to providing implications for policymakers and financial practitioners to enhance individuals' financial well-being.
The first essay focuses on the role of investment knowledge, the most dominant variable impacting investment decisions, in transaction frequency, which, in turn, influences investment performance through asset reallocation and taxes/fees. The second essay examines the association between pandemic-related payments and financial stress, aiming to understand the role of such payments and payment usage in alleviating individuals' financial strain. Lastly, the third essay investigates the relationship between investment knowledge and ownership of fixed-income investments as measured by annuities and individual bonds, which can provide reliable income streams for retirement security but are associated with relatively low market participation.
Overall, this study provides policymakers and financial practitioners with insights into individuals' investment decision-making processes and suggests strategies to improve individuals' financial well-being.Embargo status: Restricted until 06/2030. To request the author grant access, click on the PDF link to the left
Validation of TOPAS MC for modelling the efficiency of an extended-range coaxial p-type HPGe detector
No full textTOPAS MC software was used to model the efficiency of a coaxial p-type HPGe detector, type GX9023 from Canberra. The model was validated by comparing experimental efficiencies with efficiencies calculated by TOPAS MC simulations. Three different geometries of radionuclide sources, placed at different heights from the detector endcap, were used to validate the model. The imposed criteria of 5% relative difference was met for a range of radionuclides and gamma-ray energies. As a result, the created detector model with TOPAS MC was considered validated.The research was conducted under a PhD grant at Hasselt University. This work also received support from the open access scheme EUFRAT at the European Commission’s Joint Research Centre in Geel, Belgium. The author would also like to thank the developer team of TOPAS MC for the special licence allowing the use of TOPAS MC for gamma-ray spec-trometry application
Towards the identification of regulatory networks using statistical and information theoretical methods on the mammalian transcriptome.
No full textOur comprehension of the genetic machinery regulating the expression of thousands of different genes controlling cell differentiation or responding to various external signals is still highly incomplete. Furthermore, recently discovered regulatory mechanisms like those mediated by microRNAs expand our knowledge but also add an additional layer of complexity. Since all genes are primarily transcribed into RNA, the genetic activity of gene differential expression can be estimated by measuring the RNA expression. Several techniques to measure large scale gene expression on the basis of RNA have been developed. In this work, data generated with the microarray technology, one of the most commonly used methods, were analyzed towards extracting novel biological regulatory structures. In this work, several aspects on the analysis of these large gene expression data are discussed. Since this is nowadays a common task, a lot has been written about various methods in all its particulars, but often from a more technical or statistical point of view. However, the aim of a biologist planning and carrying out a microarray experiment lies on the acquisition of novel biological findings. In fact, there is still a gap between the experimentalists and the methods developing community. The experimentalists are often not too familiar with the latest fancy method based on modern statistics as it is used in e.g. information theory whereas the developing community normally does not deal extensively with current biological questions. Therefore, the author of this work tries to give an additional view on the field of microarray analysis and the applicability of diverse methods. Hence, the focus is to discuss commonly used methods towards their usage, the underlying biological assumptions and the possible interpretations, pros and cons. Furthermore, beyond ordinary differential gene expression analyses, this work also concentrates on an unbiased search for hidden information in gene expression patterns. In the first section of chapter 1, a general overview about the main biological principles is given. The term transcriptome and its composition of several RNA types will be introduced. Furthermore the mechanism controlling gene expression will be presented. The chapter further explains the basic principles of microarray technology and also discusses the advantages and limitations of this method. Finally, by means of two different biological models, commonly used and a few more specialized and less popular analysis methods will be presented. In doing so, less emphasis is given on a complete and detailed mathematical description, but more on a general applicability and the biological outcome of these tools. Chapter 2 extensively discusses the usage of a blind source separation technique, independent component analysis (ICA), on a two class microarray dataset. Monocytes extracted from human donors were differentiated into macrophages using M-CSF (Macrophage Colony-Stimulating Factor). By applying ICA to the data, so called or could be extracted. According to referring biological annotations, these sub-modes were then combined to and elaborately discussed. In this way, several known biological signalling pathways as well as regulatory mechanism involved in monocyte differentiation could be reconstructed. Furthermore, a novel biological finding, the remaining proliferative potential of macrophages could also be identified [Lutter et al., 2008]. In chapter 3, again ICA was used, but in this case applied to time-dependent microarray data, and results were compared to a very common analysis method, hierarchical clustering. Time-dependent data was derived from human monocytes infected with the intracellular pathogen F. tularensis. Using the clustering approach, groups of genes referring to distinct timepoints were identified, and a temporal behaviour of genetic immune response could be reconstructed. In parallel, ICA was used to decompose the data into expression modes (analogously to chapter 2). These modes were then mapped on the experimental time course. Compared to the clustering results, the ICA-based reconstructed immune response was more detailed and temporal activity of distinct genes could be resolved more precisely [Lutter et al., 2009]. In the following chapter 4, three different microarray datasets were used to confirm a suggested regulatory mechanism. The observation that about 50% of all microRNAs in humans and mice are intronic and therefore coupled with the expression of protein coding genes, so-called host genes, allowed for the use of established large-scale gene expression measurement techniques to approximate microRNA expression. Since a single microRNA can regulate up to dozens of other protein-coding genes, the hypothesis that this expressional linkage includes an additional functional component was investigated. Using the ordinary clustering algorithm `hierarchical clustering' and an approach based on gene annotations, this hypothesis could be basically confirmed
Soziale Strukturen des Erfolgs: Winner-take-all-Prozesse in der Kreativwirtschaft
No full textWie entstehen Erfolgskonzentrationen? Während das Winner-take-all-Phänomen bisher als Konzentrationsprozess auf der Nachfrageseite durch massenhaft gleichförmige Kaufentscheidungen der Konsumenten begriffen wurde, sind Bedingungen und Konstellationen auf der Anbieterseite wenig berücksichtigt worden. In diesem Beitrag werden sechs Ansätze diskutiert, die das Potenzial einer soziologischen Erklärung des Winner- take-all-Phänomens ausloten. Jeder der Ansätze versucht dabei, Erfolgsungleichheiten aus den sozialen Strukturen heraus zu erklären, in die die Akteure auf dem Arbeitsmarkt eingebettet sind. Der Beitrag versteht sich als erster Zugang zu einem in der Soziologie zwar noch wenig erforschten, doch wichtigen Phänomen sozialer Ungleichheit und soll den Raum für zukünftige empirische Studien öffnen.How does success accumulate? While the winner-take-all phenomenon has been viewed as a process of accumulating demand that results from a huge number of consumers making the same purchase decision, the conditions and interactions on the supply side have received scant attention in the literature. This paper investigates six ways sociology could contribute toward shedding light on the winner-take-all phenomenon, all of which seek to explain unequal success by examining the social structures of the labor market in which the actors are embedded. The author takes a preliminary, exploratory look from a sociological perspective at an aspect of inequality that is socially significant, yet poorly understood. The approaches presented open the way for future empirical study
Towards the identification of regulatory networks using statistical and information theoretical methods on the mammalian transcriptome
No full textOur comprehension of the genetic machinery regulating the expression of thousands of different genes controlling cell differentiation or responding to various external signals is still highly incomplete. Furthermore, recently discovered regulatory mechanisms like those mediated by microRNAs expand our knowledge but also add an additional layer of complexity. Since all genes are primarily transcribed into RNA, the genetic activity of gene differential expression can be estimated by measuring the RNA expression. Several techniques to measure large scale gene expression on the basis of RNA have been developed. In this work, data generated with the microarray technology, one of the most commonly used methods, were analyzed towards extracting novel biological regulatory structures.
In this work, several aspects on the analysis of these large gene expression data are discussed. Since this is nowadays a common task, a lot has been written about various methods in all its particulars, but often from a more technical or statistical point of view. However, the aim of a biologist planning and carrying out a microarray experiment lies on the acquisition of novel biological findings. In fact, there is still a gap between the experimentalists and the methods developing community. The experimentalists are often not too familiar with the latest fancy method based on modern statistics as it is used in e.g. information theory whereas the developing community normally does not deal extensively with current biological questions. Therefore, the author of this work tries to give an additional view on the field of microarray analysis and the applicability of diverse methods. Hence, the focus is to discuss commonly used methods towards their usage, the underlying biological assumptions and the possible interpretations, pros and cons. Furthermore, beyond ordinary differential gene expression analyses, this work also concentrates on an unbiased search for hidden information in gene expression patterns.
In the first section of chapter 1, a general overview about the main biological principles is given. The term transcriptome and its composition of several RNA types will be introduced. Furthermore the mechanism controlling gene expression will be presented. The chapter further explains the basic principles of microarray technology and also discusses the advantages and limitations of this method. Finally, by means of two different biological models, commonly used and a few more specialized and less popular analysis methods will be presented. In doing so, less emphasis is given on a complete and detailed mathematical description, but more on a general applicability and the biological outcome of these tools.
Chapter 2 extensively discusses the usage of a blind source separation technique, independent component analysis (ICA), on a two class microarray dataset. Monocytes extracted from human donors were differentiated into macrophages using M-CSF (Macrophage Colony-Stimulating Factor). By applying ICA to the data, so called \textit{expression modes} or \textit{sub-modes} could be extracted. According to referring biological annotations, these sub-modes were then combined to \textit{meta modes} and elaborately discussed. In this way, several known biological signalling pathways as well as regulatory mechanism involved in monocyte differentiation could be reconstructed. Furthermore, a novel biological finding, the remaining proliferative potential of macrophages could also be identified [Lutter et al., 2008].
In chapter 3, again ICA was used, but in this case applied to time-dependent microarray data, and results were compared to a very common analysis method, hierarchical clustering. Time-dependent data was derived from human monocytes infected with the intracellular pathogen F. tularensis. Using the clustering approach, groups of genes referring to distinct timepoints were identified, and a temporal behaviour of genetic immune response could be reconstructed. In parallel, ICA was used to decompose the data into expression modes (analogously to chapter 2). These modes were then mapped on the experimental time course. Compared to the clustering results, the ICA-based reconstructed immune response was more detailed and temporal activity of distinct genes could be resolved more precisely [Lutter et al., 2009].
In the following chapter 4, three different microarray datasets were used to confirm a suggested regulatory mechanism. The observation that about 50% of all microRNAs in humans and mice are intronic and therefore coupled with the expression of protein coding genes, so-called host genes, allowed for the use of established large-scale gene expression measurement techniques to approximate microRNA expression. Since a single microRNA can regulate up to dozens of other protein-coding genes, the hypothesis that this expressional linkage includes an additional functional component was investigated. Using the ordinary clustering algorithm `hierarchical clustering' and an approach based on gene annotations, this hypothesis could be basically confirmed
The productivity effects of decentralized reforms - an analysis of the Chinese industrial reforms
No full textThe empirical literature on the effects of ownership has not distinguished between the effects of ownership and the effects of control. It has also generally ignored the dynamic effects of various ownership and control rights. Using a rich set of panel data about changes in China's state-owned enterprises, the author examines the static and dynamic effects of decentralizing ownership and control rights. He finds that productivity and growth rates improved significantly when reform improved the incentives for managers and employees to learn and to work hard - for example by decentralizing the rights to control wages, make production decisions, and appoint new managers. Increasing profit-retention rates and adopting performance contracts - conventionally viewed as the most important reforms for China's state enterprises - did not improve productivity much. Overall, decentralization accounted for a least 42 percent of productivity growth in Chinese state enterprises in the 1980s. Much of that gain came from improvements in the growth rate of productivity rather than in improved levels of productivity.Labor Policies,Economic Theory&Research,Environmental Economics&Policies,Banks&Banking Reform,Public Health Promotion,Economic Theory&Research,Environmental Economics&Policies,Banks&Banking Reform,Health Monitoring&Evaluation,Municipal Financial Management
- …
