Development of an environmentally friendly method of starch oxidation by hydrogen peroxide and a complex water-soluble Iron catalyst

Oxidized starch is a key component in the paper industry, where it is used as both surfacing sizer and filler. Large quantities are annually used for this purpose; however, the methods for the oxidation are not environmentally friendly. In our research, we have studied the possibility to replace the harmful oxidation agents, such as hypochlorite or iodates and transition metal catalysts, with a more environmentally friendly oxidant, hydrogen peroxide (H2O2), and a special metal complex catalyst (FePcS), of which only a small amount is needed. The work comprised batch and semi-batch studies by H2O2, ultrasound studies of starch particles, determination of low-molecular by-products and determination of the decomposition kinetics of H2O2 in the presence of starch and the catalyst. This resulted in a waste-free oxidation method, which only produces water and oxygen as side products. The starch oxidation was studied in both semi-batch and batch modes in respective to the oxidant (H2O2) addition. The semi-batch mode proved to yield a sufficient degree of substitution (COOH groups) for industrial purposes. Treatment of starch granules by ultrasound was found to improve the reactivity of starch. The kinetic results were found out to have a rather complex pattern – several oxidation phases were observed, apparently due to the fact that the oxidation reaction in the beginning only took place on the surface, whereas after a prolonged reaction time, partial degradation of the solid starch granules allowed further reaction in the interior parts. Batch-mode experiments enabled a more detailed study of the mechanisms of starch in the presence of H2O2 and the catalyst, but yielded less oxidized starch due to rapid decomposition of H2O2 due to its high concentrations. The effect of the solid-liquid (S/L) ratio in the reaction system was studied in batch experiments. These studies revealed that the presence of the catalyst and the starch enhance the H2O2 decomposition.

Interdependence of Emerging Eastern European stock markets

One of the main developments in the global economy during the past decades has been the growth of emerging economies. Projections for their long-term growth, changes in the investment climate, corporate transparency and demography point to an increasing role for these emerging economies in the global economy. Today, emerging economies are usually considered as financial markets offering opportunities for high returns, good risk diversification and improved return-to-risk ratios. However, researchers have noted that these advantages may be in decline because of the increasing market integration. Nevertheless, it is likely that certain financial markets and specific sectors will remain partially segmented and somewhat insulated from the global economy for the year to come. This doctoral dissertation investigates several stock markets in Emerging Eastern Europe (EEE), including the ones in Russia, Poland, Hungary, the Czech Republic, Bulgaria and Slovenia. The objective is to analyze the returns and financial risks in these emerging markets from international investor’s point of view. This study also examines the segmentation/integration of these financial markets and the possibilities to diversify and hedge financial risk. The dissertation is divided into two parts. The first includes a review of the theoretical background for the articles and a review of the literature on EEE stock markets. It includes an overview of the methodology and research design applied in the analysis and a summary of articles from the second part of this dissertation and their main findings. The second part consists of four research publications. This work contributes to studies on emerging stock markets in four ways. First, it adds to the body of research on the pricing of risk, providing new empirical evidence about partial stock market segmentation in EEE. The results suggest that the aggregate emerging market risk is a relevant driver for stock market returns and that this market risk can be used to price financial instruments and forecast their performance. Second, it contributes to the empirical research on the integration of stock markets, asset prices and exchange rates by identifying the relationships between these markets through volatility and asset pricing. The results show that certain sectors of stock markets in EEE are not as integrated as others. For example, the Polish consumer goods sector, the Hungarian telecommunications sector, and the Czech financial sector are somewhat isolated from their counterparts elsewhere in Europe. Nevertheless, an analysis of the impact of EU accession in 2004 on stock markets suggests that most of the EEE markets are becoming increasingly integrated with the global markets. Third, this thesis complements the scientific literature in the field of shock and volatility spillovers by examining the mechanism of spillover distribution among the EU and EEE countries. The results illustrate that spillovers in emerging markets are mostly from a foreign exchange to the stock markets. Moreover, the results show that the effects of external shocks on stock markets have increased after the enlargement of the EU in 2004. Finally, this study is unique because it analyzes the effects of foreign macroeconomic news on geographically closely related countries. The results suggest that the effects of macroeconomic announcements on volatility are significant and have effect that varies across markets and their sectors. Moreover, the results show that the foreign macroeconomic news releases, somewhat surprisingly, have a greater effect on the EEE markets than the local macroeconomic news. This dissertation has a number of implications for the industry and for practitioners. It analyses financial risk associated with investing in Emerging Eastern Europe. Investors may use this information to construct and optimize investment portfolios. Moreover, this dissertation provides insights for investors and portfolio managers considering asset allocation to protect value or obtain higher returns. The results have also implications for asset pricing and portfolio selection in light of macroeconomic news releases.

Roles of leadership in complex environments : Enhancing Knowledge Flows in Organisational Constellations through Practice-Based Innovation Processes

Global challenges, complexity and continuous uncertainty demand development of leadership approaches, employees and multi-organisation constellations. Current leadership theories do not sufficiently address the needs of complex business environments. First of all, before successful leadership models can be applied in practice, leadership needs to shift from the industrial age to the knowledge era. Many leadership models still view leadership solely through the perspective of linear process thinking. In addition, there is not enough knowledge or experience in applying these newer models in practice. Leadership theories continue to be based on the assumption that leaders possess or have access to all the relevant knowledge and capabilities to decide future directions without external advice. In many companies, however, the workforce consists of skilled professionals whose work and related interfaces are so challenging that the leaders cannot grasp all the linked viewpoints and cross-impacts alone. One of the main objectives of this study is to understand how to support participants in organisations and their stakeholders to, through practice-based innovation processes, confront various environments. Another aim is to find effective ways of recognising and reacting to diverse contexts, so companies and other stakeholders are better able to link to knowledge flows and shared value creation processes in advancing joint value to their customers. The main research question of this dissertation is, then, to seek understanding of how to enhance leadership in complex environments. The dissertation can, on the whole, be characterised as a qualitative multiple-case study. The research questions and objectives were investigated through six studies published in international scientific journals. The main methods applied were interviews, action research and a survey. The empirical focus was on Finnish companies, and the research questions were examined in various organisations at the top levels (leaders and managers) and bottom levels (employees) in the context of collaboration between organisations and cooperation between case companies and their client organisations. However, the emphasis of the analysis is the internal and external aspects of organisations, which are conducted in practice-based innovation processes. The results of this study suggest that the Cynefin framework, complexity leadership theory and transformational leadership represent theoretical models applicable to developing leadership through practice-based innovation. In and of themselves, they all support confronting contemporary challenges, but an implementable method for organisations may be constructed by assimilating them into practice-based innovation processes. Recognition of diverse environments, their various contexts and roles in the activities and collaboration of organisations and their interest groups is ever-more important to achieving better interaction in which a strategic or formal status may be bypassed. In innovation processes, it is not necessarily the leader who is in possession of the essential knowledge; thus, it is the role of leadership to offer methods and arenas where different actors may generate advances. Enabling and supporting continuous interaction and integrated knowledge flows is of crucial importance, to achieve emergence of innovations in the activities of organisations and various forms of collaboration. The main contribution of this dissertation relates to applying these new conceptual models in practice. Empirical evidence on the relevance of different leadership roles in practice-based innovation processes in Finnish companies is another valuable contribution. Finally, the dissertation sheds light on the significance of combining complexity science with leadership and innovation theories in research.

Computational studies for the design of process equipment with complex geometries

This study combines several projects related to the flows in vessels with complex shapes representing different chemical apparata. Three major cases were studied. The first one is a two-phase plate reactor with a complex structure of intersecting micro channels engraved on one plate which is covered by another plain plate. The second case is a tubular microreactor, consisting of two subcases. The first subcase is a multi-channel two-component commercial micromixer (slit interdigital) used to mix two liquid reagents before they enter the reactor. The second subcase is a micro-tube, where the distribution of the heat generated by the reaction was studied. The third case is a conventionally packed column. However, flow, reactions or mass transfer were not modeled. Instead, the research focused on how to describe mathematically the realistic geometry of the column packing, which is rather random and can not be created using conventional computeraided design or engineering (CAD/CAE) methods. Several modeling approaches were used to describe the performance of the processes in the considered vessels. Computational fluid dynamics (CFD) was used to describe the details of the flow in the plate microreactor and micromixer. A space-averaged mass transfer model based on Fick’s law was used to describe the exchange of the species through the gas-liquid interface in the microreactor. This model utilized data, namely the values of the interfacial area, obtained by the corresponding CFD model. A common heat transfer model was used to find the heat distribution in the micro-tube. To generate the column packing, an additional multibody dynamic model was implemented. Auxiliary simulation was carried out to determine the position and orientation of every packing element in the column. This data was then exported into a CAD system to generate desirable geometry, which could further be used for CFD simulations. The results demonstrated that the CFD model of the microreactor could predict the flow pattern well enough and agreed with experiments. The mass transfer model allowed to estimate the mass transfer coefficient. Modeling for the second case showed that the flow in the micromixer and the heat transfer in the tube could be excluded from the larger model which describes the chemical kinetics in the reactor. Results of the third case demonstrated that the auxiliary simulation could successfully generate complex random packing not only for the column but also for other similar cases.

Interdependence of International Stock Markets, European Government Bond Market and Gold Market

This thesis examines the interdependence of international stock markets (the USA, Europe, Japan, emerging markets, and frontier markets), European government bond market, and gold market during the 21st century. Special focus is on the dynamics of the correlations between the markets, as well as on, spillovers in mean returns and volatility. The mean return spillovers are examined on the basis of the bivariate VAR(1) model, whereas the bivariate BEKK-GARCH(1, 1) model is employed for the analysis of the volatility spillovers. In order to analyze the spillover effects in different market conditions, the full sample period from 2000 to 2013 is divided into the pre-crisis period (2000–2006) and the crisis period (2007–2013). The results indicate an increasing interdependence especially within international stock markets during the periods of financial turbulence, and are thus consistent with the existing literature. Hence, bond and gold markets provide the best diversification benefits for equity investors, particularly during the periods of market turmoil.

Large-eddy simulation of wind flows over complex terrains for wind energy applications

Wind energy has obtained outstanding expectations due to risks of global warming and nuclear energy production plant accidents. Nowadays, wind farms are often constructed in areas of complex terrain. A potential wind farm location must have the site thoroughly surveyed and the wind climatology analyzed before installing any hardware. Therefore, modeling of Atmospheric Boundary Layer (ABL) flows over complex terrains containing, e.g. hills, forest, and lakes is of great interest in wind energy applications, as it can help in locating and optimizing the wind farms. Numerical modeling of wind flows using Computational Fluid Dynamics (CFD) has become a popular technique during the last few decades. Due to the inherent flow variability and large-scale unsteadiness typical in ABL flows in general and especially over complex terrains, the flow can be difficult to be predicted accurately enough by using the Reynolds-Averaged Navier-Stokes equations (RANS). Large- Eddy Simulation (LES) resolves the largest and thus most important turbulent eddies and models only the small-scale motions which are more universal than the large eddies and thus easier to model. Therefore, LES is expected to be more suitable for this kind of simulations although it is computationally more expensive than the RANS approach. With the fast development of computers and open-source CFD software during the recent years, the application of LES toward atmospheric flow is becoming increasingly common nowadays. The aim of the work is to simulate atmospheric flows over realistic and complex terrains by means of LES. Evaluation of potential in-land wind park locations will be the main application for these simulations. Development of the LES methodology to simulate the atmospheric flows over realistic terrains is reported in the thesis. The work also aims at validating the LES methodology at a real scale. In the thesis, LES are carried out for flow problems ranging from basic channel flows to real atmospheric flows over one of the most recent real-life complex terrain problems, the Bolund hill. All the simulations reported in the thesis are carried out using a new OpenFOAM® -based LES solver. The solver uses the 4th order time-accurate Runge-Kutta scheme and a fractional step method. Moreover, development of the LES methodology includes special attention to two boundary conditions: the upstream (inflow) and wall boundary conditions. The upstream boundary condition is generated by using the so-called recycling technique, in which the instantaneous flow properties are sampled on aplane downstream of the inlet and mapped back to the inlet at each time step. This technique develops the upstream boundary-layer flow together with the inflow turbulence without using any precursor simulation and thus within a single computational domain. The roughness of the terrain surface is modeled by implementing a new wall function into OpenFOAM® during the thesis work. Both, the recycling method and the newly implemented wall function, are validated for the channel flows at relatively high Reynolds number before applying them to the atmospheric flow applications. After validating the LES model over simple flows, the simulations are carried out for atmospheric boundary-layer flows over two types of hills: first, two-dimensional wind-tunnel hill profiles and second, the Bolund hill located in Roskilde Fjord, Denmark. For the twodimensional wind-tunnel hills, the study focuses on the overall flow behavior as a function of the hill slope. Moreover, the simulations are repeated using another wall function suitable for smooth surfaces, which already existed in OpenFOAM® , in order to study the sensitivity of the flow to the surface roughness in ABL flows. The simulated results obtained using the two wall functions are compared against the wind-tunnel measurements. It is shown that LES using the implemented wall function produces overall satisfactory results on the turbulent flow over the two-dimensional hills. The prediction of the flow separation and reattachment-length for the steeper hill is closer to the measurements than the other numerical studies reported in the past for the same hill geometry. The field measurement campaign performed over the Bolund hill provides the most recent field-experiment dataset for the mean flow and the turbulence properties. A number of research groups have simulated the wind flows over the Bolund hill. Due to the challenging features of the hill such as the almost vertical hill slope, it is considered as an ideal experimental test case for validating micro-scale CFD models for wind energy applications. In this work, the simulated results obtained for two wind directions are compared against the field measurements. It is shown that the present LES can reproduce the complex turbulent wind flow structures over a complicated terrain such as the Bolund hill. Especially, the present LES results show the best prediction of the turbulent kinetic energy with an average error of 24.1%, which is a 43% smaller than any other model results reported in the past for the Bolund case. Finally, the validated LES methodology is demonstrated to simulate the wind flow over the existing Muukko wind farm located in South-Eastern Finland. The simulation is carried out only for one wind direction and the results on the instantaneous and time-averaged wind speeds are briefly reported. The demonstration case is followed by discussions on the practical aspects of LES for the wind resource assessment over a realistic inland wind farm.

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.

Interdependence of Macroeconomics Variables, Stock Market Returns and Stock Market Volatility in Latin America

This thesis examines the interdependence of macroeconomic variables, stock market returns and stock market volatility in Latin America between 2000 and 2015. Argentina, Brazil, Chile, Colombia, Mexico and Peru were chosen as the sample markets, while inflation, interest rate, exchange rate, money supply, oil and gold were chosen as the sample macroeconomic variables. Bivariate VAR (1) model was applied to examine the mean return spillovers between the variables, whereas GARCH (1, 1) – BEKK model was applied to capture the volatility spillovers. The sample was divided into two smaller sub-periods, where the first sub-period covers from 2000 to 2007, and the second sub-period covers from 2007 to 2015. The empirical results report significant shock transmissions and volatility spillovers between inflation, interest rate, exchange rate, money supply, gold, oil and the selected markets, which suggests interdependence between the variables.