Pulsatile Blood Flow Simulations in Computed Tomography(CT) Scan-Based and Idealized Geometries of Human Aorta

Blood flow in human aorta is an unsteady and complex phenomenon. The complex patterns are related to the geometrical features like curvature, bends, and branching and pulsatile nature of flow from left ventricle of heart. The aim of this work was to understand the effect of aorta geometry on the flow dynamics. To achieve this, 3D realistic and idealized models of descending aorta were reconstructed from Computed Tomography (CT) images of a female patient. The geometries were reconstructed using medical image processing code. The blood flow in aorta was assumed to be laminar and incompressible and the blood was assumed to be Newtonian fluid. A time dependent pulsatile and parabolic boundary condition was deployed at inlet. Steady and unsteady blood flow simulations were performed in real and idealized geometries of descending aorta using a Finite Volume Method (FVM) code. Analysis of Wall Shear Stress (WSS) distribution, pressure distribution, and axial velocity profiles were carried out in both geometries at steady and unsteady state conditions. The results obtained in thesis work reveal that the idealization of geometry underestimates the values of WSS especially near the region with sudden change of diameter. However, the resultant pressure and velocity in idealized geometry are close to those in real geometry

Integrating eco-innovation principles into regional strategy and policy: case of Päijät-Häme region, Finland

This study focuses on the integration of eco-innovation principles into strategy and policy at the regional level. The importance of regions as a level for integrating eco-innovative programs and activities served as the point of interest for this study. Eco-innovative activities and technologies are seen as means to meet sustainable development objective of improving regions’ quality of life. This study is conducted to get an in-depth understanding and learning about eco-innovation at regional level, and to know the basic concepts that are important in integrating eco-innovation principles into regional policy. Other specific objectives of this study are to know how eco-innovation are developed and practiced in the regions of the EU, and to analyze the main characteristic features of an eco-innovation model that is specifically developed at Päijät-Häme Region in Finland. Paijät-Häme Region is noted for its successful eco-innovation strategies and programs, hence, taken as casework in this study. Both primary (interviews) and secondary data (publicly available documents) are utilized in this study. The study shows that eco-innovation plays an important role in regional strategy as reviewed based on the experience of other regions in the EU. This is because of its localized nature which makes it easier to facilitate in a regional setting. Since regional authorities and policy-makers are normally focused on solving its localized environmental problems, eco-innovation principles can easily be integrated into regional strategy. The case study highlights Päijät-Häme Region’s eco-innovation strategies and projects which are characterized by strong connection of knowledge-producing institutions. Policy instruments supporting eco-innovation (e.g. environmental technologies) are very much focused on clean technologies, hence, justifying the formation of cleantech clusters and business parks in Päijät-Häme Region. A newly conceptualized SAMPO model of eco-innovation has been developed in Päijät-Häme Region to better capture the region’s characteristics and to eventually replace the current model employed by the Päijät-Häme Regional Authority. The SAMPO model is still under construction, however, review of its principles points to some of its three important spearheads – practice-based innovation, design (eco-design) and clean technology or environmental technology (environment).

Applications of graph transformation in tools for domain-specific modeling languages

The use of domain-specific languages (DSLs) has been proposed as an approach to cost-e ectively develop families of software systems in a restricted application domain. Domain-specific languages in combination with the accumulated knowledge and experience of previous implementations, can in turn be used to generate new applications with unique sets of requirements. For this reason, DSLs are considered to be an important approach for software reuse. However, the toolset supporting a particular domain-specific language is also domain-specific and is per definition not reusable. Therefore, creating and maintaining a DSL requires additional resources that could be even larger than the savings associated with using them. As a solution, di erent tool frameworks have been proposed to simplify and reduce the cost of developments of DSLs. Developers of tool support for DSLs need to instantiate, customize or configure the framework for a particular DSL. There are di erent approaches for this. An approach is to use an application programming interface (API) and to extend the basic framework using an imperative programming language. An example of a tools which is based on this approach is Eclipse GEF. Another approach is to configure the framework using declarative languages that are independent of the underlying framework implementation. We believe this second approach can bring important benefits as this brings focus to specifying what should the tool be like instead of writing a program specifying how the tool achieves this functionality. In this thesis we explore this second approach. We use graph transformation as the basic approach to customize a domain-specific modeling (DSM) tool framework. The contributions of this thesis includes a comparison of di erent approaches for defining, representing and interchanging software modeling languages and models and a tool architecture for an open domain-specific modeling framework that e ciently integrates several model transformation components and visual editors. We also present several specific algorithms and tool components for DSM framework. These include an approach for graph query based on region operators and the star operator and an approach for reconciling models and diagrams after executing model transformation programs. We exemplify our approach with two case studies MICAS and EFCO. In these studies we show how our experimental modeling tool framework has been used to define tool environments for domain-specific languages.

Kernel-Based Ranking. Methods for Learning and Performance Estimation

Machine learning provides tools for automated construction of predictive models in data intensive areas of engineering and science. The family of regularized kernel methods have in the recent years become one of the mainstream approaches to machine learning, due to a number of advantages the methods share. The approach provides theoretically well-founded solutions to the problems of under- and overfitting, allows learning from structured data, and has been empirically demonstrated to yield high predictive performance on a wide range of application domains. Historically, the problems of classification and regression have gained the majority of attention in the field. In this thesis we focus on another type of learning problem, that of learning to rank. In learning to rank, the aim is from a set of past observations to learn a ranking function that can order new objects according to how well they match some underlying criterion of goodness. As an important special case of the setting, we can recover the bipartite ranking problem, corresponding to maximizing the area under the ROC curve (AUC) in binary classification. Ranking applications appear in a large variety of settings, examples encountered in this thesis include document retrieval in web search, recommender systems, information extraction and automated parsing of natural language. We consider the pairwise approach to learning to rank, where ranking models are learned by minimizing the expected probability of ranking any two randomly drawn test examples incorrectly. The development of computationally efficient kernel methods, based on this approach, has in the past proven to be challenging. Moreover, it is not clear what techniques for estimating the predictive performance of learned models are the most reliable in the ranking setting, and how the techniques can be implemented efficiently. The contributions of this thesis are as follows. First, we develop RankRLS, a computationally efficient kernel method for learning to rank, that is based on minimizing a regularized pairwise least-squares loss. In addition to training methods, we introduce a variety of algorithms for tasks such as model selection, multi-output learning, and cross-validation, based on computational shortcuts from matrix algebra. Second, we improve the fastest known training method for the linear version of the RankSVM algorithm, which is one of the most well established methods for learning to rank. Third, we study the combination of the empirical kernel map and reduced set approximation, which allows the large-scale training of kernel machines using linear solvers, and propose computationally efficient solutions to cross-validation when using the approach. Next, we explore the problem of reliable cross-validation when using AUC as a performance criterion, through an extensive simulation study. We demonstrate that the proposed leave-pair-out cross-validation approach leads to more reliable performance estimation than commonly used alternative approaches. Finally, we present a case study on applying machine learning to information extraction from biomedical literature, which combines several of the approaches considered in the thesis. The thesis is divided into two parts. Part I provides the background for the research work and summarizes the most central results, Part II consists of the five original research articles that are the main contribution of this thesis.

The Progress and Challenges of the e-Government Initiatives and Implementation in the Kurdistan Region of Iraq

The Electronic Government (e-Government) means delivering the services and information to the citizens and businesses through the use of Information and Communication Technology (ICT) in order to enable them to interact more effectively with the government, and to increase the quality of the services. As many other governments in the developed and developing countries, the Kurdistan Regional Government (KRG) has embarked on the e-Government initiatives. This study revealed that there are various challenges which affect the e-Government in the Kurdistan Region of Iraq (KRI), but also a lot of e-Government progress has happened. In addition, based on the United Nations’ e-Government maturity level benchmarking, the e-Government in the KRI is at the interactive stage. In this study the services that the citizens want from the government in order to implement an appropriate e-Government were also identified.

Simulation of moisture profiles in a Latossol in Dourados region, in the State of Mato Grosso do Sul, Brazil

The understanding of unsaturated soil water flow at process-level is essential to develop proper management actions for environmental protection in agricultural systems. One important tool for simulation of soil water flow that has been used worldwide is the SWAP model. The aim of this work was to test and to calibrate the SWAP model by inverse modeling to describe moisture profiles in a Brazilian very clayey Latossol in Dourados, State of Mato Grosso do Sul, Brazil. The SWAP model was tested in an experimental field of 0.09 ha cultivated with soybean and soil profiles were sampled eight times between December 2006 and October 2007. The SWAP input values (i.e. soil water retention curves and meteorological data) were based on in-situ measurements. Simulations with uncalibrated soil water retention curves resulted in moisture profiles that were too wet for almost all sampling dates, in particular between 0-10 cm depth. After calibration of soil water retention curves, there was a good improvement in the simulated moisture profiles, which were within the range of measured values for almost all depths and sampling dates.

Climate risk in maize crop in the northeastern of Brazil based on climate change scenarios

This study evaluates the impacts of climate change on the agricultural zoning of climatic risk in maize crop cultivated in the Northeastern of Brazil, based on the Intergovernmental Panel on Climate Change (IPCC) reports. The water balance model, combined with geospatial technologies, was used to identify areas of the study region where the crops could suffer yield restrictions due to climate change. The data used in the study were the time series of rainfall with at least 30 years of daily data, crop coefficients, potential evapotranspiration and duration of the crop cycle. The scenarios of the increasing of air temperature used in the simulations were of 1.5ºC, 3ºC and 5ºC. The sowing date of maize crop from January to March appears to be less affected by warming scenarios than the sowing in November and December or April and May.

Creating possibilities for collective creativity : Brokerage Functions in Practice-Based Innovation

In the network era, creative achievements like innovations are more and more often created in interaction among different actors. The complexity of today‘s problems transcends the individual human mind, requiring not only individual but also collective creativity. In collective creativity, it is impossible to trace the source of new ideas to an individual. Instead, creative activity emerges from the collaboration and contribution of many individuals, thereby blurring the contribution of specific individuals in creating ideas. Collective creativity is often associated with diversity of knowledge, skills, experiences and perspectives. Collaboration between diverse actors thus triggers creativity and gives possibilities for collective creativity. This dissertation investigates collective creativity in the context of practice-based innovation. Practice-based innovation processes are triggered by problem setting in a practical context and conducted in non-linear processes utilising scientific and practical knowledge production and creation in cross-disciplinary innovation networks. In these networks diversity or distances between innovation actors are essential. Innovation potential may be found in exploiting different kinds of distances. This dissertation presents different kinds of distances, such as cognitive, functional and organisational which could be considered as sources of creativity and thus innovation. However, formation and functioning of these kinds of innovation networks can be problematic. Distances between innovating actors may be so great that a special interpretation function is needed – that is, brokerage. This dissertation defines factors that enhance collective creativity in practice-based innovation and especially in the fuzzy front end phase of innovation processes. The first objective of this dissertation is to study individual and collective creativity at the employee level and identify those factors that support individual and collective creativity in the organisation. The second objective is to study how organisations use external knowledge to support collective creativity in their innovation processes in open multi-actor innovation. The third objective is to define how brokerage functions create possibilities for collective creativity especially in the context of practice-based innovation. The research objectives have been studied through five substudies using a case-study strategy. Each substudy highlights various aspects of creativity and collective creativity. The empirical data consist of materials from innovation projects arranged in the Lahti region, Finland, or materials from the development of innovation methods in the Lahti region. The Lahti region has been chosen as the research context because the innovation policy of the region emphasises especially the promotion of practice-based innovations. The results of this dissertation indicate that all possibilities of collective creativity are not utilised in internal operations of organisations. The dissertation introduces several factors that could support collective creativity in organisations. However, creativity as a social construct is understood and experienced differently in different organisations, and these differences should be taken into account when supporting creativity in organisations. The increasing complexity of most potential innovations requires collaborative creative efforts that often exceed the boundaries of the organisation and call for the involvement of external expertise. In practice-based innovation different distances are considered as sources of creativity. This dissertation gives practical implications on how it is possible to exploit different kinds of distances knowingly. It underlines especially the importance of brokerage functions in open, practice-based innovation in order to create possibilities for collective creativity. As a contribution of this dissertation, a model of brokerage functions in practice-based innovation is formulated. According to the model, the results and success of brokerage functions are based on the context of brokerage as well as the roles, tasks, skills and capabilities of brokers. The brokerage functions in practice-based innovation are also possible to divide into social and cognitive brokerage.

The Possibilities and Dosimetric Limitations of MLC-Based Intensity-Modulated Radiotherapy Delivery and Optimization Techniques

The use of intensity-modulated radiotherapy (IMRT) has increased extensively in the modern radiotherapy (RT) treatments over the past two decades. Radiation dose distributions can be delivered with higher conformality with IMRT when compared to the conventional 3D-conformal radiotherapy (3D-CRT). Higher conformality and target coverage increases the probability of tumour control and decreases the normal tissue complications. The primary goal of this work is to improve and evaluate the accuracy, efficiency and delivery techniques of RT treatments by using IMRT. This study evaluated the dosimetric limitations and possibilities of IMRT in small (treatments of head-and-neck, prostate and lung cancer) and large volumes (primitive neuroectodermal tumours). The dose coverage of target volumes and the sparing of critical organs were increased with IMRT when compared to 3D-CRT. The developed split field IMRT technique was found to be safe and accurate method in craniospinal irradiations. By using IMRT in simultaneous integrated boosting of biologically defined target volumes of localized prostate cancer high doses were achievable with only small increase in the treatment complexity. Biological plan optimization increased the probability of uncomplicated control on average by 28% when compared to standard IMRT delivery. Unfortunately IMRT carries also some drawbacks. In IMRT the beam modulation is realized by splitting a large radiation field to small apertures. The smaller the beam apertures are the larger the rebuild-up and rebuild-down effects are at the tissue interfaces. The limitations to use IMRT with small apertures in the treatments of small lung tumours were investigated with dosimetric film measurements. The results confirmed that the peripheral doses of the small lung tumours were decreased as the effective field size was decreased. The studied calculation algorithms were not able to model the dose deficiency of the tumours accurately. The use of small sliding window apertures of 2 mm and 4 mm decreased the tumour peripheral dose by 6% when compared to 3D-CRT treatment plan. A direct aperture based optimization (DABO) technique was examined as a solution to decrease the treatment complexity. The DABO IMRT technique was able to achieve treatment plans equivalent with the conventional IMRT fluence based optimization techniques in the concave head-and-neck target volumes. With DABO the effective field sizes were increased and the number of MUs was reduced with a factor of two. The optimality of a treatment plan and the therapeutic ratio can be further enhanced by using dose painting based on regional radiosensitivities imaged with functional imaging methods.

Structural and operational optimisation : applications in energy systems

In this work mathematical programming models for structural and operational optimisation of energy systems are developed and applied to a selection of energy technology problems. The studied cases are taken from industrial processes and from large regional energy distribution systems. The models are based on Mixed Integer Linear Programming (MILP), Mixed Integer Non-Linear Programming (MINLP) and on a hybrid approach of a combination of Non-Linear Programming (NLP) and Genetic Algorithms (GA). The optimisation of the structure and operation of energy systems in urban regions is treated in the work. Firstly, distributed energy systems (DES) with different energy conversion units and annual variations of consumer heating and electricity demands are considered. Secondly, district cooling systems (DCS) with cooling demands for a large number of consumers are studied, with respect to a long term planning perspective regarding to given predictions of the consumer cooling demand development in a region. The work comprises also the development of applications for heat recovery systems (HRS), where paper machine dryer section HRS is taken as an illustrative example. The heat sources in these systems are moist air streams. Models are developed for different types of equipment price functions. The approach is based on partitioning of the overall temperature range of the system into a number of temperature intervals in order to take into account the strong nonlinearities due to condensation in the heat recovery exchangers. The influence of parameter variations on the solutions of heat recovery systems is analysed firstly by varying cost factors and secondly by varying process parameters. Point-optimal solutions by a fixed parameter approach are compared to robust solutions with given parameter variation ranges. In the work enhanced utilisation of excess heat in heat recovery systems with impingement drying, electricity generation with low grade excess heat and the use of absorption heat transformers to elevate a stream temperature above the excess heat temperature are also studied.

Prevalence and risk factors for Leptospira spp. in cattle herds in the south central region of Paraná state

The aim of this study was to determine the prevalence of anti-Leptospira spp. antibodies and the risk factors for Leptospira spp. infection in breeding cattle herds in the south central region of Paraná state. It was based on the statistic delineation/serological samples and information regarding the selected farms employed in the study of bovine brucellosis for Paraná state in the context of National Program for Control and Eradication of Brucellosis and Tuberculosis. A total of 1.880 females aged >24 months from 274 non vaccinated herds were studied. Serum samples were tested for antibodies against Leptospira spp. using microscopic agglutination test (MAT) with 22 Leptospira serovars. The epidemiological questionnaire was applied on all the selected farms and aimed to obtain epidemiological data. Hundred eighty one of 274 herds were positive for Leptospira spp./presenting prevalence of positive herds of 66.06% (IC95%=60.12-71,65%). Presence of >43 cattle (OR=3.120; IC=1.418-6.867)/animal purchase (OR=2.010; IC=1.154-3.500)/rent of pastures (OR=2.925; IC=1.060-8.068) and presence of maternity paddock (OR=1.981; IC=1,068-3,676) were identified as risk factors for leptospirosis due to any serovar in the multivariate logistic regression. Risk factors for leptospirosis due to serovar Hardjo were presence of >43 cattle (OR=3.622; IC=1.512-8,677)/animal purchase (OR=3.143; IC=1.557-6.342)/rent of pastures (OR=4.070; IC=1.370-12.087) and presence of horses (OR=2.981; IC=1.321-6.726). These results indicate that Leptospira spp. infection is widespread in the south central region of Paraná state and that factors related to the herd characteristic and management are associated with the infection.

Strategic Innovation Management Based on Three Dimensions

This thesis examines innovation development needs of firms in a remote rural region. The perspective of the study is in strategic innovation management and three dimensions of innovation development: innovation environment, value delivery and innovation capability. The framework is studied with a theoretical and methodological approach in the context of the development of a regional innovation system and the defining of innovation development needs. The thesis is based on existing innovation management literature, expanding it by examining the features of the three dimensions. The empirical data of the study comprise 50 purposefully selected firms within the region of Pielinen Karelia located in Eastern Finland. Most of the firms (70%) included in the study represent manufacturing firms, and over 90% are small and medium-sized enterprises. The research data consist of two questionnaires and an interview, which were done during 2011 in the connection of a regional development project. The point of view of the research is in regional development and harnessing the innovation capability of the firms within the region. The principal research approach applies soft systems methodology. The study explores the means to foster the innovativeness of firms from the viewpoints of innovation environment, innovation capability and value delivery. In closer detail, the study examines relations between the innovation capability factors, differences in innovation development needs within the value delivery system, between sectors and between firm size categories. The thesis offers three major contributions. First, the study extends earlier research on strategic innovation management by connecting the frameworks of innovation capability, innovation environment and value delivery process to the defining of innovation development needs at the regional level. The results deepen knowledge especially concerning practice-based innovation, peripheral regions and smaller firms. Second, the empirical work, based on a case study, confirms the existence of a structural connection integrating five factors of innovation capability. Statistical evidence is provided especially for the positive impacts of the improvement of absorption capability, marketing capability and networking capability, which are the main weaknesses of firms according to the study. Third, the research provides a methodological contribution by applying the innovation matrix in the defining of the innovation development needs of firms. The study demonstrates how the matrix improves possibility to target policy instruments and innovation services more efficiently through indicating significant differences between the innovation support needs regarding various time horizons and phases of innovation process.

Testing pigs of non-technified rearing farms for serum antibodies against Taenia solium in a region of the state of São Paulo, Brazil

Abstract: Taenia solium is a zoonotic tapeworm of great importance in developing countries, due to the occurrence of human taeniasis and cysticercosis. Pigs have an important role in the biological cycle of the parasite as intermediate hosts. The scientific literature has been describing risk factors associated with the occurrence of this disease that must be avoided in countries with poor sanitation, in order to reduce the exposure of swine to the parasite eggs. This research focused on testing pigs of non-technified rearing farms for serum antibodies against Taenia solium in the region of Jaboticabal municipality, in the state of São Paulo, Brazil. The found prevalence was 6.82% (CI 95% 4.18 - 9.45) at animal level and 28.87% (CI 95% 16.74 - 40.40) at herd level. These figures are probably associated with low technification adoption during animal rearing in the studied area, which increased the exposure of the animals to risk factors associated with the occurrence of Taenia solium complex. The results found based on serological evidences of swine cysticercosis in the studied region serves as a warning to public sanitary authorities to improve public health and control T. solium.

Deregulation’s Impact on the Railway Freight Transport Sector’s Future in the Baltic Sea Region

The European transport market has confronted several changes during the last decade. Due to European Union legislative mandates, the railway freight market was deregulated in 2007. The market followed the trend started by other transport modes as well as other previously regulated industries such as banking, telecommunications and energy. Globally, the first country to deregulate the railway freight market was the United States, with the introduction of the Staggers Rail Act in 1980. Some European countries decided to follow suit already before regulation was mandated; among the forerunners were the United Kingdom, Sweden and Germany. The previous research has concentrated only on these countries, which has provided an interesting research gap for this thesis. The Baltic Sea Region consists of countries with different kinds of liberalization paths, including Sweden and Germany, which have been on the frontline, whereas Lithuania and Finland have only one active railway undertaking, the incumbent. The transport market of the European Union is facing further challenges in the near future, due to the Sulphur Directive, oil dependency and the changing structure of European rail networks. In order to improve the accessibility of this peripheral area, further action is required. This research focuses on topics such as the progression of deregulation, barriers to entry, country-specific features, cooperation and internationalization. Based on the research results, it can be stated that the Baltic Sea Region’s railway freight market is expected to change in the future. Further private railway undertakings are anticipated, and these would change the market structure. The realization of European Union’s plans to increase the improved rail network to cover the Baltic States is strongly hoped for, and railway freight market counterparts inside and among countries are starting to enhance their level of cooperation. The Baltic Sea Region countries have several special national characteristics which influence the market and should be taken into account when companies evaluate possible market entry actions. According to thesis interviews, the Swedish market has a strong level of cooperation in the form of an old-boy network, and is supported by a positive attitude of the incumbent towards the private railway undertakings. This has facilitated the entry process of newcomers, and currently the market has numerous operating railway undertakings. A contrary example was found from Poland, where the incumbent sent old rolling stock to the scrap yard rather than sell it to private railway undertakings. The importance of personal relations is highlighted in Russia, followed by the railway market’s strong political bond with politics. Nonetheless, some barriers to entry are shared by the Baltic Sea Region, the main ones being acquisition of rolling stock, bureaucracy and needed investments. The railway freight market is internationalizing, which is perceived via several alliances as well as the increased number of mergers and acquisitions. After deregulation, markets seem to increase the number of railway undertakings at a rather fast pace, but with the passage of time, the larger operators tend to acquire smaller ones. Therefore, it is expected that in a decade’s time, the number of railway undertakings will start to decrease in the deregulation pioneer countries, while the ones coming from behind might still experience an increase. The Russian market is expected to be totally liberalized, and further alliances between the Russian Railways and European railway undertakings are expected to occur. The Baltic Sea Region’s railway freight market is anticipated to improve, and, based on the interviewees’ comments, attract more cargoes from road to rail.