Use of scenario analysis in studying emerging technology – Case Grid computing

Tutkimuksen selvitettiin miten skenaarioanalyysia voidaan käyttää uuden teknologian tutkimisessa. Työssä havaittiin, että skenaarioanalyysin soveltuvuuteen vaikuttaa eniten teknologisen muutoksen taso ja saatavilla olevan tiedon luonne. Skenaariomenetelmä soveltuu hyvin uusien teknologioiden tutkimukseen erityisesti radikaalien innovaatioiden kohdalla. Syynä tähän on niihin liittyvä suuri epävarmuus, kompleksisuus ja vallitsevan paradigman muuttuminen, joiden takia useat muut tulevaisuuden tutkimuksen menetelmät eivät ole tilanteessa käyttökelpoisia. Työn empiirisessä osiossa tutkittiin hilaverkkoteknologian tulevaisuutta skenaarioanalyysin avulla. Hilaverkot nähtiin mahdollisena disruptiivisena teknologiana, joka radikaalina innovaationa saattaa muuttaa tietokonelaskennan nykyisestä tuotepohjaisesta laskentakapasiteetin ostamisesta palvelupohjaiseksi. Tällä olisi suuri vaikutus koko nykyiseen ICT-toimialaan erityisesti tarvelaskennan hyödyntämisen ansiosta. Tutkimus tarkasteli kehitystä vuoteen 2010 asti. Teorian ja olemassa olevan tiedon perusteella muodostettiin vahvaan asiantuntijatietouteen nojautuen neljä mahdollista ympäristöskenaariota hilaverkoille. Skenaarioista huomattiin, että teknologian kaupallinen menestys on vielä monen haasteen takana. Erityisesti luottamus ja lisäarvon synnyttäminen nousivat tärkeimmiksi hilaverkkojen tulevaisuutta ohjaaviksi tekijöiksi.

A Dependency Parsing Approach to Biomedical Text Mining

Biomedical research is currently facing a new type of challenge: an excess of information, both in terms of raw data from experiments and in the number of scientific publications describing their results. Mirroring the focus on data mining techniques to address the issues of structured data, there has recently been great interest in the development and application of text mining techniques to make more effective use of the knowledge contained in biomedical scientific publications, accessible only in the form of natural human language. This thesis describes research done in the broader scope of projects aiming to develop methods, tools and techniques for text mining tasks in general and for the biomedical domain in particular. The work described here involves more specifically the goal of extracting information from statements concerning relations of biomedical entities, such as protein-protein interactions. The approach taken is one using full parsing—syntactic analysis of the entire structure of sentences—and machine learning, aiming to develop reliable methods that can further be generalized to apply also to other domains. The five papers at the core of this thesis describe research on a number of distinct but related topics in text mining. In the first of these studies, we assessed the applicability of two popular general English parsers to biomedical text mining and, finding their performance limited, identified several specific challenges to accurate parsing of domain text. In a follow-up study focusing on parsing issues related to specialized domain terminology, we evaluated three lexical adaptation methods. We found that the accurate resolution of unknown words can considerably improve parsing performance and introduced a domain-adapted parser that reduced the error rate of theoriginal by 10% while also roughly halving parsing time. To establish the relative merits of parsers that differ in the applied formalisms and the representation given to their syntactic analyses, we have also developed evaluation methodology, considering different approaches to establishing comparable dependency-based evaluation results. We introduced a methodology for creating highly accurate conversions between different parse representations, demonstrating the feasibility of unification of idiverse syntactic schemes under a shared, application-oriented representation. In addition to allowing formalism-neutral evaluation, we argue that such unification can also increase the value of parsers for domain text mining. As a further step in this direction, we analysed the characteristics of publicly available biomedical corpora annotated for protein-protein interactions and created tools for converting them into a shared form, thus contributing also to the unification of text mining resources. The introduced unified corpora allowed us to perform a task-oriented comparative evaluation of biomedical text mining corpora. This evaluation established clear limits on the comparability of results for text mining methods evaluated on different resources, prompting further efforts toward standardization. To support this and other research, we have also designed and annotated BioInfer, the first domain corpus of its size combining annotation of syntax and biomedical entities with a detailed annotation of their relationships. The corpus represents a major design and development effort of the research group, with manual annotation that identifies over 6000 entities, 2500 relationships and 28,000 syntactic dependencies in 1100 sentences. In addition to combining these key annotations for a single set of sentences, BioInfer was also the first domain resource to introduce a representation of entity relations that is supported by ontologies and able to capture complex, structured relationships. Part I of this thesis presents a summary of this research in the broader context of a text mining system, and Part II contains reprints of the five included publications.

Modelling of North-West Russian market behavior considering technical restrictions of transmission grid

Nowadays the Finnish-Russian electric energy interaction is carried out through the back-to-back DC Vyborg substation and several power plants working synchronously with Finnish power system. Constant amount of energy flows in one direction — from Russia to Finland. But the process of electricity market development in Russian energy system makes the new possibilities of electrical cooperation available. The goal of master's thesis is to analyze the current state and possible evolution trends of North-West Russian system in relation with future possible change in power flow between Russia and Finland. The research is done by modelling the market of North-West Russia and examination of technical grid restrictions. The operational market models of North-West region of Russia for the years 2008 and 2015 were created during the research process. The description of prepared market models together with modelling results and their analysis are shown in the work. The description of power flow study process and results are also presented.

Development of power flows around Baltic countries with new grid links

In the Thesis main focus is done on power flow development paths around the Baltic States as well as on market-based requirements for creation of the common Baltic electricity market. Current market regulations between the countries are presented; barriers for creating competitive common Baltic power market and for electricity trading with third countries are clarified; solutions are offered and corresponding road map is developed. Future power development paths around the Baltic States are analysed. For this purpose the 330 kV transmission grid of Estonia, Latvia and Lithuania is modelled in a power flow tool. Power flow calculations are carried out for winter and summer peak and off-peak load periods in 2020 with different combinations of interconnections. While carrying out power balance experiments several power flow patterns in the Baltic States are revealed. Conclusions are made about security of supply, grid congestion and transmission capacity availability for different scenarios.

Structural studies on enzymes of biotechnological and biomedical interest

Structural studies of proteins aim at elucidating the atomic details of molecular interactions in biological processes of living organisms. These studies are particularly important in understanding structure, function and evolution of proteins and in defining their roles in complex biological settings. Furthermore, structural studies can be used for the development of novel properties in biomolecules of environmental, industrial and medical importance. X-ray crystallography is an invaluable tool to obtain accurate and precise information about the structure of proteins at the atomic level. Glutathione transferases (GSTs) are amongst the most versatile enzymes in nature. They are able to catalyze a wide variety of conjugation reactions between glutathione (GSH) and non-polar components containing an electrophilic carbon, nitrogen or sulphur atom. Plant GSTs from the Tau class (a poorly characterized class) play an important role in the detoxification of xenobiotics and stress tolerance. Structural studies were performed on a Tau class fluorodifen-inducible glutathione transferase from Glycine max (GmGSTU4-4) complexed with GSH (2.7 Å) and a product analogue Nb-GSH (1.7 Å). The three-dimensional structure of the GmGSTU4-4-GSH complex revealed that GSH binds in different conformations in the two subunits of the dimer: in an ionized form in one subunit and a non-ionized form in the second subunit. Only the ionized form of the substrate may lead to the formation of a catalytically competent complex. Structural comparison between the GSH and Nb-GSH bound complexes revealed significant differences with respect to the hydrogen-bonding, electrostatic interaction pattern, the upper part of -helix H4 and the C-terminus of the enzyme. These differences indicate an intrasubunit modulation between the G-and Hsites suggesting an induced-fit mechanism of xenobiotic substrate binding. A novel binding site on the surface of the enzyme was also revealed. Bacterial type-II L-asparaginases are used in the treatment of haematopoietic diseases such as acute lymphoblastic leukaemia (ALL) and lymphomas due to their ability to catalyze the conversion of L-asparagine to L-aspartate and ammonia. Escherichia coli and Erwinia chrysanthemi asparaginases are employed for the treatment of ALL for over 30 years. However, serious side-effects affecting the liver and pancreas have been observed due to the intrinsic glutaminase activity of the administered enzymes. Structural studies on Helicobacter pylori L-asparaginase (HpA) were carried out in an effort to discover novel L-asparaginases with potential chemotherapeutic utility in ALL treatment. Detailed analysis of the active site geometry revealed structurally significant differences between HpA and other Lasparaginases that may be important for the biological activities of the enzyme and could be further exploited in protein engineering efforts.

Biomedical Event Extraction with Machine Learning

Biomedical natural language processing (BioNLP) is a subfield of natural language processing, an area of computational linguistics concerned with developing programs that work with natural language: written texts and speech. Biomedical relation extraction concerns the detection of semantic relations such as protein-protein interactions (PPI) from scientific texts. The aim is to enhance information retrieval by detecting relations between concepts, not just individual concepts as with a keyword search. In recent years, events have been proposed as a more detailed alternative for simple pairwise PPI relations. Events provide a systematic, structural representation for annotating the content of natural language texts. Events are characterized by annotated trigger words, directed and typed arguments and the ability to nest other events. For example, the sentence “Protein A causes protein B to bind protein C” can be annotated with the nested event structure CAUSE(A, BIND(B, C)). Converted to such formal representations, the information of natural language texts can be used by computational applications. Biomedical event annotations were introduced by the BioInfer and GENIA corpora, and event extraction was popularized by the BioNLP'09 Shared Task on Event Extraction. In this thesis we present a method for automated event extraction, implemented as the Turku Event Extraction System (TEES). A unified graph format is defined for representing event annotations and the problem of extracting complex event structures is decomposed into a number of independent classification tasks. These classification tasks are solved using SVM and RLS classifiers, utilizing rich feature representations built from full dependency parsing. Building on earlier work on pairwise relation extraction and using a generalized graph representation, the resulting TEES system is capable of detecting binary relations as well as complex event structures. We show that this event extraction system has good performance, reaching the first place in the BioNLP'09 Shared Task on Event Extraction. Subsequently, TEES has achieved several first ranks in the BioNLP'11 and BioNLP'13 Shared Tasks, as well as shown competitive performance in the binary relation Drug-Drug Interaction Extraction 2011 and 2013 shared tasks. The Turku Event Extraction System is published as a freely available open-source project, documenting the research in detail as well as making the method available for practical applications. In particular, in this thesis we describe the application of the event extraction method to PubMed-scale text mining, showing how the developed approach not only shows good performance, but is generalizable and applicable to large-scale real-world text mining projects. Finally, we discuss related literature, summarize the contributions of the work and present some thoughts on future directions for biomedical event extraction. This thesis includes and builds on six original research publications. The first of these introduces the analysis of dependency parses that leads to development of TEES. The entries in the three BioNLP Shared Tasks, as well as in the DDIExtraction 2011 task are covered in four publications, and the sixth one demonstrates the application of the system to PubMed-scale text mining.

Two-phase flow measurements with spacer grid in a rod bundle geometry

Heat transfer effectiveness in nuclear rod bundles is of great importance to nuclear reactor safety and economics. An important design parameter is the Critical Heat Flux (CHF), which limits the transferred heat from the fuel to the coolant. The CHF is determined by flow behaviour, especially the turbulence created inside the fuel rod bundle. Adiabatic experiments can be used to characterize the flow behaviour separately from the heat transfer phenomena in diabatic flow. To enhance the turbulence, mixing vanes are attached to spacer grids, which hold the rods in place. The vanes either make the flow swirl around a single sub-channel or induce cross-mixing between adjacent sub-channels. In adiabatic two-phase conditions an important phenomenon that can be investigated is the effect of the spacer on canceling the lift force, which collects the small bubbles to the rod surfaces leading to decreased CHF in diabatic conditions and thus limits the reactor power. Computational Fluid Dynamics (CFD) can be used to simulate the flow numerically and to test how different spacer configurations affect the flow. Experimental data is needed to validate and verify the used CFD models. Especially the modeling of turbulence is challenging even for single-phase flow inside the complex sub-channel geometry. In two-phase flow other factors such as bubble dynamics further complicate the modeling. To investigate the spacer grid effect on two-phase flow, and to provide further experimental data for CFD validation, a series of experiments was run on an adiabatic sub-channel flow loop using a duct-type spacer grid with different configurations. Utilizing the wire-mesh sensor technology, the facility gives high resolution experimental data in both time and space. The experimental results indicate that the duct-type spacer grid is less effective in canceling the lift force effect than the egg-crate type spacer tested earlier.

Fundraising and investor relations in social enterprise: Marketing your way out of the pioneer gap in African off-grid renewable energy

Social enterprises apply the best of business for the pursuit of social or environmental mission while also generating revenues. Globally, nearly 1,3 billion people lack access to electricity, as well as another billion having access to only low quality and infrequent electricity. Off-grid renewable energy, like solar, will increasingly have a key role in the solution of the energy access issue. The pioneer gap in off-grid renewable energy consists of financing (or funding) gaps and capacity gaps, to do with both the early stage of the enterprises in question, as well as the early stage of the whole industry. The gaps are emphasised by specific characteristics of off-grid renewable energy business models and the requirements of operating in bottom-of-the-pyramid markets. The marketing perspective to fundraising is chosen to uncover the possible role enterprises themselves have in bridging the pioneer gap. The purpose of this thesis is to study how social enterprises operating in off-grid renewable energy in Africa utilise marketing activities in their investor relations in bridging the pioneer gap. This main research question is divided into the following sub-questions:  How does the pioneer gap affect fundraising for these enterprises?  How are the funding needs for these enterprises characterised?  How do these enterprises build trust in their investor relations? The theoretic framework is built on relationship marketing and investor relations, with an emphasis on creation of trust. The research is conducted as a thematical case study. Primary data is gathered via semi-structured interviews with six solar energy companies and two accelerators. According to the findings, the main components affecting trust-creation are diminished information asymmetry and perceived risk, mission alignment as well as a personal fit or relationship with the investor. Therefore, an enterprise can utilise e.g. the following marketing activities in their investor relations to bridge the pioneer gap: ensuring investor material, the enterprise story and presenting of them is clear, concise and complete to “package” the enterprise as an investment; taking investor needs and motivations into account as well as utilising existing investors as ambassadors.

Piilevien asiakastarpeiden tunnistaminen – repertory grid -tekniikka

Tässä työssä tutkitaan asiakaslähtöisyyttä tuotekehityksessä ja piilevien asiakastarpeiden selvittämistä ja hyödyntämistä asiakaslähtöisyydessä. Tavoitteena on selvittää asiakaslähtöisyyden tärkeyttä ja etuja tuotekehityksessä, sekä käydä läpi erilaisia asiakastarpeiden selvittämismetodeja erityisesti keskittyen repertory grid –tekniikkaan (RGT). Asiakaslähtöisyys tuo yrityksille merkittävää kilpailuetua tuotekehityksen alkupäähän. Erityisesti piilevät asiakastarpeet voivat mahdollistaa tuoteinnovaatioita, joilla saavutetaan huomattavasti parempaa asiakastyytyväisyyttä ja etulyöntiasemaa kilpailijoihin nähden. Piileviä asiakastarpeita voi tunnistaa repertory grid –tekniikalla, jota on avattu tässä työssä. Tekniikka mahdollistaa piilevien asiakastarpeiden tunnistamisen, mikä osaltaan edistää etulyöntiaseman antavia asiakaslähtöisiä tuoteinnovaatioita, joilla yritys voi differoitua muista markkinoilla olevista yrityksistä. Asiakaslähtöisyyden tulee tuotekehityksen lähtökohdan lisäksi olla osa yrityksen strategiaa ja kulttuuria, jotta yritys voi menestyä asiakaslähtöisyydellä.

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.

Optimization and Applications of Large-Scale Biomedical Event Networks

The overwhelming amount and unprecedented speed of publication in the biomedical domain make it difficult for life science researchers to acquire and maintain a broad view of the field and gather all information that would be relevant for their research. As a response to this problem, the BioNLP (Biomedical Natural Language Processing) community of researches has emerged and strives to assist life science researchers by developing modern natural language processing (NLP), information extraction (IE) and information retrieval (IR) methods that can be applied at large-scale, to scan the whole publicly available biomedical literature and extract and aggregate the information found within, while automatically normalizing the variability of natural language statements. Among different tasks, biomedical event extraction has received much attention within BioNLP community recently. Biomedical event extraction constitutes the identification of biological processes and interactions described in biomedical literature, and their representation as a set of recursive event structures. The 2009–2013 series of BioNLP Shared Tasks on Event Extraction have given raise to a number of event extraction systems, several of which have been applied at a large scale (the full set of PubMed abstracts and PubMed Central Open Access full text articles), leading to creation of massive biomedical event databases, each of which containing millions of events. Sinece top-ranking event extraction systems are based on machine-learning approach and are trained on the narrow-domain, carefully selected Shared Task training data, their performance drops when being faced with the topically highly varied PubMed and PubMed Central documents. Specifically, false-positive predictions by these systems lead to generation of incorrect biomolecular events which are spotted by the end-users. This thesis proposes a novel post-processing approach, utilizing a combination of supervised and unsupervised learning techniques, that can automatically identify and filter out a considerable proportion of incorrect events from large-scale event databases, thus increasing the general credibility of those databases. The second part of this thesis is dedicated to a system we developed for hypothesis generation from large-scale event databases, which is able to discover novel biomolecular interactions among genes/gene-products. We cast the hypothesis generation problem as a supervised network topology prediction, i.e predicting new edges in the network, as well as types and directions for these edges, utilizing a set of features that can be extracted from large biomedical event networks. Routine machine learning evaluation results, as well as manual evaluation results suggest that the problem is indeed learnable. This work won the Best Paper Award in The 5th International Symposium on Languages in Biology and Medicine (LBM 2013).

Wood-derived biomaterials for biomedical applications

Driven by the global trend in the sustainable economy development and environmental concerns, the exploring of plant-derived biomaterials or biocomposites for potential biomedical and/or pharmaceutical applications has received tremendous attention. Therefore, the work of this thesis is dedicated to high-value and high-efficiency utilization of plant-derived materials, with the focus on cellulose and hemicelluloses in the field of biomedical applications in a novel biorefinery concept. The residual cellulose of wood processing waste, sawdust, was converted into cellulose nanofibrils (CNFs) with tunable surface charge density and geometric size through 2,2,6,6-tetramethylpiperidinyloxy (TEMPO)-mediated oxidation and mechanical defibrillation. The sawdust-based CNFs and its resultant free-standing films showed comparable or even better mechanical properties than those from a commercial bleached kraft pulp at the same condition, demonstrating the feasibility of producing CNFs and films thereof with outstanding mechanical properties from birch sawdust by a process incorporated into a novel biorefinery platform recovering also polymeric hemicelluloses for other applications. Thus, it is providing an efficient route to upgrade sawdust waste to valuable products. The surface charge density and geometric size of the CNFs were found to play key roles in the stability of the CNF suspension, as well as the gelling properties, swelling behavior, mechanical stiffness, morphology and microscopic structural properties, and biocompatibility of CNF-based materials (i.e. films, hydrogels, and aerogels). The CNFs with tunable surface chemistry and geometric size was found promising applications as transparent and tough barrier materials or as reinforcing additive for production of biocomposites. The CNFs was also applied as structural matrices for the preparation of biocomposites possessing electrical conductivity and antimicrobial activity by in situ polymerization and coating of polypyrrole, and incorporation of silver nanoparticles, which make the material possible for potential wound healing application. The CNF-based matrices (films, hydrogels, and aerogels) with tunable structural and mechanical properties and biocompatibility were further prepared towards an application as 3D scaffolds in tissue engineering. The structural and mechanical strength of the CNF matrices could be tuned by controlling the charge density of the nanocellulose, as well as the pH and temperature values of the hydrogel formation conditions. Biological tests revealed that the CNF scaffolds could promote the survival and proliferation of tumor cells, and enhance the transfection of exogenous DNA into the cells, suggesting the usefulness of the CNF-based 3D matrices in supporting crucial cellular processes during cell growth and proliferation. The CNFs was applied as host materials to incorporate biomolecules for further biomedical application. For example, to investigate how the biocompatibility of a scaffold is influenced by its mechanical and structural properties, these properties of CNF-based composite matrices were controlled by incorporation of different hemicelluloses (O-acetyl galactoglucomanan (GGM), xyloglucan (XG), and xylan) into CNF hydrogel networks in different ratios and using two different approaches. The charge density of the CNFs, the incorporated hemicellulose type and amount, and the swelling time of the hydrogels were found to affect the pore structure, the mechanical strength, and thus the cells growth in the composite hydrogel scaffolds. The mechanical properties of the composite hydrogels were found to have an influence on the cell viability during the wound healing relevant 3T3 fibroblast cell culture. The thusprepared CNF composite hydrogels may work as promising scaffolds in wound healing application to provide supporting networks and to promote cells adhesion, growth, and proliferation.