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(2022)The Traveling Salesman Problem (TSP) is a well-known optimization problem. The time needed to solve TSP classically grows exponentially with the size of the input, placing it into the NP-hard computational complexity class–the class of problems that are at least as hard as any other problem solvable in nondeterministic polynomial time. Quantum computing gives us a new approach to searching through such a huge search space, using methods such as quantum annealing and phase estimation. Although the current state of quantum computers does not give us enough resources to solve TSP with a large input, we can use quantum computing methods to improve on existing classical algorithms. The thesis reviews existing methods to efficiently tackle TSP utilizing potential quantum resources, and discusses the augmentation of classical algorithms with quantum techniques to reduce the time complexity of solving this computationally challenging problem.
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(2020)The aim of this thesis is to predict total career racing performance of Finnish trotter horses by using trotters early career racing performance and other early career variables. This thesis presents a brief introductory of harness racing and horses used in Finnish trotting sport. The data is presented and modified for predictions, with descriptive statistics of tables and visuals. The machine learning method of Random forests for regression is introduced and used in the predictions. After training the model, this thesis presents the prediction accuracy and variables of importance of the predictions of total career racing performance for both Finnhorse trotters and Finnish Standardbred trotter population. Finally, the writer discusses on the shortages and possible improvements for future research. The data for this thesis was provided by The Finnish trotting and breeding association (Suomen Hippos ry), which included all information of harness races from 1984 to the end of 2019, raced in Finland. From almost three million rows, the data was summarised to a data table of 46704 rows of trotters, that have started their career at earliest allowed three age groups. A total of 37 independent variables were used to predict three outcomes of total career earnings, total number of career starts and total number of career first placings, as separate models. The predictors are derived from other studies that estimate the environmental and genetic factors of racing performance of a trotter. The three models performed poor to moderate, with total earnings having the highest prediction accuracy. The model predicted quite well larger amounts of earnings, but was avid to predict some earnings when there in fact were none. Prediction accuracy of total number of starts was poor, especially when the true amount of starts was low. Model that predicted total number of career first placings performed the worst. This can partially be explained by the fact that winning is a rare event for a trotter in general. The models fit better for Finnish Standardbred trotters than for Finnhorse trotters. This thesis works as a good basis for future similar research, where massive amounts of data and machine learning is used to predict trotter’s career, racing performance or other factors. The results show that predicting total career racing performance as a classification problem could be a better fit than regression. These adequate classes, as well as possible better predictors and suitable imputes for missing values, should be consulted with an audience of superior knowledge in harness racing.
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(2020)Reinforcement learning (RL) is a basic machine learning method, which has recently gained in popularity. As the field matures, RL methods are being applied on progressively more complex problems. This leads to need to design increasingly more complicated models, which are difficult to train and apply in practice. This thesis explores one potential way of solving the problem with large and slow RL models, which is using a modular approach to build the models. The idea behind this approach is to decompose the main task into smaller subtasks and have separate modules each of which concentrates on solving a single subtask. In more detail, the proposed agent will be built using the Q-decomposition algorithm, which provides a simple and robust algorithm for building modular RL agents. The problem we use as an example of usefulness of the modular approach is a simplified version of the video game Doom and we design a RL agent that learns to play it. The empirical results indicate that the proposed model is able to learn to play the simplified version of Doom on a reasonable level, but not perfectly. Additionally, we show that the proposed model might suffer from usage of too simple models for solving the subtasks. Nevertheless, taken as a whole the results and the experience of designing the agent show that the modular approach for RL is a promising way forward and warrants further exploration.
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(2021)The lidar depolarisation ratio is used for aerosol categorisation as it is indicative of aerosol shape. Commonly, depolarisation ratio is measured in short term studies at short wavelengths such as 355 nm and 532 nm. The depolarisation ratio has a spectral dependency and so exploring values at longer wavelengths could be valuable for future studies. Here, aerosol depolarisation ratio at 1565 nm is measured across Finland’s ground based remote sensing network over a four year period. The Halo Photonics StreamLine Doppler lidars instruments were found to be stable over long time periods and cloud based calibration was used to correct for the bleed though. The depolarisation ratio of elevated aerosol layers was compared to boundary layer aerosol. A higher average depolarisation ratio was found for elevated aerosol with the exception of boreal forest sites in the summer months where values were similar. Elevated aerosols over Finland were found to originate mostly from the Arctic, Europe, Russia and North America using aerosol transport models. Four case studies were looked at in more detail: Saharan dust with a depolarisation ratio of 0.249 ± 0.018, pollen with a depolarisation ratio of 0.207 ± 0.013, anthropogenic pollution with a depolarisation ratio of 0.067 ± 0.009, and a mixed layer with a depolarisation ratio of 0.152 ± 0.019 thought to be pollen and smoke. Based on this study, Halo Doppler Lidar can be used to measure elevated aerosol at 1565 nm in the long term. Future studies could use 1565 nm depolarisation ratio alongside commonly used shorter wavelengths to aid aerosol categorisation.
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(2017)Distinguishing the coronal magnetic field and its evolution can unlock key information on solar energetic eruptions such as the Coronal Mass Ejections (CMEs). CMEs are formed as magnetic flux ropes, i.e. magnetic field lines twisted about each other. They are the main drivers of space weather effects on Earth. Understanding the structure of the internal magnetic field of the CME would help determine the severity of the resulting geomagnetic storm. Predicting the onset and the orientation of the flux rope axis is a major focus of current space weather research. For this purpose, a numerical study on the kinematic emergence of a twisted flux rope into a coronal magnetic field is performed using the Magneto-frictional method (MFM). The MFM is an exciting prospect as it is sufficiently accurate and computationally inexpensive. The initiation of the eruption is through ideal Magnetohydrodynamic (MHD) kink instability. In this case, the kink instability occurs when the windings of the field lines about the flux rope axis exceeds a critical value. This thesis presents the set-up of the Fan & Gibson flux rope with different configurations. This was in hopes of studying the slow energization of the coronal field arcade with the emergence of a current carrying flux rope. The results of the simulations presented here show that the several key factors such as the height at which the flux rope is stopped and its twist play a major role in the dynamics of the flux rope in making it kink unstable. One of the main motivations was to use the results to discuss the performance of the MFM in comparison to MHD and how capable it is in capturing ideal MHD phenomenon. The simulations are also used to investigate the formation of sigmoidal current layer often seen before the onset of eruption. In the results presented here, the sigmoidal ’S’ shaped current layer is formed as the flux rope becomes kink unstable. This sigmoidal current layer is analysed for different configurations of the flux rope. These results have suggested that accurate dynamic modelling of the coronal magnetic field is essential for successful space weather prediction purposes.
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Using Three-Component Data for Seismic Interferometry Studies at the Kylylahti Mine, Eastern Finland (2019)The reflection seismic surveying method is useful when conducting mineral exploration in the crystalline bedrock because of its good depth extent and resolution. However, the traditional experiments with active sources are expensive and difficult to carry out, especially in remote areas or in conservation areas where mineral exploration is limited due to environmental reasons. Recently, a number of theoretical advances have proven that passive soundings utilizing ambient seismic noise can provide new opportunities for seismic imaging and contribute to data generation for reflection seismic surveys, without the need for explosive or vibratory sources. One of the most promising new methods is seismic interferometry (SI), where the impulse response between two receivers is reconstructed by correlating their signals with each other. COGITO-MIN is a joint project between the University of Helsinki, the Geological Survey of Finland, Polish Academy of Sciences, and industrial partners with the aim of investigating and developing new cost-effective seismic exploration methods in the crystalline bedrock. Within the framework of the project, a passive seismic experiment was carried out in which 45 three-component geophones were deployed for a month in the vicinity of the polymetallic Kylylahti Mine in Polvijärvi, northern Karelia, where the mining operator is the Swedish metal company Boliden. The original purpose of these geophones was to collect data suitable for detecting underground cavities related to underground nuclear explosions. The institute that collected the data was CTBTO (Comprehensive Test Ban Treaty Organization) whose task is to monitor the treaty in the pre-ratification stage. The purpose of this Master's thesis was to develop an SI workflow for the three-component data and to investigate the method's performance in an area where local geology is known after nearly 40 years of exploration and consequent mining operations. The specific scientific objectives of the thesis are (1) to demonstrate the usefulness of collecting three-component data in conjunction with or instead of single-component data, (2) to assess the noise-based SI methods used in previous studies and to improve their stability in the crystalline bedrock, and (3) to investigate the possibilities of SI from an operational perspective. Seismic velocities obtained through laboratory measurements were merged with geological and density models of the target area provided by Boliden. The resulting velocity and density grids were then used as the basis for waveform modelling, and the results from SI were validated against them. The starting point for SI was the noise-driven approach where 'each sample matters'. The interferometric workflow is built on the Seismic Unix suite together with self-written algorithms that are based on theoretical evaluations. SI is followed by an imaging workflow, which provides the basis for the reflectivity profiles. The thesis work focuses on five components of the Green's tensor and the vertical, radial and transverse component of the impulse response. With the horizontal components, one can access the S-wave patterns in addition to the P-waves. As a specialty, the so-called sign bit normalization (SBN) method was also tested. The technique involves destroying much of the amplitude information of the original seismograms by only retaining the sign bit of each sample. According to the results outlined in this thesis, SBN can make it easier to image the weak reflectors of the subsurface. This type of seismic interferometry seems particularly suitable for the early stage of mineral exploration, where the explorer does not yet fully understand the target they are studying. The most important advantage of seismic interferometry, however, is its cost effectiveness, and its potential for reducing risks for the environment.
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(2022)The aim of this thesis was to 1) give an exposition of how topological data analysis (TDA) can be used to look for patterns in periodic data, 2) apply it to financial data and 3) visually explore how a topological analysis of credit data using landscape distances compared to looking directly at the change in credit data in the context of stock market crashes. TDA applies algebraic topology to data. It models data sets as various-dimensional surfaces, or manifolds, and studies their structure to find patterns of interconnectedness. It is a powerful tool for studying large, complex, multi-dimensional and noisy data sets. It is often able to capture subtle patterns in such data sets much better than other methods. It is known that stock market crashes are preceded by periods of credit expansion, but we have no reliable indicator of an imminent crash. Chapter 2 covers the algebraic topological theory needed. Key concepts are simplicial complexes, homology groups and persistent homology. The central theorem is the Nerve Theorem, which establishes an equivalence between the union of a collection of convex sets and the nerve of the collection. Chapter 3 describes the method of time delay embedding to pre-process periodic data. A Vietoris-Rips filtration was applied to sliding windows of credit data. From this persistence diagrams and their corresponding persistence landscapes were obtained. The normalised persistence landscape norms (L1) were plotted to visually explore how well TDA captured the connection between credit expansion and stock market crashes. It was compared to the discrete first derivative of the credit data. Visual inspection of the graphs suggested TDA to be as good, and possibly slightly better, at predicting stock market crashes from bank credit data, than looking at the discrete first derivative directly. No obvious new indicator of an imminent crash was found, however. To unlock the true potential of TDA in analysing large, multivariate data sets, further studies could look to triangulate a better indicator of stock market crashes by combining the credit data with other economic, social and political data. It would also be useful to establish a less subjective, more transparent method for choosing the thresholds used as crash indicators, and to quantify the predictions made by different indicators to better compare them with each other.
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(2020)Ohjelmiston hyvä käytettävyys varmistaa sen, että henkilöt, joille ohjelmisto on suunnattu osaavat sitä käyttää, ja että ohjelmiston käyttö on sekä tehokasta että miellyttävää. Käytettävyyden merkitys on kasvanut, kun ohjelmistojen käyttäjiksi on tullut tietotekniikan ammattilaisten lisäksi kuluttajia. Kilpailukyvyn varmistamiseksi ohjelmistojen tulee olla käytettävän lisäksi myös saavutettavia ja niiden kokonaiskäyttökokemuksen on oltava hyvä. Saavutettavuus on myös lainmukainen vaatimus ohjelmistoille. Käytettävyyden testaaminen on perinteisesti käsityötä, jota suorittaa koulutettu käytettävyysasiantuntija tai käyttäjätutkija. Suuri osa huomiosta keskittyy käyttöliittymään, jonka kautta käyttäjä on vuorovaikutuksessa ohjelmiston kanssa. Käytettävyys on kontekstisidonnaista, joten osa testaamisesta tulee aina vaatimaan ihmisen analysointia. Osa testaamisesta voidaan kuitenkin automatisoida, ja näin vapauttaa tutkijan kapasiteettia sellaisiin osa-alueisiin, jotka vaativat kontekstin ja käyttäjän toiminnan ymmärtämistä ja tulkitsemista. Lisäksi tutkija pystyy keskittymään käytettävyysongelmia korjaavien muutosehdotusten kehittämiseen. Automaattisesti voidaan testata erityisesti käyttöliittymän yhdenmukaisuutta, käyttöliittymäelementtien havaittavuutta ja kerätä automaattisesti käyttäjän raportoimia ongelmakohtia. Tässä työssä pyritään vastaamaan kysymykseen, missä määrin käytettävyyden varmistamista pystytään automatisoimaan, ja esitellään tämän tiedon pohjalta rakennettu käytettävyyden automaattisen testaamisen työkalu, Usability Spy, jonka tarkoituksena on automatisoida käytettävyystesteihin osallistuvilta henkilöiltä koottavien käytettävyystestiaineiston keräystä. Työkalun toimintaympäristönä on DOS käyttöjärjestelmän päällä toimiva Microsoft Windows tai Microsoft Windows for Workgroups ikkunointiympäristön versio 3.1 tai uudempi.
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(2023)In this thesis we try to find the measurement accuracy of our dronebound wind measurement setup and if the quality of the measurements is high enough for operational usage. The thesis goes over the most important theoretical concepts concerning effects of wind in the boundary layer. In the thesis we analyze wind data gathered by a drone-bound anemometer, and introduce a direct method of measuring wind with a UAV. The data includes stationary wind data gathered at height of 30 metres, as well as vertical wind profiles to 500 metres above ground level. The data is compared to reference data from a 30 metre wind mast and automatic radiosoundings. The measurements were conducted in Jokioinen, Finland between the 2nd of September 2022 and 10th of October 2022. Total of 20 measurement flights were conducted, consisting of 14 stationary wind measurements and six wind profile measurements. We found out the stationary wind measurement quality to be comparable with earlier studies. The vertical wind profile measurements were found to be hard to analyze, as the reference measurement was not as compatible as we had hoped for. The difference between automatic radiosoundings and our profile measurements was distinctly greater than the difference between the stationary drone and wind mast measurements. Lastly some optimization and improvements to the measurement arrangement are discussed. The application of these improvements and modifications will be left as future endeavour for some willing individual.
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(2022)An industrial classification system is a set of classes meant to describe different areas of business. Finnish companies are required to declare one main industrial class from TOL 2008 industrial classification system. However, the TOL 2008 system is designed by the Finnish authorities and does not serve the versatile business needs of the private sector. The problem was discovered in Alma Talent Oy, the commissioner of the thesis. This thesis follows the design science approach to create new industrial classifications. To find out what is the problem with TOL 2008 indus- trial classifications, qualitative interviews with customers were carried out. Interviews revealed several needs for new industrial classifications. According to the customer interviews conducted, classifications should be 1) more detailed, 2) simpler, 3) updated regularly, 4) multi-class and 5) able to correct wrongly assigned TOL classes. To create new industrial classifications, un- supervised natural language processing techniques (clustering) were tested on Finnish natural language data sets extracted from company websites. The largest data set contained websites of 805 Finnish companies. The experiment revealed that the interactive clustering method was able to find meaningful clusters for 62%-76% of samples, depending on the clustering method used. Finally, the found clusters were evaluated based on the requirements set by customer interviews. The number of classes extracted from the data set was significantly lower than the number of distinct TOL 2008 classes in the data set. Results indicate that the industrial classification system created with clustering would contain significantly fewer classes compared to TOL 2008 industrial classifications. Also, the system could be updated regularly and it could be able to correct wrongly assigned TOL classes. Therefore, interactive clustering was able to satisfy three of the five requirements found in customer interviews.
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Utilizing Generative Artificial Intelligence for Tax and Legal Consultancy: Design Science Approach (2024)The continuous application of generative AI is increasingly employed to enhance both creative and knowledge-centric processes. With the release of OpenAI’s GPT models in 2022, generative AI utilisation has surged, facilitating the optimisation of these processes to a greater extent than before. This research has been conducted for PwC Finland and presents a solution that leverages generative AI and RAG methods to aid consultants in data retrieval and analyses. This work follows a design science methodology, whereby, an incremental and iterative process is followed to produce an IT artifact. The artifact is in the form of a Proof of Concept (POC) instantiation: a Microsoft Word Add-in that links to a backend process employing a RAG framework, generative AI, and contemporary software architecture. This research has found that utilising RAG methods with generative AI alone is not enough to produce specific analyses within the tax and legal context. With an accuracy of only 62.5%, its important to utilise metadata filtering, pre-prompting and knowledge graphs to enhance contextual understanding.
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(2019)The use of virtual reality technology in the field of city planning has grown during the recent decades, because virtual reality provides a highly informative and immersive experience of an urban area in contrast to traditionally used 2-dimensional material, such as imagery and animations. This makes communication and decision making between the stakeholders of a city planning project much more efficient and enables participating the public to the process, which is very desirable in city planning projects. The virtual reality technology has evolved dramatically during the past few years. Devices such as head-mounted displays, spatial controllers and CAVE installations, and developing environments, such as game engines and 3D modeling studios have become more available for consumers and developers. Therefore, the opportunities for virtual reality technology requires revisiting. The goal of this study is to find software development opportunities for city planning projects using virtual reality technologies and realize those opportunities by implementing a model solution for use cases that show the most potential for development. The model solution is a high-level modeling tool, which can be used for fast drafting and modeling of city-scale plans, and provides appealing visualization for presentation purposes. This allows architects to make changes to the city plan immediately as they are receiving feedback from the decision makers and other stakeholders of a city planning project. This makes the decision making much faster, as the modifications can be made during the meeting, instead of re-scheduling another meeting to see the changes to the plan. The model solution also allows viewing the plan in a highly immersive virtual reality environment, with devices such as head-mounted displays. The development of this software is executed following well known agile methodologies used in commercial software development projects and is extended with a literature review and an analysis of the functionality of the model solution. The literature review charts the existing applications and studies of virtual reality in city planning so that opportunities for further development can be found. From these opportunities, the use cases showing the most potential are chosen and refined to a concept of an application. This concept is then refined to concrete designs and implemented in iterations, producing a final solution. The final solution is then analyzed in a test setup, including 8 participants, who are given city planning tasks, and answers a survey afterward, affirming that the model solution is a useful tool for future city planning projects.
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(2019)Public transport networks are a subset of vehicular networks with some important distinctions; the actors in the network include buses and bus-stops, they are predictable and they provide reliable physical coverage of an area. The Public Transport Network of a city can also be interpreted as an opportunistic network where nodes are bus-stops and communication between these nodes occurs when a bus travels between two bus-stops. How will a data communication network perform when built upon the opportunistic network formed by the public transport system of a city? In this thesis we explore this question basing our analysis on Helsinki Region’s public bus transport system as a real example. We explore the performance of a public transport network when used for communication of data using both simulation of the network and graph analysis. The key performance factors studied are the data delivery ratio and data delivery time. Additional issues considered are the kind of applications such a system is suited for, the important characteristics governing the reliability and efficacy of such a data communications system, and the design guidelines for building such an application. The results demonstrate that data transfer applications can be built over a city’s Public Transport Network.
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(2021)Modern software systems often produce vast amounts of software usage data. Previous work, however, has indicated that such data is often left unutilized. This leaves a gap for methods and practices that put the data to use. The objective of this thesis is to determine and test concrete methods for utilizing software usage data and to learn what use cases and benefits can be achieved via such methods. The study consists of two interconnected parts. Firstly, a semi-structured literature review is conducted to identify methods and use cases for software usage data. Secondly, a subset of the identified methods is experimented with by conducting a case study to determine how developers and managers experience the methods. We found that there exists a wide range of methods for utilizing software usage data. Via these methods, a wide range of software development-related use cases can be fulfilled. However, in practice, apart from debugging purposes, software usage data is largely left unutilized. Furthermore, developers and managers share a positive attitude towards employing methods of utilizing software usage data. In conclusion, software usage data has a lot of potential. Besides, developers and managers are interested in putting software usage data utilization methods to use. Furthermore, the information available via these methods is difficult to replace. In other words, methods for utilizing software usage data can provide irreplaceable information that is relevant and useful for both managers and developers. Therefore, practitioners should consider introducing methods for utilizing software usage data in their development practices.
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(2022)Syftet med denna utvecklingsforskning är att skapa ett utvecklingsobjekt som utvecklar undervisningen inom mätning och enhetsomvandling, eftersom tidigare forskningsresultat tyder på att det finns brister i elevers kunnande inom mätning och enhetsomvandling. Syftet med utvecklingsobjektet är att eleverna bildar förmågor som de kan anpassa i sina egna liv. Som utvecklingsobjekt skapar jag uppgifter som påminner om vardagliga problemlösningssituationer, för att hämta skolmatematiken närmare vardagliga situationer. Detta hjälper eleverna att förstå att lärandet i skolan inte enbart sker för lärandets skull. Utvecklingsforskningen utförs i två cykler. I arbetets första cykel skapar jag utvecklingsobjektet på basen av den teoretiska problemanalysen som jag gör. I arbetets andra cykel gör jag en fallstudie för att få mera kunskap om hur utvecklingsobjektet fungerar under verkliga förhållanden. På basen av denna fallstudie utvecklar jag ytterligare utvecklingsobjektet. I fallstudien deltog två högstadieelever. Jag fick tillstånd av elevernas föräldrar att eleverna deltar i utvecklingsprocessen av utvecklingsobjektet. Resultaten för fallstudien tyder på ett fungerande utvecklingsobjekt. Arbetsmetoden var obekant för eleverna. Den mest centrala observationen utöver att eleverna ansåg uppgifterna vara nyttiga, var att eleverna tyckte om att diskutera matematik, även om de ansåg det vara svårt. Utvecklingsobjektet löser iallafall delvis problematiken av en traditionell, rutinbaserad undervisning genom att få eleverna att arbeta i en aktiv upptäckande roll. Dessutom uppmuntrar utvecklingsobjektet eleverna och läraren till att anamma en diskussionskultur under matematiklektionerna. I helheten är resultaten av denna utvecklingsforskning betydande för utvecklingen av realistisk matematikundervisning.
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(2020)Uudelleenkäytöllä ohjelmistokehityksessä tarkoitetaan aiemmin kehitettyjen artefaktien hyödyntämistä uudessa kohteessa. Artefaktit voivat olla käsitteellisiä, kuten suunnittelupäätökset, tai konkreettisia, kuten ohjelmakoodi, ja niitä voidaan hyödyntää ohjelmistotuotantoprosessin eri vaiheissa. Uudelleenkäyttöä tutkittiin jo vuosikymmeniä sitten, ennen internetin yleistymistä, mutta sen ei koettu olevan muodostunut tavanomaiseksi käytännöksi. Tutkielman tavoitteena on koostaa ennen internetaikakautta tunnistettuja uudelleenkäytön muotoja ja niille asetettuja odotuksia, sekä arvioida niiden toteutumista nykypäivän uudelleenkäytössä. Tässä tutkielmassa lähestytään uudelleenkäytön muotoja ja niihin kohdistettuja odotuksia sekä niiden toteutumista kirjallisuuskatsauksena. Tutkielma on kaksiosainen ja osien jakajana toimii internetin yleistymisen ajankohta, joka tässä tutkielmassa on ajoitettu vuosituhannen vaihteeseen. Nykypäivän uudelleenkäytön menetelmien tarkastelun lähtökohtana toimi Charles W. Kruegerin käyttämä nelikko: abstrahointi, valinta, erikoistaminen ja integrointi. Tutkielmasta ilmenee, että uudelleenkäytön asema ohjelmistokehityksessä on vakiintunut. Monet nykypäivän uudelleenkäytön menetelmät vastaavat hyvin historiassa esitettyjä menetelmiä ja odotukset uudelleenkäytön menestymisestä näyttävät toteutuneen. Vaikka uudelleenkäyttö on hyväksytty vakiintuneeksi käytännöksi, ei sen hyödyistä näytä olevan luotettavaa näyttöä vahvistamaan kaikkia sille asetettuja odotuksia. Tutkielmassa havaitaan myös, että internetin myötä ohjelmistokehitys on kokenut suuria muutoksia, joiden vaikutuksia menneinä vuosikymmeninä tuskin voitiin ennakoida.
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(2017)Positroniemissiotomografia eli PET-kuvantaminen perustuu positroni-elektroniannihilaatiotapahtumassa syntyvien gammakvanttien detektointiin. PET-kuvantamisessa käytetään positroniemittereitä kuten hiili-11, typpi-13, happi-15, fluori-18, gallium-68 sekä jodi124. 18F-Isotoopin ainutlaatuisuus ja sen laaja käyttö PET-kuvantamisessa selittyy sen pitkällä 109,8 minuutin puoliintumisajalla sekä beetahiukkasen lyhyellä kantamalla väliaineessa. PET-kuvantamisessa kuvan laatuun vaikuttaa radiolääkeaineen kohdespesifisyys sekä leimatun lääkeaineen poistuminen kohteen ulkoisesta kudoksesta nopeasti. Esikohdennettu kaksivaiheinen bio-ortogonaalinen kemia mahdollistaa tarkan kohdespesifisyyden, radiolääkeaineen nopean poistumisen verenkierrosta ja kuvantamiskohteen ympäröivästä kudoksesta sekä terveen kudoksen minimoidun säteilyaltistuksen. Tetratsiinin sekä trans-syklo-okteenin välinen käänteisesti elektronivajaa Diels-Alder reaktio (IEDDA) on yksi nopeimmista tunnetuista bio-ortogonaalisista reaktioista. Erikoistyön tarkoituksena oli syntetisoida sekä 18F-leimata IEDDA-reaktioon soveltuva uusi lipofiilinen tetratsiini-radiolääkeaine. Lääkeaine puhdistettiin nestekromatografisilla menetelmillä (HPLC, FPLC) ja sen rakenne varmistettiin 1D- ja 2D-NMR-spektroskopialla sekä massaspektrometrialla (ESI-TOF MS). Erikoistyössä määritettiin 18F-leimatun radiolääkeaineen lipofiilisyyttä kuvaava LogD-arvo, jonka perusteella arvioitiin lääkeaineen soveltuvuutta aivojen esikohdennettuun PETkuvantamiseen. Aivokuvantamiseen soveltuvalla lääkeaineella ideaalinen LogD-arvo sijoittuu välille 2,0–3,5, jolloin lääkeaineen voidaan olettaa ylittävän veri-aivoesteen. 18F-Leimattavan lääkeaineen esiasteen eli prekursorin synteesissä amidisidoksen muodostusreaktio sekä tert-butyylioksikarbamaatti-suojaryhmän poisto onnistuivat 65 % saannoilla. Oksiimin muodostus onnistui hyvällä 90 % saannolla. Prekursorin emäksisissä olosuhteissa tapahtuvan 18F-suoraleimauksen saannoksi saatiin 21 %. Työssä kehitettiin kaksivaiheinen leimausmenetelmä prekursorin pH-herkän oksiimisidoksen vuoksi. Kaksivaiheisessa menetelmässä 18F-leimausta seurasi oksiimin muodostusreaktio. Näin vältyttiin molekyylin hajoamiselta. Kaksivaiheisella leimauksella 18F-fluoraussaantoksi saatiin 78 %:a. Radiolääkeaineen puoliintumisaikakorjatuksi saannoksi tuotetusta kokonaisaktiivisuudesta määritettiin 16 %. Tavoitteena oli saavuttaa vähintään 10 % saanto. Radiolääkeaineelle määritettiin LogD-arvo fysiologisessa pH:ssa 7,4 Shake flask-uuttomenetelmällä. LogD-arvoksi määritettiin 2,1 ± 0,13. LogD-arvon perusteella lääkeaineella on optimaalinen lipofiilisyys veriaivoesteen läpäisylle. Radiolääkeaineen soveltuvuus aivokuvantamiseen tulee vielä selvittää stabilisuuskokeilla, määrittämällä lääkeaineen biodistribuutio sekä suorittamalla aivojen in vivo PET-kuvantaminen.
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(2018)Ilmakehän aerosolihiukkasilla on merkittäviä vaikutuksia ilmastoon ja ihmisten terveyteen. Suuri osa näistä aerosolihiukkasista on peräisin uusien pienhiukkasten muodostumistapahtumista eli NPF-tapahtumista (New Particle Formation). NPF-tapahtumissa ilmakehän höyryistä muodostuu pienhiukkasia, jotka alkavat kasvaa. Tämä tutkielma käsittelee läntisessä Saudi-Arabiassa tehdyissä aerosolimittauksissa havaittuja NPF-tapahtumia. Tarkoituksena on määrittää NPF-tapahtumien yleisyys sekä näiden voimakkuutta ja siten ilmasto- ja terveysvaikutuksellista potentiaalia kuvaavat muodostumis- ja kasvunopeudet. Lisäksi erityisen mielenkiinnon kohteena ovat nk. kutistumistapahtumat, joissa NPF-tapahtumissa syntyneiden hiukkasten moodin keskimääräisen halkaisijan havaitaan pienenevän kasvuvaiheen jälkeen. NPF-tapahtumien tunnistaminen sekä muodostumis- ja kasvunopeuksien määrittäminen perustuivat aerosolihiukkasten lukumääräkokojakaumamittausten analysointiin. Muodostumisnopeudet laskettiin vuomenetelmällä ja kasvunopeudet seuraamalla automaattisesta moodisovitusalgoritmista saatujen halkaisijoiden aikakehitystä. NPF-tapahtumien tunnistaminen perustui lukumääräkokojakaumien visuaaliseen tarkasteluun. NPF-tapahtumien piirteisiin vaikuttavia tekijöitä selvitettiin lähinnä käytettävissä olevien meteorologisten suureiden mittausten sekä dispersiomallilla laskettujen ilmamassojen lähdealueiden avulla. Lukumääräkokojakaumamittausten analysointi osoitti, että NPF-tapahtumat ovat tarkastellulla mittauspaikalla hyvin yleisiä ja voimakkaita. NPF-päivien esiintymistiheys oli 73 % luokitelluista päivistä (454 kpl) ja keskimääräiset muodostumis- ja kasvunopeudet olivat 13,5 cm-3 s-1 (J7nm) ja 8,2 nm h-1 (GR7-12nm). 76 %:ssa NPF-tapahtumista havaittiin lisäksi kutistumistapahtuma. Kutistumistapahtumien esiintymistiheys sekä muodostumis- ja kasvunopeudet olivat hieman suurempia kesäkuukausina, kun taas NPF-tapahtumien yleisyydellä ei ollut selkeää vuodenaikaisvaihtelua. NPF-tapahtumat ja niiden voimakkuus ovat mitä todennäköisimmin riippuvaisia rannikon puolelta kulkeutuvista antropogeenisistä päästöistä. Tähän viittaa erityisesti se, että pienhiukkasten muodostumista ei havaittu lainkaan sisämaasta peräisin olevissa ilmamassoissa. Kutistumistapahtumat eivät vaikuttaneet olevan yhteydessä hiukkasten haihtumista lisääviin tekijöihin (lämpötilan kasvu, ilmamassojen sekoittuminen). Ilmamassojen lähdealueiden ja antropogeenisten päästöjen horisontaalisen jakauman tarkastelu viittasivat puolestaan näennäiseen kutistumisprosessiin. Tässä moodin halkaisijan pieneneminen aiheutuisi haihtumisen sijaan heikommista NPF-tapahtumista peräisin olevien, ja siten vähemmän kasvaneiden hiukkasten havaitsemisesta.
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(2019)Tämän pro gradu -tutkielman kirjallinen osa käy läpi uusia psykoaktiivisia aineita ja niiden tunnistukseen käytettäviä laitteistoja ja menetelmiä. Tutkielman kokeellisen osan suoritus alkoi 56 uuden psykoaktiivisen aineen tunnistamisella ja viemisellä laboratorion tietokantaan vertailuaineiden avulla. Tutkimuksen toinen osa keskittyi m/zCloud –tietokannan tutkimiseen uusien psykoaktiivisten aineiden tunnistuksessa.
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(2017)Asiakasdataa on tallennettu pitkään, mutta vasta digitalisaation myötä sen käsittelyä voidaan tehokkaasti automatisoida. Asiakasdataa hyödyntämällä voidaan luoda uutta liiketoimintaa ymmärtämällä nykyisten ja tulevien asiakkaiden toimintaa ja tarpeita. Digitalisointi mahdollistaa läpinäkyvän ja mitattavan tavan automatisoida markkinointia. Uusi ohjelmoitava markkinoinnin maailma tarjoaa uusia reaaliaikaisia asiakasviestinnän keinoja. Asiakasdataa tallennetaan yrityksissä jatkuvasti lukuisiin eri järjestelmiin. Tallentamiseen liittyy monia haasteita, kuten markkinoivien organisaatioiden toimintakulttuuri ja muutosvastarinta. Asiakasdatan käsittelyä vaikeuttaa mm. organisaation eri osastojen omien tietojärjestelmien tavat käsitellä asiakasdataa. Keskitettyjen tietovarastojen puuttumisesta johtuva datan siiloutuminen aiheuttaa myös datan integriteettiin liittyviä haasteita. Tässä tutkielmassa selvitetään mitä mahdollisuuksia ja haasteita uudet teknologiat tuovat asiakasdatan keräämiseen, organisoitiin ja käsittelyyn. Dataohjatun markkinoinnin nykytilaa Suomessa selvitetään laajan tapaustutkimuksen avulla, haastattelemalla 20:a liike-elämässä aktiivisesti työskentelevää henkilöä. Tuloksena raportoidaan nykytilanne sekä asiakasdatan pirstaleisuutta ja epäyhteneväisyyttä luovat rakenteelliset ongelmat. Dataohjattu päätöksenteko on tärkeä kilpailutekijä ja sen merkityksen korostuessa analysointiosaamista tarvitaan entistä enemmän. Näiden haasteiden ymmärtäminen ja niihin varautuminen on entistä tärkeämpää jo suunniteltaessa asiakasdataa kerääviä ja käsitteleviä tietojärjestelmiä. Dataohjatun markkinoinnin yleistyminen muokkaa tietojärjestelmien lisäksi myös osaamistarpeita ja yrityskulttuureita.
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