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(2019)Tässä tutkimuksessa luodaan yleiskatsaus babylonialaiseen matematiikkaan, perehdytään sen saavutuksiin ja erityispiirteisiin ja pohditaan sen suurimpia ansioita. Lisäksi selvitetään miten babylonialainen matematiikka on vaikuttanut matematiikan kehitykseen ja miten babylonialaiset keksinnöt ovat päätyneet erityisesti kreikkalaisten matemaatikoiden käyttöön. Babylonialaisen matematiikan lisäksi tutkitaan myös babylonialaista astronomiaa soveltuvin osin. Tutkimuksessa selvitetään myös onko babylonialaisella matematiikalla yhteyksiä nykyaikaan ja erityisesti tapaan jakaa tunti 60 minuuttiin ja minuutti 60 sekuntiin ja ympyrän kehäkulma 360 asteeseen. Tutkimus toteutettiin kirjallisuuskatsauksena käyttämällä mahdollisimman laajasti sekä babylonialaista matematiikkaa koskevia perusteoksia että uusimpia artikkeleita. Matemaattisten saavutusten siirtymistä lähestyttiin tutkimalla tunnettuja kreikkalaisen matematiikan ja astronomian keskeisiä henkilöitä ja heidän yhteyksiään babylonialaiseen matematiikkaan. Näiden pohjalta muodostettiin yhteneväinen kokonaisuus babylonialaisen matematiikan saavutuksista ja tiedon siirtymisestä. Babylonialainen matematiikka käytti omaperäistä ja edistyksellistä seksagesimaalijärjestelmää, jonka kantaluku oli 60 ja joka oli ensimmäinen tunnettu numeroiden paikkajärjestelmä. Babylonialaisia matemaatikoita voidaan perustellusti sanoa antiikin parhaiksi laskijoiksi. He tunsivat monia tunnettuja lauseita kuten Pythagoraan lauseen ja Thaleen lauseen, osasivat ratkaista toisen asteen yhtälön ja käyttivät erilaisia tehokkaita algoritmeja likiarvojen laskemiseen yli tuhat vuotta ennen kreikkalaisia. Kreikkalaisten ensimmäisinä matemaatikkoina pitämät Thales ja Pythagoras oppivat ilmeisesti tunnetuimmat tuloksensa babylonialaisilta ja heidän merkityksensä on ensisijaisesti tiedon kuljettajana ja matematiikan eri osasten järjestelijöinä. Babylonialainen astronomia oli edistyksellistä ja kreikkalainen Hipparkhos hyödynsi babylonialaisten tekemien havaintojen lisäksi myös babylonialaista laskutapaa tehdessään omia tutkimuksiaan. Näiden ratkaisujen pohjalta ympyrä jaetaan vielä nykyäänkin 360 asteeseen, joista jokainen aste jakautuu 60 osaan. Samalla babylonialaiseen matematiikkaan perustuvalla periaatteella myös tunnit ja minuutit on jaettu 60 osaan.
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(2016)This thesis is a review of bandit algorithms in information retrieval. In information retrieval a result list should include the most relevant documents and the results should also be non-redundant and diverse. To achieve this, some form of feedback is required. This document describes implicit feedback collected from user interactions by using interleaving methods that allow alternative rankings of documents to be presented in result lists. Bandit algorithms can then be used to learn from user interactions in a principled way. The reviewed algorithms include dueling bandits, contextual bandits, and contextual dueling bandits. Additionally coactive learning and preference learning are described. Finally algorithms are summarized by using regret as a performance measure.
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(2014)Tutkielmassa tarkastellaan kahta tasogeometrian käsitettä: barysentristä koordinaattisysteemiä sekä pisteen konjugaatiota kolmion suhteen. Barysentriset koordinaatit ovat homogeeninen koordinaattisysteemi, jonka avulla pisteen sijainti tasossa ilmoitetaan suhteessa annettuun kolmioon. Pisteen konjugaatio kolmion suhteen on kuvaus, joka kuvaa tason pisteet toisiksi tietyillä, tyypillisesti geometrisesti luonnehdittavilla ehdoilla. Käsitteet liittyvät toisiinsa siten, että eräät mielenkiintoiset konjugaatiokuvaukset voidaan määritellä barysentristen koordinaattien avulla. Barysentriset koordinaatit otettiin käyttöön 1800-luvun alussa useamman henkilön toimesta. Ne ilmoittavat tason pisteen sijainnin suhteessa annettuun kolmioon järjestetyllä lukukolmikolla, toisin kuin yleisemmin käytetyt karteesiset koordinaatit, jotka ilmoittavat pisteen sijainnin suhteessa annettuun origoon (0,0) lukuparin avulla. Barysentriset koordinaatit voidaan ilmoittaa useammalla, keskenään ekvivalentilla tavalla, mutta niiden määrittäminen tapahtuu kuitenkin aina jonkin kolmion suhteen. Määrittely voidaan tehdä joko tutkittavan pisteen ja kolmion kärkien muodostamien kolmion sivujen jakosuhteiden avulla tai käyttäen hyväksi tutkittavan pisteen ja kolmion kärkien muodostamien kolmioiden pinta-alojen suhteita. Tutkielman kolmannessa luvussa esitetään barysentristen koordinaattien järjestelmä sekä annetaan esimerkkejä mielenkiintoisten pisteiden koordinaateista. Barysentristen koordinaattien kaltainen, toinen homogeeninen koordinaattisysteemi, trilineaariset koordinaatit esitellään myös lyhyesti. Neljännessä luvussa johdetaan muunnoskaavat trilineaaristen ja barysentristen koordinaattien sekä barysentristen ja karteesisten koordinaattien välille. Pisteen konjugaatio kolmion suhteen on eräs pistetransformaation erityistapaus. Tutkielman viidennessä luvussa tarkastellaan aluksi pistetransformaation käsitettä yleisesti, jotta pisteen konjugaatiota kolmion suhteen voidaan ymmärtää paremmin. Isotominen ja isogonaalinen konjugaatio ovat mielenkiintoiset, paljon tutkitut ja geometriassa sovelletut erikoistapaukset pisteen konjugaatiosta kolmion suhteen. Ne ovat mielenkiintoisia myös tämän työn kannalta, sillä niiden määrittelyssä käytetään sekä barysentrisiä että trilineaarisia koordinaatteja. Isotominen ja isogonaalinen konjugaatio esitellään tutkielman viimeisessä luvussa.
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(2015)This thesis describes the development of 'Bayes Academy', an educational game which aims to teach an understanding of Bayesian networks. A Bayesian network is a directed acyclic graph describing a joint probability distribution function over n random variables, where each node in the graph represents a random variable. To find a way to turn this subject into an interesting game, this work draws on the theoretical background of meaningful play. Among other requirements, actions in the game need to affect the game experience not only on the immediate moment, but also during later points in the game. This is accomplished by structuring the game as a series of minigames where observing the value of a variable consumes 'energy points', a resource whose use the player needs to optimize as the pool of points is shared across individual minigames. The goal of the game is to maximize the amount of 'experience points' earned by minimizing the uncertainty in the networks that are presented to the player, which in turn requires a basic understanding of Bayesian networks. The game was empirically tested on online volunteers who were asked to fill a survey measuring their understanding of Bayesian networks both before and after playing the game. Players demonstrated an increased understanding of Bayesian networks after playing the game, in a manner that suggested a successful transfer of learning from the game to a more general context. The learning benefits were gained despite the players generally not finding the game particularly fun. ACM Computing Classification System (CCS): - Applied computing - Computer games - Applied computing - Interactive learning environments - Mathematics of computing - Bayesian networks
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(2013)Background. DNA microarrays measure the expression levels of tens of thousands of genes simultaneously. Some differentially expressed genes may be useful as markers for the diagnosis of diseases. Available statistical tests examine genes individually, which causes challenges due to multiple testing and variance estimation. In this Master's thesis, Bayesian confirmatory factor analysis (CFA) is proposed as a novel approach for the detection of differential gene expression. Methods. The factor scores represent summary measures that combine the expression levels from biological samples under the same condition. Differential gene expression is assessed by utilizing their distributional assumptions. A mean-field variational Bayesian approximation is employed for computationally fast estimation. Results. Its estimation performance is equal to Gibbs sampling. Point estimation errors of model parameters decrease with increasing number of variables. However, mean centering of the data matrix and standardization of factor scores resulted in an inflation of the false positive rate. Conclusion. Avoiding mean centering and revision of the CFA model is required so that location parameters of factor score distributions can be estimated. The utility of CFA for the detection of differential gene expression needs also to be confirmed by a comparison with different statistical procedures to benchmark its false positive rate and statistical power.
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(2015)The thesis studies three different conditional correlation Multivariate GARCH (MGARCH) models. They are the Constant Conditional Correlation (CCC-) GARCH, Dynamic Conditional Correlation (DCC-) GARCH and Asymmetric Dynamic Conditional Correlation (ADCC-) GARCH, in which the time-varying volatilities are modelled by three univariate GARCH models with the error term assumed to have a Gaussian distribution. In order to compare the performance of these models, we apply them to the volatility analysis of two stocks. Regarding model inference, we adopt a Bayesian approach and implement a Markov Chain Monte Carlo (MCMC) algorithm, Metropolis Within Gibbs (MWG), instead of the regular maximum likelihood (ML) method. Finally, the estimated models are employed to compute Value at Risk (VaR) and their performance is discussed.
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(2023)Most graph neural network architectures take the input graph as granted and do not assign any uncertainty to its structure. In real life, however, data is often noisy and may contain incorrect edges or exclude true edges. Bayesian methods, which consider the input graph as a sample from a distribution, have not been deeply researched, and most existing research only tests the methods on small benchmark datasets such as citation graphs. As often is the case with Bayesian methods, they do not scale well for large datasets. The goal of this thesis is to research different Bayesian graph neural network architectures for semi-supervised node classification and test them on larger datasets, trying to find a method that improves the baseline model and is scalable enough to be used with graphs of tens of thousands of nodes with acceptable latency. All the tests are done twice with different amounts of training data, since Bayesian methods often excel with low amounts of data and in real life labeled data can be scarce. The Bayesian models considered are based on the graph convolutional network, which is also used as the baseline model for comparison. This thesis finds that the impressive performance of the Bayesian graph neural networks does not generalize to all datasets, and that the existing research relies too much on the same small benchmark graphs. Still, the models may be beneficial in some cases, and some of them are quite scalable and could be used even with moderately large graphs.
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(2020)Thanks to modern medical advances, humans have developed tools for detecting diseases so early, that a patient would be better off had the disease gone undetected. This is called overdiagnosis. Overdiagnosisisaproblemespeciallycommoninacts,wherethetargetpopulationofanintervention consists of mostly healthy people. Colorectal cancer (CRC) is a relatively rare disease. Thus screening for CRC affects mostly cancerfree population. In this thesis I evaluate overdiagnosis in guaiac faecal occult blood test (gFOBT) based CRC screening programme. In gFOBT CRC screening there are two goals: to detect known predecessors of cancers called adenomas and to remove them (cancer prevention), and to detect malign CRCs early enough to be still treatable (early detection). Overdiagnosis can happen when detecting adenomas, but also when detecting cancers. This thesis focuses on overdiagnosis due to detection of adenomas that are non-progressive in their nature. Since there is no clinical means to make distinction between progressive and non-progressive adenomas, statistical methods must be applied. Classical methods to estimate overdiagnosis fail in quantifying this type of overdiagnosis for couple of reasons: incidence data of adenomas is not available, and adenoma removal results in lowering cancer incidence in screened population. While the latter is a desired effect of screening, it makes it impossible to estimate overdiagnosis by just comparing cancer incidences among screened and control populations. In this thesis a Bayesian Hidden Markov model using HMC NUTS algorithm via software Stan is fitted to simulate the natural progression of colorectal cancer. The five states included in the model were healthy (1), progressive adenoma (2), screen-detectable CRC (3), clinically apparent CRC (4) and non-progressive adenoma (5). Possible transitions are from 1 to 2, 1 to 5, 2 to 3 and 3 to 4. The possible observations are screen-negative (1), detected adenoma (2), screen-detected CRC (3), clinically manifested CRC (3). Three relevant estimands for evaluating this type of overdiagnosis with a natural history model are presented. Then the methods are applied to estimate overdiagnosis proportion in guaiac faecal occult blood test (gFOBT) based CRC screening programme conducted in Finland between 2004 and 2016. The resulting mean overdiagnosis probability for all the patients that had an adenoma detected for programme is 0.48 (0.38, 0.56, 95-percent credible interval). Different estimates for overdiagnosis in sex and age-specific stratas of the screened population are also provided. In addition to these findings, the natural history model can be used to gain more insight about natural progression of colorectal cancer.
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(2020)Objectives: The objective of this thesis is to illustrate the advantages of Bayesian hierarchical models in housing price modeling. Methods: Five Bayesian regression models are estimated for the housing prices. The models use a robust Student’s t-distribution likelihood and are estimated with Hamiltonian Monte Carlo. Four of the models are hierarchical such that the apartments’ neighborhoods are used as a grouping. Model stacking is also used to produce an ensemble model. Model checks are conducted using the posterior predictive distributions. The predictive distributions are also evaluated in terms of calibration and sharpness and using the logarithmic score with leave-one-out cross validation. The logarithmic scores are calculated using Pareto smoothed importance sampling. The R^2-statistics from the point predictions averaged from the predictive distributions are also presented. Results: The results from the models are broadly reasonable as, for the most part, the coefficients of the explanatory variables and the predictive distributions behave as expected. The results are also consistent with the existence of a submarket in central Helsinki where the price mechanism differs markedly from the rest of the Helsinki-Espoo-Vantaa region. However, model checks indicate that none of the models is well-calibrated. Additionally, the models tend to underpredict the prices of expensive apartments.
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(2018)The scope of this project is to provide a set of Bayesian methods to be applied to the task of potential energy barriers prediction. Energy barriers define a physical property of atoms that can be used to characterise their molecular dynamics, with applications in quantum-mechanics simulations for the design of new materials. The goal is to replace the currently used artificial neural network (ANN) with a method that apart of providing accurate predictions, can also assess the predictive certainty of the model. We propose several Bayesian methods and evaluate them on this task, demonstrating that sparse Gaussian process (SGP) are capable of providing predictions, and their confidence intervals, with a level of accuracy equivalent to the current ANN, in a bounded computational complexity time.
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(2019)In this thesis we present a new likelihood-free inference method for simulator-based models. A simulator-based model is a stochastic mechanism that specifies how data are generated. Simulator-based models can be as complex as needed, but they must allow exact sampling. One common difficulty with simulator-based models is that learning model parameters from observed data is generally challenging, because the likelihood function is typically intractable. Thus, traditional likelihood-based Bayesian inference is not applicable. Several likelihood-free inference methods have been developed to perform inference when a likelihood function is not available. One popular approach is approximate Bayesian computation (ABC), which relies on the fundamental principle of identifying parameter values for which summary statistics of simulated data are close to those of observed data. However, traditional ABC methods tend have high computational cost. The cost is largely due to the need to repeatedly simulate data sets, and the absence of knowledge of how to specify the discrepancy between the simulated and observed data. We consider speeding up the earlier method likelihood-free inference by ratio estimation (LFIRE) by replacing the computationally intensive grid evaluation with Bayesian optimization. The earlier method is an alternative to ABC that relies on transforming the original likelihood-free inference problem into a classification problem that can be solved using machine learning. This method is able to overcome two traditional difficulties with ABC: it avoids using a threshold value that controls the trade-off between computational and statistical efficiency, and combats the curse of dimensionality by offering an automatic selection of relevant summary statistics when using a large number of candidates. Finally, we measure the computational and statistical efficiency of the new method by applying it to three different real-world time series models with intractable likelihood functions. We demonstrate that the proposed method can reduce the computational cost by some orders of magnitude while the statistical efficiency remains comparable to the earlier method.
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(2019)Tailoring a hybrid surface or any complex material to have functional properties that meet the needs of an advanced device or drug requires knowledge and control of the atomic level structure of the material. The atomistic configuration can often be the decisive factor in whether the device works as intended, because the materials' macroscopic properties - such as electrical and thermal conductivity - stem from the atomic level. However, such systems are difficult to study experimentally and have so far been infeasible to study computationally due to costly simulations. I describe the theory and practical implementation of a 'building block'-based Bayesian Optimization Structure Search (BOSS) method to efficiently address heterogeneous interface optimization problems. This machine learning method is based on accelerating the identification of a material's energy landscape with respect to the number of quantum mechanical (QM) simulations executed. The acceleration is realized by applying likelihood-free Bayesian inference scheme to evolve a Gaussian process (GP) surrogate model of the target landscape. During this active learning, various atomic configurations are iteratively sampled by running static QM simulations. An approximation of using chemical building blocks reduces the search phase space to manageable dimensions. This way the most favored structures can be located with as little computation as possible. Thus it is feasible to do structure search with large simulation cells, while still maintaining high chemical accuracy. The BOSS method was implemented as a python code called aalto-boss between 2016-2019, where I was the main author in co-operation with Milica Todorović and Patrick Rinke. I conducted a dimensional scaling study using analytic functions, which quantified the scaling of BOSS efficiency for fundamentally different functions when dimension increases. The results revealed the target function's derivative's important role to the optimization efficiency. The outcome will help people with choosing the simulation variables so that they are efficient to optimize, as well as help them estimate roughly how many BOSS iterations are potentially needed until convergence. The predictive efficiency and accuracy of BOSS was showcased in the conformer search of the alanine dipeptide molecule. The two most stable conformers and the characteristic 2D potential energy map was found with greatly reduced effort compared to alternative methods. The value of BOSS in novel materials research was showcased in the surface adsorption study of bifenyldicarboxylic acid on CoO thin film using DFT simulations. We found two adsorption configurations which had a lower energy than previous calculations and approximately supported the experimental data on the system. The three applications showed that BOSS can significantly reduce the computational load of atomistic structure search while maintaining predictive accuracy. It allows material scientists to study novel materials more efficiently, and thus help tailor the materials' properties to better suit the needs of modern devices.
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(2020)Most of the standard statistical inference methods rely on the evaluating so called likelihood functions. But in some cases the phenomenon of interest is too complex or the relevant data inapplicable and as a result the likelihood function cannot be evaluated. Such a situation blocks frequentist methods based on e.g. maximum likelihood estimation and Bayesian inference based on estimating posterior probabilities. Often still, the phenomenon of interest can be modeled with a generative model that describes supposed underlying processes and variables of interest. In such scenarios, likelihood-free inference, such as Approximate Bayesian Computation (ABC), can provide an option for overcoming the roadblock. Creating a simulator that implements such a generative model provides a way to explore the parameter space and approximate the likelihood function based on similarity between real world data and the data simulated with various parameter values. ABC provides well defined and studied framework for carrying out such simulation-based inference with Bayesian approach. ABC has been found useful for example in ecology, finance and astronomy, in situations where likelihood function is not practically computable but models and simulators for generating simulated data are available. One such problem is the estimation of recombination rates of bacterial populations from genetic data, which often is unsuitable for typical statistical methods due to infeasibly massive modeling and computation requirements. Overcoming these hindrances should provide valuable insight into evolution of bacteria and possibly aid in tackling significant challenges such as antimicrobial resistance. Still, ABC inference is not without its limitations either. Often considerable effort in defining distance functions, summary statistics and threshold for similarity is required to make the comparison mechanism successful. High computational costs can also be a hindrance in ABC inference; As increasingly complex phenomena and thus models are studied, the computations that are needed for sufficient exploration of parameter space with the simulation-comparison cycles can get too time- and resource-consuming. Thus efforts have been made to improve the efficiency of ABC inference. One improvement here has been the Bayesian Optimization for Likelihood-Free Inference algorithm (BOLFI), which provides efficient method to optimize the exploration of parameter space, reducing the amount of needed simulation-comparison cycles by up to several magnitudes. This thesis aims to describe some of the theoretical and applied aspects of the complete likelihood-free inference pipelines using both Rejection ABC and BOLFI methods. The thesis presents also use case where the neutral evolution recombination rate in Streptococcus pneumoniae population is inferred from well-studied real world genome data set. This inference task is used to provide context and concrete examples for the theoretical aspects, and demonstrations for numerous applied aspects. The implementations, experiments and acquired results are also discussed in some detail.
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(2022)Sum-product networks (SPN) are graphical models capable of handling large amount of multi- dimensional data. Unlike many other graphical models, SPNs are tractable if certain structural requirements are fulfilled; a model is called tractable if probabilistic inference can be performed in a polynomial time with respect to the size of the model. The learning of SPNs can be separated into two modes, parameter and structure learning. Many earlier approaches to SPN learning have treated the two modes as separate, but it has been found that by alternating between these two modes, good results can be achieved. One example of this kind of algorithm was presented by Trapp et al. in an article Bayesian Learning of Sum-Product Networks (NeurIPS, 2019). This thesis discusses SPNs and a Bayesian learning algorithm developed based on the earlier men- tioned algorithm, differing in some of the used methods. The algorithm by Trapp et al. uses Gibbs sampling in the parameter learning phase, whereas here Metropolis-Hasting MCMC is used. The algorithm developed for this thesis was used in two experiments, with a small and simple SPN and with a larger and more complex SPN. Also, the effect of the data set size and the complexity of the data was explored. The results were compared to the results got from running the original algorithm developed by Trapp et al. The results show that having more data in the learning phase makes the results more accurate as it is easier for the model to spot patterns from a larger set of data. It was also shown that the model was able to learn the parameters in the experiments if the data were simple enough, in other words, if the dimensions of the data contained only one distribution per dimension. In the case of more complex data, where there were multiple distributions per dimension, the struggle of the computation was seen from the results.
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(2020)Tutkielma kartoittaa bayesiläisen lähestymistavan soveltamista joukkoliikenteen lipunmyynnin dynaamiseen hinnoitteluun. Dynaamisessa hinnoittelussa muutetaan tuotteen hintaa taajaan pyrkimyksenä hiljaisempina aikoina houkutella lisää asiakkaita alemmilla hinnoilla ja hyödyntää suuremman kysynnän jaksot nostamalla hintoja. Tutkittavassa tilanteessa pyritään maksimoimaan pitkän matkan linja-autovuoron lipunmyynnistä syntyvä liikevaihto. Oletetaan lippujen myynnin odotusarvon määräytyvän jonkin kysyntäfunktion perusteella, ja että kysyntä riippuu myyntihinnasta sekä muista muuttujista. Hinnoittelija valitsee kullekin myyntijaksolle hinnan, joka tuottaa jonkin myyntitulon, ja tavoite on siis tietylle linja-autolähdölle maksimoida sen lipunmyynnin kokonaisliikevaihdon odotusarvo yli myyntijaksojen etsimällä parhaat hinnat. Hinnoittelussa esitetään noudatettavaksi seuraavaa lähestymistapaa. Oletetaan kysynnän noudattavan log-lineaarista mallia. Käytetään sen parametreille priorijakaumaa, jonka hyperparametreille lasketaan suurimman uskottavuuden estimaatit aiemmin kerätyn datan perusteella. Kussakin yksittäisessä hinnoittelujaksossa kysyntäfunktion parametreille muodostetaan myyntikauden edellisten hinnoittelujaksojen toteutuneiden myyntien perusteella uskottavuus. Sitten parametrien uskottavuuden ja priorijakauman perusteella muodostetaan parametrien posteriorijakauma, jota arvioidaan Markovin ketju -- Monte Carlo -menetelmin. Viimein posteriorijakauman antamaa tietoa kysynnästä käytetään myyntijakson hinnan optimoimiseen varmuutta vastaavan hinnoittelustrategian mukaisesti, eli olettaen parametriestimaatit virheettömiksi.
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(2013)Begreppskartor utvecklades ursprungligen på 1980-talet av Joseph Novak och Bob Gowin som ett sätt att strukturera och därmed få djupare förståelse för ny kunskap. Sedan dess har de utvecklats och undersökts som inlärnings-, utbildnings- och bedömningsmetod, inte minsta av forskare som Maria Ruiz-Primo. Den här avhandlingen utgår till stor del från Ruiz-Primos utveckling av begreppskartan som ett verktyg för att bedöma elevers prestationer och undersöker huruvida traditionella prov kunde ersättas med begreppskartsuppgifter inom fysiken. Begreppskartor har en bevisligen positiv effekt vad gäller inlärning, men de används sällan i våra skolor och det är svårt att få elever att anamma en ny och ointuitiv inlärningsmetod. Att använda begreppskartan som en bedömningsmetod i klassen skulle knuffa eleverna mot ett mer begreppsbaserat tänkande och kunde hjälpa dem i sin förståelse av ämnet. Som provuppgift är begreppskartan snabb att göra och rätta men ger läraren goda insikter i elevens förståelse för ett ämne. Ifall man kunde tänka sig att använda begreppskartan som provuppgift åtminstone diagnostiskt skulle det föra med sig en hel drös fördelar. Två klasser undersöktes, en i grundskolan på årskurs sju och en kurs i gymnasiet. Bägge grupper fick lära sig grunderna i att göra begreppskartor på förhand, gymnasisterna hade lite erfarenhet av det också från tidigare. Båda grupperna fick sedan under en kurs skriva ett traditionellt prov samt göra en begreppskarta av materialet de nyligen gått igenom. Begreppskartorna bedömdes med en femstegs-modell utvecklad av Ruiz-Primo och Shavelson och gavs ett vitsord beroende på hur väl de jämfördes med en expertkarta. Resultaten undersöktes skilt för de två grupperna och granskades för svårighetsgrad, korrelation och överensstämning enligt en statistisk metod utvecklad av Bland och Altman 1986. Svårighetsgraden för uppgiften fanns vara lämplig, bägge gruppernas medeltal var något lägre för begreppskartan än för det traditionella provet vilket kan motiveras med att eleverna trots allt har större erfarenhet av traditionella prov än begreppskartor. Korrelationen för grundskolegruppen fanns vara god, 0,825 medanden för gymnasiet var betydligt svagare, 0,412. Bland-Altman metoden gav vidare negativa resultat för gymnasiet med mycket stora kast mellan de enskilda elevernas prestationer i de två uppgifterna. Grundskolegruppen presterade lite mer konsekvent men visade en trend där de verkligt svaga och de verkligt starka eleverna gynnades av begreppskartsuppgiften medan eleverna med medeltal kring 7 gjorde sämre ifrån sig än i det traditionella provet. Korrelationen för grundskolan är så pass stark att det är tänkbart att begreppskartan kunde användas som en bedömningsmetod inom grundskolan. Grundskolans begränsade matematik gör att också en stor del av naturvetenskaperna är fenomen- och begreppsbaserade snarare än baserade på problemlösning. I gymnasiet är det tvärtom, största delen av gymnasiekursen inom fysiken går ut på matematisk uträkning av fenomen, inte på att kunna förklara dem med ord och förstå samband. Som en följd av detta är begreppskartorna mer användbara som en alternativ bedömningsmetod i grundskolan än i gymnasiet.
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(2017)In the field of scientific research, computer simulation, Internet applications, e-commerce and many other applications, the amount of data is growing at an extremely fast pace. In order to analyze and utilize these large data resources, it is necessary to rely on effective data analysis techniques. The relational database (RDBMS) model has always been a dominant database model in database management. However, the traditional relational data management technology encountered great obstacles in the scalability, as it has difficulties with the big data analysis. Today, the cloud databases and NoSQL databases are attracting widespread attentions and become optional choices besides the relational database. This thesis mainly focuses on benchmarking studies of two multi-model NoSQL databases, ArangoDB and OrientDB and discusses the use of NoSQL for the big data analysis.
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(2019)Continuous integration (CI) and continuous delivery (CD) can be seen as an essential part of modern software development. CI/CD consists of always having software in a deployable state. This is accomplished by continuously integrating the code into a main branch, in addition to automatically building and testing it. Version control and dedicated CI/CD tools can be used to accomplish this. This thesis consists of a case study which aim was to find the benefits and challenges related to the implementation of CI/CD in the context of a Finnish software company. The study was conducted with semi-structured interviews. The benefits of CD that were found include faster iteration, better assurance of quality, and easier deployments. The challenges identified were related to testing practices, infrastructure management and company culture. It is also difficult to implement a full continuous deployment pipeline for the case project, which is mostly due to the risks involved updating software in business-critical production use. The results of this study were found to be similar to the results of previous studies. The case company's adoption of modern CI/CD tools such and GitLab and cloud computing are also discussed. While the tools can make the implementation of CI/CD easier, they still come with challenges in adapting them to specific use cases.
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(2021)Isomorphic web applications combine the best parts of static Hypertext Markup Language (HTML) pages and single-page applications. An isomorphic web application shares code between the server and the client. However, there is not much existing research on isomorphic web applications. Improving the performance, user experience and development experience of web applications are popular research topics in computer science. This thesis studies the benefits and challenges of isomorphism in single-page applications. To study the benefits and challenges of isomorphism in single-page applications, a gray literature review and a case study were conducted. The articles used in the gray literature review were searched from four different websites. To make sure the gray literature could be used in this study, a quality assessment process was conducted. The case study was conducted as a developer survey, where developers familiar with isomorphic web applications were interviewed. The results of both studies are then compared and the key findings are compared together. The results of this study show that isomorphism in single-page applications brings benefits to both the developers and the end-users. Isomorphism in single-page applications is challenging to implement and has some downsides, but they mostly affect developers. The performance and search engine optimization of the application are improved. Implementing isomorphism makes it possible to share code between the server and the client, but it increases the complexity of the application. Framework and library compatibility are issues that must be addressed by the developers. The findings of this thesis give motivation for developers to implement isomorphism when starting a new project or transforming existing single-page applications to use isomorphism.
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(2013)Suomen loppusijoituskonseptin (KBS – 3) mukaan ydinvoimaloiden käytetty polttoaine tullaan sijoittamaan Olkiluodon kallioperään noin 400 metrin syvyyteen. Se eristetään elollisesta luonnosta useiden teknisten ja luonnollisten vapautumisesteiden avulla. Puristettu bentoniittisavi on yksi näistä, ja sen tarkoituksena on muun muassa estää mahdollisesti loppusijoituskohteesta vapautuneiden radionuklidien pääseminen kosketuksiin veden kanssa. Bentoniittisavesta voi vapautua suotuisissa oloissa stabiileja kolloidisia hiukkasia, jotka sekoittuvat pohjaveteen. Bentoniittikolloidien pinnan negatiivisen nettovarauksen vuoksi, ne sitovat herkästi esimerkiksi liuoksessa olevia metallikationeja. Mikäli loppusijoituskohteesta pääsisi vapautumaan radionuklideja, on mahdollista, että ne teknisistä vapautumisesteistä huolimatta joutuisivat kosketuksiin pohjaveden kanssa. Suomen loppusijoituskohteen graniittisen kallioperän kivimineraalien ominaisuuksista johtuen pohjaveteen mahdollisesti joutuneet radioaktiiviset metallikationit, esimerkiksi kolmenarvoiset aktinidit, sorboituisivat lujasti kivimineraaleihin tai saostuisivat. Stabiileilla bentoniittikolloideilla on huomattava negatiivinen pintavaraus ja vaikka niilläkin on vuorovaikutuksia kalliomateriaalin kanssa, ne yleisesti hylkivät pohjavesiverkoston kalliopintoja kulkeutuen virtaavan veden mukana pitkiäkin matkoja. Sitoessaan radionuklideja kolloidit voivat täten mobilisoida muutoin erittäin niukkaliukoisia metalleja, jolloin niiden potentiaaliseen kulkeutumiseen liittyvät fysikaaliset ja kemialliset prosessit pohjavesisysteemissä on tunnettava ja huomioitava ydinjätteen loppusijoituksen turvallisuusarvioinnissa. Tämän tutkielman kokeellisessa osiossa selvitettiin bentoniittikolloidien kulkeutumista sekä niiden vaikutusta radionuklidien (Sr-85 ja Eu-152) liikkuvuuteen graniittisissa kivimurske- ja luonnonrakokolonneissa. Kolonnien hydrodynaamiset virtausolosuhteet määritettiin kivimateriaaliin pidättymättömän merkkiaineen (I-125) avulla. ICP – MS:sta analyysia hyödynnettiin määritettäessä bentoniittikolloidien sisältämä alumiinipitoisuus, jonka avulla saatiin laskennallisesti bentoniittikolloidiliuosten sekä kolloidien kulkeutumiskokeiden näytteiden kolloidipitoisuudet. Lisäksi tutkittiin dynaamisen valonsironnan avulla määritettyjen kolloidiliuosten laskentataajuuksien sekä massaspektrometrisesti saatujen kolloidipitoisuuksien välistä riippuvuutta. Bentoniittikolloidien kulkeutumisen havaittiin riippuvan suuresti kolonnin kivimateriaalista, mutta myös eluentin virtausnopeudesta. Kivimurskekolonneilla virtausnopeuksilla 20 – 40 µL/min kolonnien läpi kulkeutui 1,5 – 6,1 % kolonniin syötetyistä bentoniittikolloideista. Rakokolonnin läpi kulkeutui puolestaan huomattavasti suurempi osa kolloideista: virtausnopeudella 10 µL/min yli 25 % ja nopeudella 20 µL/min yli 62 %. Bentoniittikolloidien havaittiin myös kasvattavan selkeästi Sr-85:n liikkuvuutta rakokolonnissa. Kolloideilla ei sen sijaan havaittu olevan vaikutusta Sr-85:n kulkeutumiseen kivimurskekolonneissa. Lisäksi bentoniittikolloidiliuosten dynaamisella valonsironta-menetelmällä saatujen laskentataajuuksien ja ICP – MS:sesti määritettyjen liuosten kolloidipitoisuuksien välillä havaittiin olevan selkeä lineaarinen riippuvuus.
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