Faculty of Science
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(Helsingin yliopisto, 2020)Open Source Software (OSS) products have become an essential component in the value-creation mechanisms of commercial software development. Therefore, many OSS development projects are driven by strong professionalism in an environment that mixes the common good interests with commercial direction. For organizations that hope to create new, or engage with existing OSS development communities, it is essential to understand this balance to create a successful collaboration. This thesis addresses the many problems of organizing and managing OSS-development model-based communities. We present mixed-method case studies of large and established, commercially influenced OSS development projects, that are orchestrated by a central organization. This evidence describes how differently the organization and governance of hybrid communities can be configured. It illustrates how a community's participatory options and knowledge flows can be used to leverage its openness. Management challenges of these complex environments are described, and practitioner-proven means for alleviating them are presented. We find that the hybrid OSS developer community can be organized as an environment for serious, goal-oriented work, and as an environment for experience-based learning. Our results highlight how important it is to consider what value each in- and outflow of knowledge brings to both the orchestrator, and to the community's contributors. Similarly, orchestrators should consider the extent to which they want to interact with the community's processes, and by what means they could support the community's contributors in achieving their goals. This thesis aims at inspiring new Open Innovation strategies for software producing organizations that want to either engage with existing, or create new hybrid OSS development model-based communities.
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(Helsingin yliopisto, 2020)Carbon dioxide (CO2) and methane (CH4) are two most significant anthropogenic greenhouse gases contributing to climate change and global warming. Indirect remote sensing measurements of atmospheric concentrations of these gases are crucial for monitoring man-made emissions and understanding their effects and related atmospheric processes. The reliability of these studies depends largely on robust uncertainty quantification of the measurements, which provides rigorous error estimates and confidence intervals for all results. The main goal of this work is to develop and implement rigorous, robust and computationally efficient means of uncertainty quantification for atmospheric remote sensing of greenhouse gases. We consider both CO2 measurements by NASA’s Orbiting Carbon Observatory 2 (OCO-2) and CH4 measurements by Sodankylä Arctic Space Center’s Fourier Transform Spectrometer (FTS), the latter being studied from the perspectives of both individual measurements, and the entire time series from 2009-2018. Our approach leverages recent mathematical results on dimension reduction to produce novel algorithms that are a step towards thorough and efficient operational uncertainty quantification in the field of atmospheric remote sensing. Mathematically, the process of inferring gas concentrations from indirect measurements is an ill-posed inverse problem, meaning that a well-defined solution doesn't exist without proper regularization. Bayesian approach utilizes probability theory to provide a regularized solution to the inverse problem as a posterior probability distribution. The posterior distribution is conventionally approximated using a Gaussian distribution, and results are reported as the mean of the distribution as a point estimate, and the corresponding variance as a measure of uncertainty. In reality, due to non-linear physics models used in the computations, the posterior is not well approximated by a Gaussian distribution, and ignoring its actual shape can lead to unpredictable errors and inaccuracies in the retrieval. Markov chain Monte Carlo (MCMC) methods offer a robust way to explore the actual properties of posterior distributions, but they tend to be computationally infeasible as the dimension of the state vector increases. In this work, the low intrinsic information content of remote sensing measurements is exploited to implement the Likelihood-Informed Subspace (LIS) dimension reduction method, which increases the computational efficiency of MCMC. Novel algorithms using LIS are implemented to abovementioned atmospheric CH4 profile and column-averaged CO2 concentration inverse problems.
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(Helsingin yliopisto, 2020)Random planar maps have been widely studied, due to their role as a discretization of the random surfaces in Liouville quantum gravity. However, there are still relatively few mathematical studies concerning random planar maps coupled to statistical physics lattice models, which in turn model Liouville quantum gravity coupled with matter fields. A canonical model of that kind is the Ising model, originally introduced to model ferromagnetic matter. This dissertation improves understanding of the geometry of random planar triangulations coupled to the Ising model at the critical temperature and away from it, starting from the discrete level, via an exploration process following the interface on an Ising-decorated random triangulation. The exploration is called the peeling process. The interface is imposed by boundary conditions consisting of two consecutive arcs of opposite spins, which is the most natural setting involving a single non-closed interface. In the first article of the thesis, it is shown that when the spins lie on the faces and the temperature is critical, there exists an infinite volume limit of the model, in the sense of local weak convergence, as the boundary size tends to infinity one arc after another. Then, the geometry of the interface is studied via certain observables derived from the peeling process. It is shown that the interface drifts almost surely towards infinity, and moreover some scaling limits related to the interface length are obtained. In the second article, the same model is studied outside the critical point, showing a phase transition predicted by the physics literature. This involves showing the local convergence of the model for any temperature and studying the geometry of the local limit in different temperatures. Moreover, the local convergence of the first article is generalized to the case where the length of the two boundary arcs tend to infinity simultaneously. The last article of the thesis concerns a similar model where the Ising spins lie on the vertices of the triangulation instead of the faces. This model has a different behaviour in the high-temperature regime, and otherwise exhibits universality with the model concerned in the first two articles. Moreover, the fact that the interface is a simple curve gives an explicit scaling limit of the interface length, revealing a connection to the continuum Liouville quantum gravity.
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(Helsingin yliopisto, 2020)This thesis presents research on two topics related to sea level in the Baltic Sea: regional sea level rise and meteotsunamis, i.e. meteorologically generated tsunami waves. While these phenomena act on very different time scales, they are both relevant for estimates of coastal flooding risks. Main objectives of this work are i) to present projections of mean sea level change in Finland by 2100 as location-specific probability distributions that can be used as a basis for decision-making in coastal management, and ii) to study the occurrence of meteotsunamis on the Finnish coast and the weather conditions that create these waves. Global mean sea level is rising in the warming climate. This will affect coastal life worldwide, but sea level does not rise uniformly around the globe. Projections of future sea level rise have large uncertainties, especially because the response of the Antarctic ice sheet to climatic changes is poorly known. This makes the upper tail of the probability distribution of sea level rise hard to pin down. In this work, an ensemble of global sea level rise projections is adjusted regionally to form a probability distribution of regional sea level rise. The results suggest that sea level rise in the Baltic Sea will be about 80% of the global mean, without including the effect of land uplift. To obtain probability distributions of mean sea level change relative to land, the effects of postglacial land uplift and wind-induced changes in mean sea level are combined with the sea level rise distributions. According to the average scenario, the sea level in the Gulf of Finland is expected to rise ca. 30 cm in 2000–2100, while mean sea level decline will continue in the Gulf of Bothnia. However, the high-end scenario projects sea level rise everywhere on the Finnish coast, ranging from 21 cm in Vaasa to 90 cm in Hamina. Meteotsunamis occur in shallow sea areas worldwide and can reach a height of several metres in extreme cases. In the Baltic Sea, such high, inexplicable sea waves are historically known as Seebär on the German-speaking southern coast and sjösprång in Swedish-speaking regions. According to old literature and recent eyewitness reports, meteotsunamis can occur all around the Baltic Sea and cause mild damage. The highest reliably documented events have been 1–1.5 metres high. After decades of no reported occurrences, three meteotsunamis were observed in Finland in the summers of 2010 and 2011. This work gives a detailed description of these events and their meteorological origin. The waves were created by air pressure disturbances propagating over the sea. The speeds of the disturbances were close to the long wave speed in the sea, which amplifies the wave. Such resonance effects, in addition to local coastal bathymetry, are central in the formation of meteotsunamis. To study the frequency of meteotsunami occurrence on the Finnish coast, meteotsunamis were detected in the original tide gauge charts and high-resolution sea level data from the Gulf of Finland over the past century. In total, 121 potential events were identified in the summer months of 1922–2014, with typical wave heights of 10–30 cm at the tide gauges. A statistically significant increasing trend in the number of meteotsunamis was found in Hamina in the eastern part of the gulf, but not in Hanko in the west. A strong connection between lightning observations (1998–2014) and meteotsunami occurrence was found: lightning numbers were over ten times higher on days when a meteotsunami was recorded compared to other summer days.
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(Helsingin yliopisto, 2020)Recent and expected future developments in the domains of artificial intelligence, intelligent software agents, and robotics will create a new kind of environment where artificial entities and human beings seamlessly operate together to offer services. The users of these services may not necessary know whether the service is actually offered by a human being or an artificial entity. This kind of environment raises a requirement for using a joint terminology between human beings and artificial entities, especially in the domain of the epistemic quality of information. The epistemic quality of information will play an important role in this kind of intelligent distributed systems. One of the main reasons is that it affects the dependability of those systems. Epistemology is the study of knowledge and justified belief including their nature, sources, limits, and forms. Human beings have been interested in epistemology since the times of ancient Greece, as knowledge is seen to be an important factor of human beings' actions and success in the actions. We are of the opinion that the scene of epistemology is changing more than ever before: artificial intelligence has entered into the domain. In this thesis we argue that first, an intelligent software entity is capable of having beliefs and second, both knowledge and justified belief will be important factors in the dependability of AI-based agents' actions and success in the actions. We carry out a theoretical analysis of the epistemological concepts - belief, justified belief, and knowledge - for the context of intelligent software agents and dependable intelligent distributed systems. We introduce enhanced definitions of justified belief and knowledge, which we call Pragmatic Process Reliabilism. These definitions can be adopted into dependable intelligent distributed systems. We enhance the dependability taxonomy in order to cope better with the situations created by learning and the variation of the epistemic quality of information. The enhancements comprise the following concepts: attributes (skillfulness, truthfulness, and serveability), fault classes (training fault and learning fault), failure (action failure and observed failure), and means (relearning and retraining). We develop a theoretical framework (Belief Description Framework - BDF) to perceive, process, and distribute information in order to verify that our ideas can be implemented. We model the framework using Unified Modelling Language in order to demonstrate its applicability for implementation. First, we define relationships between epistemological concepts and software entities (classes). Second, we show that information, belief, justified belief, and knowledge can be specified as classes and instantiated as objects. The Information class defines the environment - a kind of information ecosystem - of information. It is the central point. It has relationships with other classes: Proposition, Presentation, EpistemicQuality, Warrant, Security, Context, and ActorOnInformation. Third, we specify some important requirements for BDF. Fourth, we show by modelling BDF using the UML modelling method that BDF can be specified and implemented.
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(Helsingin yliopisto, 2020)People expose to poor air quality both outdoors and indoors. Around 90 % of people in the world are breathing polluted air. Air pollution causes negative health effects, increases mortality and leads to harmful effects on the environment. The history of air pollution shows that the air is regarded as no one’s ‘possession’, and the responsibility of it is not recognized before its pollution has seriously damaged nature and affected people’s health. Economic growth has been considered more important than clean air and human welfare. Fast changes towards better air are possible, but they require public action and strong coordinated efforts of policymakers. People demand better air, but are influenced by confusing common opinions and contradictory public argumentation. Education provides possibilities for gaining new knowledge and shaping the cognitive schemas and behavior of people. Therefore, this study aims to contribute better air quality by investigating knowledge transfer of the holistic view of air quality in higher education. The research bridges educational and air quality knowledge in the development of Modern Educational Design Framework (MEDF). It adapted the Design Based Research methodology. The empirical part was implemented in a multidisciplinary and multicultural course at the University of Helsinki because both the students who represent future experts and researchers who work as educators need appropriate knowledge on air quality and teaching of the subject. The results show that, in addition to knowledge and awareness of air quality and its impacts, people need to feel ownership of the air and self-interest to understand their responsibility for the condition of environment to change their behavior. Understanding how our actions assist improvement of air quality, gives people a feeling of empowerment in front of the problems. Therefore, understanding the regional cultural factors behind the different social and individual manners of behavior related to air pollution is important. The research produced information on which issues and pedagogical principles should be considered in building the holistic view of air quality in aligned educational setting. Understanding the development of air quality in the internal dynamics and interplay of the social, scientific and technological process in both history and the present, and the structure and dynamics of systematic knowledge building, is important. The scientific communities need to develop interdisciplinary collaboration and skills. Active and multi-form learning environments, where the linguistic aspects and students’ pre-knowledge and backgrounds are considered, enhance learning. The organizer should consider that interdisciplinary education requires more resources than traditional one. Keywords: air quality, air pollution, education, lnowledge transfer, knowledge building, interdisciplinary, multidisciplinary, multicultural, design, Design Based Research, holistic view
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(Helsingin yliopisto, 2020)The study of geographical ecology is about how species populations are distributed in space and time. Using a model-based approach to study ecological processes controlling species distributions provides us with the means to compare different ecological hypothesis, predict a distribution of a species population in locations we have not sampled, and assess uncertainty of the predictions. Such models are practical tools for environmental management as well. For this doctoral thesis, I studied statistical modeling methodologies to reveal and accommodate model uncertainties, which originate from natural variation of the environment, inconstant surveying, and lack of important ecological covariate data from a model. I apply the methods to improve decision making process in conservation planning. As a case study I examined how Arctic marine environment has changed during the recent decades and how severely the Arctic marine mammals are exposed to stress from environmental change and disturbance from marine traffic in the Siberian Shelf Sea area. First, to assess the magnitude of change of the hydrographic conditions, I applied spatially-explicit prediction method, which accounts for uncertainties especially from strong spatio-temporal variation of the environment. Second, I developed a method for jointly analyzing different types of species observations, generated by heterogeneous sampling methods. Third, I combined species distribution predictions with the locations of marine traffic routes to define the mortality risk marine oil spill accidents pose to different species. Lastly, I developed approaches for further utilizing prior information in ecological models to improve the identifiability of the different processes that control distributions of species populations. I demonstrated this approach on vegetation data in northern Norway. This thesis shows, that the western part of the Siberian Shelf Area has become warmer and less saline during 1980-2000, but the magnitudes of changes are highly uncertain. The Arctic marine mammals in that area have responded to changes in physical environment, particularly diminishing ice cover, by changing their spatial distributions (ringed seal and polar bear), or becoming less abundant in their original distributional area due their inability to change their home ranges (walrus). The risks of the Arctic marine mammals to become exposed to oil spills are uncertain due to predictive variance of the species distribution models and natural variation of the environment. On average, spring is the riskiest season for oil transportation with respect to the hazards posed to the Arctic marine mammals. Lastly, the identifiability and predictive accuracy of species distribution models were improved by steering the inference with prior restrictions on the random effects, which reflect the unobserved ecological processes. This thesis shows that methods for propagating uncertainties into model predictions from data and model structure are essential for testing ecological hypothesis and predicting into novel areas. Random effects may accommodate assumptions about varying sampling methods and unobserved ecological processes, but they need to be restricted appropriately not to allow them to run over the covariate effects. The methods developed here allow to utilize different data sets and help in tackling shortage of observational data in remote areas. They also assist in making more credible predictions for example into novel climatic conditions. Further, this thesis suggests of propagating uncertainties into areal and temporal comparisons of model predictions from predictive variance and natural variation of the environment. These are essential prediction steps to create credible information for comparing different decisions in environmental management. In general, this thesis provides tools to detect ecological and environmental changes and approaches how to adjust environmental management to those changes.
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(Helsingin yliopisto, 2020)Interactions and mergers between galaxies are among the most spectacular astrophysical phenomena that drive morphological transformations of galaxies as they evolve throughout cosmic times. Specifically, galactic encounters induce star formation due to the compression of the interstellar medium through tidal torques, ram pressure and shocks. The in-situ star formation process is in turn self-regulated by various stellar feedback processes, such as ultraviolet radiation from young massive stars and energetic supernova explosions. The thermodynamical processes in the interstellar gas with temperatures ranging from a few degrees to millions of Kelvins, coupled with the stellar lifecycle, are therefore the subjects of a wide range of ongoing observational and numerical studies. Significant technological advances in recent decades have resulted in a general framework for the formation and evolution of galaxies, but the complete astrophysical picture still remains incomplete. Here we study the evolution of galaxies undergoing mergers by running high-resolution hydrodynamical simulations. We use state-of-the-art numerical methods, post-processing methods, and observational data analysis tools. The simulations presented here span a wide range of initial conditions from gas-rich dwarf galaxies, through Milky Way-like disk galaxies, to massive early-type galaxies which include central supermassive black holes. The employed simulation methods include some of the most sophisticated astrophysical models available for galactic-scale simulations. The cooling of the star-forming gas is modelled in detail using a chemical network, and the newly formed stars sample a mass resolution down to the masses of individual massive stars. We also follow the spatially and the temporally evolving interstellar radiation field emanating from the individually modelled stars into the surrounding interstellar medium, while simultaneously accounting for dust attenuation and gas self-shielding. In this thesis we investigate how the extreme star formation environment produced by a gas-rich, low-metallicity dwarf galaxy merger can be used as a proxy for the turbulent star formation conditions present in the high-redshift Universe. Specifically, we follow the formation of a population of young star clusters during the interactions of dwarf galaxies. We show that the star cluster formation proceeds most efficiently during the starburst phase. Young star clusters are, however, already present with an observationally consistent power-law mass function after the first pericentric passage. We take special interest in the formation and early evolution of the three most massive star clusters, which form hierarchically during the most intense starburst. These objects are shown to evolve in terms of their sizes and surface mass densities to resemble the present-day globular clusters observed in the Local Group. Another simulation, specifically set up to reproduce the observed properties of the Antennae galaxy merger (NGC 4038/4039), is in turn used to study the spatially extended star formation during a disk galaxy merger. The simulation output is post-processed using radiative transfer and the results are reduced with observational data analysis methods. We compare the spatial star formation properties and the metallicity distribution to the observed present-day Antennae. We further follow the enrichment of the interstellar medium through stellar winds and supernovae, and show how the merger remnant evolves into a red and dead elliptical galaxy. We continue simulating the Antennae merger for a prolonged period of time after the coalescence of the galactic disks, and use the surface brightness and kinematic properties of the simulated remnant to search for an observational counterpart to the possible future fate of the present-day Antennae galaxies. The outputs of our numerical simulations are used as well to discern how long a period a galaxy merger can be identified in optical images of observed mergers, and the results are used in building a comprehensive picture of the origin of post-starburst galaxies. Finally, we show how the supermassive black holes, found in the centres of all massive early-type galaxies, end in binaries at the centres of merger remnants of elliptical galaxies. The binaries scour the galactic centres while producing cored surface brightness profiles often observed in ellipticals, and coalesce as a result of gravitational wave driven binary evolution.
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(Helsingin yliopisto, 2020)The present thesis focuses on two interrelated research themes that deal with interactions of biological and synthetic nanocomplexes with cell membrane surfaces. Understanding the physical and chemical principles that regulate these interactions is crucial to figure out how native peripheral protein complexes function on the surface of cell membranes, and how man-made nanoparticles with the desired properties can be utilized in the vicinity of cell membranes. In order to provide the most accurate representation of these phenomena by molecular level resolution, the research presented in this thesis has been carried out using atomic and molecular simulation methods. The first part of the thesis focuses on the interaction of fibroblast growth factor II (FGF2) with the plasma membrane. We studied the entry point of FGF2 at the inner layer of the plasma membrane and the importance of PI(4,5)P2 lipids in recruitment and oligomerization of FGF2 at the membrane surface. Understanding how to regulate the secretion of FGF2 will pave the way for biomedical applications as to the development of drugs that can prevent tumor cells from secreting FGF2. The second part of this thesis concentrates on the interactions between gold nanoparticles and cell membranes. We first investigated the role of temperature and lipid composition in regulating the intake of monoprotected gold nanoparticles into model membranes. Understanding this process is critically important in the development of means to control the translocation of man-made nanoparticles into a cell, related to the design of novel drug delivery vehicles with reduced toxicity. Second, we studied how gold nanoparticles can be exploited in single-particle tracking measurements to understand nanoscale membrane dynamics with optimal temporal resolution. Altogether, this thesis work provides novel insight into the interplay between molecular complexes and cell membrane surfaces and underlines the added value that emerges from the linking of computer simulations and experimental techniques.
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(Helsingin yliopisto, 2020)This study combines structural and geochemical approaches to investigate the evolution and origin of hydrothermal fluids responsible for sulfide and gold mineralization in the Archean and Paleoproterozoic greenstone belts in eastern and southern Finland. Structural control on fluids responsible for sulfidization of schist horizons and the spatial and temporal relationship of the schist horizon to the paleobasin architecture is established from the 2.0–1.9 Ga Tampere schist belt (TSB), south Finland. Hydrothermal minerals including tourmaline and calcite are used as a proxy of the chemical evolution and origin of hydrothermal fluids in the Archean orogenic gold deposits located in the 2.7–2.5 Ga Hattu schist belt (HSB), eastern Finland. The observed structures and architecture of the volcano-sedimentary rocks within the southeastern Tampere schist belt imply the presence of hitherto unrecognized paleothrusts within the Tampere paleobasin. The paleothrusts were critical in the localization of the fracture zones that transported and trapped sulfide-bearing hydrothermal fluids, as the sulfide mineralizations are spatially linked to the thrusts. The results suggest that the currently subvertical mineralized fault zones may have originally been more gently dipping and that their mineralization did not necessarily originate from great depths but instead may have been derived from the lower parts of the basin not exposed in the study area. The prevailing granitoids in the study area and nearby do not correlate temporarily or spatially with studied sulfide horizons, and most probably did not provide the fluids responsible for the sulfidization. If the base of the basin infill in our study area consists of volcanogenic massive sulfide (VMS) bearing rocks similar to those that are interpreted as the source for at least the deposits further in the west, they may have served as a source of mineralizing fluids. The spatial and temporal link of sulfide mineralization to the Tampere schist belt architecture helps in understanding the timing of the similar mineralizations in the Tampere schist belt in terms of tectonic evolution, the structural control of the mineralized systems, and in exploration of similar mineralization within the belt. Orogenic style gold mineralizations in the Archean Hattu schist belt are present in all major host rocks including epiclastic sedimentary and volcanogenic rocks, as well as felsic intrusives. The gold mineralization are found within hydrothermal quartz veins and as dissemination in the altered wall rocks. Hydrothermal tourmalines and calcites are often associated with the gold mineralizations occurring in the quartz veins, as alteration minerals in contacts to the metasedimentary host rocks and quartz veins (tourmalinites), but also within the metasedimentary and magmatic rocks. I characterize and compare the major, trace and rare earth element chemistry of these hydrothermal minerals from different host rocks in order to evaluate their suitability as a petrogenetic tool for tracing the chemical evolution and fluid sources of the hydrothermal system. Major and trace element compositions of hydrothermal tourmalines from the HSB appear to be predominantly controlled by the host rocks and local fluid-rock interactions, which makes discriminating the distal fluid sources difficult. However, selected trace element compositions (i.e., Li, V and Sr and REE patterns) suggest metamorphic fluid sources. As in the case of tourmalines, the control of the fluid chemistry by local host rock interaction is manifested by the compositions of hydrothermal calcites as well. The Sr, Y, Mn, (La/Lu)N and ∑REE compositions show clear correlation with their respective host rocks precluding their suitability to discriminate the original fluid sources. However, the REE patterns of the calcites are very homogenous and different from those of their local host rocks, suggesting that they are inherited from the hydrothermal fluid(s) they have precipitated from. The HREE enrichment relative to the LREE visible in the chondrite-normalized patterns are comparable to the REE patterns of calcites from hydrothermal vein type deposits of metamorphic origin elsewhere and contrasts with the REE patterns of calcites from magmatic-hydrothermal environments. The REE patterns of the hydrothermal calcites in the HSB deposits would therefore be compatible with formation from a fluid system that is essentially derived from metamorphic sources, in agreement with conclusions drawn from the published fluid inclusion chemistry. In addition, chemically variable calcite growth zones and grain populations within the calcites can be taken as evidence of chemical changes in fluid composition, probably due to introduction of a new and chemically distinct fluid generation. Based on these results, the potential of using tourmaline and calcite geochemistry to discriminate distal fluid sources is limited to only few specific elements because of the strong interaction of the fluids with the local host rocks. This challenges the straightforward use of tourmalines and calcites as a proxy of distal fluid sources without considering the reference data of the representative local host rock geochemistry. Despite the different age and style of mineralization, as well as the different methodological approaches used here, the results from both studies demonstrate that metamorphic supracrustal rocks are potentially the principal source of hydrothermal fluids associated with sulfidized schist horizons in the Paleoproterozoic TSB and in the orogenic gold deposits of the Archean HSB. In addition to common fluid sources, the sulfide schist horizons in the TSB show many similar architectural and structural features including fault zones and lithostratigraphic associations that often control the mineralizations in the Paleoproterozoic and Archean orogenic gold deposits as well. This highlights the importance of metamorphic supracrustal rocks as the fluid source as well as the architectural and structural control of fluid flow at different scale of mineralizations within the orogenic belts.
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(Helsingin yliopisto, 2020)The need to understand and predict Arctic environmental change has increased the demand to acquire comprehensive information for local communities, scientists, and policymakers. Broad reviews that summarize observations are an important tool to produce this pervasive knowledge on ecosystem properties and processes. However, our understanding about Arctic ecosystems is limited by a relatively sparse network of observations and research gaps that have not been fully identified. For example, the key drivers of fine-scale variability in the carbon cycle, which is an important ecosystem function in the Arctic, have not yet been synthesized. An improved understanding of the current knowledge in Arctic ecosystems is required to predict how Arctic ecosystems function in current and future conditions. In this thesis, I study the representativeness of field sampling locations, and knowledge gaps as well as drivers of fine-scale carbon cycling across the terrestrial Arctic. The first paper focuses on how field sampling locations are distributed across Arctic topographical, soil, and vegetation gradients within broad environmental science disciplines. In the second paper, I review the current state of knowledge in Arctic carbon dioxide (CO2) flux chamber studies which are used to measure fine-scale variability in gas exchange between the biosphere and the atmosphere. And in the third paper, I examine the drivers of fine-scale spatial variability in Arctic carbon cycling as a whole by studying both CO2 fluxes and carbon stocks, with a study design that includes in-situ climatic, soil, and plant community functional composition measurements from 80–220 plots across a tundra landscape. This thesis applies machine learning and Bayesian methods to understand the coverage of field sampling locations and drivers of carbon cycling, respectively. The underlying idea in this thesis is to examine research gaps across Arctic environmental gradients and chamber literature, explore the drivers of carbon cycling at a local scale, as well as to develop theoretical and methodological frameworks to provide a more comprehensive understanding of Arctic ecosystems in a changing climate. The results from the first two papers show that there are vast areas in the Arctic that are lacking sampling locations, particularly in the northernmost Arctic regions. The environmental coverage of field sampling locations varies across environmental science disciplines, but in general, more research is needed in extreme climatic, productivity, and soil organic carbon stock conditions which are found in the Canadian Arctic Archipelago, northern Greenland, central and eastern Siberia, and northern Taimyr region. The results from the second study demonstrate that the Arctic CO2 flux chamber literature is rather comprehensive with 93 studies published over 2000–2016. However, I discovered that knowledge gaps in Arctic CO2 flux chamber studies exist in 1) continuous and year-round measurements, 2) the quantification of other greenhouse gas fluxes together with CO2 fluxes to estimate the full greenhouse gas balance, 3) understanding the role of soil respiration, and 4) experiments that would include a broad range of global change drivers related to not only warming, but also snow conditions, soil moisture, and nutrients, for example. The drivers of fluxes have been identified in Arctic CO2 flux chamber studies, but vegetation, which is a key driver of fluxes, has not been described in a uniform way, nor are the disturbance effects on fluxes understood. The results from the third study suggest that the fine-scale variability of tundra carbon cycling was driven by the plant community functional composition which was characterized with globally comparable plant functional traits describing plant size and resource-use strategies. Plant size, in particular, had a strong and positive relationship with all CO2 fluxes and carbon stocks except soil organic carbon stocks. Moreover, the results demonstrate that tundra ecosystems form a hierarchical system where plant functional traits mediate the effects of average abiotic conditions on carbon cycling across the landscape. This has important implications for how we interpret and model the primary drivers of Arctic CO2 fluxes. This thesis suggests that generalizations about Arctic ecosystems that are derived from the current Arctic literature are based on a restricted sample of the actual spectrum of the Arctic terrestrial gradients. It highlights the importance of recognizing how well the Arctic environments are sampled as well as how fine-scale variability in carbon cycling has been studied, and how research gaps should be filled. Thus, the results help to prioritize future research efforts. Moreover, it provides a hierarchical and trait-based framework to explore fine-scale variability in Arctic carbon cycling in a way that can tease apart the drivers and investigate the effect of vegetation using globally applicable plant functional traits. These findings build towards an improved understanding of the overall view of Arctic ecosystems and their carbon cycling.
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(Helsingin yliopisto, 2020)Space-efficient data structures are an active field of research that has found many applications in combinatorial pattern matching and bioinformatics. The idea is to build data structures that occupy space close to an information-theoretic lower bound, or even less, but still support efficient query operations. In the past few decades, compact index structures have been designed for a variety of different types of data, including bit vectors, strings, trees and graphs, to name a few prominent applications. In this thesis, we design and apply compact data structures for problems related to bioinformatics, and advance the theory of Wheeler graphs, which are a class of graphs that admit a compact indexing data structure. The work is based on four published papers. In the first two papers, we propose compact solutions for two problems on strings. In Paper I, we design and implement an algorithm that computes the classical greedy approximation for the shortest common superstring problem. In Paper II, we design and implement data structures for storing a variable-order Markov model in a compact and queryable form. The last two papers of the thesis expand the theory of Wheeler graphs. In Paper III, we extend the theory into finite state automata, leading to a number of interesting algorithms for recognizing, sorting, determinizing and minimizing automata that are Wheeler graphs. We also show how to turn any acyclic automaton into the minimum equivalent Wheeler graph automaton. In Paper IV, we propose a method to compress a Wheeler graph while retaining the indexing functionality, by generalizing a recently introduced method of tunneling from the Burrows-Wheeler transform to Wheeler graphs.
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(Helsingin yliopisto, 2020)During normal operation of a nuclear reactor neutrons cause fission reactions in the uranium fuel producing energy. In addition, some of the neutrons from reactor core are also absorbed into the construction materials close to the core causing activating nuclear reactions in these materials. Radioactive characterization is the process of determining the radiological properties of reactor fuel and activated structural materials. In the early phase of a decommissioning project characterization is performed with non-destructive computational methods and by studying material samples collected from low-active outer parts of a reactor. FiR 1 is a TRIGA Mark II type research reactor operated by VTT Technical Research Centre of Finland. The reactor has been used for various research and education purposes and producing radioisotopes for both medical and industrial use for years 1962-2015. This thesis consists of the activity characterization work carried out in the FiR1 TRIGA research reactor decommissioning project during the years 2014-2019. Radionuclides in the spent nuclear fuel were studied with two separate burnup calculation models. The calculated nuclide inventories were also tested by applying them in a computational dose rate model. The results were compared with dose rates that had been measured earlier from individual fuel elements. Activated construction materials were studied by building a three dimensional neutron transport calculation model of the reactor structures to calculate the neutron fluxes during reactor operation. This data was combined with reactor operation history and material-specific activating impurity concentrations to model the production of different radionuclides in these materials. Conservative assumptions have been used especially regarding material compositions and operational history modelling simplifications to provide slightly overestimated activities. Measurements from active samples support the calculated results and indicate that the calculations have been conservative as intended. The results from activity characterization are used in a nuclear decommissioning project as input data for estimating the amount of radioactive waste, choosing optimal dismantling methods, planning the practical radiation safety during dismantling and estimating the long-term safety of final disposal of decommissioning waste. The thesis also briefly describes applying the results in these parts of the FiR 1 reactor decommissioning project.
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(Helsingin yliopisto, 2020)The proliferation of IoT devices and rapidly developing wireless techniques boost the data volume and service demand at the edge of the Internet. Meanwhile, increased requirement for low latency feedback has become a must for most popular mobile applications, e.g., Augmented Reality (AR), Virtual Reality (VR) and Connected Vehicles. To address these challenges, edge computing has emerged as an extensional solution for cloud computing. This thesis studies edge computing-facilitated mobile computing and communication systems. We first propose solutions to improve edge resource utilization regarding general edge systems. We present a mechanism to cluster user requests based on similarity for better Content Delivery Net- work (CDN) performance. This mechanism works directly on current CDN architecture and can be deployed incrementally. Then we extend the mechanism by adding cache resource grouping algorithm, so that the system directs similar requests to same servers and group those servers which receive similar requests. This iterative mechanism optimizes the edge utilization by concentrating the resource on similar requests to achieve higher cache hit ratio and computation efficiency. Thereafter, we present solutions for mobile edge systems specifically for three most promising use cases, i.e., Connected Vehicles, Mobile AR (MAR) and Smart city (traffic control). We explore the potential of edge computing in connected vehicular AR applications with real data sets. We design a lightweight edge system and data flow fit for general connected vehicular AR applications and implement a prototype. With an indoor test and real data set analysis, we find out that our system can improve the performance of vehicular AR applications with reasonable cost. To optimize the system, we formulate the problem of edge server allocation and task scheduling as a mutant multiprocessor scheduling problem and develop a two-stage edge-cloud decentralized algorithm as well as a centralized algorithm to schedule the offloading tasks on the fly. We conduct a raw road test and an extensive evaluation based on the road test results and large data sets from real world. The results show that our system improve at least twice the application performance comparing with cloud solutions. For MAR, we consider to offload tasks to multiple edge servers via multiple paths simultaneously to further improve the MAR performance. We develop a fast scheduling algorithm to split the workloads among the avail- able edge servers and show promising results with real implementations. At last, we explore the potential of combining edge computing and ma- chine learning techniques to realize intelligent traffic control by letting edge servers co-located with traffic lights learn the waiting traffic and adapt the light periods with reinforcement learning.
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(Helsingin yliopisto, 2020)Gauge symmetry invariance is an indispensable aspect of the field-theoretic models in classical and quantum physics. Geometrically this symmetry is often modelled with current groups and current algebras, which are used to capture both the idea of gauge invariance and the algebraic structure of gauge currents related to the symmetry. The Hamiltonian anomaly is a well-known problem in the quantisation of massless fermion fields, originally manifesting as additional terms in current algebra commutators. The appearance of these anomalous terms is a signal of two things: that the gauge invariance of quantised Hamiltonian operators is broken, and that consequently it is not possible to coherently define a vacuum state over the physical configuration space of equivalent gauge connections. In this thesis we explore the geometric and topological origins of the Hamiltonian anomaly, emphasising the usefulness of higher geometric structures in the sense of category theory. Given this context we also discuss higher versions of the gauge-theoretic current groups. These constructions are partially motivated by the 2-group models of the abstract string group, and we extend some of these ideas to current groups on the three-sphere S³. The study of the Hamiltonian anomaly utilises a wide variety of tools from such fields as differential geometry, group cohomology, and operator K-theory. We gather together many of these approaches and apply them in the standard case involving the time components of the gauge currents. We then proceed to extend the analysis to the general case with all space-time components. We show how the anomaly terms for these generalised current algebra commutators are derived from the same topological foundations; namely, from the Dixmier-Douady class of the anomalous bundle gerbe. As an example we then compute the full set of anomalous commutators for the three-sphere S³ as the physical space.
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(Helsingin yliopisto, 2020)One of the key challenges to overcome when designing fusion reactors is choosing appropriate materials that can withstand the intense particle irradiation and heat loads inside the reactor. The current top candidates for different parts of the plasma-facing reactor walls are tungsten, beryllium, and various advanced steels. Understanding the effects of ion and neutron irradiation in these materials requires detailed studies of the radiation-induced atom-level changes in the crystal structure, a goal achievable by a combined effort of experimental measurements and computer modelling. This thesis uses the latter to advance the understanding of radiation damage in iron, tungsten, and beryllium. The main tool is molecular dynamics simulations, with which radiation damage can be studied with atomistic resolution. A major part of the thesis is devoted to the development of interatomic potentials to allow more accurate simulations. We demonstrate how improved analytical potentials tailored to radiation damage allow us to study radiation effects in more detail and with higher accuracy than before. In particular, we investigate the formation, evolution and transformation of defect clusters such as dislocation loops, voids, and the C15 Laves phase cluster in iron and tungsten. We focus on aspects of radiation damage in fusion reactor materials that have previously received little attention. These include effects of radiation-induced collision cascades overlapping with previous damage in iron and tungsten, the stochastic stress- and temperature-driven interaction between dislocations and voids in iron, and simulations of beryllium oxide. The latter is made possible by developing the first interatomic potential for beryllium-oxygen interactions. Furthermore, we show how the use of machine learning leads to significantly more accurate modelling of radiation damage compared to analytical potentials. Specifically, we train a machine-learning potential for tungsten that significantly outperforms existing analytical potentials and makes simulations of radiation damage with quantum-level accuracy possible.
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(Helsingin yliopisto, 2020)In this thesis, we study uniformly quasiregular maps on closed Riemannian manifolds. Quasiregular maps are a generalization of conformal maps which permits branching and a bounded amount of infinitesimal distortion. Uniformly quasiregular maps form a subclass of quasiregular self-maps where the control on the infinitesimal distortion is preserved under iteration. The common theme throughout the thesis is discovering limitations for the existence of uniformly quasiregular maps on closed manifolds. To this end, we bound the size of the cohomology ring of a uniformly quasiregularly elliptic closed manifold. Moreover, we obtain several results limiting the possible values of the degree, the Julia set, and the entropy of a uniformly quasiregular map. The thesis consists of an introductory part and four scientific articles. In the first article, joint with P. Pankka, we estabilish a conformal Sobolev variant of the de Rham cohomology and a quasiregular push-forward operation for differential forms, and use these to study the cohomological behavior of uniformly quasiregular maps. In the second article, these methods are then connected to the study of invariant measures and invariant conformal structures of uniformly quasiregular maps. In particular, a dynamical version of the Bonk-Heinonen conjecture is solved in the second article. In the third article, we study limitations to the existence of automorphic quasiregular maps, which in turn are connected to uniformly quasiregular maps of the Lattès type. Finally, in the fourth article, joint with P. Pankka, Y. Okuyama and T. Sahlsten, we study the topological entropy of uniformly quasiregular maps, solving a uniformly quasiregular version of Shub's entropy conjecture in the special case where the ambient closed manifold is not a rational cohomology sphere.
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(Helsingin yliopisto, 2020)During the operation of nuclear power plants, the plant's reactor materials can be corroded. In the prevailing conditions, the corrosion products are activated. This results in radiation background which should be minimized in order to protect the plant personnel. One of the most harmful radionuclides responsible for the radiation exposure is Co-60 due to its relatively long half-life and high gamma decay energy. In order to remove the radioactive cobalt isotopes from the surface of the structures of the reactor, decontamination solutions can be used. These solutions can contain organic complexing agents, like ethylenediaminetetraacetic acid (EDTA). Thus, cobalt is present in the solutions as a non-ionic species, complexed with EDTA. This creates a challenge since the removal of cobalt-EDTA complexes may not be possible by using conventional methods, like ion exchange. In addition, the waste solutions quite often contain high concentrations of stable metals and only trace concentrations of radioactive cobalt. Even though the usage of EDTA in the nuclear industry has decreased, vast amounts of EDTA bearing radioactive liquid waste are stored in tanks. The stability of cobalt-EDTA complexes is rather high and efficient oxidation methods are required for the degradation of the complexes. Advanced Oxidation Techniques have proven their usability for the degradation of EDTA and became also the main motivation of this dissertation together with the removal of cobalt by using inorganic sorbent materials. The dissertation summarizes the usability evaluations of different inorganic sorbent materials for the cobalt uptake in EDTA bearing solutions. A commercial titania based ion exchange material CoTreat® (manufactured by Fortum Power and Heat Oy) was tested in combination with different Advanced Oxidation Techniques including ozonation and ultraviolet (UV) irradiation. When the process was divided into two stages, first the oxidation of EDTA as a pretreatment, and secondly the removal of cobalt by CoTreat®, very good results were achieved. Other sorbent materials under study were synthesized titanium antimonates and antimony oxides. The titanium antimonates were tested without the oxidation of EDTA and proved their usability to remove cobalt especially in acidic solutions. When the synthesized antimony oxides were studied, it was noticed that UV irradiation had a favourable effect on the cobalt sorption efficiency even in the absence of EDTA in the solution. It was demonstrated that a single-stage process combining the UV oxidation and removal of cobalt can be utilized with antimony oxides. Interfering ions, like calcium or nickel in the solution affected the cobalt sorption efficiency of antimony oxide. However, the obtained results were encouraging and the possibility to use antimony oxide as an efficient sorbent for cobalt from aqueous nuclear waste became clear. Keywords: cobalt; EDTA; sorption; titania; titanium antimonate; antimony oxide; Advanced Oxidation Techniques; UV-C; competing cations
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(Helsingin yliopisto, 2020)Water is fundamental for plant life, as it affects the growth, survival, and spatial patterns of vegetation. Here, I explored soil moisture and its ecosystem effects to answer: 1) What controls soil moisture variation? 2) How is water linked to vegetation? 3) Do plants influence water resources? I focused on the moisture of the top-soil layer (0 – 10 cm) in Fennoscandian mountain tundra. First, I evaluated environmental conditions controlling soil moisture variation. I used different modelling methods (generalized linear models, generalized additive models, generalized boosted regression models, and random forests) to account for the uncertainties related to each multivariate technique. On average, the model fit was R2 = 0.60 and the predictive performance R2 = 0.47. The spatial variation of soil moisture was most related to a topographic proxy of soil water accumulation and the depth of the organic soil layer. These results demonstrated that moisture can be modelled using topography and soil data. Secondly, I examined the influence of three water aspects (spatial and temporal variation of soil moisture, and fluvial disturbance) on vascular plants, mosses, and lichens. I used species distribution modelling, a framework for analysing the spatial patterns of species in relation to the environment. The species groups were most related to the spatial variation of soil moisture, albeit species had diverse responses. In general, water is not scarce in the tundra, yet the water aspects improved the models highlighting water as a multifaceted driver of the ecosystem. In addition, I investigated if plant-environment relationships were universal in the tundra. Here, I used hierarchical generalized additive models to compare sites across the hemispheres. I combined plant trait records with data on their environmental drivers. The local variation of conditions within the sites was overridden by global relationships indicating that these links are generalisable across the tundra sites. The results provide empirical evidence for a fundamental assumption in community ecology: consistent plant-environment relationships. Last, I introduced plants to my first question regarding controls of soil moisture. I considered other factors potentially influencing vegetation and soil conditions by using structural equation modelling, a theory-based hierarchical modelling technique. Woody plants correlated negatively with soil moisture, soil temperature, and soil organic carbon stocks (standardised coefficients = -0.16; -0.22; -0.27). As the abundance of woody plants increases, they feedback into the climate system through the water, energy, and carbon cycles. To conclude, plant-water relationships are strong across the tundra. Soil moisture and its spatial variation are controlled by the soil characteristics and the topographic features in the landscape, but also by the abundance of woody plants. Water conditions affect vegetation across species groups, from individuals to the communities. This knowledge unravels the importance of soil moisture in a vulnerable ecosystem undergoing rapid changes.
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(Helsingin yliopisto, 2020)Soft nanoparticles are attractive materials due to their small size, deformability, and ability to host guest molecules. Possible applications include for example carriers of active ingredients such as drugs, biomolecules, reagents, and catalyst. Soft nanoparticles can also be used to stabilize emulsions and in diagnostic systems. Poly(N-vinylcaprolactam) (PNVCL) is a potential material for the construction of such soft nanoparticles as the polymer is non-toxic and thermoresponsive. The responsiveness allows for facile synthesis of nanoparticles in water and can be utilized in the loading and releasing active compounds from the soft nanoparticles. From the broad variety of synthesis methods to produce PNVCL particles, selected methods are discussed, utilized and compared in this dissertation. First, the well-known precipitation polymerization method was used to synthesize colloidal PNVCL gel particles with semibatch method with propargyl acrylate as a comonomer to obtain particles with propargyl moieties on their outer shell. The propargyl groups were later utilized in the functionalization of the gel particles using copper catalysed azide-alkyne cycloaddition. Functionalization with azide bearing gold nanoparticles yielded particles that shrank in response to temperature, and due to AuNPs, the particles also shrank in response to blue light and to oscillating electric field. Thus, the functionalization of the PNVCL gel particles produced multi-responsive particles. Propargyl containing PNVCL particles were also functionalized with carbohydrate azides based on either maltose or glucose to obtain particles with biorelevant functions. The accessibility of the carbohydrate groups was evaluated based on protein interaction studies. Interestingly, the carbohydrate functions were accessible at room and body temperatures independently on the swelling degree of the gel particles, swollen versus partly shrunken. This is due to the prencence of the carbohydrates on the outer parts of the gel particles independent of temperature. The morphology and colloidal properties of the carbohydrate functionalized particles were thoroughly investigated and compared to the propargyl functional particles and to particles synthesized without propargyl containing comonomer. Final part of the dissertation is dedicated to the polymerization induced self-assembly (PISA) of N-vinylcaprolactam as the only monomer. The method produces soft PNVCL particles, but these are self-assemblies of PEG-PNVCL block copolymers, not chemically crosslinked gel particles as the ones usually produced using precipitation polymerization. In the PISA reactions performed, control over the molecular mass of the formed polymers was achieved and the polymerizations were made in higher concentration compared to the concentrations used in the traditional precipitation polymerization method. However, the PISA produced PEG-PNVCL self-assemblies disassembled into dissolved polymer chains upon cooling as the PNVCL block became soluble. Physical crosslinking based on a hydrogen bond donor, salicylic acid, was studied and proved to be effective in preventing the disassembly induced by cooling.
