Faculty of Science
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(Helsingin yliopisto, 2022)The vision made in the article "The Computer for the 21st Century" by Mark Weiser in the year 1991 is widely credited as laying the foundation of ubiquitous computing. In the following years, technological advances in smartphone technologies, the internet of things (IoT), and mobile networking successfully turned this vision into a reality. Users only need to carry a miniature pocket-size computer, now known as \emph{smartphones} to access service anywhere and anytime. The massive deployment of edge data centres around the world has enabled users to enjoy even those services which require a significant amount of computational resources, more than those available on the smartphone. In nutshell, mobile devices, such as smartphones, have become a gateway through which all user information flows (from the user, as well as to the user). While mobile devices have proved to be a true enabler of Mark Weiser's vision, they have also made it easy for external entities to take a peek at users' personal space more closely than ever before. Strong security measures often tend to have a high computational cost which can impede the user's quality of experience (QoE). The ubiquitous adoption of mobile devices and IoT devices such as Amazon Alexa is primarily driven by the utilities they provide to users. It is well documented that many users tend to stop using privacy/security measures after some time if they impede the quality of experience. Thus, it makes it essential to ensure that measures intended for protecting users' privacy and well-being do not impede users' QoE significantly. This thesis provides a collection of novel frameworks to enable privacy and robustness in a ubiquitous environment. It focuses on both active interactions of users with smartphones and headsets in the foreground, as well as passive interactions with smart devices deployed in the background. For enabling privacy and well-being in users' daily interactions, the thesis proposes novel frameworks to enable privacy preservation in ubiquitous short text messaging and daily conversations. At the same, the thesis also considers the need for a robust interaction mechanism in the upcoming age of the metaverse. For enabling well-being in passive interactions, the thesis proposes a robust learning framework. To ensure privacy and well-being without noticeable depreciation in the quality of experience, the thesis proposes a non-probabilistic framework for multimedia context identification which has significantly lower computational overhead. Finally, the thesis proposes a vision of a paradigm shift from the current data ecosystem to the model ecosystem which will enable privacy at a macroscopic level.
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(Helsingin yliopisto, 2022)The Sun emits a continuous, magnetized stream of charged particles, the solar wind, into our Solar System. Rich interactions take place when this solar wind plasma encounters planetary, cometary, or other objects in the Solar System. The details of these interactions depend on the solar wind parameters and the properties of the object, such as whether or not it has an atmosphere or a magnetic field. The resulting space weather effects are varied, from long-term atmospheric erosion to short-term challenges for our society. This dissertation models these interactions, from Mars to the comet 67P/Churyumov–Gerasimenko, concluding the tour in near-Earth space. The main tools of the present studies are numerical models of space plasmas, specifically two hybrid plasma models with kinetic ions and fluid electrons: the Particle-in-Cell model HYB and the Vlasov model Vlasiator. Such hybrid models rely on the separation of spatio-temporal scales of the light electrons and heavy ions to describe ion-kinetic phenomena without the need to resolve electron kinetics. This dissertation consists of introductions to space plasma physics, the numerical models used, and to the three different solar system objects discussed in the four peer-reviewed articles included thereafter. Ion-kinetic studies are first presented for the magnetospheric shielding from energetic particle precipitation at an ancient Mars analogue, where some shielding is observed even by a modest global magnetosphere. Secondly, hybrid modelling is applied at 67P/Churyumov–Gerasimenko, where a possible remote-sensing procedure for determining the existence and scale of a bow shock-like structure is modelled and discussed in relation to Rosetta observations, finding that there are observations consistent with the modelled shock-like structures. Finally, the novel electron submodule of Vlasiator, eVlasiator, is used to probe kinetic electron behaviour at a global scale in Earth’s magnetosphere, for the first time, with results compared to Magnetospheric Multiscale observations and found to be in qualitative agreement, especially near reconnection sites. In the future, eVlasiator should be able to model aspects of electron precipitation to the upper atmosphere of the Earth and place pointwise satellite electron measurements into a larger context.
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(Helsingin yliopisto, 2022)The Earth is surrounded by doughnut-shaped regions of charged particles trapped in the geomagnetic field, called the radiation belts. These belts are highly dynamic and can have major space weather effects. The electrons that form the outer radiation belt have a huge range of energies, with some of them approaching the speed of light. A large number of satellites orbit through the radiation belts and these high-energy electrons can damage their sensitive electronics. It is therefore essential to thoroughly understand the particle dynamics within the radiation belts to protect the satellites. The dynamics of radiation belt electrons can be broadly divided into three categories: acceleration, transport, and loss. Acceleration of particles to higher energies can occur due to local interactions with plasma waves, or by being transported radially inwards in a non-adiabatic process called radial diffusion. Loss of particles from the radiation belts can occur when the particles travel along the Earth’s magnetic field lines and enter the atmosphere, in a process called precipitation. Loss can also occur when the particles cross the outer boundary of the Earth’s magnetosphere and are swept away by the solar wind. This is called magnetopause shadowing, and can be enhanced when particles are transported outward beyond the magnetopause. Radial diffusion therefore contributes both to the loss and acceleration of radiation belt particles. Acceleration, transport and loss of particles all often enhance when an interplanetary coronal mass ejection (ICME) - a large-scale heliospheric transient originating from solar eruptions - impacts the Earth, causing large disruptions to the radiation belt environment. This thesis uses spacecraft observations and simulations of near-Earth space to study the transport and loss of radiation belt electrons. Observational data of electron fluxes were obtained from Polar Orbiting Environmental Satellites (POES), Meteorological Operational (MetOp) satellites, the Van Allen Probes (RBSP) and the Global Positioning System (GPS). The Vlasiator model, a hybrid-Vlasov model of near-Earth space, and the Fokker-Planck DREAM3D simulation were used to obtain the inner magnetospheric wave activity and the phase space density (PSD) of radiation belt electrons respectively. Three first-author articles are included in this thesis, which have all been published in peer-reviewed journals. The first article uses POES / MetOp data to obtain the precipitating electron fluxes in the 30 - 300 keV energy range during the impact of two ICMEs that have rotating magnetic fields of opposite polarity. The timing, strength and location of the electron precipitation was closely tied to the magnetic field orientation, with the strongest precipitation occurring during the southward phase of the ICME These precipitating fluxes were studied in relation to the radiation belt electron fluxes observed by the RBSP during these two events. The greatest variations in electron fluxes at energies ranging from 33 keV to 3.4 MeV also occurred during the southward phase of the ICME. This shows that the substructure of ICMEs can be a major factor controlling the radiation belt response. The second article included in this thesis used the hybrid-Vlasov Vlasiator simulation to develop a methodology to evaluate radial diffusion, using a theoretical formalism that goes beyond quasilinear theory. The vast majority of radial diffusion formalisms are based on quasilinear theory, which assumes that any changes in the particle’s location are small and occur slowly. During ICME-driven storms however, it is possible that radial diffusion of particles occurs more quickly and to a greater degree than can be accurately studied under quasilinear theory. The methodology developed in the second article enables a more accurate evaluation of radial diffusion during these times, using electric and magnetic field data directly from a simulation. Evaluation of radial diffusion beyond quasilinear theory allows in turn a more precise understanding of the role that radial diffusion plays in the acceleration and loss of radiation belt particles. The final paper included in this thesis studied a dropout of relativistic electrons during the impact of the ICME. This paper specifically examined the loss to the magnetopause and the effect of different treatments of the Earth’s magnetic field on the calculated loss. The outward radial diffusion, which transported particles across the magnetopause, was treated according to two models. The first radial diffusion model assumed that the Earth’s magnetic field was a dipole, while the second radial diffusion model allowed a non-dipolar geomagnetic field that accounted for features such as dayside compression and nightside stretching of the Earth’s magnetic field. Up to 10\% more loss of a given electron population to the magnetopause occurred when radial diffusion was modelled with a non-dipolar geomagnetic field than with a dipole. This study shows that over-simplified treatment of the Earth’s magnetic field can significantly affect the modelled radial diffusion of electron populations, which can then have large impact on phenomena such as dropout events, highlighting the importance of accurate treatment of both the Earth’s magnetic field and radial diffusion.
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(Helsingin yliopisto, 2022)Aromaticity - the delocalization of electrons along a closed atomic circuit - has its manifestations in the energetic, structural, electronic, and spectroscopic properties of molecules and in how they react with each other. This phenomenon is central in chemistry, and the history of chemists using the concept as an “intuition pump” to understand and design new molecules goes back to the 1800s when Kekulé first time came up with the snake-eating-its-tail model of benzene. Those days predate the discovery of electron and quantum mechanics, and the concept has evolved since. While the physics of chemistry is understood, the utility of intuitive concepts still remains. Science as of today is still a human business, and to most of us chemists the fluctuations of fermionic field, or their computational representation as tensors, don’t give much food for thought. In this PhD thesis, I present my research in which quantum chemical methods were used to study different types of aromatic compounds. The focus is on assessing their aromaticity by probing the ring currents of molecules - the net flow of electrons around when it’s placed in a magnetic field. Calculation of this magnetically induced current density and the bond currents yield an accurate measure for electron delocalization. The studied systems present different types of aromaticities and aromatic molecules: through-bond aromaticity in the substituent ring of benzene derivatives, the intricacies of current pathways in naphtalene-fused porphyrinoids and in copper coordination complexes, and the magnetic-field orientation dependence of aromaticity in gaudiene, a spherical aromatic, but not spherically aromatic compound. The presented results disprove old conclusions for some compounds and enrich the understanding of others. In addition, the thesis gives a brief overview of computational quantum chemistry, and a slightly deeper one on aromaticity, presenting the ups and downs of different methods used to assess it computationally, and taking the reader on a tour to the zoo of different types of aromatic compounds.
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(Helsingin yliopisto, 2022)There has been a stagnation in the development of power-storage devices since the lithium-ion battery in 2019, and alternative ways to save power in devices connected to the Internet-of-Things (IoT) are highly sought for. One very promising way is to produce power-saving CMOS-compatible single-electron transistors (SETs) made of silicon, insulated with silica. Excessive silicon introduced into the silica matrix, through ion-induced atomic mixing, will self-assemble into a silicon nanodot acting as the quantum island of the SET during thermal annealing. Molecular dynamics (MD) has been used for decades to simulate the dynamics of atomistic systems exposed to ion beams, which also made MD the perfect choice for this study as well. We implemented a few speed-up models to make the irradiation simulation faster and at the same time more appealing to the industry for testing out irradiation conditions before applying them experimentally. We even included a graphical user interface to make the simulator more user friendly for external audiences. During the study we came up with a reliable way of combining layers of silicon-based materials to merge together the target structures. We used the merged structures to investigate the physical processes related to the irradiation-driven atomic mixing we were looking for. An understanding of the processes helps the community to more accurately apply the irradiation condition in the already available silicon-based processes used by the semiconductor industry when creating CMOS components. We found a connection between the initial momentum transfer from ions hitting a nanopillar perpendicularly at the tip and the ion hammering effect on nanoscale. The built-up tension directed downwards during the ballistic phase of the cascade and the low-energetic collisions of the fading cascade would start to press the atoms laterally outwards. Other noticeable effects during the irradiation were crystalline-to-amorphous phase shifts and local densifications of the silica.
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(Helsingin yliopisto, 2022)Wintertime slip injuries are a very common problem in Finland as well as in other countries where winter conditions are frequent. According to surveys, on average every third person in Finland slips each winter and more than 50,000 persons are injured needing medical attention. Slipping causes human suffering as well as significant financial costs due to medical expenses and sick leaves. On some of the most slippery days, the number of slipping injuries can be so high that the hospital emergency departments are crowded with patients requiring surgery. The severity of slipping injuries typically increases with age. In addition, the number of slips and slip related injuries are more common among women than men. Finland has set a goal to increase the share of sustainable transport modes, such as walking and cycling, in the future. The aim is to reduce greenhouse gas emissions from transport and improve public health. Walking and cycling are to be the primary means of transport, especially for short distances in dense urban areas. In addition, the aim is to improve traffic safety and to develop walking and cycling infrastructure. This dissertation presents in which weather situations slips occur more than usual. In addition, the work presents a meteorological tool to help predicting weather conditions that cause pedestrian sidewalk slipperiness. Weather has a significant role in pedestrian’s wintertime slips and resulting injuries. In this dissertation, it has been investigated what are the weather situations that increase the risk of slipping and what is the spatio-temporal distribution of slips. Special attention has been given to situations with clearly more slips than usual, i.e. so called peak days of slipping injuries. The results show that snow and ice significantly increase the risk of slipping, and that most of the wintertime slips occur when the temperature is near zero degrees or slightly below it. This dissertation presents a numerical model predicting slipperiness from the pedestrian’s point of view. The model is developed at the Finnish Meteorological Institute. The thesis presents the physical principles of the model and how the slipperiness classification is implemented. The model is a tool for meteorologists to supports the decision making when issuing warnings about slippery sidewalk conditions. In addition, the model benefits winter road maintenance personnel and also public with better sidewalk condition and issued warnings. Climate change will have a major impact on future winters, especially in the northern latitudes. The winter season is shortened and near zero temperatures are becoming more frequent also during mid-winter, meaning more slippery conditions during that period. It is expected that the slip period will become shorter but at the same time more intense.
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(Helsingin yliopisto, 2022)The standard model of particle physics describes an astonishing number of phenomena. Yet at the same time it is incomplete: it does not describe e.g. gravitation. Finding explicit weaknesses in the standard model predictions has proved to be difficult, and hence precision measurements are currently one of the most promising methods towards this goal. One of the most intriguing precision measurements is that of the top quark mass (mt), which is connected for instance to the question about the meta-stability of the universe. This thesis strives multilaterally towards a more precise measurement and interpretation of the top quark mass. The work begins with efforts towards a more precise jet calibration at the CMS. Then, the possible weaknesses of a D0 mt analysis are reviewed. Finally, a mt measurement at the CMS is constructed for the legacy 2017--2018 datasets. The jet energy corrections are the most important experimental factor in the uncertainties of the top quark mass. Hence, they are closely linked with the mt measurement. The work on jets in this thesis aims for an exceptionally precise jet energy calibration for the CMS Run 2 legacy datasets. The author has made several important contributions towards the jet energy corrections in the Run 2 legacy reconstruction. The re-assessment of a D0 top quark mass measurement is performed outside of the CMS and D0 affiliations. The D0 top quark mass value is an important outlier in the top quark mass world combination, and a better understanding of the reasons behind this is desirable. In an earlier study it was shown that there are possible discrepancies in the flavor-dependent jet energy corrections at D0. In this thesis we demonstrate that these discrepancies (if they can be confirmed) shift the D0 top quark mass measurement to a value that is more in line with the other major measurements from CMS, ATLAS and CDF. The work culminates in the design and validation of the first direct CMS lepton+jets mt measurement on the 2017--2018 datasets. The analysis is executed using a new profile likelihood method, where the collected data can constrain systematic uncertainties in situ. Agreement between data and simulation is verified within the systematic uncertainties using control plots. The impact of an extensive set of systematic uncertainties on the mt measurement is assessed using simulations. Also the full effects of limited statistics in simulations are demonstrated using toy experiments. It is confirmed that the limiting systematic uncertainty in the current mt measurements is the modelling of b quark jets. This challenge can be encountered either by enhancing the b jet energy corrections or by performing the measurement on a larger amount of data. In profile likelihood analyses, the latter is also a valid approach.
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(Helsingin yliopisto, 2022)The dissertation concerns inverse problems and dynamical systems. Inverse problems, as a subfield of mathematics, studies the mathematical theory of indirect measurements. It is an active area of research with extensive mathematical theory and numerous applications. Many inverse problems are concerned with physical systems that evolve in time. A mathematical model that describes how a quantity evolves in time is called a dynamical system. X-ray computed tomography is a technique where the inner structure of an object is computed from a number of its X-ray images. In the first publication of the dissertation we consider X-ray computed tomography in a setting where the orientations in which the object was imaged are unknown. This problem is called tomography with unknown view angles; such a problem arises e.g. in cryogenic electron microscopy of viral particles. We show that under general assumptions it is possible to reconstruct the structure of the object in tomography with unknown view angles. Diffusion is the flow of a substance from areas of high concentration to areas of low concentration. In the second publication we consider an inverse problem for the space–time fractional diffusion equation. This equation models diffusion and anomalous diffusion processes, such as those sometimes observed in fractured geological formations. We show that the geometry of the underlying space can be determined by observing the evolution of a solution of the equation in a subset of the space. In the third and fourth publications we consider a dynamical system that models injection locking in a laser. Injection locking is a technique where light from one laser is injected into another laser's cavity (the part of a laser where the emitted light is created) with the intention of altering the laser's properties. The idea is to consider injection locking as a process that can provide the basis for optical computing devices. In the third publication we derive an approximation for the nonlinear relationship between the injected light and the injection-locked emitted light, and we show that it is possible to construct an optical logic gate based on this relationship. In the fourth publication we do a detailed analysis of the dynamical system that models injection locking in lasers, and based on this analysis, we propose a design for an optical neural network.
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(Helsingin yliopisto, 2022)The purpose of this thesis was to study the origin and nature of the massif-type anorthosite-related monzodioritic rocks and to investigate the possibility that they could be utilized to shed light on the melt evolution of massif-type anorthosite parental magmas. This thesis also presents new means to apply state-of-the-art thermodynamic modeling tools, the Magma Chamber Simulator (MCS) and rhyolite-MELTS, to study the petrogenesis of massif-type anorthosites and related rocks. Although the petrogenesis of anorthosites has been studied for over a century, some questions still remain open. The massif-type anorthosites are often found together with more felsic rock types in anorthosite-magnerite-charnockite-granite (AMCG) intrusions and are mainly restricted to the Proterozoic times. Related minor fine-grained high-Al gabbros and monzodioritic rocks (also referred to as jotunites, ferrodiorites, monzonites, monzonorites, etc.), have proven to play a key role in the search of melt compositions of massif-type anorthosites. Despite their simple modal compositions, the evaluation of parental magma compositions has proven to be challenging and suggestions of high-Al basaltic or monzodioritic compositions and mantle or crustal origins have been made. The 1.64 Ga Ahvenisto complex in southeastern Finland is an AMCG complex that comprises a granitic intrusion surrounded by an anorthositic envelope with minor monzodioritic rocks. The monzodioritic rocks are fine-grained and are presumed to represent near-melt compositions left after the fractional crystallization of the anorthositic cumulates. The monzodioritic rocks also show evidence melt interaction with the granitic rocks as mingled monzodioritic pillows with net-veined granites and hybrid rocks. This thesis uses routine analytical methods complemented with detailed field and petrographic descriptions to study the monzodioritic, granitic, and olivine-bearing anorthositic rocks of the Ahvenisto complex. The produced data complemented with existing data are applied to various petrological modeling to shed light on the origin of these rocks and related interaction structures in the Ahvenisto complex. A dataset of suggested parental and residual magma compositions, mantle-derived melts, and wallrock compositions was compiled for the thermodynamic modeling. Field, petrographic, and geochemical data complemented with petrological modeling indicates that the monzodioritic rocks of the Ahvenisto complex represent near-melt compositions that were produced by fractional crystallization of the anorthositic cumulates. Three different monzodioritic types were recognized: olivine, pillow, and, massive monzodiorite, which form an evolutionary trend. The compositions of the monzodioritic rocks also control the interaction with the granitic melts ¬– only the more evolved pillow and massive monzodiorites are involved in the mingling and hybridization, respectively. Mafic phases in the monzodioritic rocks show Fe-enriched compositions that are not in equilibrium with the host whole-rock compositions. Thus, the monzodioritic rocks are suggested to be formed by a local equilibrium crystallization process. The thermodynamic modeling indicates that the massif-type anorthosite parental magmas were of high-Al basaltic compositions and were produced from mantle-derived melts assimilating lower crustal material rather than by direct melting of lower crust. The models further suggest that the monzodioritic rocks represent the residual melts left after polybaric fractional crystallization of the anorthosite parental melts. The models conducted for this thesis serve as a framework for future applications of more detailed studies of AMCG suites and other intrusions.
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(Helsingin yliopisto, 2022)Metagenomics is the analysis of DNA sequencing data from samples obtained directly from the environment and containing several different organisms at once. Common tasks in metagenomics are taxonomic profiling, where the goal is to identify the organisms present in the sample and assign relative abundances to them, and taxonomic binning, where the sequencing data from the sample is divided into bins that correspond to some sensible taxonomic units. This thesis introduces methods for performing these two tasks at a high-resolution capable of distinguishing between lineages of bacterial species. The first of these methods is mSWEEP, which solves the profiling task by utilizing a collection of grouped bacterial reference sequences, pseudoalignment, and a probabilistic model. The second method, mGEMS, builds upon mSWEEP to solve the binning task using an assignment rule derived from the fundamentals of the probabilistic model used by mSWEEP. Both methods are accompanied by efficient implementations that utilize fast variational inference and pseudoalignment to fit the model in a reasonable time, rendering them applicable to large-scale datasets. Both mSWEEP and mGEMS have been developed for application in either the traditional whole community metagenomics context, where the direct-from-environment samples are analysed, or in the plate sweep metagenomics context, where the sample has been plated once on a selective medium. While the latter is not metagenomics in the traditional sense, this thesis advocates for its use when high depth sequencing data is required from some species and the other organisms are not of interest. Regardless of the type of metagenomics data used, the ultimate goal of both mSWEEP and mGEMS is to enable performing standard genomic epidemiological analyses directly from data containing several strains of the same bacteria, skipping the typically used isolation steps required to separate them. Due to the implied cost-savings from reducing the number of cultures that need to be performed as well as the better capture of variation in the samples through using metagenomics data, mSWEEP and mGEMS enable performing entirely novel types of analyses in the field of genomic epidemiology.
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(Helsingin yliopisto, 2022)Companies in the financial and insurance sector have to face a large amount and variety of risks. Some of these risks are easy to estimate and model, but there exist also risks that are unpredictable and hard to understand. Financial crises in the past show that commonly used tools do not suffice to model such large risks. In the insurance sector, for instance, the occurrence of unexpectedly large claims can easily cause large losses that result in the insolvency of a company. Catastrophic events such as earthquakes, floods, pandemics and terror or cyberattacks create big damages which lead to exceptional large costs for insurance and reinsurance companies. Observations and data from the past are not always sufficient to predict such extremal events in the future since, for example, climate change affects the occurrence of natural catastrophes like forest fires and storms whereas the development of information technology enables cyber crime. On financial markets, return rates of portfolios have similar properties. Most often changes in return rates are small and follow expected trends but it is not uncommon that unexpectedly stock market crashes happen. Therefore, one has to establish models that consider smaller claim sizes as well as large ones caused by extremal events. Mathematically, heavy-tailed distributions meet these requirements. Heavy-tailed distributions are distributions whose tail is not exponentially bounded and thus rare events with a large impact have a substantially higher probability than similar events modelled by light-tailed distributions such as the normal distribution. Usually, insurance companies operate in different lines of business, offer products of different types of insurances or operate in different regions. Hence, the companies do not only have to be aware of the risks in every single line of business but also in their interactions and dependence. For instance, a catastrophic event like an earthquake does not only affect a single line of business, but can cause large claims in different types of insurances. Viewing the yearly net payout of an insurance company as a stochastic process with underlying heavy-tailed random vectors permits to model and analyse the long-term behaviour of the solvency of the company. This work aims to understand how one can model the long-term activities of an insurance company in markets where large losses are possible and investment returns from different industry sectors can collapse at the same time. The work endeavours to analyse the nature and order of magnitudes of risks as well as their impacts in different lines of business.
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(Helsingin yliopisto, 2022)This doctoral dissertation is devoted to the study of the quotient algebra of compact-by-approximable operators (the compact-by-approximable algebra) on Banach spaces and related quotient algebras of linear operators. The compact-by-approximable algebra is a radical Banach algebra that is non-trivial only within the class of Banach spaces failing the approximation property, and it has not been much studied prior to this thesis. The primary focus is on questions about size, closed ideals, and other structural properties of such quotient algebras. We also study non-classical approximation properties associated to explicit Banach operator ideals. In the first of four research articles included in the dissertation, we show that the compact-by-approximable algebra is large for many Banach spaces. In fact, there is a linear isomorphic embedding from the space of all null sequences of scalars into the compact-by-approximable algebra for various Banach spaces, including particular closed subspaces of classical sequence spaces and specific Banach spaces due to George Willis (1992) and William B. Johnson (1972). We also exhibit an example of a non-separable compact-by-approximable algebra. The second article studies closed ideals and related properties of the compact-by-approximable algebra. We exhibit various examples of Banach spaces for which the compact-by-approximable algebra carries explicit closed ideals, where many - but not all - of the ideals are induced by Banach operator ideals. We also discuss the existence of compact non-approximable operators between closed subspaces of classical sequence spaces. The first and second article are joint works with Hans-Olav Tylli. The compact-by-approximable algebra can be generalised by considering an analogous quotient algebra associated to a general Banach operator ideal and its approximative kernel. The third article investigates such a quotient algebra for two classes of Banach operator ideals; namely, for the class of quasi p-nuclear operators introduced by Arne Persson and Albrecht Pietsch (1969), and for the class of Sinha-Karn p-compact operators of Deba P. Sinha and Anil K. Karn (2002). Our focus here is on questions about size, nilpotency, and closed ideals. The results also yield new examples of closed ideals of the compact-by-approximable algebra. Some of the results in the second and third article involve non-classical approximation properties associated to Banach operator ideals. Such approximation properties were introduced by Eve Oja (2012) and they have recently been studied for various Banach operator ideals. The fourth article focuses on approximation properties related to the Banach operator ideals of unconditionally p-compact operators introduced by Ju Myung Kim (2014) and the aforementioned Sinha-Karn p-compact operators. For instance, we show that the respective approximation properties associated to unconditionally 1-compact operators and Sinha-Karn 1-compact operators are strictly weaker properties than the classical approximation property. All four research articles use various factorisation techniques for linear operators and results from Banach space theory. A comprehension of the constructions of various Banach spaces failing the approximation property is also essential for many of the results. This doctoral dissertation contributes to the branch of functional analysis in pure mathematics, and hopes to bring new insights towards a better understanding of the elusive gap between the compact operators and the bounded finite-rank operators on infinite-dimensional Banach spaces.
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(Helsingin yliopisto, 2022)Our knowledge about mineralogy and physical conditions on airless planetary bodies in the Solar system is based mainly on remotely captured reflectance spectra. However, reflectance spectra are influenced by many effects, a major one is the space weathering. The term space weathering refers to a set of processes, also called space weathering agents, mainly the solar wind irradiation and micrometeoroid impacts, which on long timescales darken the surfaces and alter reflectance spectra of airless bodies. Here, I focused on finding the difference between the effect of the two, above-mentioned, space weathering agents on reflectance spectra of silicate rich airless planetary bodies. Firstly, I studied areas of magnetic anomalies on the Moon, so-called lunar swirls. The swirls’ spectra are influenced mostly by micrometeoroid impacts. I compared these spectra to spectra of surrounding areas, influenced by both the space weathering agents. The results suggested that there is a difference in the effect of micrometeoroid impacts and the combination of the two space weathering agents. There are also additional effects that contribute to the evolution of spectra on the Moon, such as the position with respect to the near and far side, which relates to the shape of Earth’s magnetotail and an increased shielding of the solar wind ions. During the laboratory experiments, I, with the help of colleagues, irradiated pellets made of silicates typically found on airless planetary bodies, i.e. olivine and pyroxene. To simulate the effect of solar wind, I used ions of H, He, and Ar. To simulate micrometeoroid bombardment, I used individual femtosecond laser pulses. The main conclusions were that the difference between the two space weathering agents can be seen mainly in the longer near-infrared (NIR) wavelengths (around 2 μm). Micrometeoroid impacts cause greater changes there, resulting in smaller spectral slope changes. Otherwise, the original mineralogy seemed to influence the way the weathering proceeds more significantly, which agrees with previous studies and also with observations of A-type asteroids or asteroids (4) Vesta and (433) Eros. The differences in irradiated samples were then analysed on micro-scale using electron microscopy. Ion irradiation caused only mild blistering on the surface while laser irradiation caused extended melting with associated melt splashes. The subsurface changes were also different. Ion irradiations induced vesiculation in partially amorphised topmost layers of the samples. Laser irradiation induced creation of the nanophase iron (npFe0) particles in the olivine sample, but not in the pyroxene sample. Changes in ion-irradiated samples caused alterations in the visible spectral slope, while npFe0 particlesin laser-irradiated olivine also altered the NIR spectral slope. The pyroxene sample irradiated by laser showed only a significant amorphous layer full of large vesicles. The spectral slope did not change as a result, the sample only showed alteration of the absorption bands. This analysis highlighted the significance of wavelength-sized structures on the resulting reflectance spectra. Based on these results, I gained an insight into the evolution of the spectra and subsurface structures. Nevertheless, more simulations on different minerals are needed to gain a complete understanding of the space weathering mechanism.
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(Helsingin yliopisto, 2022)Despite continuous advances in medicine, there is still a need for better diagnostics and therapeutics against various diseases including cancer. When a single system combines the diagnosis with the treatment of a disease, it is called a theranostic system. Additionally, the term nanoparticle refers to the size of a particle which exhibits in the nano-scale. Nanoparticles are currently found, for example, in food, cosmetics and medical industries. There are various types of nanoparticles that can serve as theranostic systems, and thus, they are called nanotheranostics. Hepatitis E virus nanoparticles (HEVNPs) are small (<30 nm in diameter), safe, biocompatible and are derived from the Hepatitis E virus, and therefore, they share all the physical characteristics with the virus. The main difference between the two is that HEVNPs lack the genetic material (RNA). HEVNPs have been studied regarding their potential use as nanotheranostic agents including their application in imaging modalities, such as in optical imaging. In this thesis, a highly promising nanosystem, HEVNP, was investigated as an imaging agent candidate for the high-performance molecular imaging modalities, positron emission tomography (PET) and single-photon emission computed tomography (SPECT). As a consequence, in this work various different radiolabeling methodologies were evaluated in order to successfully radiolabel HEVNPs with both PET and SPECT compliant radionuclides. Subsequently, the novel nanosystems were studied in vitro and in vivo. Specifically, two approaches of radiolabeling were explored; a direct and an indirect approach. The direct radiolabeling, in which the HEVNPs were functionalized with a radiometal coordinating chelator prior to the radiosynthesis, was found to be the most efficient approach, and thus, this method was used for the investigation of the HEVNP biodistribution in cells and animals.The radiosynthesis following the direct approach was done with both PET- and SPECT-compliant radionuclides (gallium-68 and indium-111, respectively), and the radiolabeled HEVNP nanosystems were evaluated in vivo in mice after intraveous or oral administration. Furthermore, HEVNPs targeted to integrin α3β1 were evaluated in HCT 116 colorectal tumor-bearing mice after intravenous administration of the radiolabeled HEVNPs. One of the prominent findings was that the radiolabeled HEVNPs were found to be highly hepatospecific. Additionally, excellent radiolabel stability of the HEVNPs was observed following the intravenous injections; opposed to the highly unstable oral administered HEVNPs. The evaluation of the integrin α3β1-targeted-HEVNPs revealed a modest in vivo tumor uptake, which most probably was due to the excessive liver uptake of the specific NPs. In conclusion, methods for the radiolabeling of HEVNPs with PET- and SPECT-compliant radionuclides were succesfully developed. This was achieved with excellent radiochemical yields, and the in vivo results that were obtained from the highly stable radiolabeled nanosystems are very promising. Overall, the novel HEVNP systems that were synthesized in this work could serve as candidates for multifunctional applications in nanotheranostics including their use in targeted PET/SPECT imaging for various diseases.
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(Helsingin yliopisto, 2022)Air pollution and climate change pose threats to human health and the environment. Gathering climatic and environmental data, such as visibility, temperature, relative humidity, and mass concentration of aerosol particles with diameter of 2.5 or 10 micrometers or less (PM2.5, PM10, respectively), is the first step in understanding the processes that contribute to air pollution and climate change. Ground-based in situ stations are sparsely and unevenly distributed, meaning that these stations cannot offer spatially seamless coverage for parameters. Satellite observations provide global coverage. Clouds, however, lead to missing values in some satellite products, like the land surface temperature (LST) datasets derived from thermal infrared bands. Finding a method that can automatically produce consistent identification results is essential since new particle formation (NPF), a significant source of atmospheric aerosols, is also tied to the environment and climate. By developing deep learning techniques and utilizing images that contain climatic and environmental information, this thesis seeks to answer three research questions about climate and the environment. The first study focuses on retrieving parameters from Red-Green-Blue (RGB) and hyperspectral images captured near the ground. The second study seeks to retrieve spatially seamless air temperature from satellite-derived LST products that contain missing values. The last research aims to automatically identify the new particle formation (NPF) events and obtain the related parameters such as growth rate, start time, and end time of each event. In summary, the main findings of this thesis are as follows. (1) A model was proposed to simultaneously retrieve a suite of parameters, including visibility, temperature, relative humidity, PM2.5, and PM10, from images captured near the ground. The proposed model achieves generally better retrieval results compared with three classic deep learning models. RGB images are more cost-efficient than hyperspectral images for retrieving parameters. Images with relatively lower spatial resolution can also be used for retrieval. Retrieving multiple parameters is not only possible for images captured at a fixed location but for images captured on a continental scale. (2) By leveraging a UNet model and an image-to-image training approach, it is possible to extract spatially seamless air temperature by filling the gaps in satellite-derived LST with a specified constant value. The retrieval results are with a higher spatial resolution and generally better retrieval accuracy compared with the fifth generation reanalysis for the global climate and weather (ERA5) of the European Center for Medium-Range Weather Forecasts (ECMWF). (3) Leveraging the typical features of image objects, instance segmentation methods such as Mask R-CNN can be applied to detect NPF events from aerosol number size distribution datasets. Other derivatives, such as growth rates, start times, and end times, can also be determined automatically. The proposed method improves the automatic level for analyzing the NPF events and obtains consistent results for NPF datasets collected in different sites.
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(Helsingin yliopisto, 2022)This thesis discusses how Bayesian networks can be used to improve data analytics in the field of environmental assessment and management. The data-analytic challenge is that ecosystems are complex and potentially changing, while the available data are relatively sparse both in terms of the number of observations and in which ecosystem components they cover. This thesis takes steps towards better analysis of these sparse data through combining pre-existing, uncertain information such as modelling results and expert knowledge with modern, probabilistic data analysis. The first theme of the thesis is how variable discretization in Bayesian network classifiers, particularly tree-augmented Naïve Bayes, can help understand the relationships between environmental factors at different levels. This work explores different discretizations of the class variable and discusses the implications of the differences between the resulting models, and contributes to the still relatively quiet discussion about discretization schemes in Bayesian networks. The second theme is detecting change in the ecological processes based on the sparse, often noisy data. This thesis builds dynamic Bayesian network models to detect change in the Central Baltic Sea ecosystem interactions, and explores the effect of different model structures in detecting the ecosystem change. It is shown that the hidden variables of the models can identify ecosystem change, and that this result does not depend on the exact model structure or hidden variable set-up. The third theme is decision support models that aim to integrate information regarding all interlinked aspects of the decision problem such as different parts of the ecosystems as well as economic and societal considerations. To be useful, decision support models need to be able to provide estimates of uncertainty of the different assessments and projections. This thesis reviews and evaluates various uncertainty assessment methods. Further, this thesis builds a large probabilistic meta-model to demonstrate how a Bayesian network based decision support model can be used to summarise a large body of research and model projections about potential management alternatives and climate scenarios Bayesian networks are showing their strength for different tasks of environmental data analytics. Elegant handling of missing data, explicit and rigorous handling of uncertainty, and the possibility to use prior scientific knowledge and data together in analyses in a transparent way are strong advantages for Bayesian analysis for environmental data that often contain missing values and are scarce. In addition to being flexible and, thus, able to integrate different types of information and data, they are transparent, allowing critical assessment and discussion of the models. This is important as environmental data analytics are often used to support decision making on the use of ecosystems, affecting the lives of current and future generations.
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(Helsingin yliopisto, 2022)This thesis investigates the aridification of Asian continental interiors and the underlying mechanisms affecting aridity during the Eocene-Oligocene Transition and during the Oligocene. The Eocene-Oligocene Transition, at circa 34 Ma, is widely considered to have been the most dramatic climatic shift of the past 50 million years. In the context of Asian continental environments, the Eocene-Oligocene Transition was associated with increased seasonality, dramatic cooling, turnovers in biota, and drying of the environment. The causal mechanism for environmental cooling is likely linked to geographical reorganization of continents and oceanic currents, decline in atmospheric CO2 and permanent glaciation in Antarctica. On the other hand, Oligocene climate was possibly regulated by recurring glacial-interglacial episodes in response to astronomical forcing and changing summer insolation, inducing variations in sea level, atmospheric and oceanic circulation, concentration of CO2, and temperature. However, responses of Asian terrestrial environments to Oligocene climate dynamics have remained poorly constrained. In this dissertation, lithostratigraphical and sedimentological characteristics, depositional environments as well as paleoprecipitation and weathering regimes were determined in the region of Ulantatal, situated in Inner Mongolia of China. In addition, a high-resolution temporal framework was established for the Ulantatal sediments using magnetostratigraphy in conjunction with biostratigraphical correlation. Ulantatal also hosts profuse micromammalian faunas which had previously lacked a proper temporal framework. It has now proven possible to calibrate these faunas with contemporaneous Mongolian Valley of Lakes mammal paleocommunities, where the ages are constrained by a combination of radiometric dating and magnetostratigraphy. Using the calibrated mammalian faunas in Ulantatal to anchor the magnetostratigraphy, it was possible to infer an age of around 35 to 27 Ma for the Ulantatal deposits. Sedimentological investigations revealed that the sediments have a massive structure with a bimodal grain size distribution which, together with their geochemical and mineralogical compositions, suggests that bulk of the sediments in Ulantatal are eolian in origin. In contrast, the oldest, latest Eocene beds display hydraulic structures typical of floodplain deposits. Modelling of past rainfall based on bulk sediment geochemistry, mineral magnetic properties and the mammal fossil triple oxygen isotope compositions suggested that the region around Ulantatal was in a stable semi-arid state from the latest Eocene until around 31 Ma in late Oligocene time. In contrast to some other contemporaneous records in the vicinity of the Tibetan Plateau, no dramatic shifts in the proxy records were observed across the Eocene-Oligocene Transition. Instead, the proxy records remained unchanged. However, it is typical for the terrestrial realm to respond in a heterogeneous way to global climatic forcings and may be overprinted by local or regional factors. The Late Oligocene after 31 Ma, on the other hand, is marked by increased shifts in precipitation and weathering intensity. This increased instability may reflect an intensified Asian monsoon system. Uplift of the Tibetan Plateau, changes in atmospheric CO2, retreat of the Paratethys Sea and subsequent influences on land-sea thermal contrasts, and/or changes in the Intertropical Convergence Zone have likely affected the onset and intensification of the monsoon system.
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(Helsingin yliopisto, 2022)Our devices have become accustomed to being always connected to the Internet. Our devices from handheld devices, such as smartphones and tablets, to our laptops and even desktop PCs are capable of using both wired and wireless networks, ranging from mobile networks such as 5G or 6G in the future to Wi-Fi, Bluetooth, and Ethernet. The applications running on the devices can use different transport protocols from traditional TCP and UDP to state-of-the-art protocols such as QUIC. However, most of our applications still use TCP, UDP, and other protocols in a similar way as they were originally designed in the 1980s, four decades ago. The transport connections are a single path from the source to the destination, using the end-to-end principle without taking advantage of the multiple available transports. Over the years, there have been a lot of studies on both multihoming and multipath protocols, i.e., allowing transports to use multiple paths and interfaces to the destination. Using these would allow better mobility and more efficient use of available transports. However, Internet ossification has hindered their deployment. One of the main reasons for the ossification is the IPv4 Network Address Translation (NAT) introduced in 1993, which allowed whole networks to be hosted behind a single public IP address. Unfortunately, how this many-to-one translation should be done was not standardized thoroughly, allowing vendors to implement their own versions of NAT. While breaking the end-to-end principle, the different versions of NATs also behave unpredictably when encountering other transport protocols than the traditional TCP and UDP, from forwarding packets without translating the packet headers to even discarding the packets that they do not recognize. Similarly, in the context of multiconnectivity, NATs and other middleboxes such as firewalls and load balancers likely prevent connection establishment for multipath protocols unless they are specially designed to support that particular protocol. One promising avenue for solving these issues is Software-Defined Networking (SDN). SDN allows the forwarding elements of the network to remain relatively simple by separating the data plane from the control plane. In SDN, the control plane is realized through SDN controllers, which control how traffic is forwarded by the data plane. This allows controllers to have full control over the traffic inside the network, thus granting fine-grained control of the connections and allowing faster deployment of new protocols. Unfortunately, SDN-capable network elements are still rare in Small Office / Home Office (SOHO) networks, as legacy forwarding elements that do not support SDN can support the majority of contemporary protocols. The most glaring example is the Wi-Fi networks, where the Access Points (AP) typically do not support SDN, and allow traffic to flow between clients without the control of the SDN controllers. In this thesis, we provide a background on why multiconnectivity is still hard, even though there have been decades worth of research on solving it. We also demonstrate how the same devices that made multiconnectivity hard can be used to bring SDN-based traffic control to wireless and SOHO networks. We also explore how this SDN-based traffic control can be leveraged for building a network orchestrator for controlling and managing networks consisting of heterogeneous devices and their controllers. With the insights provided by the legacy devices and programmable networks, we demonstrate two different methods for providing multiconnectivity; one using network-driven programmability, and one using a userspace library, that brings different multihoming and multipathing methods under one roof.
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(Helsingin yliopisto, 2022)Cosmic inflation, a phase of accelerated expansion of the early universe, is the most successful scenario for solving various issues in cosmology related to the observed cosmic microwave background and the large-scale structure of the universe. Higgs inflation is a particularly interesting model of inflation, as it is driven by the only fundamental scalar field in the Standard Model of particle physics, the Higgs boson, and its predictions fit the current observational constraints. Inflationary models can be considered in different formulations of general relativity. In addition to the metric curvature description of gravity, one could for example consider the Palatini formulation of general relativity. For the Einstein--Hilbert action, the descriptions are equivalent to each other. However, once a scalar field is non-minimally coupled to gravity, these formulations branch off into their own theories with different predictions. In this thesis, we show that the Higgs inflation scenario is successful in teleparallel gravity only when non-minimal couplings satisfy a particular relation. When the relation is satisfied, we find a new inflationary model where the tensor-to-scalar ratio can be arbitrarily large. We also consider Higgs inflation in a formulation of general relativity based on loop quantum gravity, and find a wide range of models that fit observational constraints. Cosmic inflation can also produce primordial black holes, a long-standing dark matter candidate, that can also act as seeds for supermassive black holes. We consider the effect of stochastic noise in ultra-slow-roll inflation on the probability distribution of the curvature perturbation, which determines the abundance of primordial black holes. We discuss a number of models inspired by Higgs inflation for various masses of primordial black holes, and show that the accurate calculation of stochastic effects changes the abundance of primordial black holes by orders of magnitude compared to the approximations commonly used in the literature.
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(Helsingin yliopisto, 2022)When the applied voltage and electric field exceed a certain threshold, a vacuum gap will breakdown, losing its insulating capability. Vacuum breakdown often occurs in devices such as vacuum circuit breakers, particle accelerators and spacecraft components, adversely affecting their normal operation. The formation of a conductive channel is crucial for a vacuum gap to complete the transition from an insulator to a conductor. A clear understanding of this process is indispensable for understanding the entire vacuum breakdown and optimizing the design of vacuum insulation. The current theories on conductive channel formation mostly agree that the generation of cathode glow/plasma is the starting point of constructing conductive channel, while the role of anode glow is controversial. The present dissertation adopted experimental methods such as electrical measurements, high-speed imaging, spectral analysis, microscopic morphology analysis, and energy spectrum analysis to obtain the electrical signals, optical radiation signals, electrode surface morphology, and electrode surface composition during vacuum breakdowns, and also established a simulation model of the breakdown process based on the PIC-MCC method. The roles played by the cathode and anode in the establishment of conductive channels are clarified from the viewpoints of sufficiency and necessity, revealing the formation mechanism of conductive channels. It is shown that the generation of cathode glow/plasma is a necessary condition for the start of vacuum breakdown, and the complete collapse of gap voltage is the indicator of conductive channel formation. Both the cathode and the anode provide atoms for the anode glow. The cathode contribution results from reflection of the ions in the expanding cathode plasma at the anode surface, which implies a major role of the cathode plasma expansion in constructing conductive channels. The atoms on the anode surface leave the anode and participate in the anode glow mainly due to the sputtering effect of the cathode ion flow, rather than the heating effect of the cathode electron flow on the anode surface. Both the establishments of anode glow and conductive channel attribute to the expansion of the cathode plasma, while neither the appearance nor the expansion of the anode glow is necessary for constructing conductive channels. It is finally proposed that the necessary and sufficient condition for conductive channel formation in vacuum breakdowns is the generation and expansion of the cathode plasma, which is also verified by the visual demonstration of vacuum breakdown process in the simulations. The conclusions obtained in this dissertation contribute in the better understanding of fundamentals of the theory for conductive channel formation, and contribute to the optimization of vacuum insulation design.
