Matemaattis-luonnontieteellinen tiedekunta
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(Helsingin yliopisto, 2019)Cloud computing has created a radical shift in expanding the reach of application usage and has emerged as a de-facto method to provide low-cost and highly scalable computing services to its users. Existing cloud infrastructure is a composition of large-scale networks of datacenters spread across the globe. These datacenters are carefully installed in isolated locations and are heavily managed by cloud providers to ensure reliable performance to its users. In recent years, novel applications, such as Internet-of-Things, augmented-reality, autonomous vehicles etc., have proliferated the Internet. Majority of such applications are known to be time-critical and enforce strict computational delay requirements for acceptable performance. Traditional cloud offloading techniques are inefficient for handling such applications due to the incorporation of additional network delay encountered while uploading pre-requisite data to distant datacenters. Furthermore, as computations involving such applications often rely on sensor data from multiple sources, simultaneous data upload to the cloud also results in significant congestion in the network. Edge computing is a new cloud paradigm which aims to bring existing cloud services and utilities near end users. Also termed edge clouds, the central objective behind this upcoming cloud platform is to reduce the network load on the cloud by utilizing compute resources in the vicinity of users and IoT sensors. Dense geographical deployment of edge clouds in an area not only allows for optimal operation of delay-sensitive applications but also provides support for mobility, context awareness and data aggregation in computations. However, the added functionality of edge clouds comes at the cost of incompatibility with existing cloud infrastructure. For example, while data center servers are closely monitored by the cloud providers to ensure reliability and security, edge servers aim to operate in unmanaged publicly-shared environments. Moreover, several edge cloud approaches aim to incorporate crowdsourced compute resources, such as smartphones, desktops, tablets etc., near the location of end users to support stringent latency demands. The resulting infrastructure is an amalgamation of heterogeneous, resource-constrained and unreliable compute-capable devices that aims to replicate cloud-like performance. This thesis provides a comprehensive collection of novel protocols and platforms for integrating edge computing in the existing cloud infrastructure. At its foundation lies an all-inclusive edge cloud architecture which allows for unification of several co-existing edge cloud approaches in a single logically classified platform. This thesis further addresses several open problems for three core categories of edge computing: hardware, infrastructure and platform. For hardware, this thesis contributes a deployment framework which enables interested cloud providers to effectively identify optimal locations for deploying edge servers in any geographical region. For infrastructure, the thesis proposes several protocols and techniques for efficient task allocation, data management and network utilization in edge clouds with the end-objective of maximizing the operability of the platform as a whole. Finally, the thesis presents a virtualization-dependent platform for application owners to transparently utilize the underlying distributed infrastructure of edge clouds, in conjunction with other co-existing cloud environments, without much management overhead.
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(Helsingin yliopisto, 2019)Synoptic situation and different meteorological phenomena can highly affect renewable energy production. Investigating different phenomena will give new information on the occurrence and characteristics of specific phenomena and their impacts on renewable energy applications. Different observational data sets and numerical models can be widely used in different phases of renewable energy projects; from planning of the project to help with the operation and the maintenance of the existing wind or solar field. In this thesis a meteorological phenomena, a low-level jet, is investigated. Thesis comprises analysis of the climatological occurrence of low-level jets, their characteristics and forcing mechanisms, as well as numerical model capability to capture the phenomena. In addition, solar radiation forecasts obtained from the operational numerical weather prediction model are evaluated and the role of cloud cover forecast skill in solar radiation forecast error is investigated. Long data sets of observational data: mainly Doppler wind lidar, ceilometer, and solar radiation observations, are used, in addition to reanalysis and operational numerical weather prediction model data. A low-level jet is a wind phenomenon that can affect wind energy production. Nighttime low-level jets are a commonly known boundary-layer phenomenon occurring during stably stratified conditions over flat terrain. In this thesis, new information on the occurrence, characteristics, and forcing mechanisms of a low-level jet was gained in different conditions: in Northern Hemisphere mid-latitude and polar regions based on reanalysis data and at two different sites in Finland and Germany based on long-term Doppler lidar observations. The low-level jet identification algorithms developed in these studies can be used to repeat the studies by using different models covering different areas or at any site operating a Doppler lidar. The low-level jet identification algorithm for Doppler lidar data can also be applied to operationally detect low-level jets, which is useful information for example from wind energy point-of-view. Solar radiation and cloud cover forecasts were evaluated at one site in Finland based on long time-series of solar radiation and ceilometer observations. The role of cloud cover forecast in solar radiation forecast error is investigated. The solar radiation and cloud cover forecasts were obtained from operational numerical weather prediction model that can be used to predict the expected power production at solar field day-ahead. It was found that there is a positive bias in the forecast incoming solar radiation even if the cloud cover forecast is correct. The study can guide model improvements as the bias is likely due to underestimation in the forecast cloud liquid water content or incorrect representation of cloud optical properties. The methods created in this study can be applied to hundreds of sites globally. In addition, the algorithms developed in this study can be further used in different applications in the field of renewable energy, for example to detect potential in-cloud icing conditions.
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(Helsingin yliopisto, 2019)Poly(2-oxazoline)s consist of a -(CH2-CH2-N)- main chain and an N-acyl substituent. They were reported for the first time in 1966/67. They have been investigated in the bulk, in solutions and in dispersions. The recent interest lies primarily in their chemical versatility and their potential for nanomedical applications. Tailoring materials for such specific applications requires a sound knowledge of their phase behaviors, which depends on intensive parameters. Amongst others, composition and temperature are of particular interest. The phase behaviors of poly(2-propyl-2-oxazoline)s (PPOxs) will be the main focus of this thesis. PPOx homopolymers are investigated as well as block copolymers (BCPs) and blends of poly(2-isopropyl-2-oxazoline) (PiPOx) and poly(lactide) (PLA). The first part describes the synthesis of the polymers. The PPOxs are prepared by cationic ring opening polymerization. They are linear, narrowly dispersed, and bear at the termini one methyl- and one azide-group. Semi-crystalline as well as amorphous PLA is synthesized by ring opening polymerization of L-lactide and DL-lactide, respectively. The linear PLAs are terminated with one propargyl- and one hydroxyl-group. Coupling of the azido- and alkyne-functional homopolymers gives a library of 18 PiPOx-b-PLA BCPs. This approach allows to compare the phase behaviors of the BCPs with those of the individual components. The next part is dedicated to a study of the solution properties of three PPOx homopolymers, namely poly(2-n-propyl-2-oxazoline) (PnPOx), poly(2-cyclopropyl-2-oxazoline) (PcyPOx) and PiPOx in water, in methanol and in water/methanol mixtures. Nuclear magnetic resonance (NMR) spectroscopy of the three polymers reveals significant differences in the side-group’s rotational freedom. Unexpectedly, these differences are reflected in the calorimetric assessment of the coil-to-globule phase transition. The phase diagrams in respect to the water/methanol composition are constructed based on transmittance measurements. Methanol is a good solvent up to its boiling point for the three PPOxs. The solubility of PnPOx in water decreases when up to 40 vol% methanol is added. This behavior termed “cononsolvency” was first reported for ternary polymer/water/methanol mixtures of poly(N-isopropyl acrylamide), a structural isomer of PnPOx and PiPOx. PiPOx and PcyPOx do not exhibit cononsolvency in the investigated ternary system. The PPOxs’ solution behaviors depend on the rotational freedom of the side-groups. In the third part, the bulk phase behavior of PiPOx, its blends with PLA, and PiPOx-b-PLA BCPs is studied. The PiPOx volume fractions in the BCPs varies from 14 to 82 %. PiPOx and PLA are miscible based on the single glass transition criterion and small angle x-ray scattering at a temperature above the melting points of the two polymers. Infrared spectroscopy indicates an attractive dipole-dipole interaction between the carbonyl moieties of the PiPOx amide and the carbonyl of the PLA ester. PiPOx and the stereo-regular PLLA are semi-crystalline. The influence of the miscibility on the crystallization is investigated by polarized optical microscopy, differential scanning calorimetry and wide-angle x-ray scattering. It is found that the presence of PLA increases the crystallization rate of PiPOx. In contrast, PLLA remains amorphous in most of the BCPs. The last part focuses on aqueous dispersions of the self-assembled PiPOx-b-PLA BCPs. The dispersions were prepared by adding a solution of a BCP in THF to water, a non-solvent of PLA but a solvent of PiPOx at low temperature. Contrary to expectation PiPOx resides in the particle interior, together with PLA. It does not form a shell of hydrated chains around the PLA core. This conclusion was attained on the basis of NMR spectroscopy and evaluation of the thermo-responsive properties of the BCP particle dispersions in water. At room temperature the particles are colloidally stable for 20 days at least. The particle morphology is investigated by cryogenic transmission electron microscopy, light scattering and small angle neutron scattering. The particles are spherical and permeated with water over the wide PiPOx volume fraction. Short segments of PiPOx reside on the particle/water interface and stabilize the dispersion. The thermo-responsive properties of the dispersions depend on the configuration and length of these segments. Attractive interactions between soluble and insoluble block are an important factor for the self-assembly of amphiphilic BCPs.
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(Helsingin yliopisto, 2019)Software experiments are presently often used by big technology pioneers, such as Microsoft, Facebook and Google, in order to learn about their users and to guide their research and development activities. Continuous experimentation (CE) is reported to be an integral part of software development in these organisations, however, how they transitioned to the approach is not publicly shared. Therefore, there is a lack of guidance for other organisations that are willing to adopt CE. In the current competitive markets, investing time and money in a new approach might be risky for these organisations, especially if they do not know how to initiate this transition process. This dissertation focuses on how organisations can initiate the transition towards CE, i.e., an approach to enhance development decisions by running experiments in an iterative and sustainable fashion. The dissertation was designed to acquire descriptive and observational knowledge through empirical studies and was con- ducted in three main phases. First, we designed and ran multiple-case studies to investigate how CE can be introduced to existing software company development teams, who want to run their first systematic experiments. We extracted descriptive knowledge from the introduction process and composed lessons learned to act as guidelines. In the second phase, we conducted a survey study with practitioners from four Nordic software companies, in order to better understand their attitudes and perception towards experiment-driven development, user involvement and ethics. Examining the results at role-to-role levels gave us an understanding of commonalities and distinctions stemming from different job functions. Furthermore, we identified patterns from the data that describe what trends exist across the dataset with respect to experiment-driven software development. Finally, in the last phase of the study, we conducted a single-case study with a mobile gaming company to investigate how CE functions as an organisational mechanism throughout the development life-cycle. The findings show that transitioning towards CE is a learning process that can be facilitated well by guidance, utilising existing resources and starting with small experiments with potentially enormous impact. Furthermore, by investigating the point of view of practitioners, we observed that software experiments represent different concepts, for instance, A/B tests and user interviews. We also observed that the role of the practitioner has a big impact not only on how experiments are understood, but also how individuals perceive the ethics involved in the experiments. For example, while managers are more cautious about company-customer relationships, UX designers were found to allow exceptions to user notification during experiments. In addition, we discovered that companies might understand and adopt experiment-driven development differently, for in- stance, influenced by their business contexts. Lastly, by examining a company’s CE practices, we found that experiments can take different forms given the development stage, and the organisational mechanism can be established to fit both the needs of the business domain and organisational goals. One of the biggest challenges of adopting CE, inaccessible real users, can be overcome with alter- native methods, such as proxy users, especially early in the development, when experiments are important in determining product value. Highly competitive markets can put pressure on organisations to avoid risks and costs when adopting a new approach. In this dissertation, we learned that by and large, software organisations and development teams can initiate their transition towards CE in an efficient and economical way. Furthermore, we conclude that the transition is a learning process that improves with practice and has to adapt to the organisational goals and contexts. The influence of human factors, such as the finding that individual perception of experiments and ethics is correlated with job functions indicates that CE is a multi-disciplinary research field, where individuals should be studied as well as experimentation processes. Software engineering research needs further studies to validate the findings in different contexts.
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(Helsingin yliopisto, 2019)Low and medium energy ion irradiation can induce different structures over the surface of semiconductors and metals depending on the parameters used for the irradiation of the surfaces. Different mathematical models have been developed in the last decades to explain the formation reasons of such structures, such as nano-dots or ripples, and to predict the pattern wavelength. These theories have been discussed and tested for several years. In this work, computational methods are used in order to predict and observe such effects. First, a mathematical model, which uses as an input the results from the computational methods, is applied to predict the pattern wavelength and at which angle the regime changes from stable to unstable. Moreover, a relaxation method to remove the background displacement in amorphous silicon, which affects the prediction is presented. Second, a simulation model of sequential irradiation consisting of the irradiation of a segment of the surface, and speeding-up the eventual modification of the surface is developed. The simulation outputs at ultra-low energy are compared with experimental results. The use of the same model at higher energies and applied to aluminum allows us to obtain conclusions on the reason of pattern formation in both materials at different energies and irradiation angles. The last part of this work contains the results obtained from homogeneously distributed irradiation. The irradiation is performed according to an accelerated molecular dynamics method which reduces the time span between impacts and allows us to reach higher fluences. This latter method allowed us to observe the direct ripple-formation and the propagation of the pattern on the surface for the first time. This study allows to explain and observe the different stages before the eventual ripple formation.
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(Helsingin yliopisto, 2019)This thesis focuses on the development of new atomic layer deposition processes for zirconium oxide and rare earth oxides. Atomic layer deposition (ALD) is a chemical thin film deposition method that is capable of generating films with excellent properties including conformality, uniformity, high density and pinhole free structure. Because of these film properties, ALD has become the best and often the only method capable of fulfilling the demands of many applications, microelectronics being the best example. The unique properties of ALD films are enabled by the self-limiting growth mechanism of these films. Traditionally, ALD metal precursors have been homoleptic, meaning that the compound contains only one type of ligands. In the search of precursors with higher thermal stability, growth rate and uniformity, heteroleptic precursors with more than one type of ligands have gained interest. However, properties of different ligand combinations are hard to predict which means that comprehensive studies on the precursors with different oxygen sources are needed. In this work, heteroleptic precursors for rare earth oxides and zirconium oxide were studied. ZrO2 and rare earth oxides are so called high-κ materials with a wide variety of applications ranging from microelectronics to fuel cells, optics and catalysis. For the ALD of ZrO2, three metal precursors, Zr(Me5Cp)(TEA), Zr(MeCp)(TMEA) and ZrCp(tBuDAD)(OiPr) were evaluated with water or ozone as the oxygen source. Self-limiting growth processes typical for ALD were found for two Zr precursors with ozone. The deposition temperature for the self-limiting Zr(Me5Cp)(TEA)/O3 process was as high as 375 °C making Zr(Me5Cp)(TEA) one of the most thermally stable precursors for zirconium. ZrO2 films with high purity were deposited with the three precursors especially when ozone was used as the oxygen source. Heteroleptic cyclopentadiene-amidinate precursors RE(iPrCp)(iPramd) were studied for Y, La, Pr, Gd and Dy. Water and ozone were studied as oxygen sources. In addition to these common oxygen sources, also ethanol as well as water and ozone in the same deposition process separated by a purge period were tested for the La2O3 deposition. Self-limiting growth was confirmed for Y2O3, La2O3 and Gd2O3.
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(Helsingin yliopisto, 2019)The Standard Model of particle physics is the most successful and precise theoretical description of fundamental physics. The discovery of the Higgs boson in 2012 by the ATLAS and CMS experiments at the LHC provided strong evidence for the Englert-Brout-Higgs-Guralnik-Hagen-Kibble mechanism, explaining how elementary particles gain their masses in the Standard Model. However, the Standard Model is known to be an incomplete description of the nature, as it cannot explain the origin of dark matter, neutrino masses or the observed matter–antimatter asymmetry. Therefore more general models with an extended Higgs sector are actively being studied. Models with at least two Higgs doublets predict the existence of electrically charged Higgs bosons. The observation of charged Higgs bosons would provide direct evidence for new physics and guide way towards a more comprehensive theory. In this thesis, a search is presented for charged Higgs bosons decaying into a tau lepton and a neutrino, based on proton-proton collision events recorded by the CMS experiment in 2016 at a center-of-mass energy of 13 TeV. The amount of data corresponds to an integrated luminosity of 35.9 inverse femtobarns. The search targets the hadronic final state with a hadronically decaying tau lepton, missing momentum due to neutrinos, and additional jets from an associated top quark decay. This analysis contains multiple methodological improvements with respect to the previous CMS results on the same search channel. The particle identification algorithms and selection criteria are optimized for good performance under challenging luminosity conditions. Categorization of events based on tau lepton helicity is used to enhance sensitivity. The background from events with jets misidentified as tau leptons is estimated from data, whereas the background from genuine-tau events is estimated from simulation. This thesis also presents a new version of the tau embedding method, which allows the estimation of the genuine-tau background using single-muon events. The transverse mass of the tau-neutrino system is reconstructed. As the data agree with the background-only hypothesis, upper limits are derived for the charged Higgs boson production rate. The search covers signal hypotheses from 80 GeV to 3 TeV, and for the first time in CMS, the hypotheses with the charged Higgs boson mass close to the top quark mass are scanned. For maximal signal sensitivity, the results are combined with those from the leptonic final states of the same search channel. The combined result is interpreted in the context of the Minimal Supersymmetric Standard Model.
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(Helsingin yliopisto, 2019)In Finland, with each breath you are inhaling a luxury product: clean air. This is not the case for most people, as 90% of the human population lives within polluted air according to the World Health Organization. In addition to gas molecules, there are tiny solid or liquid particles floating in our air. We refer to this particle and gas mixture as an aerosol. Some aerosol particles are injected directly into the atmosphere from emissions like biomass burning or industries. The other half or more (~50-70%) are created in the air from precursor vapors in what is termed new particle formation (NPF) events. However aerosols are not only infamously involved in air quality, but play a major role in climate: they scatter incoming solar radiation, and indirectly affect climate by serving as the seeds that form clouds. In both cases, aerosols have an overall cooling effect, offsetting the global warming from greenhouse gases. Yet aerosols and aerosol-cloud interactions have the largest uncertainty in our climate budget estimates. It is thus with urgency that we must concretize the sources, concentrations, and life-cycles of atmospheric particles around the world. However, while NPF events have been observed almost everywhere worldwide, global comparisons and quantification of NPF dynamics are mostly based on ideal, regional processes. As a result, a large fraction of field data is discarded from further analysis. In this thesis, we compared ideal NPF events to discarded, ambiguous or small-scale events in order to understand their potential contribution to aerosol dynamics and number concentrations. We first determined the optimal ambient conditions for regional NPF in a boreal forest at SMEAR II station in Finland: clear-sky, sunny days, with low background aerosol concentrations, moderate temperatures, low relative humidity and high concentrations of oxidized organic vapors. We then reclassified the undefined days from 11 years of data (~40% of total data) to include transported/advected events and bursts of nucleation mode ions and particles that failed to grow to larger sizes. We consequently developed an automated classification scheme that accounts for both regional NPF events and the new classes of previously undefiend days. The result is a more robust analysis of NPF processes. We observed frequent nocturnal clustering in Hyytiälä (~30% of 11 years of data). Specifically, 1.5 to 2 nm ion concentrations were ~2 times higher at night than during daytime NPF events. However, this phenomenon disappears after ~3 nm cluster sizes. We conclude that a boreal nighttime forest is an effective source of sub-3 nm clusters that fail to grow. Lastly, we compared nucleation mode aerosol at a pasture site and a rainforest site in the Amazon Basin, and present the first observations and characteristics of NPF commencing at ground level in the Amazon. No NPF occurred at the rainforest site. However, rain-enhanced intermediate ion bursts were frequent inside the forest canopy and raised ion concentrations by several orders of magnitude. To get a global overview of NPF, it is understandable that we select unambiguous NPF cases of each region for inter-comparison. In this work, however, we focused on unconventional NPF-related features, with the thesis that omitting these events may lead to oversights of potentially relevant processes to NPF. We conclude that by investigating the less-than-ideal cases, we can find new mechanisms and sources that allow us to better understand, quantify and predict the processes that lead to new particle formation.
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Applications of Bayesian computational statistics and modeling to large-scale geoscientific problems (Helsingin yliopisto, 2019)Climate change is one of the most important, pressing, and furthest reaching global challenges that humanity faces in the 21st century. Already affecting daily lives of many directly and everyone indirectly, changes in climate are projected to have many catastrophic consequences. For this reason, researching climate and climate change is needed. Studying complex geoscientific phenomena such as climate change consists of a patchwork of challenging mathematical, statistical, and computational problems. To solve these problems, local and global process models and statistical models are combined with both small in situ observation data sets with only few observations, and equally well with enormous global remote sensing data products containing hundreds of millions of data points. This integration of models and data can be done in a Bayesian inverse modeling setting if the algorithms and computational methods used are chosen and implemented carefully. The methods used in the four publications on which this thesis is based range from high-dimensional Bayesian spatial statistical models and Markov chain Monte Carlo methods to time series modeling and point estimation via optimization. The particular geoscientific problems considered are: finding the spatio-temporal distribution of atmospheric carbon dioxide based on sparse remote sensing data, quantifying uncertainties in modeling methane emissions from boreal wetlands, analyzing and quantifying the effect of climate change on growing season in the boreal region, and using statistical methods to calibrate a terrestrial ecosystem model. In addition to analyzing these problems, the research and the results help to understand model performance and how modeling uncertainties in very large computational problems can be approached, also providing algorithm implementations on top of which future efforts may be built.
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(Helsingin yliopisto, 2019)High-resolution, accurate, and updated forest structure maps are urgently required for the implementation of REDD+, payment of ecosystem services, and other climate change mitigation strategies in tropical countries. The collection of forest inventory data is usually labor intensive and costly, and remote sites can be difficult to access. Remote sensing data, for example airborne laser scanning (ALS), hyperspectral imagery, and Landsat data, complement field-based forest inventories and provide high-resolution, accurate, and spatially explicit data for mapping forest structural attributes. However, issues such as the effect of topography, pulse density, and the single and combined use of various remote sensing data on forest structural attributes prediction warrant further research. The main objective of this thesis was to assess airborne and spaceborne remote sensing techniques for modeling forest structural attributes across a montane forest landscape in the Taita Hills, Kenya. The sub-objectives focused on a) the effect of the topographic normalization of Landsat images on fractional cover (Fcover) prediction, aboveground biomass (AGB), and forest structural heterogeneity modeling using ALS and other remote sensing data and b) the analysis of the maps of forest structural attributes. In Study I, the effect of topographic normalization on ALS-based Fcover modeling was evaluated using common vegetation indices and spectral-temporal metrics based on a Landsat time series (LTS). The results demonstrate that the fit of the Fcover models did not improve after topographic normalization in the case of ratio-based vegetation indices (Normalized Difference Vegetation Index, NDVI; reduced simple ratio, RSR) or tasseled cap (TC) greenness; however, the fit improved in the case of brightness and wetness, particularly in the period of the lowest sun elevation. However, if TC indices are preferred, then topographic normalization using a Shuttle Radar Topography Mission (SRTM) digital elevation model (DEM) is recommended. In Study II, field-based AGB estimates are modeled by ALS data and a multiple linear regression. The plot-level AGB was modeled with a coefficient of determination (R2) of 0.88 and a root mean square error (RMSE) of 52.9 Mg ha-1. Furthermore, the determinants for AGB spatial distribution are examined using geospatial data and statistical modeling. The AGB patterns are controlled mainly by mean annual precipitation (MAP), the distribution of croplands, and slope, which collectively explained 69.8% of the AGB variation. Study III investigated whether the fusion of ALS with LTS and hyperspectral data, or stratification of the plots to the forest and non-forest classes, improves AGB modeling. According to the results, the prediction model based on ALS data only provides accurate models even without stratification. However, using ALS and HS data together, and employing an additional forest classification for stratification, improves the model accuracy considerably in the studied landscape. Finally, in Study IV, the potential of single and combined ALS and LTS data in modeling forest structural heterogeneity (the Gini coefficient of tree size) was assessed, and the difference between three forest remnants and forest types is evaluated based on predicted maps. If the LTS metrics were included in the models, then ALS data with lower pulse density yield similar accuracy to more expensive, high pulse-density data. Furthermore, the GC map presents forest structural heterogeneity patterns at the landscape scale an
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(Helsingin yliopisto, 2019)There are many chemical analytical techniques available to probe molecular structures. Nuclear Magnetic Resonance (NMR) spectroscopy is an analytical spectroscopy technique that exploits strong external magnetic field and is capable of elucidating and quantifying molecular structures at the atomic level. NMR spectrum is typically measured from dissolved materials or directly from solid materials. The influence of magnetic interactions in solution and solid-state define the information that is obtainable from the NMR spectrum. The molecular motion and mobility play a key role. The obtainable spectral resolution in solid-state is usually lower than in the liquid state due to the presence of anisotropic interactions, which are not averaged out like in solution state. In NMR this means that the spectra, which contain the structural information, can be visually very different. These phenomena, combined with the need to obtain quantitative information, need to be addressed with proper considerations when acquiring and interpreting NMR data. This thesis focuses on the impact of relevant magnetic interactions in NMR via selected chemical structure studies of materials such as lignin, Ziegler-Natta (ZN) and aluminosilicate catalysts and cellulose.
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(Helsingin yliopisto, 2019)Cosmic inflation is a hypothetical period in the early universe, where the expansion of space accelerated. Inflation explains many properties of the observed universe, but its cause is not known. Higgs inflation is a model where inflation is caused by the Higgs field of the Standard Model of particle physics, coupled non-minimally to gravity. In this thesis, we study various aspects of cosmology with Higgs inflation. Inflation leaves marks on the cosmic microwave background radiation, and these marks can be used to distinguish inflationary models from each other. We study hilltop Higgs inflation, a model where quantum corrections produce a local maximum into the Higgs potential, and show that there the predicted tensor-to-scalar ratio is less than or equal to 1.2 × 10^-3. This is smaller than the prediction of tree-level Higgs inflation by a factor of four or more and can be probed by next-generation microwave telescopes. We also study reheating, the process where the universe transitions from inflation to radiation domination with a thermal bath of relativistic Standard Model particles. We show that in Higgs inflation, reheating is particularly efficient in the Palatini formulation of general relativity, because there Higgs bosons are produced violently by a tachyonic instability. The duration of reheating affects, for example, the predicted spectral index of the primordial perturbations. Finally, we discuss the production of primordial black holes in Higgs inflation. We show that large quantities of such black holes can be produced, but in order to satisfy observational constraints on large scales, they must be so small that they would have evaporated by now by Hawking radiation. However, if the evaporating black holes left behind Planck mass relics, these could constitute part or all of the dark matter, the dominant, unknown matter component of the universe. Together, these studies show that even though the ingredients that go into Higgs inflation are simple, they lead to a rich phenomenology and offer valuable insights into inflation, gravitational degrees of freedom and the origin of dark matter.
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(Helsingin yliopisto, 2019)Understanding the gas-phase chemistry of secondary organic aerosol (SOA) formation is critical for accurate estimation of the effect of these aerosol on Earth's radiative balance. Additionally, the direct detection of the precursor molecules involved in these chemical reactions at atmospheric pressure without pre-treatment is valuable. In this work, computational and experimental methods are employed to 1) elucidate the thermodynamics and the mechanisms of selected key radical-radical reactions in the atmosphere and 2) investigate the efficiency of some of the chemical ionization mass spectrometry methods in detecting the atmospherically relevant acids and precursor compounds involved in the formation of SOA. The main oxygen containing radical species in our atmosphere, and also the key focus of this study, are hydroxy (OH), hydroperoxy (HO2), alkoxy (RO) and peroxy (RO2) radicals. Our computational study on the favorability of the radical recycling product channels of RO2 + HO2 and RO2 + RO2 reactions (RO + OH + O2 and RO + RO + O2, respectively) for RO2s derived from the oxidation of a set of the highest globally emitted monoterpenes showed that the two reactions were thermodynamically favorable for all the studied systems, and that for some of them, especially the O3 oxidized systems, the rate-limiting transition state energies can be low enough to render the reactions competitive in atmospheric conditions. Peroxy radical reactions with the atmospheric oxidant OH and alkoxy radicals RO were found to first form a trioxide adduct (ROOOH and ROOOR, respectively). While the former rapidly decompose to RO + HO2 and R(O)OH + O2 products for the model β-oxo and acetyl RO2 systems, respectively, the ROOOR adducts from the latter can have lifetimes in the range of 10 - 100 s (for the homo and hetero alkyl and β-oxo systems). If the reacting RO2 and RO radicals are sufficiently large and oxidized, the product adducts can directly be involved in SOA formation. The modeling of iodide-based chemical ionization mass spectrometer (iodide-CIMS) using computational methods showed that relatively low-level computational theory can produce reasonable correlation between molecule•I- cluster binding enthalpies and iodide-CIMS instrumental sensitivities. While some outliers were observed (lower than expected binding enthalpies for clusters that were detected at the maximum possible sensitivity of the instrument, for example), the method outlined in our study can be a quick indicator of the detectibility of an analyte by an iodide-CIMS. Additionally, the direct detection of the HO2 radical experimentally using an iodide-CIMS was demonstrated. The comparison of iodide- and nitrate-CIMS spectra for a cyclohexene ozonolysis experiment showed that the iodide-CIMS method was capable of detecting the less oxidized (oxygen:carbon O/C ratio of 0.5 - 0.66) molecules more efficiently than nitrate-CIMS. Higher oxidized molecules (O/C ratio 1 - 1.5) were detected equally well by both methods. Finally, the use of a new chemical ionization inlet (Multi-scheme chemical IONization inlet, MION, Karsa Ltd, Helsinki, Finland), which is capable of switching between two different reagent ions, bromide and nitrate, in 1 s timescales was demonstrated and used to detect the ozonolysis products of cyclohexene and α-pinene. The successful demonstration of the MION inlet opens up the possibility to use multiple CIMS methods concurrently and detect a widest possible range of volatile organic compound (VOC) oxidation products.
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(Helsingin yliopisto, 2019)The amount of user-generated web content has grown drastically in the past 15 years and many social media services are exceedingly popular nowadays. In this thesis we study social media content creation and consumption through large volume measurements of three prominent social media services, namely Twitter, YouTube, and Wikipedia. Common to the services is that they have millions of users, they are free to use, and the users of the services can both create and consume content. The motivation behind this thesis is to examine how users create and consume social media content, investigate why social media services are as popular as they are, what drives people to contribute on them, and see if it is possible to model the conduct of the users. We study how various aspects of social media content be that for example its creation and consumption or its popularity can be measured, characterized, and linked to real world occurrences. We have gathered more than 20 million tweets, metadata of more than 10 million YouTube videos and a complete six-year page view history of 19 different Wikipedia language editions. We show, for example, daily and hourly patterns for the content creation and consumption, content popularity distributions, characteristics of popular content, and user statistics. We will also compare social media with traditional news services and show the interaction with social media, news, and stock prices. In addition, we combine natural language processing with social media analysis, and discover interesting correlations between news and social media content. Moreover, we discuss the importance of correct measurement methods and show the effects of different sampling methods using YouTube measurements as an example.
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(Helsingin yliopisto, 2019)Late first-row transition metals, namely copper, nickel, and cobalt, are pivotal materials in many modern and future applications. Because of its low resistivity, Cu has for long been the metal of choice for interconnects in microelectronic devices. Co is needed in the smallest features of the 10-nm technology node interconnects, as it is more robust than Cu toward electromigration, a phenomenon causing damage to the interconnects. Being ferromagnetic, Co and Ni are in the focal point of developing faster and more durable magnetic memories capable of handling the exponentially increasing amounts of data being generated annually. The development of faster yet smaller electronic devices requires a constant increase in computational power. To improve the performance without increasing device size, the components on integrated circuits should be shrunk and packed more closely. The shrinking is achieved by using thin films with nanoscale thicknesses preferably arranged in three-dimensional forms. For downscaling to continue, accurate thin film deposition methods are needed. Atomic layer deposition (ALD) provides atomic level accuracy and is thus the number one thin film deposition technique for modern and future devices. ALD is based on a cyclically repeated alternate supply of gaseous precursors that react on a substrate and form a uniform layer of material, atom by atom, even on complex three-dimensional structures. ALD is based solely on chemistry; to benefit from the many advantages the method has to offer, suitable precursors must first be found for each of the desired materials. ALD has been employed to deposit a myriad of materials ranging from pure elements to, for example, oxides, nitrides, and chalcogenides, but the deposition of metals has been hindered by a lack of reactive precursors and reducing agents. Thermal ALD processes exist mostly for noble metals, but mere thermal activation has often proven insufficient for the reduction of the late first-row transition metals. The aim of this thesis was to find and develop new precursors and processes for the ALD of high-quality Cu, Ni, and Co thin films, thus promoting the development of better microelectronics. Within the scope of this thesis, several new metal precursors for the ALD of the late first-row transition metals were developed and tested. Out of all of them, the diamine adducts of Co(II) and Ni(II) chlorides showed the best performance in the ALD experiments. In addition to the new metal precursors, the focus of this thesis was also on finding more efficient alternatives for the conventional reducing agents, H2 and NH3. Tert-butylhydrazine showed high reactivity to produce Cu and Ni3N by ALD, providing significant improvement on film purity and resistivity over the existing processes. Tributyltin hydride, another powerful reducing agent, was studied for the ALD of Co and Ni. Instead of producing metallic Co or Ni, intermetallic Co3Sn2 and Ni3Sn2 were deposited unveiling a new field of ALD: the ALD of intermetallics. The same approach was also applied to the ALD of Ni2Ge thin films. Postdeposition reduction of the corresponding metal oxides and nitrides was also explored as an alternative route for the preparation of metal thin films.
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(Helsingin yliopisto, 2019)Magnetic fields alter the properties of molecules, affecting the electron distribution, the electron configuration and the molecular geometry. In weak magnetic fields, the changes are subtle. Electrons as charged particles placed in magnetic field start following specific pathways, giving rise to magnetically induced ring currents. They follow the contour of the molecule, as well as form vortices around certain molecular rings and chemical bonds. Strong ring currents arise near atomic nuclei due to the core electrons. Magnetically induced currents are a unique fingerprint of the molecular structure but they also serve as an indicator for electron delocalisation, aromatic properties and applicability in optoelectronics. Various organic molecules were investigated using the gauge-including magnetically-induced current density approach. It has been demonstrated that heteroatoms alter the ring-current pathways and the current strength, and thereby affect molecular aromaticity.The topology of Möbius systems has been shown to depend both on the twist of the molecular rings of a series of [40]annulenes, as well as on their spatial folding (writhe). The investigation of a series of toroidal carbon nanotubes showed helical current flow in one of the chiral molecules in the study, which is a pre-requisite for the generation of anapole moment when the molecule is placed in a magnetic field. Very strong magnetic fields beyond achievable on Earth cause major changes in the electron configuration of atoms and molecules. Orbitals with high angular momentum and high-spin configurations become lower in energy than the typical zero-field occupation. Weak magnetic fields can be studied as a perturbation to the zero-field Hamiltonian. However, as the field strength increases, the magnetic interaction becomes equally strong as the electrostatic one. The explicit treatment of the magnetic field strength involves the angular momentum operator in the \schr, thus leading to complex orbitals. Therefore, new quantum chemistry software is necessary. A benchmark study for the performance of a traditional implementation based on Gaussian-type orbitals versus a fully numerical code has been done at the \acl{hf} level. After determining the accuracy of the method, small hydrocarbon molecules have been investigated, which showed that they exist as bound molecules in high-spin configurations where only the core electrons of the carbon atom are paired.
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(Helsingin yliopisto, 2019)Wintertime weather conditions can be hazardous for road traffic. Icy roads and poor visibility caused by snowfall increase the accident risk. Accurate forecasting of road conditions is important, because reliable and precise forecasts help the road maintenance personnel to plan their operations accordingly. Well timed maintenance operations increase safety and enable economical savings as unnecessary actions can be avoided. Drivers can also adjust their route plan and driving behaviour appropriately when warnings of hazardous conditions are given well beforehand. Road conditions are forecasted in the Finnish Meteorological Institute (FMI) with specialized road weather model. Before executing the actual forecast, the model is first initialized by feeding it with observation data. The quality of this data is essential for forecast accuracy, as the forecast is greatly dependent of the initial model state. Road weather stations have traditionally been one of the main sources of information, but their density is sparse especially in rural areas. Road surface temperature can vary considerably across the road network, so observations should be done in dense enough spatial scale. Nowadays it is possible to gather real time information from vehicles. Mobile sources provide observations with high spatial density and thus facilitate detecting the road stretches most prone to freezing. However, the quality of mobile observations should be assessed before implementing them to the road weather forecasting systems. This dissertation aims to answer to two research questions. Firstly, it has been studied how to best use available surface temperature observations in the road weather model initialization. Secondly, it has been studied how differences in two road weather models' physics affect to the surface temperature forecast accuracy. A method called coupling was implemented to the FMI road weather model. The main idea of the method is to adjust the incoming radiation flux so that the modelled surface temperature fits to the last observed value. The results show that this method improves considerably the short range surface temperature forecasts. Mobile surface temperature observations done with Teconer RTS411 were compared to road weather station measurements to assess the mobile data quality. According to the results, the mobile observations were on average 0.62 ͦC warmer than the road weather station measurements at 0 ͦC and in dry conditions. It was found out that the difference between mobile observations and road weather station measurements was dependent on the road status. A calibration equation for mobile observations was developed using linear mixed models to get mobile observations more in line with road weather station measurements. The effect of the mobile observations to the road surface temperature forecast accuracy was studied. According to the results, using the mobile observations calibrated with the developed equation improved the accuracy of road surface temperature forecasts compared to a theoretical situation where there would not be other surface temperature observations available. However, for an area with a dense road weather station network the accuracy of forecasts assimilating mobile observations with correction were on par with the accuracy of forecast assimilating interpolated surface temperature values. Studying model physics and comparing behaviour of different models is beneficial for model development. In this work, the verification results of the FMI's and the Royal Netherlands Meteorological institute's (KNMI) road weather models were compared to each other. In addition, the model physics were studied to find out the reasons for differences in the surface temperature forecasts. The forecasts of the KNMI model were found to be slightly more accurate than the forecasts of the FMI model. Although the core physics of the models were rather similar, there were large differences in some physical parameters and the number and the thickness of the ground layers. Individual reason for the better performance of the KNMI model could not be found, as the effects of different physical properties eventually sum up to surprisingly similar modelled surface temperature values.
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(Helsingin yliopisto, 2019)Vacuum breakdown is a process that limits the performance of many modern electronic devices like fusion reactors, vacuum interrupters, satellite systems and linear colliders. To optimize cost and performance of the Compact Linear Collider in CERN, an extensive study has been initiated to clarify the mechanisms that lead to vacuum arcing. Next to the experiments, computer simulations have found their place in obtaining valuable insight into the process. In addition to the financial advantages, simulations cover spatio-temporal resolution that is beyond capabilities of the modern experimental physics. For this reason, the numerical simulations based on well-motivated physical models are often the only tools which can provide interesting insight on an atomic scale. For practical use, these tools must meet requirements of well-balanced computational efficiency and desired accuracy. A promising way to achieve this is to combine continuous-space calculations with atomistic simulations. In the current PhD project, we develop a multiscale-multiphysics tool that combines molecular dynamics, finite element method and particle-in-cell techniques. With this tool we make a self-consistent connection between atomistic and continuum simulations that helps to combine different physics and optimize computational cost without significantly reducing the level of accuracy. We use various stages of the tool to simulate thermal and shape evolution of intensively electron emitting metal nanotips that are considered as a source of electrons and neutrals for plasma. More specifically, we study the shape memory effect and thermal runaway in Cu nanoprotrusions under a high electric field. The shape memory effect and spontaneous reorientation are the pair of mechanisms that may cause up to 34% fluctuation in aspect ratio of Cu nanotip under cyclic local electric field of 20 GV/m or higher. The study reveals that a high electric field increases the critical temperature of spontaneous reorientation, making thin Cu nanotips less prone to a collapse. By applying macroscopic electric field of 0.6 GV/m on h=93 nm Cu nanotip, we demonstrate a thermal runaway process. The growth of the tip starts after its apex melts and the resulting positive feedback loop leads to an intense evaporation of the tip. Such decay happens in cycles, so that intense evaporation alternates with the mild cooling periods. The comparison with previous works shows that in case of a sufficiently narrow tip, the evaporation is intense enough to ignite a plasma in vacuum.
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(Helsingin yliopisto, 2019)The Sun, besides being fundamental for life on Earth, is also characterised by intense activity and magnetism. Such activity manifests often in the form of eruptions, that can consist of large amounts of plasma and magnetic flux that are ejected into interplanetary space. These phenomena are known as coronal mass ejections (CMEs). CMEs may impact Earth and harm the performance and reliability of space- and ground-based technological systems, such as satellites in orbit, power grids, and systems utilising navigation and positioning applications. The increased radiation that follows a CME eruption can endanger the health of astronauts involved in space missions. The effects of solar activity on Earth are called collectively "space weather". The ability of a CME to drive space weather effects on Earth (or "geoeffectiveness") depends on its internal magnetic structure, morphology, and speed. The magnetic structure of a CME is often described with a flux rope morphology, that is a helical magnetic tube whose magnetic field can be divided into two main components: the axial field, which runs through the centre of the tube, and the helical field, which wraps around the tube. In this thesis, the magnetic structure of CMEs and their geoeffectiveness at 1 AU are investigated using a combination of observational and modelling techniques. The magnetic structure of flux ropes at the time of eruption can be inferred from multiwavelength remote-sensing observations of the CME source region, by taking into account features as coronal loops, filaments, flare ribbons, and photospheric structures. However, the results of the analysis show that the magnetic structure of such flux ropes may differ significantly when measured at 1 AU, i.e. around Earth’s orbit. This is because CMEs can experience dramatic evolution after lifting off from the Sun, e.g. through deflections, rotations, and deformations. The results presented in this thesis highlight that CME evolution is an important factor to take into account in numerical models and in space weather forecasting. Furthermore, the turbulent sheath regions that often travel ahead of CMEs may contain geoeffective components. Another aspect that contributes to making CME forecasting a challenging task is represented by those CMEs whose impact is less "obvious," e.g., because they are not entirely Earth-directed or because their signatures are unclear in remote-sensing data. During periods of significant solar activity there can be multiple CMEs launched from the same or nearby source regions. This thesis utilises recent multi-instrument observations from different vantage points to analyse periods of successive CME eruptions and their possible interactions in the corona and inner heliosphere. Magnetohydrodynamic modelling of CME propagation is also used, especially for problematic CMEs and multi-eruption periods, to provide a global heliospheric context necessary to interpret the multi-spacecraft observations. This thesis thus contributes to the improvement of our current understanding of CME evolution and space weather forecasting. Its results can be used as inputs, validation, and refinement for space weather forecasting tools and their modelling results. Finally, its comprehensive Sun–to–1 AU approach to analyse periods of enhanced eruptive activity and the subsequent heliospheric evolution of multiple CME events emphasises the importance of combining observations from multiple vantage points and heliospheric modelling for making progress in space weather forecasting.
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(Helsingin yliopisto, 2019)This work considers with the origin, age and geological environment of the concealed continental crust of Vestfjella, western Dronning Maud Land, Antarctica (WDML). In the Jurassic, the bedrock of Vestfjella experienced the latest major period of extension and rifting. The WDML Jurassic crust has been correlated with the Karoo Large Igneous Province of Africa, and with the Archean and Proterozoic domains, where exposed, of the Archean Kaapvaal Craton and Mesoproterozoic Natal Belt of Africa. The lamproite-hosted xenoliths investigated in this study show metamorphic (including metasomatic) modification from their primary geochemical composition. In the classification of the examined samples, the mineral mode proved to be superior to geochemical classification in protolith identification. The zircon populations of arc affinity metatonalite, quartz metadiorite and metagranite xenoliths record a thermal event at 1150–590 Ma. However, the evolution of the WDML Proterozoic crust began earlier, in the Mesoproterozoic, with arc magmatism at ca. 1450–1300 Ma. The accretion of arc terrains and development of the continental Namaqua–Natal–Maud belt by the Grenvillean-Kibaran orogeny was followed by the break-up of the Rodinia Supercontinent. Granite crystallization at ca. 1100–1090 Ma and at 1050–990 Ma records crustal anatexis, cooling and Neoproterozoic mylonitic deformation. The Proterozoic zircon ages are similar to the crustal domains in the Natal Belt of southern Africa, the Maud Belt of central Dronning Maud Land and remote Mesoproterozoic basement exposed in the West Falkland Islands and Haag nunataks, West Antarctica. The initial εNd (1450) of +7.1 for a pargasite-rich garnet-free metagabbro and the initial εNd (180) of -8.5 for a garnet-bearing metagabbro resemble the isotopic signature of enriched lithospheric mantle and old enriched crust. The present-day Nd isotope composition of these xenoliths conforms to the array of the Triassic Karoo igneous province gabbroic rocks and granulite xenoliths (Proterozoic or undefined), similar to the Lesotho lower crustal xenoliths. The youngest xenolith zircon age, 165 Ma, records crustal heating and A-type granite magmatism post-dating the Karoo magmatism in WDML. The Vestfjella crust cooled below 300 °C at ca. 100 Ma ago (Rb-Sr). This work provides new direct information on the concealed Precambrian of East Antarctica, the regional geology of East Antarctica and southern Africa, and geological processes in the Vestfjella bedrock. The results may be used to resolve the palaeogeography of the supercontinents Rodinia and Gondwana and to interpret existing and forthcoming chronological, geochemical and geophysical data.
