Faculty of Science
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(Helsingin yliopisto, 2021)Soil is one of the most critical components in Earth systems in connecting the lithosphere, hydrosphere, atmosphere, and biosphere. The responsiveness of soil temperature to climatic changes has been tackled in a number of studies. However, a timely understanding of the recent shallow–depth soil temperature evolution in response to changing climate and environment at inter-annual time scale over northern Eurasia is inadequate and needed for assessing the land–atmosphere thermal interactions and consequences at a broad spatiotemporal scale. This thesis backs up the aforementioned knowledge gaps by 1) quantifying and contrasting soil temperature changes in northern Eurasia and subregions; 2) investigating discordant changes in soil and air temperatures and their links with environmental changes; 3) assessing and examining the stability of soil–air temperature coupling at an inter-annual time scale and the underlying mechanisms. This thesis demonstrated that shallow–depth soil temperature significantly increased over the region since the 1970s in terms of the annual mean, maximum, and minimum soil temperatures. However, the warming rates were spatially heterogenous at different depths and over the subregions divided by the extent of frozen ground. In the region as a whole, the average increases in the annual mean, maximum and minimum temperatures ranged between 0.30–0.31, 0.33–0.44, and 0.24–0.25 °C/decade, respectively, at multiple depths. As such, the overall faster increase in the annual maximum temperature than minimum temperature led to an increase in intra-annual variability of soil temperature over the years. The soil temperature changes were consistently smaller than the air temperature changes except in the seasonal frost area at a depth of 0.2 m. Such discordant changes in soil and air temperatures led to inter-annual variations of soil–air temperature difference over the period. Furthermore, the environmental changes essentially explained the variability of temperature difference. Among the examined variables, changes in the snow cover characters showed overriding effects. Other environmental changes such as surface net solar radiation, liquid precipitation, and soil moisture had comparatively smaller impacts but still significant, which cannot be ignored at some ground depths. It is also revealed that coupling, which is defined as the linear regression slope between the mean annual soil and air temperatures, was not a stable property and significantly decreased from 1984 to 2013. The declined coupling was likely due to the compound effects of changing thawing/freezing-degree days of air temperature and snow cover characteristics. The findings further question the rationality of using air temperature to proxy soil temperature (or vice versa) at inter-annual and long-term time scales. This thesis provides a thorough picture of the recent shallow–depth soil temperature evolution in response to climate changes and related consequences over northern Eurasia. The findings will help understand land–atmosphere thermal interactions and the role of environmental components at inter-annual and long-term time scales. Considering soil temperature as a critical parameter for Earth systems research, the findings can be referred¬¬ to and implemented for various aims, such as climate projection, land–surface model development, and agricultural management.
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(Helsingin yliopisto, 2021)The overall aim of this thesis is to understand the chemistry of different types of ionic liquids (ILs) and their application in cellulose processing, as well as in the chemical analyses of pulp. ILs are applied either on their own or as electrolytes with dimethyl sulfoxide (DMSO) or gamma valerolactone (GVL) as co-solvents. All of the ILs utilised in this work were synthesised in our laboratory via Menshutkin reaction followed by metathesis to different counter anions or via acid-base chemistry. The beginning of our work focused on the investigation of the C2 chemistry of imidazolium ionic liquids (IMILs), one of the first class of ionic liquids utilised in biomass processing, resulting in the understanding of the C2 chemistry of IMILs under neutral and acidic conditions, which complete the comprehension on the mechanistic scenario of the C2 chemistry of IMILs. In this investigation, the importance of the quality of the ILs is remarked. The studies continued to investigate the activation of the chemical reactivity of pulps using tetrabutylphosphonium acetate ([P4444][OAc]), a more thermal stable ionic liquid. The non-dissolving pre-treatment of pulps by [P4444][OAc] demonstrated a reduction in the crystallinity of the pulp to be directly related to its chemical reactivity enhancement. Additionally, [P4444][OAc]:d6-DMSO (20:80 wt.%) was also investigated in both the regioselectivity studies of acetylation reactions and the oxidised nanocellulose nuclear magnetic resonance (NMR) analyses. Quantitative heteronuclear single quantum correlation (HSQC) NMR was a suitable experiment for the quantitation of the oxidation level achieved in the nanocellulose, demonstrating the potential of this method for cellulose analyses. Finally, the research concluded with the studies on other cellulose solvent systems, such as 1,8-diazabicyclo[5.4.0]undec-7-ene (DBU) with dimethylsulphoxide (DMSO), which resulted on the discovery of the unexpected reactivity of 1,1,3,3-tetramethylguanidinium acetate ([TMG][OAc]). Furthermore, this investigation led us to the design and synthesis of a novel task-specific ionic liquid (TSIL), so called ‘TMG2SA’. This IL was investigated with regard to cellulose chemical modification, resulting in the high yield production of nanocellulose-type materials by a low demanding energy step, with a different approach than previously reported.
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(Helsingin yliopisto, 2021)The metabolic disorder type 2 diabetes characterized by insulin resistance and a reduced production of insulin is one of the fastest growing health and economic menaces for the society. Although, the increasing number of individuals suffering from type 2 diabetes and insulin resistance may have different diabetic characteristics and therefore a risk of severe diabetic complications such as diabetic kidney disease, most individuals still undergo the same forms of treatment and medication. Usually, the first line therapy option is the drug metformin. However, the use of metformin is contraindicated in many cases especially when kidney function declines. Apart from metformin the only insulin sensitizer available on the market is pioglitazone but its risks involving cardiovascular effects are highly debated. More recently, several alternative drugs with different drug targets have been introduced. However, they may possess poor efficiency or safety or are expensive. Consequently, new alternative and more individualized forms of treatment are desperately needed. The SH2-domain containing inositol 5´-phosphatase 2 (SHIP2) is a little studied target for the treatment of type 2 diabetes and insulin resistance. The study presented in this thesis mainly focuses on the design and synthesis of novel blood glucose lowering agents that potentially act as SHIP2 inhibitors and insulin sensitizers. In an in silico screening a group of compounds that bind to SHIP2 were found and were biologically validated in vitro by showing their inhibition of SHIP2. Interestingly, one of the compounds was the most widely used anti-diabetic drug metformin. Additionally, completely novel compounds that significantly inhibited SHIP2 were found. Based on these findings two sets of sulfonanilides were designed and synthesized and their biological efficacy was validated by a functionalized glucose uptake assay. Our best candidates were non-toxic and increased the glucose uptake into cells at lower concentrations than metformin. This indicates that our sulfonanilides can lower the blood glucose levels. We also provide insights into the protein-ligand interactions that may have some impact for future SHIP2 drug design. Large molecular libraries around the most promising candidate from the screening were designed and synthesized. In these sets of 181 molecules many compounds significantly increased the glucose uptake into cells. The druglike properties and in vitro efficacy of the top six compounds were investigated more closely. All compounds were non-toxic and soluble at the effective concentrations. However, some additional modifications and improvements are needed to optimize the metabolic stability of these compounds. Our aim in the future is also to show that these compounds protect podocytes from apoptosis which is an important factor considering the severe diabetic complication, diabetic kidney disease. In view of a huge unmet medical need, we aim to patent the specific new chemical entities after optimization of the metabolic stability properties. Our findings confirm that SHIP2 is an interesting alternative target that may be used for designing novel treatments for type 2 diabetes and insulin resistance. In addition, we have also shown the potential of our compounds for increasing the glucose uptake into cells thus lowering the blood glucose levels.
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(Helsingin yliopisto, 2021)Cell surface receptor proteins are important gatekeepers. They enable cells to react to their surroundings by receiving and propagating chemical signals through the cell membrane. Countless such reactions occur in the cells of our bodies every moment to keep us alive. Hence, understanding how these molecules operate helps us to improve our health and the quality of our life. In this Thesis, we discuss the activation of two cell signaling-related proteins, CD44 and JAK2. CD44 is a cell surface receptor for a carbohydrate called hyaluronan. Through hyaluronan binding, CD44 is involved in various signaling cascades that regulate, e.g., cell–cell interactions as well as cell differentiation, proliferation, and survival. JAK2 is a non-receptor tyrosine kinase — an intracellular signaling protein that binds to cytokine receptors, forming a receptor–JAK signaling complex. Through this interaction, JAK2 mediates central physiological functions, including hematopoiesis and immune response. Despite their physiological significance, the understanding of these proteins is incomplete because their atomic-level operating principles have not yet been fully elucidated. Therefore, we use biomolecular simulations to shed light into the function and regulation of these proteins and their cognate molecules. In the case of CD44, we expand the current knowledge on the details of its hyaluronan binding. We also show how N -glycosylation of the receptor can modulate the ligand binding by altering its binding site preference. In the case of JAK2, we find how the activation of the signaling complex is controlled by intracellular dimerization and proper orientation through specific membrane binding. Our work provides novel atomic-level insight into the functions and interactions of the studied proteins. The results can be useful in drug development — especially in the search for new drug binding sites, for example, at glycosylation, dimerization, or membrane binding interfaces of proteins. Finally, this work highlights the added value gained by bridging computer simulations with experimental techniques.
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(Helsingin yliopisto, 2021)Data from digital media are increasingly used in conservation science. They help researchers and policymakers learn about the beliefs and values of people and their interactions with non-human nature, and observe relevant news and events on a global scale. In the ongoing global biodiversity crisis, this knowledge becomes crucial. It is predominantly human activities that drive plant and animal species into extinction and destroy sensitive ecosystems (on which our wellbeing and survival depends). Knowing about people’s environmental perceptions is an important stepping stone in promoting a change away from unsustainable habits and a transition to more sustainable lifestyles. In this thesis, I explore, develop, and evaluate novel methods and approaches to unlock the new opportunities digital media offer to conservation science. The five individual publications that form chapters of the thesis build upon each other: first, I present a comprehensive review of relevant data sources and methods. In the second chapter, I discuss the data privacy implications of using digital media data in conservation science research. The third chapter presents a general framework to collect, filter, and identify social media data related to illegal wildlife trade. The fourth and fifth chapters, then, combine the findings of the first three: I demonstrate how to identify conservation-relevant events from the public opinion on social media and in online news, and investigate the online songbird trade in Indonesia, its price structure and the spatial characteristics of its supply chain. The concepts, the methods, and the openly-available tools presented in this thesis can guide conservation scientists and practitioners, as well as researchers in other fields, and inspire them to use digital media data in their own work.
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(Helsingin yliopisto, 2021)The Luumäki gem-beryl bearing pegmatite belongs to the miarolitic pegmatites, a comparatively rare and little studied pegmatite class. However, it is the miarolitic pegmatites that give raise to the scientific hypothesis about the involvement of aqueous fluids during pegmatite formation. The controversy regarding the involvement of a separate aqueous fluid was renewed in view of recent interpretations of giant crystal growth and graphic granite textures, which are both quite unique for pegmatites. And recent understanding favors a model where pegmatites are being formed by rapid cooling and crystallization from a volatile-rich melt, instead of very slow cooling of an undercooled, water-saturated granite melt. Some argue that H2O is essential for reducing the viscosity of granite melts and the element transport to form giant crystals in pegmatites, based on fluid inclusion studies and the water-saturated melt model proposed by Richard Jahns and Wayne Burnham. Recently this model has been redefined by studies on melt and fluid inclusions suggesting a melt-melt immiscibility with subsequent aqueous fluid exsolution, as suggested by Rainer Thomas. Others reason that refining of a melt, similar to the metallurgical process of zone refining, is key for pegmatite consolidation, and a separate H2O phase plays a minor role if any at all. The proposed model is the constitutional zone refining (CZR) by David London. My study supports the view of a separate aqueous phase in the late stage of pegmatite crystallization and demonstrates that in the case of the Luumäki pegmatite, an aqueous phase separated at the onset of pocket formation at about 380 °C and 1.2 kbar. Furthermore, it is shown that a pegmatite melt is very heterogeneous and within the same pegmatite body one miarolitic pocket can clearly show evidence for a hydrothermally dominated system, while another represents a magmatic dominated process of formation without clear evidence of the involvement of an aqueous fluid. Also, pegmatite bodies within the same geological framework can differ in their appearance, showing that within a small area of just a few hundred of meters one pegmatite body can show extensive hydrothermal activity whereas the other lacks any evidence of an aqueous fluid separation. The research of pegmatites and their fluid inclusions is a somewhat complicated endeavor, similar to solving a puzzle, in which most pieces appear very similar to one another, and many more studies need to be conducted to solve such a puzzle. Powerful analytical methods such as in-situ fluid inclusion analysis by means of LA-ICP-MS is a step in the right direction and will hopefully encourage many more to engage in this tedious, yet gratifying search for the bigger picture by looking at the small individual pieces in greater detail as ever before.
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(Helsingin yliopisto, 2021)How stars form is one of the most central issues in astrophysics. Many questions remain open, in particular regarding the roles of the gas dynamics and self-gravity in determining the location, mass, and multiplicity of the future stars as they form within molecular clouds. Moreover, what is the contribution of feedback processes, from outflows and intense ultraviolet radiation from newly-born stars to the dynamical processes? Combining observations at various wavelengths with numerical simulations, it is possible to address these questions by characterizing the energy balance of star-forming regions. In this thesis work, we have explored the possibilities and limitations of combining observations and modelling of star-forming regions. The first and third paper explore the correlations between the gas and dust through observations, analysing the dynamical and physical properties of star forming regions. The clouds studied in these papers are different in both morphology and environment, from a large filamentary structure to a compact star forming clump. However, in both clouds, several compact cores could be identified from the observations, showing clear signs of star formation. Changes in the properties of dust grains should be reflected in how the grains scatter light at near-infrared (NIR) wavelengths and in the thermal emission at far-infrared (FIR). Thus, in the second paper, a method to study dust properties with NIR scattering was developed. The method was shown to be able to provide relevant information on dust properties, but high-precision observation and further constraints on the strength of the radiation field are required. The radiation field can be constrained by observing the thermal emission from dust, and in the fourth and fifth papers constraints on the dust properties were derived with simultaneous modelling at NIR and FIR wavelengths. The models can reproduce the observed emission, while the scattered light can only be matched with dust models that include evolution of dust grains. However, the modelled scattered light in the diffuse regions is clearly overestimated. Thus further numerical work is required to provide a self-consistent model of the region.
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(Helsingin yliopisto, 2021)Multi-Model DataBases (MMDBs) are database management systems that utilize a single platform to store, manipulate, and query data in various data models, e.g., relational, tree, and graph models. Numerous MMDBs have been developed to facilitate multi-model data management, but they differ fundamentally in data storage, query language, and query processing. Existing tools are not suitable for benchmarking MMDBs because they do not consider the multi-model data and workloads. Therefore, it is of paramount importance to provide a new benchmark to evaluate the performance of MMDBs. Furthermore, MMDBs bring new issues for query optimization as traditional techniques cannot properly optimize the queries due to the lack of consideration of multi-model operators and storage. Thus, it calls for new approaches to optimize multi-model queries. This thesis is divided into two parts to accomplish the above two goals, respectively. In the first part, we developed a new benchmark system for MMDBs in a scenario of social commerce with five data models, i.e., relational, JSON, XML, graph, and key-value. This benchmark system is an end-to-end tool that consists of various components, including synthetic data generation, workload specification, parameter curation, and execution client. Moreover, we leveraged the developed system to conduct a holistic experimental evaluation of the state-of-the-art MMDBs to compare their performance and identify their performance bottlenecks in handling multi-model workloads. In the second part, we proposed two query optimization techniques for MMDBs. Firstly, we proposed a kernel density estimation (KDE)-based model to estimate the selectivity of multi-model joins that involve predicates for relational and tree data models. The estimation method can serve as a building block for selecting an optimal joining order in a cross-model query execution plan. We also proposed two approaches, the max-min approximation (MMA) and grid-based approximation (GBA) models, to approximate the KDE contribution while improving the estimation efficiency for large data samples. Secondly, we studied the problem of view selection in the relational-based graph databases to avoid the costly joins in the relational engine. Particularly, we proposed an end-to-end system that can automatically create, evaluate, and select views for accelerating the query processing. We proposed an extended graph view that can answer the subgraph and supergraph queries simultaneously. We devised a filtering-and-verification framework that enables the verification of the query containment by graph views. We formalized the view selection problem as a 0-1 Knapsack problem, then we developed a view selection algorithm, named graph gene algorithm (GGA), which explores the graph view transformations to reduce the view space and optimize the view benefit. Overall, the present thesis advances MMDBs in three aspects, i.e., performance benchmarking, join selectivity estimation, and automatic view selection.
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(Helsingin yliopisto, 2021)Computational creativity has received a good amount of research interest in generating creative artefacts programmatically. At the same time, research has been conducted in computational aesthetics, which essentially tries to analyse creativity exhibited in art. This thesis aims to unite these two distinct lines of research in the context of natural language generation by building, from models for interpretation and generation, a cohesive whole that can assess its own generations. I present a novel method for interpreting one of the most difficult rhetoric devices in the figurative use of language: metaphors. The method does not rely on hand-annotated data and it is purely data-driven. It obtains the state of the art results and is comparable to the interpretations given by humans. We show how a metaphor interpretation model can be used in generating metaphors and metaphorical expressions. Furthermore, as a creative natural language generation task, we demonstrate assigning creative names to colours using an algorithmic approach that leverages a knowledge base of stereotypical associations for colours. Colour names produced by the approach were favoured by human judges to names given by humans 70% of the time. A genetic algorithm-based method is elaborated for slogan generation. The use of a genetic algorithm makes it possible to model the generation of text while optimising multiple fitness functions, as part of the evolutionary process, to assess the aesthetic quality of the output. Our evaluation indicates that having multiple balanced aesthetics outperforms a single maximised aesthetic. From an interplay of neural networks and the traditional AI approach of genetic algorithms, we present a symbiotic framework. This is called the master-apprentice framework. This makes it possible for the system to produce more diverse output as the neural network can learn from both the genetic algorithm and real people. The master-apprentice framework emphasises a strong theoretical foundation for the creative problem one seeks to solve. From this theoretical foundation, a reasoned evaluation method can be derived. This thesis presents two different evaluation practices based on two different theories on computational creativity. This research is conducted in two distinct practical tasks: pun generation in English and poetry generation in Finnish.
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(Helsingin yliopisto, 2021)Cancer is a critical health concern worldwide. Although significant progress in cancer diagnosis and therapy has been made to date, the lack of efficient delivery of active compounds to the target site and adverse systemic effects remain a major challenge in cancer treatment. For those reasons, different therapeutic strategies have been developed to improve the target specificity and reduce side effects in conventional therapy. Nanomedicines are innovative nanoparticulate drug delivery systems constructed from biocompatible, biodegradable, and nontoxic materials. Nanoparticles can transport diagnostic and therapeutic agents with increased bioavailability, reduced dosing frequency, and less off-target side effects. Recently, cellulose nanocrystals (CNC NPs) and lignin nanoparticles (LNPs) have attracted attention as abundant natural nanomaterials that can be derived from various bioresources, especially plant-based lignocellulosic biomass. CNC NPs and LNPs have been intensively explored as material scaffolds in numerous biomedical applications due to their unique physicochemical and biological properties. Nuclear molecular imaging techniques, including single-photon emission computed tomography (SPECT) and positron emission tomography (PET) are non-invasive and sensitive imaging technologies that allow the tracking of a tracer dose of radiopharmaceuticals in vivo to determine their target occupancy, circulation, biodistribution profiles, and elimination kinetics. Nanomaterials tagged with a radioactive label can be traced after systemic administration through the detection of gamma photons emitted by the radioactive isotopes outside the body (single γ photon for SPECT and annihilation of two anti-parallel 511 keV photons for PET). Moreover, several radioisotopes can concomitantly release diagnostic γ radiation and ionizing particles (α or β-) during their decay, enabling the consolidation of diagnostic imaging and radiotherapy. The combination of imaging labels and therapeutic agents into a single nanoparticle platform creates a theranostic nanosystem, which can be used for simultaneous imaging and therapy of cancer. This thesis aimed to develop theranostic nanoparticle drug delivery systems based on CNC NPs and LNPs. The project comprised of several studies: 1) radiolabeling chemistry development, 2) in vitro cytotoxicity and cellular uptake investigation, 3) in vivo biodistribution and imaging studies in tumor-bearing animal models with the developed tracers, and 4) a theranostic nanosystem development and biological evaluation based on the observations from 1–3. Firstly, CNC- and LNP-based imaging probes for nuclear and optical imaging were developed for the in vitro and in vivo investigation using two modification strategies: site-specific hydrazone linkage to the terminal aldehyde of the CNC and non-site-specific conjugation using CDI activation. Both multimodal CNC NPs and LNPs demonstrated low cytotoxicity and favorable interactions with macrophage and cancer cell lines. Following extensive validation in material characterization and in vitro cell models, radiometal chelator DOTA-modified CNC NPs from both synthetic pathways were selected to further explore the in vivo behavior through the labeling with diagnostic radionuclide 111In in both healthy and 4T1 breast tumor-bearing mouse models. The ex vivo biodistribution and SPECT/CT imaging revealed comparable pharmacokinetic profiles where the accumulation of all developed 111In-labeled CNC NPs was primary in the lung, liver, and spleen, which are the clearance organs of nontargeted nanoparticles. Due to high retention of the CNC in the lung capillaries, theranostic CNC NPs were further developed for co-delivery of radiotherapeutic 177Lu and chemotherapeutic vemurafenib to target YUMM1.G1 metastatic melanoma in the lung through vascular trapping. The theranostic CNC NPs exhibited excellent radiolabel stability and sustained drug release profiles in vitro. The therapeutic studies also showed that the lifespan of tumor-bearing animals treated with theranostic CNC NPs was increased about twice from the median survival time of animals receiving only the vehicle or CNC NPs carrying only a single component of the theranostic system. In conclusion, the work presented in this thesis demonstrates the successful development of novel CNC- and LNP-based molecular imaging nanoprobes and the theranostic CNC NPs for the delivery of radio- and chemotherapeutic agents with enhanced therapeutic efficacy compared to the conventional chemotherapy in metastatic melanoma. The studies provide a breakthrough on the development of systemically administered CNC drug delivery systems and warrant further investigation on the potential of CNC NPs as a renewable scaffold for theranostic drug delivery systems.
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(Helsingin yliopisto, 2021)Nucleation of liquid, or solid, clusters in the gas phase is ubiquitous in nature. It plays a major role in various fields of studies, and especially those dealing with the atmosphere and climate change. A significant portion of atmospheric particles is formed through nucleation, and these small particles can affect the climate by either absorbing or scattering sunlight, or acting as seeds for cloud formation. On the other hand, nucleation can also be used in technologies aiming at climate change mitigation: e.g., a novel nucleation-based approach using supersonic separators for CO2 capture provides an environmentally friendly alternative to traditional approaches involving toxic compounds. Despite the fact that nucleation has been studied for over a century, the theoretical picture remains incomplete. In the context of this thesis, nucleation is determined by kinetics and thermodynamics as special attention needs to be paid to the phase and energy of the nucleating clusters. These basic aspects of nucleation are not resolved in most of the measurement methods as the early stages of nucleation involve sub-nanometer clusters evolving very rapidly in time. Classical nucleation theories are usually based on simple models and properties of bulk materials, neglecting atomistic details. Atomistic simulations and numerical modelling, as employed in this work, can provide valuable insight into the nanoscale details of nucleation. In this thesis, our investigations are limited to homogeneous nucleation, and the studied systems vary from simple Lennard-Jonesium to fully atomistic models of complex molecular clusters. We have used various state-of-the-art equilibrium and non-equilibrium computational methods to shed light on the most important nucleation mechanisms in different conditions. Atomistic interactions were described by empirical force fields or quantum chemistry methods, as required by the system. The simulation results, reviewed in the context of both classical and non-classical nucleation theory, enabled us to uncover the nucleation pathways, cluster thermodynamics and structural (and energetic) evolution of the nucleating clusters. Based on the results presented in this thesis, molecular-level modelling is necessary to capture the microscopic effects related to the formation of the clusters. In addition, predicting nucleation rates of strongly bound atmospheric clusters requires non-classical treatment of both nucleation pathways and collision rates, as cluster-cluster collisions (not only monomer-cluster collisions) need to be accounted for, and the collision rate coefficients are affected by attractive long-range interactions. For loosely bound clusters, however, we have demonstrated that these phenomena can be ignored and thus the underlying framework of classical nucleation theory is working relatively well. We have further analysed the structural and energetic details of strongly undercooled clusters during the nucleation stage, and after equilibration.
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(Helsingin yliopisto, 2021)Despite efforts to reverse the current global environmental crisis that threat-ens biodiversity and human well-being, many indicators suggest we are still far from changing the main trajectory towards sustainability. With privately owned land covering large areas of the world, private land conservation (PLC) has been recognized as a promising strategy to complement protected area networks in meeting biodiversity conservation objectives. However, the over-all success of PLC depends on designing and implementing a suite of policies according to geographical contexts and to the needs, values, and ca-pabilities of different stakeholders. In my doctoral thesis, I aim to identify challenges and opportunities to foster PLC at different geographical scales by understanding the main trends and gaps in a global PLC literature review and by assessing landowners’ preferences and needs at national and local levels. In order to do so I followed transdisciplinary approaches, combining theories and methods from the natural and social sciences in collaboration with stakeholders outside academia. By following a transdisciplinary approach my thesis contributes to identifying and addressing research gaps in PLC at different scales with practical implications for biodiversity conservation, sustainability, and policy-making in Uruguay and elsewhere in the world in similar contexts. In addition, my thesis highlights the need for future research to disentangle the main contextdependent dimensions driving PLC effectiveness but also to identify general principles that could inform the design, governance and im-plementation of legitimate and equitable policies across contexts.
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(Helsingin yliopisto, 2021)This thesis looks into two privacy threats of cellular networks. For their operations, these networks have to deal with unique permanent user identities called International Mobile Subscriber Identity (IMSI). One of the privacy threats is posed by a device called IMSI catcher. An IMSI catcher can exploit various vulnerabilities. Some of these vulnerabilities are easier to exploit than others. This thesis looks into fixing the most easily exploitable vulnerability, which is in the procedure of identifying the subscriber. This vulnerability exists in all generations of cellular networks prior to 5G. The thesis discusses solutions to fix the vulnerability in several different contexts. One of the solutions proposes a generic approach, which can be applied to any generation of cellular networks, to fix the vulnerability. The generic approach uses temporary user identities, which are called pseudonyms, instead of using the permanent identity IMSI. The thesis also discusses another solution to fix the vulnerability, specifically in the identification procedure of 5G. The solution uses Identity-Based Encryption (IBE), and it is different from the one that has been standardised in 5G. Our IBE-based solution has some additional advantages that can be useful in future works. The thesis also includes a solution to fix the vulnerability in the identification procedure in earlier generations of cellular networks. The solution fixes the vulnerability when a user of a 5G network connects to those earlier generation networks. The solution is a hybridisation of the pseudonym-based generic solution and the standardised solution in 5G. The second of the two threats that this thesis deals with is related to the standards of a delegated authentication system, known as Authentication and Key Management for Applications (AKMA), which has been released in July 2020. The system enables application providers to authenticate their users by leveraging the authentication mechanism between the user and the user's cellular network. This thesis investigates what requirements AKMA should fulfil. The investigation puts a special focus on identifying privacy requirements. It finds two new privacy requirements, which are not yet considered in the standardisation process. The thesis also presents a privacy-preserving AKMA that can co-exist with a normal-mode AKMA.
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(Helsingin yliopisto, 2021)Strings are ubiquitous. When being collected from various sources, strings are often inconsistent, which means that they can have the same or similar meaning expressed in different forms, such as with typographical mistakes. Finding similar strings given such inconsistent datasets has been researched extensively during past years under an umbrella problem called approximate string matching. This thesis aims to enhance the quality of the approximate string matching by detecting similar strings using their meanings besides typographical errors. Specifically, this thesis focuses on utilising synonyms and taxonomies, since both are commonly available knowledge sources. This research is to use each type of knowledge to address either a selection or join tasks, where the first task aims to find strings similar to a given string, and the second task is to find pairs of strings that are similar. The desired output is either all strings similar to a given extent (i.e., all-match) or the top-k most similar strings. The first contribution of this thesis is to address the top-k selection problem considering synonyms. Here, we propose algorithms with different optimisation goals: to minimise the space cost, to maximise the selection speed, or to maximise the selection speed under a space constraint. We model the last goal as a variant of an 0/1 knapsack problem and propose an efficient solution based on the branch and bound paradigm. Next, this thesis solves the top-k join problem considering taxonomy relations. Three algorithms, two based on sorted lists and one based on tries, are proposed, in which we use pre-computations to accelerate list scan or use predictions to eliminate unnecessary trie accesses. Experiments show that the trie-based algorithm has a very fast response time on a vast dataset. The third contribution of this thesis is to deal with the all-match join problem considering taxonomy relations. To this end, we identify the shortcoming of a standard prefix filtering principle and propose an adaptive filtering algorithm that is tuneable towards the minimised join time. We also design a sampling-based estimation procedure to suggest the best parameter in a short time with high accuracy. Lastly, this thesis researches the all-match join task by integrating typographical errors, synonyms, and taxonomies simultaneously. Key contributions here include a new unified similarity measure that employs multiple measures, as well as a non-trivial approximation algorithm with a tight theoretical guarantee. We furthermore propose two prefix filtering principles: a fast heuristic and accurate dynamic programming, to strive for the minimised join time.
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(Helsingin yliopisto, 2021)The amount and properties of atmospheric aerosol particles vary both in time and space depending on the proximity of the sources, atmospheric chemistry, and meteorological con-ditions. Atmospheric particulate matter worsens air quality and therefore affects human health. Aerosol particles have a notable effect also on the Earth’s climate by scattering and absorbing the solar radiation and via aerosol-cloud interactions. The absorbing fraction of particles warms the climate, but due to the aerosol-cloud interactions and the greater fraction of scattering particles, the total effect of aerosols on the climate is cooling. To determine the effect that particles have on the climate, it is crucial to know aerosol optical properties (AOPs) that describe the ability of atmospheric aerosol particles to scatter and absorb light at different wavelengths. The AOPs are determined by the size distribution, chemical composition, shape, and mixing state of the particles. This thesis aims to deepen the understanding of the AOPs and their relationships to the aerosol size distribution and chemical composition by combining comprehensive measurements of these parameters. The measurements were conducted at a rural boreal forest measurement site SMEAR II. This thesis also studies the spatial and temporal variation of aerosols, by utilizing long-term aerosol measurements from different environments that vary from background sites to urban locations. The study of the spatio-temporal variation focuses on the variation in equivalent black carbon (eBC), which stands for optically measured black carbon (BC). A majority of the aerosol absorption is caused by BC, and therefore it represents the aerosol particles that have a warming effect on the climate. Since BC is emitted mainly by anthropogenic activi-ties as a by-product of incomplete combustion, measurements of eBC give additional infor-mation on the health effects of aerosol particles since particles emitted from combustion sources are more harmful to health than aerosols from other sources. Studying the spatio-temporal variation in aerosol particles and especially in eBC concentration indicates the effect of anthropogenic activities on the aerosol concentration. The measurements of the AOPs are rather robust, cheap and easy to run, which is why the AOPs are commonly measured properties. However, challenges arise with absorption and eBC measurements, which are typically measured by filter-based methods. In optical filter measurements, also the filter interacts with the radiation causing nonlinearities and uncer-tainties in the measurements. In addition to understand better the AOPs and the spatio-tem-poral variation in the atmospheric particles, this thesis aims to improve the filter-based measurements and to understand better the effect of different instruments and filter loading correction algorithms on the measured AOPs.
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(Helsingin yliopisto, 2021)Bayesian models capture the intrinsic variability of a data-generating process as a posterior distribution over the parameters of the model for the process. Decisions that are optimal for a user-defined loss are obtained by minimizing expectation of the loss over the posterior. Because posterior inference is often intractable approximations of the posterior are obtained either via sampling with Monte Carlo Markov chain methods or through variational methods which minimize a discrepancy measure between an approximation and the true posterior. Probabilistic programming offers practitioners tools that combine easy model specification with automatic approximate inference techniques. However, these techniques do not yet accommodate posterior calibrations that yield decisions that are optimal for the expected posterior loss. This thesis develops efficient and flexible variational approximations as well as density function transformations for flexible modeling of skewed data for use in probabilistic programs. It also proposes extensions to the Bayesian decision framework and a suite of automatic loss-sensitive inference techniques for decision-making under posterior approximations. Briefly, we make four concrete contributions: First, we exploit importance sampling to approximate the objective gradient and show how to speed up convergence in stochastic gradient and stochastic average gradient descent for variational inference. Next, we propose a new way to model skewed data in probabilistic programs by prescribing an improved version of the Lambert W distribution amenable to gradient-based inference. Lastly, we propose two new techniques to better integrate decision-making into probabilistic programs - a gradient-based optimization routine for the loss-calibrated variational objective, specifically for the challenging case of continuous losses, and an amalgamation of learning theory and Bayesian decision theory that utilizes a separate decision-making module to map the posterior to decisions minimizing the empirical risk.
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(Helsingin yliopisto, 2021)Physical characterization of asteroid surfaces by studying the scattered light is challenging as the light-scattering processes are affected by the particle sizes, shapes, and materials, which in most cases are unknown. When interpreting remote-sensing observations, it is important to choose the correct methods for realistic analyses. In the past decades, several extensive studies have been carried out to understand asteroid surfaces, however, none of the previously used models are able to interpret spectroscopy, photometry, and polarimetry at the same time with sufficient precision. In the thesis, light-scattering methods were developed and utilized together with laboratory measurements to characterize asteroid regoliths and meteorite surfaces. The light-scattering methods presented in the thesis take into account both wavelength-scale particles and particles larger than the wavelength of the incident light, which is important as the models used for each wavelength domain are different, and the resonance region is difficult to account for. First, the reflectance spectra of three meteorite samples are simulated using a model combining olivine, pyroxene, and iron. The results are promising as we are able to match the simulated spectra fairly well with the measured spectra. Second, spectroscopic, photometric, and polarimetric modeling of asteroid (4) Vesta shows good results as the reflectance spectra can be modeled with reasonable precision, and the modeled photometry and polarimetry produce non-linear brightening and negative degree of linear polarization in the backscattering direction that is also seen in the observations. Finally, asteroid taxonomic classification is analysed by performing lightcurve inversion for 491 asteroids using convex and ellipsoid shapes. We retrieve phase curve slope parameters, rotation periods, pole orientations, shapes, reference phase curves, and absolute magnitudes in the G band of the ESA Gaia space telescope. Our analysis indicates that there can be mis-classifications in the current taxonomic systems. Asteroid photometry complements the existing classifications based on spectroscopy and provides us with a way to find the correct taxonomy. The forward methods of modeling the scattering properties of surfaces used in the thesis are vital in future studies as they can be applied to other Solar System objects, such as comets and satellite surfaces. Furthermore, retrieving the scattering properties of asteroid surfaces plays a vital role in the future space missions, including asteroid mining. Asteroid lightcurve inversion is useful especially when carrying out taxonomic classification as it provides additional information on the physical properties of the surface. The upcoming Gaia Data Release 3 will be extensive enough for rotational pole retrievals and will improve our current knowledge of the asteroids' physical properties.
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(Helsingin yliopisto, 2021)Snow has an important impact on hydrology, agriculture, climate and weather, infrastructure and different forms of both aerial and land transportation. The accumulation and properties of snow are inherently connected to the microphysical processes through which the falling ice particles grow. Furthermore, snow processes affect rainfall as well, since the vast majority of rain events originate as melted snow. For monitoring precipitation, the spatial coverage and resolution of radar instrumentation are unmatched. The quality of quantitative precipitation estimation using radars depends on our ability to establish meaningful relations between microphysical and electromagnetic scattering properties of hydrometeors. Especially for snow particles, these properties are diverse and the relations between them complex involving prominent uncertainties and knowledge gaps. Furthermore, the properties are constantly evolving as the falling particles undergo series of microphysical processes including growth from vapour, aggregation and riming. This dissertation work addresses these knowledge gaps by parametrizing microphysical properties of falling snow using ground based measurements, investigating the links between the properties and ice processes, and further studying their manifestations in collocated and off-site radar observations. A novel method is introduced for retrieving ensemble mean density of falling snow using a video disdrometer and a precipitation gauge. These retrievals are used in identifying triple frequency radar signatures of rimed particles and low density aggregates, and to develop a method for retrieving rime mass fraction. Based on the rime mass fraction retrievals, the effect of riming to snowfall is quantified. Using multifrequency Doppler radar and scanning C band radar observations we show that the downward streching of melting layer is linked primarily to precipitation intensity and secondarily to riming. Machine learning methods are employed in objectively documenting and automatically detecting known polarimetric fingerprints of ice microphysical processes in vertical profiles of radar variables. Automated ice process detection is anticipated to open the door for adaptive radar retrieval methods of snowfall rate.
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(Helsingin yliopisto, 2021)Majority of precipitation in mid- to high-latitudes originates from ice clouds. In these clouds, atmospheric ice particles grow through various microphysical processes and may precipitate to the surface in the form of snowfall or rainfall. A large fraction of these clouds contain supercooled liquid water, which affects microphysical properties of ice particles. However, despite the importance of ice microphysics in mixed-phase clouds to the development of precipitation, our understanding of underlying processes is still lacking. In past decades, long-term continuous observations of clouds and precipitation have shown promise for addressing this challenge. To provide such observations, remote sensing instruments, such as weather and research cloud radars, have been widely utilized. In this thesis, operational weather radars and cloud radars are used to address some challenges specific to ice microphysics. Using dual-polarization weather radar observations collected over four years, we show how the shape of ice particles depends on rime mass fraction and present the parametrization of this dependence. This study also investigates the potential of using radar dual-polarization signatures to identify riming extent. Furthermore, the complexity of ice microphysics and the ambiguity of corresponding radar signatures motivate search for additional information, which can be used to infer ice microphysics. This work illustrates how radar characteristics of the melting layer can be linked to ice growth processes such as riming and aggregation. In natural clouds, ice particles are usually characterized by a large variety of habits. However, our interpretation of the melting layer usually assumes presence of a single class of ice particles with a certain shape. This study reports that two types of ice particles can produce different radar polarimetric signals in the melting layer. The melting signal of ice needles is employed to evaluate current melting layer detection methods. The melting layer of precipitation also plays a negative role, because it attenuates radio waves. Due to this largely unknown attenuation at milimeter wavelengths, cloud properties in rainfall are poorly documented by ground-based cloud radars. In this study, the melting layer attenuation at Ka- and W-bands is quantified using the differential attenuation technique based on multifrequency radar Doppler spectra observations. In addtion, the retrievals are used to evaluate previous modelling results.
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(Helsingin yliopisto, 2020)The interaction of X-rays with matter provides us with a variety of tools to investigate the properties and dynamics of materials in multiple length scales, from microscopic to macroscopic ones. By studying the interactions, we can obtain information on the atomic scale configuration and quantum states of materials, which helps us to understand why materials behave as they do. The art and science of measuring the spectral changes of X-rays is known as X-ray spectroscopy. Most contemporary X-ray spectrometers in the hard X-ray regime with mid-to-high energy resolution (order of 0.01--1 eV) are based on diffraction of X-rays from crystals in which the atoms are organised in a periodic structure. To optimise the performance of such instruments, the crystal optical elements are often bent to collect divergent X-rays over a larger solid angle, monochromatise, and focus them on a detector. Bending, however, causes internal deformations in the crystal which have an often adverse effect to its energy-resolving capabilities. To understand the influence of strain in crystal optics is crucial in optimising the performance of X-ray spectrometers at state-of-art synchrotron and X-ray free electron laser lightsources, and laboratory-scale X-ray spectrometers which have seen a re-emergence in recent years. Multiple approaches based on e.g. Takagi-Taupin theory or multilamellar model have been utilised to understand the influence of bending to X-ray diffraction curves. The approaches using a so-called depth-dependent strain field for the crystal deformation have been successful in understanding the diffraction curves (i.e. intensity of diffracted X-rays as a function of the photon energy or angle of incidence) of cylindrically and spherically bent crystal wafers with small surface area. However, using only the depth-dependent strain field is insufficient to explain experimentally determined diffraction curves of spherically bent crystal wafers with larger surface area. This work presents a theoretical approach to model the X-ray diffraction curves of arbitrarily shaped toroidally bent crystal wafers with large surface areas. The key idea of the work is to include an in-plane stretching component to the deformation field due to bending in addition to the depth-dependent part. The work presents two separate theoretical approaches to calculate the in-plane component: 1) a model based on geometrical considerations to derive the in-plane component for a circular, elastically anisotropic spherically bent crystal wafer, and 2) a more general approach to calculate the aforementioned component for an arbitrarily shaped, anisotropic toroidally bent crystal wafers based on the minimization of mechanical stretching energy. The validity of the models is assessed by comparing the predictions with experimentally measured curves and they are found to explain the observed features in a quantitatively accurate manner. An open-source Python implementation of the latter approach is provided for the community for the ease of adoption of the presented method. In addition, the work introduces a measurement protocol to mitigate the adverse effect of the in-plane stretching to the X-ray diffraction curves by utilizing position-sensitive X-ray detectors providing a higher energy-resolution for instruments equipped with such detectors without a loss of collected X-ray photons.
