Browsing by study line "ingen studieinriktning"

Fault management in mobile networks is required for detecting, analysing, and fixing problems appearing in the mobile network. When a large problem appears in the mobile network, multiple alarms are generated from the network elements. Traditionally Network Operations Center (NOC) process the reported failures, create trouble tickets for problems, and perform a root cause analysis. However, alarms do not reveal the root cause of the failure, and the correlation of alarms is often complicated to determine. If the network operator can correlate alarms and manage clustered groups of alarms instead of separate ones, it saves costs, preserves the availability of the mobile network, and improves the quality of service. Operators may have several electricity providers and the network topology is not correlated with the electricity topology. Additionally, network sites and other network elements are not evenly distributed across the network. Hence, we investigate the suitability of a density-based clustering methods to detect mass outages and perform alarm correlation to reduce the amount of created trouble tickets. This thesis focuses on assisting the root cause analysis and detecting correlated power and transmission failures in the mobile network. We implement a Mass Outage Detection Service and form a custom density-based algorithm. Our service performs alarm correlation and creates clusters of possible power and transmission mass outage alarms. We have filed a patent application based on the work done in this thesis. Our results show that we are able to detect mass outages in real time from the data streams. The results also show that detected clusters reduce the number of created trouble tickets and help reduce of the costs of running the network. The number of trouble tickets decreases by 4.7-9.3% for the alarms we process in the service in the tested networks. When we consider only alarms included in the mass outage groups, the reduction is over 75%. Therefore continuing to use, test, and develop implemented Mass Outage Detection Service is beneficial for operators and automated NOC.

Tailoring a hybrid surface or any complex material to have functional properties that meet the needs of an advanced device or drug requires knowledge and control of the atomic level structure of the material. The atomistic configuration can often be the decisive factor in whether the device works as intended, because the materials' macroscopic properties - such as electrical and thermal conductivity - stem from the atomic level. However, such systems are difficult to study experimentally and have so far been infeasible to study computationally due to costly simulations. I describe the theory and practical implementation of a 'building block'-based Bayesian Optimization Structure Search (BOSS) method to efficiently address heterogeneous interface optimization problems. This machine learning method is based on accelerating the identification of a material's energy landscape with respect to the number of quantum mechanical (QM) simulations executed. The acceleration is realized by applying likelihood-free Bayesian inference scheme to evolve a Gaussian process (GP) surrogate model of the target landscape. During this active learning, various atomic configurations are iteratively sampled by running static QM simulations. An approximation of using chemical building blocks reduces the search phase space to manageable dimensions. This way the most favored structures can be located with as little computation as possible. Thus it is feasible to do structure search with large simulation cells, while still maintaining high chemical accuracy. The BOSS method was implemented as a python code called aalto-boss between 2016-2019, where I was the main author in co-operation with Milica Todorović and Patrick Rinke. I conducted a dimensional scaling study using analytic functions, which quantified the scaling of BOSS efficiency for fundamentally different functions when dimension increases. The results revealed the target function's derivative's important role to the optimization efficiency. The outcome will help people with choosing the simulation variables so that they are efficient to optimize, as well as help them estimate roughly how many BOSS iterations are potentially needed until convergence. The predictive efficiency and accuracy of BOSS was showcased in the conformer search of the alanine dipeptide molecule. The two most stable conformers and the characteristic 2D potential energy map was found with greatly reduced effort compared to alternative methods. The value of BOSS in novel materials research was showcased in the surface adsorption study of bifenyldicarboxylic acid on CoO thin film using DFT simulations. We found two adsorption configurations which had a lower energy than previous calculations and approximately supported the experimental data on the system. The three applications showed that BOSS can significantly reduce the computational load of atomistic structure search while maintaining predictive accuracy. It allows material scientists to study novel materials more efficiently, and thus help tailor the materials' properties to better suit the needs of modern devices.

Acute lymphoblastic leukemia (ALL) is a hematological malignancy that is characterized by uncontrolled proliferation and blocked maturation of lymphoid progenitor cells. It is divided into B- and T-cell types both of which have multiple subtypes defined by different somatic genetic changes. Also, germline predisposition has been found to play an important role in multiple hematological malignancies and several germline variants that contribute to the ALL risk have already been identified in pediatric and familial settings. There are only few studies including adult ALL patients but thanks to the findings in acute myeloid leukemia, where they found the germline predisposition to consider also adult patients, there is now more interest in studying adult patients. The prognosis of adult ALL patients is much worse compared to pediatric patients and many are still lacking clear genetic markers for diagnosis. Thus, identifying genetic lesions affecting ALL development is important in order to improve treatments and prognosis. Germline studies can provide additional insight on the predisposition and development of ALL when there are no clear somatic biomarkers. Single nucleotide variants are usually of interest when identifying biomarkers from the genome, but also structural variants can be studied. Their coverage on the genome is higher than that of single nucleotide variants which makes them suitable candidates to explore association with prognosis. Copy number changes can be detected from next generation sequencing data although the detection specificity and sensitivity vary a lot between different software. Current approach is to identify the most likely regions with copy number change by using multiple tools and to later validate the findings experimentally. In this thesis the copy number changes in germline samples of 41 adult ALL patients were analyzed using ExomeDepth, CODEX2 and CNVkit.

Kirjallisuuskatsauksessa käydään läpi erilaisia menetelmiä C3-substituoitujen indolien synteeseihin 2-alkenyylianiliinityyppisistä lähtöaineista, joiden bentsyylinen asema oli substituoitu. Erityistä huomiota kiinnitetään menetelmiin, joiden reaktiomekanismeiksi ehdotettiin radikaalimekanismeja. Myös näitä ehdotettuja radikaalimekanismeja esitellään tutkielmassa. Kokeellisessa työssä tutkittiin C3-subsituoitujen indolien hapettavaa synteesiä hiilikatalyytin avulla. Lähtöaineina käytettiin bentsyylisesti aryylisubstituoituja 2-alkenyylianiliinijohdannaisia. Joidenkin lähtöaineiden typpeen oli kiinnitetty metoksipyridiini, jonka kiinnitystä varten kehitettiin Buchwald-katalyysi. Hiilikatalyysit tuottivat hyviä saantoja. Korkea elektronitiheys, etenkin aniliinin bentseenirenkaan ja/tai typen aromaattisen substituentin korkea elektronitiheys, oli eduksi. Reaktion mekanismin ehdotetaan alkavan hapettumisella radikaalikationiksi, ja näitä hapetuspotentiaaleja laskettiin aiemmin raportoidun menetelmän mukaisesti. Mikäli indolin 5-renkaan substituutiot (N1, C2, C3) olivat tarpeeksi samankaltaisia, korkea elektronitiheys, matala hapetuspotentiaali ja hyvä saanto korreloivat. Indolin 5-renkaan substituutio on kuitenkin merkittävämpi tekijä kuin hapetuspotentiaali ja/tai korkea elektronitiheys. Pyridiini typen suojaryhmänä toimi katalyysissä ja se onnistuttiin poistamaan helposti. Metoksipyridiini toimi katalyysissä hyvin, mutta sen kvantitatiivinen poistaminen ei onnistunut.

This paper features two parts; a literature review discussing the recent development in using electrochemical gas sensors for pollutant detection and the use of sensor nodes in real-life locations, and an experimental section focusing on the kinetic study of nitrogen containing compounds utilizing in-tube extraction device. Growing interest towards personal safety have led to development of low-cost electrochemical sensors for personal safety, indoor air quality and leak detection applications. Heterojunctions and light illumination have emerged as an effective way to improve sensor performance, but the selectivity of electrochemical sensors remains relatively poor. Multiple sensors can be combined to create ‘E-noses’ which significantly improve the selectivity and compound identification. These E-noses have been deployed in some indoor locations, either being stationary in sensor networks or moved around by a robot or drone. All approaches have benefits and caveats associated to them, with the differences between individual sensors limiting sensor network use, and slow response and recovery times limiting the use of moving sensors. A novel micropump system was constructed to be used in the active air sampling together with in tube extraction (ITEX) and thermal desorption gas-chromatography (TD-GC-MS). The repeatability of this method was tested in a kinetic study of 10 selected nitrogen containing compounds in a custom-built permeation chamber. The breakthrough times and volumes of the compounds were investigated. Kinetic modelling was successful for 9 out of the 10 compounds with 1 compound behaving significantly different from the rest. The breakthrough times were always over 20 minutes and breakthrough volumes were around the 1000 ml region. Reproducibility was tested with multiple ITEX’s and samples were taken from five indoor locations. Three of the tested compounds were found in some of the samples.

Tiivistelmä – Referat – Abstract Molecular imaging is visualization, characterization and quantification of biological processes at molecular and cellular levels of living organisms, achieved by molecular imaging probes and techniques such as radiotracer imaging, magnetic resonance imaging and ultrasound imaging. Molecular imaging is an important part of patient care. It allows detection and localization of disease at early stages, and it is also an important tool in drug discovery and development. Positron emission tomography (PET) is a biomedical imaging technique considered as one of the most important advances in biomedical sciences. PET is used for a variety of biomedical applications: i.e. imaging of divergent metabolism, oncology and neurology. PET is based on incorporation of positron emitting radionuclides to drug molecules. As prominent radionuclides used in PET are of short or ultra-short half-lives, the radionuclide is most often incorporated to the precursor in the last step of the synthesis. This has proven to be a challenge with novel targeted radiotracers, as the demand for high specific activity leads to harsh reaction conditions, often with extreme pH and heat which could denature the targeting vector. Click chemistry is a synthetic approach based on modular building blocks. The concept was originally developed for purposes of drug discovery and development. It has been widely utilized in radiopharmaceutical development for conjugating prosthetic groups or functional groups to precursor molecules. Click chemistry reactions are highly selective and fast due to thermodynamic driving force and occur with high kinetics in mild reaction conditions, which makes the concept ideal for development and production of PET radiopharmaceuticals. Isotope exchange (IE) radiosynthesis with trifluoroborate moieties is an alternative labeling strategy for a reasonably high yield 18F labeling of targeted radiopharmaceuticals. As the labeling conditions in IE are milder than in commonly utilized nucleophilic fluorination, the scope of targeting vectors can be extended to labile biomolecules expressing highly specific binding to drug targets, resulting to higher contrast in PET imaging. A trifluoroborate functionalized prosthetic group 3 was synthetized utilizing click chemistry reactions, purified with SPE and characterized with HPLC-MS and NMR (1H , 11B-, 13C-, 19F-NMR). [18F]3 was successfully radiolabeled with RCY of 20.1 %, incorporation yield of 22.3 ± 11.4 % and RCP of >95 %. TCO-functionalized TOC-peptide precursor 6 was synthetized from a commercial octreotide precursor and a commercially available click chemistry building block via oxime bond formation. 6 was characterized with HPLC-MS and purified with semi preparative HPLC. Final product [18F]7 was produced in a two-step radiosynthesis via IEDDA conjugation of [18F]3 and 6. [18F]7 was produced with RCY 1.0 ± 1.0 %, RCP >95 % and estimated molar activity of 0.7 ± 0.8 GBq/µmol. A cell uptake study was conducted with [18F]7 in AR42J cell line. Internalization and specific binding to SSTR2 were observed in vitro.

Programming courses often receive large quantities of program code submissions to exercises which, due to their large number, are graded and students provided feedback automatically. Teachers might never review these submissions therefore losing a valuable source of insight into student programming patterns. This thesis researches how these submissions could be reviewed efficiently using a software system, and a prototype, CodeClusters, was developed as an additional contribution of this thesis. CodeClusters' design goals are to allow the exploration of the submissions and specifically finding higher-level patterns that could be used to provide feedback to students. Its main features are full-text search and n-grams similarity detection model that can be used to cluster the submissions. Design science research is applied to evaluate CodeClusters' design and to guide the next iteration of the artifact and qualitative analysis, namely thematic synthesis, to evaluate the problem context as well as the ideas of using software for reviewing and providing clustered feedback. The used study method was interviews conducted with teachers who had experience teaching programming courses. Teachers were intrigued by the ability to review submitted student code and to provide more tailored feedback to students. The system, while still a prototype, is considered worthwhile to experiment on programming courses. A tool for analyzing and exploring submissions seems important to enable teachers to better understand how students have solved the exercises. Providing additional feedback can be beneficial to students, yet the feedback should be valuable and the students incentivized to read it.

Plasmonic is an emerging field which has showed application for photocatlysis. Here we investigate a gold/platinum bimetallic catalytic system, and try to show how the catalytic properties of gold nanoparticles can be us to harvest visible light energy to increase the catalytic activity of platinum. Platinum being are rare and expensive metal, we also took the opportunity to find the optimal amount of catalyst to reduce platinum use. The catalyst is composed of a core spherical gold nanoparticles, of around 15 nm diameter. They were synthesized using an inversed Turkevich method, based on trisodium citrate, gold precursor salt and done in solution. Various amount of platinum was deposited on those nanoparticles using seeded growth method. The amount of platinum varied for single atoms to an atomic monolayer. This suspension of nanoparticles was deposited on ultrafine silica powder to be used for certain reaction and characterization. The material was characterized via several technics. UV-Visible and Diffuse Reflectance Spectroscopy were used to characterize its optical properties and showed a absorption peak around 524 nm characteristic of gold nanoparticles of this size. Imaging was done using electron microscopy (SEM and TEM) to study the morphology and showed monodisperse and spherical particles. The exact composition of the different catalyst were obtain using Atomic Emission Spectroscopy. The study was conducted by using reduction reaction as tests to investigate differences in conversion and selectivity under dark and monochromatic 525 nm and 427 nm light conditions. We chose to work on reduction of 4-nitrophenol, phenylacetylene and nitrobenzene, because they are widely used both in research and industry, and are easy to set up. Some catalyst showed good enhancement under 525 nm light, especially the one with the least amount of platinum. Different selectivity were also observed, indicating the presence of different reaction pathways under light conditions.

This thesis is about exploring descriptors for atmospheric molecular clusters. Descriptors are needed for applying machine learning methods for molecular systems. There is a collection of descriptors readily available in the DScribe-library developed in Aalto University for custom machine learning applications. The question of which descriptors to use is up to the user to decide. This study takes the first steps in integrating machine learning into existing procedure of configurational sampling that aims to find the optimal structure for any given molecular cluster of interest. The structure selection step forms a bottleneck in the configurational sampling procedure. A new structure selection method presented in this study uses k-means clustering to find structures that are similar to each other. The clustering results can be used to discard redundant structures more effectively than before which leaves fewer structures to be calculated with more expensive computations. Altogether that speeds up the configurational sampling procedure. To aid the selection of suitable descriptor for this application, a comparison of four descriptors available in DScribe is made. A procedure for structure selection by representing atmospheric clusters with descriptors and labeling them into groups with k-means was implemented. The performance of descriptors was compared with a custom score suitable for this application, and it was found that MBTR outperforms the other descriptors. This structure selection method will be utilized in the existing configurational sampling procedure for atmospheric molecular clusters but it is not restricted to that application.

Machine learning operations (MLOps) tools and practices help us continuously develop and de- ploy machine learning models as part of larger software systems. Explainable machine learning can support MLOps, and vice versa. The results of machine learning models are dependent on the data and features the models use, so understanding the features is important when we want to explain the decisions of the model. In this thesis, we aim to understand how feature stores can be used to help understand the features used by machine learning models. We compared two existing open source feature stores, Feast and Hopsworks, from an explainability point of view to explore how they can be used for explainable machine learning. We were able to use both Feast and Hopsworks to aid us in understanding the features we extracted from two different datasets. The feature stores have significant differences, Hopsworks being a part of a larger MLOps platform, and having more extensive functionalities. Feature stores provide useful tools for discovering and understanding the features for machine learning models. Hopsworks can help us understand the whole lineage of the data – where it comes from and how it has been transformed – while Feast focuses on serving the features consistently to models and needs complementing services to be as useful from an explainability point of view.