Browsing by study line "ingen studieinriktning"

At low concentrations, biogenic amines (BA) promote natural physiological activity, but at higher concentrations they can cause a wide variety of health hazards, especially for more sensitive individuals. The BA determination in wine is challenging due to the variation in physicochemical properties and the potential matrix effects of other compounds in the sample. It is important to develop efficient sample purification methods to minimize matrix interference. Derivatization is required for most biogenic amines due to the absence of chromophores. The conditions that promote the origin or formation of biogenic amines in wines are not yet fully understood, as many factors contribute to their formation. The main sources or stages of BA formation during wine-making should be identified in order to reduce BA levels by corrective measures. Currently, the analytical community is striving for more environmentally friendly methods. The literature review examines methods for determination of biogenic amines in wines from 2005 until 2020. The methods are high-performance liquid chromatography, ultra-high-performance liquid chromatography, high-temperature liquid chromatography, nano-liquid chromatography, micellar liquid chromatography, capillary electrophoresis, micromachined capillary electrophoresis, gas chromatography, immunoassay, sensor, colorimetric method, thin-layer chromatography and ion chromatography. The health disadvantages of biogenic amines and the problem areas associated with their determination from a complex wine matrix, such as matrix effect and derivatization, are also surveyed. In addition, changes in the BA profile during different stages of winemaking and storage, as well as the effect of the grape variety and lactic acid bacterial strain on the BA profile, are surveyed. Validation determines the suitability of a method for its intended use. In the methods for determining the literature review, measurement uncertainty - possibly the most important validation parameter - had not been determined in any of the validations. The aim of the research project was to obtain a functional and validated method for the determination of biogenic amines in wines for the Alcohol Control Laboratory at Alko Inc. In the method tested, histamine, tyramine, putrescine, cadaverine, phenylethylamine and isoamylamine derivatized with diethyl ethoxymethylene malonate were determined by high-performance liquid chromatography and diode array detector. The method was not sufficiently reliable, so a competitive enzyme-linked immunosorbent assay for the determination of histamine in wines was introduced, which provided a useful method for the Alcohol Control Laboratory. The validation determined specificity/selectivity, recovery, repeatability, systematic error, estimation of random error, measurement uncertainty, expanded measurement uncertainty, limit of detection and limit of quantification. The European Food Safety Authority has confirmed histamine and tyramine as the most toxic amines. The International Organization of Vine and Wine has not set legal limits for BA levels, but some European countries have had recommended maximum levels for histamine. Many wine importers in the European Union require a BA analysis even in the absence of regulations. Based on the literature review, high BA levels were found in the wines under study, including levels of histamine, tyramine, and phenylethylamine that exceeded the toxicity limits. Some wines had biogenic amines below the detection limit, so the production of low-amine wines is possible. In addition, certain strains of lactic acid bacteria were found to significantly reduce the BA levels in wine. High-performance liquid chromatography is the most widely used determination method. An increasing trend is to develop simpler methods such as the portable sensor-based method.

Estimating the error level of models is an important task in machine learning. If the data used is independent and identically distributed, as is usually assumed, there exist standard methods to estimate the error level. However, if the data distribution changes, i.e., a phenomenon known as concept drift occurs, those methods may not work properly anymore. Most existing methods for detecting concept drift focus on the case in which the ground truth values are immediately known. In practice, that is often not the case. Even when the ground truth is unknown, a certain type of concept drift called virtual concept drift can be detected. In this thesis we present a method called drifter for estimating the error level of arbitrary regres- sion functions when the ground truth is not known. Concept drift detection is a straightforward application of error level estimation. Error level based concept drift detection can be more useful than traditional approaches based on direct distribution comparison, since only changes that affect the error level are detected. In this work we describe the drifter algorithm in detail, including its theoretical basis, and present an experimental evaluation of its performance in virtual concept drift detection on multiple datasets consisting of both synthetic and real-world datasets and multiple regression functions. Our experi- ments show that the drifter algorithm can be used to detect virtual concept drift with a reasonable accuracy.

Social networks represent a public forum of discussion for various topics, some of them controversial. Twitter is such a social network; it acts as a public space where discourse occurs. In recent years the role of social networks in information spreading has increased. As have the fears regarding the increasingly polarised discourse on social networks, caused by the tendency of users to avoid exposure to opposing opinions, while increasingly interacting with only like-minded individuals. This work looks at controversial topics on Twitter, over a long period of time, through the prism of political polarisation. We use the daily interactions, and the underlying structure of the whole conversation, to create daily graphs that are then used to obtain daily graph embeddings. We estimate the political ideologies of the users that are represented in the graph embeddings. By using the political ideologies of users and the daily graph embeddings, we offer a series of methods that allow us to detect and analyse changes in the political polarisation of the conversation. This enables us to conclude that, during our analysed time period, the overall polarisation levels for our examined controversial topics have stagnated. We also explore the effects of topic-related controversial events on the conversation, thus revealing their short-term effect on the conversation as a whole. Additionally, the linkage between increased interest in a topic and the increase of political polarisation is explored. Our findings reveal that as the interest in the controversial topic increases, so does the political polarisation.

Interest towards indoor air quality has increased for several decades from human health perspective. In order to evaluate the quality of indoor air in terms of volatile organic compound (VOC) levels, robust analytical procedures and techniques must be used for indoor air VOC measurements. Since indoor building materials are the greatest source of indoor VOC emissions, same kind of procedures must be used for analysis of emission rates from building materials and their surfaces. Theory part of this thesis reviews background of VOCs and human health, legislation and guideline values, common building materials with emissions and used sampling techniques/approaches for indoor air sampling and surface material emission rate sampling & analysis. Discussed sampling techniques include, for example, material emission test chambers, field and laboratory test emission cells, solid phase microextraction (SPME) fibre applications and Radiello passive samplers. Also new innovative approaches are discussed. Used common analysis instruments are Gas Chromatography (GC) with Mass Spectrometer (MS) or Flame Ionization Detector (FID) for VOCs and High-Performance Liquid Chromatography-Ultraviolet/Visible light detector (HPLC-UV/VIS) for carbonyl VOCs (e.g. formaldehyde) after suitable derivatization. Analytical procedures remain highly ISO 16000 standard series orientated even in recent studies. In addition, potential usage of new modern miniaturized sample collection devices SPME Arrow and In-tube extraction (ITEX) used in experimental part of this thesis are discussed as an addition to indoor air and VOC emission studies. The aim of the experimental part of this thesis was to develop calibrations for selected organic nitrogen compounds with SPME Arrow and ITEX sampling techniques and test the calibration with indoor and outdoor samples. A calibration was successfully carried out with SPME Arrow (MCM-41 sorbent), ITEX (MCM-TP sorbent) and ITEX (Polyacrylonitrile (PAN) 10 % sorbent) with permeation system combined with GC-MS for the following selected organic nitrogen compounds: triethylamine, pyridine, isobutyl amine, allylamine, trimethylamine, ethylenediamine, dipropyl amine, hexylamine, 1,3-diaminopropane, 1-methyl-imidazole, N, N-dimethylformamide, 1,2-diaminocyclohexane, 1-nitropropane and formamide. The overall quality of the calibration curves was evaluated, and the calibrations were compared in terms of linear range, relative standard deviation (RSD) % for accepted calibration levels and obtained Limits of Detection (LOD) values. Also, ways to improve the calibrations were discussed. The calibration curves were tested with real indoor and outdoor samples and quantitative, as well as semi-quantitative, results were obtained.

The rapid growth of artificial intelligence (AI) and machine learning (ML) solutions has created a need to develop, deploy and maintain AI/ML those to production reliably and efficiently. MLOps (Machine Learning Operations) framework is a collection of tools and practices that aims to address this challenge. Within the MLOps framework, a concept called the feature store is introduced, serving as a central repository responsible for storing, managing, and facilitating the sharing and reuse of extracted features derived from raw data. This study gives first an overview of the MLOps framework and delves deeper into feature engineering and feature data management, and explores the challenges related to these processes. Further, feature stores are presented, what they exactly are and what benefits do they introduce to organizations and companies developing ML solutions. The study also reviews some of the currently popular feature store tools. The primary goal of this study is to provide recommendations for organizations to leverage feature stores as a solution to the challenges they encounter in managing feature data currently. Through an analysis of the current state-of-the-art and a comprehensive study of organizations' practices and challenges, this research presents key insights into the benefits of feature stores in the context of MLOps. Overall, the thesis highlights the potential of feature stores as a valuable tool for organizations seeking to optimize their ML practices and achieve a competitive advantage in today's data-driven landscape. The research aims to explore and gather practitioners' experiences and opinions on the aforementioned topics through interviews conducted with experts from Finnish organizations.

Many beyond the Standard Model theories include a first order phase transition in the early universe. A phase transition of this kind is presumed to be able to source gravitational waves that might be be observed with future detectors, such as the Laser Interferometer Space Antenna. A first order phase transition from a symmetric (metastable) minimum to the broken (stable) one causes the nucleation of broken phase bubbles. These bubbles expand and then collide. It is of importance to examine how the bubbles collide in depth, as the events during the collision affect the gravitational wave spectrum. We assume the field to interact very weakly or not at all with the particle fluid in the early universe. The universe also experiences fluctuations due to thermal or quantum effects. We look into how these background fluctuations affect the field evolution and bubble collisions during the phase transition in O(N) scalar field theory. Specifically, we numerically simulate two colliding bubbles nucleated on top of the background fluctuations, with the field being a N-dimensional vector in the O(N) group. Due to the symmetries present, the system can be examined in cylindrical coordinates, lowering the number of simulated spatial dimensions. In this thesis, we perform the calculation of initial state fluctuations and simulate them and two bubbles numerically. We present results of the simulation of the field, concentrating on the effects of fluctuations on the O(N) scalar field theory.

Investment funds are continuously looking for new technologies and ideas to enhance their results. Lately, with the success observed in other fields, wealth managers are taking a closes look at machine learning methods. Even if the use of ML is not entirely new in finance, leveraging new techniques has proved to be challenging and few funds succeed in doing so. The present work explores de usage of reinforcement learning algorithms for portfolio management for the stock market. It is well known the stochastic nature of stock and aiming to predict the market is unrealistic; nevertheless, the question of how to use machine learning to find useful patterns in the data that enable small market edges, remains open. Based on the ideas of reinforcement learning, a portfolio optimization approach is proposed. RL agents are trained to trade in a stock exchange, using portfolio returns as rewards for their RL optimization problem, thus seeking optimal resource allocation. For this purpose, a set of 68 stock tickers in the Frankfurt exchange market was selected, and two RL methods applied, namely Advantage Actor-Critic(A2C) and Proximal Policy Optimization (PPO). Their performance was compared against three commonly traded ETFs (exchange-traded funds) to asses the algorithm's ability to generate returns compared to real-life investments. Both algorithms were able to achieve positive returns in a year of testing( 5.4\% and 9.3\% for A2C and PPO respectively, a European ETF (VGK, Vanguard FTSE Europe Index Fund) for the same period, reported 9.0\% returns) as well as healthy risk-to-returns ratios. The results do not aim to be financial advice or trading strategies, but rather explore the potential of RL for studying small to medium size stock portfolios.

Lenders assess the credit risk of loan applicants from both affordability and indebtedness perspective. The affordability perspective involves assessing the applicant’s disposable income after accounting for regular household expenditures and existing credit commitments, a measure called money-at-disposal or MaD. Having an estimate of the applicant’s expenditures is crucial, but simply asking applicants for their expenditures could lead to inaccuracies. Thus, lenders must produce their own estimates based on statistical or survey data about household expenditures, which are then passed to the MaD framework as input parameters or used as control limits to ascertain expenditure information reported by the applicant is truthful or at least adequately conservative. More accurate expenditure estimates in the loan origination would enable lenders to quantify mortgage credit risk more precisely, tailor loan terms more aptly, and protect customers against over-indebtedness better. Consequently, this would facilitate the lenders to be more profitable in their lending business as well as serve their customers better. But there is also a need for interpretability of the estimates stemming from compliance and trustworthiness motives. In this study, we examine the accuracy and interpretability of expenditure predictions of supervised models fitted to a microdataset of household consumption expenditures. To our knowledge, this is the first study to use such a granular and broad dataset to create predictive models of loan applicants’ expenditures. The virtually uninterpretable "black box" models we used, aiming at maximizing predictive power, rarely did better accuracy-wise than interpretable linear regression ones. Even when they did, the gain was marginal or in predicting minor expenditure categories that contributed only a low share of the total expenditures. Thus, ordinary linear regression is what we suggest generally provides the best combination of predictive power and interpretability. After careful feature selection, the best predictive power was attained with 20-54 predictor variables, the number depending on the expenditure category. If a very simple interpretation is needed, we suggest either a linear regression model of three predictor variables representing the number of household members, or a model based on the means within 12 "common sense groups" that we divided the households in. An alternative solution with a predictive power somewhere between the full linear regression model and the two simpler models is to use decision trees providing easy interpretation in the form of a set of rules.

Resonant inelastic X-ray scattering (RIXS) is one of the most powerful synchrotron based methods for attaining information of the electronic structure of materials. Novel ultra-brilliant X-ray sources, X-ray free electron lasers (XFEL), offer new intriguing possibilities beyond the traditional synchrotron based techniques facilitating the transition of X-ray spectroscopic methods to the nonlinear intensity regime. Such nonlinear phenomena are well known in the optical energy range, less so in X-ray energies. The transition of RIXS to the nonlinear region could have significant impact on X-ray based materials research by enabling more accurate measurements of previously observed transitions, allowing the detection of weakly coupled transitions on dilute samples and possibly uncovering completely unforeseen information or working as a platform for novel intricate methods of the future. The nonlinear RIXS or stimulated RIXS (SRIXS) on XFEL has already been demonstrated in the simplest possible proof of concept case. In this work a comprehensive introduction to SRIXS is presented from a theoretical point of view starting from the very beginning, thus making it suitable for anyone with the basic understanding of quantum mechanics and spectroscopy. To start off, the principles of many body quantum mechanics are revised and the configuration interactions method for representing molecular states is introduced. No previous familiarity with X-ray matter interaction or RIXS is required as the molecular and interaction Hamiltonians are carefully derived, based on which a thorough analysis of the traditional RIXS theory is presented. In order to stay in touch with the real world, the basic experimental facts are recapped before moving on to SRIXS. First, an intuitive picture of the nonlinear process is presented shedding some light onto the term \textit{stimulated} while introducing basic terminology and some X-ray pulse schemes along with futuristic theoretical examples of SRIXS experiments. After this, a careful derivation of the Maxwell-Liouville-von Neumann theory up to quadrupole order is presented for the first time ever. Finally, the chapter is concluded with a short analysis of the experimental status quo on XFELs and some speculation on possible transition metal samples where SRIXS in its current state could be applied to observe quadrupole transitions advancing the field remarkably.

Background: Electroencephalography (EEG) depicts electrical activity in the brain, and can be used in clinical practice to monitor brain function. In neonatal care, physicians can use continuous bedside EEG monitoring to determine the cerebral recovery of newborns who have suffered birth asphyxia, which creates a need for frequent, accurate interpretation of the signals over a period of monitoring. An automated grading system can aid physicians in the Neonatal Intensive Care Unit by automatically distinguishing between different grades of abnormality in the neonatal EEG background activity patterns. Methods: This thesis describes using support vector machine as a base classifier to classify seven grades of EEG background pattern abnormality in data provided by the BAby Brain Activity (BABA) Center in Helsinki. We are particularly interested in reconciling the manual grading of EEG signals by independent graders, and we analyze the inter-rater variability of EEG graders by building the classifier using selected epochs graded in consensus compared to a classifier using full-duration recordings. Results: The inter-rater agreement score between the two graders was κ=0.45, which indicated moderate agreement between the EEG grades. The most common grade of EEG abnormality was grade 0 (continuous), which made up 63% of the epochs graded in consensus. We first trained two baseline reference models using the full-duration recording and labels of the two graders, which achieved 71% and 57% accuracy. We achieved 82% overall accuracy in classifying selected patterns graded in consensus into seven grades using a multi-class classifier, though this model did not outperform the two baseline models when evaluated with the respective graders’ labels. In addition, we achieved 67% accuracy in classifying all patterns from the full-duration recording using a multilabel classifier.