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Browsing by study line "Mathematics / Computer and data science / Physics / Chemistry"

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  • Tanskanen, Ville (2020)
    Microbial volatile organic compounds are emitted by diverse set of microbial organisms and they are known to cause health hazards when present in indoor air. Early detection of fungal contaminated buildings and species present is crucial to prevent health problems caused by fungal secondary metabolites. This thesis focuses on analysing emission profiles of different insulation materials and fungal cultures, which allows, in further studies, to develop efficient new ways to detect fungi from contaminated buildings. Studied insulation materials consisted of cellulose and glass wool, which were analysed in multiple different conditions. Humidity of atmosphere was varied between 0-10 microliters and temperature was varied between 30°C and 40°C. In fungal emission profile study 24 different cultures were analysed in two different atmospheres, ambient and micro- aerophilic, and in multiple different inoculums. Analysis for both insulation materials and fungal cultures was done using headspace solid phase microextraction Arrow -tool and headspace in tube extraction –tool together with gas chromatography – mass spectrometry. One goal for this thesis was also test suitability of these methods for detection of fungal secondary metabolites. Comprehensive fungal emission profiles were successfully formed and new information from behaviour of insulation materials in different settings was found. In addition, new information about analysis methods and fungal behaviour in different atmospheres was found. Headspace solid phase microextraction Arrow with gas chromatography – mass spectrometry was found to be efficient, sensitive and timesaving method for indoor air study purposes. There were also many potential fungal culture specific biomarker compounds found for further study purposes.
  • Barin Pacela, Vitória (2021)
    Independent Component Analysis (ICA) aims to separate the observed signals into their underlying independent components responsible for generating the observations. Most research in ICA has focused on continuous signals, while the methodology for binary and discrete signals is less developed. Yet, binary observations are equally present in various fields and applications, such as causal discovery, signal processing, and bioinformatics. In the last decade, Boolean OR and XOR mixtures have been shown to be identifiable by ICA, but such models suffer from limited expressivity, calling for new methods to solve the problem. In this thesis, "Independent Component Analysis for Binary Data", we estimate the mixing matrix of ICA from binary observations and an additionally observed auxiliary variable by employing a linear model inspired by the Identifiable Variational Autoencoder (iVAE), which exploits the non-stationarity of the data. The model is optimized with a gradient-based algorithm that uses second-order optimization with limited memory, resulting in a training time in the order of seconds for the particular study cases. We investigate which conditions can lead to the reconstruction of the mixing matrix, concluding that the method is able to identify the mixing matrix when the number of observed variables is greater than the number of sources. In such cases, the linear binary iVAE can reconstruct the mixing matrix up to order and scale indeterminacies, which are considered in the evaluation with the Mean Cosine Similarity Score. Furthermore, the model can reconstruct the mixing matrix even under a limited sample size. Therefore, this work demonstrates the potential for applications in real-world data and also offers a possibility to study and formalize identifiability in future work. In summary, the most important contributions of this thesis are the empirical study of the conditions that enable the mixing matrix reconstruction using the binary iVAE, and the empirical results on the performance and efficiency of the model. The latter was achieved through a new combination of existing methods, including modifications and simplifications of a linear binary iVAE model and the optimization of such a model under limited computational resources.
  • Muiruri, Dennis (2021)
    Ubiquitous sensing is transforming our societies and how we interact with our surrounding envi- ronment; sensors provide large streams of data while machine learning techniques and artificial intelligence provide the tools needed to generate insights from the data. These developments have taken place in almost every industry sector with topics such as smart cities and smart buildings becoming key topical issues as societies seek more sustainable ways of living. Smart buildings are the main context of this thesis. These are buildings equipped with various sensors used to collect data from the surrounding environment allowing the building to adapt itself and increasing its operational efficiency. Previously, most efforts in realizing smart buildings have focused on energy management and au- tomation where the goal is to improve costs associated with heating, ventilation, and air condi- tioning. A less studied area involves smart buildings and their indoor environments especially relative to sub-spaces within a building. Increased developments in low-cost sensor technologies have created new opportunities to sense indoor environments in more granular ways that provide new possibilities to model finer attributes of spaces within a building. This thesis focuses on modeling indoor environment data obtained from a multipurpose building that serves primarily as a school. The aim is to explore the quality of the indoor environment relative to regulatory guidelines and also exploring suitable predictive models for thermal comfort and indoor air quality. Additionally, design science methodology is applied in the creation of a proof of concept software system. This system is aimed at demonstrating the use of Web APIs to provide sensor data to clients that may use the data to render analytics among other insights to a building’s stakeholders. Overall, the main technical contributions of this thesis are twofold: (i) a potential web-application design for indoor air quality IoT data and (ii) an exposition of modeling of indoor air quality data based on a variety of sensors and multiple spaces within the same building. Results indicate a software-based tool that supports monitoring the indoor environment of a building would be beneficial in maintaining the correct levels of various indoor parameters. Further, modeling data from different spaces within the building shows a need for heterogeneous models to predict variables in these spaces. This implies parameters used to predict thermal comfort and air quality are different in varying spaces especially where the spaces differ in size, indoor climate control settings, and other attributes such as occupancy control.
  • Partovi, Fariba (2021)
    Utilization of pesticides in the modern agriculture is often indispensable for gaining good crops. However, pesticides are abundantly being used in too hight quantities which leads to potential health risks for the consumers. Currently there are no pre-screening methods for monitoring the levels of pesticides in food, but only a negligible small percentage of all goods are being tested using the laborious standardized methods. This master’s thesis is an investigation, that was carried out in the wet laboratory of KARSA Oy Ltd, on 10 different pesticides: Glyphosate, Thiabendazole, 2-phenylphenol, Chlorpyrifos, Fludioxonil, Chlormequat, Bupirimate, Diflubenzuron, Fenpyrazamine and 2,4-dichlorophenoxyacetic acid. Pesticides were ionized using straight radiation chemical ionization (SRCI) in positive and negative modes without any added reagent and also using bromide, nitrate, acetonylacetone and acetone as reagents. Charged target molecules and adducts were detected using Thermo fisher Iontrap/Orbitrap (LTQ Orbitrap velos pro upgraded) mass spectrometer. After the initial method development and scoping measurements pesticides were studied both individually and as a mixture of all 10 pesticides. Sample solutions were first injected with syringe so that the solvent and targets evaporated at the same time inside the desorber heating block of SRCI inlet. In these syringe injection measurements, the desorber temperature was set at 150 °C. Mass range at 125–750 has been used for all the pesticides except for Chlormequat (100–750). After the syringe injection measurements, the mixture of 10 pesticides was analysed by TCM filters. Target solutions of 1 µl volume were placed on filters and after the solvents had evaporated the filters were heated from room temperature to 245°C using the same setup as with the syringe injections. In conclusion, with syringe injections 7 pesticides out of 10 were detected using positive and negative mode without any added reagent. The highest target intensities were recorded from TCM filters. Overall, applying the SRCI-Orbitrap setup for pesticide pre-screening from target solutions resulted in the detection of 9 pesticides out of 10.