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In software configuration management, branching is a common practice, which can enable efficient parallel development between developers and teams. However, the developers might not be aware of the different branching practice options and how to exactly formulate a branching strategy. This could lead to an opposite effect towards productivity, and other issues as well. The focus of this thesis is in what branching practices are considered as beneficial, what affects their usability, what risks are involved, and how to plan these practices in a structured manner. There are plenty of branching practices presented in the literature, which can either complement each other or be completely incompatible. A lot of the practices' beneficiality depends on the surrounding context, such as the tools in use and project characteristics. The most relevant risk to branching is merge conflicts, but there are other risks as well. The approaches for planning a branching strategy, however, are found to be too narrow in the reviewed literature. Thus, Branching Strategy Formulation and Analysis Method (BSFAM) is proposed to help teams and organizations plan their branching strategy in a structured manner. Additionally, the issues of branching are explored in the context of an organization that has multiple concurrent projects ongoing for a single product. Information on this is gathered through a survey, semi-structured interviews, and available documentation. The issues that were found can be attributed to a lack of proper base strategy, difficulties in coordination and awareness, and test automation management in relation to branching. The proposed method is then applied in that same context in order to provide solutions to the organization's issues, and to provide an example case. BSFAM will be taken into use in upcoming projects in the organization, and it will be improved if necessary. If the proposed method is to be adopted more widely and its resulting information published, it could provide further research towards how different branching practices fit in different contexts. Additionally, it could help in new, generally better, branching practices to emerge.

ALICE (A Large Ion Collider Experiment) is an experiment at CERN (European Organization for Nuclear Research), where a heavy-ion detector is dedicated to exploit the unique physics potential of nucleus-nucleus interactions at LHC (Large Hadron Collider) energies. In a part of that project, 716 so-called type V4 modules were assembles in Detector Laboratory of Helsinki Institute of Physics during the years 2004 - 2006. Altogether over a million detector strips has made this project the most massive particle detector project in the science history of Finland. One ALICE SSD module consists of a double-sided silicon sensor, two hybrids containing 12 HAL25 front end readout chips and some passive components, such has resistors and capacitors. The components are connected together by TAB (Tape Automated Bonding) microcables. The components of the modules were tested in every assembly phase with comparable electrical tests to ensure the reliable functioning of the detectors and to plot the possible problems. The components were accepted or rejected by the limits confirmed by ALICE collaboration. This study is concentrating on the test results of framed chips, hybrids and modules. The total yield of the framed chips is 90.8%, hybrids 96.1% and modules 86.2%. The individual test results have been investigated in the light of the known error sources that appeared during the project. After solving the problems appearing during the learning-curve of the project, the material problems, such as defected chip cables and sensors, seemed to induce the most of the assembly rejections. The problems were typically seen in tests as too many individual channel failures. Instead, the bonding failures rarely caused the rejections of any component. One sensor type among three different sensor manufacturers has proven to have lower quality than the others. The sensors of this manufacturer are very noisy and their depletion voltage are usually outside of the specification given to the manufacturers. Reaching 95% assembling yield during the module production demonstrates that the assembly process has been highly successful.

The human brain is divided into left and right hemisphere, and there are functional differences between the hemispheres. A hemispheric difference is called the lateralization of the brain function, and the degree of lateralization is described by the laterality index. The most investigated domain of the lateralized brain functions is language, which is a left hemisphere dominant function in the majority of the population. Functional magnetic resonance imaging provides a noninvasive method for studying the brain functions indirectly through the bloodoxygenation-level-dependent effect. The language-related functional magnetic resonance imaging can be used in the localization of the Broca’s speech area and determination of the dominant hemisphere in epileptic patients. The purpose of this thesis is to assess a method for calculating the laterality index from functional magnetic resonance imaging data. The data is acquired during three language task paradigms with five subjects and analyzed statistically. The methods used for the laterality index calculations are reviewed, and a new calculation method is presented. Result tables of laterality indices and hemispheric dominances per used regions of interest are generated. The presented laterality index calculation method successfully determined the speech laterality of three subjects out of five as a left hemispheric dominance. The language laterality of two subjects was not successful due to corrupted functional data and contradicted results between different paradigms. The major source of error is the subject’s head motion during the functional imaging. Together with the information about the head motion’s extent, the generated table could provide relevant extra information to epileptic patients’ functional magnetic resonance imaging data and could serve for clinical purposes in the future.

Norway spruce (Picea abies (L.) Karst.) is one of the economically most important tree species in Finland. It is known to be drought-sensitive species and expected to suffer from the warming climate. In addition, warmer temperatures benefit pest insect Eurasian spruce bark beetle (Ips typographus L.) and pathogen Heterobasidion parviporum, which both use Norway spruce as their host and can make the future of Norway spuce in Finland even more difficult. In this thesis, adult Norway spruce mortality was studied from false colour aerial photographs taken in years between 2010 and 2021. Dead trees were detected from the photos by visual inspection, and mortality was calculated based on the difference in the number of dead trees in the photos from different years. The aim was to find out if Norway spruce mortality in Finland had increased over time, and what were the factors that had been driving tree mortality. The results indicate that tree mortality was the highest in the last third of the studied 10-year period, so it was concluded that tree mortality had increased over time. Various possible tree mortality drivers were analysed and found to be connected to tree mortality. Each driver was analysed individually by testing correlation with tree mortality. In addition, linear regression analysis and segmented linear regression with one breakpoint were used with the continuous variables. Increased tree mortality correlated with higher stand mean age, mean height, mean diamater, and mean volume, supporting the findings in earlier research. Mortality was connected to the proportion of different tree species in the stand: the higher the proportion of spruce, the higher the mortality, and the higher the proportion of deciduous trees, the lower the mortality. Of different fertility classes, tree mortality was the highest in the second most fertile class, herb-rich heat forest, and mortality decreased with decreasing fertility. Dead trees were also found to be located closer to stand edges than the stand centroid. Increased temperature resulted in increased mortality. Increased vapour pressure deficit (VPD) and drought, which was analysed with Standardized Precipitation Evapotranspiration Index (SPEI) of different time scales, were also connected with increased tree mortality. Further research is required for understanding and quantifying the joint effect of all the interacting mortality drivers. Nevertheless, it seems that for Norway spruce, the warmer future with increased mortality is already here, and it should be taken into consideration in forest management. Favouring mixed stands could be one of the solutions to help Norway spruce survive in the warming climate.

Piperine represents the major plant alkaloid encountered in various Piperaceae species and has received in recent years considerable attention because of its broad range of favorable biological and pharmacological activities, including antioxidant, immunostimulant, bioavailability-enhancing and anti-carcinogenic properties. The literature part of this thesis gives a selective overview of advanced methods for the quantitative analysis of piperine in plant-base materials, and various approaches employed for instrumental analysis, including spectroscopic, chromatographic, and electrochemical techniques. An effort was made to evaluate the potential of the reported methods based on the analytical figures of merit, such as total sample throughput capacity, analytical range, precision, accuracy, limit of detection and limit of quantification. The objective of the experimental part of the thesis focused on the development of a convenient, robust, simple, efficient and reliable method to quantify piperine in pepper fruits. The analytical method established in this thesis involves liberation of piperine by continuous liquid extraction of ground pepper fruits with methanol, and cleanup of the crude extracts with reversed phase solid phase extraction. Analyte quantitation was accomplished using gradient reversed phase High Performance Liquid Chromatography with mass spectrometric detection, using Electrospray Ionization-Ion Trap Mass Spectrometry. To enable reliable internal standardization, deuterium labelled piperine surrogate (piperine-D10) was synthesized from piperine in three steps in a reasonable overall yield (65 %) and standard-level purity (99.7 %). It may be worth mentioning that the commercial market value of the amount of piperine-D10 synthesized in-house exceeds 167,400 euros. One of the major challenges encountered during the development and optimization of the analytical method was the extreme photosensitivity of piperine and piperine-D10, both suffering in solution extensive photoisomerization upon exposure to ambient light within matter of minutes. This issue was addressed by carrying out all tasks associated with synthesis, sample preparation and analytical measurements under dark conditions. For the preparation of calibrators, a fully automated procedure was developed, being controlled by custom-written injector programs and executed in the light-protected sample compartment of a conventional autosampler module. In terms of merits, the developed analytical method offers good sample throughput capacity (run time 20 min, retention time 8.2 min), excellent selectivity and high sensitivity (Limit of Detection= 0.012 ppm, Limit of Quantification= 0.2 ppm). The method is applicable over a linear range of 0.4 to 20 ng of injected mass (r2= 0.999). The stability of standards and fully processed samples was found to be excellent, with less than 5% of variations in concentrations occurring after a 3-week (calibrators) or 4-month (samples) storage at 4 °C and 23 °C respectively, under dark conditions. Intra-day repeatability were better than 2.95 %. Preliminary validation data also suggest satisfactory inter-operator reproducibility. To test the applicability of the developed LC-MS method, it was employed to quantify piperine in a set of 15 pepper fruit samples, including black, white, red and green varieties of round and long peppers, purchased from local markets and retailers. The piperine contents obtained were in the range of 17.28 to 56.25 mg/g (piperine/minced sample) and generally in good agreement with the values reported in the scientific literature. It is justified to assume that the developed analytical method may directly be applicable to the quantitation of related pepper alkaloids in herbal commodities, and after some modifications in the sample preparation strategy, also for the monitoring of piperine in biological fluids, such as serum and urine.

Lipids can be found in all living organisms, and complex lipids are typically determined from biological samples and food products. Samples are usually prepared prior to analysis. Liquid-liquid extraction (LLE) is obviously the most often used technique to isolate lipids. Two fundamental protocols are Folch and Bligh & Dyer methods. In both methods, the extraction is based on lipid partitioning between chloroform and water-methanol phases. Methyl-tert-butyl ether offers an environmentally friendly alternative to chloroform. Total lipid fraction can be further separated by solid-phase extraction. Complex lipids are typically isolated from other lipid species with silica SPE cartridges. Three main techniques used in quantitative determination of complex lipids are thin layer chromatography (TLC), high performance liquid chromatography (HPLC) and direct infusion mass spectrometry (MS). Thin layer chromatography is a traditional technique, but its applicability is limited due to poor resolution and requirement of post-column derivatization. Instead, HPLC provides an efficient separation and it is easily coupled with several detectors. HPLC methods are the most commonly used in lipid analysis. Direct infusion mass spectrometry is the incoming technique. Lipid molecules can be precisely identified during the fast measurement. Other advantages are excellent selectivity and sensitivity. New method for glycolipids was developed during the experimental period. Glycolipids were isolated from bio oil samples using solid phase extraction cartridges. Normal phase liquid chromatography was utilized to separate glycolipids, and detection was carried out with parallel tandem mass spectrometry (MS/MS) and evaporative light scattering detection (ELSD). Quantification was based on ELSD measurements, whereas MS/MS was adopted to confirm the identification. Developed method was validated and following parameters were determined: linearity, trueness, precision, measurement uncertainty, detection and quantification limits. Precisions were successful and they were mainly between 5-15 %. Trueness results were however more undesired, because measured concentrations were typically higher than theoretical concentrations. Results were dependent on analyte, but generally they varied between 66 % and even 194 %. Validation pointed out that method needs further development. Mass spectrometric quantification can be considered, if appropriate internal standards would be available.

Quantum computing has an enormous potential in machine learning, where problems can quickly scale to be intractable for classical computation. Quantum machine learning is research area that combines the interplay of ideas from quantum computing and machine learning. Powerful and useful machine learning is dependent on having large-scale datasets used to train the models to be able to solve real-life problems. Currently, quantum machine learning lacks a plethora of large-scale quantum datasets required to further develop the models and test the quantum machine learning algorithms. Lack of large datasets is currently limiting the quantum advantage in the field of quantum machine learning. In this thesis, the concept of quantum data and different types of applied quantum datasets used to develop quantum machine learning models is studied. The research methodology is based on a systematic and comparative literature review of the state of the art articles in quantum computing and quantum machine learning in the recent years. We classify datasets into inherent and non-inherent quantum data based on the nature of the data. The preliminary literature review addresses patterns in the applied quantum machine learning. Testing and benchmarking QML models primarily uses non-inherent quantum data, or classical data encoded into the quantum system, while separate research is focused on generating inherent quantum datasets.

Using quantum algorithms to carry out ML tasks is what is known as Quantum Machine Learning (QML) and the methods developed within this field have the potential to outperform their classical counterparts in solving certain learning problems. The development of the field is partly dependent on that of a functional quantum random access memory (QRAM), called for by some of the algorithms devised. Such a device would store data in a superposition and could then be queried when algorithms require it, similarly to its classical counterpart, allowing for efficient data access. Taking an axiomatic approach to QRAM, this thesis provides the main considerations, assumptions and results regarding QRAM and yields a QRAM handbook and comprehensive introduction to the literature pertaining to it.

The measurement of quantum states has been a widely studied problem ever since the discovery of quantum mechanics. In general, we can only measure a quantum state once as the measurement itself alters the state and, consequently, we lose information about the original state of the system in the process. Furthermore, this single measurement cannot uncover every detail about the system's state and thus, we get only a limited description of the system. However, there are physical processes, e.g., a quantum circuit, which can be expected to create the same state over and over again. This allows us to measure multiple identical copies of the same system in order to gain a fuller characterization of the state. This process of diagnosing a quantum state through measurements is known as quantum state tomography. However, even if we are able to create identical copies of the same system, it is often preferable to keep the number of needed copies as low as possible. In this thesis, we will propose a method of optimising the measurements in this regard. The full description of the state requires determining multiple different observables of the system. These observables can be measured from the same copy of the system only if they commute with each other. As the commutation relation is not transitive, it is often quite complicated to find the best way to match the observables with each other according to these commutation relations. This can be quite handily illustrated with graphs. Moreover, the best way to divide the observables into commuting sets can then be reduced to a well-known graph theoretical problem called graph colouring. Measuring the observables with acceptable accuracy also requires measuring each observable multiple times. This information can also be included in the graph colouring approach by using a generalisation called multicolouring. Our results show that this multicolouring approach can offer significant improvements in the number of needed copies when compared to some other known methods.

Interfaces of solid and liquid helium exhibit many physical phenomena. At very low temperatures the solid-liquid interface becomes mobile enough to allow a periodic melting-freezing wave to pro-pagate along the surface. These crystallization waves were experimentally confirmed in ^4He decades ago, but in ^3He they are only observable at extremely low temperatures (well below 0.5 mK). This presents a difficult technical challenge to create a measurement scheme with very low dissipation. We have developed a method to use a quartz tuning fork to probe oscillating helium surfaces. These mechanical oscillators are highly sensitive to interactions with the surrounding medium, which makes them extremely accurate sensors of many material properties. By tracking the fork's resonant frequency with two lock-in amplifiers, we have been able to attain a frequency resolution below 1 mHz. The shift in resonant frequency can then be used to calculate the corresponding change in surface level, if the interaction between the fork and the helium surface is understood. One of the main goals of this thesis was to create interaction models that could provide quantitative estimates for the calculations. Experimental results suggest that the liquid-vapour surface forms a column of superfluid that is suspended from the tip of the fork. Due to the extreme wetting properties of superfluids, the fork is also coated with a thin (∼ 300 Å) layer of helium. The added mass from this layer depends on the fork-surface distance. Oscillations of the surface level thus cause periodic change in the effective mass of the fork, which in turn modulates the resonant frequency. For the solid-liquid interface the interaction is based on the inviscid flow of superfluid around the moving fork. The added hydrodynamic mass increases when the fork oscillates closer to the solid surface. Crystallization waves below the fork will thus change the fork's resonant frequency. We were able to excite gravity-capillary and crystallization waves in ^4He with a bifilarly wound capacitor. Using the quartz tuning fork detection scheme we measured the spectrum of both types of waves at 10 mK. According to the interaction models developed in this thesis, the surface level resolution of this method was ∼ 10 μm for the gravity-capillary waves and ∼ 1 nm for the crystallization waves. Thanks to the low dissipation (∼ 20 pW) of the measurement scheme, our method is directly applicable in future ^3He experiments.