This thesis encompasses two research fields in astronomy: astrometry and astronomy education and they are discussed in two parts. These parts represent two sides of a coin; astrometry, which is about constructing 3D representations of the Universe, and AER, where for this thesis, the goal is to investigate university students’ and lecturers’ disciplinary discernment vis-à-vis the structure of the Universe and extrapolating three-dimensionality. Part I presents an investigation of stellar surface structures influence on ultra-high-precision astrometry. The expected effects in different regions of the HR-diagram were quantified. I also investigated the astrometric effect of exoplanets, since astrometric detection will become possible with projects such as Gaia. Stellar surface structures produce small brightness variations, influencing integrated properties such as the total flux, radial velocity and photocenter position. These properties were modelled and statistical relations between the variations of the different properties were derived. From the models it is clear that for most stellar types the astrometric jitter due to stellar surface structures is expected to be of order 10 μAU or greater. This is more than the astrometric displacement typically caused by an Earth-sized exoplanet in the habitable zone, which is about 1–4 μAU, making astrometric detection difficult. Part II presents an investigation of disciplinary discernment at the university level. Astronomy education is a particularly challenging experience for students because discernment of the ‘real’ Universe is problematic, making interpretation of the many disciplinary-specific representations used an important educational issue. The ability to ‘fluently’ discern the disciplinary affordances of these representations becomes crucial for the effective learning of astronomy. To understand the Universe I conclude that specific experiences are called. Simulations could offer these experiences, where parallax motion is a crucial component. In a qualitative study, I have analysed students’ and lecturers’ discernment while watching a simulation video, and found hierarchies that characterize the discernment in terms of three-dimensionality extrapolation and an Anatomy of Disciplinary Discernment. I combined these to define a new construct: Reading the Sky. I conclude that this is a vital competency needed for learning astronomy and suggest strategies for how to implement this in astronomy education.

This thesis encompasses two research fields in astronomy: astrometry and astronomy education and they are discussed in two parts. These parts represent two sides of a coin; astrometry, which is about constructing 3D representations of the Universe, and AER, where for this thesis, the goal is to investigate university students’ and lecturers’ disciplinary discernment vis-à-vis the structure of the Universe and extrapolating three-dimensionality. Part I presents an investigation of stellar surface structures influence on ultra-high-precision astrometry. The expected effects in different regions of the HR-diagram were quantified. I also investigated the astrometric effect of exoplanets, since astrometric detection will become possible with projects such as Gaia. Stellar surface structures produce small brightness variations, influencing integrated properties such as the total flux, radial velocity and photocenter position. These properties were modelled and statistical relations between the variations of the different properties were derived. From the models it is clear that for most stellar types the astrometric jitter due to stellar surface structures is expected to be of order 10 μAU or greater. This is more than the astrometric displacement typically caused by an Earth-sized exoplanet in the habitable zone, which is about 1–4 μAU, making astrometric detection difficult. Part II presents an investigation of disciplinary discernment at the university level. Astronomy education is a particularly challenging experience for students because discernment of the ‘real’ Universe is problematic, making interpretation of the many disciplinary-specific representations used an important educational issue. The ability to ‘fluently’ discern the disciplinary affordances of these representations becomes crucial for the effective learning of astronomy. To understand the Universe I conclude that specific experiences are called. Simulations could offer these experiences, where parallax motion is a crucial component. In a qualitative study, I have analysed students’ and lecturers’ discernment while watching a simulation video, and found hierarchies that characterize the discernment in terms of three-dimensionality extrapolation and an Anatomy of Disciplinary Discernment. I combined these to define a new construct: Reading the Sky. I conclude that this is a vital competency needed for learning astronomy and suggest strategies for how to implement this in astronomy education.

This thesis is concerned with the problem of predicting the remaining part of the trajectory of a golf ball as it travels through the air where only the three-dimensional position of the ball is captured. The approach taken to solve this problem relied on recurrent neural networks in the form of the long short-term memory networks (LSTM). The motivation behind this choice was that this type of networks had led to state-of-the-art performance for similar problems such as predicting the trajectory of pedestrians. The results show that using LSTMs led to an average reduction of 36.6 % of the error in the predicted impact position of the ball, compared to previous methods based on numerical simulations of a physical model, when the model was evaluated on the same driving range that it was trained on. Evaluating the model on a different driving range than it was trained on leads to improvements in general, but not for all driving ranges, in particular when the ball was captured at a different frequency compared to the data that the model was trained on. This problem was solved to some extent by retraining the model with small amounts of data on the new driving range. / Detta examensarbete har studerat problemet att förutspå den fullständiga bollbanan för en golfboll när den flyger i luften där endast den tredimensionella positionen av bollen observerades. Den typ av metod som användes för att lösa problemet använde sig av recurrent neural networks, i form av long short-term memory nätverk (LSTM). Motivationen bakom detta var att denna typ av nätverk hade lett till goda resultatet för liknande problem. Resultatet visar att använda sig av LSTM nätverk leder i genomsnitt till en 36.6 % förminskning av felet i den förutspådda nedslagsplatsen för bollen jämfört mot tidigare metoder som använder sig av numeriska simuleringar av en fysikalisk modell, om modellen användes på samma golfbana som den tränades på. Att använda en modell som var tränad på en annan golfbana leder till förbättringar i allmänhet, men inte om modellen användes på en golfbana där bollen fångades in med en annan frekvens. Detta problem löstes till en viss mån genom att träna om modellen med lite data från den nya golfbanan.

The aim of this Master's thesis is to describe the past and future demographic development of selected EU countries. The second aim is to utilize methods for multicriteria evaluation of alternatives in the demography. The last aim is to describe methods used to extrapolate mortality curves. The theoretical part describes demographic indicators, methods used to extrapolate mortality curves and computational processes for methods for multicriteria evaluation of alternatives. The practical part includes the computation of demographic indicators and describes the progress of the indicators in the future. The methods for multicriteria evaluation of alternatives are used for ranking selected EU countries according to the demographic indicators. The last part of the thesis includes the application of methods used for extrapolating mortality curves and compares the achieved results with the actual progress.