Developments in computer vision has sought to design deep neural networks which trained on a large set of images are able to generate high quality artificial images which share semantic qualities with the original image set. A pivotal shift was made with the introduction of the generative adversarial network (GAN) by Goodfellow et al.. Building on the work by Goodfellow more advanced models using the same idea have shown great improvements in terms of both image quality and data diversity. GAN models generate images by feeding samples from a vector space into a generative neural network. The structure of these so called latent vector samples show to correspond to semantic similarities of their corresponding generated images. In this thesis the DCGAN model is trained on a novel data set consisting of image sequences of the growth process of basil plants from germination to harvest. We evaluate the trained model by comparing the DCGAN performance on benchmark data sets such as MNIST and CIFAR10 and conclude that the model trained on the basil plant data set achieved similar results compared to the MNIST data set and better results in comparison to the CIFAR10 data set. To argue for the potential of using more advanced GAN models we compare the results from the DCGAN model with the contemporary StyleGAN2 model. We also investigate the latent vector space produced by the DCGAN model and confirm that in accordance with previous research, namely that the DCGAN model is able to generate a latent space with data specific semantic structures. For the DCGAN model trained on the data set of basil plants, the latent space is able to distinguish between images of early stage basil plants from late stage plants in the growth phase. Furthermore, utilizing the sequential semantics of the basil plant data set, an attempt at generating an artificial growth sequence is made using linear interpolation. Finally we present an unsuccessful attempt at visualising the latent space produced by the DCGAN model using a rudimentary approach at inverting the generator network function. / Utvecklingen inom datorseende har syftat till att utforma djupa neurala nätverk som tränas på en stor mängd bilder och kan generera konstgjorda bilder av hög kvalitet med samma semantiska egenskaper som de ursprungliga bilderna. Ett avgörande skifte skedde när Goodfellow et al. introducerade det generativa adversariella nätverket (GAN). Med utgångspunkt i Goodfellows arbete har flera mer avancerade modeller som använder samma idé uppvisat stora förbättringar när det gäller både bildkvalitet och datamångfald. GAN-modeller genererar bilder genom att mata in vektorer från ett vektorrum till ett generativt neuralt nätverk. Strukturen hos dessa så kallade latenta vektorer visar sig motsvara semantiska likheter mellan motsvarande genererade bilder. I detta examensarbete har DCGAN-modellen tränats på en ny datamängd som består av bildsekvenser av basilikaplantors tillväxtprocess från groning till skörd. Vi utvärderar den tränade modellen genom att jämföra DCGAN-modellen mot referensdataset som MNIST och CIFAR10 och drar slutsatsen att DCGAN tränad på datasetet för basilikaväxter uppnår liknande resultat jämfört med MNIST-dataset och bättre resultat jämfört med CIFAR10-datasetet. För att påvisa potentialen av att använda mer avancerade GAN-modeller jämförs resultaten från DCGAN-modellen med den mer avancerade StyleGAN2-modellen. Vi undersöker också det latenta vektorrum som produceras av DCGAN-modellen och bekräftar att DCGAN-modellen i enlighet med tidigare forskning kan generera ett latent rum med dataspecifika semantiska strukturer. För DCGAN-modellen som tränats på datamängden med basilikaplantor lyckas det latenta rummet skilja mellan bilder av basilikaplantor i tidiga stadier och sena stadier av plantor i tillväxtprocessen. Med hjälp av den sekventiella semantiken i datamängden för basilikaväxter gjörs dessutom ett försök att generera en artificiell tillväxtsekvens med hjälp av linjär interpolation. Slutligen presenterar vi ett misslyckat försök att visualisera det latenta rummet som produceras av DCGAN-modellen med hjälp av ett rudimentärt tillvägagångssätt för att invertera den generativa nätverksfunktionen.

This study investigated the relationship between Sweden’s CO2e (Carbon Dioxide Equivalent) emissions and key macroeconomic factors, for the period 2008Q1- 2022Q3. The aim was to enhance the understanding of the link between macroeconomic factors and greenhouse gas emissions in a post-industrial economy, using multiple regression analysis. The study identified several significant macroeconomic factors affecting CO2e emissions and examined the extent to which these variables explain the fluctuations in Sweden’s emissions. Additionally, the study assessed the validity of the Environmental Kuznets Curve and Porter Hypothesis within Sweden’s environmental context. In the study, two multiple regression models were developed. Model 1 had an R^2 of 0.90, using the macroeconomic variables Industry Fuel Consumption, Population, Net Export, and Oil Prices. However, since the first model displayed moderate autocorrelation, a second model was also built by introducing a lagged dependent variable which yielded an R^2 of 0.92. / Denna studie undersökte förhållandet mellan Sveriges CO2e (koldioxidekvivalent) utsläpp och centrala makroekonomiska faktorer för perioden 2008K1-2022K3. Syftet var att öka förståelsen för sambandet mellan makroekonomiska faktorer och växthusgasutsläpp i en postindustriell ekonomi, med användning av multipel regressionsanalys. Studien identifierade flera betydande makroekonomiska faktorer som påverkar CO2e-utsläpp och undersökte i vilken utsträckning dessa variabler förklarar fluktuationerna i Sveriges utsläpp. Dessutom utvärderade studien giltigheten av Miljökuznetskurvan och Porters hypotes inom ramen för Sveriges miljökontext. I studien skapades två multipel regressionsmodeller. Modell 1 hade ett R^2 på 0,90, med de makroekonomiska variablerna Industriell Bränsleförbrukning, Befolkning, Nettoexport och Oljepriser. Eftersom den första modellen visade måttlig autokorrelation byggdes dock även en andra modell genom att införa en fördröjd beroende variabel, vilket resulterade i ett R^2 på 0,92.

In today’s educational landscape, digitalization, driven by economic interests, has become increasingly prominent. This development has sparked interest in understanding how high school students’ technical capital and mathematical self-efficacy influence their outcomes. To deepen the understanding of this interplay, this study integrates Selwyn’s Bourdieusian capital theory on technical capital with Bandura’s theories on mathematical self-efficacy. Empirical data were collected through a survey administered to 208 high school students across three schools in Sweden, ensuring anonymity and integrity. Linear regression was employed, controlling for gender, academic program, socioeconomic factors, and completed coursework. The results demonstrate that technical capital significantly predicts mathematical self-efficacy, with a predictive capacity of up to 59.6%. Moreover, tolerance for deviation in the model increased the predictive capacity to 94.2%. No significant differences in predictability were observed based on gender or academic program dependencies. However, gender differences revealed a more linear relationship between technical capital and digital/technical competence among women compared to men. Additionally, both genders displayed equivalent performance in knowledge-based questions. This suggests that men tend to overestimate their digital competence relative to their technical self-efficacy, while women do not exhibit the same tendency toward overconfidence. In conclusion, this study offers insights into how technical capital and self-efficacy in mathematics shape students’ educational outcomes.

This thesis explores the use of a column generation method, a subgradient method, and a logic-based Benders decomposition method on a minimized makespan K-rural postman problem. The K-rural postman problem here describes a search and rescue mission using multiple identical unmanned aerial vehicles (UAVs) to cover an area, represented as a complete graph. Each decomposition method has a separate problem for each UAV. In the subgradient and column generation case, a heuristic is used to find an improved upper bound for the makespan. This upper bound can in turn be used to decrease the feasible regions of the subproblems. Moreover, because the subproblems are slow to solve, a maximum calculation time is used, resulting in a feasible solution and a lower bound for each subproblem. These two modifications to the decomposition methods result in a non-standard behaviour.  Multiple fictional problem instances of different sizes and numbers of UAVs were generated and used for evaluating the methods. A maximal time limit is used in these instances. We conclude that solving the original, non-decomposed, problem for smaller instances with a standard solver is faster and gives better results than the decomposition methods. For larger instances, solving the non-decomposed model led to memory issues on several occasions. However, the suggested subgradient and column generation methods can solve every problem. The logic-based Benders decomposition method performed best on instances with multiple UAVs, but had issues when fewer UAVs are utilized. / Den här masteruppsatsen utforskar användningen av en kolumngenereringsmetod, en subgradientmetod och en logikbaserad Benders dekompositionsmetod på en variant av lantbrevbärarproblemet. Vårat brevbärarprolem beskriver sök- och räddningsuppdrag där $K$ drönare används för att avsöka ett område med målfunktionen att minimera flygtiden för den långsammaste drönaren. Varje dekompositionsmetod använder sig av ett problem för varje drönare. I subgradient- och kolumngenereringsmetoden användes en heuristik för att hitta en bättre övre begränsning till drönarnas flygtid. Den förbättrade övre begränsningen kunde sedan användas för att minska det tillåtna området för de mindre problemen. Eftersom de mindre problem var svårlösta, användes en maximal beräkningstid vilket resulterade i att en tillåten lösning och undre gräns gavs för varje mindre problem. Dessa två modifikationer resulterade i icke typiska beteenden.  Metoderna utvärderades på flera fiktiva testinstanser av olika storlekar där antalet drönare varierar. En tidsbegränsning används på varje probleminstans. Slutsatserna från uppsatsen är de original brevbärare problemet ger bäst lösning och snabbast lösningstid i de mindre instanserna. Vid lösning av större probleminstanser, gav original problemet flerfaldiga gånger minnesproblem. Subgradient- och kolumngenereringsmetoden kunde däremot lösa varje probleminstans inom tidsbegränsningen, vilket gjorde de mer pålitliga. Logikbaserade Benders dekompositionsmetoden presterade bättre i instanser med flera drönare, men stötte på problem i instanser med färre drönare.

The main objective in sampling is to select a sample from a population in order to estimate some unknown population parameter, usually a total or a mean of some interesting variable. When the units in the population do not have the same probability of being included in a sample, it is called unequal probability sampling. The inclusion probabilities are usually chosen to be proportional to some auxiliary variable that is known for all units in the population. When unequal probability sampling is applicable, it generally gives much better estimates than sampling with equal probabilities. This thesis consists of six papers that treat unequal probability sampling from a finite population of units. A random sample is selected according to some specified random mechanism called the sampling design. For unequal probability sampling there exist many different sampling designs. The choice of sampling design is important since it determines the properties of the estimator that is used. The main focus of this thesis is on evaluating and comparing different designs. Often it is preferable to select samples of a fixed size and hence the focus is on such designs. It is also important that a design has a simple and efficient implementation in order to be used in practice by statisticians. Some effort has been made to improve the implementation of some designs. In Paper II, two new implementations are presented for the Sampford design. In general a sampling design should also have a high level of randomization. A measure of the level of randomization is entropy. In Paper IV, eight designs are compared with respect to their entropy. A design called adjusted conditional Poisson has maximum entropy, but it is shown that several other designs are very close in terms of entropy. A specific situation called real time sampling is treated in Paper III, where a new design called correlated Poisson sampling is evaluated. In real time sampling the units pass the sampler one by one. Since each unit only passes once, the sampler must directly decide for each unit whether or not it should be sampled. The correlated Poisson design is shown to have much better properties than traditional methods such as Poisson sampling and systematic sampling.

The Standard Model of Elementary Particle Physics (SM) is the present theoryfor the elementary particles and their interactions and is a well-established theorywithin the physics community. The SM is a combination of Quantum Chromodynamics(QCD) and the Glashow{Weinberg{Salam (GWS) electroweak model. QCDis a theory for the strong force, whereas the GWS electroweak model is a theoryfor the weak and electromagnetic forces. This means that the SM describes allfundamental forces in Nature, except for the gravitational force. However, the SMis not a nal theory and some of its problems will be discussed in this thesis.In the rst part of this thesis, several properties of baryons are studied suchas spin structure, spin polarizations, magnetic moments, weak form factors, andnucleon quark sea isospin asymmetries, using the chiral quark model (QM). TheQM is an eective chiral eld theory developed to describe low energy phenomena of baryons, since perturbative QCD is not applicable at low energies. The resultsof the QM are in good agreement with experimental data.The second part of the thesis is devoted to the concept of quantum mechanicalneutrino oscillations. Neutrino oscillations can, however, not occur within the GWSelectroweak model. Thus, this model has to be extended in some way. All studiesincluding neutrino oscillation are done within three avor neutrino oscillationmodels. Both vacuum and matter neutrino oscillations are considered. Especially,global ts to all data of candidates for neutrino oscillations are presented and alsoan analytical formalism for matter enhanced three avor neutrino oscillations usingtime evolution operators is derived. Furthermore, investigations of matter eectswhen neutrinos traverse the Earth are included.The thesis begins with an introductory review of the QM and neutrino oscillationsand ends with the research results, which are given in the nine accompanyingscientic articles. / QC 20100616

Denna rapport redogör framtagningen av en beräkningsmodell för slagtider av pneumatiska cylindrar och manöverdon. Slagtiderna för ett manöverdon kan bestämmas genom experimentella tester. För att underlätta och minska tiden som krävs i samband med testerna skapas en beräkningsmodell som presenterar teoretiska värden för slagtiderna. Denna beräkningsmodell stämmer kvalitativt överens med de experimentella tester som också genomförs i detta arbete. Testerna genomförs först på en enkel pneumatisk cylinder vilket bidrar till kunskaper om slagkarakteristik och slagtider. Denna kunskap är till hjälp för utveckling av beräkningsmodellen. Under testerna mäts bland annat slagtid, kammartryck och kolvens förflyttning vid en mängd olika driftförhållanden. Testerna visar att en av de mest kritiska parametrarna för beräkningsmodellen är C-värdet, en parameter som beskriver flödeskarakteristiken för pneumatiska komponenter. För att få beräkningsmodellen att fungera väl behöver ett så korrekt C-värde som möjligt användas. Beräkningsmetodiken består i stora drag av samband för fyllning och tömning av pneumatiska volymer samt tryckförändringar i cylinderkamrarna vid kompression och expansion. Med en kombination av dessa ekvationer är det möjligt att beräkna slagtiden. Eftersom beräkningsmodellen vill hållas relativt enkel görs ett antal antaganden om systemets parametrar. Dessa antaganden utvärderas efter deras påverkan på slagtiden. Validering mot experimentella resultat visar att beräkningsmodellen generellt fungerar bättre vid höga matningstryck och kritiska flöden. När matningstrycket är lågt och underkritiska flöden erhålls påverkas slagtiden av många fler parametrar, vilket gör att beräkningsmodellen får något sämre precision. Detta resultat är inte helt oväntat eftersom sambandet för kritiskt flöde är relativt enkelt. / This thesis work describes the development of a calculation model for stroke times of pneumatic cylinders and actuators. The stroke time of an actuator can be determined by experimental tests. To facilitate and reduce the time required in connection with the tests, a calculation model is created which presents theoretical values of the stroke time. This calculation model is qualitatively consistent with the experimental tests carried out in this work. The tests are first carried out on a simple pneumatic cylinder, which contributes to knowledge of stroke characteristics and stroke times. This knowledge is helpful for the development of the calculation model. During the tests the stroke time, chamber pressure and piston movement are measured in a variety of operating conditions. The tests show that one of the most critical parameters for the calculation model is the C value, a parameter that describes the flow characteristics of pneumatic components. To make the calculation model reliable, a reasonable C value need to be used. The calculation method consists largely of equations for filling and emptying of pneumatic volumes as well as pressure changes in the cylinder chambers during compression and expansion. With a combination of these equations it is possible to calculate the stroke time. Since the calculation model wants to be kept relatively simple, several assumptions are made about parameters in the system. These assumptions are evaluated according to their potential and impact on the stroke time. Validation experiments show that the calculation model generally works better at high supply pressures and critical flows. When the supply pressure is low and subcritical flow are obtained, the stroke time is affected by many more parameters, which lower the precision of the calculation model. This result is not entirely unexpected since the critical flow equations are relatively simple.

An automated manufacturing industry makes use of many interacting moving parts and sensors. Data from these sensors generate complex multidimensional data in the production environment. This data is difficult to interpret and also difficult to find patterns in. This project provides tools to get a deeper understanding of Swedsafe’s production data, a company involved in an automated manufacturing business. The project is based on and will show the potential of the multidimensional production data. The project mainly consists of predicting deviations from predefined threshold values in Swedsafe’s production data. Machine learning is a good method of finding relationships in complex datasets. Supervised machine learning classification is used to predict deviation from threshold values in the data. An investigation is conducted to identify the classifier that performs best on Swedsafe's production data. The technique sliding window is used for managing time series data, which is used in this project. Apart from predicting deviations, this project also includes an implementation of live graphs to easily get an overview of the production data. A steady production with stable process values is important. So being able to monitor and predict events in the production environment can provide the same benefit for other manufacturing companies and is therefore suitable not only for Swedsafe. The best performing machine learning classifier tested in this project was the Random Forest classifier. The Multilayer Perceptron did not perform well on Swedsafe’s data, but further investigation in recurrent neural networks using LSTM neurons would be recommended. During the projekt a web based application displaying the sensor data in live graphs is also developed.

The viscosities of a set of silicone oils containing different size ranges of charcoal or paraffin particles as well as the viscosities of silicone oil foams were measured at room temperature in order to determine the effect of dispersed phase on the viscosity of a liquid and its effect on foaming ability. The effective viscosity of the samples increased with volume fraction of the second phase. The foaming ability was improved by the presence of the particles. The improved foaming effect was for the most part not a result of the increased viscosity. No connection between the particle size and the effective viscosity could be determined. On the other hand particle morphology and the particle size distribution had effect on the effective viscosity. The viscosity data were compared with a number of existing equations for the estimation of effective viscosity. Einstein-Roscoe equation is suitable for two-phase mixtures containing globular particles with narrow particle size distribution and low interfacial tension. New mathematical models are required for effective viscosity prediction, where the suspending phase viscosity, effect of the interfacial tension, as well as the particle morphology should be taken in consideration.

Introducing more intermittent renewable energy sources in the energy system makes the role of balancing power more important. Furthermore, an increased infeed from intermittent renewable energy sources also has the effect of creating lower and more volatile electricity prices. Hence, investing in balancing power is prone to high risks with respect to expected profits, which is why a good representation of electricity prices is vital in order to motivate future investments. We propose a stochastic multi-factor model to be used for simulating the long-run dynamics of electricity prices as input to investment valuation of power generation assets. In particular, the proposed model is used to assess the impact of electricity price dynamics on investment decisions with respect to balancing power generation, where a combined heat and power plant is studied in detail. Since the main goal of the framework is to create a long-term representation of electricity prices so that the distributional characteristics of electricity prices are maintained, commonly cited as seasonality, mean reversion and spikes, the model is evaluated in terms of yearly duration which describes the distribution of electricity prices over time. The core aspects of the framework are derived from the mean-reverting Pilipovic model of commodity prices, but where we extend the assumptions in a multi-factor framework by adding a functional link to the supply- and demand for power as well as outdoor temperature. On average, using the proposed model as a way to represent future prices yields a maximum 9 percent overand underprediction of duration respectively, a result far better than those obtained by simpler models such as a seasonal profile or mean estimates which do not incorporate the full characteristics of electricity prices. Using the different aspects of the model, we show that variations of electricity prices have a large impact on the investment decision with respect to balancing power. The realized value of the flexibility to produce electricity in a combined heat and power plant is calculated, which yields a valuation close to historical realized values. Compared with simpler models, this is a significant improvement. Finally, we show that by including characteristics such as non-constant volatility and spiky behavior in investment decisions, the expected value of balancing power generators, such as combined heat and power plants, increases. / I takt med att fler intermittenta förnyelsebara energikällor tillför el i dagens energisystem, blir också balanskraftens roll i dessa system allt viktigare. Vidare så har en ökning av andelen intermittenta förnyelsebara energikällor även effekten att de bidrar till lägre men också mer volatila elpriser. Därmed är även investeringar i balanskraft kopplade till stora risker med avseende på förväntade vinster, vilket gör att en god representation av elpriser är central vid investeringsbeslut. Vi föreslår en stokastisk flerfaktormodell för att simulera den långsiktiga dynamiken i elpriser som bas för värdering av generatortillgångar. Mer specifikt används modellen till att utvärdera effekten av elprisers dynamik på investeringsbeslut med avseende på balanskraft, där ett kraftvärmeverk studeras i detalj. Eftersom huvudmålet med ramverket är att skapa en långsiktig representation av elpriser så att deras fördelningsmässiga karakteristika bevaras, vilket i litteraturen citeras som regression mot medelvärde, säsongsvariationer, hög volatilitet och spikar, så utvärderas modellen i termer av årlig prisvaraktighet som beskriver fördelningen av elpriser över tid. Kärnan i ramverket utgår från Pilipovic-modellen av råvarupriser, men där vi utvecklar antaganden i ett flerfaktorramverk genom att lägga till en länkfunktion till tillgång- och efterfrågan på el samt utomhustemperatur. Vid användande av modellen som ett sätt att representera framtida priser, fås en maximal över- och underprediktion av prisvaraktighet om 9 procent, ett resultat som är bättre än det som ges av enklare modellering såsom säsongsprofiler eller enkla medelvärdesestimat som inte tar hänsyn till elprisernas fulla karakteristika. Till sist visar vi med modellens olika komponenter att variationer i elpriser, och därmed antaganden som används i långsiktig modellering, har stor betydelse med avseende på investeringsbeslut i balanskraft. Det realiserade värdet av flexibiliteten att producera el för ett kraftvärmeverk beräknas, vilket ger en värdering nära faktiska realiserade värden baserade på historiska priser och som enklare modeller inte kan konkurrera med. Slutligen visar detta också att inkluderandet av icke-konstant volatilitet och spikkarakteristika i investeringsbeslut ger ett högre förväntat värde av tillgångar som kan producera balanskraft, såsom kraftvärmeverk.