To address inefficiency, waste, and the negative consequences of electricity generation, companies and government entities are looking to behavioural change among residential consumers. To drive behavioural change, consumers need better feedback about their electricity consumption. A monthly or quarterly bill provides the consumer with almost no useful information about the relationship between their behaviours and their electricity consumption. Smart meters are now widely dispersed in developed countries and they are capable of providing electricity consumption readings at an hourly resolution, but this data is mostly used as a basis for billing and not as a tool to assist the consumer in reducing their consumption. One component required to deliver innovative feedback mechanisms is the capability to forecast hourly electricity consumption at the household scale. The work presented by this thesis is an evaluation of the effectiveness of a selection of kernel based machine learning methods at forecasting the hourly aggregate electricity consumption for different sized sets of households. The work of this thesis demonstrates that k-Nearest Neighbour Regression and Gaussian process Regression are the most accurate methods within the constraints of the problem considered. In addition to accuracy, the advantages and disadvantages of each machine learning method are evaluated, and a simple comparison of each algorithms computational performance is made. / För att ta itu med ineffektivitet, avfall, och de negativa konsekvenserna av elproduktion så vill företag och myndigheter se beteendeförändringar bland hushållskonsumenter. För att skapa beteendeförändringar så behöver konsumenterna bättre återkoppling när det gäller deras elförbrukning. Den nuvarande återkopplingen i en månads- eller kvartalsfaktura ger konsumenten nästan ingen användbar information om hur deras beteenden relaterar till deras konsumtion. Smarta mätare finns nu överallt i de utvecklade länderna och de kan ge en mängd information om bostäders konsumtion, men denna data används främst som underlag för fakturering och inte som ett verktyg för att hjälpa konsumenterna att minska sin konsumtion. En komponent som krävs för att leverera innovativa återkopplingsmekanismer är förmågan att förutse elförbrukningen på hushållsskala. Arbetet som presenteras i denna avhandling är en utvärdering av noggrannheten hos ett urval av kärnbaserad maskininlärningsmetoder för att förutse den sammanlagda förbrukningen för olika stora uppsättningar av hushåll. Arbetet i denna avhandling visar att "k-Nearest Neighbour Regression" och "Gaussian Process Regression" är de mest exakta metoder inom problemets begränsningar. Förutom noggrannhet, så görs en utvärdering av fördelar, nackdelar och prestanda hos varje maskininlärningsmetod.

The aim of this study is to act as a pre-study for AirSon Engineering AB regarding a small scale wind turbine they want to install. This by means of collecting data about the windspeeds present at said location, taking into consideration local regulations and doing calculations on the turbulence in the wind, which is affected by nearby obstacles and by the house which the wind turbine is planned to be installed next to. The study puts specific focus on three main questions, namely: What kind of production is to be expected? What is the economy like for the installation of the wind turbine? What are the possibilities/limitations from a construction perspective? An analysis of the location of the installation and a comparison of the selected wind turbines and their respective dimensions, potential for production and economics is presented in this study. The emphasis of the analysis is on examining the respective wind turbines and determining which of them that best fits AirSon with regard to all three aspects. Different graphs have been used to compile wind data and the program used for this study is Matlab. In addition to that the program Excel has been used to compile and present the results for the various wind turbines.  A total of nine small scale vertical axis wind turbines with rated output powers between 1 kW and 10 kW have been examined and are presented as potential suggestions for installation. The manufacturers whose wind turbines are presented are Aeolos, Toyoda and Ropatec. By the end of this study a recommendation from the authors, to AirSon is presented for which windturbine the authors think might fit best. The plan for this study is furthermore to act as a guidance so that AirSon can, following up on the study, directly work toward acquiring and installing said wind turbine. / Syftet med detta arbete är att genomföra en förstudie åt AirSon Engineering AB rörande ett småskaligt vindkraftverk som de vill installera. Arbetet innefattar insamling av data om vindhastigheter från den befintliga platsen samt hänsynstagande till de lokala omständigheterna, till exempel vad gäller turbulensen i vinden, som påverkas av närliggande hinder och av huset vilket vindkraftverket planeras att installeras intill. Examensarbetet har sitt fokus specifikt på tre huvudfrågor, nämligen: Vad för produktion förväntas från platsen? Hur ser ekonomin ut för installationen av vindkraftverket? Vad finns det för möjligheter/hinder ur ett kontruktionsperspektiv? I arbetet presenteras en analys av platsen som vindkraftverket ska installeras på samt en analys av utvalda vindkraftverk med hänsyn till storlek, produktion och ekonomi. Analysens tyngdpunkt ligger i att undersöka det vindkraftverk som passar in bäst för AirSon med hänsyn till alla tre aspekterna. Till analysen har olika grafer använts för att sammanställa vinddata och programmet som användes till detta är Matlab. För att sammanställa och presentera de olika vindkraftverken har Excel använts.  Totalt sett har nio småskaliga vertikalaxlade vindkraftverk med märkeffekter mellan 1 kW och 10 kW undersökts och tagits fram som potentiella förslag för installation. De tillverkare vars vindkraftverk presenteras är Aeolos, Toyoda och Ropatec. I slutet av detta arbete presenteras en rekommendation för vilket vindkraftverk som författarna anser vara lämpligast för AirSon. Avsikten med arbetet är att vägleda AirSon tillräckligt mycket för att de ska kunna använda analysen för att installera verket.

This study proposes a performance comparison between machine learning regression algorithms and Artificial Neural Network (ANN). The regression algorithms used in this study are Multiple linear, Least Absolute Selection Operator (Lasso), Ridge, Random Forest. Moreover, this study attempts to analyse the correlation between variables to determine the most important factors that affect house prices in Malmö, Sweden. There are two datasets used in this study which called public and local. They contain house prices from Ames, Iowa, United States and Malmö, Sweden, respectively.The accuracy of the prediction is evaluated by checking the root square and root mean square error scores of the training model. The test is performed after applying the required pre-processing methods and splitting the data into two parts. However, one part will be used in the training and the other in the test phase. We have also presented a binning strategy that improved the accuracy of the models.This thesis attempts to show that Lasso gives the best score among other algorithms when using the public dataset in training. The correlation graphs show the variables' level of dependency. In addition, the empirical results show that crime, deposit, lending, and repo rates influence the house prices negatively. Where inflation, year, and unemployment rate impact the house prices positively.

Indiana University-Purdue University Indianapolis (IUPUI) / Exodontia, or extraction of teeth, has been a well-documented dental treatment that forms one of the foundations of dentistry. The steps associated with extracting teeth have changed little in the last century and these steps are largely part of the dogma of dentistry. One such step is that of socket compression post-extraction. Rationale for socket compression after extraction is manifold. They include: shorter healing times, fewer dry sockets and re-approximating walls that were stretched in the elevation and delivery stages of extractions. The purpose of this study was to determine if post-extraction ridge compression negatively affected alveolar ridge dimensions when compared to sites that are not compressed post-extraction. Secondary outcome measures will identify if socket compression/re-approximation affects the rate of soft tissue closure or occurrence of alveolar osteitis. In this study, 14 subjects were recruited. Eight subjects formed the compression group, while six formed the non-compression group. The subjects in the compression group received compression of their alveolar ridges after extraction to approximate their original pre-extraction width. The subjects in the non-compression group did not receive ridge compression. Each subject had pre-extraction and post-extraction CBCT scans along with post-operative follow up visits at 1, 2, and 4 weeks post-extraction. The present investigation found that with respect to changes in ridge width, sites that were compressed did not lose significantly more dimension than those that were not. With respect to ridge height, sites that were compressed did not lose significantly more dimension than those that were not. Sites that were compressed and sites that were not, healed at approximately the same rate, with respect to soft tissue closure. While the results showed a lack of statistical significance between both groups, there appears to be a trend towards the ridge compression group having a smaller ridge width. Such a trend was not noted with soft tissue closure, thereby invalidating the rationale for socket compression after extraction. One of the limitations of this pilot study is the small sample size. Further validation of these results must be done with a larger sample size in order to provide clinical guidance to dental practitioners.

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Fodder maize cultivation under low mountain conditions in Central Europe presents obstacles for organic dairy farmers; low temperatures and high precipitation values in spring delay the juvenile development of maize, which leads to lower and fluctuating yields. Increasing the soil temperature during the critical growth phase of maize in spring is beneficial for maize cultivation. For this reason, 0.15 m high ridge-row cultivation (RCM) of maize was compared to a typical flat surface cultivation method (FCM) with 0.75 m row spacing in three environments (En) in 2017, 2018 and 2020 on-farm at low mountain sites in Germany. In the experiment, with randomised block design and one-factorial arrangement, soil temperature (ST) at 0.05 m soil depth at midday, field emergence (FE) 4, 8, 16 and 20 days after sowing (DAS), dry matter yields (DM) in every En and plant development and N, P, K content in En 2020 were investigated. RCM led to a significantly higher ST 4 DAS in every En, 12 and 20 days in 2018 and 8 and 16 DAS in 2020. RCM did not accelerate maize FE but positively impacted plant development and starch content. RCM generated a higher dry matter (DM) yield of whole maize plants and corn cobs, and a higher protein yield than FCM. RCM slightly increased the plant-available P and Mg content from 0 to 0.3 m and influenced significantly the mineral N content from 0 to 0.3 m at the beginning of grain development. RCM, a simple cultivation technique, demonstrated benefits for maize cultivation, particularly for climatically marginal locations in Germany.

The AER has a vital role in directing embryonic limb development. Several models have been developed that attempt to explain how the AER directs limb development, but none of them are fully supported by existing data. I provide evidence that FGFs secreted from the AER induce a gradient of Wnt5a. I also demonstrate that limb mesenchyme grows toward increasing concentrations of Wnt5a. We hypothesize that the changing shape of the AER is critical for patterning the limb along the proximal to distal axis. To better understand the pathway through which Wnt5a elicits its effects, we have performed various genetic studies. We demonstrate that Wnt5a does not signal via the Wnt/β-catenin pathway. However, we show that Wnt5a mutants share many common defects with Vangl2 mutants suggesting that Wnt5a signals through the Wnt/planar cell polarity (PCP) pathway.

Public transport is essential for both residents and city planners because of its environmentally and economically beneficial characteristics. During the past decade climatechange, coupled with fuel and energy crises have attracted significant attention toward public transportation. Increasing the demand for public transport on the one hand and its complexity on the other hand have made the optimum network design quite challenging for city planners. The ridership is affected by numerous variables and features like space and time. These fluctuations, coupled with inherent uncertaintiesdue to different travel behaviors, make this procedure challenging. Any demand and supply mismatching can result in great user dissatisfaction and waste of energy on the horizon. During the past years, due to recent technologies in recording and storing data and advances in data analysis techniques, finding patterns, and predicting ridership based on historical data have improved significantly. This study aims to develop forecasting models by regressing boardings toward population, time of day, month, and station. Using the available boarding dataset for blue bus line number 4 in Stockholm, Sweden, seven different machine learning algorithms were assessed for prediction: Multiple Linear Regression, Decision Tree, Random Forest, Bayesian Ridge Regression, Neural Networks, Support Vector Machines, K-Nearest Neighbors. The models were trained and tested on the dataset from 2012 to 2019, before the start of the pandemic. The best model, KNN, with an average R-squared of 0.65 in 10-fold cross-validation was accepted as the best model. This model is then used to predict reduced ridership during the pandemic in 2020 and 2021. The results showed a reduction of 48.93% in 2020 and 82.24% in 2021 for the studied bus line.

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