This dissertation studies a framework in support electric vehicle (EV) charging station expansion and management decisions. In the first part of the dissertation, we present mathematical model for designing and managing electric vehicle charging stations, considering both long-term planning decisions and short-term hourly operational decisions (e.g., number of batteries charged, discharged through Battery-to-Grid (B2G), stored, Vehicle-to-Grid (V2G), renewable, grid power usage) over a pre-specified planning horizon and under stochastic power demand. The model captures the non-linear load congestion effect that increases exponentially as the electricity consumed by plugged-in EVs approaches the capacity of the charging station and linearizes it. The study proposes a hybrid decomposition algorithm that utilizes a Sample Average Approximation and an enhanced Progressive Hedging algorithm (PHA) inside a Constraint Generation algorithmic framework to efficiently solve the proposed optimization model. A case study based on a road network of Washington, D.C. is presented to visualize and validate the modeling results. Computational experiments demonstrate the effectiveness of the proposed algorithm in solving the problem in a practical amount of time. Finding of the study include that incorporating the load congestion factor encourages the opening of large-sized charging stations, increases the number of stored batteries, and that higher congestion costs call for a decrease in the opening of new charging stations. The second part of the dissertation is dedicated to investigate the performance of a collaborative decision model to optimize electricity flow among commercial buildings, electric vehicle charging stations, and power grid under power demand uncertainty. A two-stage stochastic programming model is proposed to incorporate energy sharing and collaborative decisions among network entities with the aim of overall energy network cost minimization. We use San Francisco, California as a testing ground to visualize and validate the modeling results. Computational experiments draw managerial insights into how different key input parameters (e.g., grid power unavailability, power collaboration restriction) affect the overall energy network design and cost. Finally, a novel disruption prevention model is proposed for designing and managing EV charging stations with respect to both long-term planning and short-term operational decisions, over a pre-determined planning horizon and under a stochastic power demand. Long-term planning decisions determine the type, location, and time of established charging stations, while short-term operational decisions manage power resource utilization. A non-linear term is introduced into the model to prevent the evolution of excessive temperature on a power line under stochastic exogenous factors such as outside temperature and air velocity. Since the re- search problem is NP-hard, a Sample Average Approximation method enhanced with a Scenario Decomposition algorithm on the basis of Lagrangian Decomposition scheme is proposed to obtain a good-quality solution within a reasonable computational time. As a testing ground, the road network of Washington, D.C. is considered to visualize and validate the modeling results. The results of the analysis provide a number of managerial insights to help decision makers achieving a more reliable and cost-effective electricity supply network.

Schizophrenia is a severe psychiatric disorder with unsatisfactory treatment options and poorly under- stood etiology. Genetic models are a suitable tool for studying this disorder with its high heritability. However, currently available animal models do not cover the broad range of schizophrenia symptoms and are not disorder-specific. Ribonucleic acid binding motif protein 12 gene (RBM12), a novel, high- risk gene for schizophrenia, was recently identified. This thesis aimed to assess the social behavior of schizophrenia-like phenotype in RBM12 zebrafish mutants. The social behavior of mutated adult zebrafish was assessed during free-swimming. Trajectories of each zebrafish were obtained from recordings by the usage of idtracker.ai. Parameters selected to quantify the social behavior of the zebrafish were chosen based on common symptoms of humans with schizophrenia. Inter-fish distance was examined as an indicator of preferred personal space since humans diagnosed with schizophre- nia have an increased need for a greater personal space compared to mentally healthy individuals. Wall-hugging, increased speed and bottom-dwelling were studied as indicators of anxiety, a common comorbid symptom of schizophrenia. The RBM12 mutants exhibited a greater inter-fish distance than their wild-type siblings during three-dimensional recordings. They however, did not demonstrate an increased inter-fish distance during two-dimensional recordings. The mutated zebrafish displayed a higher average speed and greater wall-hugging, indicating anxiety. It can be concluded that RBM12 mutation produces partial symptomatology consistent with humans diagnosed with schizophrenia, providing a promising animal model. The current work provided novel insight into the neural substrates of schizophrenia and for potential drug screening for this disorder. Further research is needed to fully characterise schizophrenia-like symptoms in this RBM12 animal model.

[pt] Esta dissertação tem por objetivo investigar a possibilidade de obtenção de retornos anormais – utilizando-se o período entre jan/2005 e dez/2014 como espaço amostral – no mercado acionário brasileiro. Investigou-se, então, a hipótese de reversão à média móvel de 21 dias para os ativos integrantes do Índice Brasil 100 – IBrX-100. Estratégias contrárias com carteiras compradas em ações cujos preços estivessem abaixo da média móvel e vendidas em ações cujos preços estivessem acima da média móvel foram montadas e testadas para os referidos períodos. Por fim, não foram encontradas evidências em favor da reversão à média móvel de 21 dias para o período estudado. / [en] The goal of this study is to investigate the possibility of obtaining abnormal returns – using the period between January/2005 and December/2014 –in the Brazilian stock market. The main hypothesis in focus is the moving average of 21 days reversion of the securities of the Index Brasil 100 – IBrX 100. Contrarian strategies were used with portfolios built by buying stocks whose prices were below the moving average and selling stocks whose prices are above the moving average. There is no evidence in favor of the reversion and in favor of the possibility of abnormal returns in the study period.

The purpose of this thesis is to fill the previous research gap concerning automation in the horticulture industry by discovering the adoption of automation in the U.S. and Canada, exploring the possibilities of introducing autonomous solutions and provide recommendations as to how this could create opportunities for small robotics companies targeting the industry. A case company in the U.S. and Canada was used as an example of a small robotics company for the case study. Two research questions were formulated: RQ1: Which major tasks in the horticulture industry should a small robotics company aim to automate? RQ2: What are the barriers for companies in the horticulture industry to invest in automated solutions? A mixed methods research with a pragmatic, inductive and exploratory approach was employed. The primary source of data was gathered from surveys, due to the geographical diversity of the region studied. The surveys reveal that the average level of automation across all respondents averaged at 47%. Given the strategy of the case company, a small robotics company is argued to aim to automate the following tasks: placing plant liners, sticking cuttings and planting seed, spacing of plants and containers, plant pruning, harvesting and grading production, and pesticide application. The horticulture industry is showing low barriers to invest in automation. The relatively high levels of automation are leading to increased trust in automation and further investments in automation. This is shown in the technology being perceived as useful amongst 75-85% of respondents and perceived as easy to use amongst 94% of respondents. / Syftet med denna avhandling är att fylla det tidigare forskargapet om automatisering inom hortikultur, genom att utforska acceptansen av automatisering i USA och Kanada, utforska möjligheterna att införa autonoma lösningar och ge rekommendationer om hur detta kan skapa möjligheter för små robotföretag som riktar sig mot branschen. En fallstudie på ett robotföretag i USA och Kanada användes som ett exempel på ett litet robotföretag. Två forskningsfrågor formulerades: RQ1: Vilka stora uppgifter inom hortikultur bör ett litet robotföretag sträva efter att automatisera?RQ2: Vilka hinder finns för företag inom hortikultur att investera i automatiserade lösningar? En blandad metodforskning med ett pragmatiskt, induktivt och utforskande tillvägagångssätt användes. Den primära källan till data samlades från undersökningar, på grund av den geografiska mångfalden i regionen som studerades. Undersökningarna visar att den genomsnittliga automatiseringsgraden för alla svarande i genomsnitt uppgick till 47%. Med tanke på bolagets strategi rekommenderas ett litet robotföretag att automatisera följande uppgifter: rada upp plantor, stick och plantera frön, skapa avstånd mellan växter och behållare, beskära och kvalitetsgranska skördar, och applicera bekämpningsmedel. Hortikulturindustrin visar låga hinder för investeringar i automatisering. De relativt höga automatiseringsnivåerna leder till ökat förtroende för automatisering och ytterligare investeringar i automation. Detta framgår av tekniken som uppfattas som användbar bland 75–85% av de svarande och uppfattas som lätt att använda bland 94% av de svarande.

Sweden was one of the few countries in the EU that in most cases decided to keep their elementary schools open during the COVID-19 pandemic. The purpose of this study was to investigate potential outcomes COVID-19 has had on students´ average merit scores as well as group-specific effects across genders and school types. To further estimate which factors might have also influenced students´ success, we decided to consider the share of higher educated parents and the municipality income for compulsory schools in our research. Using the fixed effects method, a few models were constructed to analyze the different effects. We found that in general, COVID-19 had a significant and positive effect on the average merit score of students across all elementary schools in Sweden, suggesting that there have been other factors influencing their academic performance. Furthermore, it was found that gender disparities and the share of higher educated parents have affected the average academic performance, whereas the municipality income for compulsory schools did not. Lastly, private schools were found to perform less positively compared to public schools.

京都大学 / 0048 / 新制・論文博士 / 博士(工学) / 乙第13278号 / 論工博第4181号 / 新制||工||1726(附属図書館) / (主査)教授 角 哲也, 教授 寶 馨, 准教授 Sameh Kantoush, 教授 立川 康人 / 学位規則第4条第2項該当 / Doctor of Philosophy (Engineering) / Kyoto University / DFAM

Experience and key data suggest that snow and ice lead to increased numbers of cyclist falls during the winter months. Reliable in-depth data concering the extent and characteristics of this issue are currently not available in most countries. In Germany, this is due to the high level of under-reporting in official statistics, particularly for incidents involving only one bicyclist. In combination with the lack of knowledge on exposure this causes difficulties to quantify risks for cyclist falls. This study addresses these gaps. lt aims at quantifying the risk of single bicycle accidents in inclement weather conditions. This study focusses on icy and snowy conditions as these are of relevance for the risk to fall. Cyclists are particularly affected by slippery icy and snowy road conditions; these might exist in clear, cloudy, or foggy weather, in situations with high or low humidity and with higher or lower wind speed. Variables from official weather data are purposefully combined in this study to identify time periods with snow or ice on the roads and to allow for the comparison of those with all other time periods ('other weather''). We address the above-mentioned problems of exposure and underreporting by using multiple data sources for quantifying the risk of falls. This approach allows to compute clear risk ratios for icy/snowy and the other weather conditions and thus contributes to the scarce and fragmented literature that has generated such values so far. [from Background, AIM]

Kvantkemiska studier av reaktiviteten hos guldnanopartiklar Au3-Au11 och Au13 mot O- centrerade molekylradikaler OH , OOH , OCH3   och H2O undersöks. Olika molekylära ytegenskaper tas med i beräkningen, elektrostatiska ytpotentialen, den genomsnittliga lokala joniseringsenergin, electron attachment energy och spinndensiteten (VS(r), IS(r), TS(r), ES(r) och S(r)). De erhållna resultaten gäller slutna och öppna skalsystem. Där system med slutna skal bildas från växelverkan mellan en guldklusterradikal och en fri radikal, och system med öppna skal bildas från växelverkan mellan ett jämnt antal guldatomer med en fri radikal. För system med slutna skal Aux-R (där x = 3, 5, 7, 9 eller 11 och R är en O-centrerad radikal) finns det en övergripande trend av bindningsenergin gentemot ES(r), vilket återspeglar elektrofilictiten hos guldnanopartiklar. Multivariata modeller visar vidare hur de olika parametrarna korrelerar gentemot varandra för system med slutna skal.För strukturerna Aux-R (där x=3-11) medl ägst bindningsenergi, dvs. inklusive både slutna och öppna skalsystem, är den tydligaste trenden bindningsenergi vs minimum i ES(r) och parametern TS(r). Vid jämförelse av resultaten av interaktionerna med de fria radikalerna med H2O är trenden alltid tydligast för H2O. I linje med tidigare studier finns det även en korrelation av bindningsenergierna med VS,max och ES,min för H2O. Slutligen sträcker sig trenden med bindningsenergi vs ES,min vidare till systemet som innehåller den icke-plana Au13-strukturen. Denna studie visar kopplingen mellan reaktiviteten hos guldnanopartiklar mot fria radikaler till den lokala ES(r), samtidigt som bidraget från andra ytegenskaper visas. Detta kan vara av betydelse för fortsatta studier kring naturen av interaktioner av guldnanopartiklar. / The nature of gold nanoparticle interactions towards molecular radicals are investigated. Quantum chemical studies of the reactivity of gold nanoparticles Au3-Au11 and Au13 towards O-centered molecular radicals OH , OOH , OCH3   and H2O are performed. Different molecular surface properties are taken into account; the surface electrostatic potential, average local ionization energy, electron attachment energy and spin density (VS(r), IS(r), TS(r), ES(r) and S(r)). The obtained results concern closed and open shell systems. Where closed shell systems are formed from the interaction of a radical gold cluster and a free radical, and open shell systems are formed from the interaction of an even number of gold atoms with a free radical. For closed shell systems Aux-R (where x = 3, 5, 7, 9 or 11 and R is an O-centered radical) there is an overall trend of the binding energy vs the local electron attachment energy, reflecting the electrophilicity of the gold nanoparticles. Multivariate plots further show how the different parameters correlate together for closed shell systems. Looking at the lowest energy structures Aux-R (where x = 3-11), i.e. including closed and open shell systems, the clearest trend is of binding energy vs minima in the local electron attachment energy ES,min and the TS(r) parameter. When comparing the results of the interactions with the free radicals with H2O, the trend is always clearest for H2O. Concurring with previous trends, there is a correlation of the binding energies with VS,max and ES,min for H2O. Lastly, the trend of Binding energy vs ES,min further extends to systems containing the non-planar Au13 structure. This study extends the reactivity of gold nanoparticles towards free radicals to the local electron attachment energy, while showing the contribution of other surface properties. This might be of importance for further studies concerning the nature of gold nanoparticle interactions.

En övergång till bilar utrustade med avancerade automatiska säkerhetssystem (ADAS) och även utvecklingen mot självkörande fordon innebär ökad trafik på den lokala databussen. Det finns således ett behov av att både minska den faktiska mängden data som överförs, samtidigt som värdet på datat ökas. Data aggregation tillämpas i dagsläget inom områden såsom trådlösasensornätverk och mindre mobila robotar (WMR’s) och skulle kunna vara en del av en lösning. Denna rapport avser undersöka aggregation av sensordata i ett tidskänsligt system. För ett användarfall gällande halka under konvojkörning testas en aggregationsstrategi genom implementation på en fysisk demonstrator. Demonstratorn består av ett autonomt fordon i mindre skala som befinner sig i en konvoj med ett annat identiskt fordon. Resultaten pekar mot att ett viktat medelvärde, som i realtid anpassar sin viktning baserat på specifika sensorers koherens, med fördel kan användas för att estimera fordonshastighet baserat på individuella hjuls sensordata. Därefter kan en slip ratio beräknas, vilket avgör om fordonet befinner sig i ett tillstånd av halka eller ej. Begränsningar för den undersökta strategin inkluderar antalet icke-halkande hjul som behövs för tillförlitliga resultat. Simulerade resultat antyder att extra hastighetsreferenser behövs för tillförlitliga resultat. Relaterat till användarfallet konvojkörning föreslås att andra fordon används som hastighetsreferens. Detta skulle innebära en ökad precision för estimeringen av fordonshastigheten samt utgöra en intressant sammanslagning av områdena samarbetande cyberfysiska system (CO-CPS) och dataaggregation. / With an impending shift to more advanced safety systems and driver assistance (ADAS) in the vehicles we drive, and also increased autonomousity, comes increased amounts of data on the internal vehicle data bus. There is a need to lessen the amount of data and at the same time increase its value. Data aggregation, often applied in the field of environmental sensing or small mobile robots (WMR’s), could be a partial solution. This thesis choses to investigate an aggregation strategy applied to a use case regarding slip detection in a vehicle convoy. The approach was implemented in a physical demonstrator in the shape of a small autonomousvehicle convoy to produce quantitative data. The results imply that a weighted adaptive average can be used for vehicle velocity estimation based on the input of four individual wheel velocities. There after a slip ratio can be calculated which is used to decide if slip exists or not. Limitations of the proposed approach is however the number of velocity references that is needed since the results currently apply to one-wheel slipon a four-wheel vehicle. A proposed future direction related to the use case of convoy driving could be to include platooning vehicles as extra velocity references for the vehicles in the convoy, thus increasing the accuracy of the slip detection and merging the areas of CO-CPS and data aggregation.

The stock market is a non-linear field, but many of the best-known portfolio optimization algorithms are based on linear models. In recent years, the rapid development of machine learning has produced flexible models capable of complex pattern recognition. In this paper, we propose two different methods of portfolio optimization; one based on the development of a multivariate time-dependent neural network,thelongshort-termmemory(LSTM),capable of finding lon gshort-term price trends. The other is the linear Markowitz model, where we add an exponential moving average to the input price data to capture underlying trends. The input data to our neural network are daily prices, volumes and market indicators such as the volatility index (VIX).The output variables are the prices predicted for each asset the following day, which are then further processed to produce metrics such as expected returns, volatilities and prediction error to design a portfolio allocation that optimizes a custom utility function like the Sharpe Ratio. The LSTM model produced a portfolio with a return and risk that was close to the actual market conditions for the date in question, but with a high error value, indicating that our LSTM model is insufficient as a sole forecasting tool. However,the ability to predict upward and downward trends was somewhat better than expected and therefore we conclude that multiple neural network can be used as indicators, each responsible for some specific aspect of what is to be analysed, to draw a conclusion from the result. The findings also suggest that the input data should be more thoroughly considered, as the prediction accuracy is enhanced by the choice of variables and the external information used for training. / Aktiemarknaden är en icke-linjär marknad, men många av de mest kända portföljoptimerings algoritmerna är baserad på linjära modeller. Under de senaste åren har den snabba utvecklingen inom maskininlärning skapat flexibla modeller som kan extrahera information ur komplexa mönster. I det här examensarbetet föreslår vi två sätt att optimera en portfölj, ett där ett neuralt nätverk utvecklas med avseende på multivariata tidsserier och ett annat där vi använder den linjära Markowitz modellen, där vi även lägger ett exponentiellt rörligt medelvärde på prisdatan. Ingångsdatan till vårt neurala nätverk är de dagliga slutpriserna, volymerna och marknadsindikatorer som t.ex. volatilitetsindexet VIX. Utgångsvariablerna kommer vara de predikterade priserna för nästa dag, som sedan bearbetas ytterligare för att producera mätvärden såsom förväntad avkastning, volatilitet och Sharpe ratio. LSTM-modellen producerar en portfölj med avkastning och risk som ligger närmre de verkliga marknadsförhållandena, men däremot gav resultatet ett högt felvärde och det visar att vår LSTM-modell är otillräckligt för att använda som ensamt predikteringssverktyg. Med det sagt så gav det ändå en bättre prediktion när det gäller trender än vad vi antog den skulle göra. Vår slutsats är därför att man bör använda flera neurala nätverk som indikatorer, där var och en är ansvarig för någon specifikt aspekt man vill analysera, och baserat på dessa dra en slutsats. Vårt resultat tyder också på att inmatningsdatan bör övervägas mera noggrant, eftersom predikteringsnoggrannheten.