Bilar av ett visst märke, modell, år och uppsättning funktioner börjar med ett pris som fastställs av tillverkaren. När de åldras och säljs vidare som de används, är de föremål för prissättning av utbud och efterfrågan för deras speciella uppsättning funktioner, utöver deras unika historia. Ju mer detta skiljer dem från jämförbara bilar, desto svårare blir de att utvärdera med traditionella metoder. Genom att använda maskininlärning algoritmer för att bättre utnyttja data om alla mindre vanliga egenskaper hos en bil kan man mer exakt bedöma ett fordons värde. Denna studie jämför prestandan för algoritmer för Linjär Regression, Ridge Regression, Lasso Regression och Random Forest Regression när det gäller att förutsäga priset på begagnade bilar. En viktig kvalifikation för ett prisförutsägelseverktyg är att avskrivningar kan representeras för att bättre utnyttja tidigare data för aktuell prisförutsägelse. Denna studie jämför därför även den skattade prisavtagningen hos algoritmerna. Studien har genomförts med en stor offentlig datauppsättning av begagnade bilar. Resultaten visar att Random Forest Regression visar den högsta prisförutsägelseprestanda för alla mätvärden som används. Den kunde också representera den genomsnittliga avskrivningen mycket närmare verkligheten än de andra algoritmerna, med 13,7 % förutspådd årlig geometrisk prisavtagning för datasetet oberoende av fordonets ålder. / Cars of a particular make, model, year, and set of features start out with a price set by the manufacturer. As they age and are resold as used, they are subject to supply-and-demand pricing for their particular set of features, in addition to their unique history. The more this sets them apart from comparable cars, the harder they become to evaluate with traditional methods. Using Machine Learning algorithms to better utilize data on all the less common features of a car can more accurately assess the value of a vehicle. This study compares the performance of Linear Regression, Ridge Regression, Lasso Regression, and Random Forest Regression ML algorithms in predicting the price of used cars. An important qualification of a price prediction tool is that depreciation can be represented to better utilize past data for current price prediction. The study has been conducted with a large public dataset of used cars. The results show that Random Forest Regression demonstrates the highest price prediction performance across all metrics used. It was also able to represent average depreciation much more closely than the other algorithms, at 13.7% predicted annual geometric depreciation for the dataset independent of vehicle age.

UCO (Ultra Clean Oil) module, an innovative adaption of existing used rolling oil treatment in the Aluminum industry, is being studied for its potential market in China. In this article, conventional used rolling oil treatment process in China has been evaluated and compared with the UCO module in technical and economic aspects. This article also discusses the effect of these two processes on the environment. The UCO module presented in this article, has the potential to significantly reduce the impurities in the used rolling oil and also save the cost for the raw materials, manpower, maintenance and operation for more than 1,600,000 Kr a year. With the new ― state of art‖ equipment, the UCO module has less negative effect on the environment. The UCO module appears to be more efficient but profitability was found to be lower than the conventional process in China.

Nowadays, with concerns about environmental and health issues, the awareness of more environmentally friendly and sustainable waste disposal is growing. However, due to the widespread disposal of waste items in the fashion field, such as landfills and incineration, the problems caused by these disposal methods cannot meet people’s sustainable needs for waste treatment. Therefore, the project is based on consumers, focusing on the sustainable treatment of used garments, and provides some possibilities for solving problems related to other types of used products.  In order to achieve this goal, this project starts from the literature review of garments disposal behavior, and through the understanding of the theories such as life cycle assessment and waste hierarchy, as well as the collection and analysis of user survey data, completed the design of the project solution. By reference to the theoretical framework and application of the methodological framework, the project finally proposed a solution composed of three consumer-oriented design ideas, mainly in the form of social design to facilitate people’s recycling, reuse and other sustainable disposals of used garments.

[pt] Este trabalho apresenta o desenvolvimento de um sistema computacional para ser utilizado na diagramação (encaixe) de figuras planas em cartões retangulares na indústria de embalagens de papel. O sistema aparece como o primeiro passo para a automação total da fabricação de embalagens. Consta de quatro subsistemas: a edição de figuras; e edição de cores das arestas destas figuras; a confecção e edição de encaixes,onde se leva em conta a relação entre as cores das arestas das figuras de acordo com diferentes tipos de problema; a saída destes encaixes por plotter. É apresentada uma discussão detalhada sobre os cálculos de distância entre figuras e sua utilização no esquema de encaixe. Uma série de exemplos gráficos e apresentada cobrindo as diversas situações possíveis. / [en] This research work consists of the development of a computer system to be used in the nesting of plane figures on rectangular cardboards in the paper industry. This system is a first step towards the total automation of the manufacture of paper packs. The system consists of four parts: a figure editor; a subsystem for editing the colors of the edges; the nesting subsystem, in which the colors of the edges are taken into account; and a subsystem for outputing the drawings. A detailed discussion on the calculation of the distance between the figures and its influence on the nesting system is presented. Finally, graphical examples which cover the various possible situations are presented.

京都大学 / 新制・課程博士 / 博士(工学) / 甲第23869号 / 工博第4956号 / 新制||工||1774(附属図書館) / 京都大学大学院工学研究科都市環境工学専攻 / (主査)教授 藤原 拓, 准教授 西村 文武, 講師 中田 典秀 / 学位規則第4条第1項該当 / Doctor of Philosophy (Engineering) / Kyoto University / DFAM

The growth of the market for electric vehicles (EVs) is increasing each year with record numbers of newly registered electric vehicles in 2022. Electric vehicles are considered the key component of a sustainable transportation sector. The knowledge of influential factors on purchase decisions for new EVs is high. The research attention within the field has been huge over the last decade. As the number of EVs in the transport sector increases, the number of used EVs is increasing. However, basically no research about influential factors for purchase decisions of used EVs can be found. As the prices of new EVs are high, the market for such vehicles is constrained to certain consumer groups. To succeed with the transformation of the transport sector, more consumer groups need to have access to EVs, which can be accomplished with a well-established market for used EVs. The knowledge about financial factors for used EVs need to increase. The purpose of this study is to understand if well-known financial factors influencing purchase decisions for new EVs have similar effects on purchase decisions for used EVs. As in previous studies, this study applies a quantitative research approach where an online survey has been designed with liker-scale questions. A total of 90 respondents in varying age groups answered the survey based on five categories of financial factors: financial incentives, purchasing price, operational costs, maintenance costs, and residual value. The data has been analyzed using Spearman correlation, principal component analysis (PCA), and ordered logistic regression. The Spearman correlation found positive significance between the financial factors and the purchase intention of new and used EVs. The factors identified in the PCA are in line with the expected factors as the predefined research model. The ordered logistic regression could however support significant relation with purchase intensions of new EVs the aspect of financial incentives and residual value. For used EVs the regression was not successful. This study shows that the influence of financial incentives on the purchase intention of EVs, are similar to both new EVs as for used EVs. This result is however based on a small data sample that is much smaller than previous studies. For future research it is important to retrieve a larger data sample to improve reliability of the result. The regression analysis did not support the analytical outcome of the Spearman correlation and it needs to be further analyzed why a significant regression model could not be defined in relation to used EVs.

No description available.

No description available.

Silicones elastomers are widely used all over the world due to their unusual properties when compared to their carbon-based counterparts. Synthetic methods for their synthesis are still quite limited and the traditional silicone products are not able to completely meet the requirement for modern materials. Silicone elastomers with customized structures and with higher levels of sustainability will be the research focus for the development of next generation materials. The element sulfur and its functional groups are growing players in modern polymer and materials science, since sulfur reactions are exceptionally versatile. The incorporation of sulfur reactions into the design and preparation of silicone materials can lead to silicones with unique properties for various research interests. Initial exploration was focused on the creation of general and simple methods for 3D printing silicone elastomers using thiol-ene chemistry. However, silicone inks suitable for 3D printing are still quite limited. Photo-initiated thiol-ene chemistry was proposed to design a rapid cure silicone ink for extrusion 3D printing. Unlike other radical reactions, the relatively oxygen insensitive thiol-ene was able to provide the necessary rapid reaction rate and build up the necessary viscosity for practical printing in less than 2 seconds in the presence of air. Various customized silicone structures with different moduli were obtained with a relative fast printing speed. The use of thiol oxidation reactions in the synthesis of silicone elastomers is also demonstrated in this thesis. Reductive cleavage of the resulting disulfide bridge was successfully performed with the presence of hydrosilane and B(C6F5)3 catalyst. Herein, a synthetic method to reversible silicone elastomers based on the disulfide linkage is described. This method could be extended to cleave the disulfide and polysulfide linkage in used automotive rubber materials. Various kinds of sulfur-cured rubbers were successfully devulcanized to polymeric oil. This simple and efficient method could potentially offer a solution for the huge amount of tire waste produced every year. Finally, a new method for preparing thermoplastic silicone elastomers with ionic linkages is reported. A novel dicarboxylic acid-modified silicone was synthesized though thiol-Michael additions. The resulting ionic crosslinked networks were built though the neutralization between carboxylic and amino silicone. Thermoplastic silicone elastomers with unique viscoelastic behavior can be obtained. In summary, the thesis demonstrates that sulfur chemistry is an exceptional synthetic tool for the silicone chemist. / Thesis / Doctor of Philosophy (PhD)

Master of Science