Este trabalho tem por objetivo apresentar a Teoria da Resposta ao Item (TRI), por meio de uma abordagem pedagógica, aos professores da educação básica, que mencionaram esta necessidade por meio de pesquisa realizada pelo autor. Levar parte do conhecimento teórico que embasa esta teoria ao conhecimento do docente, principalmente a construção da curva de probabilidade de acerto do item, favorecerá a compreensão, a análise e o monitoramento do processo avaliativo educacional. Este material apresenta as principais definições e conceitos da avaliação externa em larga escala, além de fornecer insumos para a compreensão das suposições realizadas para aplicação da metodologia. Neste sentido, o texto foi estruturado de forma a apresentar didaticamente as etapas do processo de implementação de uma avaliação, desde a construção do item até a apuração e divulgação dos resultados. Todo enfoque será dado à construção do modelo da TRI com um parâmetro (dificuldade do item), também conhecido como modelo de Rasch, o que simplifica e facilita a compreensão da metodologia. O modelo utilizado nas avaliações externas em larga escala (modelo com três parâmetros) será introduzido a partir das considerações realizadas na abordagem que explicita o pensamento da construção do modelo de um parâmetro. Acredita-se que esta compreensão possa colaborar com o professor na exploração das habilidades/competências dos alunos durante os anos escolares. / This study aims to present the Item Response Theory (IRT), through a pedagogical approach, to teachers of basic education, which mentioned this necessity through research conducted by the author. To take part of the theoretical knowledge that supports this theory to the teacher\'s knowledge, especially the construction of probability curve of item correct response, it will favor for understanding, analysis and monitoring the evaluation educational process. This material presents the main definitions and concepts of the external evaluation in large scale, besides providing inputs for understanding the assumptions made to apply the methodology. In this sense, the text was structured in order to present the implementation process stages of a large scale assessment, from the item construction to the results calculation and dissemination. The focus will be given to the IRT model construction of one-parameter (difficulty of the item), also known as Rasch model, since it simplifies and facilitates the understanding of methodology. The model used in the external assessment on a large scale (three-parameter model) will be introduced from the considerations made in the approach that explicit the thought of one-parameter model construction. It is believed that understanding can collaborate with teacher in exploration of the students\' skills/competences during the school year.

近年來，在中國大陸市場有大量公司進行掛牌上市的同時，也有越來越多的公司出現債務逾期甚至是違約的情況。考慮到目前中國經濟增速放緩，處在轉型發展的複雜階段，銀行信貸等資金供應鏈需要謹慎評估企業出現財務困境的風險。但是我們發現金融機構在平常管理信貸業務的時候會盲目地看重高額利潤的回報而忽略借款者潛在的財務危機，而且投資人在進行投資分析的時候往往也會忽略企業的財務狀況而使自己遭受損失，因此從企業的財務狀況入手對其進行財務困境機率的評估有非常重大的現實意義。 本文通過對企業財務指標進行相關分析以構建公司財務困境機率評估模型。本文選取了不良貸款率最高的製造業作為研究對象，將2015年滬深兩地的124家上市製造業公司的財務資料作為訓練樣本，將2014年120家上市公司的財務資料作為檢驗樣本，將交易所特別處理公司劃分為非正常組公司，其餘為正常組。本文通過篩選得出23個財務指標作為研究變數，引入了 Logistic 模型與 SVM 模型，針對單一模型的預測結果在準確率和穩定性方面不理想的問題引入了基於 Logistic 模型、SVM 模型的組合模型，並用檢驗樣本進行了四個模型的相關實證分析，比較了四個模型之間的準確度。 對四個模型進行實證分析的結果表明：Logistic模型穩健性好、可解釋性強、建模過程簡單易操作，但分類精度略低於 SVM 模型；SVM雖然分類精度高，但缺乏可解釋性和穩定性，且建模過程依賴專家知識和經驗；Logistic -SVM 組合模型則兼具其優點，預測精確度較單一模型均有提高，而且研究發現異態並行結構優於串型結構。通過本文建立的模型可以計算出企業的陷入財務困境的機率，有效評估企業的違約風險，進而為相關金融機構和投資者提供放款或投資的判斷依據。 / At present, more and more companies are listed in the Chinese mainland market. At the same time, more and more companies are frequently at risk of default and overdue. Given the slowdown in China's economic growth and the complex environment of transformation and development, the supply of funds such as bank loans and other capital needs to be cautious, debt default, loan overdue cases are still likely to occur one after another. However, we find that financial institutions blindly value the return of high profits while ignoring the potential financial crisis of borrowers in the normal management of credit business, it is of great significance to start with the financial status of a company to assess the probability of financial distress. This paper builds a company default probability assessment model by analyzing the financial indicators of enterprises. This paper selects the manufacturing industry with the highest NPL as the research object. Taking the financial data of 124 listed manufacturing companies in Shanghai and Shenzhen in 2015 as the training samples, using the financial data of 120 listed companies in 2014 as the test sample, Exchange special treatment companies divided into non-normal group companies, the rest for the normal group. According to the data of its 2015 financial indicators, 23 financial indicators were screened out as research variables, and a comprehensive analysis was carried out. The Logistic model and SVM model were introduced. Combined model was introduced based on the Logistic model and SVM model to solve the problem that the prediction accuracy and stability of the single model were not ideal,. Finally, empirical analysis of the four models is carried out using the sample data of listed companies in 2014, and the accuracy of the four models is compared. The results of empirical analysis of the four models show that Logistic regression model has no strict assumptions on the data, a better stability and interpretation, but the classification accuracy is slightly lower than the SVM model. The SVM model has higher classification accuracy, but the disadvantage is the lack of interpretability and stability, the modeling process depends on expert knowledge and experience. In order to balance the stability of Logistic model and the accuracy of SVM model, this paper introduces a combined model based on Logistic model and SVM model. The analysis shows that the prediction accuracy of combined model is higher than that of single model, the combination of Logistic regression model and SVM model based on Parallel structure has a higher prediction accuracy than Sequential structure. The model established in this paper can calculate the default probability of an enterprise, effectively assess companies’ risk of financial distress, and then provide the judgment basis for the relevant financial institutions and investors to lend or invest.

Wind power is one of the fastest growing energy sources, which have advantages in terms of delivering clean, cheap and fast energy. Many actors and organizations have realized this potential, which has lead to exponential growth of the wind power over recent couple of years. Despite promising future of clean and green energy through wind there are still areas to be improved to tackle main hinders for further development on a larger scale. The larger scale development of wind power has up-to-date been reached in only few countries such asDenmarkandGermany. The most potential can be found through offshore wind turbines due to, among other, lesser height and noise restrictions than inland wind turbines as well as better wind resources. This thesis is mainly characterized by the mixed-method method, which is in its turn characterized by mixing different research methods such as induction, deduction and abduction as they might be suitable to apply during the different stages of the research process. In this thesis the system approach will be considered to model the scope of this study’s context. As this thesis was constructed as a theoretical study the systematic literature review was used as the main source for data collection. The synthesis of the reviewed articles was initially performed in a broad manner to show overall picture of research related to the scope of this thesis. In the following step, the adopted LCM tool was used to extract study specific data from the reviewed and additional articles. This enabled to link some of the current problems, in wind power area, and ‘unfulfilled’ stakeholder requirements.    This thesis aim was to identify important stakeholders and to address their respective requirements within the scope of supportability engineering applied in the wind power context, particularly stakeholder requirements that have not been considered by previous research. The purpose of it was to compare different requirements with current issues in the wind power sector. Conducted systematic literature review showed significant and costly (development) issues related to the supportability engineering such as reliability, availability, maintainability, accessibility problems etc. This was fully in line with the stakeholders’ requirements, which in several cases demanded higher dependability i.e. availability performance and its influencing factors.  The thesis also included definition of supportability engineering framework, through comparing several widely accepted and standardized theoretical concepts. This comparison enabled definition of more focused approach with requirements on the ‘supported system’ and to lesser extent the ‘support system’. These requirements are usually mixed within the different theoretical concepts.  The main findings from this study are that application of supportability engineering framework would lead to earlier identification of important stakeholders and their requirements. Considering these requirements, for instance availability and maintainability of the wind turbines, at earlier stages of the life cycle through better design and improved supportability infrastructure could potentially decrease amount of current problems in the wind energy sector in particular for the offshore part. Another result was that different stakeholders and their requirements were identified. Thesis contribution overall was a new ‘refined approach’ to deal with supportability issues through supportability engineering framework, whose main focus is the ‘supported system’.

Graduation theses is dealing New logistic concept, which is in these days implemented in concern Volkswagen. Author, employee of Škoda company, describes New logistic concept at all in its five moduls and he is evaluating its principles with the modernest logistic theory. In practical part he analyses factuality of concrete Milkrun establishing in order of New logistic concept principles. Work results with confirmation of funcionality of new principles through concrete exam of Milkrun. It containes also common recomendation for successful realisation the whole concept.

Higher-level education is very important these days, which can create very high thresholds for admission on popular programs on certain universities. In order to know what grade will be needed to be admitted to a program, a student can look at the threshold from previous years. We explored whether it was possible to generate accurate predictions of what the future threshold would be. We did this by using well-established machine learning classification models and admission data from 14 years back covering all applicants to the Computer Science and Engineering Program at KTH Royal Institute of Technology. What we found through this work is that the models are good at correctly classifying data from the past, but not in a meaningful way able to predict future thresholds. The models could not make accurate future predictions solely based on grades of past applicants. / Eftergymnasiala studier är väldigt viktiga numera, vilket kan leda till mycket höga antagningskrav på populära program på vissa universitet och högskolor. För att veta vilket betyg som krävs för att komma in på en utbildning så kan studenten titta på gränsen från tidigare år och utifrån det gissa sig till vad gränsen kommer vara kommande år. Vi undersöker om det är möjligt att, med hjälp av väletablerade, klassificerande Maskininlärnings-modeller kunna förutse antagningsgränsen i framtiden. Vi tränar modellerna på data med antagningsstatistik som sträcker sig tillbaka 14 år med alla ansökningar till civilingenjörs-programmet Datateknik på Kungliga Tekniska Högskolan. Det vi finner genom detta arbete är att modellerna är bra på att korrekt klassificera data från tidigare år, men att de inte, på ett meningsfullt sätt, kan förutse betygsgränsen kommande år. Modellerna kan inte göra detta endast genom data på betyg från tidigare år.

Automatiserad dokumentklassificering är en process eller metod som syftar till att bearbeta ochhantera dokument i digitala former. Många företag strävar efter en textklassificeringsmetodiksom kan lösa olika problem. Ett av dessa problem är att klassificera och organisera ett stort antaldokument baserat på en uppsättning av fördefinierade kategorier.Detta examensarbete syftar till att hjälpa Medius, vilket är ett företag som arbetar med fakturaarbetsflöde, att klassificera dokumenten som behandlas i deras fakturaarbetsflöde till fakturoroch icke-fakturor. Detta har åstadkommits genom att implementera och utvärdera olika klassificeringsmetoder för maskininlärning med avseende på deras noggrannhet och effektivitet för attklassificera finansiella dokument, där endast fakturor är av intresse.I denna avhandling har två dokumentrepresentationsmetoder "Term Frequency Inverse DocumentFrequency (TF-IDF) och Doc2Vec" använts för att representera dokumenten som vektorer. Representationen syftar till att minska komplexiteten i dokumenten och göra de lättare att hantera.Dessutom har tre klassificeringsmetoder använts för att automatisera dokumentklassificeringsprocessen för fakturor. Dessa metoder var Logistic Regression, Multinomial Naïve Bayes och SupportVector Machine.Resultaten från denna avhandling visade att alla klassificeringsmetoder som använde TF-IDF, föratt representera dokumenten som vektorer, gav goda resultat i from av prestanda och noggranhet.Noggrannheten för alla tre klassificeringsmetoderna var över 90%, vilket var kravet för att dennastudie skulle anses vara lyckad. Dessutom verkade Logistic Regression att ha det lättare att klassificera dokumenten jämfört med andra metoder. Ett test på riktiga data "dokument" som flödarin i Medius fakturaarbetsflöde visade att Logistic Regression lyckades att korrekt klassificeranästan 96% av dokumenten.Avslutningsvis, fastställdes Logistic Regression tillsammans med TF-IDF som de övergripandeoch mest lämpliga metoderna att klara av problmet om dokumentklassficering. Dessvärre, kundeDoc2Vec inte ge ett bra resultat p.g.a. datamängden inte var anpassad och tillräcklig för attmetoden skulle fungera bra. / Automated document classification is an essential technique that aims to process and managedocuments in digital forms. Many companies strive for a text classification methodology thatcan solve a plethora of problems. One of these problems is classifying and organizing a massiveamount of documents based on a set of predefined categories.This thesis aims to help Medius, a company that works with invoice workflow, to classify theirdocuments into invoices and non-invoices. This has been accomplished by implementing andevaluating various machine learning classification methods in terms of their accuracy and efficiencyfor the task of financial document classification, where only invoices are of interest. Furthermore,the necessary pre-processing steps for achieving good performance are considered when evaluatingthe mentioned classification methods.In this study, two document representation methods "Term Frequency Inverse Document Frequency (TF-IDF) and Doc2Vec" were used to represent the documents as fixed-length vectors.The representation aims to reduce the complexity of the documents and make them easier tohandle. In addition, three classification methods have been used to automate the document classification process for invoices. These methods were Logistic Regression, Multinomial Naïve Bayesand Support Vector Machine.The results from this thesis indicate that all classification methods used TF-IDF, to represent thedocuments as vectors, give high performance and accuracy. The accuracy of all three classificationmethods is over 90%, which is the prerequisite for the success of this study. Moreover, LogisticRegression appears to cope with this task very easily, since it classifies the documents moreefficiently compared to the other methods. A test of real data flowing into Medius’ invoiceworkflow shows that Logistic Regression is able to correctly classify up to 96% of the data.In conclusion, the Logistic Regression together with TF-IDF is determined to be the overall mostappropriate method out of the other tested methods. In addition, Doc2Vec suffers to providea good result because the data set is not customized and sufficient for the method to workwell.

Composite endpoints are widely used in clinical trials. The outcome of a clinical trial can affect many individuals and it is therefore of importance that the methods used are as effective and correct as possible. Improvements of the standard method of testing composite endpoints have been proposed and in this thesis, the alternative method using nonparametric combination methodology is compared to the standard method. Performing a simulation study, the power of three combining functions (Fisher, Tippett and the Logistic) are compared to the power of the standard method. The performances of the four methods are evaluated for different compositions of treatment effects, as well as for independent and dependent components. The results show that using the nonparametric combination methodology leads to higher power in both dependent and independent cases. The combining functions are suitable for different compositions of treatment effects, the Fisher combining function being the most versatile. The thesis is written with support from Statisticon AB.

Entrepreneurs are portrayed as salient drivers of regional development and for a number of years nascent entrepreneurs have been studied in a large number of countries as part of the Global Entrepreneurship Monitor project and the Panel Study of Entrepreneurial Dynamics. Scholars have devoted much effort to investigating factors that determine how individuals engage in entrepreneurial activities, with most of the discussion limited to business start-ups. However, since this type of project does not follow identical nascent entrepreneurs over time, limited knowledge exists about their development and whether they stay in this nascent phase for a long time. In practice, it is common for entrepreneurs to run a business and at the same time work in wage work, so-called combining entrepreneurs. In Sweden, almost half of all business owners combine wage work with a business. However, not all combining entrepreneurs will eventually decide to leave the wage work and invest fully in the business. Consequently, much research has focused on the first step of entering entrepreneurship full time, but less has focused on the second step, the transition from the combining phase to full-time self-employment. The aim of this thesis is therefore to contribute to the theory of entrepreneurship by gaining a deeper understanding of combining entrepreneurs and their motives and intentions.   In the context of combining entrepreneurs, the theory of identity, resources and choice overload has been used to examine how entrepreneurs’ age (when starting the business), entrepreneurial tenure (the length of engagement in the side-business), hours spent (weekly involvement in the side-business), involvement in entrepreneurial teams (leading the business with one or more partners) and involvement in networks (business networks) influence their passion for engaging in entrepreneurship while sustaining wage work. Different categories of combining entrepreneurs and their intentions have also been examined.   A survey was administered to 1457 entrepreneurs within the creative sector in two counties in Sweden (Gävleborgs County and Jämtlands County). Since there were no separate mailing lists to only combining entrepreneurs, the survey was sent to all entrepreneurs within the chosen industry and counties. The total response rate was 33.5 percent and of them 57.6 percent combined, yielding 262 combining entrepreneurs who answered the questionnaire. The survey was then followed up with eight focus group interviews and two single interviews to validate the answers from the questionnaire.   The results indicate three types of combining entrepreneurs: nascent – with the intention to leave the combining phase for a transition into full-time self-employment, lifestyle – with the intention to stay in the combining phase, and occasional – with the intention to leave the combining phase for full-time wage work and close down the business. Transitioning fully to self-employment increases with the individual’s age. Also, a positive interactive effect exists with involvement in entrepreneurial networks. The results also indicate that the ability to work with something one is passionate about is the top motive for combining wage work with a side-business. Passion is also more likely to be the main motive behind the combining form among individuals who are older at business start-up, but passion is less likely to be the main motive behind the combining form among individuals who spend more time on the business. The longer the individual has had the side-business, the less likely passion is the main motive behind the combining form, and passion is less likely to be the main motive among those who are part of an entrepreneurial team. / <p>Avhandlingen baseras på fem delarbeten, tre var opublicerade vid tidpunkten för disputationen, två länkas här.</p>

A bridge approach is usually built to provide a smooth and safe transition for vehicles from the roadway pavement to the bridge structure. However, differential settlement between the roadway pavement resting on embankment fill and the bridge abutment built on more rigid foundation often creates a bump in the roadway. Previous work examined this issue at a microscopic level and presented new methods for eliminating or minimizing the effects at specific locations. This research studies the problem at a macroscopic level by determining methods to predict settlement severity to assist designers in developing remediation plans during project development to minimize the lifecycle costs of bridge bump repairs. The study is based on historic data from a wide range of Kentucky roads and bridges relating to bridge approach inspection and maintenance history. A macro method considering a combination of maintenance times, maintenance measures, and observed settlement was used to classify the differential settlement scale as minimal, moderate, and severe, corresponding to the approach performance status good, fair, and poor. A series of project characteristics influencing differential settlement were identified and used as parameters to develop a model to accurately predict settlement severity during preliminary design. Eighty-seven bridges with different settlement severities were collected as the first sample by conducting a survey of local bridge engineers in 12 transportation districts. Sample two was created by randomly selecting 600 bridges in the inspection history of bridges in Kentucky. Ordinal and/or multinomial logistic regression analyses were implemented to identify the relationships between the levels of differential settlement and the input variables. Two predictive models were developed. Prediction of bridge approach settlement can play an important role in selecting proper design, construction, and maintenance techniques and measures. The users can select one or two models to predict the approach settlement level for a new bridge or an existing bridge with different purposes. The significance of this study lies in its identification of parameters that had the most influence on the settlement severity at bridge ends, and how those parameters interacted in developing of a prediction model. The important parameters include geographic regions, approach age, average daily traffic (ADT), the use of approach slabs, and the foundation soil depth. The regression results indicate that the use of approach slabs can improve the performance of approaches on mitigating the problem caused by differential settlement. In addition, current practices regarding differential settlement prediction and mitigation were summarized by surveying the bridge engineers in 5 transportation districts.

Entrepreneurs are portrayed as salient drivers of regional development and for a number of years nascent entrepreneurs have been studied in a large number of countries as part of the Global Entrepreneurship Monitor project and the Panel Study of Entrepreneurial Dynamics. Scholars have devoted much effort to investigating factors that determine how individuals engage in entrepreneurial activities, with most of the discussion limited to business start-ups. However, since this type of project does not follow identical nascent entrepreneurs over time, limited knowledge exists about their development and whether they stay in this nascent phase for a long time. In practice, it is common for entrepreneurs to run a business and at the same time work in wage work, so-called combining entrepreneurs. In Sweden, almost half of all business owners combine wage work with a business. However, not all combining entrepreneurs will eventually decide to leave the wage work and invest fully in the business. Consequently, much research has focused on the first step of entering entrepreneurship full time, but less has focused on the second step, the transition from the combining phase to full-time self-employment. The aim of this thesis is therefore to contribute to the theory of entrepreneurship by gaining a deeper understanding of combining entrepreneurs and their motives and intentions.   In the context of combining entrepreneurs, the theory of identity, resources and choice overload has been used to examine how entrepreneurs’ age (when starting the business), entrepreneurial tenure (the length of engagement in the side-business), hours spent (weekly involvement in the side-business), involvement in entrepreneurial teams (leading the business with one or more partners) and involvement in networks (business networks) influence their passion for engaging in entrepreneurship while sustaining wage work. Different categories of combining entrepreneurs and their intentions have also been examined.   A survey was administered to 1457 entrepreneurs within the creative sector in two counties in Sweden (Gävleborgs County and Jämtlands County). Since there were no separate mailing lists to only combining entrepreneurs, the survey was sent to all entrepreneurs within the chosen industry and counties. The total response rate was 33.5 percent and of them 57.6 percent combined, yielding 262 combining entrepreneurs who answered the questionnaire. The survey was then followed up with eight focus group interviews and two single interviews to validate the answers from the questionnaire.   The results indicate three types of combining entrepreneurs: nascent – with the intention to leave the combining phase for a transition into full-time self-employment, lifestyle – with the intention to stay in the combining phase, and occasional – with the intention to leave the combining phase for full-time wage work and close down the business. Transitioning fully to self-employment increases with the individual’s age. Also, a positive interactive effect exists with involvement in entrepreneurial networks. The results also indicate that the ability to work with something one is passionate about is the top motive for combining wage work with a side-business. Passion is also more likely to be the main motive behind the combining form among individuals who are older at business start-up, but passion is less likely to be the main motive behind the combining form among individuals who spend more time on the business. The longer the individual has had the side-business, the less likely passion is the main motive behind the combining form, and passion is less likely to be the main motive among those who are part of an entrepreneurial team.