Today’s power systems are based on a centralised system and distribution networks that are considered as passive terminations of transmission networks. The high penetration of Distributed Generation (DG) at the distribution network level has created many challenges for this structure. New tools and simulation approaches are required to address the subject and to quantify the dynamic characteristics of the system. A distribution network or part of it with DG, Active Distribution Network Cell (ADNC), can no longer be considered as passive. An equivalent dynamic model of ADNC is therefore extremely important, as it enables power system operators to quickly estimate the impact of disturbances on the power system’s dynamic behaviour. A dynamic equivalent model works by reducing both the complexity of the distribution network and the computation time required to run a full dynamic simulation. It offers a simple and low-order representation of the system without compromising distribution network dynamic characteristics and behaviour as seen by the external grid. This research aims to develop a dynamic equivalent model for ADNC. It focuses on the development of an equivalent model by exploiting system identification theory, i.e. the grey-box approach. The first part of the thesis gives a comprehensive overview and background of the dynamic equivalent techniques for power systems. The research was inspired by previous work on system identification theory. It further demonstrates the theoretical concept of system identification, system load modelling and the modelling of major types of DG. An equivalent model is developed, guided by the assumed structure of the system. The problem of equivalent model development is then formulated under a system identification framework, and the parameter estimation methodology is proposed. The validation results of the effectiveness and accuracy of the developed model are presented. This includes the estimation of the parameter model using a clustering algorithm to improve the computational performance and the analysis of transformer impedance effects on the ADNC responses. The evaluation of probability density function, eigenvalue analysis and parameter sensitivity analysis for the model parameters are also presented. Typical model parameters for different network topologies and configurations are identified. Finally, the developed equivalent model is used for a large power system application. The accuracy and robustness of the developed equivalent model are demonstrated under small and large disturbance studies for various types of fault and different fault locations.

The purpose of this paper is to investigate the impact of cross-listing on companies' performance. It is divided into two aspects, one in short-term and the other in long-term. In short-run study, 6 companies cross-listing in NYSE and Chinese market are in the sample. In pre-cross-listing period, the abnormal returns are mostly positive and remain stable; the cumulative abnormal returns are close to 0 and the difference among them is very small; but on the cross-listing day, all the companies' abnormal returns decline, and after that day, the abnormal returns still fluctuate around 0 while most of them are negative, and the difference among each company's cumulative abnormal return become large. In long-run study, by using multiple regression of 99 Chinese companies listed in th U.S. markets form 2007 to 2012, there is a significant positive relationship between total asset turnover and cross-listing at 5% significance level and there is a significantly negative relation between market value and cross-listing at 10%significance level; return on equity and return on asset are both positive with cross-llisting, but not significant.

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This dissertation is designed to answer the following questions: (1) Which measurement model is better to contribute to the research model in different areas? (2) Within a given model, how does the data size influence the performance of a neural network (NN) and some other methods? (3) Compared to partial least square (PLS), ordinary least square (OLS), XGBoost, how is the performance of NN? Essay 1 systematically compares PLS-SEM to ANN and builds the hybrid vehicle purchasing intention model (HVPIM). It investigates different models that have been previously applied to study the theory of planned behavior (TPB). The methods find those factors that significantly correlated with consumer purchase intention. Essay 2 posits, develops, and tests a PNN model with healthcare data. A research survey is designed and distributed to undergraduate students from a major research school in the U.S. southwest region. Research hypotheses are tested using PLS-SEM and PNN. Essay 3 targets on testing the performance of the NN model with panel data from the soccer transfer market. To achieve this purpose, the essay posits and develops an empirical test built on game theory. The NN model is tested and compared to OLS and XGBoost. As the research compares the different types of NN models with other linear models, NN does have extensive applications and outperforms some other methods. The contribution of this research falls into both academia and industry. While theoretical science and practical applications may be mutually complementary, real demand is often the driving force behind technological growth. Currently, the practicality of neural networks has many accomplishments, such as smart control, pattern recognition, robot control, etc. It can be expected that using NN algorithms to process the necessary data will improve its applicability in real life.

Cookies är idag ett utbrett fenomen som nyttjas vid digital insamling av information. Informationen som samlas in är ofta av personlig karaktär och används bland annat för att individanpassa användarupplevelser på webbsidor. Ovissheten kring digital insamling av personlig information skapar en oro som idag är mycket omdebatterad. Detta arbete utgörs av en kvantitativ studie med syfte att undersöka möjliga samband mellan universitetsstudenters medvetenhet, attityd och beteende gällande digital insamling av information. Undersökningen består av en onlinebaserad enkät. Det insamlade materialet har analyserats utifrån det teoretiska ramverket Communication Privacy Management (CPM) med hjälp av analysmetoden Partial Least Squares (PLS) samt IBM Statistical Package for Social Sciences (SPSS) för att finna relevanta samband. Resultat påvisade att majoriteten universitetsstudenter känner till fenomenet cookies, men färre känner till dess användningsområden. Det konstaterades även att medvetenhet kring cookies har en påverkan på både beteende och attityd. Det fastställdes även att oavsett om universitetsstudenter har en negativ attityd förändras inte dess beteende. / Cookies are a widespread phenomenon and the main technique for digital collection of information. The collected information is often of personal nature and is used, among other things, to personalize user experiences on web pages. The uncertainty regarding digital collection of personal information creates privacy concerns that is significantly debated today. This essay consists of a quantitative study with the aim to investigate possible relations between university students awareness, attitude and behaviour regarding digital collection of information. The survey consist of an online-based poll. The gathered material has been analyzed by the theoretical framework Communication Communication Management (CPM) with the analysis method Partial Least Squares (PLS) and the IBM Statistical Package for Social Sciences (SPSS) to find relevant relations. Results showed that the majority of university students are familiar with the phenomenon of cookies, but few are aware of its area of use. It was also found that awareness of cookies has an influence on both behaviour and attitude. It can be established that regardless of whether university students have a negative or positive attitude regarding cookies, it does not affect their behaviour.

Made available in DSpace on 2016-03-15T19:26:30Z (GMT). No. of bitstreams: 1 Anderson Luis Saber Campos.pdf: 1013366 bytes, checksum: 2e9d466c92f8d404ca44d38a91e676e8 (MD5) Previous issue date: 2007-09-11 / Coordenação de Aperfeiçoamento de Pessoal de Nível Superior / From the theory on structure of capital and the application of structural equations a model was considered to evaluate the indebtedness of the public companies in Brazil. The effect of direct and indirect costs of bankruptcy, tax benefits, agency costs of free cash flow and the agency costs of borrowing. Computed the results, which was opted to analyzing an alternative model that indicates the relevance of the capital demand and offers effects in the level of companies indebtedness. One met evidences on the relevance of direct and indirect costs of bankruptcy and agency costs of borrowing in the determination of the capital structure in analyzed companies. / A partir da teoria sobre estrutura de capital e da aplicação de equações estruturais foi proposto um modelo para avaliar o endividamento das companhias abertas no Brasil. Foram considerados os efeitos das dificuldades financeiras, benefícios fiscais, agency de capital próprio e de capital de terceiros. Computado os resultados optou-se por analisar um modelo alternativo segundo o qual encontramos indícios e relevância dos efeitos da demanda e oferta de capital no nível de endividamento das empresas. Encontrou-se evidências que dificuldades financeiras e agency de capital de terceiros influem na determinação da estrutura de capital das empresas analisadas.