SANTOS, Luan Falcão Daniel. Network effects, conformism and misbehavior in Brazilian classrooms. -2016. 79f. Dissertação (mestrado). - Universidade Federal do Ceará, Programa de Pós Graduação em Economia, CAEN, Fortaleza, 2016. / Submitted by Mônica Correia Aquino (monicacorreiaaquino@gmail.com) on 2017-07-28T19:24:46Z No. of bitstreams: 1 2016_dis_lfdsantos.pdf: 1161764 bytes, checksum: 657efde122aaf7a32fa71aa91069e159 (MD5) / Approved for entry into archive by Mônica Correia Aquino (monicacorreiaaquino@gmail.com) on 2017-07-28T19:25:15Z (GMT) No. of bitstreams: 1 2016_dis_lfdsantos.pdf: 1161764 bytes, checksum: 657efde122aaf7a32fa71aa91069e159 (MD5) / Made available in DSpace on 2017-07-28T19:25:16Z (GMT). No. of bitstreams: 1 2016_dis_lfdsantos.pdf: 1161764 bytes, checksum: 657efde122aaf7a32fa71aa91069e159 (MD5) Previous issue date: 2016 / For understanding how networks a ect the behavior of individuals, speci - cally the behavior os students in the last year of high school inside the classroom, we estimate a model of Network e ects, the Local-average model for two behavioral variables: doing an exam without being prepared and cheating in an exam, in order to understand how the behavior of individual's friends a ects his or her behavior. It was found a positive and statistically signi cant e ect for the network e ect of the probability of doing an exam without being prepared, and a positive, but not signi cant e ect for the network e ect of the probability of cheating in an exam. This result shows that policies or actions aiming the reduction of the probability of a student do an exam without being prepared have what is called social multiplier e ect, because besides this policy change the behavior of the student regarding this variable, his or her change of behavior a ects positively the behavior of people in his or her network. / Para entender de que forma as networks afetam o comportamento dos indiv duos, em espec co o comportamento de estudantes no ultimo ano do ensino m edio dentro da sala de aula, estimamos um modelo de Network e ects, o Localaverage Model para duas vari aveis comportamentais: fazer uma prova ou teste sem ter se preparado e colar em uma prova, a m de entender como o comportamento dos amigos de um indiv duo afeta o comportamento do mesmo. Encontrou-se uma efeito positivo e estatisticamente signi cante para o network e ect da probabilidade de se fazer uma prova ou teste sem ter se preparado, e um efeito positivo, mas n~ao signi cante para o network e ect da probabilidade de se colar em uma prova. Este resultado mostra que pol ticas ou a c~oes que visam a redu c~ao da probabilidade de um estudante fazer um exame sem se preparar tem o que se chama social multiplier e ect, pois al em dessa pol tica mudar o comportamento do estudante em rela c~ao a essa vari avel, sua mudan ca de comportamento afeta positivamente o comportamento das pessoas em sua network.

This thesis presents experimental studies on the relationship between external surface pressure and the perceived discomfort in seated body areas, in particular those under the ischial tuberosity and the mid-thigh. It consists of three parts. Part one provides a comprehensive review of the existing knowledge concerning seated discomfort. The current assessment methods of seated discomfort are summarised, with the emphasis on the validity and reliability of the rating scale methods. The implications of surface pressure to seated people are outlined from the perspective of clinical, sensory and perceptual, and ergonomics domains. A brief review of current technologies for pressure measurement is also provided. Part two presents the experimental work. It starts with an exploratory assessment model of seated discomfort, based on pressure measures. Two preliminary experiments were conducted to test the feasibility of the model. Three further psychophysical experiments were carried out to test the validity and reliability of the selected six rating scales, and to investigate the effects of surface pressure levels on perceived pressure intensity and discomfort in the seated mid-thigh and ischial tuberosity areas. Surface pressure stimuli were applied to a seated body area of 3,318 mm2• Subjects judged three items of sensations: pressure intensity, local discomfort, and the overall discomfort. The main results are: I) A 50-point category partitioning scale was identified to be most sensitive and reliable for scaling pressure intensity and discomfort; 2) Sensations of pressure intensity and discomfort linearly increase with the logarithm of the pressure stimulus level; 3) Thresholds for pressure intensity and discomfort in the seated ischium and thigh areas were derived; 4) The sensitivity of intensity and discomfort to the stimuli differs between the locations .The mid-thigh is more sensitive to surface pressure than the ischium. It is considered that this is due to differences in load adaptation, body tissue composition and deformation; 5) Local pressure discomfort dominates the overall discomfort, and ratings of the local discomfort are higher than those of overall discomfort. Part three discusses the findings from this research. Four integration models of the overall discomfort from local discomfort components were proposed. The Weighted Average model asserts that the overall discomfort is a linear combination of local discomfort components, and that the weight of each local discomfort is the proportion of this component out of the arithmetic sum of all local discomfort components. The mechanisms of discomfort were analysed. The fundamental research presented herein uniquely contributes to the knowledge on the human perception of seated pressure discomfort. Although this is not application based, the findings contribute to the methods of seating comfort evaluation as well as provide criteria by which seat designers may formulate design requirements.

More power semiconductors are applying to the aircraft power system to make the system smaller, lighter and more reliable. Average models provide a good solution to system simulation and can also serve as the basis to derive the small signal model for system-level study using linear control theory. A new average modeling approach for three-phase and nine-phase diode rectifiers with improved ac and dc dynamics is proposed in this dissertation. The key assumption is to model the load current using its first-order Taylor Series expansion throughout the entire averaging time span. A thorough comparison in the time domain is given of this model and two additional average models that were developed based on different load current assumptions, using the detailed switching models as the benchmark. The proposed average model is further verified by experimental results. In the frequency domain, the output impedance of a nine-phase diode rectifier is derived, and the sampling effect in the average model is investigated by Fourier analysis. The feeder's impedance before the rectifier is modeled differently in the output impedance in contrast in the equivalent commutation inductance. The average model is applied to the resonance study in a system composed of a synchronous generator, a nine-phase diode rectifier and a motor drive. The Thevenin's and Norton's equivalent circuits are derived to construct a linearized system. The equivalent impedance are derived from the average models, and the source are obtained from the switching circuit by short-circuit or open-circuit. Transfer functions are derived from the harmonic sources to the bus capacitor voltage for resonance study. The relationship between the stability and the resonance is analyzed, and the effect of controllers on the resonance is investigated. Optimization is another system-level application of the average model. A half-bridge circuit with piezoelectric actuator as its load is optimized using genetic algorithm. The optimization provides the possibility to design the actuator and its driving circuit automatically. / Ph. D.

Multi-pulse transformer/rectifier systems are becoming increasingly popular in power distribution systems. These topologies can be found in aircraft power systems, motor drives, and other applications that require low total harmonic distortion (THD) of the input line current. This increase in the use of multi-pulse transformer topologies has led to the need to study large systems composed of said units and their interactions within the system. There is also an interest in developing small-signal models so that stability issues can be studied. This thesis presents a procedure for the average model of multi-pulse transformer/rectifier topologies. The dq rotating reference frame was used to develop the average model and parameter estimation is incorporated through the use of polynomial fits. The average model is composed of nonlinear dependent sources and linear passive components. A direct benefit from this approach is a reduction in simulation time by two orders of magnitude. The average model concept demonstrates that it accurately predicts the dynamics of the system being studied. In particular, two specific topologies are studied, the 12-pulse hexagon transformer/rectifier (hex t/r) and the 18-pulse autotransformer rectifier unit (ATRU). In both cases, detailed switching model results are used to verify the operation of the average model. In the case of the hex t/r, the average model is further validated with experimental data from an 11 kVA prototype. The hex t/r output impedance, obtained from the linearized average model, has also been verified experimentally. / Master of Science

Sweden, together with Norway, Finland and Denmark, have created a multi-national electricity market called NordPool. In this market, producers and retailers of electricity can buy and sell electricity, and the retailers then offers this electricity to end consumers such as households and industries. Previous studies have shown that pricing at the NordPool market is functioning quite well, but no other study has to my knowledge studied if pricing in the retail market to consumers in Sweden is well functioning. If the market is well functioning, with competition and low transaction costs when changing electricity retailer, we would expect that a homogeneous good such as electricity would be sold at the approximately same price, and that price changes would be highly correlated, in this market. Thus, the aim of this study is to test whether the price of Vattenfall, the largest energy firm in the Swedish market, is highly correlated to the price of other firms in the Swedish retail market for electricity. Descriptive statistics indicate that the price offered by Vattenfall is quite similar to the price of other firms in the market. In addition, regression analysis show that the correlation between the price of Vattenfall and other firms is as high as 0.98.

The dissertation deals with simulations of the DC-DC converters in their basic configurations (Buck, Boost, Buck-boost, Cuk, SEPIC). In the first part of the thesis derivation of transfer functions Line-to-Output (LTO) and Control-To-Output (CTO) can be found. These symbolic responses are derived for three types of basic converters (Buck, Boost, Buck-boost) using well-known average model [1]. Derived expressions are very complicated. For reduction of these expressions symbolic approximation method was used, however the generality is lost. The average model was used to for decreasing the computational effort of analysis of DC-DC converters in the time domain. For these simulations VHDL-AMS language was used. The main topic of the thesis is harmonic balance method, which was adapted to DC-DC converters. Because conditions and assumptions for LTO and CTO functions are very different, harmonic balance method was derived into two variants. For obtaining of LTO response, duty cycle of switching signal can be considered as constant in time. Spectrum of this signal is simple as follows from well-known sinc function. For obtaining of CTO response PWM modulation must be used. Compared to sinc function spectrum of PWM modulation is richer (contains more combination frequencies). Many types of PWM modulation is described in [31]. For simulation PWM modulation with uniform sampling in two variants (single and double edge) was used. Non-ideal switching of PWM switch was modeled by PWM pulse with defined slew rate. Last section deals with comparison of all derived functions (LTO, CTO, modulation type, defined slew rate) with well-known averaged model.

The idea of time series forecasting techniques is that the past has certain information about future. So, the question of how the information is encoded in the past can be interpreted and later used to extrapolate events of future constitute the crux of time series analysis and forecasting. Several methods such as qualitative techniques (e.g., Delphi method), causal techniques (e.g., least squares regression), quantitative techniques (e.g., smoothing method, time series models) have been developed in the past in which the concept lies in establishing a model either theoretically or mathematically from past observations and estimate future from it. Of all the models, time series methods such as autoregressive moving average (ARMA) process have gained popularity because of their simplicity in implementation and accuracy in obtaining forecasts. But, these models were formulated based on certain properties that a time series is assumed to possess. Classical decomposition techniques were developed to supplement the requirements of time series models. These methods try to define a time series in terms of simple patterns called trend, cyclical and seasonal patterns along with noise. So, the idea of decomposing a time series into component patterns, later modeling each component using forecasting processes and finally combining the component forecasts to obtain actual time series predictions yielded superior performance over standard forecasting techniques. All these methods involve basic principle of moving average computation. But, the developed classical decomposition methods are disadvantageous in terms of containing fixed number of components for any time series, data independent decompositions. During moving average computation, edges of time series might not get modeled properly which affects long range forecasting. So, these issues are to be addressed by more efficient and advanced decomposition techniques such as Wavelets and Empirical Mode Decomposition (EMD). Wavelets and EMD are some of the most innovative concepts considered in time series analysis and are focused on processing nonlinear and nonstationary time series. Hence, this research has been undertaken to ascertain the predictability of nonstationary time series using wavelet and Empirical Mode Decomposition (EMD) based ARMA models. The development of wavelets has been made based on concepts of Fourier analysis and Window Fourier Transform. In accordance with this, initially, the necessity of involving the advent of wavelets has been presented. This is followed by the discussion regarding the advantages that are provided by wavelets. Primarily, the wavelets were defined in the sense of continuous time series. Later, in order to match the real world requirements, wavelets analysis has been defined in discrete scenario which is called as Discrete Wavelet Transform (DWT). The current thesis utilized DWT for performing time series decomposition. The detailed discussion regarding the theory behind time series decomposition is presented in the thesis. This is followed by description regarding mathematical viewpoint of time series decomposition using DWT, which involves decomposition algorithm. EMD also comes under same class as wavelets in the consequence of time series decomposition. EMD is developed out of the fact that most of the time series in nature contain multiple frequencies leading to existence of different scales simultaneously. This method, when compared to standard Fourier analysis and wavelet algorithms, has greater scope of adaptation in processing various nonstationary time series. The method involves decomposing any complicated time series into a very small number of finite empirical modes (IMFs-Intrinsic Mode Functions), where each mode contains information of the original time series. The algorithm of time series decomposition using EMD is presented post conceptual elucidation in the current thesis. Later, the proposed time series forecasting algorithm that couples EMD and ARMA model is presented that even considers the number of time steps ahead of which forecasting needs to be performed. In order to test the methodologies of wavelet and EMD based algorithms for prediction of time series with non stationarity, series of streamflow data from USA and rainfall data from India are used in the study. Four non-stationary streamflow sites (USGS data resources) of monthly total volumes and two non-stationary gridded rainfall sites (IMD) of monthly total rainfall are considered for the study. The predictability by the proposed algorithm is checked in two scenarios, first being six months ahead forecast and the second being twelve months ahead forecast. Normalized Root Mean Square Error (NRMSE) and Nash Sutcliffe Efficiency Index (Ef) are considered to evaluate the performance of the proposed techniques. Based on the performance measures, the results indicate that wavelet based analyses generate good variations in the case of six months ahead forecast maintaining harmony with the observed values at most of the sites. Although the methods are observed to capture the minima of the time series effectively both in the case of six and twelve months ahead predictions, better forecasts are obtained with wavelet based method over EMD based method in the case of twelve months ahead predictions. It is therefore inferred that wavelet based method has better prediction capabilities over EMD based method despite some of the limitations of time series methods and the manner in which decomposition takes place. Finally, the study concludes that the wavelet based time series algorithm could be used to model events such as droughts with reasonable accuracy. Also, some modifications that could be made in the model have been suggested which can extend the scope of applicability to other areas in the field of hydrology.

金融市場是個變化莫測的環境，看似隨機，在隨機中卻隱藏著某些特性與關係。不論是自然現象中的氣象預測或是金融領域中對下一時刻價格的預測, 都有相似的複雜性。 時間序列的預測一直都是許多領域中重要的項目之一, 金融時間序列的預測也不例外。在本論文中我們針對金融時間序列的非線性與非穩態關係引入類神經網路(ANNs) 與集合經驗模態分解法(EEMD), 藉由ANNs處理非線性問題的能力與EEMD處理時間序列信號的優點，並進一步與傳統上使用於金融時間序列分析的自回歸滑動平均模型(ARMA)進行複合式的模型建構，引入燭型圖概念嘗試進行高頻下的台指期貨TAIEX交易。在不計交易成本的績效測試下本研究的高頻交易模型有突出的績效，證明以ANNs、EEMD方法與ARMA組成的混合式模型在高頻時間尺度交易下有相當的發展潛力，具有進一步發展的價值。在處理高頻時間尺度下所產生的大型數據方面，引入平行運算架構SPMD(single program, multiple data)以增進其處理大型資料下的運算效率。本研究亦透過分析高頻時間尺度的本質模態函數(IMFs)探討在高頻尺度下影響台指期貨價格的因素。 / Financial market is complex, unstable and non-linear system, it looks like have some principle but the principle usually have exception. The forecasting of time series always an issue in several field include finance. In this thesis we propose several version of hybrid models, they combine Ensemble Empirical Mode Decomposition (EEMD), Back-Propagation Neural Networks(BPNN) and ARMA model, try to improve the forecast performance of financial time series forecast. We also found the physical means or impact factors of IMFs under high-frequency time-scale. For processing the massive data generated by high-frequency time-scale, we pull in the concept of big data processing, adopt parallel computing method ”single program, multiple data (SPMD)” to construct the model improve the computing performance. As the result of backtesting, we prove the enhanced hybrid models we proposed outperform the standard EEMD-BPNN model and obtain a good performance. It shows adopt ANN, EEMD and ARMA in the hybrid model configure for high-frequency trading modeling is effective and it have the potential of development.

The inability to effectively hedge against unfavourable exchange rate movements, using the current forward exchange rate as the only guideline, is a key inhibiting factor of international trade. Market participants use the current forward exchange rate quoted in the market to make decisions regarding future exchange rate changes. However, the current forward exchange rate is not solely determined by the interaction of demand and supply, but is also a mechanistic estimation, which is based on the current spot exchange rate and the carry cost of the transaction. Results of various studies, including this study, demonstrated that the current forward exchange rate differs substantially from the realized future spot exchange rate. This phenomenon is known as the exchange rate puzzle. This study contributes to the dynamics of modelling exchange rate theories by developing an exchange rate model that has the ability to explain the realized future spot exchange rate and the exchange rate puzzle. The exchange rate model is based only on current (time t) economic fundamentals and includes an alternative approach of incorporating the impact of the interaction of two international financial markets into the model. This study derived a unique exchange rate model, which proves that the exchange rate puzzle is a pseudo problem. The pseudo problem is based on the generally excepted fallacy that current non–stationary, level time series data cannot be used to model exchange rate theories, because of the incorrect assumption that all the available econometric methods yield statistically insignificant results due to spurious regressions. Empirical evidence conclusively shows that using non–stationary, level time series data of current economic fundamentals can statistically significantly explain the realized future spot exchange rate and, therefore, that the exchange rate puzzle can be solved. This model will give market participants in the foreign exchange market a better indication of expected future exchange rates, which will considerably reduce the dependence on the mechanistically derived forward points. The newly derived exchange rate model will also have an influence on the demand and supply of forward exchange, resulting in forward points that are a more accurate prediction of the realized future exchange rate. / Thesis (Ph.D. (Risk management))--North-West University, Potchefstroom Campus, 2011.

The inability to effectively hedge against unfavourable exchange rate movements, using the current forward exchange rate as the only guideline, is a key inhibiting factor of international trade. Market participants use the current forward exchange rate quoted in the market to make decisions regarding future exchange rate changes. However, the current forward exchange rate is not solely determined by the interaction of demand and supply, but is also a mechanistic estimation, which is based on the current spot exchange rate and the carry cost of the transaction. Results of various studies, including this study, demonstrated that the current forward exchange rate differs substantially from the realized future spot exchange rate. This phenomenon is known as the exchange rate puzzle. This study contributes to the dynamics of modelling exchange rate theories by developing an exchange rate model that has the ability to explain the realized future spot exchange rate and the exchange rate puzzle. The exchange rate model is based only on current (time t) economic fundamentals and includes an alternative approach of incorporating the impact of the interaction of two international financial markets into the model. This study derived a unique exchange rate model, which proves that the exchange rate puzzle is a pseudo problem. The pseudo problem is based on the generally excepted fallacy that current non–stationary, level time series data cannot be used to model exchange rate theories, because of the incorrect assumption that all the available econometric methods yield statistically insignificant results due to spurious regressions. Empirical evidence conclusively shows that using non–stationary, level time series data of current economic fundamentals can statistically significantly explain the realized future spot exchange rate and, therefore, that the exchange rate puzzle can be solved. This model will give market participants in the foreign exchange market a better indication of expected future exchange rates, which will considerably reduce the dependence on the mechanistically derived forward points. The newly derived exchange rate model will also have an influence on the demand and supply of forward exchange, resulting in forward points that are a more accurate prediction of the realized future exchange rate. / Thesis (Ph.D. (Risk management))--North-West University, Potchefstroom Campus, 2011.