<p>The inverse problem of modeling biochemical processes mathematically from measured time course data falls into the category of system identification and parameter estimation. Analyzing the time course data would provide valuable insights into the model structure and dynamics of the biochemical system. Based on the types of biochemical reactions, such as metabolic networks and genetic networks, several modeling frameworks have been proposed, developed and proved effective, including the Michaelis-Menten equation, the Biochemical System Theory (BST), etc. One bottleneck in analyzing the obtained data is the estimation of parameter values within the system model.</p> <p>As most models for molecular biological systems are nonlinear with respect to both parameters and system state variables, estimation of parameters in these models from experimental measurement data is thus a nonlinear estimation problem. In principle, all algorithms for nonlinear optimization can be used to deal with this problem, for example, the Gauss-Newton iteration method and its variants. However, these methods do not take the special structures of biological system models into account. When the number of parameters to be determined increases, it will be challenging and computationally expensive to apply these conventional methods.</p> <p>In this research, several methods are proposed for estimating parameters in two classes of widely used biological system models: the S-system model and the linear fractional model (LFM), by utilizing their structure specialties. For the S-system, two estimation methods are designed. 1) Based on the two-term structure (production and degradation) of the model, an alternating iterative least squares method is proposed. 2) A separation nonlinear least squares method is proposed to deal with the partially linear structure of the model. For the LFM, two estimation methods are provided. 1) The separation nonlinear least squares method can also be adopted to treat the partially linear structure of the LFM, and moreover a modified iterative version is included. 2) A special strategy using the separation principle and the weighted least squares method is implemented to turn the cost function into a quadratic form and thus the estimates for parameters can be analytically solved. Simulation results have demonstrated the effectiveness of the proposed methods, which have shown better performance in terms of estimation accuracy and computation time, compared with those conventional nonlinear estimation methods.</p>

This paper uses the panel data of 11 OECD countries over a period from 1971 to 2006. Unlike the traditional cointegration model which omitted the impact of structural breaks in the analysis, this paper applies panel cointegration with structural break test proposed by Westerlund (2006), panel unit root test, and panel dynamic OLS test. The empirical results indicate that health care expenditure and economic growth (GDP per capita) are non-stationary in the series; and between the two variables, a long-term cointegration relationship exists. Moreover, a positive correlation between HCE and economic growth is found in the panel dynamic OLS model. The researcher concludes that investing in health capital improves human capital and that boosts economic growth in the sample countries, and vice versa. More importantly, allowing structural breaks in the cointegration analysis obtains reliability in the estimation and proves more detailed and specific information on the consequence of the momentous events on the two variables; and thus enables policy makers and health economists to propose more effective strategies.

There are many reasons for companies to hold real estate, including for operating business, production, sales, and providing services. Previous researches show that corporate real estate (CRE) is an important part of company assets, and it will affect stock returns and risk of company. The main object of this study is to investigate the impact of changes in CRE on stock returns and risk of company in Taiwan. Moreover, this study analyzes how CRE affect toward different industry during each business cycle period. Then, we provide some suggestions to stockholders and managers. The data set from 1992 through 2011 in Taiwan stock market, the relationship between CRE and stock returns and risk are analyzed using two stage least squares regression model. The empirical results show that, on average, higher CRE appears to be associated with higher abnormal return performance and higher total risk. On the other hand, CRE show negative impact on business operation such as lower adjusted return on assets and higher risk of bankruptcy. Furthermore, CRE factor is associated with higher abnormal return performance and higher firm value when company with small asset size, high P/E ratio or newly establish characters. Results also indicate that the impact of CRE on firm¡¦s stock price and risk depend on industries, business cycle period, and firm characters. CRE show negative impact on Textile, Tourism, and Trading and Consumers' Goods Industry. In Food Industry, higher CRE factor is associated with lower system risk and positive impact on business operation.

Direct sequence code division multiple access (DS-CDMA) technique is one of the significant multiplexing technologies used in wireless communication services. In the DS-CDMA framework, all users have been assigned distinct signature code sequence to achieve multiple accesses within the same frequency band, and allow signal separating at the receiver. Under multipath fading environment with near-far effect, the current CDMA systems employed the RAKE receiver, to enhance the system performance. It is known that if training data is available the minimum mean squares error (MMSE) multiuser receiver, in which the average power of the receiver output is minimized subject to appropriate constraints, could be obtained by solving directly by the constrained Wiener estimation solution. However, if this is not the case, the blind multiuser receiver is an alternative approach to achieve desired performance closed to the one with the MMSE approach. In this thesis, based on the max/min criterion, the blind multiuser receiver, with linear constraints, is devised. Here constraint equations are written in parametric forms, which depend on the multipath structure of the signal of interest. Constraint parameters are jointly optimized with the parameters of the linear receiver to obtain the optimal parameters. In consequence, the sliding window linearly constrained RLS (SW-LC-RLS) algorithm is employed to implement the optimal blind receiver, with max/min approach. This new proposed scheme can be used to deal with multiple access interference (MAI) suppression for the environments, in which the narrow band interference (NBI) due to other systems is joined suddenly to the DS-CDMA systems, and having serious near-far effect. Under such circumstance, the channel character due to the NBI and near-far effect will become violent time varying, such that the conventional LC-RLS algorithm as well as LC-LMS algorithms could not perform well. Via computer simulation it confirms that our proposed scheme has better capability for MAI suppression in DS-CDMA systems than other existing schemes, and is more robust against the NBI and near-far problems.

In the last one hundred years, the mankind has invented a lot of convenient tools for pursuing beautiful and comfortable living environment. Computer is one of the most important inventions, and its operation ability is incomparable with the mankind. Because computer can deal with a large amount of data fast and accurately, people use this advantage to imitate human thinking. Artificial intelligence is developed extensively. Methods, such as all kinds of neural networks, data mining, fuzzy logic, etc., apply to each side fields (ex: fingerprint distinguishing, image compressing, antennal designing, etc.). We will probe into to prediction technology according to the decision tree and fuzzy clustering. The fuzzy decision tree proposed the classification method by using fuzzy clustering method, and then construct out the decision tree to predict for data. However, in the distance function, the impact of the target space was proportional inversely. This situation could make problems in some dataset. Besides, the output model of each leaf node represented by a constant restricts the representation capability about the data distribution in the node. We propose a more reasonable definition of the distance function by considering both input and target differences with weighting factor. We also extend the output model of each leaf node to a local linear model and estimate the model parameters with a recursive SVD-based least squares estimator. Experimental results have shown that our improved version produces higher recognition rates and smaller mean square errors for classification and regression problems, respectively.

Machine learning methods have been promising tools in science and engineering fields. The use of these methods in chemistry and drug design has advanced after 1990s. In this study, molecular electrostatic potential (MEP) surfaces of PCP-like compounds are modelled and visualized in order to extract features which will be used in predicting binding affinity. In modelling, Cartesian coordinates of MEP surface points are mapped onto a spherical self-organizing map. Resulting maps are visualized by using values of electrostatic potential. These values also provide features for prediction system. Support vector machines and partial least squares method are used for predicting binding affinity of compounds, and results are compared.

When using data for estimating the parameters in a bivariate distribution, the tradition is to assume that data comes from a bivariate normal distribution. If the distribution is not bivariate normal, which often is the case, the maximum likelihood (ML) estimators are intractable and the least square (LS) estimators are inefficient. Here, we consider two independent sets of bivariate data which come from non-normal populations. We consider two distinctive distributions: the marginal and the conditional distributions are both Generalized Logistic, and the marginal and conditional distributions both belong to the Student&rsquo / s t family. We use the method of modified maximum likelihood (MML) to find estimators of various parameters in each distribution. We perform a simulation study to show that our estimators are more efficient and robust than the LS estimators even for small sample sizes. We develop hypothesis testing procedures using the LS and the MML estimators. We show that the latter are more powerful and robust. Moreover, we give a comparison of our tests with another well known robust test due to Tiku and Singh (1982) and show that our test is more powerful. The latter is based on censored normal samples and is quite prominent (Lehmann, 1986). We also use our MML estimators to find a more efficient estimator of Mahalanobis distance. We give real life examples.

The e-government phenomena has become more important with the ever increasing number of implementations world wide. A model explaining the e-government adoption and the related measurement instrument a survey had been developed and validated in this study. In a post technology acceptance model (TAM) approach, theory of planned behavior (TPB) was extended to t the requirements of e-government context. The adoption of student loans service of the higher education student loans and accommodation association (KYK) was investigated to obtain data for empirical validation. The instrument was administered to over four-hundred students and partial least squares path modeling was employed to analyze the data. The results indicate that the model was an improvement over TAM in terms of predictive power. The constructs investigated in the study successfully explained the intention to use an e-government service.

Through the rapid expansion of information technologies, Learning Management Systems have become one of the most important innovations for delivery of education. Successful implementation and management of these systems are primarily based on the instructors&#039 / adoption. However, too few researches have been conducted to evaluate instructors&rsquo / adoption towards e-learning system as taking higher education as base. This study aims to understand behavioral intentions of higher education instructors towards Learning Management Systems and further to identify the influencing factors. A research model has been proposed based on the belief variables of the Technology Acceptance Model. Additionally, Application Characteristics, Individual, Social and Technological dimensions were considered to identify the effects of key variables on behavioral intention of users. A survey instrument has been developed and conducted with 224 academicians after a pilot study through its reliability and validity has been assured. Although the items of the survey instrument were based on the literature, an explanatory factor analysis was performed to strictly determine which items belong to which factors. Then, in order to assess the measurement model Convergent validity and Discriminant validity were conducted via confirmatory factor analyses. After the required prior analyses, Component based Structural Equation Modeling (Partial Least Square - PLS) was used to validate the predictive power of the proposed research model. Consequently, relationships between the influencing factors were detected and the results showed that the factors related with Belief dimension directly influenced behavioral intention of instructors. Also, the factors under the Individual, Social and Technological dimensions indirectly affected behavioral intention of users towards learning management system use. Additionally, structured and informal interviews were performed with ten instructors and the findings of the research model were explained with the opinions of system users. The indications of this research will be valuable for implementation, management and continuous improvement of learning management systems.

This thesis explores the transformation of public spaces, with reference to power relations and the struggle for power. In this regard Tehran has been chosen as the main concern and the case of the study, while in its short history of being the political center of the country, the city has been hosting several uprisings and political tensions that are projected on the body of the city. The agencies of this power struggle will be analyzed sociologically and politically, to comprehend the way public spaces of the city and the conception of space are transformed. The spatial analysis of the case of the study in different periods of its history, in relation to socio-political elements of effect, will cause the study to evolve around a simultaneous concentration on spatial transformation and power relations. With such a framework this thesis will question the role of architecture and urban design in the transformation of space, which is dominated by the power struggle, and its balance. The primary aim here is to understand how public space becomes a political apparatus in using urban public spaces historically, in the struggle over power, and how the ruling power represents its ideology in public spaces and how in response the resisting forces of the society manifest their demand for change in public spaces and appropriate those v spaces to live in. Alongside the theoretical discussions, the case of Tehran will provide a multi dimensional source for these explorations / the discussions will mainly focus on a great public square of Tehran (Azadi Square), and the entrance of Tehran University, as the critical and symbolic nodes of public gatherings in the recent history of this city, to analyze how public spaces which are created by one authority of power might totally change in terms of function and meaning, and be transformed into a new entity with similarities and contradictions with the previous one.