This study examines whether different corporate real estate (CRE) strategies affect the stock outperformance and systemic risk of various companies. The sample of 443 listed companies of 17 industries in Taiwan during 2000 to 2010 was divided into four groups for the different corporate real estate strategies. The pairwise abnormal return and systemic risk of composite and business (without the affect from real estate market) series were empirically examined and compared using a partial adjustment model. This study also conducts the two-stage least squares procedure to determine whether four CRE strategies were considered diversifiable factors when evaluating the firm¡¦s value The results do not indicate an increasingly abnormal return performance associated with the company implementing a certain CRE strategy, but companies with a stable profession and consistent adjustment strategies are considered a good diversifier by stock investors. Aggressive adjustment strategies do not diversify the systematic risk to overall industry, otherwise the scale of total assets would be considered a diversification in companies with aggressive strategies. The companies using an aggressive profession strategy to increase leverage are regarded as risky phenomen for stock investors, and companies with stable profession strategies face higher systemic risk if their book value is greater than their market value. Therefore, this study determines that CRE strategies affect companies¡¦ systematical risk.

Singular value decomposition (SVD) is a popular decomposition method for solving least-squares estimation problems. However, for large datasets, SVD is very time consuming and memory demanding in obtaining least squares solutions. In this paper, we propose a least squares estimator based on an iterative divide-and-merge scheme for large-scale estimation problems. The estimator consists of several levels. At each level, the input matrices are subdivided into submatrices. The submatrices are decomposed by SVD respectively and the results are merged into smaller matrices which become the input of the next level. The process is iterated until the resulting matrices are small enough which can then be solved directly and efficiently by the SVD algorithm. However, the iterative divide-and-merge algorithms executed on a single machine is still time demanding on large scale datasets. We propose two distributed algorithms to overcome this shortcoming by permitting several machines to perform the decomposition and merging of the submatrices in each level in parallel. The first one is implemented in MapReduce on the Hadoop distributed platform which can run the tasks in parallel on a collection of computers. The second one is implemented on CUDA which can run the tasks in parallel using the Nvidia GPUs. Experimental results demonstrate that the proposed distributed algorithms can greatly reduce the time required to solve large-squares problems.

In economy, banking sector has been considered as the main issue for development. Using panel data analyzing to test the relationship between banks performance and economic freedom indexes for 9 emerging market countries in Asia, the paper¡¦s result shows that there is the positive effect between monetary freedom, business freedom, financial freedom and banks performance while investment freedom has a negative impact. It suggests that emerging market countries¡¦ government and the policy maker need to focus deeply on the operation, the efficiency of regulation and improving the economic freedom.

Parameter identification of an induction machine is of great importance in numerous industrial applications, including the assessment of machine performance and design of control schemes. Parameter identification is based on the input-output signals and the model used. Many researches have applied the inverter drive to control the exciting signal of the induction machine in the identifying process. This study proposed a method to identify all parameter of the induction machine with a no-load low-voltage starting test. The method has a simple structure without needing extra hardware, which could significantly simplify the procedures and save cost. Based on the curves of resistance and reactance, the user can obtain the machine¡¦s equivalent circuit parameters. With the identified parameters of the equivalent circuit, input voltage, and rotor speed, the user can find the torque. From the torque and rotor speed, the user can find the mechanical parameters. A least mean square (LMS) method was used with a particle swarm optimization (PSO) method to solve the aforementioned problem. From various tests, the practicability and accuracy of this method can been proven. This study also proposes a method to rapidly analyze power parameters. This method uses two adjacent data to compute the fundamental frequency component of voltage or current. The parameters of fundamental frequency component include frequency, amplitude, and phase. Under the condition of varied parameters, the frequency and phase are dependent. This method fixes the frequency and computes the amplitude and phase, and then stable results will be obtained.

In the process of urbanization, the population and economy are rapidly growing. To achieve the demand of urbanization, natural areas are developed into construction areas in the city. Nature areas began to shrink and fragment, causing a great deal of damage, such as species extinction. The ability of nature to control the flood, adjust the rain, and filter pollution has been lost. This study is based on the city of Kaohsiung and Kaohsiung County. The study uses a large scale to re-examine the spatial structure of this area to propose an effective and reasonable planning policy for the environment and ecosystem. The study introduces the concept of green infrastructure to develop a green network system for the city. Green infrastructure is a network connecting natural areas and open spaces that can save ecological value and retain the ecosystem function. A green infrastructure network consists of hubs and links, and the network pattern can be made for different purposes. The study chose green land with an area of more than twenty hectares for the hubs and chose the terrestrial animal for the target species. The study uses the least-cost path analysis to plan the links for the terrestrial animal. The least-cost path, which is the path of least resistance when the terrestrial animal passes through the landscape. All the hubs generated 903 links. To create the green infrastructure networks, this study uses the gravity model to evaluate which corridor is the most important. Using the graph theory, the study developed three plans for a green infrastructure network to meet different demands: the Paul Revere, Traveling Salesman, and Least Cost to User networks. Planners can see different demands dictating the choice between different green infrastructure networks. Finally, the study discusses the three-network structure, finding the important hubs and links for the terrestrial animal. These areas should be protected to maintain the green infrastructure network.

This thesis proposes an adaptive control approach to regulate the hydrogen feed of a fuel cell. The goal of the controller is to maintain the so-called hydrogen excess ratio, defined as the ratio between the hydrogen fed to the cell stake and those consumed in the stake, at a desired level when the fuel cell is under load variation. Maintaining the hydrogen excess ratio at an appropriate level would avoid hydrogen starvation, which is crucial for slowing degeneration of the fuel cell membranes and prolonging the life of the cell stake. The control approach we propose is based on the receding horizon linear quadratic optimal control algorithm with an on-line turning scheme which updates the plant model according to real-time measurement. To ease the computational complexity and make real-time turning realizable, we adopt a simple autoregressive with external disturbance (ARX) model to approximate the complicate chemical/electrical process of the fuel cell. The proposed adaptive control approach is implemented on an experimental platform. The experimental results show that the proposed control works with reasonably good performance.

While prior studies on ERP implementation have largely focused on the importance of best practices, this paper examines the impact of knowledge transfer climate and the competence of the players (i.e., the implementing firm and the consultant team). The model divides factors that influence the result of knowledge transfer during ERP implementation into three categories: those implemented by the firm, those implemented by the consultant, and those related to the impact of the knowledge transfer climate. Competence factors from the first two aspects facilitate the building of a better knowledge transfer climate. Survey results from 101 respondents were subjected to multivariate analysis. The significance of player competence factors is verified, and an understanding of the role that the knowledge transfer climate plays in the knowledge transfer process and the impact on the transfer process is developed. This paper provides a broader, richer model of knowledge transfer networks to promote insight into successful ERP implementation. In practice, the key to effective knowledge transfer is the establishment a positive knowledge transfer climate. To achieve a successful ERP implementation, practitioners should focus on possessing robust competences with ERP implementation partners. Additional research may help ERP implementation project teams more effectively promote knowledge transfer in a wider range of conditions with greater confidence and precision.

This paper studies the role of fluctuation of the aggregate consumption-wealth ratio(cay) for predicting Taiwan stock return. The effect of cay on U.S. stock return has been recently confirmed by Lettau and Ludvigson (2001) with a two stage method. In the first step, estimate the ratio used a dynamic least square(DLS) technique. Second, to investigate the performance of cay, they use in-sample and out-of-sample test. In this paper, we follow the method which Lettau and Ludvigson(2001) use to examine the predictability of cay. Using quarterly market data from 1998 to 2010, we find cay is strong predictors of excess return in out-of sample test. We also find that this ratio is a better forecaster of future returns at intermediate horizons compared to short time.

In this research, least-squares based finite element formulations and their applications in fluid mechanics are presented. Least-squares formulations offer several computational and theoretical advantages for Newtonian as well as non-Newtonian fluid flows. Most notably, these formulations circumvent the inf-sup condition of Ladyzhenskaya-Babuska- Brezzi (LBB) such that the choice of approximating space is not subject to any compatibility condition. Also, the resulting coefficient matrix is symmetric and positive-definite. It has been observed that pressure and velocities are not strongly coupled in traditional leastsquares based finite element formulations. Penalty based least-squares formulations that fix the pressure-velocity coupling problem are proposed, implemented in a computational scheme, and evaluated in this study. The continuity equation is treated as a constraint on the velocity field and the constraint is enforced using the penalty method. These penalty based formulations produce accurate results for even low penalty parameters (in the range of 10-50 penalty parameter). A stress based least-squares formulation is also being proposed to couple pressure and velocities. Stress components are introduced as independent variables to make the system first order. The continuity equation is eliminated from the system with suitable modifications. Least-squares formulations are also developed for viscoelastic flows and moving boundary flows. All the formulations developed in this study are tested using several benchmark problems. All of the finite element models developed in this study performed well in all cases. A method to exploit orthogonality of modal bases to avoid numerical integration and have a fast computation is also developed during this study. The entries of the coefficient matrix are calculated analytically. The properties of Jacobi polynomials are used and most of the entries of the coefficient matrix are recast so that they can be evaluated analytically.

The conventional finite element models (FEM) of problems in structural mechanics are based on the principles of virtual work and the total potential energy. In these models, the secondary variables, such as the bending moment and shear force, are post-computed and do not yield good accuracy. In addition, in the case of the Timoshenko beam theory, the element with lower-order equal interpolation of the variables suffers from shear locking. In both Euler-Bernoulli and Timoshenko beam theories, the elements based on weak form Galerkin formulation also suffer from membrane locking when applied to geometrically nonlinear problems. In order to alleviate these types of locking, often reduced integration techniques are employed. However, this technique has other disadvantages, such as hour-glass modes or spurious rigid body modes. Hence, it is desirable to develop alternative finite element models that overcome the locking problems. Least-squares finite element models are considered to be better alternatives to the weak form Galerkin finite element models and, therefore, are in this study for investigation. The basic idea behind the least-squares finite element model is to compute the residuals due to the approximation of the variables of each equation being modeled, construct integral statement of the sum of the squares of the residuals (called least-squares functional), and minimize the integral with respect to the unknown parameters (i.e., nodal values) of the approximations. The least-squares formulation helps to retain the generalized displacements and forces (or stress resultants) as independent variables, and also allows the use of equal order interpolation functions for all variables. In this thesis comparison is made between the solution accuracy of finite element models of the Euler-Bernoulli and Timoshenko beam theories based on two different least-square models with the conventional weak form Galerkin finite element models. The developed models were applied to beam problems with different boundary conditions. The solutions obtained by the least-squares finite element models found to be very accurate for generalized displacements and forces when compared with the exact solutions, and they are more accurate in predicting the forces when compared to the conventional finite element models.