In this study numerical modelling of a moderate pressure DC glow discharge plasma is car- ried out in 1D and 2D geometry. The governing equations include continuity equations for the plasma species (electrons, positive ions and metastable atoms), the electron energy equation (EEE), Poisson equation for the electric

This thesis examines the relationship between the bilateral real exchange rate and the trade balances of 20 industries in which majority of the trade between Turkey and her leading partner Germany is carried out, both for the short and long run, in search of the existence of any J-curve effect. Using quarterly data over the period 1989:1-2011:3, the relationship is analyzed empirically through the bounds testing approach to cointegration and error correction modeling. The findings show that, although the pattern created by a depreciation does not follow the compl ete J curve in any of the industries, still the exchange rate as well as foreign and domestic real incomes are effective determinants of bilateral trade balance between Turkey and Germany in majority of the cases both in the short and in the long run. Moreover, this thesis provides strong support for the assertion that at the disaggregate level industries exhibit unique and distinct trade balance responses to exchange rate fluctuations, by showing that these responses vary significantly across different sectors both in the short and long run.

Recently, millions of databases have been used and we need a new technique that can automatically transform the processed data into useful information and knowledge. Data mining is the technique of analyzing data to discover previously unknown information and spatial data mining is the branch of data mining that deals with spatial data. In spatial data mining, clustering is one of useful techniques for discovering interesting data in the underlying data objects. The problem of clustering is that give n data points in a d-dimensional metric space, partition the data points into k clusters such that the data points within a cluster are more similar to each other than data points in different clusters. Cluster analysis has been widely applied to many areas such as medicine, social studies, bioinformatics, map regions and GIS, etc. In recent years, many researchers have focused on finding efficient methods to the clustering problem. In general, we can classify these clustering algorithms into four approaches: partition, hierarchical, density-based, and grid-based approaches. The k-means algorithm which is based on the partitioning approach is probably the most widely applied clustering method. But a major drawback of k-means algorithm is that it is difficult to determine the parameter k to represent ``natural' cluster, and it is only suitable for concave spherical clusters. The k-means algorithm has high computational complexity and is unable to handle large databases. Therefore, in this thesis, we present an efficient clustering algorithm for large spatial databases. It combines the hierarchical approach with the grid-based approach structure. We apply the grid-based approach, because it is efficient for large spatial databases. Moreover, we apply the hierarchical approach to find the genuine clusters by repeatedly combining together these blocks. Basically, we make use of the Hilbert curve to provide a way to linearly order the points of a grid. Note that the Hilbert curve is a kind of space-filling curves, where a space-filling curve is a continuous path which passes through every point in a space once to form a one-one correspondence between the coordinates of the points and the one-dimensional sequence numbers of the points on the curve. The goal of using space-filling curve is to preserve the distance that points which are close in 2-D space and represent similar data should be stored close together in the linear order. This kind of mapping also can minimize the disk access effort and provide high speed for clustering. This new algorithm requires only one input parameter and supports the user in determining an appropriate value for it. In our simulation, we have shown that our proposed clustering algorithm can have shorter execution time than other algorithms for the large databases. Since the number of data points is increased, the execution time of our algorithm is increased slowly. Moreover, our algorithm can deal with clusters with arbitrary shapes in which the k-means algorithm can not discover.

Large off-shore wind farms raise the concern of widespread tripping of off-shore wind generator in the presence of system faults and corresponding voltage dips that could potentially cause system wide blackout. In this thesis an offshore wind farm and three different types of power transmission are modeled and studied using simulation software. Off-shore wind farm composed of fixed speed induction generators and HVAC interconnection, HVAC interconnection plus STATCOM and HVDC interconnections are studied. Onshore grid faults are simulated for each interconnection. Voltage tolerance curves are established to assess fault ride through capability of each interconnection and compared with different grid transmission ride through capacity required by grid operator.

Queries on spatial data commonly concern a certain range or area, for example, queries related to intersections, containment and nearest neighbors. The Continuous Nearest Neighbor (CNN) query is one kind of the nearest neighbor queries. For example, people may want to know where those gas stations are along the super highway from the starting position to the ending position. Due to that there is no total ordering of spatial proximity among spatial objects, the space filling curve (SFC) approach has proposed to preserve the spatial locality. Chen and Chang have proposed efficient algorithms based on SFC to answer nearest neighbor queries, so we may perform a sequence of individually nearest neighbor queries to answer such a CNN query in the centralized system by one of Chen and Chang's algorithms. However, each searched range of these nearest neighbor queries could be overlapped, and these queries may access several same pages on the disk, resulting in many redundant disk accesses. On the other hand, Zheng et al. have proposed an algorithm based on the Hilbert curve for the CNN query for the wireless broadcast environment, and it contains two phases. In the first phase, Zheng et al.'s algorithm designs a searched range to find candidate objects. In the second phase, it uses some heuristics to filter the candidate objects for the final answer. However, Zheng et al.'s algorithm may check some data blocks twice or some useless data blocks, resulting in some redundant disk accesses. Therefore, in this thesis, to avoid these disadvantages in the first phase of Zheng et al.'s algorithm, we propose a local expansion approach based on the Peano curve for the CNN query in the centralized system. In the first phase, we determine the searched range to obtain all candidate objects. Basically, we first calculate the route between the starting point and the ending point. Then, we move forward one block from the starting point to the ending point, and locally spread the searched range to find the candidate objects. In the second phase, we use heuristics mentioned in Zheng et al.'s algorithm to filter the candidate objects for the final answer. Based on such an approach, we proposed two algorithms: the forward moving (FM) algorithm and the forward moving* (FM*) algorithm. The FM algorithm assumes that each object is in the center of a block, and the FM* algorithm assumes that each object could be in any place of a block. Our local expansion approach can avoid the duplicated check in Zheng et al.'s algorithm, and determine a searched range with higher accuracy than that of Zhenget al.'s algorithm. From our simulation results, we show that the performance of the FM or FM* algorithm is better than that of Zheng et al.'s algorithm, in terms of the accuracy and the processing time.

The aim of the research is the analysis and motion curve synthesis for cam-gear intermittent mechanisms. Based on the grooved-cam-controlled motion of rocker planet gear and motion superposition of the planet gear trains, cam-gear intermittent mechanisms transfer continuous rotation to intermittent rotation. Interpolation of non-parametric rational B-splines motion curve is first introduced in this thesis, and it provides better motion characteristics. Based on rigid body transformation, the analytical grooved cam profile can be determined, and the pressure angle and radius of curvature can be calculated. By applying vector operation, rigid body dynamic analysis is also performed. An application example for cam-gear intermittent mechanisms used in the paper delivery system of the die cutting and creasing machine is served to support the content of this research and to improve the kinematic and dynamic characteristics of the paper delivery system.

Intermittent Mechanisms are widely used in automation equipment, including delivery, assembly, and indexing systems. The aim of this research is to set up a systematic design and analysis procedure of a conjugate intermittent gear cam mechanism. The output of a conjugate intermittent gear cam mechanism is the intermittent motion of sun gear, which has a dwell function in a working period. The intermittent of sun gear is the combination of carrier constant rotation and planet gear variable rotation that is influenced by the conjugate cam profiles. This research first is to set up the design and analysis procedure including applications of rational B-splines to synthesis of output intermittent motion curve and the ALM optimization method for motion tuning to meet specific demands. Secondly, for kinematic analysis, the rigid body transformation methos is used to determinate the conjugate cam profiles so that the geometric analysis can be performed. Then, the rigid body dynamic behavior of the mechanism is analyzed. Finally, to verify the usefulness and effectiveness of the developed procedure, it is conducted to design and analyze a real paper conveyor system of a die cutting and creasing machine. The research results obtained in this study have been applied in the industry due to its validation.

The simplicity of the Environmental Kuznets (EKC) curve concept motivated this study of the relationships between environmental, economic and social indicators at the country, city/regional and manufacturing facility scale. The study builds on almost 20 years of research on the EKC, which has shown conflicting results for confirmation of the EKC hypothesis that the environment first degrades, then improves, with increasing economic wealth. Most EKC studies use country-scale income or GDP as the primary economic indicator of interest; this study experiments with city/regional GDP at the local scale and a country-scale "market maturity" indicator commonly used by the corporation studied. The manufacturing facility scale analysis is new territory in the EKC literature. Firm-scale studies in the past have been just that, evaluating firm environmental performance across a specific industry. This effort evaluates manufacturing facility performance within the same firm across a set of 21 countries of interest to the corporation. This study is unique in a few other ways. Including multiple scales in the same study is not common in the EKC literature. Typically, a study would focus on one or a few indicators at one specific scale. The actual environmental and social outcome variables used here are also somewhat unique. Generally speaking, the results reported here will fall into the "mixed" bucket relative to the 20 years of existing EKC literature; however, a possible research platform is established based on the possible nesting of multiple scales within the same research effort.

<p> </p><p>The purpose of this dissertation would be to find the relationship between CO₂ emission and GDP. We found that in case of the majority of countries the CO₂emission is related to national income and follows an inverted-U shaped curve. In our analysis we used the regression technique on 139 countries within the time period 1985-2004 to model and analyze the mentioned relationship and define the variables, that describe it. As it will be proved, Environmental Kuznets Curve validate the model and our hypothesis confirm other researches, therefore the inverse-u relationship proves to be correct.</p><p> </p>

<p>The purpose of this thesis is to examine how a depreciation in the exchange rate affects the trade balance in an economy over time. The outcomes of a depreciation are possible to analyze through the J-curve phenomenon that shows the relation between the exchange rate and the trade balance both in the short-run and the long-run. The data used in this thesis cover 39 countries and their quarterly changes in exchange rate between 1982 and 2005. The largest depreciation for each country during these years was detected and is the base for this research. In this thesis, focus is on the trade ratio rather than the trade balance for empirical purposes. The relation between the largest depreciations and its effect on the trade ratio are examined in two sets of regressions. The results show no evidence of a J-curve in neither one of the sets of regressions, even though the trade ratio is positively affected by the depreciation. When testing only for significantly large depreciations in the exchange rate the affect on the trade ratio is stronger, all else equal. According to the findings in this thesis, a depreciation in the real effective exchange rate causes the trade ratio to increase immediately and then decrease over time. The conclusion is that the findings are not in line with the J-curve phenomenon tested for; however, they support standard trade theory with the Marshall-Lerner condition being met i.e. a depreciation in the exchange rate will affect the trade balance positively.</p>