Ternary chalcopyrite compounds are the semiconductors with suitable properties to be used as absorber materials in thin film solar cells. AgInSe2 is a promising candidate with its several advantages over the widely used CuInSe2. The purpose of this study was to optimize the physical properties of the Ag-In-Se (AIS) thin films that were deposited by e-beam evaporation of Ag3In5Se9 single crystal powder for solar cell applications by means of post-annealing process under nitrogen atmosphere. The as-grown AIS thin films were annealed at 200, 300 and 400oC and their structural, optical, electrical and photoelectrical properties were examined to observe the effects of post-annealing process. Structural characterization of the films was performed by X-ray diffraction (XRD), energy dispersive X-ray spectroscopy (EDS), X-ray photoelectron spectroscopy (XPS) and atomic force microscopy (AFM) analyses. Optical properties of the films were investigated by optical transmittance measurements. Electrical and photoelectrical properties of the films were examined by temperature dependent conductivity, photoconductivity under different illumination intensities and spectral photoresponse measurements. It was discovered that the annealing of AIS thin films at 200oC resulted in the best physical properties for solar cell applications. The obtained films were polycrystalline with mixed binary and ternary crystalline phases, such as Ag3In5Se9, AgInSe2 and InSe, and showed n-type conductivity with room temperature conductivity value of 2.3x10-6 (Ohm&shy / cm)-1. The band gap energy of the 200oC-annealed films was determined as 1.68 eV from spectral photoresponse measurements. The results of the study revealed that the inadequate Ag incorporation and segregation and/or reevaporation of Se atoms at high annealing temperatures were the major problems encountered in producing single phase polycrystalline AgInSe2 thin films. The required stoichiometry of thin films should be maintained during the growth of the films by means of an alternative deposition procedure and the films should be selenized during post-annealing process.

Census operations are very important events in the history of a nation. These operations cover every bit of land and property of the country and its citizens. Census data is also known as demographic data providing valuable information to various users, particularly planners to know the trends in the key areas. Since 2006, Turkey aims to produce this census data not as &ldquo / de-facto&rdquo / (static) but as &ldquo / de-jure&rdquo / (real-time) by the new Address Based Register Information System (ABPRS). Besides, by this new register based census, personal information is matched with their address information and censuses gained a spatial dimension. Data obtained from this kind of a system can be a great input for the creation of &ldquo / small statistical areas (SSAs)&rdquo / which can compose of street blocks or any other small geographical unit to which social data can be referenced and to establish a complete census geography for Turkey. Because, statistics on large administrative units are only necessary for policy design only at an extremely abstracted level of analysis which is far from &quot / real&quot / problems as experienced by individuals. In this thesis, it is aimed to employ some spatial clustering and districting methodologies to automatically produce SSAs which are basically built upon the ABPRS data that is geo-referenced with the aid of geographical information systems (GIS) and thus help improving the census geography concept which is limited with only higher level administrative boundaries in Turkey. In order to have a clear idea of what strategy to choose for its realization, small area identification criteria and methodologies are searched by looking into the United Nations&rsquo / recommendations and by taking some national and international applications into consideration. In addition, spatial clustering methods are examined for obtaining SSAs which fulfills these criteria in an automated fashion. Simulated annealing on k-means clustering, only k-means clustering and simulated annealing on k-means clustering of Self-Organizing Map (SOM) unified distances are deemed as suitable methods. Then these methods are implemented on parcel and block datasets having either raw data or socio-economic status (SES) indices in nine neighborhoods of Ke&ccedil / i&ouml / ren whose graphical and non-graphical raw data are manipulated, geo-referenced and combined in common basemaps. Consequently, simulated annealing refinement on k-means clustering of SOM u-distances is selected as the optimum method for constructing SSAs for all datasets after making a comparative quality assessment study which allows us to see how much each method obeyed the basic criteria of small area identification while creating SSA layers.

The primary goal for the conceptual design phase of a generic air-to-ground missile is to reach an optimal external configuration which satisfies the flight performance requirements such as flight range and time, launch mass, stability, control effectiveness as well as geometric constraints imposed by the designer. This activity is quite laborious and requires the examination and selection among huge numbers of design alternatives. This thesis is mainly focused on multi objective optimization techniques for an air to-ground missile design by using heuristics methods namely as Non Dominated Sorting Genetic Algorithm and Multiple Cooling Multi Objective Simulated Annealing Algorithm. Futhermore, a new hybrid algorithm is also introduced using Simulated Annealing cascaded with the Genetic Algorithm in which the optimized solutions are passed to the Genetic Algorithm as the intial population. A trade off study is conducted for the three optimization algorithm alternatives in terms of accuracy and quality metrics of the optimized Pareto fronts.

Nano scale processing of IC chips has become the prime production technique as the microelectronic industry aims towards scaling down product dimensions while increasing accuracy and performance. Accurate control of temperature and a good monitoring mechanism for thickness of the deposition layers during epitaxial growth are critical parameters influencing a good yield. The two-fold objective of this thesis is to establish the feasibility of an alternative to the current pyrometric and ellipsometric techniques to simultaneously measure temperature and thickness during wafer processing. TAP-NDE is a non-contact, non-invasive, laser-based ultrasound technique that is employed in this study to contemporarily profile the thermal and spatial characteristics of the wafer. The Gabor wavelet transform allows the wave dispersion to be unraveled and the group velocity of individual frequency components to be extracted from the experimentally acquired time waveform. The thesis illustrates the formulation of a theoretical model that is used to identify the frequencies sensitive to temperature and thickness changes. The group velocity of the corresponding frequency components is determined and their corresponding changes with respect to temperature for different thickness are analytically modeled. TAP-NDE is then used to perform an experimental analysis on Silicon wafers of different thickness to determine the maximum possible resolution of TAP-NDE towards temperature sensitivity, and to demonstrate the ability to differentiate between wafers of different deposition layer thickness at temperatures up to 600?C. Temperature resolution is demonstrated for ?10?C resolution and for ?5?C resolution; while thickness differentiation is carried out with wafers carrying 4000? and 8000? of aluminum deposition layer. The experimental group velocities of a set of selected frequency components extracted using the Gabor Wavelet time-frequency analysis as compared to their corresponding theoretical group velocities show satisfactory agreement. As a result of this work, it is seen that TAP-NDE is a suitable tool to identify and characterize thickness and temperature changes simultaneously during thermal annealing that can replace the current need for separate characterization of these two important parameters in semiconductor manufacturing.

A hybrid method that addresses the design and optimization of long-span steel trusses is presented. By utilizing advancements in present day computing and biologically inspired analysis and design, an effort has been made to automate the process of evolving optimal trusses in an unstructured problem domain. Topology, geometry and sizing optimization of trusses are simultaneously addressed using a three stage methodology. Multi-objective genetic algorithms are used to optimize the member section sizes of truss topologies and geometries. Converting constraints into additional objectives provides a robust algorithm that results in improved convergence to the pareto-optimal set of solutions. In addition, the pareto-curve plotted based on how well the different objectives are satisfied helps in identifying the trade-offs that exist between these objectives, while also providing an efficient way to rank the population of solutions during the search process. A comparison study between multi-objective genetic algorithms, simulated annealing, and reactive taboo search is conducted to evaluate the efficiency of each method with relation to its overall performance, computational expense, sensitivity to initial parameter settings, and repeatability of finding near-global optimal designs. The benefit of using a three stage approach, and also implementing the entire model on parallel computers, is the high level of computational efficiency that is obtained for the entire process and the near-optimal solutions obtained. The overall efficiency and effectiveness of this method has been established by comparing the truss design results obtained using this method on bridge and roof truss benchmark problems with truss designs obtained by other researchers. One of the salient features of thisresearch is the large number of optimal trusses that are produced as the final result. The range of designs available provides the user with the flexibility to select the truss design that best matches their design requirements. By supporting human-computer interactions between these stages, the program also incorporates subjective aesthetic criteria, which assist in producing final designs in consonance with the user's requirements.

An Equivalent Current Injection¡]ECI¡^based Preventive Security- Constrained Optimal Power Flow¡]PSCOPF¡^is presented in this paper and a particle swarm optimization (PSO) algorithm is developed for solving non-convex Optimal Power Flow (OPF) problems. This thesis integrated Simulated Annealing Particle Swarm Optimization¡]SAPSO¡^ and Multiple Particle Swarm Optimization¡]MPSO¡^, enabling a fast algorithm to find the global optimum. Optimal power flow is solved based on Equivalent- Current Injection¡]ECIOPF¡^algorithm. This OPF deals with both continuous and discrete control variables and is a mixed-integer optimal power flow¡]MIOPF¡^. The continuous control variables modeled are the active power output and generator-bus voltage magnitudes, while the discrete ones are the shunt capacitor devices. The feasibility of the proposed method is exhibited for a standard IEEE 30 bus system, and it is compared with other stochastic methods for the solution quality. Security Analysis is also conducted. Ranking method is used to highlight the most severe event caused by a specific fault. A preventive algorithm will make use of the contingency information, and keep the system secure to avoid violations when fault occurs. Generators will be used to adjust the line flow to the point that the trip of the most severe line would not cause a major problem.

Polymer chain microstructure, including characteristics such as molecular weight and branch length, can impact the end-use properties of the polymer. The assumptions contained in deterministic models prevent examination of the structure of individual polymer chains, so removal of these assumptions is necessary to gain insight into molecular-level mechanisms that determine chain microstructure. The work presented here uses a combination of stochastic and deterministic models to examine two significant mechanistic issues in free radical polymerization. The zero-one assumption concerning the number of radicals is often made for miniemulsion polymerization using oil-soluble initiators because of accelerated termination due to radical confinement. Although most of the initiator is present inside the particles, opposing viewpoints exist as to whether the locus of radical generation is the particle phase or the aqueous phase. A well-mixed kinetic Monte Carlo (KMC) model is used to simulate the molecular weight distribution and the results are compared to estimated molecular weights for several chain-stopping events, with the finding that the dominant nucleation mechanism varies with reaction temperature and particle size. Intramolecular chain transfer to polymer, or backbiting, is often assumed to produce only short-chain branches. Using a lattice KMC model, a cumulative distribution function (CDF) is obtained for branch lengths produced by backbiting. Implementation of the CDF in both a rate-equation model and the well-mixed KMC model shows that, for the butyl acrylate solution polymerization system used for comparison, backbiting is responsible for most of the branches, including long-chain branches, even though overlap of the polymer coils in the solution is predicted, a condition which would normally be expected to lead to significant intermolecular chain transfer to polymer. The well-mixed KMC model provides a more thorough analysis of chain microstructure while the rate-equation model is more computationally efficient.

A random search optimization method based off an analogous process for the slow cooling of metals is explored and used to find the optimum solution for a number of regression models that analyze nuclear, radiological, biological,and chemical terrorism targets. A non-parametric simulation based off of historical data is also explored. Simulated series of 30 years and a 30 year extrapolation of historical data are provided. The inclusion of independent variables used in the regression analysis is based off existing work in the reviewed literature. CBRN terrorism data is collected from both the Monterey Institute's Weapons of Mass Destruction Terrorism Database as well as from the START Global Terrorism Database. Building similar models to those found in the literature and running them against CBRN terrorism incidence data determines if conventional terrorism indicator variables are also significant predictors of CBRN terrorism targets. The negative binomial model was determined to be the best regression model available for the data analysis. Two general types of models are developed, including an economic development model and a political risk model. From the economic development model we find that national GDP, GDP per capita, trade openness, and democracy to significant indicators of CBRN terrorism targets. Additionally from the political risk model we find corrupt, stable, and democratic regimes more likely to experience a CBRN event. We do not find language/religious fractionalization to be a significant predictive variable. Similarly we do not find ethnic tensions, involvement in external conflict, or a military government to have significant predictive value.

In this thesis we address the problem of designing codes for specific applications. To do so we make use of the relationship between clusters and codes. Designing a block code over any finite dimensional space may be thought of as forming the corresponding number of clusters over the particular dimensional space. In literature we have a number of algorithms available for clustering. We have examined the performance of a number of such algorithms, such as Linde-Buzo-Gray, Simulated Annealing, Simulated Annealing with Linde-Buzo-Gray, Deterministic Annealing, etc, for design of codes. But all these algorithms make use of the Eucledian squared error distance measure for clustering. This distance measure does not match with the distance measure of interest in the error correcting scenario, namely, Hamming distance. Consequently we have developed an algorithm that can be used for clustering with Hamming distance as the distance measure. Also, it has been observed that stochastic algorithms, such as Simulated Annealing fail to produce optimum codes due to very slow convergence near the end. As a remedy, we have proposed a modification based on the code structure, for such algorithms for code design which makes it possible to converge to the optimum codes.

Dilute Mg alloys are exclusively identified for wrought structural applications in automotive industry. Any improvement in mechanical properties of alloys is possible only by grain size refinement and by the development of suitable texture. The grain size, grain size anisotropy and texture in these alloys affect the compatibility stresses in a very complex manner. To launch a full scale study towards understanding the complex deformation mechanisms operating in these alloys, it is necessary to understand the effect of grain size and texture on the mechanical behavior of Mg alloys in a broad or semi-quantitative manner first. Current literature lacks such broad study. In this present study, the effect of grain size, grain size anisotropy and texture evolution on the mechanical properties are examined in order to develop an understanding of the deformation mechanism that control the mechanical properties of a dilute conventionally extruded Mg alloy, AZ21. The approach adopted was to first study the microstructure and texture evolution in this conventionally extruded alloy. Since the grain sizes in these alloys vary over a wide range, it is hence necessary to study the microstructure evolution in a highly quantitative manner. In understanding texture, the present study is only limited to qualitatively evaluating the evolution of fibre component of texture using X-Ray Diffraction spectra. For truly quantitative microstructure evolution results in materials were grains sizes are spread over a wide range, it is critical to study a statistically enough no. of grains. Hence to avoid any sampling error, large montages (about 0.3 sq. mm) were constructed out of a series of high resolution images captured using an optical microscope. The montages so constructed are subjected to extensive image enhancement and various other operations are performed to convert these coloured to binary montages. Information like grain size, diameter etc., can be easily extracted from these binary montages and used for further analysis. Fibre texture in these conventionally extruded dilute Mg alloys generally develops due to alignment of basal planes along the direction of extrusion. The Critical Resolved Shear Stress for basal slip is very low when compared to that of non-basal planes. And also since there are very limited primary slip systems in these dilute Mg alloys, the development of strong fibre texture drastically changes the compatibility stresses and hence the mechanical properties . To broadly study the effect of microstructure-texture on mechanical proerties, after post extrusion annealing, heat treatments representing typical microstructure-texture combinations were identified. Effect of each microstructure-texture combination on the tensile and completely reversed cyclic fatigue properties are studied and qualitatively interpreted. The fibre texture showed pronounced effect on tensile ductility but it hardly affected the yield strength. With just 10% reduction in BPI, the ductility reduced by about 50%. A small change in average grain size did not alter the yield strength. Unlike tensile ductility, fatigue endurance stress was not altered drastically by the change in grain size or texture. But there appeared to be a significant effect of residual stress. In ending, a small change in microstructure-texture combination in these conventionally extruded alloys has a pronounced effect on ductility or in other words plastic properties. But a it has minimal effect on yield strength and fatigue endurance stress.