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This thesis proposes the platform for the observation of a new type of electromagnetic interface mode, Coupled Interface Mode, and studies the utilization of this mode in second order nonlinear interaction in AlGaAs. The dispersion relations for theoretical examination of the modes are developed and used to design a waveguide structure that accommodates a three wave mixing process utilizing coupled interface modes. The waveguides are fabricated according to optimized fabrication recipes and characterized for linear and nonlinear properties. Second harmonic generation is adopted for the demonstration of nonlinear interaction, due to its convenient experimental set-up. Three different laser sources are used to pump the waveguides and second harmonic light is generated and characterized. Coupled interface modes in conjunction with other types of modes also existing within the same structures, offer the possibility to explore three-wave mixing processes such as sum and difference frequency generation.

This thesis proposes the platform for the observation of a new type of electromagnetic interface mode, Coupled Interface Mode, and studies the utilization of this mode in second order nonlinear interaction in AlGaAs. The dispersion relations for theoretical examination of the modes are developed and used to design a waveguide structure that accommodates a three wave mixing process utilizing coupled interface modes. The waveguides are fabricated according to optimized fabrication recipes and characterized for linear and nonlinear properties. Second harmonic generation is adopted for the demonstration of nonlinear interaction, due to its convenient experimental set-up. Three different laser sources are used to pump the waveguides and second harmonic light is generated and characterized. Coupled interface modes in conjunction with other types of modes also existing within the same structures, offer the possibility to explore three-wave mixing processes such as sum and difference frequency generation.

abstract: Emergent processes can roughly be defined as processes that self-arise from interactions without a centralized control. People have many robust misconceptions in explaining emergent process concepts such as natural selection and diffusion. This is because they lack a proper categorical representation of emergent processes and often misclassify these processes into the sequential processes category that they are more familiar with. The two kinds of processes can be distinguished by their second-order features that describe how one interaction relates to another interaction. This study investigated if teaching emergent second-order features can help people more correctly categorize new processes, it also compared different instructional methods in teaching emergent second-order features. The prediction was that learning emergent features should help more than learning sequential features because what most people lack is the representation of emergent processes. Results confirmed this by showing participants who generated emergent features and got correct features as feedback were better at distinguishing two kinds of processes compared to participants who rewrote second-order sequential features. Another finding was that participants who generated emergent features followed by reading correct features as feedback did better in distinguishing the processes than participants who only attempted to generate the emergent features without feedback. Finally, switching the order of instruction by teaching emergent features and then asking participants to explain the difference between emergent and sequential features resulted in equivalent learning gain as the experimental group that received feedback. These results proved teaching emergent second-order features helps people categorize processes and demonstrated the most efficient way to teach them. / Dissertation/Thesis / Masters Thesis Psychology 2015

This article aims to analyse the theory of second order elections and its application in elections at the local level, both generally and specifically in the Czech Republic. At first it presents the theory of second order elections and the possibilities of its application. It also talks about the progress of the electoral system at local level, the impact of its revisions on outcomes of election and the position of political parties in elections at local level. Then it focuses on the testing of the theory of second order elections for local elections in the Czech Republic in 2002 and 2006. Generally it evaluates the results of these local elections and compares the theoretical preconditions with the outcomes. It also discuses the observed factors which have influence over theory-testing at the local level in the Czech Republic. Powered by TCPDF (www.tcpdf.org)

The purpose of this dissertation is to address various geometric aspects of second-order scalar hyperbolic partial differential equations in two independent variables and one dependent variable F(x, y, u, u_x, u_y, u_xx, u_xy, u_yy )= 0 (1) We find a characterization of hyperbolic Darboux integrable equations at level k (1) in terms of the vanishing of the generalized Laplace invariants and provide an invariant characterization of various cases in the Goursat general classification of hyperbolic Darboux integrable equations (1). In particular we give a contact invariant characterization of equations integrable by the methods of general and intermediate integrals. New relative invariants that control the existence of the first integrals of the characteristic Pfaffian systems are found and used to obtain an invariant characterization for the class of -Gordon equations. A notion of a hyperbolic Darboux system is introduced and we show by examples that the classical Laplace transformation is just a special case of a diffeomorphism of hyperbolic Darboux systems. We also construct new examples of homomorphisms between certain hyperbolic systems. We characterize Monge-Ampere equations and explicitly exhibit two invariants whose vanishing is a necessary and sufficient condition for the equation to be of the Monge-Ampere type. The solution to the inverse problem of the calculus of variations for hyperbolic equations (1) in terms of the generalized Laplace invariants is presented. We also obtain some partial results on symplectic conservation laws. We characterize, up to contact equivalence, some classical equations using the generalized Laplace invariants. These results contain characterizations of the wave, Liouville, Klein-Gordon, and certain types of Euler-Poisson equations.

Researching efficient removal techniques is essential due to the toxicity of heavy metals, even at low concentrations, and their pervasiveness in a variety of environmental settings. According to WHO, Mercury is one of the most dangerous pollutants for human health. It causes severe damage to the ecosystem and other living beings. But because of its favorable physical-chemical properties, it has been widely used in the industrial activities. Unfortunately, several rivers and aquifers are getting contaminated by this hazardous chemical and inevitably putting importance on how to solve this problem. Most importantly a cost effective and environmentally friendly methods are needed to get a sustainable solution to this contamination. In this study Pinecones and pecan shells were chosen because of its abundance in nature and they are completely free of cost to get. Though some of the studies has been performed to remove some heavy metals by utilizing these two agricultural waste materials, none of the previous study investigated this two-potential bio-sorbents for removing Mercury from water solutions. In addition, there is a chance that metals and other ions will coexist in the environment, which is a complicated situation where there would be a competition among the ions for active cites on the sorbent surface. This study presents the effective removal of Hg2+ at a trace level concentration through adsorption on the grounded pinecones (PC), pecan shells (PS) and Chemically modified pinecones (PC), and pecan shells (PS). The FTIR analysis showed the functional group present in each specimen and pHPZC of each sample was determined to understand the surface chemistry and reactivity of the materials. Chemical modification might result in the increased surface area, porosity, functional groups as compared to the unmodified samples. The factors affecting adsorption efficiency were pH, adsorbent dosage, ionic strength, contact time and metal concentration. The Hg2+ removal efficiency in aqueous solution was found 90-92% for PS and APC, 92-95% for APS, and 80-85% for PC at room temperature with 1 mg/mL dose and pH their optimum pH condition. However, for unmodified PC and PS, the adsorption efficiency was less for all situation as compared to the acid modified PC and PS. The base activated PC and PS were found to be less effective than even the unmodified materials. Thus, results indicate that modification of PC and PS with Nitric acid (HNO3) increases metal adsorption efficiency as compared to unmodified samples. Furthermore, all of the materials tested found to be following the Freundlich's adsorption isotherm in aqueous solutions. Besides, ionic mercury can be readily converted to organic mercury through methylation, and as organic mercury builds up in the food chain, it is very harmful to human health even at a low level. Thus, to provide appropriate protection, the US Environmental Protection Agency (EPA) has set the maximum contamination level of mercury in drinking water at 2.0 ppb. As a result, it becomes very crucial to invent a very sensitive and selective approach for monitoring the low concentration of Hg2+ in the environment. This study aims to create an incredibly sensitive assay for the detection and quantification of Hg2+ (aq) using the single-particle inductively coupled plasma mass spectrometry (spICP-MS). The well-known thymine (T)-Hg2+-T complex forms when AuNPs modified with single-stranded DNA are exposed to Hg2+ (aq) and this formation causes AuNPs to aggregate. By determining the overall reduction in the number of identified AuNPs or NP aggregates the degree of aggregation can quantified. This spICP-MS-based approach has been reported to obtain a substantially lower detection limit of 0.031 part-per-trillion (155 fM) and a larger (10,000-fold) linear range up to 1 ppb when compared to most other Hg assays that use the similar principle of aggregation-dispersion with DNA modified AuNPs. Besides, this approach showed low interference from the sample matrix. Considering the aforementioned advantages, this study focuses on quantifying aqueous Hg2+ using single stranded DNA-gold nanoparticles conjugates with the help of single-particle inductively coupled plasma mass spectrometry (spICP-MS).

No description available.

Dynamical systems are mathematical models characterized by a set of differential or difference equations. Model reduction aims to replace the original system with a reduced system of significantly smaller dimension that still describes the important dynamics of the large-scale model. Interpolatory model reduction methods define a reduced model that interpolates the full model at selected interpolation points. The reduced model may be obtained through a Krylov reduction process or by using the Iterative Rational Krylov Algorithm (IRKA), which iterates this Krylov reduction process to obtain an optimal ℋ₂ reduced model. This dissertation studies interpolatory model reduction for first-order descriptor systems, second-order systems, and DAEs. The main computational cost of interpolatory model reduction is the associated linear systems. Especially in the large-scale setting, inexact solves become desirable if not necessary. With the introduction of inexact solutions, however, exact interpolation no longer holds. While the effect of this loss of interpolation has previously been studied, we extend the discussion to the preconditioned case. Then we utilize IRKA's convergence behavior to develop preconditioner updates. We also consider the interpolatory framework for DAEs and second-order systems. While interpolation results still hold, the singularity associated with the DAE often results in unbounded model reduction errors. Therefore, we present a theorem that guarantees interpolation and a bounded model reduction error. Since this theorem relies on expensive projectors, we demonstrate how interpolation can be achieved without explicitly computing the projectors for index-1 and Hessenberg index-2 DAEs. Finally, we study reduction techniques for second-order systems. Many of the existing methods for second-order systems rely on the model's associated first-order system, which results in computations of a 2𝑛 system. As a result, we present an IRKA framework for the reduction of second-order systems that does not involve the associated 2𝑛 system. The resulting algorithm is shown to be effective for several dynamical systems. / Ph. D.

In large-scale air quality simulations we are interested in the influence factors which cause changes of pollutants, and optimization methods which improve forecasts. The solutions to these problems can be achieved by incorporating adjoint models, which are efficient in computing the derivatives of a functional with respect to a large number of model parameters. In this research we employ first order adjoints in air quality simulations. Moreover, we explore theoretically the computation of second order adjoints for chemical transport models, and illustrate their feasibility in several aspects. We apply first order adjoints to sensitivity analysis and data assimilation. Through sensitivity analysis, we can discover the area that has the largest influence on changes of ozone concentrations at a receptor. For data assimilation with optimization methods which use first order adjoints, we assess their performance under different scenarios. The results indicate that the L-BFGS method is the most efficient. Compared with first order adjoints, second order adjoints have not been used to date in air quality simulation. To explore their utility, we show the construction of second order adjoints for chemical transport models and demonstrate several applications including sensitivity analysis, optimization, uncertainty quantification, and Hessian singular vectors. Since second order adjoints provide second order information in the form of Hessian-vector product instead of the entire Hessian matrix, it is possible to implement applications for large-scale models which require second order derivatives. Finally, we conclude that second order adjoints for chemical transport models are computationally feasible and effective. / Master of Science