719 research outputs found

    Unconventional rf photoresponse from a superconducting spiral resonator

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    Superconducting thin film resonators employing strip geometries show great promise in rf/microwave applications due to their low loss and compact nature. However, their functionality is limited by nonlinear effects at elevated rf/microwave powers. Here, we show that by using a planar spiral geometry carrying parallel currents in adjacent turns, this limitation can be minimized. We investigate the rf current distributions in spiral resonators implemented with Nb thin films via laser scanning microscopy. The rf current density profile along the width of the individual turns of the resonators reveals an unconventional trend: maximum current in the middle of the structure and decaying toward its edges. This unusual behavior is associated with the circular nature of the geometry and the cancellation of magnetic field between the turns, which is favorable for handling high powers since it allows the linear characteristics to persist at high rf current densities.Comment: 8 pages, 7 figure

    Some properties of hyperbolic Yamabe solitons

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    We define the hyperbolic Yamabe flow and obtain some properties of its stationary solutions, namely, hyperbolic Yamabe solitons. We also consider immersed submanifolds as hyperbolic Yamabe solitons and prove that, under certain assumptions, a hyperbolic Yamabe soliton hypersurface is pseudosymmetric or metallic shaped. Further we characterize the hyperbolic Yamabe soliton factor manifolds of a multiply twisted, multiply warped, doubly warped, and warped product manifold, and we provide a classification for a complete gradient hyperbolic Yamabe soliton factor manifold. Finally we determine the conditions when the factor manifolds are hyperbolic Yamabe solitons if the manifold is a hyperbolic Yamabe soliton and illustrate this result for the Robertson--Walker spacetime.Comment: 19 page

    A Computational Intelligence Approach to System-of-Systems Architecting Incorporating Multi-Objective Optimization

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    A computational intelligence approach to system-of-systems architecting is developed using multi-objective optimization. Such an approach yields a set of optimal solutions (the Pareto set) which has both advantages and disadvantages. The primary benefit is that a set of solutions provides a picture of the optimal solution space that a single solution cannot. The primary difficulty is making use of a potentially infinite set of solutions. Therefore, a significant part of this approach is the development of a method to model the solution set with a finite number of points allowing the architect to intelligently choose a subset of optimal solutions based on criteria outside of the given objectives. The approach developed incorporates a meta-architecture, multi-objective genetic algorithm, and a corner search to identify points useful for modeling the solution space. This approach is then applied to a network centric warfare problem seeking the optimum selection of twenty systems. Finally, using the same problem, it is compared to a hybrid approach using single-objective optimization with a fuzzy logic assessor to demonstrate the advantage of multi-objective optimization

    Establishing Rules for Self-Organizing Systems-Of-Systems

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    Self-organizing systems-of-systems offer the possibility of autonomously adapting to new circumstances and tasking. This could significantly benefit large endeavors such as smart cities and national defense by increasing the probability that new situations are expediently handled. Complex self-organizing behaviors can be produced by a large set of individual agents all following the same simple set of rules. While biological rule sets have application in achieving human goals, other rules sets may be necessary as these goals are not necessarily mirrored in nature. To this end, a set of system, rather than biologically, inspired rules is introduced and an agent-based model is used to simulate and analyze the behavior produced with various parameters. Agents represent systems and their decisions are defined by the given rule set and parameters. The environment provides a variety of time-critical missions on an ongoing basis. The effectiveness of a particular rule or set of rules is measured by a set of key performance metrics such as the rate at which missions achieve their required capabilities within a given deadline and the average time required to do so. Different rules will be compared using this criterion along with an assessment of their ability to demonstrate beneficial self-organizing behavior

    Classical Analogue of Electromagnetically Induced Transparency with a Metal-Superconductor Hybrid Metamaterial

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    Metamaterials are engineered materials composed of small electrical circuits producing novel interactions with electromagnetic waves. Recently, a new class of metamaterials has been created to mimic the behavior of media displaying electromagnetically induced transparency (EIT). Here we introduce a planar EIT metamaterial that creates a very large loss contrast between the dark and radiative resonators by employing a superconducting Nb film in the dark element and a normal-metal Au film in the radiative element. Below the critical temperature of Nb, the resistance contrast opens up a transparency window along with a large enhancement in group delay, enabling a significant slowdown of waves. We further demonstrate precise control of the EIT response through changes in the superfluid density. Such tunable metamaterials may be useful for telecommunication because of their large delay-bandwidth products.Comment: 4 pages, 4 figure

    What Kind Of Capitalism For Azerbaijan? A Comparative Analysis From Economic View

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    Every country has experienced various capital accumulation processes due to their own specific conditions. Differences in these conditions have ensured various countries to enter the process of economic development in dissimilar historical periods. Due to the central characteristics of the previous command economic system and the impact of powerful heritage from the USSR on the bureaucratic administration, Azerbaijan is still having difficulties in transitioning to a free-market economy. Today, the transition to an open market economy for Azerbaijan is not completely realized. This research attempts to investigate the major factors of the formation process of the capitalist economic structure in Azerbaijan before and after the demise of the Soviet Union. It focused on the fundamental role of oil and relatively, the agricultural sector and also looked into the types of capitalism the country is currently experiencing based upon certain criteria and statistical indicators

    Microscopic examination of hot spots giving rise to nonlinearity in superconducting resonators

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    We investigate the microscopic origins of nonlinear rf response in superconducting electromagnetic resonators. Strong nonlinearity appearing in the transmission spectra at high input powers manifests itself through the emergence of jumplike features near the resonant frequency that evolve toward lower quality factor with higher insertion loss as the rf input power is increased. We directly relate these characteristics to the dynamics of localized normal regions (hot spots) caused by microscopic features in the superconducting material making up the resonator. A clear observation of hot-spot formation inside a Nb thin film self-resonant structure is presented by employing the microwave laser scanning microscope, and a direct link between microscopic and macroscopic manifestations of nonlinearity is established.Comment: 5 pages, 4 figure

    A Hierarchial Neural Network Implementation for Forecasting

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    In this paper, a hierarchical neural network architecture for forecasting time series is presented. The architecture is composed of two hierarchical levels using a maximum likelihood competitive learning algorithm. The first level of the system has three experts each using backpropagation and a gating network to partition the input space in order to map the input vectors to the output vectors. The second level of the hierarchical network has an expert using fuzzy ART for producing the correct trend coming from the first level. The experiments show that the resulting network is capable of forecasting the changes in the input and identifying the trends correctl
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