Amenities, local conditions and fiscal determinants of factor growth in rural America

This paper examines how amenities, asset indicators, and fiscal factors influence the growth in factors of production from 1972 to 1999 in the 466 non-metropolitan labor market areas in the continental United States. In developing our model of non-metropolitan factor markets, we combine the emphasis of Brown et al. (2003) on the affect of taxes and public expenditure policy on labor and capital formation with the emphasis of Beeson et al. (2001) on the importance of climate and natural features on localized population growth. We develop our own measure of capital stock in non-metropolitan areas using data from the Census of Manufacturing for 1967, 1972, 1977, 1982, 1987, and 1992. Results indicate that local taxes discourage both employment growth and manufacturing capital formation, but that local public infrastructure investment and the level of local entrepreneurship encourages employment growth. Amenities such as a favorable climate and the presence of surface water encourage the growth of employment, and greater local wealth, as measured by dividend, interest, and rent income, encourages the formation of manufacturing capital stock. Results fail to support an “export base” approach for rural economies where greater manufacturing capital stock encourages greater employment in a region.Rural areas ; Rural development

Distributed processing of a fractal array beamformer

Fractals have been proven as potential candidates for satellite flying formations, where its different elements represent a thinned array. The distributed and low power nature of the nodes in this network motivates distributed processing when using such an array as a beamformer. This paper proposes such initial idea, and demonstrates that benefits such as strictly limited local processing capability independent of the array’s dimension and local calibration can be bought at the expense of a slightly increased overall cost

Automation of NLO QCD and EW corrections with Sherpa and Recola

This publication presents the combination of the one-loop matrix-element generator Recola with the multipurpose Monte Carlo program Sherpa. Since both programs are highly automated, the resulting Sherpa+Recola framework allows for the computation of -in principle- any Standard Model process at both NLO QCD and EW accuracy. To illustrate this, three representative LHC processes have been computed at NLO QCD and EW: vector-boson production in association with jets, off-shell Z-boson pair production, and the production of a top-quark pair in association with a Higgs boson. In addition to fixed-order computations, when considering QCD corrections, all functionalities of Sherpa, i.e. particle decays, QCD parton showers, hadronisation, underlying events, etc. can be used in combination with Recola. This is demonstrated by the merging and matching of one-loop QCD matrix elements for Drell-Yan production in association with jets to the parton shower. The implementation is fully automatised, thus making it a perfect tool for both experimentalists and theorists who want to use state-of-the-art predictions at NLO accuracy.Comment: 38 pages, 29 figures. Matches the published version (few typos corrected

Cognitive Radio Assisted OLSR Routing for Vehicular Sensor Networks

AbstractVehicular Sensor Network (VSN) emerged due to recent developments in Wireless Sensor Network (WSN) and functioning as a way for observing metropolitan environments and enabling vehicles to share relevant sensor data to assist safety, convenience and commercial applications. Data dissemination is an important aspect of these networks and requires timely delivery of important sensor information. In VSNs, rapid mobility of the vehicles causes recurrent topography modifications. The possibility of on-demand protocols that makes routing decisions reactively in Vehicular Networks are restricted owing to its structural instability and current routing protocols, operating in a table-driven fashion like OLSR are unable to cope up with the high demands imposed by vehicular applications. Furthermore, sensor data transmissions are accompanied by rapid fluctuations in the convention of licensed spectrum and acquire more number of channels to transmit huge bandwidth data and result in spectrum scarcity. Existing works on OLSR protocol failed to examine spectrum conditions and calculate utilization of channel. Cognitive Radio (CR) is a possible solution for guiding OLSR to discover unused frequency bands and utilize them opportunistically. This paper presents an optimal OLSR routing for efficient data communication using Cognitive Radio enabled Vehicular Sensor Networks (CR-VSNs). The proposed model was tested under simulated traffic of Chennai urban road map. Delay is observed to be minimal for data communications in CR-VSN

A quartz crystal biosensor for measurement in liquids

The detection of anti-human immunodeficiency virus (HIV) antibodies by means of synthetic HIV peptide immobilized on a piezoelectric quartz sensor is demonstrated. The measurement set-up consists of an oscillator circuit, a suitably modified AT-cut thickness-shear-mode quartz crystal with gold electrodes, which is housed in a special reaction vessel, and a computer-controlled frequency counter for the registration of the measured frequency values. The quartz crystal is adapted for a steady operation in liquids at a frequency of 20 MHz. In phosphate-buffered saline solution the oscillator reaches a stability of about 0.5 Hz within a few seconds, of about 2 Hz within 10 min and about 30 Hz within 1 h. The frequency shift due to the adsorption of various proteins to the uncoated sensor surface has been investigated. It can be shown that a stable adsorptive binding of proteins to an oscillating gold surface is feasible and can be used for the immobilization of a receptor layer (e.g. HIV peptide). Specific binding of the anti-HIV monoclonal antibody to the HIV peptide immobilized on the quartz sensor is demonstrated. Control experiments show, however, additional unspecific binding. According to the experiments, the Sauerbrey formula gives a sufficiently accurate value for the decrease of the resonant frequency due to adsorption or binding of macromolecular proteins on the quartz crystal surface

Anderson transition and thermal effects on electron states in amorphous silicon

I discuss the properties of electron states in amorphous Si based on large scale calculations with realistic several thousand atom models. A relatively simple model for the localized to extended (Anderson) transition is reviewed. Then, the effect of thermal disorder on localized electron states is considered. It is found that under readily accessible conditions, localized (midgap or band tail) states and their conjugate energies may fluctuate dramatically. The possible importance of non-adiabatic atomic dynamics to doped or photo-excited systems is briefly discussed.Comment: Was presented at ICAMS18, Snowbird UT, August 1999. Submitted to J. of Non-Cryst. Solid

Analysing the performance of divide-and-conquer sequential matrix diagonalisation for large broadband sensor arrays

A number of algorithms capable of iteratively calculating a polynomial matrix eigenvalue decomposition (PEVD) have been introduced. The PEVD is an extension of the ordinary EVD to polynomial matrices and will diagonalise a parahermitian matrix using paraunitary operations. Inspired by recent work towards a low complexity divide-and-conquer PEVD algorithm, this paper analyses the performance of this algorithm - named divide-and-conquer sequential matrix diagonalisation (DC-SMD) - for applications involving broadband sensor arrays of various dimensionalities. We demonstrate that by using the DC-SMD algorithm instead of a traditional alternative, PEVD complexity and execution time can be significantly reduced. This reduction is shown to be especially impactful for broadband multichannel problems involving large arrays

Impact of fast-converging PEVD algorithms on broadband AoA estimation

Polynomial matrix eigenvalue decomposition (PEVD) algorithms have been shown to enable a solution to the broadband angle of arrival (AoA) estimation problem. A parahermitian cross-spectral density (CSD) matrix can be generated from samples gathered by multiple array elements. The application of the PEVD to this CSD matrix leads to a paraunitary matrix which can be used within the spatio-spectral polynomial multiple signal classification (SSP-MUSIC) AoA estimation algorithm. Here, we demonstrate that the recent low-complexity divide-and-conquer sequential matrix diagonalisation (DC-SMD) algorithm, when paired with SSP-MUSIC, is able to provide superior AoA estimation versus traditional PEVD methods for the same algorithm execution time. We also provide results that quantify the performance trade-offs that DC-SMD offers for various algorithm parameters, and show that algorithm convergence speed can be increased at the expense of increased decomposition error and poorer AoA estimation performance

Polynomial subspace decomposition for broadband angle of arrival estimation

In this paper we study the impact of polynomial or broadband subspace decompositions on any subsequent processing, which here uses the example of a broadband angle of arrival estimation technique using a recently proposed polynomial MUSIC (P-MUSIC) algorithm. The subspace decompositions are performed by iterative polynomial EVDs, which differ in their approximations to diagonalise and spectrally majorise s apce-time covariance matrix.We here show that a better diagonalisation has a significant impact on the accuracy of defining broadband signal and noise subspaces, demonstrated by a much higher accuracy of the P-MUSIC spectrum

Energy-efficient blockchain implementation for Cognitive Wireless Communication Networks (CWCNs)

Abstract Considering the computation resources available with sensor devices and the value and validity of Cognitive Wireless Communication Network (CWCN), traditional blockchain is not feasible for CWCN. Further, considering the security and privacy for CWCN that can directly impact human life (as in the case of ambient assisted living applications), blockchain provides a good solution for such applications, however, with some simplicity in the computation of Proof of Work (PoW). Therefore, the fourth objective solution comes up with a simplified energy-efficient blockchain implementation for CWCN that consumes less energy in computation time. The energy-hungry blockchain has been implemented on resource-constrained CWCN for ambient assisted living applications specialized for elderly care. The process includes a collection of physical environmental parameters on a single board computer-based CWCN. The implementation includes possible simplification in the most energy-consuming process, i.e., the mining process, which makes it energy efficient in computation time as energy consumption is a computation time factor