STORRE Community: This community contains the ePrints and eTheses produced by Computing Science and Mathematics staff and students.
http://hdl.handle.net/1893/35
This community contains the ePrints and eTheses produced by Computing Science and Mathematics staff and students.Thu, 23 May 2019 10:50:48 GMT2019-05-23T10:50:48ZLeontovich-Fock Parabolic Equation Method in the Neumann Diffraction Problem on a Prolate Body of Revolution
http://hdl.handle.net/1893/29548
Title: Leontovich-Fock Parabolic Equation Method in the Neumann Diffraction Problem on a Prolate Body of Revolution
Author(s): Kirpichnikova, Anna S; Kirpichnikova, Natalya Yakovlevna
Abstract: This paper continues a series of publications on the shortwave diffraction of the plane wave on prolate bodies of revolution with axial symmetry in the Neumann problem. The approach, which is based on the Leontovich–Fock parabolic equation method for the two parameter asymptotic expansion of the solution, is briefly described. Two correction terms are found for the Fock's main integral term of the solution expansion in the boundary layer. This solution can be continuously transformed into the ray solution in the illuminated zone and decays exponentially in the shadow zone. If the observation point is in the shadow zone near the scatterer, then the wave field can be obtained with the help of residue theory for the integrals of the reflected field, because the incident field does not reach the shadow zone. The obtained residues are necessary for the unique construction of the creeping waves in the boundary layer of the scatterer in the shadow zone. Bibliography: 16 titles. We consider a shortwave diffraction of a plane incident wave on the strictly convex, prolate body of revolution. The geometric characteristics of the scatterer (i.e., radii of curvatures of the surface of body of revolution) are assumed to be much larger than the incident wavelength. The incident wave propagates along the axis of revolution. The total wave field U is the sum of the incident U inc and reflected U ref waves, U = U inc + U ref. The field is constructed in the vicinity of the light-shadow border (i.e., in the penumbra of Fock's region, [1]), which is the " seed " zone for fields both in the vicinity of the limit rays and in the shadowed part of the body. The shortwave field in the illuminated area near the scatterer is described by means of the ray method. The field U satisfies the Helmholtz equation with Neumann or Dirichlet boundary conditions. Fock's boundary layer O(sk 1 3) = O(1), O(nk 2 3) = O(1) is introduced in a neighborhood of point s = 0, which belongs to the geometric border (Equator) of the shadow; here k is the wave number, n is the distance along the outer normal on the scatterer, and s is the arclength of the geodesic. The ray method does not work in the vicinity of the light-shadow border, i.e., in the Fock's boundary layer. The total wave field in the Fock's zone can be represented as U = e iks (W inc + W ref), where e iks is the oscillating factor of the wave field along the geodesic; the function W is called the attenuation function. Introducing dimensionless coordinates σ, ν instead of s and n, and rewriting e ikz in the new coordinates σ, ν, we obtain the first three terms of the expansion W inc in the form W (σ, ν) = W inc 0 + W inc 1 k 1 3 + W inc 2 k 2 3 + O(k −1), k 1, here W inc 0 is the main, W inc 1 is the first, and W inc 2 is the second terms of the asymptotic expansion. The functions W inc i , i = 0, 1, 2, have the form of integrals of linear combinations of the Airy function v(t) and its derivative v (t) with polynomials in the dimensionless normal coordinate ν in Fock's region. We apply the Leontovich–Fock parabolic equation method [1,4] to the function under investigation W ref (σ, ν) = W ref 0 + W ref 1 k 1 3 + W ref 2 k 2 3 + O(k −1), k 1.Wed, 01 May 2019 00:00:00 GMThttp://hdl.handle.net/1893/295482019-05-01T00:00:00ZSparse Modelling and Multi-exponential Analysis (Meeting Report)
http://hdl.handle.net/1893/29535
Title: Sparse Modelling and Multi-exponential Analysis (Meeting Report)
Author(s): Cuyt, Annie; Labahn, George; Sidi, Avraham; Lee, Wen-shin
Abstract: The research fields of harmonic analysis, approximation theory and computer algebra are seemingly different domains and are studied by seemingly separated research communities. However, all of these are connected to each other in many ways. The connection between harmonic analysis and approximation theory is not accidental: several constructions among which wavelets and Fourier series, provide major insights into central problems in approximation theory. And the intimate connection between approximation theory and computer algebra exists even longer: polynomial interpolation is a long-studied and important problem in both symbolic and numeric computing, in the former to counter expression swell and in the latter to construct a simple data model. A common underlying problem statement in many applications is that of determining the number of components, and for each component the value of the frequency, damping factor, amplitude and phase in a multi-exponential model. It occurs, for instance, in magnetic resonance and infrared spectroscopy, vibration analysis, seismic data analysis, electronic odour recognition, keystroke recognition, nuclear science, music signal processing, transient detection, motor fault diagnosis, electrophysiology, drug clearance monitoring and glucose tolerance testing, to name just a few. The general technique of multi-exponential modeling is closely related to what is commonly known as the Padé-Laplace method in approximation theory, and the technique of sparse interpolation in the field of computer algebra. The problem statement is also solved using a stochastic perturbation method in harmonic analysis. The problem of multi-exponential modeling is an inverse problem and therefore may be severely ill-posed, depending on the relative location of the frequencies and phases. Besides the reliability of the estimated parameters, the sparsity of the multi-exponential representation has become important. A representation is called sparse if it is a combination of only a few elements instead of all available generating elements. In sparse interpolation, the aim is to determine all the parameters from only a small amount of data samples, and with a complexity proportional to the number of terms in the representation. Despite the close connections between these fields, there is a clear lack of communication in the scientific literature. The aim of this seminar is to bring researchers together from the three mentioned fields, with scientists from the varied application domains.Fri, 01 Jan 2016 00:00:00 GMThttp://hdl.handle.net/1893/295352016-01-01T00:00:00ZThe impact of fungicide treatment and Integrated Pest Management on barley yields: Analysis of a long term field trials database
http://hdl.handle.net/1893/29474
Title: The impact of fungicide treatment and Integrated Pest Management on barley yields: Analysis of a long term field trials database
Author(s): Stetkiewicz, Stacia; Burnett, Fiona J; Ennos, Richard A; Topp, Cairistiona F E
Abstract: This paper assesses potential for Integrated Pest Management (IPM) techniques to reduce the need for fungicide use without negatively impacting yields. The impacts of three disease management practices of relevance to broad acre crops –disease resistance, forecasting disease pressure, and fungicide use – were analysed to determine impact on yield using a long-term field trials database of Scottish spring barley, with information from experiments across the country regarding yield, disease levels, and fungicide treatment. Due to changes in data collection practices, data from 1996 to 2010 were only available at trial level, while data from 2011 to 2014 were available at plot level. For this reason, data from 1996 to 2014 were analysed using regression models, while a subset of farmer relevant varieties was taken from the 2011–2014 data, and analysed using ANOVA, to provide additional information of particular relevance to current farm practice. While fungicide use reduced disease severity in 51.4%of a farmer-relevant subset of trials run 2011–2014, and yields were decreased by 0.62 t/ha on average, this was not statistically significant in 65% of trials. Fungicide use had only a minor impact on profit in these trials, with an average increase of 4.4% for malting and 4.7% for feed varieties, based on fungicide cost and yield difference; potential savings such as reduced machinery costs were not considered, as these may vary widely. Likewise, the1996–2014 database showed an average yield increase of 0.74 t/ha due to fungicide use, across a wide range of years, sites, varieties, and climatic conditions. A regression model was developed to assess key IPM and site factors which influenced the difference between treated and untreated yields across this 18-year period. Disease resistance, season rainfall, and combined disease severity of the three fungal diseases were found to be significant factors in the model. Sowing only highly resistant varieties and, as technology improves, forecasting disease pressure based on anticipated weather would help to reduce and optimise fungicide use.Mon, 01 Apr 2019 00:00:00 GMThttp://hdl.handle.net/1893/294742019-04-01T00:00:00ZGoogle Trends in Infodemiology and Infoveillance: Methodology Framework (Forthcoming/Available Online)
http://hdl.handle.net/1893/29470
Title: Google Trends in Infodemiology and Infoveillance: Methodology Framework (Forthcoming/Available Online)
Author(s): Mavragani, Amaryllis; Ochoa, Gabriela
Abstract: Background: The use of Internet data is increasingly integrated in Health Informatics research and is becoming a useful tool in exploring human behavior. The most popular tool for examining online behavior is Google Trends, an open tool that provides information on what is trending and on the variations of the online interest in selected keywords and topics over time. Online search traffic data from Google have been shown to be useful in analyzing human behavior towards health topics and in predicting diseases’ occurrence and outbreaks. Objective: Despite the large number of Google Trends studies during the last decade, the literature on the subject lacks a specific methodology framework. This article aims at providing an overview of the tool and data, and at presenting the first methodology framework in using Google Trends in Infodemiology and Infoveillance, consisting of the main factors that need to be taken into account for a solid methodology base. Methods: We provide a step-by-step guide for the methodology that needs to be followed when researching with Google Trends; essential for robust results in this line of research. Results: At first, an overview of the tool and the data are presented, followed by the analysis of the key methodological points for ensuring the robustness of the results, i.e., selecting the appropriate keyword(s), region(s), period, and category. Conclusions: In the era of Big Data, the analysis of online queries is all the more integrated in health research. This article presents and analyzes the key points that need to be considered for a solid methodology basis when using Google Trends data, which is crucial for ensuring the value and validity of the results.Sat, 23 Mar 2019 00:00:00 GMThttp://hdl.handle.net/1893/294702019-03-23T00:00:00Z