1,486 research outputs found
Reel Notes
https://orc.library.atu.edu/atu_reel390/1002/thumbnail.jp
A feynman path integral representation for elastic wave scattering by anisotropic weakly perturbations
We write a space-time Feynman path integral representation for scattered
elastic wave fields from a weakly compact supported anisotropic
non-homogeneity.Replacement by a new version where We (I!) propose a new
tomographic inversion methodology based solely in the wave sampling of the Ray
paths through Monte Carlo path integral sampling Holding thus great
potentiality for Navy's advanced Sonar detection .Comment: 8 page
An electromyographic evaluation of dual role breathing and upper body muscles in response to front crawl swimming
The upper body trunk musculature is key in supporting breathing, propulsion, and stabilization during front crawl swimming. The aim of this study was to determine if the latissimus dorsi, pectoralis major, and serratus anterior contributed to the development of inspiratory muscle fatigue observed following front crawl swimming. Fourteen trained swimmers completed a 200-m front crawl swim at 90% of race pace. Maximal inspiratory and expiratory mouth pressures (PImax and PEmax) were assessed before (baseline) and after each swim, and electromyography was recorded from the three muscles. Post-swim PImax fell by 11% (P < 0.001, d = 0.57) and the median frequency (MDF: a measure of fatigue) of the latissimus dorsi, pectoralis major, and serratus anterior fell to 90% (P = 0.001, d = 1.57), 87% (P = 0.001, r = −0.60) and 89% (P = 0.018, d = 1.04) of baseline, respectively. The fall in serratus anterior MDF was correlated with breathing frequency (r = 0.675, P = 0.008) and stroke rate (r = 0.639, P = 0.014). The results suggest that the occurrence of inspiratory muscle fatigue was partly caused by fatigue of these muscles, and that breathing frequency and stroke rate particularly affect the serratus anterior
Modelling the structure of star clusters with fractional Brownian motion
The degree of fractal substructure in molecular clouds can be quantified by
comparing them with Fractional Brownian Motion (FBM) surfaces or volumes. These
fields are self-similar over all length scales and characterised by a drift
exponent , which describes the structural roughness. Given that the
structure of molecular clouds and the initial structure of star clusters are
almost certainly linked, it would be advantageous to also apply this analysis
to clusters. Currently, the structure of star clusters is often quantified by
applying analysis. values from observed targets are
interpreted by comparing them with those from artificial clusters. These are
typically generated using a Box-Fractal (BF) or Radial Density Profile (RDP)
model. We present a single cluster model, based on FBM, as an alternative to
these models. Here, the structure is parameterised by , and the standard
deviation of the log-surface/volume density . The FBM model is able to
reproduce both centrally concentrated and substructured clusters, and is able
to provide a much better match to observations than the BF model. We show that
analysis is unable to estimate FBM parameters. Therefore, we
develop and train a machine learning algorithm which can estimate values of
and , with uncertainties. This provides us with a powerful method for
quantifying the structure of star clusters in terms which relate to the
structure of molecular clouds. We use the algorithm to estimate the and
for several young star clusters, some of which have no measurable BF
or RDP analogue.Comment: 11 Pages, accepted by MNRA
Mechanical Systems: Symmetry and Reduction
Reduction theory is concerned with mechanical systems with symmetries. It constructs a
lower dimensional reduced space in which associated conservation laws are taken out and
symmetries are \factored out" and studies the relation between the dynamics of the given
system with the dynamics on the reduced space. This subject is important in many areas,
such as stability of relative equilibria, geometric phases and integrable systems
Characterising lognormal fractional-Brownian-motion density fields with a Convolutional Neural Network
In attempting to quantify statistically the density structure of the
interstellar medium, astronomers have considered a variety of fractal models.
Here we argue that, to properly characterise a fractal model, one needs to
define precisely the algorithm used to generate the density field, and to
specify -- at least -- three parameters: one parameter constrains the spatial
structure of the field; one parameter constrains the density contrast between
structures on different scales; and one parameter constrains the dynamic range
of spatial scales over which self-similarity is expected (either due to
physical considerations, or due to the limitations of the observational or
numerical technique generating the input data). A realistic fractal field must
also be noisy and non-periodic. We illustrate this with the exponentiated
fractional Brownian motion (xfBm) algorithm, which is popular because it
delivers an approximately lognormal density field, and for which the three
parameters are, respectively, the power spectrum exponent, , the
exponentiating factor, , and the dynamic range, . We then
explore and compare two approaches that might be used to estimate these
parameters: Machine Learning and the established -Variance procedure.
We show that for and , a suitably
trained Convolutional Neural Network is able to estimate objectively both
(with root-mean-square error ) and (with ). -variance is also able to
estimate , albeit with a somewhat larger error () and with some human intervention, but is not able to estimate
Voltera's Solution of the Wave Equation as Applied to Three-Dimensional Supersonic Airfoil Problems
A surface integral is developed which yields solutions of the linearized partial differential equation for supersonic flow. These solutions satisfy boundary conditions arising in wing theory. Particular applications of this general method are made, using acceleration potentials, to flat surfaces and to uniformly loaded lifting surfaces. Rectangular and trapezoidal plan forms are considered along with triangular forms adaptable to swept-forward and swept-back wings. The case of the triangular plan form in sideslip is also included. Emphasis is placed on the systematic application of the method to the lifting surfaces considered and on the possibility of further application
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