Nonsimilar boundary layers on the leeside of cones at incidence

It is known that the similarity equations for the leeward line of symmetry of a cone have no solutions for $\text{?1 < k}{}_2{}^1\text{< k < k}{}_1\text{1 < 0}$, where k is the incidence parameter of Moore, and $\text{k}{}_1{}^1\text{, k}{}_2{}^1$ depend on the external Mach number and enthalpy ratio. In this paper we present evidence that a leeside integration for such k terminates at a finite distance from the vertex in a singularity of the type analyzed by Stewartson and Simpson for entry flow in a curved pipe, and compare the theory with a representative numerical solution at $\text{k = ?}\text{1}\text{2}$. A possible interpretation of this singularity is that the boundary layers growing from the windward line of symmetry have collided, and support for this view is given by windward to leeward integrations for a range of values of k. For k such that the similarity equations have solutions, these are interpreted as limits of more general solutions.     

Averaging shifted histograms

A method which consists in shifting different histograms of the same spectrum and then taking their average is presented in order to smooth the data and to increase the localization accuracy and separation of the peaks. The statistical properties of this method are investigated. The average of two histograms with shifted bin limits is studied. It is shown that for histograms with random bin limits, distributed according to

$\text{F}{}_{\mathrm{i}}\text{(x)=}\text{∫}\text{?∞}\text{x}\text{?}{}_{\mathrm{i}}\text{(ξ, μ}{}_{\mathrm{i}}\text{, σ)}\text{d}\text{ξ}$

; where the standard deviation σ is very small compared to the difference of the means (μ_i+1 ? μ_i) for ll i the zero order approximation to the variance of this histogram is given by:

$\text{var}\text{∽}\text{(H)=}\text{∑}\text{i=0}\text{m}\text{(A}{}_{\mathrm{i+1}}\text{?a}{}_{\mathrm{i}}\text{)}{}^2\text{F}{}_{\mathrm{i+1}}\text{(x)(1?F}{}_{\mathrm{i+1}}\text{(x))}$

, where

$\text{a}{}_{\mathrm{i}}\text{=}\text{1}\text{x}{}_{\mathrm{i=1}}\text{?x}{}_{\mathrm{i}}\text{∫}\text{x}{}_{\mathrm{i}}\text{x}{}_{\mathrm{i+1}}\text{g(ξ)}\text{d}\text{ξ}$

and g is an unknown function fitted by the histogram. Formula (1) gives also the relation:

$\text{v}\text{a}\text{?}\text{r}\text{(}\text{(H}{}_1\text{+ H}{}_2\text{)}\text{2}\text{) =}\text{1}\text{4}\text{(}\text{v}\text{a}\text{?}\text{r}\text{(H}{}_1\text{(x)) +}\text{v}\text{a}\text{?}\text{r}\text{(H}{}_2\text{(x))}$

, when H₁ and H₂ have stochastically independent bin limits.When the histogram H is considered as a spline function S of order one it is shown that for the minimization criterion with respect to the coefficient of the spline:

$\text{M}{}_1\text{=}\text{min}\text{∫}\text{x}{}_1\text{x}{}_{\mathrm{m+1}}\text{(g(x) ? S}{}_1\text{(x))}{}^2\text{d}\text{x}$

, the following result holds: $\text{M}{}_{\mathrm{a}}\text{?}{}^1{}_2\text{(M}{}_1\text{+ M}{}_2\text{)}$, where $\text{S}{}_{\mathrm{a}}\text{(x) =}{}^1{}_2\text{(S}{}_1\text{(x) + S}{}_2\text{(x))}$. If the number of shifted histograms tends to infinity, then

$\text{S}{}_{\mathrm{\infty }}\text{(x) = [Γ(x + h) + Γ(x ? h) ? 2Γ(x)]/h}{}^2$

, where $\text{Γ(x) =}\text{∫}\text{?∞}\text{x}\text{∫}\text{?∞}\text{η}\text{g(ξ)}\text{d}\text{ξ}\text{d}\text{η}$, and h is a constant bin size. Then

$\text{M}{}_{\mathrm{\infty }}\text{≈}\text{h}{}^4\text{144}\text{∫}\text{x}{}_1\text{x}{}_{\mathrm{m+1}}\text{g″}{}^2\text{(x)}\text{D}\text{x}$

. Extensions to two-dimensional histograms and to higher order (empirical distributions) are presented.     

Normal forms for nonautonomous difference equations

We extend Henry Poincaré's normal form theory for autonomous difference equations χ_{k + 1} = f(χ_k) to nonautonomous difference equations χ_{k + 1} = f_k(χ_k). Poincaré's nonresonance condition $\text{α}{}_{\mathrm{j}}\text{-П}{}^{\mathrm{n}}{}_{\mathrm{i=1}}\text{=1α}{}^{\mathrm{qi}}{}_{\mathrm{i}}\text{≠0}$ for eigenvalues is generalized to the new nonresonance condition λ_j∪$\text{α}{}_{\mathrm{j}}\text{⊔П}{}^{\mathrm{n}}{}_{\mathrm{i=1}}\text{α}{}^{\mathrm{qi}}{}_{\mathrm{i}}\text{≠0}$ for spectral intervals.     

On some convergence results for FDM with irregular mesh

The proof of convergence of the finite difference method with arbitrary irregular meshes for some class of elliptic problems is presented. By the use of the truncation error technique and stability analysis it was showed that $\text{max}{}_{\mathrm{i}}\text{¦u}{}^{\mathrm{i}}\text{? u}{}_{\mathrm{h}}{}^{\mathrm{i}}\text{¦? Ch}$, i.e., the solution u_h converges linearly with the size of the star. Correctness of this theorem was also confirmed by numerical tests.     

Mathematical foundations of Bézier's technique

Bezier's method is one of the most famous in computational geometry. In his book Numerical control Bezier gives excellent expositions of the mathematical foundations of this method. In this paper a new expression of the functions {f_n,i(u)}

$\text{f}{}_{\mathrm{n,i}}\text{(u)=1?}\text{Σ}\text{p=0}\text{i?1}\text{C}{}^{\mathrm{p}}{}_{\mathrm{n}}\text{u}{}^{\mathrm{p}}\text{(1?u)}{}^{\mathrm{n?p}}\text{(i=1,2,…,n)}$

is obtained.Using this formula, we have not only derived some properties of the functions {f_n,i(u)} (for instance $\text{f}{}_{\mathrm{n,n}}\text{(u) < f}{}_{\mathrm{n,n?1}}\text{(u)<...<f}{}_{\mathrm{n,1}}\text{(u)}\text{u}\text{? [0, 1]}$ and functions {f_n,i(u)} increase strictly at [0, 1] etc) but also simplified systematically all the mathematical discussions about Bezier's method.Finally we have proved the plotting theorem completely by matrix calculation.     

The relation between information theory and the differential geometry approach to statistics

?encov has shown that the Riemannian metric on the probability simplex ∑x_i = 1 defined by $\text{(ds)}{}^2\text{=}\text{∑(dx}{}_{\mathrm{i}}\text{)}{}^2\text{x}{}_{\mathrm{i}}$ has an invariance property under certain probabilistically natural mappings. No other Riemannian metric has the same property. The geometry associated with this metric is shown to lead almost automatically to measures of divergence between probability distributions which are associated with Kullback, Bhattacharyya, and Matusita. Certain vector fields are associated in a natural way with random variables. The integral curves of these vector fields yield the maximum entropy or minimum divergence estimates of probabilities. Some other consequences of this geometric view are also explored.     

Gibbs energies of formation of Mn3C,M(Fe,Mn)3C and Mn23C6 from the ternary phase equilibria in the FeMnC system

The high temperature phase relations in the FeMnC system have been analyzed in light of the recently developed thermodynamic method by the authors to obtain the Gibbs energies of formation of Mn₂₃C₆ and Mn,C. A new thermodyn/amic treatment is outlined and applied to obtain the stability of the ternary carbide M(Fe,Mn)₃C without any a priori assumption of a solution model for the M₃C phase. The recommended Gibbs energies of formation for the Mn carbides, Mn₃C and Mn₂₃C₆ With γ-Mn (graphite) as the Standard states are:

The present method can be extended to obtain a consistent set of thermodynamic data for binary and ternary carbides from various ternary metal-metal-carbon phase relations.     

Crosscorrelation between linearly and nonlinearly distorted versions of a given signal

A given deterministic signal x(.) is distorted by passing it through a linear time-invariant filter and also by subjecting it to the action of an instantaneous nonlinearity. The resulting time crosscorrelation of the two distorted versions of the original signal is expressed by the function

$\text{R}{}_2\text{(s)?∫}{}_{\mathrm{?\infty }}{}^{\mathrm{\infty }}\text{?∫}{}_{\mathrm{?\infty }}{}^{\mathrm{\infty }}\text{g[x(t)]k(t?t′)x(t?s)dt dt′}$

, where x(.) is the given signal, k(.) is the nonnegative definite impulse response of the linear filter, and g(.) is the output-input characteristic of the zero-memory nonlinear device. The problem considered is that of determining conditions on the pair (x,g) such that R₂(s) ? R₂(0) for all s and any choice of nonnegative definite filter function k; the principal result is the formulation of a necessary and sufficient condition for R₂ to have a global maximum at the origin. In particular, the peak value occurs at the origin if and only if $\text{G}{}_{\mathrm{x}}{}^1\text{(ω)X(ω)}$ is real and nonnegative for all ω ? 0, where G_x(.) and X(.) are the Fourier transforms of g[x(.)] and x(.), respectively. An equivalent condition is that the correlation function

$\text{R}{}_2\text{(s)?∫}{}_{\mathrm{?\infty }}{}^{\mathrm{\infty }}\text{g[x(t)]x(t?s)dt}$

, previously studied by Richardson, be nonnegative definite.Several examples are given, and it is shown that, unlike the case for R₁(.), monotonicity of g(.) is not a sufficient condition for R₂(.) to have a global maximum at s = 0 independently of the choice of filter characteristic k. Certain extensions of these results are given for the case when x(.) is a Gaussian random input.     

Insertion languages

The operations of insertion ( ← ) and iterated insertion ( ←₁ ) are simple variants of Kleene's operations · and 1 [15] in a manner similar to the operations shuffle and iterated shuffle (see e.g. [13, 23, 20, 14]). Using the operation of iterated insertion, we can generate both the semi-Dyck and the two-sided Dyck languages from certain finite subsets of these languages. Thus the class of languages of the form $\text{S}{}^{\mathrm{\leftarrow }}{}^1$ for finite S forms a natural class of generalized Dyck languages. This class is equivalent to the class of pure unitary languages discussed in [6]. We investigate this class further, by examining for it the problems of equivalence, ambiguity, and determinism, all of which are easily decidable. On the other hand, we show that the problem “$\text{S}{}^{\mathrm{\leftarrow }}\text{1 ∩ T}{}^{\mathrm{\leftarrow }}\text{1 = {λ}?}$” is undecidable for finite, unambiguous S and T. Furthermore, by extending the regular expressions to include the operations ← and $\text{←}{}_1$, we obtain the class of insertion languages which generalizes both the regular languages and the Dyck languages, but is properly contained within the class of context-free languages. Our main result here is that the problem “$\text{L = ∑}{}^1\text{?}$” is undecidable for the class of insertion languages. From this result, it follows that the equivalence problem and the problem “IsL regular?” are also undecidable for this class.     

Asymptotic properties of minimax trees and game-searching procedures

The model most frequently used for evaluating the behavior of game-searching methods consists of a uniform tree of height h and a branching degree d, where the terminal positions are assigned random, independent and identically distributed values. This paper highlights some curious properties of such trees when h is very large and examines their implications on the complexity of various game-searching methods.If the terminal positions are assigned a WIN-LOSS status with the probabilities P₀ and 1 ? P₀, respectively, then the root node is almost a sure MIN or a sure LOSS, depending on whether P₀ is higher or lower than some fixed-point probability $\text{P1(d)}$. When the terminal positions are assigned continuous real values, the minimax value of the root node converges rapidly to a unique predetermined value $\text{v1}$, which is the $\text{(1 ? P1)-}\text{fractile}$ of the terminal distribution.Exploiting these properties we show that a game with WIN-LOSS terminals can be solved by examining, on the average, $\text{O}\text{[(d)}{}^{\mathrm{<mtext>h</mtext><mtext>2</mtext>}}\text{]}$ terminal positions if positions if $\text{P}{}_0\text{≠ P1}$ and $\text{O}\text{[(}\text{P1}\text{(1 ? P1)}\text{)}{}^{\mathrm{h}}\text{]}$ positions if $\text{P}{}_0\text{= P1}$, the former performance being optimal for all search algorithms. We further show that a game with continuous terminal values can be evaluated by examining an average of $\text{O}\text{[(}\text{P1}\text{(1 ? P1)}\text{)}{}^{\mathrm{h}}\text{]}$ positions, and that this is a lower bound for all directional algorithms. Games with discrete terminal values can, in almost all cases, be evaluated by examining an average of $\text{O}\text{[(d)}{}^{\mathrm{<mtext>h</mtext><mtext>2</mtext>}}\text{]}$ terminal positions. This performance is optimal and is also achieved by the ALPHA-BETA procedure.     

Some polynomials that are hard to compute

Using the result of Heintz and Sieveking [1], we show that the polynomials $\text{Σ}{}_{\mathrm{1?j?d}}\text{b}{}^{\mathrm{<mtext>1</mtext><mtext>i</mtext>}}\text{X}{}^{\mathrm{j}}$ with b positive real different from one, and Σ_1?j?dj^rX^j with r rational not integer, are hard to compute.     

Laser-induced marine bioluminescence measurements and the potential for airborne remote sensing

Bioluminescence in cultures of the dinoflagellate Pyrocystis lunula at various temperatures were stimulated using a pulsed dye laser and Rhodamine 6G dye having an optimum lasing wavelength of $\text{586 ± 30}\text{nm}$. Following an intense “first flash” response, the flash intensity decayed in logarithmic fashion with successive laser shots. Samples pulsed to exhaustion were found to completely recover during the 12 h photophase. The total stimulable light (TSL) was calculated to be between $\text{4.5 × 10}{}^{\mathrm{?10}}$ J cell^?1 and $\text{38.5 × 10}{}^{\mathrm{?10}}$ J cell^?1. The time from stimulation to maximum light emission ($\text{t}{}_{\mathrm{m}}$) was found to vary with temperature logarithmically from approximately 11°C to 28°C. The corresponding regression equation was found to predict temperatures to within ±0.4°C. These results provide the basis for predicting the feasibility of an airborne laser transceiver for mapping the distribution of ocean bioluminescence. The potential exists for determining ocean surface/near surface temperature from measurements of the response pulse time parameters.     

A theory of wave scatter from an inhomogeneous medium with a slightly rough boundary and its application to sea ice

An analytical theory of electromagnetic wave scattering from an inhomogeneous medium with a slightly rough boundary surface is formulated. The inhomogeneity in the medium is assumed to vary continuously in the vertical direction. In addition, it is also assumed to have a small random variation in the horizontal direction. The medium is assumed to consist of two layers. Maxwell's equations are solved by using the small perturbation method together with Fourier transform technique. The resulting differential equations are solved by using WKB and variation of parameter methods. Field amplitudes in each medium are determined by taking boundary conditions into account. The expressions for first order polarized radar backscatter cross-section δ⁰ are obtained. An attempt is made to apply the developed theory to compute sea ice scatter. The complex permittivity of sea ice, which depends on both the temperature and salinity, varies with the depth of sea ice. In addition, there is certainly some variation in the horizontal direction. Thus, the developed model may be able to give useful estimates when applied to sea ice scattering. Numerical calculations are performed for polarized radar backscatter cross-section ($\text{σ}{}_{\mathrm{<mtext>vv</mtext>}}{}^{\mathrm{<mtext>0</mtext>}}\text{and}\text{σ}{}_{\mathrm{<mtext>HH</mtext>}}{}^{\mathrm{<mtext>O</mtext>}}$) at two frequencies, 13.3 GHz and 400 MHz. It can be shown that WKB method is applicable at both of these frequencies. These theoretical results are compared with the experimental results obtained from NASA Earth Resources Program mission 126. Theoretical results give the same absolute value of σ^O and the relative variation among the six ice types as is given by the experimental results.     

Some remarks on a conjecture in parameter adaptive control

A.S. Morse has raised the following question: Do there exist differentiable functions

$\text{f:R}{}^2\text{→ R}\text{and}\text{g:R}{}^2\text{→ R}$

with the property that for every nonzero real number λ and every (x₀, y₀) ∈ $\text{R}$² the solution (x(t),y(t)) of

$\text{x}\text{?}\text{(t) = x(t) + λf(x(t),y(t))}$

,

$\text{y}\text{?}\text{(t) = g(x(t),y(t))}$

,

$\text{x(0) = x}{}_0\text{, y(0) = y}{}_0$

, is defined for all t ? 0 and satisfies

$\text{lim}\text{t → + ∞}$

and y(t) is bounded on [0,∞)? We prove that the answer is yes, and we give explicit real analytic functions f and g which work. However, we prove that if f and g are restricted to be rational functions, the answer is no.     

Entropy and set covering

If the set covering constraints are Ax ? 1 and x_j ∈ {0,1}, the prior probability that the jth subset participates in an optimal covering (independently of subset costs) is shown to be given by the principal row eigenvector of A¹A, where $\text{a}{}_{\mathrm{ji}}{}^1\text{= 1 ? a}{}_{\mathrm{ij}}$. These probabilities lead to new and interesting objective functions, which are shown to be equivalent to cross entropy or weighted cross-entropy. The probabilities can also be used to obtain better bounds for heuristic solutions to optimal covering and set representation problems.     

Physical parallel devices are not much faster than sequential ones

It is proven that any (uniform) family of physical parallel devices, recognizing a language C? with time-complixity T_P(n), can be simulated by a (uniform) family of sequential devices with time-complexity $\text{T}{}_{\mathrm{P}}\text{(n)}{}^{\mathrm{<mtext>1</mtext><mtext>d</mtext>}}$ (d is a constant, depending on the technology, but not greater than 13).