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Alpha limit point

Now let jt ) be a sequence of real numbers which tends to negative infinity as n tends to infinity. If P = ir(x, t ) converges to a point P, then P is said to be an alpha limit point of x. The set of all such alpha limit points is called the alpha limit set of x, denoted a(x). It enjoys similar properties if the trajectory lies in a compact set for t < 0. [Pg.8]

We review the basic definitions and set up the semidynamical system appropriate for systems of the form (D.l). Let A" be a locally compact metric space with metric d, and let be a closed subset of X with boundary dE and interior E. The boundary, dE, corresponds to extinction in the ecological problems. Let tt be a semidynamical system defined on E which leaves dE invariant. (A set B in A" is said to be invariant if n-(B, t) = B.) Dynamical systems and semidynamical systems were discussed in Chapter 1. The principal difficulty for our purposes is that for semidynamical systems, the backward orbit through a point need not exist and, if it does exist, it need not be unique. Hence, in general, the alpha limit set needs to be defined with care (see [H3]) and, for a point x, it may not exist. Those familiar with delay differential equations are aware of the problem. Fortunately, for points in an omega limit set (in general, for a compact invariant set), a backward orbit always exists. The definition of the alpha limit set for a specified backward orbit needs no modification. We will use the notation a.y(x) to denote the alpha limit set for a given orbit 7 through the point x. [Pg.278]

Figure 2.9. The confidence interval for an individual result CI( 3 ) and that of the regression line s CLj A are compared (schematic, left). The information can be combined as per Eq. (2.25), which yields curves B (and S, not shown). In the right panel curves A and B are depicted relative to the linear regression line. If e > 0 or d > 0, the probability of the point belonging to the population of the calibration measurements is smaller than alpha cf. Section 1.5.5. The distance e is the difference between a measurement y (error bars indicate 95% CL) and the appropriate tolerance limit B this is easy to calculate because the error is calculated using the calibration data set. The distance d is used for the same purpose, but the calculation is more difficult because both a CL(regression line) A and an estimate for the CL( y) have to be provided. Figure 2.9. The confidence interval for an individual result CI( 3 ) and that of the regression line s CLj A are compared (schematic, left). The information can be combined as per Eq. (2.25), which yields curves B (and S, not shown). In the right panel curves A and B are depicted relative to the linear regression line. If e > 0 or d > 0, the probability of the point belonging to the population of the calibration measurements is smaller than alpha cf. Section 1.5.5. The distance e is the difference between a measurement y (error bars indicate 95% CL) and the appropriate tolerance limit B this is easy to calculate because the error is calculated using the calibration data set. The distance d is used for the same purpose, but the calculation is more difficult because both a CL(regression line) A and an estimate for the CL( y) have to be provided.
According to Theorem C.6, the limit set can be deformed to a compact invariant set A, without rest points, of a planar vector field. By the Poin-care-Bendixson theorem, A must contain at least one periodic orbit and possibly entire orbits which have as their alpha and omega limits sets distinct periodic orbits belonging to A. Using the fact that A is chain-recurrent, Hirsch [Hil] shows that these latter orbits cannot exist. Since A is connected it must consist entirely of periodic orbits that is, it must be an annulus foliated by closed orbits. Monotonicity is used to show... [Pg.274]

Proof of the nature of the interchain linkages came from examination of the structures of the limit dextrins produced by the action of highly purified alpha-amylases on starch and glycogen. These oligosaccharides contain both (l- 4)- and (l- 6)-a-D-glucosidic linkages, but no others. It has proved much easier to characterize the nature of the branch points... [Pg.303]

One or two wild data points (outliers) in a small sample can distort the mean and hugely inflate the variance, making it nearly impossible to make inferences—at least meaningful ones. Therefore, before experimenters become heavily invested in a research project, they should have an approximation of what the variability of the data is and establish the tolerable limits for both the alpha (a) and beta Q3) errors, so that the appropriate sample size is tested. [Pg.20]

Alloys with compositions less than the point where the a transus meets the composition axis are termed a alloys. The CP alloy discussed in this chapter is an example of an a alloy. Those with compositions greater than the point where the 3 transus meets the axis are termed 3 alloys. Those with compositions in between have a microstructure of a and 3 phases at ambient temperature under equilibrium conditions. Two types of these alloys can be identified. One type has composition limits between the a transus and the Mj curve and can be described by the term a-P alloy. The T1-6A1-4V alloy discussed later in this chapter is a common a-p alloy. The second type is given the name metastable p alloy. Composition limits for metastable p alloys fall between the and the p transus. Metastable beta alloys can best be described as alpha-beta alloys that contain an appreciable level of beta stabilizers. The low difhisivity of the beta stabilizers promotes complete retention of beta phase to room temperature at moderate cooling rates. The Ti-15V-3Cr-3Al-3Sn and Beta 21-S alloys are common metastable beta alloys. [Pg.124]

In case of the metallocene-catalyzed random copolymer of ethylene and alpha-olefin (POP and POE), incorporating more comonomer along the polymer backbone reduces density and crystallinity and hence, increases flexibility and softness. However, as the density is decreased, the melting point, crystallization peak temperature and heat resistance decrease and cycle times in injection molding increase. These deficiencies have limited the use of POEs in applications where heat resistance, high temperature compression set, and faster cycle times are desired. [Pg.92]


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See also in sourсe #XX -- [ Pg.8 ]




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