Figure 14 definition
Figure 14. Health Issues of Primary Concern in Respondent’s County: ▇▇▇▇▇ County 17.7% Figure 15: Resources Perceived by Provider Survey- Respondents Figure 16: Barriers to Patients Access Care Perceived by Provider Survey Respondents
Figure 14. Two of the four different n z adopted in our comparison. The dotted curves refer to the case of Gaussian photometric distributions with σz = 0.05 1 z as discussed in the text (see Sect. 2.6.1), the dashed curves to broader Gaussians (σz = 0.1 1 z ), while the solid lines are the n z built from the broader Gaussians with the inclusion of ‘catastrophic outliers’ and more pronounced peaks in the distributions.
2.6.1 (solid orange lines), then we broaden them by increasing the standard deviation per bin, σbroad = 0.1(1 + z) (dashed green lines): this / – increases the amplitude of the II power spectra in the off-diagonal terms, due to the overlap of the tails of the distributions from different adjacent bins; on the diagonal terms, the broadening slightly reduces the II power spectrum. We then introduce ‘catastrophic outliers’, which we generate as Gaussian islands centred on random points extracted from the original dN dz, with a similar approach as ▇▇▇▇▇▇▇▇ et al. (2020b). The presence of the outliers increases the II contribution (dash-dotted magenta lines): this is particular prominent at low redshift, for highly separated z bins, where the outliers introduce correlated pairs between bins that would otherwise been uncorrelated. (i=1, j=1) σz = 0.05(1 + z) σz = 0.1(1 + z) σz = 0.1(1 + z) + catastrophic outlier (i=2, j=1) (i=2, j=2) (i=3, j=1) (i=3, j=2) (i=3, j=3) (i=4, j=1) (i=4, j=2) (i=4, j=3) (i=4, j=4) (i=5, j=1) (i=5, j=2) (i=5, j=3) (i=5, j=4) (i=5, j=5) (i=6, j=1) (i=6, j=2) (i=6, j=3) (i=6, j=4) (i=6, j=5) (i=6, j=6) l2|C(i,j)(l)| l2|C(i,j)(l)| l2|C(i,j)(l)| l2|C(i,j)(l)| l2|C(i,j)(l)| l2|C(i,j)(l)| 10−7 10−7 10−7 10−7 10−7 10−7 103 103 103 103 103 103 76 ( ) ( )
Figure 14. The rule in Listing 11 in Belief Logic Programming × → When combining input atoms, the degree of the output atom is specified via combination functions. Formally, let D be the set of all sub-intervals of [0, 1], a function Φ : D D D is called a combination function if it is associative and commutative. These associativity and commutativity properties make it easy to extend a combination function to three or more arguments, and the order of the arguments are immaterial. [Wan and ▇▇▇▇▇, 2009] shows that Belief Logic Programming is a specific case of the ▇▇▇▇▇▇▇▇-▇▇▇▇▇▇’▇ theory [▇▇▇▇▇▇▇▇, 1967], where the combination functions are the special forms of ▇▇▇▇▇▇▇▇’▇ belief functions. The authors introduced the following three combination functions:
Examples of Figure 14 in a sentence
An example of the large increase in bandwidth required to tolerate Byzantine faults using the well-known algorithms is shown in Figure 14.
As an example, compare SAFEbus against the two-fault scenario given in Figure 14.
As illustrated in Figure 14 below, GVA figures for 2010 show that Greater Dublin Areas accounted for 42% of total national GVA due to the high proportion of the workforce working in high value-added sectors, particularly finance, business services and information technology.
Figure 14 also illustrates the productivity gap between NUTS III regions.
All of the main input and output files are illustrated in Figure 1.4 and a short summary of each file is given.
More Definitions of Figure 14
Figure 14. Pathway of a service managed by SAV of a packaging waste collection in Valencia. The route starts in the SAV facilities, the truck collects the waste in the center of Valencia, then it travels towards the sorting plant in Picassent and it finish the journey coming back to the SAV facilities. Colour indicates speed of the vehicle. Total duration of the route was 6 hours and 17 minutes with a pathway of 96 km. Maximum speed was 82 km/h. Figure 15: Pathway of a route collecting packaging waste in Valencia. Colour indicates speed of the vehicle. Driver showed a good driving behaviour as speed does not overcome 50 km/h in urban areas and 90 km/h in the highway. Speed, RPM and engine load were obtained from the OBD II system.
Figure 14. On the left: physical expectation value of i1. On the right: physical expectation value of i1i2. The solid line is the expected behavior. 8 Semiclassical states for quantum gravity The concept of semiclassical states is a key ingredient in the semiclassical analysis of LQG. Semiclassical states are kinematical states peaked on a prescribed intrinsic and extrinsic geometry of space. The simplest semiclassical geometry one can consider is the one associated to a single node of a spin-network with given spin labels. The node is labeled by an intertwiner, i.e. an invariant tensor in the tensor product of the representations meeting at the node. However a generic intertwiner does not admit a semiclassical interpretation because expectation values of non-commuting geometric operators acting on the node do not give the correct classical result in the large spin limit. For example, the 4-valent intertwiners de ▇▇▇ with the virtual spin do not have the right semiclassical behavior; one has to take a superposition of them with a speci c weight in order to construct semiclassical intertwiners. The ▇▇▇▇▇▇▇-▇▇▇▇▇▇▇▇ quantum tetrahedron described in section 8.2 is an example of semiclassical geometry; there the weight in the linear superposition of virtual links is taken as a Gaussian with phase. The ▇▇▇▇▇▇▇-▇▇▇▇▇▇▇▇ quantum tetrahedron is actually equivalent to the ▇▇▇▇▇▇-▇▇▇▇▇▇▇▇ coherent intertwiner with valence 4 (introduced section 8.1); more precisely, the former constitutes the asymptotic expansion of the latter for large spins. Coherent intertwiners, introduced for general valence in the next section, are de ▇▇▇ on a robust mathematical setting as the geometric quantization of the classical phase space associated to the degrees of freedom of a tetrahedron. But from the point of view of LQG they are only a rst step. The missing step is to de ne in the most physically motivated way semiclassical states associated to a spin-network graph; we expect them to be a superposition over spins of spin-network states, as we shall argue in a moment. In the recent graviton propagator calculations [91, 92, 93, 79, 94, 95, 96], semiclassical states associated to a spin-network graph Γ have been already considered. The states used in the de nition of semiclassical n-point functions (see chapter 10) are labeled by a spin j0 and an angle ξe per link e of the graph, and for each node a set of unit vectors →n, one for each link at that node. Such variables are suggested by the simpl...
Figure 14. Relative profitability as a function of DW. CS1 Table 11: Output design parameters, Case study 2, without restriction Relative values of Re (RRe) Design variables Main dimensions ratio Table 12: Output design parameters, Case study 2, with restriction Relative values of Re (RRe) Design variables Main dimensions ratio Figure 15: Relative profitability as a function of DW for restricted and non-restricted breadth, CS2
Figure 14. Portion of representative west (left) to east (right) sub-bottom profile image (line 423, see Figure 8 for line location) obtained in the target area, transecting westernmost Resource Area 1. Note the absence of an identifiable ravinement surface. See sections 3.3.1 of this report for additional discussion about this image. Depth is reported in meters below sea surface with an assumed sound velocity of 1524 m s-1.
Figure 14. Size of packets sends for all group events.
Figure 14. Observed Core to edge Sr:Ca, Ba:Ca and δ18O profiles in otoliths of young of the year collected in the Mediterranean.
Figure 14. In the image the rectangle highlight the “status” of the added contribution with the validation in the “pending”. In this line it is possible to edit and modify the integration, to delete it or to approve it