Figure 14. Health Issues of Primary Concern in Respondent’s County: Xxxxx 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. 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 Xxxxx, 2009] shows that Belief Logic Programming is a specific case of the Xxxxxxxx-Xxxxxx’x theory [Xxxxxxxx, 1967], where the combination functions are the special forms of Xxxxxxxx’x belief functions. The authors introduced the following three combination functions:
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 Xxxxxxxx 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 ( ) ( )
Examples of Figure 14 in a sentence
For multi-volume AIs, DoDIs, or DoDMs, the purpose consists of one basic statement pertaining to the issuance’s purpose in its entirety (i.e., all the volumes) and a volume-specific statement summarizing the content of the subject volume (see Figure 14).
In Figure 14, meta- model concepts have been added that include KPIs, which are:25• Global process KPI - which measures the overall performance of a business process• Event KPI - which measures event performance• Lane KPI - measures tape performance• Activity KPI - measures activity performance• Subprocess KPI - measures subprocess performance• Task KPI - measures task performance Figure 14 - Meta model with KPI extension Source: Wannes, Aicha, and Sonia Ghannouchi.
The new regulation has changed the land transfer process and abolished the demolition permit (Figure 1.4).
However, as the local opacity increases moving outwards, diffuse photons start to be absorbedDownloaded from https://academic.oup.com/mnras/article/411/3/1678/972366 by guest on 17 February 2021 =Figure 14.
Flowchart of steps involved in making a routine wetland determination when an onsite visit is necessary (Continued) Figure 14.
More Definitions of Figure 14
Figure 14. Loading of a model given its URI Figure 15: Supplying user credentials Figure 16: Model details Figure 17: Model parameters
Figure 14. Size of packets sends for all group events.
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
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. Results were obtained using sensed humidity & temperature values
Figure 14. The Westminster Local Desktop Grid and the contribution of different campuses Figure 16: Performance of the infrastructure (WLDG) Figure 17: Press release about the WLDG
Figure 14. Hand exposure: Plot of 95% CIs (grey lines), geometric means (red lines) and medians (black dashed lines) for the log10(model outputs) with the log10(measured potential dermal exposure (hands)*) (purple squares=boom sprayer, orange crosses=hand-held sprayers) (single M&L period) *including data sets which have model inputs that were imputed 1e+07 log10(Potential dermal exposure (µg/hands)) 1e+06 100000 10000 1000 100 Figure 15: Hand exposure: Plot of log10(measured potential dermal exposure (hands)*) and log10(geometric mean(model outputs)) (all M&L periods combined) * including data sets which have model inputs that were imputed 1e+08 log10 (Measured potential dermal exposure(µg/hands)) 1e+07 1e+06 100000 10000 1000 100 10 1