Figure 14. Health Issues of Primary Concern in Respondent’s County: Xxxxx County Health Issues of Primary Concern in Respondent's County 45% 40% 35% 30% 25% 20% 15% 10% 5% 0% 39.0% 23.8% 17.7% 9.2% 6.7% 2.4% 1.2% Obesity Substance Mental health Maternal/infant Tobacco use Teen pregnancy Injury and abuse health violence Providers were asked if they were aware of resources in their communities to address or prevent the primary health concern. • Of those who mentioned obesity as the top concern, 39% said there were no resources in the community to help address the problem, or they did not know of any (Figure 15). • More than half who considered mental health to be the top concern did not know or believed they had no resources in their community to address that problem. • Nearly half of those who reported substance abuse as the top concern did not know of or believe there were resources in the community to address that problem. Figure 15: Resources Perceived by Provider Survey- Respondents Resources to Combat the Primary Health Concern 100% 90% 80% 70% 60% 50% 40% 30% 20% 10% 0% 15.6% 17.9% 3.4% 17.2% 6.7% 13.3% 18.2% 9.1% 23.4% 50.0% 30.8% 41.4% 100.0% 80.0% 72.7% 60.9% 51.3% 50.0% 37.9% Obesity Substance Mental health Maternal/infant Tobacco use Teen pregnancy Injury and abuse health violence Yes No Don't Know / Not Sure No Answer When asked about barriers to patients accessing care or healthcare services in their communities, providers indicated that cost was the primary obstacle their clients faced (Figure 16). A lack of insurance or underinsurance was the second most prevalent. Access, education and health literacy, and transportation were also common. Cost was the top concern that emerged in the open-ended response from the community survey (see Figure 8 above), indicating that the cost of basic healthcare is a problem worthy of attention in this region. Figure 16: Barriers to Patients Access Care Perceived by Provider Survey Respondents Barriers to Patients Acessing Care or Services Cost 26.4% Uninsured / Underinsured 20.8% Education / Health Literacy 18.9% Access
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.
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).
Figure 14 is an example of Markings on Briefing Slides and Figure 15 for Multiple Source Listing on Briefing Slides.
All noise associated with the construction and use of the Exploration Decline and Underground Exploration shall be measured on or close to the boundary of any residentially zoned site or the notional boundary of any occupied rural dwelling site not owned by the consent holder or related Company, or not subject to an agreement with the consent holder or related Company as shown on Figure 1-4 of the Assessment of Environmental Effects (attached to this consent as Attachment A).
The conclusion must be reliable for it to be valid Classifiable Elements(GXFDWRU¶V 0DQXDOOnline Safety for Children Figure 14: Classifiable Elements and their Effect on Age Restrictions The Classification Guidelines prescribe mandatory and voluntary classifiable elements that are used to classify the content of the film, game, or publication.
Figure 14 shows the predicted cases using the SIR model (non-MCO with 100% population).
More Definitions of Figure 14
Figure 14. Top: Measured space-correlation function values (circles) for different frequencies at the Bevagna test site and best-fitting Bessel function (black line). Red circles indicate values discarded by the iterative grid- search procedure, because they lie outside two standard deviations. Bottom: The respective RMS error versus phase velocity curves. The ESAC method was adopted to derive the phase velocities for all frequencies composing the Xxxxxxx spectrum of the data. Figure 14 (top) shows four examples of the space-correlation values computed from the data together with the Bessel function they fit best to; below the corresponding RMS errors as function of the tested phase velocities are shown, exhibiting clear minima. For high frequencies, the absolute minimum sometimes corresponds to the minimum velocity chosen for the grid search procedure. This solution is then discarded, because a smooth variation of the velocity between close frequencies is required. At frequencies higher than a certain threshold the phase velocity might increase linearly. This effect is due to spatial aliasing limiting the upper bound of the usable frequency band. It depends on the S-wave velocity structure at the site and the minimum inter-station distance. At low frequencies (around 1.5 Hz for this test site) the RMS error function quite clearly indicates the lower boundary for acceptable phase velocities, but not the higher ones (plateau, see Figure 14, bottom left). The frequency, from which phase differences cannot be resolved any more, depends on the maximum inter-station distance and the S-wave velocity structure below the site: a wide range of velocities will then explain the observed small phase differences. Xxxxx et al. (2004) clearly pointed out this problem in Equation (3a) of their article. Figure 15 shows the final dispersion curve. Figure 15 – Phase velocity dispersion curves obtained by ESAC, and f-k analysis at the Bevagna test site.
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. 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. Top panel: horizons digitized in a cross section. Lowest panel: result of interpolation with horizons resulting in coloured meshes Figure 15: Render of the full extent of the layers modelled in MOVE in western Crommelin.
Figure 14. Size of packets sends for all group events.
Figure 14. The Westminster Local Desktop Grid and the contribution of different campuses The installed desktop Grid software is the Local SZTAKI Desktop Grid package. This is a modified version of the original BOINC installation that focuses on the requirements of non-public DG infrastructures. In these scenarios the resources are controlled centrally (in the case of the WLDG by the central computing services of the university), and there is no need for a public Website or the credit system to attract donors. All computers connect to the desktop grid server using the same user account, and the installation and upgrade of the clients are automatic using Novel ZEN Works objects. The WLDG can be utilized by the university’s researchers to run their computation intensive tasks. It is also connected to Service Grids by the gLite to BOINC Bridge allowing EGI users to run validated applications on the WLDG. Users can access the infrastructure via an easy-to-use generic portal interface, the WS-PGRADE portal. Currently 9 different applications are supported by the WLDG from diverse disciplines, including bio-molecular simulations, 3-D video rendering, x-ray profile analysis and digital signal processing. The following screenshots show the number of work units and performance of the infrastructure during the first half of November 2011. Figure 15: Total number of work units (WLDG) Figure 16: Performance of the infrastructure (WLDG) After a successful half a year’s uptime, a press release had been published on the success of the WLDG. The purpose of this article was to share the opportunity for saving up money with setting up a Private Desktop Grid. Figure 17: Press release about the WLDG Within the DEGISCO project a number of private Desktop Grids have been set up in ICPC countries such as ChinaGrid, OurGrid. And there are other examples scattered across Europe. For instance the Erasmus Computing Grid 15 collects capacity of 12.000 desktop computers at the Erasmus MC and the neighboring Hogeschool Rotterdam 16. Erasmus MC uses this Grid mainly for genome matching, an application perfectly suited for a Desktop Grid. Buying and maintaining the clusters for making available the same computing capacity would have cost them hundreds of thousands of Euro each year. It could be argued that inside an organization you do not need Volunteer Desktop Grid technology, which is then understood to be equivalent with BOINC, to achieve efficient use of Desktop computers. And, of course, this ...
Figure 14. Relative profitability as a function of DW. CS1 The output design parameters for the Case study 2 are presented in Table 11 and Table 12. The impact of the restricted breadth leads to a relative lengthening of the ship and increasing the block coefficient, which may explain the reduction of the efficiency (see Figure 15) [9]. The relatively short voyages and associated lower freight rate, in comparison to Case study 1, which reduces the profitability about two to three times. Table 11: Output design parameters, Case study 2, without restriction DW, tons 4,000 4,500 5,000 5,500 Relative values of Re (RRe) RRe 1.000 1.112 1.219 1.307 Design variables Ns 2.186 1.997 1.844 1.720 Lpp. m 98.809 101.332 104.215 109.985 B. m 16.145 16.878 17.229 18.075 d. m 5.74 5.859 5.861 5.858 D. m 7.258 7.461 7.551 7.633 CB 0.65 0.662 0.693 0.695 Main dimensions ratio Lpp/B 6.120 6.004 6.049 6.085 B/d 2.813 2.881 2.940 3.086 Lpp/D 13.614 13.582 13.801 14.409 Table 12: Output design parameters, Case study 2, with restriction DW. tons 4,000 4,500 5,000 5,500 Relative values of Re (RRe) RRe 0.983 1.094 1.188 1.220 Design variables Ns 2.193 2.002 1.854 1.737 Lpp. m 102.379 108.310 115.114 121.491 B. m 16.001 16.000 16.001 16.001 d. m 5.484 5.594 5.471 5.294 D, m 7.010 7.265 7.194 7.535 CB 0.669 0.693 0.738 0.794 Main dimensions ratio Lpp/B 6.398 6.769 7.194 7.593 B/d 2.918 2.860 2.925 3.022 Lpp/D 14.605 14.908 16.001 16.124 Figure 15: Relative profitability as a function of DW for restricted and non-restricted breadth, CS2 However, in the case of a ship with a design constraint due to the SME construction limitation and without shipbuilding restriction in the cargo transportation condition of Case study 2, the effectiveness of the two design ships is not very different, which is in the range of 2 % (see Figure 15).