Table 15 definition

Table 15. Exception rate methodology %= Level of activity requiring non-automated processing Total level of activity

Split View

AI-Powered Contracts

Draft, Review & Redline at the Speed of AI

Get StartedSchedule a Demo

Table 15. SMH services provided at MCH Rohri center in FY15 (Oct 14–Sep 15)

Table 15. Annual subsidy given to private agents per charging station in euros 68 Table 16: Cumulative government expenditures for infrastructure investments and subsidies in million euros 68 Table 17: Charging price in euro/kWh in 2025 and 2030 69 Table 18: Number of charging stations and respective share deployed by the private sector in 2030 70 Table 19: Share of charging stations deployed by the private sector in 2030 71 Table 20: Total available charging stations per time period for the three scenarios ........................................................................................................................................ 72

Examples of Table 15 in a sentence

• Table 1.5 below lists the Support Subscriptions offered by Red Hat and the Unit description that is used to measure your use of the Support Subscription(s).

• The Exhibits listed in Table 1.5 contain additional information concerning the scope of the Support Subscriptions and how Red Hat provides Subscription Services to you.

• The number of 20-second intervals that had each type of error for this period is shown in Table 15.

• The code of an iDSL study Section Study Scenario BiPlane_Image_Processing_run DesignSpace (offset {"0" "10000" "20000" "30000"} ) Table 15.

• The noncancer toxicity criteria for the COPCs are listed in Table 15 and discussed in Section 5.3 for each chemical.

• The costs shown in Table 15 are aggregate amounts and have not been apportioned between the NDES and ADES.

• The SFs, RfDs, and USEPA classifications for COPCs are presented in Table 15 and discussed in Section 5.3.

• This method was attempted to see how results compared to the presence/absence logistic regression model used above, with the results presented in Table 14 and Table 15.

• DRCCm is the Demand Response Capacity Charge in Settlement Month “m” and is calculated as set out in Table 1.5 of this Schedule 1.

• Groundwater elevations in mean sea level (MSL) were calculated and are presented along with well construction details in Table 15.

More Definitions of Table 15

Table 15. Montana Youth Risk Behavior Survey (cont.) Injury and Violence—Percentage of students who: 1999 2001 2003 2005 2007 2009 2011 • Drove a car when they had been drinking alcohol 28.2 26.6 23.8 21.5 20.7 18.1 14.6 • Texted or e-mailed while driving a car 42.8 • Talked on a cell phone while driving a car 45.8 • Carried a weapon such as a gun, knife, or club 30.3 31.5 25.7 31.3 33.3 28.8 32.2 • Did not go to school because they felt unsafe at school or on their way to or from school 5.9 8.1 7.1 9.0 7.9 8.0 10.2 • Had been threatened or injured with a weapon on school property 9.7 14.0 11.5 12.2 12.5 10.2 13.5 • Were in a physical fight 41.4 42.5 38.8 42.7 44.8 41.7 36.6 • Were injured in a physical fight that required medical treatment 21.3 20.9 16.4 20.0 20.3 17.1 18.2 • Had been bullied on school property 33.0 38.3

Table 15. Links in the Gas Distribution Network.

Table 15. Anticipated impacts contextualised pathways in Northern Ireland Social care provider (SCP) Health care provider (HCP) Third-sector care provider (TSCP)

Table 15. Industry Specific Questions Line Item Question Response *

Table 15. Planned results from approximation approach using departure frequency, com- pared to planned solutions from CVRP and TDVRP solvers. CVRP Duration Approximation Duration TDVRP Duration Waiting Table 16 show the approximation using the frequency of the ferry combined with the sailing time implemented in the TDVRP solver. The results show that this approximation yield far better results than the approximation based on sailing time on most of the instances. The approach helps to avoid ferries that yield much waiting time thus the result for the instance OS-58 is signi cantly better than the result provided by the CVRP solver. Table 16: Results from CVRP solver and approximation using frequency implemented in the TDVRP solver, compared to result from TDVRP solver. CVRP Duration Waiting Approximation Duration Waiting TDVRP Duration Waiting OS-27 958.25 70.21 958.25 70.21 909.75 1.86 OS-31 424.60 65.37 425.59 67.18 420.09 38.88 OS-58 1517.03 419.33 1248.71 109.46 1189.77 51.45 OS-116 5022.99 597.62 4757.85 371.23 4635.25 212.15 Nortura-97 6620.79 911.15 6458.83 589.90 6064.39 141.42 Nortura-273 20629.00 3857.87 20851.20 3828.05 17745.90 765.03 Table 17 is similar to Table 14 for the approximation approach using departure frequency. Results show that the approach helps when ferries have less frequent departures (as in OS-58 and OS-116 ), but only provide slightly better or even worse results than the CVRP solver. Table 17: Comparison of approximation using departure frequency to CVRP and TDVRP solvers. Improvement from CVRP Improvement potential It is important to note that even though the approximation yield fairly good results to some of the instances tested in this thesis, using time-dependent travel times is a better approach. As mentioned, the approximation using frequency of departures helps the solver to avoid ferry connections with less frequent departures. If this ferry connection has to be used to reach a customer, the approximation does not optimize the routes to connect with the departure as the solver using time-dependent travel times do.