Table 5. Core HIA Process: Boundaries and Interfaces with Councils Activity Shared Service Councils Home Improvement Risk Assessment Maintain risk assessment of activity & use this to inform work planning Contribute information to the risk assessment Work Planning Formulate an annual plan of activity Consider, influence and accept the annual plan Work Allocation Determine how resources to be allocated and when activity will occur Agree the timing of activity and make available resources to support this Reporting Report on activity to Councils Share reports with stakeholders

Table 5. Top Five Government or Education Customers Line Item 22. Provide a list of your top five government, education, or non-profit customers (entity name is optional), including entity type, the state or province the entity is located in, scope of the project(s), size of transaction(s), and dollar volumes from the past three years. Entity Name Entity Type * State / Province * Scope of Work * Size of Transactions * Dollar Volume Past Three Years * Chicago Government Illinois - IL Vendor managed inventory Multi-year contract. Agency 2020 YTD: $55,365,549 Transit Authority services. Master warehouse that is billed a monthly fee for 2019: $70,838,159 services 20 City bus and rail operational expenses. 2018: $68,722,731 garages for the second-largest Separate billing is transit authority in the US. provided for all parts and supplies procured monthly * through the contract. Parts transactions number in the thousands as we source through hundreds of vendors. City of Government Illinois - IL Vendor managed inventory Multi-year contract. Agency 2020 YTD: $20,323,676 Chicago Fleet services for 14 on-site parts is billed a monthly fee for 2019: $28,098,757 rooms servicing the City's fire, operational expenses. 2018: $27,280,538 police, sanitation, parks, aviation Separate billing is and general fleets. provided for all parts and supplies procured monthly * through the contract. Parts transactions number in the thousands as we source through hundreds of vendors. City of New Government New York - NY Vendor managed inventory Multi-year contract. Agency 2020 YTD: $19,639,682 York Fleet services for 17 on-site parts is billed a monthly fee for 2019: $23,925,781 rooms servicing the City's fire, operational expenses. 2018: $20,285,905 police, sanitation, DOT, parks, Separate billing is corrections and general fleets. provided for all parts and supplies procured monthly * through the contract. Parts transactions number in the thousands as we source through hundreds of vendors. Ohio Government Ohio - OH Vendor managed inventory Multi-year contract. Agency 2020 YTD: $19,899,819 Department of services with onsite personnel in is billed a monthly fee for 2019: $21,439,285 Transportation each of 12 DOT districts operational expenses. 2018: N/A servicing more than 100 downline Separate billing is shops statewide supporting all DOT fleet operations, including provided for all parts and supplies procured monthly * snow removal, mowing, highway through the contract. Parts maintenance and...

Examples of Table 5 in a sentence

For UL Tx switching across 3 or 4 bands in inter-band UL CA with 1 SUL band, the mapping between Tx chains and UL transmission antenna ports can be defined as in Table 5 and Table 6.
Summary statistics Peer group Variable The average number of employees in the peer group is lower than for the buyout group see Table 5.
Table 4 presents summary statistic for the buyout firms and Table 5 for the peer group firms.
For instance, a curriculum characteristic "Curriculum prescribes that educators re- sponsible for a professional part of the program, should have professional experience in the field they teach" and the corresponding category were added to Cluster 1 as an additional Russian category (see Table 5).
However, we decided not to combine these two inductively developed curriculum characteristics, and corresponding analysis categories in common country-specific units, as in the above-described example of the characteristic (see Table 5), due to a broader meaning of the Chinese curriculum characteristic.

More Definitions of Table 5

Table 5. Wilcoxon Rank Test and Sign Test. Wilcoxon Rank Test Sign Test ORS Element Wilcoxon p-value Ho: p value observed ≤ interview Ho: p value collected ≥ interview Crawling 1.00 0.66 0.66 Crouching 0.86 0.50 0.59 Kneeling 0.08 0.97 0.05 Stooping <.01 <.01 1.00 Reaching overhead 0.57 0.81 0.25 Reaching At/Below Shoulder Level <.01 <.01 1.00 Communicating Verbally 0.07 0.95 0.07 Keyboarding 0.78 0.68 0.44 Keyboarding- Touchscreen 0.56 0.35 0.79 Keyboarding- 10key 0.41 0.29 0.87 Keyboarding- Other 0.01 <.01 1.00 Fine Manipulation <.01 <.01 1.00 Gross Manipulation <.01 <.01 1.00 Pushing/Pulling with Hands and Arms <.01 <.01 1.00 Pushing/Pulling with Feet and Legs 0.21 0.22 0.84 Pushing/Pulling with Feet 0.02 0.02 1.00 Climbing Ramps/Stairs 0.07 0.98 0.05 Climbing Ladders/Ropes/Scaffolding 0.14 0.96 0.21 The variables with longer duration associated with observation are stooping, reaching at or below the shoulder, other keyboarding, fine manipulation, gross manipulation, pushing and/or pulling with hands and arms, and pushing and pulling with feet. When we measure the modes of these elements, only one shows a difference in mode between the collected and observed values – reaching at or below the shoulder. The value of the mode for this element is occasionally (2% up to one-third of the day) in the interview data and constantly in the job observation data (two- thirds or more of the day). We noted earlier that missing duration was identified as an issue with ORS pre-production. In the case of reaching at or below the shoulder, 53 of the job observation duration measures were unable to be compared with interview duration data due to missing duration. It is notable that among the 53 missing quotes in pre-production, the job observation test recorded durations of frequently or constantly in 64% of the quotes. This is a common pattern among those elements where the sign test rejected the null of observation duration equal or below pre-production – the missing data in pre-production align with observed durations above the median and mode. From this, it appears that the “underestimate” of duration from the interview data is due to the missing duration being more likely to correlate with long duration observed. As a counter-example, 46 observed quotes had reaching overhead categorized as present with unknown duration in pre-production and only one of these was classified as frequently or constantly in the observation test.

Table 5. The Range of Feasible Group Sizes for Complete Networks for Some Given ↵ ↵ < 0.08 ↵ < 0.26 ↵ < 0.41 ↵ < 0.47 ↵ < 0.56 ↵ < 0.71 n =2 X X X X X X n =3 X X X X X n =4 X X X X n =5 X X X . X . n = 10 X X . . n = 50 X Using the proof of Proposition 5, one could simply verify that for ↵¯ = 0.47 (i.e., for any ↵ < 0.47), the borrower welfare increases in n = 2, 3, 4, while for ↵¯ = 0.71, the only feasible group size is n = 2. Table 5 provides more examples of this kind and shows that larger group sizes are feasible for a smaller chance of project success. As shown in this table, if ↵ > 0.56, the only feasible group size will be n = 2. Thus, according to Proposition 5, the borrower welfare can increase in group size only if ↵ < 0.56 and the given 6 is such that 6ˆ(n, ↵) < 6 < 6˜(n, ↵).

Table 5. The energy supply of Orkney Supply Capacity/ quantity Production Notes Wind onshore 48.3 MW 149.7 GWh [18], EnergyPLAN [1] PV 1.3 MW 1.2 GWh [18], [22] Transmission line / Import 40 MW 10.8 GWh [1], [18] Solar (thermal) collectors 150 m2 0.05 GWh Estimation, [10] Biomass > 500 t 2.0 GWh Estimation, [18] Oil (for heating and transport) > 48 kt 598.8 GWh EnergyPLAN output Natural Gas > 7 kt 71.5 GWh EnergyPLAN output Coal > 3 kt 21.1 GWh [18], [21] As presented in Table 5, the total share of renewable sources in the primary energy supply reaches 17%. While the wind power production is good on Orkney, the transport and heating sector still rely on other fuels, such as oils. For the hourly simulation in EnergyPLAN, the wind and solar productions are specified by their distribution profiles throughout the whole year with a one hour temporal resolution – here the reference year 2014. The following figures present these distribution profiles for wind power, PV power and solar thermal production. Since no measured production data is available, the distribution of the electricity production from wind and PV is modelled with a renewable energy simulation website [7], [8], resulting in Figure 12 and Figure 13. For the profile of wind power production, the turbine type Enercon E44 is used for modelling, as it is also a type common on Orkney. Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec Figure 12: Wind energy production simulation for 2014 for Orkney [8] Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec Figure 13: PV energy production simulation for 2014 on Orkney [7] The distribution of wind and PV power production varies strongly throughout the year, as further illustrated with Figure 14. With wind production much lower in summer than in winter and with a much smaller capacity of PV, the electricity demand might not be possible to be covered with RES in some months, while other months have abundant amount of electricity from wind. It has to be kept in mind that the PV production is around 1.2 GWh annually, while wind produces 150 GWh. 18% 16% 14% 12% 10% 8% 6% 4% 2% 0% Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec Solar energy Wind energy Figure 14: Monthly distribution of energy production from wind and PV for 2015 on Orkney Solar radiation is not only used for the production of electricity through PV systems, but also to produce heat through solar collectors. Only a small number of thermal collector systems are listed in the energy audit, while the statistics fo...

Table 5. Specification of wagons for wagon load analysis. Type Hiirrs‐tt DB 324 Habbins 274‐1 VEL‐WL Haimmrs WW US jumbo box Type Hiirrs‐tt Habbins VEL‐WL Haimmrs US jumbo Axles/wagon 4 4 4 6 4 Axle load tonnes 22,5 22,5 22,5 20,0 32,4 Xxx xxxxx weight/wagon tonnes 90,0 90,0 90,0 120,0 129,7 Tare weight/wagon tonnes 31,6 26,0 30,0 33,7 46,3 Max load weight/wagon tonnes 58,0 64,0 60,0 50,0 83,4 Load volyme Number of TEU:s per wagon Loading length Total loading length m3 number fot m 210,0 25,6 166,0 22,0 196,0 24,5 180,0 20,2 323,0 28,2 Total length 1,25 28,4 23,3 25,8 25,8 26,4 Energy consumption KWh/grosstonkm 0,0152 0,0152 0,0153 0,0152 0,0160 Mainetnance cost SEK/km 0,20 0,21 0,21 0,28 0,23 Investment cost 1000 SEK 1 400 1 350 1 400 1 700 1 550 5 Economic analysis of VEL-wagon for wagonload Finally these wagons have been choosen for economic analysis: • 4‐axle DB Hiirss‐tt • 4‐axle DB Habbins • 4‐axle VEL high cube • 6‐axle DB Hiirss‐tt • 4‐axle US jumbo box car The specifications of wagons are shown in table 5. The calculations have been made for a 600 km long distance in Sweden with 100% load factor in cubic meters m3 and 50% empty running (i.e. no back haul). The train consists of 20 VEL wagons and an equivalent number of the other types of wagons to transport the same amount of approx. 4 000 m3. The transport cost per cubic meter m3 has been compared with total capacity in m3. The result is shown in Figure 15. As can be seen in Figure 15 the VEL wagon is not so competitive compared with the specially built wagons for large volume. Compared with Habbins it is more effective but compared with the specially designed Hirrss-tt it the transport cost per m3 is 4% higher. Compared with an ordinary Habbins is 5% less expensive per m3. The most expensive seems to be the specially designed 6-axles Haimmrs, but perhaps this wagon is built for particular dimensions. The US box car offers the lowest transport cost but the differences in cost per m3 are not so big. Another way to analyze trains and wagons is to compare trains with the same number of wagons. One example is shown by Figure 16. Here still the Hirrss-tt is most competitive but VEL is rather good. The US box car is however extreme efficient with only 73% of the cost per m3 compared with Hirrss-tt. In Figures 17 and 18 the cost per m3 according to total capacity of the train between 2000 and 7000 m3 is shown. There is a range from approx. 5€/m3 at 2000 m3 to approx. 2.5€/m3 at 7000 m3 that means that the transp...

Table 5. Existing FTAs among TPPA member countries in 0000 Xxxxxxxxx Xxxxxx Xxxxxx Chile Japan Malaysia Mexico New Zeala Peru Singapore USA Vietnam Australia ✓ ✓ ✓ ✓ ✓ ✓ ✓ Brunei ✓ ✓ ✓ ✓ ✓ ✓ ✓ Canada ✓ ✓ ✓ ✓ Chile ✓ ✓ ✓ ✓ ✓ ✓ ✓ ✓ ✓ ✓ ✓ Japan ✓ ✓ ✓ ✓ ✓ ✓ ✓ Malaysia ✓ ✓ ✓ ✓ ✓ ✓ ✓ Mexico ✓ ✓ ✓ ✓ ✓ New Zeala ✓ ✓ ✓ ✓ ✓ Peru ✓ ✓ ✓ ✓ ✓ ✓ Singapore ✓ ✓ ✓ ✓ ✓ ✓ ✓ ✓ ✓ USA ✓ ✓ ✓ ✓ ✓ ✓ Vietnam ✓ ✓ ✓ ✓ ✓ ✓ Table 6 reports the existing tariff profiles of TPPA12. It can be seen that the average applied MFN tariffs are quite low for some countries like Singapore and New Zealand. But these could differ widely across sectors. Canada, Malaysia, Mexico and Vietnam have average MFN applied tariff as high as 16% in agriculture sector while Mexico and Vietnam have around 8% applied tariffs in non-agriculture sector. Table 6: Average Applied MFN Tariffs (%) Total Agriculture Non-Agriculture Australia 2.7 1.2 2.9 Brunei 2.5 0.1 2.9 Canada 4.3 16.2 2.4 Xxxxx 0 0 0 Xxxxx 4.6 16.6 2.6 Malaysia 6.5 11.2 5.8 Mexico 7.8 16.1 8.4 New Zealan 2 1.4 2.2 Peru 3.7 4.1 3.6 Singapore 0.2 1.4 0 USA 3.4 4.7 3.2 Vietnam 9.5 16.1 8.4 These variations become even more evident when product level tariffs are observed in some sectors and in some countries. Table 7 provides highest tariffs by product category in TPPA countries. These products include dairy, clothing, beverages, tobacco, sugar and electrical machinery. Table 7: Highest tariffs by product in TPPA countries Country Product Average Applied MFN Tariffs Australia Clothing 8.9 Brunei Electrical machinery 13.9 Canada Dairy Products 246.8 Chile Most Products 6.0 Japan Dairy Products 178.5 Malaysia Beverages and Tobacco 119.7 Mexico Sugar and confectionary 59.3 New Zealand clothing 9.6 Peru clothing 13 Singapore Beverages and tobacco 2.4 USA Dairy 19.1 Vietnam Beverages and tobacco 43.6 Source: WTO Tariff profile 2012 and Xxxxxxxx (2013), CRS Report for Congress in USA

Table 5. FSR Submission Schedule Period Covered Due Date September 1st through February 28th March 31st March 1st through August 31st August 31st Final Financial Status Report March 1st through August 31st October 15th X. Xxxxxxx will be paid on a cost-reimbursement basis and in accordance with the budget for the corresponding year under this Contract. ATTACHMENT B CATEGORICAL BUDGET Categorical Budget Budget Period: Contract Effective Date To August 31, 2024 Budget Period: September 1, 2024 To August 31, 2025 Total Contract Amount Personnel $125,407.50 $125,407.50 $250,815.00 Fringe Benefits $45,815.50 $45,815.50 $91,631.00 Travel $0.00 $0.00 $0.00 Equipment $0.00 $0.00 $0.00 Supplies $0.00 $0.00 $0.00 Contractual $0.00 $0.00 $0.00 Other $0.00 $0.00 $0.00 Total Direct Charges $171,223.00 $171,223.00 $342,446.00 Indirect Charges $0.00 $0.00 $0.00 Total $171,223.00 $171,223.00 $342,446.00 Health and Human Services Contract Number HHS001315700014 Attachment C CONTRACT AFFIRMATIONS For purposes of these Contract Affirmations, HHS includes both the Health and Human Services Commission (HHSC) and the Department of State Health Services (DSHS). System Agency refers to HHSC, DSHS, or both, that will be a party to this Contract. These Contract Affirmations apply to all Contractors and Grantees (referred to as “Contractor”) regardless of their business form (e.g., individual, partnership, corporation). By entering into this Contract, Contractor affirms, without exception, understands, and agrees to comply with the following items through the life of the Contract:

Table 5. The COBACORE Core Feature Set A more extensive description of the core feature set can be found in D3.1, and on the project Sharepoint site. In order to conform with the ‘No Regrets Features’ list a number of key assumptions need to be considered within the confines of the data framework design: Pre-Crisis Implementation There is an expectation that the research framework being proposed within COBACORE will be in position prior to the onset of a disaster event. It is considered impossible to implement and set up a data model within the dynamic nature of the disaster. Much of the baseline information profiling an area is ‘static’ in nature and can be accessed/assembled pre-crisis. This affords the benefits of instant access for emergency responders in the event of a crisis. Moreover, a number of research studies (Bharosa et al, 2010; Xxxxxx et al, 2004; Xxxxxxxxx et al, 2003) have demonstrated that Inter-Organizational Information Systems (IOIS) that are not used on a regular basis prior to a disaster will never be fully utilized or bring significant operational benefits in an actual disaster. Pre-crisis awareness, application and usage of IOISS systems is essential in order to promote system familiarity and confidence from an end-user perspective be they professional responders or members of the affected community. It is important that the COBACORE platform has sufficient functionality and regular updating to warrant usage in the non-crisis situation (See SF72 framework). Framework Interoperability When considering the design features of the COBACORE data framework a key requisite was the recognition that the system will not exist in isolation. Although a number of stand-alone systems do exist including web-based and client/server solutions, such as WebEOC (ESi, 2007) and L-3 CRISIS (Ship Analytics, 2007) the requirement for all parties involved in crisis management to use a single (and often centralised) system has ultimately hindered uptake. The COBACORE data framework must recognise and embrace interoperability through recognition of and conformance with common standards, both between different implementations of Crisis Information Management System (CIMS), as well as between CIMS and other types of software used by the professional responder community. However, whilst many countries have implemented standardised terminology, principles and command structures for crisis management by developing their own Information Management Systems (IMS) (such as AII...

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