Table 5 Sample Clauses

Table 5. Allowances Rates from the first full pay period on or after 1 July 2022 and 1 July 2023. Item Allowance Clause From the Operative Date of the Agreement $ 1 July 2022 $ 1 July 2023 $ 1 Uniform Allowance when uniform is not supplied Per shift 22.3(b) 1.49 1.49 1.49 Per week 22.3(b) 7.56 7.56 7.56 2 Laundry Allowance (excluding Nursing Classifications) Per shift or part thereof 22.3(c) 0.39 0.39 0.39 Per week 22.3(c) 1.81 1.81 1.81 3 Laundry Allowance (Nursing Classifications Only) Per week 22.3(c) 5.45 5.45 5.45 Meal Allowance when no meal is provided* 4 When required to work more than one hour beyond usual finishing time 22.4(a) 13.83 14.11 14.39 5 Further payment when overtime exceeds 4 hours (Aged Care, Health Professionals and Nursing Classifications only) 22.4(a)(ii) 12.47 12.72 12.97 6 Further payment when overtime exceeds 4 hours (Home Care Classifications only) 22.4(a)(ii) 13.83 14.11 14.39 On Call Allowance (Nursing classifications only)* 7 Between rostered shifts Monday to Friday 22.5(a)(i) 23.03 23.49 23.96 8 Between rostered shifts or on a Saturday 22.5(a)(ii) 34.70 35.39 36.10 9 Between rostered shifts or ordinary hours on a Sunday, public holiday or a day when not rostered to work 22.5(a)(iii) 45.09 45.99 46.91 On Call Allowance (Home care classifications only)* 10 Finishing duty on Monday to finishing duty on Friday 22.6(a) 20.63 21.04 21.46 11 Any other period or public holiday 22.6(b) 40.84 41.66 42.49 12 Mileage Allowance 22.7(a) & (c) 0.92 0.92 0.92 Continuing Education Allowance (Nursing Classifications Only) 13 RN - post grad certificate in clinical field 22.8(g) 20.39 20.39 20.39 14 RN - post grad diploma or degree in clinical field 22.8(h) 34.01 34.01 34.01 15 RN - relevant master's degree or doctorate in clinical field 22.8(i) 40.78 40.78 40.78 16 EN - certificate IV qualification in a clinical field 22.8(j) 13.58 13.58 13.58 In-Charge Allowance (Nursing Classifications only) 17 RN – in charge of facility of less than 100 beds on day, evening or night 22.9(a) 24.30 24.30 24.30 18 RN – in charge of facility of more than 100 beds on day, evening or night 22.9(a) 39.16 39.16 39.16 19 RN in charge of a shift in a section of a facility 22.9(b) 24.29 24.29 24.29 Leading Hand Allowance (Aged Care Classifications only)* 20 - in charge of 2 - 5 Employees 22.10(b) 26.17 26.69 27.22 21 - in charge of 6 - 10 Employees 37.34 38.09 38.85 22 - in charge of 11 - 15 Employees 47.14 48.08 49.04 23 - in charge of 16-19 Employees 57.63 58.78 59.96 24 Sle...
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Table 5. Panel logistic regression models of the annual moving propensity of couples between t and t+1 Variable (observed at wave t) Model 1 Model 2 Model 3 Model 4 Model 5 Coeff. S.E. Coeff. S.E. Coeff. S.E. Coeff. S.E. Coeff. S.E. Housing satisfaction (ref=both satisfied) Man dissatisfied 0.977*** 0.081 0.690*** 0.079 0.226** 0.092 Woman dissatisfied 1.033*** 0.074 0.790*** 0.073 0.308*** 0.085 Both dissatisfied 1.751*** 0.078 1.100*** 0.077 0.130 0.091 Dislike neighbourhood (ref=both like) Man dislikes 0.460*** 0.120 0.410*** 0.117 -0.122 0.135 Woman dislikes 0.649*** 0.111 0.620*** 0.106 0.068 0.124 Both dislike 0.953*** 0.115 0.968*** 0.109 -0.081 0.127 Desire to move (ref=neither desire) Man desires 0.756*** 0.098 0.646*** 0.098 0.629*** 0.100 Woman desires 0.475*** 0.104 0.386*** 0.105 0.322** 0.108 Both desire 0.969*** 0.077 0.879*** 0.077 0.825*** 0.083 Expect to move (ref=neither expect) Man expects 1.817*** 0.125 1.417*** 0.127 1.414*** 0.128 Woman expects 2.120*** 0.115 1.738*** 0.116 1.720*** 0.117 Both expect 3.735*** 0.085 3.200*** 0.084 3.197*** 0.084 Highest age -0.033*** 0.003 -0.024*** 0.003 -0.024*** 0.003 Cohabit (ref=married) -0.022 0.067 -0.179** 0.078 -0.181** 0.078 Couple type (ref=couple, no children) Preschool chldren -0.231** 0.084 -0.111 0.099 -0.121 0.099 School age children -0.753*** 0.081 -0.499*** 0.091 -0.513*** 0.091 Children of both ages -0.657*** 0.110 -0.261** 0.125 -0.266** 0.125 Non-dependent children -0.634*** 0.115 -0.360** 0.125 -0.361** 0.125 Other 0.336** 0.169 -0.146 0.201 -0.158 0.202 Change in n children (ref=no change) Increased at t+1 0.170 0.096 -0.046 0.114 -0.049 0.115 Decreased at t+1 -0.080 0.143 0.010 0.162 0.009 0.162 Unknown at t+1 2.075*** 0.204 1.975*** 0.231 1.987*** 0.231 Highest education level (ref=very low) Low 0.206 0.122 0.089 0.132 0.085 0.132 Medium 0.131 0.117 -0.088 0.126 -0.098 0.126 High 0.378** 0.128 -0.081 0.140 -0.090 0.140 Employment status (ref=no earner) Dual earner -0.344*** 0.098 -0.372*** 0.110 -0.375*** 0.110 Single earner -0.190** 0.095 -0.312** 0.107 -0.310** 0.107 Change in n employed (ref=no change) Increased at t+1 -0.007 0.112 0.002 0.129 -0.005 0.129 Decreased at t+1 0.459*** 0.093 0.448*** 0.107 0.450*** 0.107 Unknown at t+1 -0.052 0.184 -0.030 0.207 -0.033 0.207 Real household income/10,000 0.043*** 0.011 0.034** 0.011 0.035** 0.011 Housing tenure (ref=homeowner) Social renter -0.256** 0.087 -0.164 0.097 -0.170 0.099 Private renter 1.303*** 0.081 0.983*** 0.093 0.962*** 0.09...
Table 5. 1: Employment outcomes of full-time first degree students (%) 2002/03 03/04 04/05 Employment 62.9% 62.7% 62.8% Employment and further study 7.9% 8.6% 8.1% Further study only 14.8% 15.0% 14.9% Assumed unemployed 7.0% 6.4% 6.5% Not available for employment 5.2% 5.0% 4.9% Other 1.0% 1.0% 1.1% Question not answered 1.2% 1.4% 1.8% Source: HESA, 2005 The potential of the lifelong learning sector to generate benefits for the UK economy should therefore not be underestimated. In addition, in Autumn 2006, the Financial Times newspaper ran a series of comprehensive reports on the state of the nation. One key conclusion was that investment in people, through better schooling and in infrastructure, and where the social benefit outweighed the cost, offered more permanent solutions than public spending on health and housing in poorer regions. Hence, policy interventions and other responses to economic, social and demographic changes could help to bring about enhanced participation in lifelong learning and increase attainment rates, which, in turn, could improve economic productivity considerably in the coming decades.
Table 5. A 8-round linear trail for Friet-PC in the form of masks at the output of ξ in the 8 successive rounds. round δa δb δc weight 0 ...............................1 2 1 ...............................1 ...............................1 ...............................1 2 2 ................8............... ................................ ...............................1 2 3 ................8...8..........1 ................8............... ................8..1............ 6 4 ................4..18...8......1 ....................8..........1 .......1........8...8..........1 10 5 ....4..1........4..14...8...8... ................4..1....8....... ....8...........4..18...8..1...1 14 6 8...c..14.......2...c......18..1 ....4..1............4.......8... ....4..18......14...4.......8. 22 7 8.......c......16...a...8..1...1 8...8...4.......2...8......1...1 8..18..14.......2...c......1...1 22 5.6 Combined Resistance Against 1st Order DPA and SIFA‌ A straightforward Friet-P implementation is vulnerable to SIFA [17] and SIFA- like attacks [28]. A realistic attack scenario would be the following. An adversary has access to the outer part of the state at a given time and can inject a fault during the computation of the permutation in order to recover some information on the inner part of the state. Provided that she can redo the attack multiple times on the same initial state, She could then try to inject a fault in the first round to modify one of the inputs of the AND operation in ξ. A bitflip in an input of a binary AND only propagates to its output if the other input is 1 and hence is only effective in that case. It can hence be simply be derived from the behavior of the fault-detection mechanism. Simulating probabilistic or less precise fault models such as, e.g., the random-AND fault model or a byte-based fault model would also yield exploitable results, although the adversary might need to profile the fault behavior of the device in advance with fault templates [28].
Table 5. Postoperative complications.** RT+TME TME n=695 n=719 n % n % Infectious wound infection 43 6 45 6 abscess 31 5 20 3 haematoma 7 1 2 <1 sepsis/fever 63 9 50 7 other 2 <1 2 <1 Any infectious complication 120 17 105 15 General cardiac 36 5 22 3 # multi-organ failure 11 2 10 1 pulmonary 53 8 57 8 thrombo-embolism 11 2 12 2 line-sepsis 9 1 9 1 neurological 10 1 12 2 psychological disorders 28 4 10 1 * renal 4 1 6 1 other 25 4 23 3 Any general complication 000 00 00 00 # Surgical leakage (LAR) 49 11 56 12 perforation 8 1 7 1 intestinal necrosis 6 1 7 1 fistula 8 1 14 2 stoma complications 14 2 12 2 bleeding 23 3 29 4 abdominal dehiscence 16 2 25 4 perineal complications (APR) 61 29 39 18 diarrhoea 11 2 2 <1 # ileus 37 5 48 7 other 22 3 10 1 # Any surgical complication 209 30 191 27 Any complication 336 48 297 41 * # P<0.05 * P<0.01 ** The numbers and percentages of the separate complications do not summate "any complication" since some patients had more than one complication. They were registered for each separate complication, but for "any complication" they were counted as one.
Table 5. Ablation studies of the gradual sparsity increase schedule. The number of training epochs are 3, 5 and 5 for MNLI, QQP and FEVER respectively. The subnetworks are at 90% sparsity. The numbers in the subscripts are standard deviations. gradual soft MNLI HANS QQP PAWSqqp PAWSqqp FEVER Symm1 Symm2 fixed hard 72.090.92 72.630.31 52.560.92 52.820.47 fixed hard 71.641.85 77.080.66 55.701.92 46.483.55 49.591.84 49.380.98 fixed hard 49.565.09 72.800.95 27.452.94 46.670.73 29.754.40 52.330.75 0.2∼0.9 73.610.28 75.060.31 53.900.87 54.991.28 0.2∼0.9 75.790.39 77.540.47 51.570.69 50.920.97 47.940.98 48.860.89 0.2∼0.9 73.531.36 77.01 . 46.471.66 49.87 . 52.421.39 56.57 . 0.5∼0.9 gradual soft 0.5∼0.9 gradual soft 0.5∼0.9 0 43 0 95 0 22 0.7∼0.9 76.840.46 56.720.75 0.7∼0.9 79.490.58 46.591.81 51.150.73 0.7∼0.9 79.010.68 51.740.71 58.170.33 ∼ Table 6: Results of XxXXXXx-base and XXXX-large on the NLI task. We conduct mask training with XxX loss on the standard fine-tuned PLMs. “0.5 0.7" denotes gradual sparsity increase. The numbers in the subscripts are standard deviations. XxXXXXx-base MNLI XXXX full model std 87.140.21 68.330.88 xxx 86.560.18 76.151.35 0.5 85.400.14 75.170.55 XXXX-large MNLI HANS full model std 86.840.13 69.442.39 xxx 86.250.17 76.271.55 0.5 85.470.28 75.400.64 mask train 0.7 83.480.29 68.631.33 mask train 0.7 77.546.10 60.197.56 0.5∼0.7 84.410.15 71.951.23 0.5∼0.7 84.830.26 70.182.24
Table 5. Objective 2: To ensure that any use of waterbirds in the Agreement area is sustainable Progress Target Indicator Summary and reference 2.1: The use of lead shot for hunting in wetlands is phased out in all CPs All CPs have adopted national legislation prohibiting the use of lead shot (in wetlands) No authenticated report of continued use of lead shot for hunting in wetlands in the Agreement area is received by the Secretariat 24% of the Contracting Parties have fully phased out the use of lead shot with an additional 7% having introduced partial ban. Change since MOP5: Slightly negative. Although the proportion of CPs with full ban has been retained, only 7% report partial ban as opposed to 16% at MOP5. Reference: Analysis of AEWA National Reports for the triennium 2012- 2014 (document AEWA/MOP 6.13) 2.2: Internationally coordinated collection of harvest data is developed and implemented Internationally coordinated harvest data collection in place involving at least 25% of the CPs 41% of the Contracting Parties (CPs) have confirmed harvest data collection systems in place and for 13% of the CPs these systems cover all AEWA species, the whole territory of the country and all harvesting activities. However, the international coordination and synchronization of these national schemes is still lacking. Change since MOP5: Negative. 9% less countries confirmed harvest data collection systems in place and 18% CPs less countries reported comprehensive systems covering all species, the whole territory and all harvesting activities. Reference: Analysis of AEWA National Reports for the triennium 20012- 2014 (document AEWA/MOP 6.14) Progress Target Indicator Summary and reference 2.3: Measures to reduce, and as far as possible eliminate, illegal taking of waterbirds, the use of poison baits and non- selective methods of taking are developed and implemented All CPs have pertinent legislation in place which is being fully enforced 52% of the Contracting Parties (CPs) confirmed that measures are in place to reduce/eliminate illegal taking of waterbirds within their country, while only 20% of the CPs consider the effectiveness of these measures to be high. Only 34% of the CPs have indicated that all non-selective methods of taking, as listed in the AEWA Action Plan, including poison baits, have been prohibited. Change since MOP5: Negative (lowered category of progress – from good progress to limited progress). With sliding down proportions of CPs with pertinent and effect...
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Table 5. 2 below shows the odour emissions generated from scenarios with and without water column under the laboratory study. The sediments were collected from northern KTAC with incubation temperature of 25oC. Table 5.2 Comparison of Odour Emissions with and without Water Column (Incubation Temperature: 25oC) Scenario Description Sediment Source Water Depth (m) AVS (mg-S/kg) Odour Concentration (ou/m3) Sediment Only Northern KTAC N/A 2,400 1,075 Sediment and Water Column 0.4 3,300 269 0.8 3,400 347 1.2 2,000 349
Table 5. Parental indoor cigarette smoked per day: We did not find an association between the parental indoor number of cigarette smoked per day and the log-transformed urinary lead across communities (p-value 0.8044). However, found a significant positive association with the log- transformed urinary cadmium and urinary arsenic when the number of cigarettes smoked at least four per day (p-value < 0.0001). (Table 5). Diet Based on our findings, the results revealed as followings, a positive association was found between urinary arsenic concentration and seafood consumption three days before urine sample collection (p-value 0.0079), however, the log-transformed urinary arsenic concentration was not significantly associated by the amount of seafood consumed per week. The association between seafood consumption rate per week and log-transformed urinary arsenic in both locations were the same. The research team further assessed the effect of the seafood at different consumption rates, the results exhibited no evidence of the association. When we further investigated the effect of rice and flour-ingredient snack consumption rate on the log-transformed urinary arsenic concentration exposure, we found that there was no significant association between these parameters, even though the consumption rate between the communities was significantly different (p-value 0.0101). (Table 5).
Table 5. Author Manuscript Author Manuscript Author Manuscript Author Manuscript Agreement (Kappa and Intra-class correlation coefficients) for individual items and total scores for the UHDRS measures Variable Kappa ICC (95% CI) Total Functional Capacity (TFC) Occupation 0.58 0.92 (.90, .93) Chores 0.17 0.12 (−.05, .27) ADL 0.34 0.47 (.37, .56) Finances 0.47 0.86 (.84, .87) Care Level 0.45 0.13 (−.08, .30) TOTAL FUNCTIONAL CAPACITY TOTAL SCORE 0.14 0.89 (.87, .91) Functional Assessment Could the subject engage in gainful employment in his/her accustomed work? 0.74 0.85 (.81, .88) Could the subject engage in any kind of gainful employment? 0.64 0.78 (.73, .83) Could the subject engage in any kind of volunteer or non-gainful work? 0.53 0.69 (.62, .75) Could the subject manage his/her finances (monthly) without any help? 0.63 0.78 (.72, .82) Could the subject shop for groceries without help? 0.66 0.80 (.75, .84) Could the subject handle money as a purchaser in a simple cash (store) transaction? 0.78 0.64 (.55, .71) Could the subject supervise children without help? 0.63 0.78 (.72, .82) Could the subject operate an automobile safely and independently? 0.85 0.92 (.90, .94) Could the subject do his/her own housework without help? 0.62 0.76 (.70, .81) Could the subject do his/her own laundry (wash/dry) without help? 0.79 0.88 (.85, .91) Could the subject prepare his/her own meals without help? 0.61 0.76 (.70, .81) Could the subject use the telephone without help? 0.30 0.46 (.33, .57) Could the subject take his/her own medications without help? 0.62 0.77 (.71, .81) Could the subject feed himself/herself without help? 0.52 0.69 (.61, .75) Could the subject dress himself/herself without help? 0.66 0.80 (.75, .84) Could the subject bathe himself/herself without help? 0.77 0.87 (.84, .90) Could the subject use public transportation to get places without help? 0.68 0.81 (.76, .85) Could the subject walk to places in his/her own neighborhood without help? 0.73 0.85 (.81, .88) Could the subject walk without falling? 0.35 0.52 (.41, .62) Could the subject walk without help? 0.59 0.75 (.68, .80) Could the subject comb hair without help? 0.40 0.57 (.47, .66) Could the subject transfer between chairs without help? 0.49 0.66 (.58, .73) Could the subject use the toilet/commode without help? 0.85 0.92 (.90, .94) Could the subject’s care still be provided at home? 0.55 0.71 (.64, .77) FUNCTIONAL ASSESSMENT TOTAL SCORE 0.23 0.94 (.92, .95) Independence Scale
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