Table 7. The code of a small iDSL process with an EDF Section Process ProcessModel image_processing_application seq { atom edf_values load EDF with values 6 8 10 atom edf_files load EDF from file "measurements.dat"
Table 7. Analysis 1: metric selection results. Metric - stressor correlation was consistent (yes) if the sign of the correlation was as expected. Xxxxxxxx rank correlation between the EQR, calculated using the formula EQR2, and the stressor is reported. A metric was redundant (redundancy=yes) if correlated (r>0.
Table 7. Overview of pre- and post-2012 WP activities at LSE
Table 7. Members according to occupational background. Occupation Ocak Nahiye Kaza Vilayet Total Lawyer 4 1 2 2 9 Doctor 2 0 1 0 3 Pharmacist 1 0 2 2 5 Veterinarian 0 0 0 0 0 Land owner 122 31 6 1 160 Teacher 23 15 6 0 44 Engineer 0 0 0 0 0 Retired officer 3 3 0 0 6 Retired Civil Servant 13 12 2 0 27 Merchant 308 66 43 4 421 Farmer 2047 154 12 0 2213 Worker 318 2 0 0 320
1. This second report that gives the Administrative Committees’ membership for the entire province has to be read together with the Xxxx Xxxxxx’x report that is giving the members of the Administrative Committees of the central sub-province, that is the city of Balıkesir from the Vilayet to neighborhood level (ocak), plus the Administrative Committee members of the towns in the Province (sub province level). In Sirmen’s report, i.e. in Balıkesir and the towns of the province, farmers form a tiny, unimportant percentage of the Party Administrative Committee members, while the 1941 report about the entire province reveals a striking 68,9% of farmer members. A 92,4% of these farmer members were registered in the ocak Party level. The ocak is the smallest Party structure corresponding to neighborhoods and, mostly, villages. As for the mighty percentages the consortium of merchants, artisans, and civil servants achieve in the sub-province (kaza) and Vilayet level, it retreats to a tiny 17% of the membership of the entire Party structures of the Province. If we consider these statistics from a different perspective, the percentage of merchant members, for instance, decreases as we descend to the ocak (village) level; from 44% (Vilayet administrative committee), to 58% (Kaza level), to 23% (nahiye), to a 10% at the ocak level. In other words, as we climb the ladder towards the upper echelons of the Party that were definitely more important in terms of decision-making, we observe the preponderance of civil servants, professionals and merchants, or else of urban elites. If we move downwards, towards the villages, the Party membership statistics tend to overlap with the overall population, an observation made by Xxxx Xx as well.238 Another rough indication of this tendency can be also demonstrated by a simple comparison of the percentage of illiterates between the Administrative Committee members and the overall Party members of the province of Balıkesir. The 19/3/1941 report239 of the members of the İdare Heyetleri gives a 23% of illiterate members, while the 1942 biannual report240 of the Party membership ...
Table 7. 4 below summarises the proposed monitoring frequency and water quality parameters for the impact monitoring.
Table 7. The limits for vehicle in configuration "RESS charging mode coupled to the power grid" with input current > 16 A and ≤ 75 A per phase and subjected to conditional connection are given in paragraph 7.4.2.2. Table 8. Figure 1 Measurement device
Table 7. Frequency and percentage distribution of autonomy-related characteristics across the four states for baseline and follow-up period among married rural women, 1998-1999 and 2002-2003 [B: Baseline, F: Follow-up] Financial Autonomy 1,205 (62.4) 1,105 (57.3) 330 (59.1) 406 (72.8) 209 (52.4) 249 (62.9) 536 (74.4) 593 (82.3) 2,280 (63.2) 2,353 (65.3) Involved in healthcare decisions 742 (38.4) 880 (45.6) 360 (64.5) 354 (63.4) 148 (37.1) 169 (42.4) 382 (53.1) 456 (63.3) 1632 (45.2) 1859 (51.5) Involved in jewelry purchase decisions 608 (31.5) 867 (44.9) 347 (62.2) 389 (69.7) 170 (42.6) 161 (40.4) 460 (63.8) 502 (69.6) 1585 (43.9) 1919 (53.2) Involved in visit decisions 646 (33.5) 892 (46.3) 341 (61.1) 391 (70.2) 124 (31.1) 159 (40.0) 416 (57.8) 370 (51.3) 1527 (42.3) 1812 (50.3) Overall High Decision- making 881 (45.6) 1225 (63.6) 442 (79.2) 457 (82.1) 235 (58.9) 223 (56.0) 576 (80.1) 630 (87.4) 2134 (59.2) 2535 (70.4) Permission not needed 240 (12.4) 378 (19.6) 197 (35.3) 133 (23.8) 118 (29.6) 154 (38.6) 527 (73.1) 670 (92.9) 1082 (30.0) 1335 (37.0) Permission needed 1,569 (81.3) 1,255 (65.0) 292 (52.3) 323 (58.0) 188 (47.1) 184 (46.1) 170 (23.6) 41 (5.7) 2219 (61.5) 1803 (50.0) Not allowed to go 240 (12.4) 298 (15.4) 69 (12.4) 102 (18.3) 93 (23.2) 61 (15.3) 24 (3.3) 10 (1.4) 308 (8.5) 471 (13.0) Permission not needed 233 (12.1) 1038 (53.8) 183 (32.8) 389 (69.7) 80 (20.1) 205 (51.4) 335 (46.5) 554 (76.8) 831 (23.0) 2186 (60.6) Permission needed 1620 (83.9) 772 (40.0) 348 (62.4) 160 (28.7) 312 (78.2) 185 (46.4) 375 (52.0) 165 (22.9) 2655 (73.6) 1282 (35.5) Not allowed to go 78 (4.0) 121 (6.3) 27 (4.8) 9 (1.60) 7 (1.8) 9 (2.3) 11 (1.5) 2 (0.3) 123 (3.4) 141 (3.9)
Table 7. 1: Comparison between the variables contained in a standard and an extended file. Variables in magenta are present only in standard file, those in cyan are only in extended file, orange variables are in both file with different content (see section.7) standard file extended file double time(time) double time(time) char L1b_id(time, len_L1bid) char L1b_id(time, len_L1bid) int processor_patchlevel(time) int processor_patchlevel(time) byte auxdata_subversion(time) byte auxdata_subversion(time) int orbit_id(time) int orbit_id(time) int scan_id(time) int scan_id(time) byte obs_mode_fiag(time) byte obs_mode_fiag(time) fioat chi2(time) fioat chi2(time) fioat cost_function(time) fioat cost_function(time) int gauss_iterations(time) int marquardt_iterations(time) fioat lambda_marq(time) fioat lambda_marq(time, targets) byte day_night(time) byte day_night(time) fioat longitude(time) fioat longitude(time) fioat latitude(time) fioat latitude(time) fioat solar_zenith_angle(time) fioat solar_zenith_angle(time) fioat orbital_coordinate(time) fioat orbital_coordinate(time) fioat ECMWF_altitude_shift(time) fioat ECMWF_altitude_shift(time) byte conv_id(time) byte conv_id(time) byte quality_fiag(time) byte post_quality_fiag(time) fioat longitude_profile(time,level) fioat latitude_profile(time,level) fioat orbital_coordinate_profile(time,level) fioat pressure(time, level) fioat pressure(time,level) fioat pressure_error(time, level) fioat pressure_error(time,level) fioat height(time, level) fioat height(time, level) fioat height_error(time, level) fioat temperature(time, level) fioat temperature(time, level) fioat temperature_error(time, level) fioat temperature_error(time, level) fioat profile(time, level) fioat profile_error(time, level) fioat covariance_matrix(time, cmdim) fioat averaging_kernel(time, level, level) int nparam_per_target(time, targets) byte param_units_fiag(time, targets) int selected_occupation_matrixfiag(time) byte effective_occupation_matrix(time, mwindows, level) byte retrieval_vectors(time, species, level) fioat state_vector(time, parameters) fioat full_covariance_matrix(time, cmdim) fioat full_averaging_kernel(time, parameters, parameters) fioat extended_height(time, extended_level) fioat extended_height(time, extended_level) fioat extended_pressure(time, extended_level) fioat extended_profile(time, extended_level) fioat a_priori_profile(time, level) fioat a_priori_covariance(time, cmdim) fioat error_p_t_cm(time, cmdim) fioat extended_left_gradient(time...
Table 7. Baseline Characteristics for Sites utilizing TB Directly Observed Therapy (DOT) and Video Directly Observed Therapy (VDOT)
Table 7. The means for the own personal growth as a driver M1 SD1 M2 SD2 Time of the measurement 3.65 .82 3.82 .69 Form of teaching safe driving course 3.80 .81 3.97 .64 theory lesson 3.51 .81 3.61 .71 Educational background working 3.77 .98 3.78 .71 studying 3.58 .68 3.82 .67 Age ≤ 20 3.62 .76 3.78 .65 >20 3.54 .90 4.04 .73 Gender male 3.68 .86 3.81 .71 female 3.65 .79 3.83 .66 1= first measurement 2= second measurement Table 8. Analysis of variance for Personal growth as a Source df F η p Form of teaching (F) 1 3.33 .02 .07 Educational background (E) 1 0.14 .00 .70 Age (A) 1 .54 .00 .46 Gender (G) 1 .04 .00 .85 F x G 1 .64 .00 .42 M x G 1 .41 .00 .52 A x G 1 .06 .00 .81 E x G 1 .21 .00 .65 M x F 1 .03 .00 .86 F x A 1 .07 .00 .79 F x E 1 .73 .00 .39 M x E 1 2.72 .02 .10 E x A 1 .01 .00 .94 error 173 p p The time of the measurement had a main effect on the experiences of personal growth (F1,173=5.70, p<.05, η 2=.03). Thus, the driving school education following the rules of coaching offered the subjects a better possibility to experience personal growth as a driver than the normal driving school education. The time of the measurement and age had an interaction on the experiences of personal growth (F1,173=3.94, p<.05, η 2=.02) (Figure 2).
