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. Overview of pre- and post-2012 WP activities at LSE Age group (school year) Pre-2012 activity 2012 and 2013 activity 2014, 2015, 2016 and 2017 activity Pre 14 (Years 6 to 8) Moving On Student tutoring Student mentoring Black Achievement Conference Introduction to the Social Sciences Promoting Potential Spring/Summer School (for African- Caribbean boys) Develop and improve the WP programme and maintain numbers on existing outreach activities Improved targeting of LPN pupils, LAC pupils and disabled pupils. Integrated approach with LSE Careers, Disability and Well-being Service, SU, EDI, Teaching and Learning, LSE Life, and academic departments Expand LSE WP student network and target support to students Collaborative work with Xxxxxxx Group and University of London networks. Build on work with black African- Caribbean pupils Continually review the strategic targeting of outreach work and explore expansion outside of London to further support key target groups Advisers/teachers /schools Advising the Advisers Talks and visits to state schools Close school links Targeted admissions information and feedback for low- performing schools Implement and utilise a new CRM system for communication and management of pre-entry work with participants, schools/colleges, and parents/carers. Ensure a clear pipeline of WP activity from Primary to Post-16 education with multiple interventions encouraged through regular communication with current and former participants. Evaluation As outlined in our Access Agreements since 2013, we use evaluation feedback and data to inform our WP strategy and individual WP projects. The LSE OFFA monitoring submissions for 2014 and 2015 entry contained detailed information regarding the evaluation of our access work. We also use our performance against HESA benchmarks to inform our overall direction of travel, hence the increased focus on low participation neighbourhood students since 2014. Additionally, in line with the strategic priorities in recent Access Agreement guidance, taking an evidence led approach remains a key xxxxx of our Access Agreement work. Please see section 7, Monitoring and Evaluation, for more information. Collaborative working LSE has been committed to working in partnership to support our Widening Participation activity. We have developed a solid basis of collaborative work over a number of years. Examples of this in 2018 include: Pathways projects: In 2016 LSE successfully re-bi...
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>0.8)
Table 7. Example Phase I Outcome Payment Calculation for Group I, assuming N1=1000 Final Employment Outcome: 4.000 percentage points 4.000 percentage points < 5.000 percentage point threshold Threshold not met Final Recidivism Outcome: 191.215 bed days 191.215 bed days >= 36.800 bed day threshold Threshold met Final Transitional Job Outcome: 650 PFS Participants12 191.215 bed days >= 36.800 bed day threshold Threshold met Final Employment Outcome: 4.000 percentage points N/A = $0 Final Recidivism Outcome: 191.215 bed days 191.215 bed days * 1,000 * $85 = $16,253,275 Final Transitional Job Outcome: 650 PFS Participants 650 PFS Participants * $3,120 = $2,028,000 ESTIMATED PUBLIC SECTOR BENEFITS = $18,281,275 100% of Public Sector Benefits from Final Employment Outcome = $0 100% of Public Sector Benefits from Final Recidivism and Transitional Job Outcomes up to value of Phase I Drawdown Amount = $6,832,000 50% of Public Sector Benefits from Final Recidivism and Transitional Job Outcomes thereafter = $5,724,638 = 50% * ($16,253,275+ $2,028,000 - $6,832,000) PHASE I OUTCOME PAYMENT, capped at the Maximum Outcome Payment for Phase I ($11,095,000) = $11,095,000 (e) Release of Outcome Payment 12 Assumes that Average Hours Worked for PFS Participants that Engage in Transitional Jobs is greater than or equal to 111.
Table 7. 3 below summarizes the proposed monitoring frequency and water quality parameters for baseline monitoring.
Table 7. 4.3. 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. Annex 12 - Appendix 1 Figure 1 Vehicle in configuration "RESS charging mode coupled to the power grid" Annex 13 Method(s) of testing for emission of radiofrequency conducted disturbances on AC or DC power lines from vehicle.
Table 7. Estimated target percentages of acceptances that should have the OAC flag, for the given degree subject(s). Production of these estimates is detailed in the Appendix, and was based on 2014 and 2015 data. The Actual proportions shown are based on Table 3 acceptances data for the same years, as discussed above. These estimates indicate that for Sciences and Arts subjects separately and combined (“All subjects excl. Maths”), a reasonable target is that approximately 8.0-8.1% of accepted students should have the OAC flag. The actual proportion of Arts acceptances was already close to this in 2014 and 2015 (7.9%), although it was lower for the Sciences (7.4%). The estimate for Mathematics was that 10.5% of accepted students should have the OAC flag, whereas only 7.5% of acceptances actually did have the flag in 2014-15, but the estimate did not take into account STEP which is of critical importance for Mathematics. The target of ~8.2% produced for “All subjects incl. Maths” might be slightly affected by this, but nonetheless we decided that this target figure was the most appropriate to take forward into our Access Agreement with OFFA, because it covers admissions to the collegiate University for all subjects, including Mathematics. The estimates for “All subjects” discussed in the above paragraph all used subject information in their calculation. By this, we mean that - although they apply to “All subjects” once calculated - information about degree subject was taken into account when calculating them (see Appendix for further detail). However, if we had not had, or had not used, this information about degree subject applied for, we could nonetheless have produced a less accurate target estimate. As shown in Table 7, the less accurate estimated target (including Mathematics) would have been 8.7%. In conclusion, although there are several caveats (e.g., this does not take into account choice of A Level subject, takers of alternative KS5 qualifications, or STEP results for Mathematics applicants), we decided that a reasonable admissions target for OAC-flagged applicants based on 2014-15 Cambridge applicant data is approximately 8.2%.
Table 7. 1 Source parameters used for the Umbria-Marche event 26.09.1997 (9:40, MW = 6.0). (from Xxxxxxxxx et al., 2004). HYPOCENTER [°N,°E,Z] M0 [N m] L x W [km] Strike [deg] Dip [deg] Rake [deg] Depth of upper points [km] Vr [km/s] rise time IJ [s]
Table 7. 1.1 Summary results of proportion of eastern individuals (pE) assigned with different methods (genetics, stable isotopes, otolith shape) throughout the different Phases of the GBYP program. n=sample size (only individuals with probabilities > 0.7 have been assigned to an area of origin). Reference samples (i.e. YOY) are not included in this summary. Genetics Stable isotopes Otolith Shape n pE n pE n pE Eastern Mediterranean 80 1,00 41 0,83 Levantine Sea (North) 80 1,00 41 0,83 Central Mediterranean 165 0,97 96 0,96 Adriatic Sea 37 1,00 25 0,88 Malta 19 1,00 71 0,99 Sicily (East Sicily and Ionian Sea) 69 0,96 Gulf of Syrta 40 0,95 Western Mediterranean 205 0,97 53 0,98 Balearic 28 1,00 32 0,97 Gulf of Lion, Catalan 39 1,00 Ligurian: Italian artisanal fleet 34 0,88 Sardinia 66 0,95 15 1,00 Tyrrhenian Sea 38 1,00 6 1,00 Northeast Atlantic 182 0,96 509 0,84 70 0,81 Bay of Biscay 64 0,98 126 0,96 Gibraltar 38 1,00 94 0,95 21 0,76 Madeira, Canary Islands 16 0,88 43 0,79 Morocco 33 0,94 148 0,70 21 0,86 Portugal 31 0,90 98 0,85 28 0,82 Central North Atlantic 55 0,82 361 0,63 19 1,00 Central and North Atlantic 55 0,82 361 0,63 19 1,00 Northwest Atlantic 17 0,59 Canada (Gulf Saint Xxxxxxxx) 17 0,59
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...
