Model Validation. The Manager shall cooperate with the Company and the FRBNY in the manner set forth below to validate the conceptual soundness and implementation of models used by the Manager in its performance of services under this Agreement if such model is used in such a way that an error related to the model’s formulation or implementation is likely to have a material adverse effect on the Company, including a significant financial loss, a significant error of analytical outputs including cash flows, discount rates, valuations, or statistics relating to those outputs (such as expected values, variances, percentiles, or stress estimates), or a violation of applicable law or (each, a “Material Model”). For purposes of this Section 8.5, as of the Effective Date, the Manager has identified as “Material Models” those models used in the performance of services that are based on BlackRock Solutions Aladdin interest rate modeling and yield curve construction techniques utilized for the generation of cash flows, projection of floating rate coupons, and discounting, in support of the regular reporting and analytics to be delivered pursuant to Section 9.1, as agreed upon with FRBNY, including the Manager’s Shifted Lognormal
Model Validation. All models provided to CEB for use in dynamic simulations shall be validated against site measurements. The Independent Engineer shall certify that the behaviour shown by the model under simulated conditions is representative of the behaviour of the Facility under equivalent conditions. For validation purposes, Facility Owner shall ensure that appropriate tests are performed and measurements are taken to assess the validity of the dynamic model. Facility Owner shall provide all available information showing how the predicted behaviour of the dynamic model to be verified with the actual observed behaviour of a prototype or similar PV modules/Inverter under laboratory conditions and / or actual observed behaviour of the real Facility as installed and connected to the CEB System. If the on-site measurements or other information provided indicate that the dynamic model is not valid in one or more respects, Facility Owner shall provide a revised model whose behaviour corresponds to the observed on-site behaviour as soon as reasonably practicable. The conditions validated should as far as possible be similar to those of interest, e.g. low short circuit level at Interconnection Boundary, large frequency and voltage excursions, primary resource variations.
Model Validation. Multiple regression analyses (Xxxxxx et al., 1995, Xxxxxxx et al., 2002, Xxxx et al., 2007) and indices of adiposity were assessed (Xxxxxx et al., 2000) to address two questions relating to the estimation of total abdominal visceral fat using DXA adiposity and a range of anthropometric measures: 1) which of these predictive models for the “gold standard” CT measure of visceral fat, best fit our validation sample of 54 females; and 2) in relation to previous discussions (Xxxxxx et al., 2000), can visceral fat be reliably estimated based upon anthropometry alone. CT and DXA scans for the same individuals were date-matched to between 0.23 - 2.5 years of one another. The difference in scan date for the validation sample was included in all visceral fat regression models as a nuisance factor. A Xxxxx-Xxxxxx analysis was conducted to assess if the predicted VAT error term was constant or varied across the range of CT-measured VAT area.
Model Validation. We examined the validation approach for each of the 34 outcomes (clinical endpoints of the studies). Single random split was used 17 times (50.0%), with the data split into single train-test or train-validation-test parts. When the data are split into train-test parts the best model for training data is chosen based on model’s performance on test data, whereas when the data are split into train-validation-test sets the best model for training data is selected based on the performance of the model on validation data. Then the test data are used to internally validate the performance of the model on new patients. Resampling (cross-validation or nested cross-validation) was used 9 times (26.5%). External validation (testing the original prediction model in a set of new patients from a different year, location, country etc.) was used 4 times (11.8%). External validation involved the chronological split of data into training and test parts 3 times (temporal validation), and validation of a new dataset 1 time. Multiple random split was used 2 times (5.9%), with the data split into train-test or train-validation-test data multiple times. Validation was not performed for 2 datasets (5.9%). We recommend reporting the steps of the validation approach in detail, to avoid misconceptions. In case of complex procedures, a comprehensive representation of the validation procedures can be insightful. Researchers should aim at performing both internal and external validations, if possible, to maximize the reliability of the prediction models. Table 5.3 shows the performance measures used for model validation in the 24 studies. A popular measure in the survival field, the C-index, was employed in 8 studies (33.3%, as C-index or time-dependent C-index) and AUC in 5 studies (20.8%). Notably, during the screening process, several manuscripts were identified where AUC and C-statistic were used interchangeably. While there is a link between the dynamic time-dependent AUC and the C-index (the AUC can be interpreted as a concordance index employed to assess model discrimination) [55], the two are not identical and some caution is required. Apart from the C-index, there was no other established measure in the 24 studies (large variability). This issue is of paramount importance as validation (and development) of the SNNs depends on a suitable performance measure. Any candidate measure should take into account the censoring mechanism. By employing performance measures that are common...
Model Validation. The SUFEHM developed by Xxxx et al. [35] was validated under RADIOSS code against intracranial pressure data from Xxxxx’s experiments. The intracranial response was recorded at 5 locations and compared with the experimental results. A good agreement was found for both impact force and head acceleration curves when compared with experimental data. Also the pressure data at five locations were match very well with less than 7% deviation of peak pressure from experimental peak pressure values. This head model was validated under RADIOSS code against intracranial pressure data of Trosseille et al. [41] experiments. Five tests from Xxxxxxxxxx’s experiments were replicated and a reasonable agreement was observed between simulation and experimental pressure and acceleration curves. In the context of APROSYS SP5 investigations have been completed to try and determine a suitable state-of- the-art numerical head model with which to develop numerical based head injury criteria and to identify the principle head injury mechanisms. The choice of models evaluated was partly based on the willingness of the developer of each head model to provide predictions of intra- cerebral pressure, skull deformation and rupture and brain skull displacement for six impact conditions, detailed in published PMHS impact tests (Xxxxx et al. [37], Xxxxxxxxxx et al. [41], Xxxxxxxxxx et al. [58], Xxxxx et al. [32]). SUFEHM model was one of the “state of the art” model. A comparison of the SUFEHM results under RADIOSS code with the other existing FE head model were published by Xxxx and Xxxxxxxxx in 2009 [31].
Model Validation. A time domain validation of the Strasbourg University Head-Neck Model (SUFE-HN-Model) was proposed by Xxxxx et al. [49] under LS-DYNA and it has been carried out in comparison to the N.B.D.L tests [15] under front, oblique and lateral impacts. This time analysis permitted to validate the model in accordance with the classic validation procedures systematically chosen in the literature. Finally temporal validation was completed by simulating Xxx et al. [52] experience in order to evaluate the relative cervical motion under rear impact. Furthermore SUFE-HN is validated in the frequency domain. In past studies, Xxxxxxx et al. [42] and Xxxxx et al. [49] showed that a validation in the time domain is not sufficient to reproduce the dynamic behavior of the neck. In fact, a great amount of responses may exist in a given corridor. And these responses do not correspond to a same mechanical behavior. More recently, Xxxxxx et al. [48] produced an extend of the head/neck system characterization in the frontal and horizontal plane. Two kinds of experimental devices were therefore realized. The first one is the same than the one used by Xxxxxxx et al. [42] and the second one consists in a rotational solicitation of the thorax. The results obtained thanks to the FEM of the head/neck system are summarized in the Table 10. Table 10. Results of experimental test and simulation in terms of natural frequencies Mode Mode -Illustration Average Volunteer Natural frequency Head-Neck FEM Natural frequency Flexion-Extension 1.68±0.2 Hz 2.8 Hz Inclination 1.7±0.2 Hz 2.6 Hz Axial rotation 3.2±0.3 Hz 3.4 Hz S-Shape 8.8±0.5 Hz 11 Hz Lateral retraction 9.5±1.4 Hz 9.6 Hz
Model Validation a. The ENGINEER will compare results with current effective and previously prepared Rush Creek hydrology models. Comparisons will be made on a basin by basin analysis, since the new Rush Creek model will have no channel routing for hydraulic study channels.
b. The ENGINEER will compare results as reasonable with other local stream hydrology models that are available and have a similar overall basin and sub‐basin size. These models could be provided by the ENGINEER or the PMC. However, since the Rush Creek model will have no channel routing, attenuation will not be factored into this comparison for much of the watershed. Results will need to be examined on an individual sub‐basin basis based on basin size, land use, basin slope, and other contributing factors.
c. The ENGINEER will assist the PMC on a more detailed model validation once the hydraulic studies are complete with comparisons to previously prepared Rush Creek models and other local stream hydrology models. Model validations may be needed for the September 2010 storm event (TS Xxxxxxx) and other past events. The ENGINEER will provide assistance on all model validation runs. Documentation
o A summary of the Task and a description of methodologies and assumptions used. o Any relevant correspondences, discussions, and technical decisions regarding model set‐up and testing, including review comments and special issues.
Model Validation. March 2012 – December 2012: To be performed after hydrology completion with additional validation after hydraulic completion.
Model Validation. The validation of the simulation results with the observations is done using data from four meteorological stations: an urban station (DEUSTO), an inland suburban station (BASAURI) and two rural stations, one near the coast (GALEA) and the other (DERIO) in a valley parallel to Bilbao. The observed data are provided by the Basque Meteorological Agency (EUSKALMET), see Table 3 for a description of the stations. Urban Deusto -2.966 43.283 0.6 City centre Suburban - Inland Basauri -2.883 43.243 0.4 Resid. high-dense Rural - Coastal Punta galea -3.033 43.373 0.0 None Rural Inland Derio −2.852 43.293 0.0 None Data MEAN Sigma ME MSE RMSE RMSEub a) TEMPERATURE Urban OBS 20.78 2.12 URB 20.02 2.51 -0.77 3.39 1.84 1.68 CTRL 19.80 2.69 -0.98 4.13 2.03 1.78 Coastal OBS 19.80 1.38 URB 18.78 1.24 -1.02 2.10 1.45 1.03 CTRL 18.85 1.23 -0.96 2.01 1.42 1.04 Hinterland OBS 21.17 2.53 URB 19.25 2.80 -1.92 7.57 2.75 1.97 CTRL 19.33 2.95 -1.84 7.19 2.68 1.95 b) RELATIVE HUMIDITY Urban OBS 82.45 6.50 URB 68.47 12.28 -3.98 297.87 17.26 10.11 CTRL 70.76 13.50 -11.69 255.15 15.97 10.88 Coastal OBS 91.71 5.46 URB 81.89 8.78 -9.82 191.74 13.85 9.76 CTRL 77.98 6.40 -13.73 222.20 14.91 5.80 Hinterland OBS 94.17 8.55 URB 73.81 14.29 -20.36 560.77 23.68 12.09 CTRL 73.61 14.99 -20.56 571.01 23.90 12.18 c) WIND Urban OBS 2.31 1.27 URB 1.78 0.96 -0.52 1.55 1.24 1.129 CTRL 1.90 1.43 -0.40 1.80 1.34 1.281 Coastal OBS 3.60 2.21 URB 2.69 1.46 -0.91 4.69 2.16 1.965 CTRL 2.74 1.63 -0.86 4.29 2.07 1.885 Hinterland OBS 1.91 1.37 URB 1.64 1.08 -0.26 1.05 1.02 0.989 CTRL 1.78 1.40 -0.13 0.87 0.93 0.923
Model Validation. As mentioned before, UrbClim was applied to three cities: Antwerp, London, and Bilbao. The specification of the domain and periods covered for each of the cities is provided in Table 1.
