Table 2 Sample Clauses

Table 2. Determinations Determination Concerning Determiner 2.8.2.7 Complaints and Grievances Under the Decision Making Model

Table 2. Software Subscription Use Case OpenShift Enterprise OpenShift Enterprise Broker Infrastructure OpenShift Enterprise is intended to be used as a platform as a service and will be supported only when used in that capacity. OpenShift Enterprise is not supported on non-server hardware such as desktops or workstations. OpenShift Enterprise is intended for use on a dedicated Physical Node or Virtual Guest; running other applications and/or programs of any type on the Physical Node or Virtual Guest can have a negative impact on the function and/or performance. Red Hat JBoss Enterprise Application Platform for OpenShift and/or Red Hat JBoss EAP for xPaaS will be supported in accordance with the terms of Exhibit 1.B.

Table 2. Software Subscription Use Case OpenShift Enterprise OpenShift Enterprise Broker Infrastructure OpenShift Enterprise is intended to be used as a platform as a service and will be supported only when used in that capacity. OpenShift Enterprise is not supported on non-server hardware such as desktops or workstations. OpenShift Enterprise is intended for use on a dedicated Physical Node or Virtual Guest; running other applications and/or programs of any type on the Physical Node or Virtual Guest can have a negative impact on the function and/or performance. Red Hat CloudForms is supported solely for the purpose of managing OpenShift Enterprise Software. Red Hat JBoss Middleware will be supported in accordance with the terms of Exhibit 1.B.

Table 2. Minutes per year with the corresponding probability and the inverse cumulative probability as a function of standard deviation. In the third step, the value rqh, which represents the same range for an allowed normal distribution of the quarter-hourly average frequency deviation, is calculated based on the assumption that the two signals are not correlated: In the fourth step, the ranges which correspond to the probabilities required by SO GL Article 128(3) are calculated taking rqh as basis. The probabilities are calculated as follows: For the calculation of the ranges, the inverse cumulative probabilities of and will be used.

Table 2. 11: Earliest and latest Firm Train Slots Earliest and latest Firm Train Slots (FTS)

Table 2 the Approved EU Standard Contractual Clauses with optional provisions as set forth in Section 2.2.

Table 2. Author Manuscript

Table 2. 4.4.4-2 PoP-to-PoP Average Availability SLA Credits...........

Table 2. 1: Names of folders from the 2018 survey fieldwork data collection

Table 2. The fact that MTT values in our study were less affected than were CBF and CBV values when the dose was low- ered is in concordance with the results of a study by Xxxxxxx et al (21) in which several types of reconstruction algo- rithms were compared when the total CBF 0.911 ,.0001 .094 .118 dose was decreased by one-half. Those CBV 0.906 ,.0001 .025 .089 authors found, however, that the CBV MTT 0.864 ,.0001 .075 .371 values of gray matter actually were un- Gray matter derestimated by 18.6% in all patients, CBF 0.895 ,.0001 .408 .923 and the CBV values of white matter CBV 0.917 ,.0001 .094 .477 were overestimated by 1.5% with fil- increased noise levels lead to increased small-scale gradients on the intensity curves, which are erroneously inter- preted as increased blood flow and vol- ume during the deconvolution process inherent in the perfusion analysis. Ac- cording to the central volume principle (20), MTT is less affected. tered back projection as the recon- struction algorithm, but they gave no explanation for this observation. Our optimal point of dose reduction was lower than the reduction of 33% (from 190 mAs to 125 mAs, no effective dose values were reported) suggested by Ju- luru et al (22). These authors found lit- tle effect of lower dose settings on per- fusion values, although in their study, Xxxxxx et al investigated five dose set- tings with simulated noise, whereas we used real measured noise. To our knowledge, previously pub- lished work is limited primarily because of the difficulty of realistic simulation of patient data at different dose levels. In other studies, this was achieved by simply adding Gaussian noise (22), by reconstructing only a part of the total number of raw projections (21), or by using dual-source CT scanners and let- ting the two tubes operate at different milliampere and kilovolt settings simul- taneously during acquisition (23). An alternative to our approach would be to use dedicated low-dose simulators (24,25). Our approach combined real patient tissue curves with multiple CT scans at a wide range of tube current settings to construct patient-specific digital phantoms. In van den Boom et al (14) these patient-specific phantoms were described and validated. Linear fits in the scatterplots of the perfusion values and corresponding digital xxxx- xxxx yielded slope and R2 values, re- spectively, of 1.03 and 0.96 for CBF, 1.06 and 0.97 for CBV, and 1.02 and 0.73 for MTT. Kudo et al (26) also used digital phantoms, but ...