Figure 8. The number of children aged 0-5 years on the child protection register 2009-2013
Figure 8. Current in the secondary winding versus time.
Figure 8. The blue lines indicate the ground tracks of the Xxxxx missions within 400 km from the coast. The blue patches at the coast correspond to the part of the coastlines that have a ground track within 30 km of the coast. This product consists of along-track high-resolution (20 Hz, i.e., ~350 m) sea level anomalies and coastal sea level trends derived from a complete reprocessing of the Xxxxx altimetry data in several coastal regions worldwide. This product is only available for some regions of the world. In Figure 8 is shown the tracks of the Xxxxx series of missions less than 400 km from the coast. When fully validated this type of dataset can produce estimation of the coastal sea level trend at few km from the coast (between 4 and 10 km) for the period 2002-onward. It is worthwhile mentioning that in the future, the higher spatial resolution of the ESA Sentinel altimetry missions (Sentinel-3 and -6) should also provide valuable estimates of sea level trends at the coast. Nevertheless, the in situ data from tide gauges will always be required to validate this satellite altimetry products.
Examples of Figure 8 in a sentence
Ending Date: Need to Learn Does Ok Does Great Need to Learn Does Ok Does Great Example: Sub Novice Heel on Leash X X Figure 8 on leash X X (Add additional pages as needed.) Goal Evaluation Goals help you to achieve more when you review them each year.
They shall produce the specified spray pattern (see instrument calibration in Figure 8).
Ending Date: Need to Learn Does Ok Does Great Need to Learn Does Ok Does Great Example: Sub Novice Heel on Leash X X Figure 8 on leash X X Goal Evaluation Goals help you to achieve more when you review them each year.
As shown in Figure 8, in the proposed scheme, the total traffic gain volume is gradually increasing as the number of SMs are increasing, which is quite obvious.
The results of the total traffic volume gain (in bits) at the SP presented in Figure 8 by varying the number of SMs. In our analysis, we consider one session per SM that sends consumption usages data to the SP.
More Definitions of Figure 8
Figure 8. Concept Map: ”Robot Relations” RULE { IF NOT Predicate.Can_Move(THIS) THEN { DO {Action.Check_Battery(THIS..battery);} } } RULE { IF NOT Predicate.Can_Move(THIS) AND Action.Get_Battery(THIS..battery) > 0.5 THEN { DO {Action.Get_Dist(THIS, Action.Get_Closest(THIS, ENV.Thing..Obstacle)); } } } Rules, also can be used to imply predicates, e.g.: RULE { IF Action.Get_Battery(THIS..battery) > 0.9 THEN { Predicate.Charged(THIS..battery) } ELSE { NOT Predicate.Charged(THIS..battery) } } In additon, constraints may be used to constraint the behavior of the system or impose predicates, e.g.: CONSTRAINT { IF Action.Get_Battery(THIS..battery) < 0.1 THEN { NOT Action.Move(THIS) } CONSTRAINT { IF Xxxxxxxxx.Xx_Operational(THIS.locomotion_system) THEN { Action.Get_Battery(THIS..battery) > 0.5 AND Xxxxxxxxx.Xx_Operational (THIS..wheel[1]) AND Xxxxxxxxx.Xx_Operational (THIS..wheel[2]) AND Xxxxxxxxx.Xx_Operational (THIS..wheel[3]) AND Xxxxxxxxx.Xx_Operational (THIS..wheel[4]) AND Xxxxxxxxx.Xx_Operational (THIS..wheel[5]) AND Xxxxxxxxx.Xx_Operational (THIS..engine) AND Xxxxxxxxx.Xx_Operational (THIS..locomotion_soft) AND Xxxxxxxxx.Xx_Running (THIS..locomotion_soft) } } Constraints can also be used to impose data restrictions, e.g., let’s presume we want two robots to have different first goals: CONSTRAINT { robot[1].goal[1] <> robot[2].goal[1]; } Finally, we need to specify important situations and policies driving the system in those situations. The following examples represent the LoadedAndOperational situation and the policy ReturnAndUnload Figure 9: Gripper Concept and its Realization specified to eventually handle that situation. Recall that we need also to specify a relation that connects these two structures if we want the policy to handle the situation (see Section 2.4.2). CONCEPT_SITUATION LoadedAndOperational { CHILDREN {} PARENTS {SC.Thing..Situation} SPEC { SITUATION_STATES { SC.Thing..Robot.operational , SC.Thing..Gripper.locked_stable } SITUATION_ACTIONS { SC.Action.Move , SC.Action.Lay_dwn } } } CONCEPT_POLICY ReturnAndUnload { SPEC { POLICY_GOAL { UnloadGripper } POLICY_SITUATIONS { LoadedAndOperational } POLICY_RELATIONS {. } POLICY_ACTIONS {. } POLICY_MAPPINGS { MAPPING { CONDITIONS {SC.Action.Get_position = B } DO_ACTIONS {SC.Action.Lay_dwn } } MAPPING { CONDITIONS {SC.Action.Get_position <> B } DO_ACTIONS {SC.Action.Plan_trip, SC.Action.Move } } } } } 4 The Pyramid of Awareness The ultimate goal of our KR approach is to allow for awareness and self-awaren...
Figure 8. A mobile application provides the interface to experience the story. Virtual characters enact the narrative and guide the user to participate.
Figure 8. Anatomy of nigrostriatal pathway in DRD mice. Representative sections immunostained for TH (A) or DAT (B) from striatum or midbrain of normal and DRD mice (scale bars = 1.5 mm, striatum; 200 µm, midbrain). (C) Stereological cell counts of TH-positive neurons in the SN (p>0.1, Student’s t test) and VTA (p>0.1, Student’s t test). (D) Stereological assessment of striatal volume (p>0.1, Student’s t test). Values represent mean ± SEM. Microstructural changes to corticostriatal and thalamostriatal connectivity We further examined the anatomical consequences of the p.382Q>K mutation to basal ganglia circuitry by examining glutamatergic cortical and thalamic inputs to striatum. These inputs act in concert with DA in the striatum to coordinate normal movement (Bamford et al., 2004, Costa et al., 2006). Further, glutamatergic synapses in the striatum undergo complex changes in other animal models with striatal DA depletion (Xxxxxx et al., 1998, Xxxxxxxx et al., 2009, Xxxxxxxx and Xxxxx, 2013). Thus, despite normal DAT-positive innervation and striatal volume (Fig. 8), other changes to striatal circuitry could result from the p.382Q>K mutation. We first assessed the gross distribution of glutamatergic input to striatum by staining brain sections for vGluT1, a selective marker of corticostriatal terminals, and vGluT2 a selective marker of thalamostriatal terminals. Using densitometry to quantify vGluT1 and vGluT2 staining intensity, no significant differences between genotypes were observed in any of the subregions of striatum examined (Fig. 9; p>0.1 for all subregions). Next, we examined the density of corticostriatal and thalamostriatal terminals at the synaptic level in the dorsolateral striatum, counting the number of labeled synaptic boutons in images collected by electron microscopy (Fig. 10A and 10B). In agreement with the light microscopy densitometry data, no significant differences were observed in the density of either population of terminals between normal and DRD mice (Fig. 10C; p>0.1). Further, the number of perforated vGluT1-positive (p>0.1) or vGluT2-positive (p>0.1) synapses did not significantly differ between genotypes (not shown). However, the ratio of axo-spinous to axo-dendritic synaptic contacts was significantly smaller for vGluT1-positive terminals in DRD mice compared to normal mice (Fig. 10D; p<0.05), and there was a similar trend for vGluT2-positive terminals (p=0.052). The functional significance of this shift toward more dendritic contact...
Figure 8. Assembly window (1: treeview; 2: viewer; 3: timeline; 4: operation) The assembly window is divided into several sub-windows:
Figure 8. The iron loss power density in the magnetic core of the transformer. Use the Magnetic Fields interface to model the magnetic fields of the transformer. Model the primary and secondary windings with Coil features. Connect the primary and secondary windings to an external circuit with the AC voltage source and resistors using an Electrical Circuit interface. Add a Coil Geometry Analysis study step to calculate the current in the coils. Perform a Time Dependent study to determine the voltage and currents in both the primary and secondary windings. Add the Loss Calculation subfeatures in the Windings and Core and perform a Time to Frequency Losses study to compute the corresponding losses. Application Library path: ACDC_Module/Other_Industrial_Applications/ ecore_transformer Modeling Instructions From the File menu, choose New. NEW In the New window, click Model Wizard. MODE L WIZARD 1 In the Model Wizard window, click 3D.
Figure 8. The three-layered model of socio-technical change, as suggested by Xxx (2012: 161).................................................................................................................. 20 Figure 9: From niche dynamics to regime shift (adapted from Schot/Xxxxx 2008: 540) 21 Figure 10: Emerging service trajectory varried out by local projects (adapted from Schot/Xxxxx 2008: 544) 22 Figure 11: AN EU climate services market building policy implementation map (Pietrosanti 2016: 7) 23 Figure 12: Three patterns of an iot-based business ecosystem (Xxxx et al. 2015: 51) 37 Figure 13: The suite of interactional formats for the Helsinki workshop 57 Figure 14: The CTA workshop flow for the WP4 helsinki workshop 58 Figure 15: The workshop flow for graz (WP3) 59 Figure 16: The WP2 CTA stakeholder interaction 60 Figure 17: A preliminary set of the institutional framework within which climate services operate in austria (Damm et al. 2017) 68 Figure 18: A preliminary set of Themes for providing climate services in tourism (Damm et al. 2017) 69 Figure 19: The value proposition canvas 71 Figure 20: The graz workshop flow regarding business design/value proposition (WP3) 72