Simulations. The simulated proporuon of subjects with peak-and trough levels within the target range are displayed in Figure 6. Applying tdm using saliva led to a higher percentage of sub- jects reaching target aчainment compared to no tdm (>75% vs 48%, respecuvely). How- ever, saliva tdm led to a lower percentage of target aчainment compared to blood tdm. Obtaining more than four samples for saliva tdm did not result in increased tdm per- formance. On the contrary, obtaining addiuonal samples at 18h and 1h pre-dose led to a slightly decreased performance (-3% and -4%, respecuvely) compared to the strategy using four samples.
Simulations. We provide simulations to support the presented theory. ≤ e−(λmin(Bi+1 T Bi+1)−ǁBT Bi+1ǁδl)∆t Vi+1 (ti,i+1) ≤ e−(λmin(Bi+1 ·Wc (ti,i+1) T ≤ e−(λmin(Bi+1 √ Bi+1)−ǁBT Bi+1)−ǁBT Bi+1ǁδl)∆t Bi+1ǁδl)∆t i+1 N(N 1) The first simulation involves four agents navigating under quantized communication and under a static tree structure. In fact, the communication sets of the four agents are chosen as N1 = {2}, N2 = {1, 3}, N3 = {2, 4}, N4 = {3}, · N (N −1) Vi,i+1 (ti,i+1) T so that the corresponding graph is a line graph. We can (λ (BT B )−ǁB B ǁδ )∆ N(N−1) λmin(BT B) · ǁBe ≤ e− min √ ti+1 compute ǁBT Bǁ
Simulations. Providing formative and summative evaluations.
Simulations. Four facilities, all motion-based generic simulators with six degree of freedom, were involved in ARISTOTEL (see Figure 2): two for fixed wing research - FS-102 simulator at TsaGI and GRACE (Generic Research Aircraft Cockpit Environment) at NLR – and two for rotorcraft research - XXXXXX (Simulation Motion and Navigation Technologies) Research Simulator at Delft University and HELIFLIGHT-R at The Bibby flight simulation facility at the University of Liverpool. First simulator tests were performed in March 2012 and the results are under analysis. The use of multiple simulation facilities brings with it a number of advantages: • The occurrence of A/RPCs is greatly dependent on the evaluation pilot, his or her training and instructions and the evaluation task the pilot is asked to perform. Simulators can be used to explore different approaches and assess their effectiveness in predicting A/RPC events. • A simulator‟s level of fidelity influences its ability to reliably predict A/RPC occurrences. Accuracy of the mathematical model, realism of the control feel system, quality of visual and vestibular cues all play a role in shaping the pilot‟s behaviour. A systematic study to identify the relative importance of these aspects, as well as the development of guidelines for adjusting a simulator‟s characteristics, can help A/RPC researchers focus their efforts on tuning their simulator in ways that maximize the accuracy of RPC predictions. The project involves also biodynamic tests. Trials have taken place in February and July 2011 (XXXXXX and HELIFLIGHT-R) and April 2011 (FS-102). The goal of these biodynamic tests is to understand what particular helicopter vibrations induce adverse biodynamic couplings (BDC) effects and what mission tasks are more prone to such effects. For helicopters, the results revealed some important conclusions, for example: • BDC depends on the control tasks: for the different control tasks (i.e., different neuromuscular settings), a different level of BDC was measured; • BDC depends also on the control (disturbance) axis: the highest level of BDC is measured in sway direction, followed by the surge direction. The least amount of BDC is measured in the heave direction. This demonstrates that the biodynamic couplings (coming only from neuromuscular adaptation in this experiment) depend not only on more obvious features such as pilot weight and posture (which can vary from pilot to pilot) but also on more elusive factors such as pilot worklo...
Simulations. CUSTOMER shall perform the pre-layout simulation and post-layout simulation and release to HSA for delivery to FOUNDRY, a GDSII formatted data base tape conforming to FOUNDRY process design rules and which is subject to acceptance by FOUNDRY. FOUNDRY shall perform design rule checks (“DRC”) on the CUSTOMER database. Should the CUSTOMER database have design rule (DRC) errors, those errors shall be reported in writing and the data base tape shall be returned to CUSTOMER for correction. Upon completion of an error free design rules check, written authorization of the CUSTOMER and the written acceptance of HSA and FOUNDRY, FOUNDRY shall release the data base for fabrication of Prototypes.
Simulations. Information established from another vessel of similar power, rudder, propeller, and hull form, or
Simulations simus movement simulations, each individual was assigned a random home range center within 500 m (A = 1 km) of a linear road bisecting a uniform landscape. This landscape size was selected to ensure that the model had a high likelihood of simulating all snakes with a chance to cross the road; using A = 1 km, snakes had a less than 0.005% chance of crossing a road from that distance if the snake moved directly toward the road. Each time step was considered one day, and each simulation was run for 31 days. We calculated the proportion of snakes that crossed the road on the 31st time step of the simulation to estimate daily road crossing probability, and then divided by 8 h (assuming all activity occurs between 9 am and 5 pm) to calculate hourly individual road crossing probability (ρ). We simulated the movement of snakes under different movement scenarios. For each replicate simulation, we specified the following movement parameters: mean vector length (parameter defining turning angle distribution), strength of bias in response to road or home range center, and mean step size. Mean step size was a measure of the net distance a snake moved per day on average; this was parameterized using only daily relocations from the radiotelemetry data. The radiotelemetric data in our case study included limited numbers of road crossings, and thus we were unable to precisely parameterize the road bias component of our model. We therefore simulated a range of possible values for road bias, including both road avoidance and road attraction, and explored the sensitivity of our model output to assumptions about road behavior. The road bias parameter as defined in our model ranged from ‐1 to 1. A road bias value of 0 indicated that the snake biased its movement toward the home range center and displayed no behavioral response to the road. We considered this scenario our ‘null’ road bias scenario. A road bias value of 0.1 indicated that the snake biased its movement 10% toward the road and 90% toward the home range center. Similarly, a road bias value of ‐0.1 indicated that the snake biased its movement 10% away from the road and 90% toward the home range center (Examples of movement paths: Fig 3). The mean vector length was a measure of the straightness of a snake’s movement path – a mean vector length of 0 indicates a fully random walk and a mean vector length of 1 indicates a completely straight movement path (100% probability of turning 0 degrees). We explored the sensitivi...
Simulations. Xxxxxxx and Szamel [33] observed fundamental differences in 2D and 3D simulations approaching the glass transition, that we have explained through our experiments. In this section we show that the analysis used on our experiments reveals the same qualitative results in simulations.
Simulations. We did 2D simulations in Xxxxxxx-Xxxxx systems, similar to Sec.
Simulations. The objectives of the following simulations are multi-fold. First, we evaluate the performance of the traditional ZIP model, both in the presence and absence of false zeros. Second, we evaluate the performance of the newly proposed hierarchical ZIP models in the presence of false zeros. We evaluate these two objectives considering both a mild and extreme relationship between reporting occurrence and YTRUE. In the mild (or weak) relationship between YTRUE and Pr(reporting), fewer cases are less likely to be reported and more likely to be false zeros in YOBS, the observed outcome. In the extreme (or strong) relationship between YTRUE and Pr(reporting), fewer cases are much less likely to be reported and much more likely to be false zeros in YOBS. For all simulations, we focus on the ability of the models to accurately estimate β0CASES and β1CASES, which are parameters defining the mean case intensity. For the traditional ZIP model, we qualitatively examine the ability of the model to distinguish between true distributional zeros and true excess zeros. For the new hierarchical ZIP models, we quantitatively and qualitatively examine the ability of the model to distinguish between excess true zeros and excess false zeros.
