Figure 11. Example: Map EU, NUTS 0 Figure 12: Example: Map EU, NUTS 2 Figure 13: Example: Spain, NUTS 2 Figure 14: Example: Euregio Xxxx-Xxxxx
Figure 11. Bar chart indicating percent of Ae. albopictus females to lay eggs following ingestion of dietary PPF by treatment level (untreated controls treated with 10 mg/mL sugar solution with no PPF). Percent oviposition calculated from the proportion of surviving females that laid eggs; standard error calculated from the binomial distribution for a treatment group and its proportional oviposition. No statistically significant differences were observed between treatments and control (p > 0.05) Figure 12: Box plots indicating the distribution of fecundity (above) and fertility (below) of Ae. albopictus females following ingestion of dietary PPF by treatment level (untreated controls treated with 10 mg/mL sugar solution with no PPF). Statistically significant differences in fecundity were observed between 1 mg/mL and 5 mg/mL treatments and control (p < 0.001). No statistically significant differences in fertility were observed between treatments and control (p > 0.05).
Figure 11. Calculation of baseline values based on model results With the help of these factors the baseline values will be calculated directly within the modelling framework depending on the requirements of the MAtchUP KPI calculation framework (c.f. Deliverable 5.1).
Examples of Figure 11 in a sentence
Although Silicon Valley 26Calculations in Figure 11 use the “Log-Returns” line in Tables 13 and 14 and not the log- approximation.
Figure 1-1 shows a schematic of this idea, with examples of specific dimensions that influence quality care at each time point.
Figure 11 plots the Xxxxxx ratio of the crypto carry trade as a function of the bankruptcy likelihood, λ.
More Definitions of Figure 11
Figure 11. PLI aware virtual optical network composition Once the VON slice request is received FOAM then pushes the slice information to flowvisor which uses optical openflow extensions to create the slice. The reason for keeping the optical planner and verifier module outside flowvisor is that if in the future flowvisor virtualization is replaced by a new virtualization engine then we can still use the module.
Figure 11. Schematic representation of a hybrid fuzzy-mechanistic simulation model (Xxxxxx & Xxxxx, 2007). Bayesian belief networks (BBNs) BBNs are models with a network structure that focus on the explicit representation of ‘cause- and-effect’ relationships between variables, representing in this case ecosystem components. The network architecture is linked to probability distributions that allow it to deal with variability and uncertainty in the models. This is particularly useful for the description of ecological systems (Xxxxx, 2002). Bayesian belief networks are probabilistic expert systems in which the knowledge base has two components: (1) a network of causal relationships between variables and (2) a set of conditional probability matrices that relate each variable to its causal variables (Figure 12). Bayesian models are based on the concept that defines the formalism of updating a belief about a hypothesis (or a priori probability) in the light of new evidence (e.g. new data). The updated probability is called the posterior probability. A common application of Bayesian models is stock assessment especially in fish ecology and fish management (Xxxxx & Xxxxxxxx, 2003; Xxxxx et al, 2006). Bayesian models are considered to be a powerful tool to increase knowledge about aquatic ecosystems by the integrative analysis of probabilities of models and observation data (Lek et al., 2005).
Figure 11. The Impact of Degrees of Coupling in Different Degrees of Turbulence High tu Medium tu Low tu 0.9
Figure 11. Timescale steering tests performed using historical data. The performance is as expected from the results obtained with simulated data: fluctuations are smaller than 1 ns during one month in the case of high uptime (left), while they remain below 6 ns over 6 months in the case of low uptime/long gaps (right).
Figure 11. “Maribor in the past and today” The winning entry was the video “Maribor in the past and today”. The video was produced by Mihael Muršec (a second grade high school student ) with members of the school computer club from II High school, Maribor: Xxxx Xxxxxx, Patrik Rek, Nejc-Xxxxx Xxxxxxx. “I've been interested on multimedia since the primary school” says Mihael Muršec. The jury’s motivation Mihael Muršec’s video is a lively exploration of the town of Maribor then and now. There’s a nice mixture of modern images and videos, with the inclusion of screen captures of searches on Europeana showing how the archives were discovered. The video shows how the town has changed and also how some aspects of people’s lives continue from the past to today. The final sequence compares footage of modern sports/adventure activities to archive images of skiing and other sports about the town giving a strong impression of life in the town then and now.