Figure 11 definition

Figure 11. Schematic representation of a hybrid fuzzy-mechanistic simulation model (Xxxxxx & Xxxxx, 2007). 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

Figure 11. Example: Map EU, NUTS 0 Figure 12: Example: Map EU, NUTS 2

Examples of Figure 11 in a sentence

• We find that both the control and 70-μm sample lie where expected for star-forming galaxies with EW(Hδ) > 1 and Dn4000 > 1; however, their spread over the Dn4000-Hδ plane is sub- stantially different: the control sample spans a large part of the plane, whereas the 70-μm sources have low Dn4000 (<1.3) and most have high EW (Hδ) (>5) indicative of young stellar populations Figure 11.

• The horizontal axis in the Resources Classification (Figure 1.1) defines the range of uncertainty in estimates of the quantities of recoverable, or potentially recoverable, petroleum associated with a project.

• Figure 1-1 outlines the proposed schedule to be followed with respect to the D.C. by-law adoption process.

• Figure 1-1 outlines the proposed schedule to be followed with respect to the development charge by-law adoption process.

• The LAR institutional arrangement for the Project is shown in Figure 1-1 below.

• Moreover, once the ionization equilibrium has been reached, there is an analytical approximation for the ratio between the diffuse and === =Figure 11.

• Figure 11 Distribution Diagram of Total Velocity Field at 100% Load with Baffle AddedAs it is can be seen from FIG.

• In many cases, a combination of approaches is used.Use of consistent terminology (Figure 1.1) promotes clarity in communication of evaluation results.

• Figure 1-1 provides an overview of the Order Audit Trail System reporting process.

• Figure 1-1 helps depict the geological aspects that are within Imperial County and outside of Imperial County that affect air quality.

More Definitions of Figure 11

Figure 11. Calculation of baseline values based on model results

Figure 11. PLI aware virtual optical network composition

Figure 11. “Maribor in the past and today” The jury’s motivation

Figure 11. The figures report three different kinds of information. In abscissa the (anonymised) name of the Areti substations and, in the ordinates, the rain value taken by different rain gauges spread over the city territory. The rain datum is the day integral of rain (in mm). Colour bars represent CIPCast alerts (xxxxx bars) and Areti reported faults (green bars). Close substations are thus located in nearby loca- tions, as they are compared to a given rain abundance (i.e. close to the same rain gauge).

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. 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).