Observing the Figures 1, 2, 3 and 4 shown as example, parameters such as CO, Benzene and Toluene show different trend between night and daily values, whit daily mean values bigger than night ones.
The estimates of, for example, parameters such as population structure, migration rates and effective size can be quite accurately estimated with the use of the old tools (e.g. a multilocus microsatellite study).
For example, gait parameters such as walking speed, cadence, step length and plantar pressure have been measured for fall [2], diabetic foot [3], and osteoarthritis [4].
For example, GA contains parameters, such as mutation and crossover probabilities.
Wireless sensor networks (WSNs) consist of a number of sensors deployed in a field to collect information, for example, measuring physical parameters such as temperature and humidity.
Secrecy of various broadcast data and control messages is important in such a scenario: for example, the sensory parameters (such as sampling periods and thresholds) that would from time to time be updated using broadcast mechanisms need to be kept secret, and software upgrades need to be kept confidential to prevent exploitation of code weaknesses.
For example, changes in parameters such as sucrose intake and preference, and total liquid intake during different phases of the course of stress exposure were observed over a several years in our lab, under identical experimental conditions [ 39, 47].
The accelerating effect of fiber-nucleated spherulites on the overall crystallization process is demonstrated with simple examples in which parameters such as the fiber volume fraction, the fiber radius, the nucleation density and an induction time are changed.
For example, measurements of certain parameters, such as size, are dependent on the instrumentation and the sample preparation method; this information needs to be captured in the form of metadata to fully interpret such measurements.
Similarly, with further process optimization of the sugar extraction/recovery section, for example, by optimizing operating parameters such as acid/base type and loading, temperature, and pH, the effective acid/base contribution can be significantly reduced (see, for example, [ 20, 21]).
The rules are characterized by various parameters, such as examples (i.e., number of objects covering a given rule), strength (i.e., the proportion of objects covered by premise that are also covered by conclusion), or confirmation (i.e., measure that is quantifying the degree to which premise provides evidence for conclusion).
