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The model is formulated in the frequency domain and leads to relatively simple closed-form solutions for the variables which describe the dynamic interaction.
These models are based on a number of predictor variables which describe habitat variation measured at several farms in Oxfordshire, UK.
We generated more than 78 flow variables, which describe the frequency, magnitude, duration, rate of change, and timing of flow events.
However, on the other hand, displacement variables, which describe the kinematics of the shell structures, are defined using Equivalent Single Layer (ESL), Zig-Zag (ZZ) and LW shell theories.
First, from the large number of flame image data, principal component analysis is used to discover the principal components or combinational variables, which describe the important trends and variations in the operation data.
They include gender, age (in categories), socio-professional status, holding of a driving license, and a combination of variables which describe the role of the individual (head, spouse or child) and the household structure (single or couple; with child under 12 or not; zero, one or two workers).
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For each sample, we maintain a variable which describes the associated effect of the assigned intervention.
We model the uncertain demand as a hybrid variable which describes the quantities with fuzziness and randomness.
where D n (t) is a random variable which describes the number of packets that are successfully received by node n during [0,t].
Let X be a distributed ℳ O random variable which describes the lifetime relative to each individual in the population with a vector of p-covariates z= z1,…,z p )⊤, where ⊤ denotes the transposition operator.
where w(t) and k(t) are zero-mean Gaussian distributed processes, b t) is a {0,1}-random variable which describes the state of the channel and t is the time index.
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com