VM i is characterized by a three-parameter tuple: text{VM}_{i}= text{name},t_{c},p_{l}) (1).
Each subMDP i is characterized by its own state, action set, transition probability, and reward functions and is denoted by (text {subMDP}_{i}={mathcal {S}_{i}, mathcal {A}_{i}, mathcal {T}_{i}, mathcal {R}_{i}}).
Here, each subMDP i is characterized by its own state, action set, transition probability, and reward functions and is denoted by (text {subMDP}_{i}={mathcal {S}_{i},mathcal {A}_{i}, mathcal {T}_{i},mathcal {R}_{i}}).
Each field F i is characterized by its duration T i, its constant, or variable nature State of its transported data Payload: F i = { T i, State, Payload }. (2).
Each node i is characterized by its interest in class m at time t, denoted as (R_i^m(t)), where (sum _{forall m} R_i^m(t)le 1, ~forall i) (e.g., normalization over all classes).
It predicts that the citation history of paper i is characterized by three fundamental parameters: the relative fitness,, capturing a paper's importance relative to other papers; μi; and σi.
Each traffic flow i is characterized by a packet arrival rate, token generation rate, token pool size, and a counter to keep track of the number of tokens borrowed from or given to the token bank.
Each species i is characterized by a set of reproduction parameters, cik, each of which defines the potential local reproductive rate of species i in community k, and a set of mortality rates, mik.
Each Ag A i is characterized by its effective abundance a i, which is a normalized quantity accounting for the probability that A i is presented by APCs.
Each individual i was characterized by its genotype g i (i.e. the allelic combination carried at each of the four loci of the network) and by its phenotype P i.
Each individual, or swarm particle, x i is characterized by its position in the problem space and its current travel velocity v i (t) that allows it to move in the problem space.
