To get more physical insight into the model, we express scale factor in three useful forms [17, 18, 19, 20, 21, 22] (emergent, logamediate, and intermediate scenarios) under which the universe expands differently.
Similar as the energy arrival model, we express the state transition probability matrix between E t and (tilde {E}_{t}) as a policy matrix P with elements p i,j ∈{0,1},∀i,j denoting the event of using (i−j) unites of energy in state E t =i.
To establish the age depth model, we expressed the depth as MTD-corrected depth.
In this model, we expressed regret as a fraction of the loss of potential utilities [ 24].
In order to incorporate uncertainty in the model, we expressed these probabilities as beta distributions [ 29, 30].
To test this model, we expressed the wild-type pak-1 A isoform cDNA behind the lag-2 promoter, which drives expression in DTCs but not surrounding cells (Blelloch et al. 1999).
To further test this model, we ectopically expressed Esg in differentiated ECs using the EC-specific, temperature-inducible driver MyoIA-Gal4, UAS-GFP, tub-Gal80 ts (MyoIA ts ).
To generate a humanized CML model, we retrovirally expressed BCR-ABL1 in the cord blood CD34+ cells and transplanted these into NOD-SCID (non-obese diabetic/severe-combined immunodeficient) interleukin-2-receptor γ-deficient mice.
Firstly, we express the model's surface by the wavelet decomposition expressions and obtain a reconstructed surface and then validate its accuracy.
In the physical models, we can express the physical propagation from transmit array and receive array by using the double-directional impulse response, h t,τ,θ t,ϕ t,θ r,ϕ r ), where t is the observed time, τ is the delay, and (θ t,ϕ t ) and (θ r,ϕ r ) are the (elevation,azimuth) angles of departure and arrival, respectively [20].
In Section 3 we express the model problem as an inequality and Laplacian constrained variational optimization problem.
