It is clear that any G-metric space where G derives from an underlying metric via G s or G m in Example 1.1 is symmetric.
We note that for the graph Γ ( S M 6 ) in Example 1, MWB ( Γ ( S M 6 ) ) = 5.
The reference rectangle ABCD now is as shown in Fig. 10 a with a priory known the information: (A D //(B C),(A B //(C D) and the width of the lane (3 m in this example).
Since b is no longer constant, we use b w, a weighted average of the risk differences b i in the groups with weights depending on the within-group exposure variances (b w is also obtained by regressing the individual-level data while adjusting for group; see appendix 3): (b e - b w ) = (b c - b w ) M In the example, b w is approximately 0.296 (appendix 6).
The nonlinear case ( m ≠ 1 ) is discussed in Example 3.4 on p.181, and the linear case ( m = 1 ) in Examples 4.2-4.3 4.2-4.325-227.
We show the figure of M-NSC in Example 1 in Figure 1.
Combining Propositions 1 and 2, we can get the first main theorem which shows us the equivalence relationship between (l_{2,0} -minimization and (l_{2,0} -minimization. Figure 1 M-NSC in Exandl_{2,p} -minimization
To compare the impact of the TRL changes on the efficiencies of the algorithms, we have plotted the energy consumption of network versus the number of sources for the TRL values of 200, 400, and 800 m in the case of, for example, ψ(π/6,800 m,64,8) for both the SPCPM and MPCP algorithms.
We focused on the second peak, around 20 ms in the example in Fig. 3.
Thus, the minimum DL after an infinite number of looks is bounded by 2.2 ms, and the maximum SI is bounded by 9.5 ms in this example.
