In this paper, we introduce the novel parameters indicating Normalized Tenacity ($T_N$) and Normalized Toughness ($t_N$) by a modification on existing Tenacity and Toughness parameters.  Using these new parameters enables the graphs with different orders be comparable with each other regarding their vulnerabilities. These parameters are reviewed and discussed for some special graphs as well.

Diabases, pillow lavas and basaltic flows in the Nain-Dehshir ophiolites are marked with flat to slightly depleted pattern in REE chondrite-normalized diagram and are characterized by depletion in HFSE and enrichment in LILE. This geochemical behavior can be considered for lavas erupted in arc-related environments. Clinopyroxene in these rocks shows low content of TiO2 and resemble those found ...

Network topologies are considered to be the fundamental factors that govern network’s macroscopic diagram (MFD) and performance. Despite its significance, this domain remains underexplored. This study employed empirical data as evidence examine explicit connections between network parameters of a specific MFD, in form Underwood’s model. Sixty-three 1 × km dense urban networks were sampled Hong ...

In receptor-mediated endocytosis, cells exercise biochemical control over the mechanics of adhesion to engulf foreign particles, featuring a variable adhesion strength. Here we present a thermodynamic model with which we elucidate that the variable adhesion strength critically governs the cellular uptake, yielding an uptake phase diagram in the space of ligand density and particle size. We iden...

We present a normalized LMS (NLMS) algorithm with robust regularization. Unlike conventional NLMS with the fixed regularization parameter, the proposed approach dynamically updates the regularization parameter. By exploiting a gradient descent direction, we derive a computationally efficient and robust update scheme for the regularization parameter. In simulation, we demonstrate the proposed al...

Since both the least mean-square (LMS) and least mean-fourth (LMF) algorithms suffer individually from the problem of eigenvalue spread, so will the mixed-norm LMS-LMF algorithm. Therefore, to overcome this problem for the mixed-norm LMS-LMF, we are adopting here the same technique of normalization (normalizing with the power of the input) that was successfully used with the LMS and LMF separat...