Comment on "Nonlinear charge--voltage relationship in constant phase element" [AEU-Int. J. Electron. Commun. 117, 153104 (2020)]

In this comment, we show a dimensional inconsistency that plagues one of the main founding equations, Eq. (5), of the manuscript, [Fouda et al., AEU-Int. J. Electron. Commun. 117, 153104 (2020)]. Also, a resolution of the inconsistency as well as a generalized yet a better version of the equation are suggested.Comment: 3 page

Hidden jerk in universal creep and aftershocks

Most materials exhibit creep-memory under the action of a constant load. The memory behavior is governed by Andrade's power-law of creep. The creep law has an inherent connection with the Omori-Utsu law that describes the frequency of earthquake aftershocks. Both the laws are empirical and they lack a deterministic interpretation. Interestingly, the creep response of a fractional dashpot in anomalous viscoelastic modeling is given by Andrade's law. Consequently, fractional derivatives are invoked but they are plagued by curve-fits. Here we establish an analogous physical mechanism that underlies both the laws. In this Letter, we relate the parameters of the two laws with the macroscopic properties of the material. Surprisingly, the derivation necessitates the existence of a rheological property that relates strain with the first-order time-derivative of stress. The obtained results are validated in light of the established observations.Comment: 12 pages, 1 figur

REMI: Constraint-based method for integrating relative expression and relative metabolite levels into a thermodynamically consistent metabolic model

Flux balance analysis (FBA) allows steady-state flux predictions using optimization principles and often does not result in a unique steady-state flux distribution. Therefore, integration of omics data, such transcriptomics, metabolomics has been employed as additional constraints to reduce the solution space of feasible flux phenotypes. Here, we present a computational method, termed REMI, which integrates relative expression along with relative metabolomics into genome-scale metabolic models (GEMs) to estimate the differential fluxes at GS level. First, we integrated relative expression data into an E.coli GEM using our approach and an existing GX-FBA method (Navid & Almaas, 2012; Orth et al, 2011). The results of our method are more robust and in better agreement with experiments as compared to GX-FBA, because our method facilitates alternative solution enumeration. High frequency solutions analysis between the alternatives may guide in understanding of a biological system physiology. Furthermore, to further reduce the flux space and obtain predictions closer to actual physiological state first we add thermodynamic constraints into models and then employed relative expression as well as relative metabolomics as additional constraints (Henry et al, 2007). The constraint model, resulted in reduced feasible flux space as one can expect, and predicts flux distributions that were in better agreement with experiments. References Henry CS, Broadbelt LJ, Hatzimanikatis V (2007) Thermodynamics-Based Metabolic Flux Analysis. Biophysical Journal 92: 1792-1805 Navid A, Almaas E (2012) Genome-level transcription data of Yersinia pestis analyzed with a New metabolic constraint-based approach. BMC Systems Biology 6: 150 Orth JD, Conrad TM, Na J, Lerman JA, Nam H, Feist AM, Palsson BO (2011) A comprehensive genome-scale reconstruction of Escherichia coli metabolism-2011. Molecular Systems Biology 7: