Reliability analysis incorporating exponentiated inverse Weibull distribution and inverse power law

2021 ◽  
Author(s):  
Tabassum Naz Sindhu ◽  
Abdon Atangana
Keyword(s):  
Weibull Distribution ◽  
Reliability Analysis ◽  
Power Law ◽  
Inverse Power ◽  
Inverse Power Law ◽  
Inverse Weibull Distribution

Reliability analysis using exponentiated Weibull distribution and inverse power law

2019 ◽  
Vol 35 (4) ◽  
pp. 1219-1230 ◽  
Author(s):  
Luis Carlos Méndez‐González ◽  
Luis Alberto Rodríguez‐Picón ◽  
Delia Julieta Valles‐Rosales ◽  
Alejandro Alvarado Iniesta ◽  
Abel Eduardo Quezada Carreón
Keyword(s):  
Weibull Distribution ◽  
Reliability Analysis ◽  
Power Law ◽  
Inverse Power ◽  
Inverse Power Law ◽  
Exponentiated Weibull Distribution ◽  
Exponentiated Weibull

Inverse Power Law Behavior in a Memory Scanning Experiment

2006 ◽  
Author(s):  
Gerardo Ramirez ◽  
Sonia Perez ◽  
John G. Holden
Keyword(s):  
Power Law ◽  
Inverse Power ◽  
Memory Scanning ◽  
Inverse Power Law

scholarly journals Inverse-power-law behavior of cellular motility reveals stromal–epithelial cell interactions in 3D co-culture by OCT fluctuation spectroscopy

Optica ◽  
2015 ◽  
Vol 2 (10) ◽  
pp. 877 ◽  
Author(s):  
Amy L. Oldenburg ◽  
Xiao Yu ◽  
Thomas Gilliss ◽  
Oluwafemi Alabi ◽  
Russell M. Taylor ◽  
...  
Keyword(s):  
Epithelial Cell ◽  
Power Law ◽  
Cell Interactions ◽  
Inverse Power ◽  
Cellular Motility ◽  
Inverse Power Law

Anomalies in Strongly Coupled Harmonic Quantum Brownian Motion

2013 ◽  
Vol 20 (01) ◽  
pp. 1350002 ◽  
Author(s):  
F. Giraldi ◽  
F. Petruccione
Keyword(s):  
Time Evolution ◽  
Power Law ◽  
Weak Coupling ◽  
Inverse Power ◽  
Strong Coupling Regime ◽  
Weak Coupling Regime ◽  
Spectral Densities ◽  
Inverse Power Law ◽  
Critical Configurations ◽  
Long Time

The exact dynamics of a quantum damped harmonic oscillator coupled to a reservoir of boson modes has been formally described in terms of the coupling function, both in weak and strong coupling regime. In this scenario, we provide a further description of the exact dynamics through integral transforms. We focus on a special class of spectral densities, sub-ohmic at low frequencies, and including integrable divergencies referred to as photonic band gaps. The Drude form of the spectral densities is recovered as upper limit. Starting from special distributions of coherent states as external reservoir, the exact time evolution, described through Fox H-functions, shows long time inverse power law decays, departing from the exponential-like relaxations obtained for the Drude model. Different from the weak coupling regime, in the sub-ohmic condition, undamped oscillations plus inverse power law relaxations appear in the long time evolution of the observables position and momentum. Under the same condition, the number of excitations shows trapping of the population of the excited levels and oscillations enveloped in inverse power law relaxations. Similarly to the weak coupling regime, critical configurations give arbitrarily slow relaxations useful for the control of the dynamics. If compared to the value obtained in weak coupling condition, for strong couplings the critical frequency is enhanced by a factor 4.

scholarly journals Modeling Epidemics in Seed Systems and Landscapes To Guide Management Strategies: The Case of Sweet Potato in Northern Uganda

2019 ◽  
Vol 109 (9) ◽  
pp. 1519-1532 ◽  
Author(s):  
K. F. Andersen ◽  
C. E. Buddenhagen ◽  
P. Rachkara ◽  
R. Gibson ◽  
S. Kalule ◽  
...  
Keyword(s):  
Sweet Potato ◽  
Power Law ◽  
Inverse Power ◽  
Centrality Measures ◽  
Northern Uganda ◽  
Seed Systems ◽  
Negative Exponential Function ◽  
Improved Varieties ◽  
Inverse Power Law ◽  
Negative Exponential

Seed systems are critical for deployment of improved varieties but also can serve as major conduits for the spread of seedborne pathogens. As in many other epidemic systems, epidemic risk in seed systems often depends on the structure of networks of trade, social interactions, and landscape connectivity. In a case study, we evaluated the structure of an informal sweet potato seed system in the Gulu region of northern Uganda for its vulnerability to the spread of emerging epidemics and its utility for disseminating improved varieties. Seed transaction data were collected by surveying vine sellers weekly during the 2014 growing season. We combined data from these observed seed transactions with estimated dispersal risk based on village-to-village proximity to create a multilayer network or “supranetwork.” Both the inverse power law function and negative exponential function, common models for dispersal kernels, were evaluated in a sensitivity analysis/uncertainty quantification across a range of parameters chosen to represent spread based on proximity in the landscape. In a set of simulation experiments, we modeled the introduction of a novel pathogen and evaluated the influence of spread parameters on the selection of villages for surveillance and management. We found that the starting position in the network was critical for epidemic progress and final epidemic outcomes, largely driven by node out-degree. The efficacy of node centrality measures was evaluated for utility in identifying villages in the network to manage and limit disease spread. Node degree often performed as well as other, more complicated centrality measures for the networks where village-to-village spread was modeled by the inverse power law, whereas betweenness centrality was often more effective for negative exponential dispersal. This analysis framework can be applied to provide recommendations for a wide variety of seed systems.[Formula: see text] Copyright © 2019 The Author(s). This is an open access article distributed under the CC BY 4.0 International license .

Inverse-Power-Law Light Scattering in Fused-Silica Optical Fiber

1988 ◽  
Vol 61 (12) ◽  
pp. 1388-1391 ◽  
Author(s):  
S. H. Perlmutter ◽  
M. D. Levenson ◽  
R. M. Shelby ◽  
M. B. Weissman
Keyword(s):  
Light Scattering ◽  
Optical Fiber ◽  
Power Law ◽  
Fused Silica ◽  
Inverse Power ◽  
Inverse Power Law ◽  
Silica Optical Fiber
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