On the analysis of power law distribution in software component sizes

2021 ◽  
Author(s):  
Shachi Sharma ◽  
Parag C. Pendharkar
Keyword(s):  
Power Law ◽  
Software Component ◽  
Power Law Distribution

scholarly journals The structure of co-publications multilayer network

2021 ◽  
Vol 8 (1) ◽  
Author(s):  
Ghislain Romaric Meleu ◽  
Paulin Yonta Melatagia
Keyword(s):  
Power Law ◽  
Degree Distribution ◽  
Preferential Attachment ◽  
Small World ◽  
Generation Model ◽  
Small World Network ◽  
Power Law Distribution ◽  
Multilayer Networks ◽  
Multilayer Network ◽  
Scientific Papers

AbstractUsing the headers of scientific papers, we have built multilayer networks of entities involved in research namely: authors, laboratories, and institutions. We have analyzed some properties of such networks built from data extracted from the HAL archives and found that the network at each layer is a small-world network with power law distribution. In order to simulate such co-publication network, we propose a multilayer network generation model based on the formation of cliques at each layer and the affiliation of each new node to the higher layers. The clique is built from new and existing nodes selected using preferential attachment. We also show that, the degree distribution of generated layers follows a power law. From the simulations of our model, we show that the generated multilayer networks reproduce the studied properties of co-publication networks.

scholarly journals Power-law behavior of transcription factor dynamics at the single-molecule level implies a continuum affinity model

2021 ◽  
Author(s):  
David A Garcia ◽  
Gregory Fettweis ◽  
Diego M Presman ◽  
Ville Paakinaho ◽  
Christopher Jarzynski ◽  
...  
Keyword(s):  
Transcription Factor ◽  
Single Molecule ◽  
Power Law ◽  
Dwell Time ◽  
Specific Binding ◽  
Power Law Distribution ◽  
Single Molecule Level ◽  
Binding Behavior ◽  
Chromatin Template ◽  
Nuclear Domains

Abstract Single-molecule tracking (SMT) allows the study of transcription factor (TF) dynamics in the nucleus, giving important information regarding the diffusion and binding behavior of these proteins in the nuclear environment. Dwell time distributions obtained by SMT for most TFs appear to follow bi-exponential behavior. This has been ascribed to two discrete populations of TFs—one non-specifically bound to chromatin and another specifically bound to target sites, as implied by decades of biochemical studies. However, emerging studies suggest alternate models for dwell-time distributions, indicating the existence of more than two populations of TFs (multi-exponential distribution), or even the absence of discrete states altogether (power-law distribution). Here, we present an analytical pipeline to evaluate which model best explains SMT data. We find that a broad spectrum of TFs (including glucocorticoid receptor, oestrogen receptor, FOXA1, CTCF) follow a power-law distribution of dwell-times, blurring the temporal line between non-specific and specific binding, suggesting that productive binding may involve longer binding events than previously believed. From these observations, we propose a continuum of affinities model to explain TF dynamics, that is consistent with complex interactions of TFs with multiple nuclear domains as well as binding and searching on the chromatin template.

scholarly journals Explaining the power-law distribution of human mobility through transportationmodality decomposition

2015 ◽  
Vol 5 (1) ◽  
Author(s):  
Kai Zhao ◽  
Mirco Musolesi ◽  
Pan Hui ◽  
Weixiong Rao ◽  
Sasu Tarkoma
Keyword(s):  
Power Law ◽  
Human Mobility ◽  
Power Law Distribution

MASS FUNCTION OF HALOS: A NEW ANALYTICAL APPROACH

2004 ◽  
Vol 13 (07) ◽  
pp. 1345-1349 ◽  
Author(s):  
JOSÉ A. S. LIMA ◽  
LUCIO MARASSI
Keyword(s):  
Power Law ◽  
Free Parameter ◽  
Analytical Approach ◽  
Mass Function ◽  
New Method ◽  
Power Law Distribution ◽  
The Press ◽  
The Universe ◽  
Special Case

A generalization of the Press–Schechter (PS) formalism yielding the mass function of bound structures in the Universe is given. The extended formula is based on a power law distribution which encompasses the Gaussian PS formula as a special case. The new method keeps the original analytical simplicity of the PS approach and also solves naturally its main difficult (the missing factor 2) for a given value of the free parameter.

scholarly journals Assessing the power law distribution of TGFs

2011 ◽  
Vol 116 (A10) ◽  
pp. n/a-n/a ◽  
Author(s):  
Andrew B. Collier ◽  
Thomas Gjesteland ◽  
Nikolai Østgaard
Keyword(s):  
Power Law ◽  
Power Law Distribution

Network model of deviation from power-law distribution in complex network

2010 ◽  
Vol 79 (1) ◽  
pp. 29-33 ◽  
Author(s):  
J. Jiang ◽  
R. Wang ◽  
Q. A. Wang
Keyword(s):  
Complex Network ◽  
Network Model ◽  
Power Law ◽  
Power Law Distribution

scholarly journals Predicting observational signatures of coronal heating by Alfvén waves and nanoflares

2007 ◽  
Vol 3 (S247) ◽  
pp. 279-287
Author(s):  
Patrick Antolin ◽  
Kazunari Shibata ◽  
Takahiro Kudoh ◽  
Daiko Shiota ◽  
David Brooks
Keyword(s):  
Power Law ◽  
Alfven Waves ◽  
Alfvén Waves ◽  
Power Law Distribution ◽  
Longitudinal Modes ◽  
Heating Mechanism ◽  
Set Up ◽  
Power Law Index ◽  
Power Law Distributions ◽  
Release Processes

AbstractAlfvén waves can dissipate their energy by means of nonlinear mechanisms, and constitute good candidates to heat and maintain the solar corona to the observed few million degrees. Another appealing candidate is the nanoflare-reconnection heating, in which energy is released through many small magnetic reconnection events. Distinguishing the observational features of each mechanism is an extremely difficult task. On the other hand, observations have shown that energy release processes in the corona follow a power law distribution in frequency whose index may tell us whether small heating events contribute substantially to the heating or not. In this work we show a link between the power law index and the operating heating mechanism in a loop. We set up two coronal loop models: in the first model Alfvén waves created by footpoint shuffling nonlinearly convert to longitudinal modes which dissipate their energy through shocks; in the second model numerous heating events with nanoflare-like energies are input randomly along the loop, either distributed uniformly or concentrated at the footpoints. Both models are based on a 1.5-D MHD code. The obtained coronae differ in many aspects, for instance, in the simulated intensity profile that Hinode/XRT would observe. The intensity histograms display power law distributions whose indexes differ considerably. This number is found to be related to the distribution of the shocks along the loop. We thus test the observational signatures of the power law index as a diagnostic tool for the above heating mechanisms and the influence of the location of nanoflares.

TRACER TIME DISTRIBUTION OF PARTICLES IN POROUS MEDIA

Fractals ◽  
1993 ◽  
Vol 01 (04) ◽  
pp. 1075-1079
Author(s):  
MARIELA ARAUJO
Keyword(s):  
Porous Medium ◽  
Transit Time ◽  
Power Law ◽  
Time Distribution ◽  
Tracer Particle ◽  
Power Law Distribution ◽  
Low Velocity ◽  
Constant Flow Rate ◽  
Variable Permeability ◽  
Tracer Particles

We study the transit time distributions of tracer particles in a porous medium through which a constant flow rate is established. Our model assumes that non-Gaussian dispersion is due to the presence of low velocity zones or channels in parallel with a faster flow path. Each channel is represented as a trap and simulates the existence of variable permeability blocks inside the porous medium. The time the tracer particle spends inside each channel is related to the heterogeneity of the sample, and is assumed here to have a power-law distribution. We compare the transit time distribution of these particles for the case in which the traps are Poisson distributed with the one in which the trap distribution is a power-law function.

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