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Collection and Extraction of Occupational Air Samples for Analysis of Fungal DNA

Collection and Extraction of Occupational Air Samples for Analysis of Fungal DNA

Published on: May 2, 2018

Percolation with Distance-Dependent Site Occupational Probabilities.

Eleftherios Lambrou¹, Panos Argyrakis¹

¹Department of Physics and Complexity Center, University of Thessaloniki, GR-54124 Thessaloniki, Greece.

Entropy (Basel, Switzerland)

|January 28, 2026

Summary

This summary is machine-generated.

This study introduces an inverse percolation model where site removal probability depends on distance from the center. This approach models tumor microenvironment effects, significantly reducing the percolation threshold and impacting cluster dynamics.

Keywords:

Monte-Carlo simulations distance-dependent occupational probability hypoxia heterogeneity inverse percolation oncology radiation modeling tumor radiotherapy

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Area of Science:

Physics
Computational Biology
Materials Science

Background:

Standard percolation models assume uniform site occupancy.
Tumor microenvironments exhibit non-uniform conditions, affecting therapeutic efficacy.
Tumor-induced depletion of key elements like oxygen and calcium impacts cancer therapies.

Purpose of the Study:

To develop a novel inverse percolation model with distance-dependent site removal.
To simulate the impact of a central 'tumor' on system properties.
To investigate how varying peripheral removal probabilities affect percolation thresholds and cluster formation.

Main Methods:

Implementation of an inverse percolation model with a distance-dependent removal probability, q(d).
Modeling the central point as a tumor, causing higher removal probability proximally.
Analyzing system properties, including the critical percolation threshold (pc), as a function of the boundary removal probability (qp).

Main Results:

A significant decrease in the critical percolation threshold (pc) was observed as qp decreased.
The pc dropped from the standard 0.5927 to approximately 0.20.
The size of the spanning cluster and the total number of clusters showed a strong dependence on qp.

Conclusions:

The developed inverse percolation model effectively captures the non-uniform conditions of a tumor microenvironment.
Distance-dependent site removal significantly alters percolation dynamics, lowering the critical threshold.
This model provides insights into how localized environmental changes impact system-wide properties, relevant for cancer therapy optimization.