Pattern formation | EPFL Graph Search

The science of pattern formation deals with the visible, (statistically) orderly outcomes of self-organization and the common principles behind similar patterns in nature. In developmental biology, pattern formation refers to the generation of complex organizations of cell fates in space and time. The role of genes in pattern formation is an aspect of morphogenesis, the creation of diverse anatomies from similar genes, now being explored in the science of evolutionary developmental biology or evo-devo. The mechanisms involved are well seen in the anterior-posterior patterning of embryos from the model organism Drosophila melanogaster (a fruit fly), one of the first organisms to have its morphogenesis studied, and in the eyespots of butterflies, whose development is a variant of the standard (fruit fly) mechanism. Patterns in nature Examples of pattern formation can be found in biology, physics, and science, and can readily be simulated with computer graphics, as described

Collective behavior and emerging patterns in dense dry aligning active matter

Elena Sesé Sansa

In this thesis, we study systems of active particles interacting via generic torques of different nature. We analyze the phase behavior of these systems, which results from the interplay between self-propulsion, excluded-volume and torques.We tackle the problem from two different perspectives. On the one hand, we derive a continuum field theory that describes a system of self-propelled particles subjected to generic torques. At the mean-field level, the linear stability analysis of the field equations unveils different instabilities of the homogeneous and isotropic state, leading to pattern formation and phase separation.On the other hand, we explore the phase diagrams of collections of aligning active Brownian particles by means of numerical simulations. We specifically focus on understanding what happens to motility-induced phase separation in the presence of different types of velocity alignment interactions. We study Vicsek-like alignment rules as well as dipolar interactions, which can be regarded as an alternative way of introducing effective alignment to the system. We extend the numerical simulations to also explore the phase behavior of mixtures of aligning active particles with different motilities. Here, we report a coupling between the fast and slow species, by which the fast species enhances the slow-species' motility. Finally, we address, at a fundamental level, what are the minimal ingredients leading to the emergence of a polarized phase in systems of aligning active particles. To do so, we propose a Hamiltonian model that could admit a transition to collective motion fulfilling the conservation of total linear momentum and derive a suitable algorithm to properly integrate the equations of motion.

EPFL2022

Simulating Biogenic Secondary Organic Aerosol During Summertime in China

Mengmeng Li, Athanasios Nenes

Biogenic secondary organic aerosol (BSOA) contributes significantly to summertime organic particulate matter. In this study, a simulation of BSOA for summer 2012 over China is performed using the Community Multiscale Air Quality model with an explicit representation of BSOA formation via reactive uptake of isoprene-derived intermediates, species-specific parameters for monoterpene-derived BSOA production, and a multigenerational oxidation scheme. Simulated BSOA tracks the measurements well (normalized mean bias of 1% and r(2) of 0.59). Elevated BSOA occurs in the Sichuan Basin and southeastern China (except the coastal areas) where concentrations are mostly within the range of 5-7.5 mu g/m(3). Nitrate oxidation dominates fresh BSOA originating from monoterpenes. Simulated monoterpene-formed BSOA increases by a factor of 3-11 with multigenerational oxidation. Isoprene-formed BSOA shows high concentrations in southwestern China, formed primarily by two methyltetrols (2-MT) and organosulfates (62%-83%). High concentrations in this region are driven by the abundance of IEPOX and high particle surface area of sulfate-laden aerosols. NOx emissions influence the formation pathways of isoprene-formed BSOA, as a high ratio of two-methylglyceric acid (2-MG)/2-MT is found in the North China Plain and part of the Yangtze River Delta Region. The simulated isoprene-formed BSOA is more strongly correlated with sulfate than with particle water mass concentration or particle acidity, in part because sulfate increases the surface area increasing uptake for heterogeneous reactions. Decreases in isoprene-formed BSOA were simulated with a pH increase of 2, implying that particle acidity mediates the BSOA formation. Plain Language Summary Emissions from plants contribute to particulate matter formation when they interact with emissions due to human activities. With intense anthropogenic emissions in China, how biogenic aerosols are formed as well as how the formation processes are impacted by anthropogenic emissions are not clear yet. This study applied an advanced modeling approach to simulate formation of biogenic aerosols over continental China in summer. We modeled high levels of biogenic aerosols in the Sichuan Basin and southeastern China, not only because of more forests in these areas than elsewhere but also due to particulate matter of anthropogenic origin. Based on the simulation, there is a clear evidence that anthropogenic emissions impact the chemical formation and composition of biogenic aerosols and particle acidity. Therefore, a decrease (increase) in biogenic aerosols and particle acidity would be a cobenefit (side effect) of anthropogenic emission controls.

2018

Exploring The Borderlands Of The Gathering Problem

El Mahdi El Mhamdi, Rachid Guerraoui, Alexandre David Olivier Maurer, Vladislav Tempez

Problems of pattern formation have been extensively studied in distributed computing. One of this problems is the gathering problem: agents must gather at a same position in a distributed manner. When gathering is not possible, a close problem is the convergence problem. In this article, we investigate the two following questions: (1) Can processes gather when each process cannot see more that one other process at the same time? (2) Can a gathering behavior be learned by processes? Regarding the first point, we introduce a new model with an extremely restricted visibility: each process can only see one other process (its closest neighbor). Our goal is to see if (and to what extent) the gathering and convergence problems can be solved in this setting. We first show that, surprisingly, the problem can be solved for a small number of processes (at most 5), but not beyond. This is due to indeterminacy in the case where there are several "closest neighbors" for a same process. By removing this indeterminacy with an additional hypothesis (choosing the closest neighbor according to an order on the positions of processes), we then show that the problem can be solved for any number of processes. We also show that up to one crash failure can be tolerated for the convergence problem. Regarding the second point, we present the first experimental evidence that a gathering behavior can be learned without explicit communication in a partially observable environment. The learned behavior has the same properties as a self-stabilizing distributed algorithm, as processes can gather from any initial state (and thus tolerate any transient failure). Besides, we show that it is possible to scale and then tolerate the brutal loss of up to 90% of agents without significant impact on the behavior.

2019

Simulating Biogenic Secondary Organic Aerosol During Summertime in China

Mengmeng Li, Athanasios Nenes

2018