다중혈관 관상동맥 환자에서 y-문합을 이용하여 양쪽 내흉동맥만을 사용한 우회술의 조기 성적

This article presents an overview of recent work on ant algorithms, that is, algorithms for discrete optimization that took inspiration from the observation of ant colonies' foraging behavior, and introduces the ant colony optimization (ACO) metaheuristic. In the first part of the article the basic biological findings on real ants are reviewed and their artificial counterparts as well as the ACO metaheuristic are defined. In the second part of the article a number of applications of ACO algorithms to combinatorial optimization and routing in communications networks are described. We conclude with a discussion of related work and of some of the most important aspects of the ACO metaheuristic.

http://www.iro.umontreal.ca/~dift6751/paper_aco.pdf

Ant algorithms for discrete optimization

Differently from passive Brownian particles, active particles, also known as self-propelled Brownian particles or microswimmers and nanoswimmers, are capable of taking up energy from their environment and converting it into directed motion. Because of this constant flow of energy, their behavior can be explained and understood only within the framework of nonequilibrium physics. In the biological realm, many cells perform directed motion, for example, as a way to browse for nutrients or to avoid toxins. Inspired by these motile microorganisms, researchers have been developing artificial particles that feature similar swimming behaviors based on different mechanisms. These man-made micromachines and nanomachines hold a great potential as autonomous agents for health care, sustainability, and security applications. With a focus on the basic physical features of the interactions of self-propelled Brownian particles with a crowded and complex environment, this comprehensive review will provide a guided tour through its basic principles, the development of artificial self-propelling microparticles and nanoparticles, and their application to the study of nonequilibrium phenomena, as well as the open challenges that the field is currently facing.

/pdf/active-particles-in-complex-and-crowded-environments-4pyirdi1aj.pdf

Active Particles in Complex and Crowded Environments

https://www.case.edu/affil/sigmaxi/documents/collectiveMemoryAndSpatialSortingInAnimalGroups_CouzinKrauseJamesRuxtonFranks.pdf

Collective memory and spatial sorting in animal groups.

Active Brownian particles, also referred to as microswimmers and nanoswimmers, are biological or manmade microscopic and nanoscopic particles that can self-propel. Because of their activity, their behavior can only be explained and understood within the framework of nonequilibrium physics. In the biological realm, many cells perform active Brownian motion, for example, when moving away from toxins or towards nutrients. Inspired by these motile microorganisms, researchers have been developing artificial active particles that feature similar swimming behaviors based on different mechanisms; these manmade micro- and nanomachines hold a great potential as autonomous agents for healthcare, sustainability, and security applications. With a focus on the basic physical features of the interactions of active Brownian particles with a crowded and complex environment, this comprehensive review will put the reader at the very forefront of the field of active Brownian motion, providing a guided tour through its basic principles, the development of artificial self-propelling micro- and nanoparticles, and their application to the study of nonequilibrium phenomena, as well as the open challenges that the field is currently facing.

/pdf/active-brownian-particles-in-complex-and-crowded-3ym4we2m8l.pdf

Active Brownian Particles in Complex and Crowded Environments

We review the observations and the basic laws describing the essential aspects of collective motion -- being one of the most common and spectacular manifestation of coordinated behavior Our aim is to provide a balanced discussion of the various facets of this highly multidisciplinary field, including experiments, mathematical methods and models for simulations, so that readers with a variety of background could get both the basics and a broader, more detailed picture of the field The observations we report on include systems consisting of units ranging from macromolecules through metallic rods and robots to groups of animals and people Some emphasis is put on models that are simple and realistic enough to reproduce the numerous related observations and are useful for developing concepts for a better understanding of the complexity of systems consisting of many simultaneously moving entities As such, these models allow the establishing of a few fundamental principles of flocking In particular, it is demonstrated, that in spite of considerable differences, a number of deep analogies exist between equilibrium statistical physics systems and those made of self-propelled (in most cases living) units In both cases only a few well defined macroscopic/collective states occur and the transitions between these states follow a similar scenario, involving discontinuity and algebraic divergences

Collective Motion

Social groups often need to take decisions and solve problems together, with each member contributing to the solution in a different way. Zafeiris et al. provide a family of models that allow the definition of the ideal distribution of competences in a group to solve a given task.

/pdf/group-performance-is-maximized-by-hierarchical-competence-3vygsb6xfx.pdf

Group performance is maximized by hierarchical competence distribution

This book is concerned with the various aspects of hierarchical collective behaviour which is manifested by most complex systems in nature. From the many of the possible topics, we plan to present a selection of those that we think are useful from the point of shedding light from very different directions onto our quite general subject. Our intention is to both present the essential contributions by the existing approaches as well as go significantly beyond the results obtained by traditional methods by applying a more quantitative approach then the common ones (there are many books on qualitative interpretations). In addition to considering hierarchy in systems made of similar kinds of units, we shall concentrate on problems involving either dominance relations or the process of collective decision-making from various viewpoints.

Why We Live in Hierarchies?: A Quantitative Treatise

Why We Live in Hierarchies

Humans make decisions based on the information they obtain from several major sources, among which the comments of others in Internet forums play an increasing role. Such forums cover a wide spectrum of topics and represent an essential tool in choosing the best products, manipulating views or optimizing our decisions regarding a number of aspects of our everyday life. However, many forums have extremely controversial topics and contents including those which radicalize the readers or spread information about dangerous products and ideas (e.g., drugs, weapons or aggressive ideologies). These just mentioned activities are taking place mainly on the so called “dark web” allowing the hiding of the identity of members using dark forums. We use network theoretical approaches to analyze the data we obtained by studying the connectivity features of the members and the threads within a wide selection of forums (including dark and semi-dark) and establish several characteristic behavioral patterns. Our findings reveal both common and rather different features in the two types of behavior. In particular, we show that the various distributions of quantities, like the activity of the commenters, the dynamics of the threads (defined using their lifetime) or the degree distributions corresponding to the three major types of forums we have investigated display characteristic deviations. This knowledge can be useful, for example, in identifying an activity typical for the dark web when it appears in the public web (since the public web can be accessed and used much more easily).

Anna Zafeiris

Papers

Collective Motion

Group performance is maximized by hierarchical competence distribution

Why We Live in Hierarchies?: A Quantitative Treatise

Why We Live in Hierarchies

Differences in structure and dynamics of networks retrieved from dark and public web forums