scholarly journals A “random-walk” simulation model of multiple-pattern learning in a radial-arm maze

1996 ◽  
Vol 24 (2) ◽  
pp. 206-210 ◽  
Author(s):  
Ian Neath ◽  
E. J. Capaldi
Keyword(s):  
Random Walk ◽  
Simulation Model ◽  
Radial Arm Maze ◽  
Pattern Learning ◽  
Random Walk Simulation

scholarly journals Random-Walk, Agent-Level Pandemic Simulation (RAW-ALPS) for Analyzing Effects of Different Lockdown Measures

2021 ◽  
Vol 7 ◽  
Author(s):  
Anuj Srivastava
Keyword(s):  
Random Walk ◽  
Simulation Model ◽  
Random Walks ◽  
Stochastic Simulation ◽  
Preventive Measures ◽  
Infection Rates ◽  
Physical Proximity ◽  
Stochastic Simulation Model ◽  
Restrictive Measures ◽  
Effective Use

This article develops an agent-level stochastic simulation model, termed RAW-ALPS, for simulating the spread of an epidemic in a community. The mechanism of transmission is agent-to-agent contact, using parameters reported for the COVID-19 pandemic. When unconstrained, the agents follow independent random walks and catch infections due to physical proximity with infected agents. Under lockdown, an infected agent can only infect a coinhabitant, leading to a reduction in the spread. The main goal of the RAW-ALPS simulation is to help quantify the effects of preventive measures—timing and durations of lockdowns—on infections, fatalities, and recoveries. The model helps measure changes in infection rates and casualties due to the imposition and maintenance of restrictive measures. It considers three types of lockdowns: 1) whole population (except the essential workers), 2) only the infected agents, and 3) only the symptomatic agents. The results show that the most effective use of lockdown measures is when all infected agents, including both symptomatic and asymptomatic, are quarantined, while the uninfected agents are allowed to move freely. This result calls for regular and extensive testing of a population to isolate and restrict all infected agents.

scholarly journals Spatial pattern learning in the radial arm maze

2006 ◽  
Vol 34 (1) ◽  
pp. 102-108 ◽  
Author(s):  
Michael F. Brown ◽  
Gary W. Giumetti
Keyword(s):  
Spatial Pattern ◽  
Radial Arm Maze ◽  
Pattern Learning

An Approach Toward Emulating Molecular Interaction with a Graph

2006 ◽  
Vol 59 (12) ◽  
pp. 869 ◽  
Author(s):  
Hideaki Suzuki
Keyword(s):  
Random Walk ◽  
Molecular Interaction ◽  
Hard Sphere ◽  
Dimensional Space ◽  
Three Dimensional ◽  
Atomic Clusters ◽  
Experimental Results ◽  
Random Walk Simulation ◽  
Mathematical Graph ◽  
Three Dimensional Space

Network artificial chemistry (NAC) uses a mathematical graph to emulate molecular interaction in a solvent. To emulate molecules' movement in a three-dimensional space, rewiring rules for NAC graphs’ edges must be designed to enable the edges to imitate the relations between molecules or atomic clusters. Our research formulated the ‘network energy’ representing this constraint and rewired the NAC graph to minimize the required energy. Experimental results for the NAC rewiring are compared with a hard-sphere random walk simulation.

Random-walk simulation of the dielectric constant of a composite material

1990 ◽  
Vol 42 (8) ◽  
pp. 5342-5344 ◽  
Author(s):  
R. I. Cukier ◽  
S. Y. Sheu ◽  
J. Tobochnik
Keyword(s):  
Dielectric Constant ◽  
Composite Material ◽  
Random Walk ◽  
Random Walk Simulation

Random-walk simulation of diffusion-controlled processes among static traps

1989 ◽  
Vol 39 (16) ◽  
pp. 11833-11839 ◽  
Author(s):  
Sang Bub Lee ◽  
In Chan Kim ◽  
C. A. Miller ◽  
S. Torquato
Keyword(s):  
Random Walk ◽  
Controlled Processes ◽  
Diffusion Controlled ◽  
Random Walk Simulation

The Random Walk Drainage Simulation Model as a Teaching Exercise

1972 ◽  
Vol 71 (1) ◽  
pp. 41-51
Author(s):  
Colin High ◽  
Paul Richards
Keyword(s):  
Random Walk ◽  
Simulation Model
Sign in / Sign up

Export Citation Format

Share Document