scholarly journals Area per Ligand as a Function of Nanoparticle Radius: A Theoretical and Computer Simulation Approach

Langmuir ◽  
2009 ◽  
Vol 25 (3) ◽  
pp. 1352-1359 ◽  
Author(s):  
Robert J. B. Kalescky ◽  
Wataru Shinoda ◽  
Preston B. Moore ◽  
Steven O. Nielsen
Keyword(s):  
Computer Simulation ◽  
Simulation Approach ◽  
Nanoparticle Radius

A Computer Simulation Approach for Engineering Air-Jet Spun Yarns

1997 ◽  
Vol 67 (3) ◽  
pp. 223-230 ◽  
Author(s):  
Rangaswamy Rajamanickam ◽  
Steven M. Hansen ◽  
Sundaresan Jayaraman
Keyword(s):  
Computer Simulation ◽  
Computer Simulations ◽  
Fiber Length ◽  
Experimental Investigations ◽  
Simulation Approach ◽  
Air Jet ◽  
Fiber Fineness ◽  
Spun Yarns ◽  
Yarn Count ◽  
Wrap Angle

A computer simulation approach for engineering air-jet spun yarns is proposed, and the advantages of computer simulations over experimental investigations and stand-alone mathematical models are discussed. Interactions of the following factors in air-jet spun yarns are analyzed using computer simulations: yarn count and fiber fineness, fiber tenacity and fiber friction, fiber length and fiber friction, and number of wrapper fibers and wrap angle. Based on the results of these simulations, yarn engineering approaches to optimize strength are suggested.

Gamma ray imaging using coded aperture masks: a computer simulation approach

1991 ◽  
Vol 30 (5) ◽  
pp. 549 ◽  
Author(s):  
J. Jimenez ◽  
Pedro Olmos ◽  
J. L. de Pablos ◽  
J. M. Perez
Keyword(s):  
Computer Simulation ◽  
Gamma Ray ◽  
Coded Aperture ◽  
Simulation Approach ◽  
Gamma Ray Imaging

scholarly journals Hospital beds and personal protective equipment planning for COVID-19 pandemic: A computer simulation approach

2020 ◽  
Author(s):  
Yiruo Lu ◽  
Yongpei Guan ◽  
Jennifer Fishe ◽  
Thanh Hogan ◽  
Xiang Zhong
Keyword(s):  
Computer Simulation ◽  
Personal Protective Equipment ◽  
Census Data ◽  
Discrete Event ◽  
Hospital Setting ◽  
Case Fatality ◽  
Protective Equipment ◽  
Simulation Approach ◽  
Number Of Patients ◽  
Hospital Resources

Abstract Health care systems are at the frontline to fight the COVID-19 pandemic. An emergent question for each hospital is how many general ward and intensive care unit beds are needed and how much personal protective equipment to be purchased. However, hospital pandemic preparedness has been hampered by a lack of sufficiently specific planning guidelines. In this paper, we developed a computer simulation approach to evaluating bed utilizations and the corresponding supply needs based on the operational considerations and constraints in individual hospitals. We built a data-driven SEIR model which is adaptive to control policies and can be utilized for regional forecast targeting a specific hospital’s catchment area. The forecast model was integrated into a discrete-event simulation which modeled the patient flow and the interaction with hospital resources. We tested the simulation model outputs against patient census data from UF Health Jacksonville, Jacksonville, FL. Simulation results were consistent with the observation that the hospital has ample bed resources to accommodate the regional COVID patients. After validation, the model was used to predict future bed utilizations given a spectrum of possible scenarios to advise bed planning and stockpiling decisions. Lastly, how to optimally allocate hospital resources to achieve the goal of reducing the case fatality rate while helping a maximum number of patients to recover was discussed. This decision support tool is tailored to a given hospital setting of interest and is generalizable to other hospitals to tackle the pandemic planning challenge.

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