scholarly journals Artificial intelligence and computer simulation models in critical illness

2020 ◽  
Vol 9 (2) ◽  
pp. 13-19 ◽  
Author(s):  
Amos Lal ◽  
Yuliya Pinevich ◽  
Ognjen Gajic ◽  
Vitaly Herasevich ◽  
Brian Pickering
1996 ◽  
Vol 33 (9) ◽  
pp. 39-47 ◽  
Author(s):  
John W. Davies ◽  
Yanli Xu ◽  
David Butler

Significant problems in sewer systems are caused by gross solids, and there is a strong case for their inclusion in computer simulation models of sewer flow quality. The paper describes a project which considered methods of modelling the movement of gross solids in combined sewers. Laboratory studies provided information on advection and deposition of typical gross solids in part-full pipe flow. Theoretical considerations identified aspects of models for gross solids that should differ from those for dissolved and fine suspended pollutants. The proposed methods for gross solids were incorporated in a pilot model, and their effects on simple simulations were considered.


2014 ◽  
Vol 22 ◽  
pp. S57-S58
Author(s):  
W. Hui ◽  
D.A. Young ◽  
A.D. Rowan ◽  
T.E. Cawston ◽  
C.J. Proctor

1993 ◽  
Vol 8 (1) ◽  
pp. 24-27
Author(s):  
K. Leroy Dolph ◽  
Gary E. Dixon

Abstract Erroneous predictions of forest growth and yield may result when computer simulation models use extrapolated data in repeated or long-term projections or if the models are used outside the range of data on which they were built. Bounding functions that limit the predicted diameter and height growth of individual trees to maximum observed values were developed to constrain these erroneous predictions in a forest growth and yield simulator. Similar techniques could be useful for dealing with extrapolated data in other types of simulation models. West. J. Appl. For. 8(1):24-27.


Sign in / Sign up

Export Citation Format

Share Document