Deterministic and Stochastic Three-Sector Dynamic Growth Model with Endogenous Labour Supply

2013 ◽  
Vol 89 (284) ◽  
pp. 99-111 ◽  
Author(s):  
Loretti Dobrescu ◽  
Mihaela Neamtu ◽  
Dumitru Opris
Keyword(s):  
Growth Model ◽  
Labour Supply ◽  
Dynamic Growth

A dynamic growth model of macroalgae: Application in an estuary recovering from treated wastewater and earthquake-driven eutrophication

2014 ◽  
Vol 148 ◽  
pp. 59-69 ◽  
Author(s):  
Jeffrey S. Ren ◽  
Neill G. Barr ◽  
Kristin Scheuer ◽  
David R. Schiel ◽  
John Zeldis
Keyword(s):  
Growth Model ◽  
Treated Wastewater ◽  
Dynamic Growth

Development and Validation of a Dynamic Growth Model for Listeria monocytogenes in Fluid Whole Milk

1999 ◽  
Vol 62 (2) ◽  
pp. 170-176 ◽  
Author(s):  
S. H. ALAVI ◽  
V. M. PURI ◽  
S. J. KNABEL ◽  
R. H. MOHTAR ◽  
R. C. WHITING
Keyword(s):  
Experimental Data ◽  
Listeria Monocytogenes ◽  
Growth Model ◽  
Extreme Values ◽  
Exponential Growth Phase ◽  
Lag Phase ◽  
Maximum Relative Error ◽  
Whole Milk ◽  
Dynamic Growth ◽  
Fluid Milk

Listeria monocytogenes, a psychrotrophic microorganism, has been the cause of several food-borne illness outbreaks, including those traced back to pasteurized fluid milk and milk products. This microorganism is especially important because it can grow at storage temperatures recommended for milk (≤7°C). Growth of L. monocytogenes in fluid milk depends to a large extent on the varying temperatures it is exposed to in the postpasteurization phase, i.e., during in-plant storage, transportation, and storage at retail stores. Growth data for L. monocytogenes in sterilized whole milk were collected at 4, 6, 8, 10, 15, 20, 25, 30, and 35°C. Specific growth rate and maximum population density were calculated at each temperature using these data. The data for growth rates versus temperature were fitted to the Zwietering square root model. This equation was used to develop a dynamic growth model (i.e., the Baranyi dynamic growth model or BDGM) for L. monocytogenes based on a system of equations which had an intrinsic parameter for simulating the lag phase. Results from validation of the BDGM for a rapidly fluctuating temperature profile showed that although the exponential growth phase of the culture under dynamic temperature conditions was modeled accurately, the lag phase duration was overestimated. For an α0 (initial physiological state parameter) value of 0.137, which corresponded to the mean temperature of 15°C, the population densities were under-predicted, although the experimental data fell within the narrow band calculated for extreme values of α0. The maximum relative error between the experimental data and the curve based on an average α0 value was 10.42%, and the root mean square error was 0.28 log CFU/ml.

A dynamic growth model of Ulva prolifera: Application in quantifying the biomass of green tides in the Yellow Sea, China

2020 ◽  
Vol 428 ◽  
pp. 109072 ◽  
Author(s):  
Ke Sun ◽  
Jeffrey S. Ren ◽  
Tao Bai ◽  
Jihong Zhang ◽  
Qing Liu ◽  
...  
Keyword(s):  
Growth Model ◽  
Yellow Sea ◽  
Ulva Prolifera ◽  
The Yellow Sea ◽  
Green Tides ◽  
Dynamic Growth

Development of a dynamic growth model for sweet chestnut coppice: A case study in Northwest Spain

2019 ◽  
Vol 409 ◽  
pp. 108761
Author(s):  
Marta Prada ◽  
Marta González-García ◽  
Juan Majada ◽  
Celia Martínez-Alonso
Keyword(s):  
Growth Model ◽  
Sweet Chestnut ◽  
Dynamic Growth ◽  
Chestnut Coppice
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