Diffusion of fish from a single release point

2002 ◽  
Vol 59 (5) ◽  
pp. 844-853 ◽  
Author(s):  
Claus R Sparrevohn ◽  
Anders Nielsen ◽  
Josianne G Støttrup
Keyword(s):  
Differential Equation ◽  
Partial Differential Equation ◽  
Population Density ◽  
Diffusion Theory ◽  
Detectable Effect ◽  
Young Fish ◽  
Release Point ◽  
Pde Model ◽  
Psetta Maxima ◽  
Daily Displacement

In a field experiment, 3529 turbot (Psetta maxima) were released in order to estimate and describe the movements of hatchery-reared fish by applying diffusion theory. After liberation, the development of the population density was estimated during the following 9 days, and from that, the rate of diffusion and the advection were determined. Two approaches were followed to describe the data: a normal distribution approximation (NDA) model and a partial differential equation (PDE) model. In the latter, it was possible to include the effect of sampling. The two models gave similar results, indicating that the sampling of fish during the experiment did not have any detectable effect on the population density. The activity of the released turbot resulted in an individual daily displacement of 151 m·day–1, except for the first 2 days at liberty, where the displacement was estimated to be considerably lower. Advection was significant and was related to the displacement of the water body. Further, it was possible to estimate the postrelease mortality as 14%·day–1 and the catchability of the turbot when caught with a young fish trawl as 28%.

scholarly journals A SEMI-LINEAR ELLIPTIC PDE MODEL FOR THE STATIC SOLUTION OF JOSEPHSON JUNCTIONS

1995 ◽  
Vol 06 (02) ◽  
pp. 241-262 ◽  
Author(s):  
JEAN-GUY CAPUTO ◽  
NIKOS FLYTZANIS ◽  
EMMANUEL VAVALIS
Keyword(s):  
Differential Equation ◽  
Partial Differential Equation ◽  
Elliptic Partial Differential Equation ◽  
Static Solution ◽  
Elliptic Pde ◽  
Computational Results ◽  
Computer Implementation ◽  
Pde Model ◽  
Modeling Power ◽  
Zero Voltage

In this study we derive a semi-linear Elliptic Partial Differential Equation (PDE) problem that models the static (zero voltage) behavior of a Josephson window junction. Iterative methods for solving this problem are proposed and their computer implementation is discussed. The preliminary computational results that are given, show the modeling power of our approach and exhibit its computational efficiency.

scholarly journals Research on Odor Interaction between Aldehyde Compounds via a Partial Differential Equation (PDE) Model

Sensors ◽  
2015 ◽  
Vol 15 (2) ◽  
pp. 2888-2901 ◽  
Author(s):  
LuchunYan ◽  
Jiemin Liu ◽  
Chen Qu ◽  
Xingye Gu ◽  
Xia Zhao
Keyword(s):  
Differential Equation ◽  
Partial Differential Equation ◽  
Partial Differential ◽  
Pde Model

scholarly journals A generalized Hankel transform and its use for solving certain partial differential equations

1999 ◽  
Vol 41 (1) ◽  
pp. 105-117 ◽  
Author(s):  
I. Ali ◽  
S. Kalla
Keyword(s):  
Differential Equation ◽  
Partial Differential Equation ◽  
Partial Differential Equations ◽  
Integral Transform ◽  
Diffusion Theory ◽  
Ground Surface ◽  
Ground Level ◽  
Hankel Transform ◽  
Partial Differential ◽  
Special Cases

AbstractWe introduce a generalized form of the Hankel transform, and study some of its properties. A partial differential equation associated with the problem of transport of a heavy pollutant (dust) from the ground level sources within the framework of the diffusion theory is treated by this integral transform. The pollutant concentration is expressed in terms of a given flux of dust from the ground surface to the atmosphere. Some special cases are derived.

A Simple and Stable Numerical Solution for the Population Density Equation

2003 ◽  
Vol 15 (9) ◽  
pp. 2129-2146 ◽  
Author(s):  
M. de Kamps
Keyword(s):  
Differential Equation ◽  
Partial Differential Equation ◽  
Population Density ◽  
Finite Difference ◽  
Method Of Characteristics ◽  
Difference Schemes ◽  
Finite Difference Schemes ◽  
Integrate And Fire ◽  
Large Populations ◽  
Density Equation

A population density description of large populations of neurons has generated considerable interest recently. The evolution in time of the population density is determined by a partial differential equation (PDE). Most of the algorithms proposed to solve this PDE have used finite difference schemes. Here, I use the method of characteristics to reduce the PDE to a set of ordinary differential equations, which are easy to solve. The method is applied to leaky-integrate-and-fire neurons and produces an algorithm that is efficient and yields a stable and manifestly nonnegative density. Contrary to algorithms based directly on finite difference schemes, this algorithm is insensitive to large density gradients, which may occur during evolution of the density.

scholarly journals Disparity Computation Through PDE and Data-Driven CeNN Technique

2021 ◽  
Vol 38 (4) ◽  
pp. 1051-1059
Author(s):  
Mahima Lakra ◽  
Sanjeev Kumar
Keyword(s):  
Neural Network ◽  
Differential Equation ◽  
Partial Differential Equation ◽  
Experimental Study ◽  
Variational Approach ◽  
Cellular Neural Network ◽  
Energy Functional ◽  
Data Driven ◽  
Pde Model ◽  
Distance Regularization

This paper proposes a variational approach by minimizing the energy functional to compute the disparity from a given pair of consecutive images. The partial differential equation (PDE) is modeled from the energy function to address the minimization problem. We incorporate a distance regularization term in the PDE model to preserve the boundaries' discontinuities. The proposed PDE is numerically solved by a cellular neural network (CeNN) algorithm. This CeNN based scheme is stable and consistent. The effectiveness of the proposed algorithm is shown by a detailed experimental study along with its superiority over some of the existing algorithms.

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