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E' simile al D model (https://insightmaker.com/insight/206054/D-model-curve-di-Richards) ma qui la fase lag è esplicitamente inversamente proporzionale a mu. Questo semplifica alcuni calcoli quando mumax non è costante ma dipendente dalla temperatura. 

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Il modello è basato su questo Insight https://insightmaker.com/insight/206861/D-model-curve-di-Richards-con-ln-alpha-lag-mu

Dynamic simulation modelers are particularly interested in understanding and being able to distinguish between the behavior of stocks and flows that result from internal interactions and those that result from external forces acting on a system.  For some time modelers have been particularly interested in internal interactions that result in stable oscillations in the absence of any external forces acting on a system.  The model in this last scenario was independently developed by Alfred Lotka (1924) and Vito Volterra (1926).  Lotka was interested in understanding internal dynamics that might explain oscillations in moth and butterfly populations and the parasitoids that attack them.  Volterra was interested in explaining an increase in coastal populations of predatory fish and a decrease in their prey that was observed during World War I when human fishing pressures on the predator species declined.  Both discovered that a relatively simple model is capable of producing the cyclical behaviors they observed.  Since that time, several researchers have been able to reproduce the modeling dynamics in simple experimental systems consisting of only predators and prey.  It is now generally recognized that the model world that Lotka and Volterra produced is too simple to explain the complexity of most and predator-prey dynamics in nature.  And yet, the model significantly advanced our understanding of the critical role of feedback in predator-prey interactions and in feeding relationships that result in community dynamics.The Lotka–Volterra model makes a number of assumptions about the environment and evolution of the predator and prey populations:

As differential equations are used, the solution is deterministic and continuous. This, in turn, implies that the generations of both the predator and prey are continually overlapping.[23]

When multiplied out, the prey equation becomes

dx/dt = αx - βxy

 The prey are assumed to have an unlimited food supply, and to reproduce exponentially unless subject to predation; this exponential growth is represented in the equation above by the term αx. The rate of predation upon the prey is assumed to be proportional to the rate at which the predators and the prey meet; this is represented above by βxy. If either x or y is zero then there can be no predation.

The Lotka–Volterra model makes a number of assumptions about the environment and evolution of the predator and prey populations:

As differential equations are used, the solution is deterministic and continuous. This, in turn, implies that the generations of both the predator and prey are continually overlapping.[23]

When multiplied out, the prey equation becomes

dx/dt = αx - βxy

 The prey are assumed to have an unlimited food supply, and to reproduce exponentially unless subject to predation; this exponential growth is represented in the equation above by the term αx. The rate of predation upon the prey is assumed to be proportional to the rate at which the predators and the prey meet; this is represented above by βxy. If either x or y is zero then there can be no predation.

F(t) = 1 - e ^ -λt 

Where  

• F(t) is the probability of failure  

• λ is the failure rate in 1/time unit (1/h, for example) 

• t is the observed service life (h, for example)

If we follow the slope from the leftmost start to where it begins to flatten out this can be considered the first period. The first period is characterized by a decreasing failure rate. It is what occurs during the “early life” of a population of units. The weaker units fail leaving a population that is more rigorous.

The next period is the flat bottom portion of the graph. It is called the “useful life” period. Failures occur more in a random sequence during this time. It is difficult to predict which failure mode will occur, but the rate of failures is predictable. Notice the constant slope.  

The third period begins at the point where the slope begins to increase and extends to the rightmost end of the graph. This is what happens when units become old and begin to fail at an increasing rate. It is called the “wearout” period. 

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E' un modello dinamico che assume che la fase lag sia dovuto all'accumulo di un composto essenziale (la cui quantità iniziale riflette lo stato iniziale delle cellule nell'ambiente E1, da cui provengono), secondo una cinetica di primo ordine, ad una velocità che dipende dall'ambiente E2. Il modello è lievemente modificato rispetto all'originale, per evitare che la quantità del prodotto essenziale tenda all'infinito. 

​Predator-prey models are the building masses of the bio-and environments as bio masses are become out of their asset masses. Species contend, advance and scatter essentially to look for assets to support their battle for their very presence. Contingent upon their particular settings of uses, they can take the types of asset resource-consumer, plant-herbivore, parasite-have, tumor cells- immune structure, vulnerable irresistible collaborations, and so on. They manage the general misfortune win connections and thus may have applications outside of biological systems. At the point when focused connections are painstakingly inspected, they are regularly in actuality a few types of predator-prey communication in simulation. 

 Looking at Lotka-Volterra Model:

The well known Italian mathematician Vito Volterra proposed a differential condition model to clarify the watched increment in predator fish in the Adriatic Sea during World War I. Simultaneously in the United States, the conditions contemplated by Volterra were determined freely by Alfred Lotka (1925) to portray a theoretical synthetic response wherein the concoction fixations waver. The Lotka-Volterra model is the least complex model of predator-prey communications. It depends on direct per capita development rates, which are composed as f=b−py and g=rx−d. 

A detailed explanation of the parameters:

• The parameter b is the development rate of species x (the prey) without communication with species y (the predators). Prey numbers are reduced by these collaborations: The per capita development rate diminishes (here directly) with expanding y, conceivably getting to be negative. 
• The parameter p estimates the effect of predation on x˙/x. 
• The parameter d is the death rate of species y without connection with species x. 
• The term rx means the net rate of development of the predator population in light of the size of the prey population.

Reference:

http://www.scholarpedia.org/article/Predator-prey_model