Simulation of how tiger population and anti poaching efforts effect the black market value of tiger organs.

Simulation of how tiger population and anti poaching efforts effect the black market value of tiger organs.

WIP Clone of IM-9085 for multiscale frameworks based on ecography article. Vital Signs From NAP Toward Quality Measures for Population Health and the Leading Health Indicators Report  WHO NCD Framework picture and IHI Whole system measures 2.0 (Added Nov 2016)

Een dynamisch model over een prooi predator relatie tussen verschillende populaties onder invloed van abiotische factoren.

The SEQ Koala Population over recent years has suffered due to a number of factors; habitat loss, predators, natural disasters, health issues and road fatalities to name a few.  All the while conservation efforts are being made to aid the population growth of  the national icon.

This insight draws together these contributing factors into a single population model (simulation).  This model begins with the known 2006 population and it projected based on current decline rates.  Accuracy is limited, however the downward trend is clearly evident.

Developed by Patrick O'Shaughnessy

Adapted from Hartmut Bossel's "System Zoo 3 Simulation Models, Economy, Society, Development."

​Population model where the population is summarized in four age groups (children, parents, older people, old people). Used as a base population model for dealing with issues such as employment, care for the elderly, pensions dynamics, etc.

Simulation of MTBF with controls


The inverse curve is the trust time
On the right the increase in failures brings its inverse which is loss of trust and move into suspicion and lack of confidence.
This can be seen in strategic social applications with those who put economy before providing the priorities of the basic living infrastructures for all.

This applies to policies and strategic decisions as well as physical equipment.
A) Equipment wears out through friction and preventive maintenance can increase the useful lifetime, 
B) Policies/working practices/guidelines have to be updated to reflect changes in the external environment and eventually be replaced when for instance a population rises too large (constitutional changes are required to keep pace with evolution, e.g. the concepts of the ancient Greeks, 3000 years ago, who based their thoughts on a small population cannot be applied in 2013 except where populations can be contained into productive working communities with balanced profit and loss centers to ensure sustainability)

Early Life
Useful Life

Wearout

Modelagem do estado psicológico de uma população. Inicialmente, todos os indivíduos estão no estado "Calmo". Com o passar do tempo e com as interações mútuas, há o surgimento e progressivo aumento do total de indivíduos com raiva (estado "Raivoso"). Deste estado e, com o passar do tempo, os indivíduos podem evoluir mentalmente e atingirem o estado "Indiferente", nos quais eles se tornam indiferentes à qualquer interação. Outra possibilidade é o indivíduo se enriquecer e, assim, atingir a felicidade (estado "Feliz").

EIGENE MODIFIKATIONEN

Adapted from Hartmut Bossel's "System Zoo 3 Simulation Models, Economy, Society, Development."

​Population model where the population is summarized in four age groups (children, parents, older people, old people). Used as a base population model for dealing with issues such as employment, care for the elderly, pensions dynamics, etc.

This model is under construction, not at all ready, don't use it for any purposes (my suggestion ☺) yet.

WIP from Eric Pruyt Netherlands

Adapted from Hartmut Bossel's "System Zoo 3 Simulation Models, Economy, Society, Development."

​Population model where the population is summarized in four age groups (children, parents, older people, old people). Used as a base population model for dealing with issues such as employment, care for the elderly, pensions dynamics, etc.

The Logistic Map is a polynomial mapping (equivalently, recurrence relation) of degree 2, often cited as an archetypal example of how complex, chaotic behaviour can arise from very simple non-linear dynamical equations. The map was popularized in a seminal 1976 paper by the biologist Robert May, in part as a discrete-time demographic model analogous to the logistic equation first created by Pierre François Verhulst. 

Mathematically, the logistic map is written

where:

 is a number between zero and one, and represents the ratio of existing population to the maximum possible population at year n, and hence x0 represents the initial ratio of population to max. population (at year 0)r is a positive number, and represents a combined rate for reproduction and starvation. To generate a bifurcation diagram, set 'r base' to 2 and 'r ramp' to 1
To demonstrate sensitivity to initial conditions, try two runs with 'r base' set to 3 and 'Initial X' of 0.5 and 0.501, then look at first ~20 time steps

Shows the ecological impact of population.

Simulation of MTBF with controls


The inverse curve is the trust time
On the right the increase in failures brings its inverse which is loss of trust and move into suspicion and lack of confidence.
This can be seen in strategic social applications with those who put economy before providing the priorities of the basic living infrastructures for all.

This applies to policies and strategic decisions as well as physical equipment.
A) Equipment wears out through friction and preventive maintenance can increase the useful lifetime, 
B) Policies/working practices/guidelines have to be updated to reflect changes in the external environment and eventually be replaced when for instance a population rises too large (constitutional changes are required to keep pace with evolution, e.g. the concepts of the ancient Greeks, 3000 years ago, who based their thoughts on a small population cannot be applied in 2013 except where populations can be contained into productive working communities with balanced profit and loss centers to ensure sustainability)

Early Life
Useful Life

Wearout

UDB101 Assignment 1D

Koala Population Case Study

Sian Phillips

This is a population model designed for local health and care systems (United Kingdom). This model does not simulation male/female, but rather everyone in 5-year age groups.

Simulation of MTBF with controls


The inverse curve is the trust time
On the right the increase in failures brings its inverse which is loss of trust and move into suspicion and lack of confidence.
This can be seen in strategic social applications with those who put economy before providing the priorities of the basic living infrastructures for all.

This applies to policies and strategic decisions as well as physical equipment.
A) Equipment wears out through friction and preventive maintenance can increase the useful lifetime, 
B) Policies/working practices/guidelines have to be updated to reflect changes in the external environment and eventually be replaced when for instance a population rises too large (constitutional changes are required to keep pace with evolution, e.g. the concepts of the ancient Greeks, 3000 years ago, who based their thoughts on a small population cannot be applied in 2013 except where populations can be contained into productive working communities with balanced profit and loss centers to ensure sustainability)

Early Life
Useful Life

Wearout

Show relation of birth and death rate over time, creating the elements of the demographic transition. This one is for Sweden. You can clone this insight for other nations, just plug in the new crude birth and death rates and find the starting population in 1960.

A collaborative class project with each participant creating an animal/plant sub-model​ to explore the greater population/community dynamics of the Yellowstone ecosystem.

Adapted from Hartmut Bossel's "System Zoo 3 Simulation Models, Economy, Society, Development."

​Population model where the population is summarized in four age groups (children, parents, older people, old people). Used as a base population model for dealing with issues such as employment, care for the elderly, pensions dynamics, etc.