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Clone of Clone of BATHTUB MEAN TIME BETWEEN FAILURE (MTBF) RISK

Ivan Stamenkovic
Simulation of MTBF with controls

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)
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 LifeIf 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.
Useful Life
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.  
Wearout
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. 

Environment Economics Finance Mathematics Physics Biology Health Fractals Chaos TURBULENCE Engineering Navier Stokes Science Demographics Population Growth BIFURCATIONS MTBF Risk Failure Strategy

  • 5 years 11 months ago

Clone of BATHTUB MEAN TIME BETWEEN FAILURE (MTBF) RISK

atif
Simulation of MTBF with controls

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)
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 LifeIf 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.
Useful Life
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.  
Wearout
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. 

Environment Economics Finance Mathematics Physics Biology Health Fractals Chaos TURBULENCE Engineering Navier Stokes Science Demographics Population Growth BIFURCATIONS MTBF Risk Failure Strategy

  • 7 years 10 months ago

Clone of FORCED GROWTH INTO TURBULENCE

Sayantan Das
FORCED GROWTH GROWTH GOES INTO TURBULENT CHAOTIC DESTRUCTION 
 BEWARE pushing increased growth blows the system!
(governments are trying to push growth on already unstable systems !)

The existing global capitalistic growth paradigm is totally flawed

The chaotic turbulence is the result of the concept and flawed strategy of infinite bigness this has been the destructive influence on all empires and now shown up by Feigenbaum numbers and Dunbar numbers for neural netwoirks

See Guy Lakeman Bubble Theory for more details on keeping systems within finite limited size working capacity containers (villages communities)

Environment Economics Finance Mathematics Physics Biology Health Fractals Chaos TURBULENCE Engineering Navier Stokes Science Demographics Population Growth BIFURCATIONS MTBF Strategy Weather

  • 7 years 7 months ago

Ireland Population

Novelyn Verran
Birth Rate​
https://www.irishtimes.com/news/ireland/irish-news/baby-boom-puts-ireland-top-of-eu-birth-rate-table-1.3150045
Death Rate
https://www.google.com/search?q=ireland+death+rate&rlz=1CADEAC_enUS712US712&oq=ire&aqs=chrome.4.69i57j69i60l3j69i59l2.6191j0j7&sourceid=chrome&ie=UTF-8

Immigration Rate
https://knoema.com/atlas/Ireland/topics/Demographics/Population/Net-migration-rate

Emigration Rate
https://www.google.com/search?rlz=1CADEAC_enUS712US712&ei=uJgMWrTmBsuZjwST9ZqABw&q=ireland+emigration+rate&oq=ireland+emigration+rate&gs_l=psy-ab.3..0i30k1j0i8i30k1l4.2635.3926.0.4687.6.6.0.0.0.0.150.700.2j4.6.0....0...1.1.64.psy-ab..0.3.374...33i21k1j0i13i30k1j0i8i13i30k1.0._x0RnrywMJE



Biology Population

  • 3 years 2 months ago

Proyecto Molecular

David Lora
Regulación de la expresión en múltiples niveles de un gen hipotético de oxidoreductasa. Parametros; H2O2: moleculas iniciales de H2O2; Tasa Oxidativa: velocidad a la que la celula produce nuevo H2O2, Mutación TR1 y ProtS: Activa las mutaciones en estos genes (ver panel de información); miARN: activa la regulación por miARNs.

Biology Molecular Biology

  • 7 months 5 days ago

Clone of THE BROKEN LINK BETWEEN SUPPLY AND DEMAND CREATES CHAOTIC TURBULENCE (+controls)

Charles Møller
THE BROKEN LINK BETWEEN SUPPLY AND DEMAND CREATES TURBULENT CHAOTIC DESTRUCTION

The existing global capitalistic growth paradigm is totally flawed

Growth in supply and productivity is a summation of variables as is demand ... when the link between them is broken by catastrophic failure in a component the creation of unpredictable chaotic turbulence puts the controls ito a situation that will never return the system to its initial conditions as it is STIC system (Lorenz)

The chaotic turbulence is the result of the concept of infinite bigness this has been the destructive influence on all empires and now shown up by Feigenbaum numbers and Dunbar numbers for neural netwoirks

See Guy Lakeman Bubble Theory for more details on keeping systems within finite working containers (villages communities)

Environment Economics Finance Mathematics Physics Biology Health Fractals Chaos TURBULENCE Engineering Navier Stokes Supply Demand Strategy

  • 7 years 2 weeks ago

Bio103 Bathtub Model

John Petersen
It is sometimes the case that the flow into a stock is not dependent on the amount of stuff stored in that stock.  A bathtub is often used as an example.  Think of the water stored in the bathtub as a stock.   Turn the spigot on and walk away and the flow into the bathtub is not determined by the stock.  The same is true for river flow into Lake Erie -- the rivers have no capacity to adjust flow based on how much water is already in the Lake.  Of course in both cases the stock has the capacity to overflow if teh stock exceeds some maximum capacity -- over the sides of tub or down Niagara Falls.

Biology Growth

  • 3 years 9 months ago

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