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Attempts to model in the social dynamics of  Pavilion host aquisition
Advanced Pavilion 2 Host Conversion Model
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From Stephen Toulmin's Book The Uses of Argument Cambridge University Press 2003. See wikipedia  Also Francis Miller Claim Hexagon 2025 web article

Toulmin and Miller Argument Models
4 months ago
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Rich picture version of Tanner's Clinical Judgment Model, with the addition of clinical reasoning cycle concepts from T Levett-Jones et al Nurse Education Today 30 (2010) 515-520

Digital Literacy & Electronic Healthcare Record Integration
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Based on the Market and Price simulation model in System Zoo 3, Z504. In this model the profit calculations were not realistic. They were based on the per unit profit, which does not take items not sold into account. Also the model was not very clear on profit since it was included in the total production costs and consequently in the unit costs and subsequently profit was calculated by subtracting unit costs of the market price. Thus profit had a double layer which does not make the model better accessible. I have tried to remedy both in this simplified version.
Simplified and changed Z504 Market and Price - System Zoo 3
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Based on the Market and Price simulation model in System Zoo 3, Z504. I made some more intrusive changes that make the model more realistic, or more 'economic', in another version 'simplified and improved'. 
Simplified Z504 Market and Price - System Zoo 3
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We start with an SEIR social virality model and adapt it to model social media adoption of Playcast Hosts.  *Note that this model does not attempt to model WOM emergent virality.  

Social Media Virality3
10 months ago
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Simulation d'un MRU d'un corps qui avance avec une régulation de vitesse réaliste.
serie 08 ex4 Une regulation de vitesse plus realiste V1
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Simple box model for atmospheric and ocean carbon cycle, with surface and deep water, including DIC system, carbonate alkalinity, weathering, O2, and PO4 feedbacks.
Lab 10 (predictive model eg. keeling curve)
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Problem 3 - Instagram
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VA - socio-economic
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Model of the autonomi network economics
autonomi
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This version of the CAPABILITY DEMONSTRATION model has been further calibrated (additional calibration phases will occur as better standardized data becomes available).  Note that the net causal interactions have been effectively captured in a very scoped and/or simplified format.  Relative magnitudes and durations of impact remain in need of further data & adjustment (calibration). In the interests of maintaining steady progress and respecting budget & time constraints, significant simplifying assumptions have been made: assumptions that mitigate both completeness & accuracy of the outputs.  This model meets the criteria for a Capability demonstration model, but should not be taken as complete or realistic in terms of specific magnitudes of effect or sufficient build out of causal dynamics.  Rather, the model demonstrates the interplay of a minimum set of causal forces on a net student progress construct -- as informed and extrapolated from the non-causal research literature.
Provided further interest and funding, this  basic capability model may further de-abstracted and built out to: higher provenance levels -- coupled with increased factorization, rigorous causal inclusion and improved parameterization.
Version 6A NULL PRACTICE Student-Home-Teachers-Classroom
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plus realiste
mru 4(1)
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This model tries to show the effect of car-sharing (CS) and its possible effect on reducing CO2 emission over a time period of 20 years. The main target of car-sharing is to reduce individual car ownership and the total number of cars on. In addition to that, with more fuel-efficient cars and the increased use of electric cars it could be an effective tool to reduce CO2 emissions.

We assumed that the total travel demand (yearly driven passenger units) shifts from private car ownership to CS services over a time of 20 years [1]. The possibility of buying a private car and a CS car is calculated by dividing the travel demand and the maximal travel demand possible, multiplied by the number of families without a private car respectively with the number of CS families. Private cars will abrade increasing the number of families without a car. However, in our model they will decide to join CS thereby increasing the number of CS families. By this the number of private cars will decrease while number of CS cars will increase. Gasoline CS cars will change over time into electric CS cars with a benefit for the CO2 emission due to lower lifecycle emission. Since CS cars are utilized more, they will abrade faster. However, this will overall result in more fuel efficient cars with another benefit for CO2 emissions.

Private Autos(100.000) Familien ohne private Autos(20.000) CS Familien(1.000) CS Autos - Benzin( 5.000) Bedarf private Autos(1.920.000.000 yearly passanger distance) Umstiegsrate(0.25) Kraftstoffeffizienz(0.15) CO2 emission of gasoline car(24 t CO2) [2] CO2 emission of electric car(19 t CO2)[2]

Thing to try (with influence on CO2 emission of CS cars):
Change the exchange-rate with which CS cars with gasoline motor are exchanged by electric motor  Change the fuel-efficiency of CS cars Both will influence the reduction of CO2 emission by CS cars

Extending the model:Demand for public transportation Would create a more realistic model since there are not only the options of having a private car or using CS.Public transportation could help reduce overall CO2 emissions.

Credits and references:
Cornelius Frank & Ameur GlouiaDepartment of Bioengineering SciencesWeihenstephan-Triesdorf University of Applied Science85354 Freising, Germanycornelius.frank@student.hswt.de / ameur.glouia@student.hswt.de

[1] Kawaguchi, T. (2019). Scenario Analysis of Car- and Ride-Sharing Services Based on Life Cycle Simulation. Procedia CIRP. 80: 328-333
[2] Low Carbon Vehicle Partnership. (2015). Lifecycle emissions from cars.


Effect of Car-sharing on CO2 Emission - Projectwork Dyn. Sim. SS19 HSWT - CorneliusFrankAmeurGlouia v2.0
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Flow of successful SBC
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SMA project
CO2 Emissions by Passenger Cars in SG
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Peircean process approach to Causation from Menno Hulswit's article. See also Peirce thought insight 

Peircean Causation and Causality
2 months ago
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This is a clone of "Fast Fashion ISCI 360 Solutions Final submission" created by user "V B" which we are using as the foundation for an exercise in the DTU course 12100 "Quantitative sustainability".

The model takes into account clothing production and textile waste on a global scale while incorporating Vancouver's own "Fast Fashion" issue into the model.

Please refer to the notes for each variable and stock to see which links were hidden from the model.

Part 2: Our solution for the issue surrounding "Fast Fashion" focuses on increasing individuals education about sustainability and how they can help reduce negative impacts on the environment by shopping less, recycling and donating. This effect of education on sustainability is seen in the "Online Shopping" equation where the impact of "Education on Sustainability" is increased by x1.5 which impacts the entire model. Furthermore, components of the feedback loop on the right are also influenced by increasing education on sustainability and thus, those values were altered accordingly. These values were chosen arbitrarily by taking into account that doubling any value is not realistic so the change should be between x1.0 and x2.0.
Clone of Clone of Fast Fashion ISCI 360 Solutions Final Edit
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Simple box model for atmospheric and ocean carbon cycle, with surface and deep water, including DIC system, carbonate alkalinity, weathering, O2, and PO4 feedbacks.
Lab 11, coastal model
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Spring, 2020: in the midst of on-line courses, due to the pandemic of Covid-19.

With the onset of the Covid-19 coronavirus crisis, we focus on SIRD models, which might realistically model the course of the disease.

We start with an SIR model, such as that featured in the MAA model featured in
https://www.maa.org/press/periodicals/loci/joma/the-sir-model-for-spread-of-disease-the-differential-equation-model

Without mortality, with time measured in days, with infection rate 1/2, recovery rate 1/3, and initial infectious population I_0=1.27x10-4, we reproduce their figure

With a death rate of .005 (one two-hundredth of the infected per day), an infectivity rate of 0.5, and a recovery rate of .145 or so (takes about a week to recover), we get some pretty significant losses -- about 3.2% of the total population.

Resources:
  1. http://www.nku.edu/~longa/classes/2020spring/mat375/mathematica/SIRModel-MAA.nb
  2. https://www.maa.org/press/periodicals/loci/joma/the-sir-model-for-spread-of-disease-the-differential-equation-model
SD USA
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This version of the CAPABILITY DEMONSTRATION model has been further calibrated (additional calibration phases will occur as better standardized data becomes available).  Note that the net causal interactions have been effectively captured in a very scoped and/or simplified format.  Relative magnitudes and durations of impact remain in need of further data & adjustment (calibration). In the interests of maintaining steady progress and respecting budget & time constraints, significant simplifying assumptions have been made: assumptions that mitigate both completeness & accuracy of the outputs.  This model meets the criteria for a Capability demonstration model, but should not be taken as complete or realistic in terms of specific magnitudes of effect or sufficient build out of causal dynamics.  Rather, the model demonstrates the interplay of a minimum set of causal forces on a net student progress construct -- as informed and extrapolated from the non-causal research literature.
Provided further interest and funding, this  basic capability model may further de-abstracted and built out to: higher provenance levels -- coupled with increased factorization, rigorous causal inclusion and improved parameterization.
Version 6B: Calibrated Student-Home-Teachers-Classroom
Insight diagram
This is a clone of "Fast Fashion ISCI 360 Solutions Final submission" created by user "V B" which we are using as the foundation for an exercise in the DTU course 12100 "Quantitative sustainability".

The model takes into account clothing production and textile waste on a global scale while incorporating Vancouver's own "Fast Fashion" issue into the model.

Please refer to the notes for each variable and stock to see which links were hidden from the model.

Part 2: Our solution for the issue surrounding "Fast Fashion" focuses on increasing individuals education about sustainability and how they can help reduce negative impacts on the environment by shopping less, recycling and donating. This effect of education on sustainability is seen in the "Online Shopping" equation where the impact of "Education on Sustainability" is increased by x1.5 which impacts the entire model. Furthermore, components of the feedback loop on the right are also influenced by increasing education on sustainability and thus, those values were altered accordingly. These values were chosen arbitrarily by taking into account that doubling any value is not realistic so the change should be between x1.0 and x2.0.
Fast Fashion ISCI 360 Solutions Final Edit
9 months ago
Insight diagram
This model simulates the electricity supply and demand dynamics for the Napanee community in Ontario, with a focus on the impact of micro-generated solar electricity from households and businesses.

Units Used in the Model:
  • Electricity Supply: Measured in MWh/h (megawatt-hours per hour), representing the total amount of electricity available from both external sources and local solar generation.
  • Electricity Demand: Measured in MWh/h, covering both household and business consumption.
  • Micro-Generated Solar Electricity: Solar power generated by households and businesses, measured in MWh/h.
Key Components:
  1. Electricity Supply: The total electricity provided by both external sources (the grid) and solar generation.
  2. Electricity Demand: The total community demand, which includes household and business consumption and varies over a 24-hour period based on usage patterns.
  3. Micro-Generated Solar: Solar power generated by both households and businesses. This power reduces the demand for grid electricity and follows a sinusoidal pattern, peaking during the day and dropping to zero at night.
  4. Total Demand: The remaining electricity that the grid needs to supply after accounting for micro-generated solar power.
Napanee Community Electricity Model
Insight diagram
This simulation allows you to compare different approaches to influence flow, the Flow Times and the throughput of a work process.

By adjusting the sliders below you can 
  • observe the work process without any work in process limitations (WIP Limits), 
  • with process step specific WIP Limits* (work state WIP limits), 
  • or you may want to see the impact of the Tameflow approach with Kanban Token and Replenishment Token 
  • or see the impact of the Drum-Buffer-Rope** method. 
* Well know in (agile) Kanban
** Known in the physical world of factory production

The "Tameflow approach" using Kanban Token and Replenishment Token as well as the Drum-Buffer-Rope method take oth the Constraint (the weakest link of the work process) into consideration when pulling in new work items into the delivery "system". 

You can also simulate the effects of PUSH instead of PULL. 

Feel free to play around and recognize the different effects of work scheduling methods. 

If you have questions or feedback get in touch via twitter @swilluda

The work flow itself
Look at the simulation as if you would look on a kanban board

The simulation mimics a "typical" software delivery process. 

From left to right you find the following ten process steps. 
  1. Input Queue (Backlog)
  2. Selected for work (waiting for analysis or work break down)
  3. Analyse, break down and understand
  4. Waiting for development
  5. In development
  6. Waiting for review
  7. In review
  8. Waiting for deployment
  9. In deployment
  10. Done
Kanban Board Simulation - WIP Limit, Tameflow Kanban Token and Drum-Buffer-Rope