Assignments WS 2023/2024

Kopd05 - Simulation of pandemic spread

• The aim of the simulation will be to investigate the spread of different viruses, according to their coefficient of spread, recovery or mortality.
• The simulation can be used by epidemiologists to study the spread of viruses and predict the evolution of a pandemic.
• The simulation will be developed using the NetLogo agent-based model
• The following variables will be included in the simulation:
  • Virus spread rate
  • Chance of recovery of the individual
  • Risk of death
  • Number of individuals
  • Number of people infected
  • % of immune individuals (random)
  • Vaccination rate
  • isImmune
  • isInfected
  • isVaccinated
  • ChanceofCure (random)

• Data can be based on real virus data or can be set individually.
  For example, the spread of a virus will be based on the reproduction number of R (can be looked up on the internet)

• Everything will be based on freely available online data related to the topic, virus, etc.

Kopd05 (talk) 21:02, 12 December 2023 (CET)

This course is in English. We accept English versions only. Tomáš (talk)

Very general. Please, specify in deep. What kind(s) of viruses, how will you model the population, how should it look like, etc. etc. Tomáš (talk) 19:15, 20 December 2023 (CET)

Tutd00 - Aquatic ecosystem simulation

• The simulation is inspired by the classic "predator and prey" model. The goal is to model the behavior of fish, plants and predators in the generated aquatic environment.
• The simulation will be developed using the NetLogo - Agent based model
• The following variables will be in the simulation:
  • The water temperature
  • Pollution level
  • Number of fish
  • Number of sharks
  • Number of plants
  • Time of death of fish and shark (random)
  • Fish and shark breeding time (random, but only after feeding)
  • and also variables that depend on the basic ones: number of dead fish and sharks, number of plants eaten

• The user has the option to set the number of fish, plants, sharks, water temperature and pollution level when starting the simulation.
• The rules: Cold water temperatures accelerate the rate of reproduction of predators, while warm water speeds up the rate of reproduction of fish.High water pollution slows down the reproduction of fish and sharks, but speeds up the growth of plants. The user can change settings during the simulation.
• This simulation allows the user to investigate how different parameter configurations affect ecosystem stability and dynamics and could be modified and used to model real aquatic systems.
• The data used in the simulation is based on real sources dedicated to the topic of aquatic systems (example: https://www.nalms.org/wp-content/uploads/2018/09/31-2-5.pdf), but will be implemented in a simplified form.

DariaTut (talk) 10:47, 18 December 2023 (CET)

This course is in English. We accept English versions only. Tomáš (talk) 19:58, 19 December 2023 (CET)

Edited DariaTut (talk) 20:27, 19 December 2023 (CET)

Predator-prey is one of the most widely implemented models. Hence, you should suggest something what make your implementation special and new. What is the added value compared to the predator-prey? Tomáš (talk) 19:19, 20 December 2023 (CET)

Kubs09 - Simulation of Passenger Behavior at the Main Train Station

• Topic: Passenger behavior when boarding trains at the entire Main Train Station
• Utilization: This could be utilized, for instance, by Czech Railways/main station administrators to better adjust trains and their arrival positions.
• Method: Agent-based modelling, Netlogo
• Variables:
  • Number of passengers
  • Number of trains
  • Passengers positions
  • Timetable (train departures/arrivals)
  • Delays (random)
  • isCollision
  • isDelay(random)

• Sources: For this simulation, predominantly sources from Google Scholar would be used, or scientific articles found in the E-library of VSE.

Kubs09 (talk) 20:35, 19 December 2023 (CET)

This course is in English. We accept English versions only. Tomáš (talk) 19:58, 19 December 2023 (CET)

Edited.

Vysj06 - Simulation of Agricultural Production and Climate Change

• Objective of the simulation: To model the impact of climate change on agricultural production, including soil fertility, crop yields, and irrigation.
• Usage: To provide farmers, scientists, and policymakers with tools for better planning and adaptation to climate changes.
• Method: Agent-based modeling, using NetLogo.
• Variables:
  • Types of crops (grains, vegetables, fruits).
  • Soil conditions and their changes.
  • Amount and distribution of precipitation.
  • Temperature changes.
  • Irrigation methods and their efficiency.

• Data: Based on real climate and agricultural data, including historical trends and forecasts. Option to configure parameters.
• Output: The simulation will provide users with the ability to visualize and understand the impact of various climate scenarios on agricultural production and possible adaptation strategies.

Vysj06 (talk) 21:25, 19 December 2023 (CET)

This course is in English. We accept English versions only. Tomáš (talk) 19:59, 19 December 2023 (CET)

Edited

Doba00 - Factors that influence beer fermentation

• Simulation: This simulation will focus on addressing external and internal factors that have an effect on beer fermentation and influence alcohol percentage in the final product.
  • Can be used by brewery makers in the food industry to better evaluate beer-making conditions in order to make preferred outcomes of alcohol levels.
• Incorporated variables:
  • temperature,
  • yeast type,
  • gravity of the wort,
  • carbon dioxide production,
  • cooling,
  • yeast reuse

• Goal: The simulation aims to analyze factors affecting the beer fermentation process and evaluate the best conditions for getting preferred outcomes in alcohol percentage in the final product.

• Method: Vensim

• Data:
  • https://www.phind.com/search?cache=uu8fcm0sqdc1fyznvi0o0rhx&fbclid=IwAR30Y-m8maeJI3WFcW-fLvqTlRov_63mfnCGPxC9RiANZQbkQaYNO8MDl0M
  • https://www.researchgate.net/publication/284725490_Biochemistry_of_beer_fermentation
  • chrome-extension://efaidnbmnnnibpcajpcglclefindmkaj/http://ucitelchemie.upol.cz/materialy/vkpch/vyroba_piva_text_pro_ucitele.pdf
  • https://grainfather.com/beer-fermentation-process/#:~:text=Typically%20the%20cooler%20the%20temperature,unwanted%20attributes%20in%20the%20beer.

• Author: Doba00 (talk) 16:25, 19 December 2023 (CET)

Please provide us with the reference to particular data, you wil base your simulation on. How exactly will your simulation work? How will you simulate the dynamics of food prices in the food industry? From what data you will derive the formulas neccesary for it? Oleg.Svatos (talk) 17:17, 19 December 2023 (CET)

Edited. After a thorough consideration, I have decided to change the topic completely. I hope it is more suitable now. Doba00 (talk) 15:51, 20 December 2023 (CET)

Tata05 - Simulation of the Ocean Carbon Uptake and Atmospheric Carbon Dioxide

• Problem definition: I want to simulate the process of the Life cycle of processing carbon dioxide from the atmosphere and increasing the stored carbon dioxide on the ocean floor. This process influences ocean acidification and affects the entire climate. The ocean absorbs carbon dioxide from the atmosphere wherever air meets water. Regarding scientists oceans absorb 30% of our emissions, driven by a huge carbon pump.
• Method: Agent-based simulation, NetLogo.
• Variabels:
  • Solar Energy
  • Atmospheric CO2
  • Changes in temperature
  • Change in water acidity
  • CO2 dissolving
  • Carbon capture and storage
• Resource: Information from National Oceanic and Atmospheric Administration, Nasa Global Climate, https://www.soest.hawaii.edu/oceanography/faculty/zeebe_files/Publications/ZeebeWolfEnclp07.pdf

Tata05 (talk) 16:46, 19 December 2023 (CET)

Please provide us with the reference to literature with formulas you will base your simulation on. Without it, it is impossible to evaluate wheather the simulation will make sense. Oleg.Svatos (talk) 17:13, 19 December 2023 (CET)

Reference: https://www.soest.hawaii.edu/oceanography/faculty/zeebe_files/Publications/ZeebeWolfEnclp07.pdf

Edited Tata05 (Tata05) 21:49, 19 December 2023 (CET)

akee00== Urban Traffic flow and Pollution Control

• Primary objective: To analyze the impact of different traffic management strategies on urban traffic flow and air pollution levels.
• Problem to solve: Determining the most effective traffic management strategy that minimizes traffic congestion and reduces air pollution in an urban environment.
• Context: With growing urban populations, traffic congestion and pollution have become critical issues. This simulation aims to explore how various traffic control measures can alleviate these problems.
• Method and Simulation Environment:
  • Agent based Modelling
  • Simulation Tool: Netlogo.
• Environment Setup: A simulated urban area with a grid of streets, traffic signals, vehicles, and pollution indicators.
• Variables and Data:
• Random variables:
  • Vehicle breakdowns,
  • Driver behaviour: route choice and speed variability
  • Traffic incidents
  • Weather conditions
  • Vehicle emission rates.
• Incorporated (deterministic) variables:
  • Vehicle agents: count, types
  • Traffic signal agents: signal timing, adaptive signals
  • Pollution measurement: Baseline emission levels, Air quality index
  • Traffic Management Strategies
  • Road layout.
• Data source:
  • Traffic and transportation data for Prague from praha.eu
• Expected outcome: The simulation should reveal the most effective traffic management strategies for reducing congestion and pollution. By comparing these results with real-world data, urban planners can make informed decisions to improve traffic flow and air quality in cities.

This is generally a good topic, however the scope you suggest is really large and you would be hardly able to deliver results. Limit the model reasonably, e.g. choose just a limited area or limit the model different way. Tomáš (talk) 20:04, 19 December 2023 (CET)

vala18 - Space Junk: a satelite/debris collision simulator

• I would like to simulate a movement of satellites and space debris (AKA space junk) in a subsection of earths orbit (represented as 2D plane), where the satellites move on predetermined trajectories, but the junk's motion is somewhat randomized. If debris collides with another debris, it changes direction and creates little bit more debris, if debris collides with a satelite, the satelite is destroyed and a lot of debris is created. Then I would run the simulation to calculate a meantime between collisions based on starting conditions, such as the number of satellites, debris density and debris multiplication (how many pieces of additional debris collisions create).
• Space debris poses a significant issue due to the growing amount of defunct satellites, spent rocket stages, and fragments in Earth's orbit. The escalating debris increases collision risks, jeopardizing operational satellites and future space missions. This problem threatens space infrastructure, exacerbates space congestion, and raises the specter of generating even more fragments through collisions, potentially creating a self-sustaining cycle of space debris proliferation. Addressing this issue is crucial to ensuring the sustainability of space activities and preventing long-term consequences for Earth's orbital environment and space exploration. Proposed simulator could help two show and calculate conditions uder which such a self-sustaining debris proliferation arises; albeit with a simplified model, and hopefully educate about the aforementioned issue.
• NetLogo - agent based modeling (two types of agents: satellites and debris)
  • The model will make the following simplifications:
    • subsection of Earth's orbit will be represented as a 2D "map" of fixed size (pixels) forming a grid
    • objects (satellites and debris) can move on the map only in 8 distinct directions
    • all objects move at the same speed (one pixel per turn)
    • objects are squares; the minimal size is 1x1 pixel, then 2x2 pixels, etc...
    • when objects collide the resulting number of debris is a random integer between 1 and the sum of sizes (pixels) of colliding objects.
    • mass of objects is proportional to their size, i.e. 1 (pixel) = 1 unit of mass
    • all objects follow Newton's laws of motion
• Variables for starting conditions
  • map size
  • number of satellites
  • number of debris
  • starting position and direction of objects
• Variables of agents (satellites and debris)
  • size
  • mass
  • direction
• In order to simulate realistic scenarios - Data from NASA and other relevant studies regarding density of debris and number of satellites in orbit will be obtained to inform the starting conditions of the proposed model. Namely, I plan to use data obtained and published by:
  • ORBITAL DEBRIS PROGRAM OFFICE
  • Low Earth Orbit Visualization

vala18(AdamValtr) (talk) 16:57, 20 December 2023 (CET)