Assignments WS 2023/2024

Kopd05 - Simulace šíření pandemie

• Cílem simulace bude zkoupání šíření různých virů, podle jejich koeficientu šíření, uzdravování či úmrtnosti.
• Simulaci mohou využívat epidemiologové, ke zkoumání šířitelnosti virů a predikci vývoje pandemie.
• Simulace bude vypracována pomoci NetLogo - Agent based modelu
• V simulaci budou následující proměnné:
  • Rychlost šíření viru
  • Šance na uzdravení jedince
  • Riziko smrti
  • Počet jedinců
  • Počet nakažených
  • % imunních jedinců (náhodná)
  • Rychlost vakcinace
  • jeImunní
  • jeNakažený
  • jeOčkovaný
  • šanceNaUzdravení (náhodná)

• Data mohou být založena na reálných datech o virech, či mohou být nastaveny individuálně.
  Například šiřitelnost virusu bude založena na reprodukčním čísle R (lze dohledat na internetu)

• Vše bude založeno na volně dostupných datech online, které souvisí s daným tématem, virem, apod.

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

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

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
  • and also variables that depend on the basic ones: number of dead fish and sharks, number of plants eaten

• Rules:
  • Fish eat plants. If the fish does not eat any plant, it loses strength and eventually dies.
  • Sharks hunt fish. If the shark doesn't catch any fish, it dies.
  • Fish, plants and adults have the ability to reproduce and die over time.
  • Warmer water promotes faster reproduction of fish.
  • Colder water promotes faster reproduction of sharks.
  • A higher level of pollution slows down the reproduction process of fish and sharks, but on the contrary accelerates the reproduction of plants.

• The user has the option to set the number of fish, plants, sharks, water temperature and pollution level when starting 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 water systems.
• The data used in the simulation is based on real sources dedicated to the topic of aquatic systems (sources: Google Scholar, ProQuest), 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[User:DariaTut|DariaTut]] (talk) 20:27, 19 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)

Vysj06 - Simulace zemědělské výroby a klimatických změn

• Cíl simulace: Modelovat dopad klimatických změn na zemědělskou výrobu, včetně úrodnosti půdy, výnosů plodin a zavlažování
• Využití: Poskytnout zemědělcům, vědcům a politikům nástroje pro lepší plánování a adaptaci na klimatické změny.
• Metoda: Agent-based modelování, využití NetLogo.
• Proměnné:
  • Typy plodin (obilí, zelenina, ovoce).
  • Půdní podmínky a jejich změny.
  • Množství a rozložení srážek.
  • Teplotní změny.
  • Zavlažovací metody a jejich efektivita.

• Data: Založeno na reálných klimatických a zemědělských datech, včetně historických trendů a předpovědí. Možnost konfigurace parametrů.
• Výstup: Simulace poskytne uživatelům možnost vizualizace a porozumění vlivu různých klimatických scénářů na zemědělskou produkci a možné adaptační strategie.

Vysj06 (talk) 14:58, 19 December 2023 (CET)

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

Doba00 - Pricing in the Food Industry

• Simulation: The simulation will model the dynamics of food prices in the food industry, focusing on the interactions between different factors influencing it.
  • Can be used by policymakers in the food industry to better evaluate policy proposals.
  • Incorporated variables: production costs, inflation rates, consumer demand, policy interventions
  • Random variables: weather

• Goal: The goal of the simulation is to analyze the factors influencing food prices and to evaluate the stability and resilience of the food industry's pricing system under different scenarios.

• Method: Vensim

• Data: https://www.czso.cz/

• 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)

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

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)