The population dynamics of rabbits (prey) and foxes (predator) under different birth and death rates for both 

The UC Davis study gives yields for California, but the yields in Ontario are much less due to the less favourable growing conditions and season length (source:https://www.ontario.ca/page/dayneutral-strawberries). The revenue streams are different for the June-bearing and day-neutral strawberries depending on how many acres of each we have due to seasonal and off-season price differences and different yearly yields. The startup cost is the sum of the equipment, irrigation, and plant establishment costs, all of which depend on acres and set rates from the UC Davis study or the "Ontario Berry Crops Establishment and Production Costs 2022 Economic Report". The yearly expenses are labour costs, water cost, and fuel cost, which all depend on the number of acres and price rates for these aspects of the farm.

This complex system models the dynamics and impacts of transportation efficacy and efficiency of sustainable urban transportation in Durham Region. Within the Regional Municipality of Durham, there are eight local Municipalities consisting of Ajax, Brock, Clarington, Oshawa, Pickering, Scugog, Uxbridge, and Whitby, which account for a total population of approximately 750,000 individuals as of 2023. As Durham Region continues to expand and increase in population, road structures will be faced with an increasing traffic load that will generate a significant amount of carbon emissions. Due to the prevailing climate concerns, the need for sustainable urban transportation is evident.

Urban transportation systems are a cornerstone of city life. They connect communities and businesses, while providing access to services, supporting economic and social activities. Maintaining a vast network of transportation systems as cities grow, becomes increasingly challenging. These challenges, consisting of traffic congestion, environmental impact, and diverse transportation needs, become of paramount concern, such that political campaigns run solely on these platforms.

Within this stock and flow model are the main variables that comprise transportation emissions within Durham Region. The urban transportation model highlights different modes of transportation in the form of stocks, which consist of public transit users (bus), fossil fuel car drivers, and EV car drivers, as well as the inflows and outflows that are influenced by various variables. 

All data used within this model was obtained from the various sources on the internet. The data used within the model is based off of estimate values. Data pertaining to population values and cities/towns that comprise Durham Region, ON obtained from the following source(s): 

Regional Municipality of Durham. (n.d.). Demographics and statistics. Durham Region Economic Development and Tourism. Retrieved November 24, 2024, from https://www.durham.ca/en/economic-development/invest-and-grow/demographics-and-statistics.aspx

Regional Municipality of Durham. (n.d.). Local municipalities. Durham Region. Retrieved from https://www.durham.ca/en/regional-government/local-municipalities.aspx

A model demonstrating the differences in productivity and cost in an arbitrary workforce (this could be one company/institution or an entire area). I used AI to assist me in creating this model, by explaining to it what I would like to have as my main stocks and what I'd like to model, and then had it suggest variables, flows, and links (and thus equations for the flows and variables as well). The actual initial values were also suggested by the AI.

This complex system models the dynamics and impacts of transportation efficacy and efficiency of sustainable urban transportation in Durham Region. Within the Regional Municipality of Durham, there are eight local Municipalities consisting of Ajax, Brock, Clarington, Oshawa, Pickering, Scugog, Uxbridge, and Whitby, which account for a total population of approximately 750,000 individuals as of 2023. As Durham Region continues to expand and increase in population, road structures will be faced with an increasing traffic load that will generate a significant amount of carbon emissions. Due to the prevailing climate concerns, the need for sustainable urban transportation is evident.

Urban transportation systems are a cornerstone of city life. They connect communities and businesses, while providing access to services, supporting economic and social activities. Maintaining a vast network of transportation systems as cities grow, becomes increasingly challenging. These challenges, consisting of traffic congestion, environmental impact, and diverse transportation needs, become of paramount concern, such that political campaigns run solely on these platforms.

Within this stock and flow model are the main variables that comprise transportation emissions within Durham Region. The urban transportation model highlights different modes of transportation in the form of stocks, which consist of public transit users (bus), fossil fuel car drivers, and EV car drivers, as well as the inflows and outflows that are influenced by various variables. 

All data used within this model was obtained from the various sources on the internet. The data used within the model is based off of estimate values. Data pertaining to population values and cities/towns that comprise Durham Region, ON obtained from the following source(s): 

**NOTE: Kindly note we have used 10 sources in total that we have referenced - 2 are listed below as these 2 sources were used for the above description - the other references are located in the variables where the sources were used. 

Regional Municipality of Durham. (n.d.). Demographics and statistics. Durham Region Economic Development and Tourism. Retrieved November 24, 2024, from https://www.durham.ca/en/economic-development/invest-and-grow/demographics-and-statistics.aspx

Regional Municipality of Durham. (n.d.). Local municipalities. Durham Region. Retrieved from https://www.durham.ca/en/regional-government/local-municipalities.aspx

Group 8 Members: Leda Alizada (100821720), Pritika Lally (100867821), Mahad Rashid (100779108), Rileigh Rodych (100515185), Sami Siddique (100460897)

A model demonstrating the differences in productivity and cost in an arbitrary workforce (this could be one company/institution or an entire area). I used AI to assist me in creating this model, by explaining to it what I would like to have as my main stocks and what I'd like to model, and then had it suggest variables, flows, and links (and thus equations for the flows and variables as well). The actual initial values were also suggested by the AI.

The changing dynamics of distance and velocity over time according to several variables including, constant acceleration, constant mass, force and work. 

The dynamics of Codfish (prey) and Shark (predator) populations. 

The model showcases a connected population growth of Codfish and Sharks in an ocean. The model has two positive feedback loops being the birth-rate of codfish and the birthrate of sharks; and two negative feedback loops being the death-rate of codfish and the death-rate of sharks. The stock components of the model link to each other through feedback loops. The feedback loops are the variables of codfish death-rate which depends on the stock of number of sharks, and the variable of shark’s birth-rate which depends on the stock of number of codfish in the ocean/model. 

Group 4:

Nami Zuha (100821467)

Zachary Wayne (100814747)

The purpose of the InsightMaker model is to model how Waste-to-Energy (WtE) technology impacts waste management efficiency, energy output and greenhouse gas emissions for the scale of ten years (assuming the WtE technology integration in an urban setting). This will determine if WtE has the capability of minimizing the reliance on waste landfills as well as assisting reaching renewable energy targets. This model will shine light on Waste-to-Energy sustainability opportunities and challenges.

Orange variables are associated with calculating waste volume, green variables are associated with calculating energy generation.

Canadian population dynamics where growth rate (birth rate plus immigration rate) depends on density ...... When the population is at carrying capacity, the growth rate is equal to the mortality rate; when the population is below carrying capacity, the growth rate is greater than the mortality rate; when the population is above carrying capacity, the growth rate is lower than the mortality rate.

This model simulates the basic dynamics of a water reservoir, including the impact of rainfall, community water consumption, conservation efforts, and evaporation. The model shows how the reservoir’s water level changes over time based on natural inflows and human , nature water use.

This population dynamics model simulates the growth and decline of the Javan Rhinoceros population over time (over course of 50 years). The model focuses on the balance between births and deaths within the population, providing insight into how different factors affect the species' survival and growth rates. By using constant fertility and death rates, the model reflects the natural dynamics of the Javan Rhinoceros population, illustrating the interplay between reproduction and mortality.

Group 4:

Nami Zuha (100821467)

Zachary Wayne (100814747)

The purpose of the InsightMaker model is to model how Waste-to-Energy (WtE) technology impacts waste management efficiency, energy output and greenhouse gas emissions for the scale of ten years (assuming the WtE technology integration in an urban setting). This will determine if WtE has the capability of minimizing the reliance on waste landfills as well as assisting reaching renewable energy targets. This model will shine light on Waste-to-Energy sustainability opportunities and challenges.

Orange variables are associated with calculating waste volume, green variables are associated with calculating energy generation.

In this model, I will be demonstrating my understanding of Modelling a Human Population by using Oshawa as a current example. For this model, we begin with a current population of 170,000. However, with Oshawa's "theoretically new" (just to demonstrate my understanding) neighbourhoods being built the population will change to reflect that and the city's appropriate carrying capacity! By using variable factors such as "Moving in/inflow rate" and "Moving out/outflow rate" to reflect the number of current residents residing within Oshawa (nResidents).

The changing dynamics of distance and velocity over time according to several variables including, constant acceleration, constant mass, force and work. 

Group 4:

Nami Zuha (100821467)

Zachary Wayne (100814747)

The purpose of the InsightMaker model is to model how Waste-to-Energy (WtE) technology impacts waste management efficiency, energy output and greenhouse gas emissions for the scale of ten years (assuming the WtE technology integration in an urban setting). This will determine if WtE has the capability of minimizing the reliance on waste landfills as well as assisting reaching renewable energy targets. This model will shine light on Waste-to-Energy sustainability opportunities and challenges.

Orange variables are associated with calculating waste volume, green variables are associated with calculating energy generation.

The changing dynamics of distance and velocity over time according to several variables including, constant acceleration, constant mass, force and work. 

This model explores the relationship between AI resource investment and the resulting gains in efficiency and effectiveness. The purpose is to illustrate how allocating resources to AI can drive improvements in performance but may also reach a point of diminishing returns.
The model helps users understand how different investment rates impact AI-driven improvements in efficiency and effectiveness.

By adjusting the Investment Rate and observing changes in Efficiency Benefit and Effectiveness Benefit, users can gain insight into the tradeoffs of AI investment, observing points where added investment yields smaller returns. This visualization is valuable for decision-makers seeking to optimize resource allocation for maximum performance.

The dynamics of mouse (prey) and cat (predator) population

This complex system models the dynamics and impacts of transportation efficacy and efficiency of sustainable urban transportation in Durham Region. Within the Regional Municipality of Durham, there are eight local Municipalities consisting of Ajax, Brock, Clarington, Oshawa, Pickering, Scugog, Uxbridge, and Whitby, which account for a total population of approximately 750,000 individuals as of 2023. As Durham Region continues to expand and increase in population, road structures will be faced with an increasing traffic load that will generate a significant amount of carbon emissions. Due to the prevailing climate concerns, the need for sustainable urban transportation is evident.

Urban transportation systems are a cornerstone of city life. They connect communities and businesses, while providing access to services, supporting economic and social activities. Maintaining a vast network of transportation systems as cities grow, becomes increasingly challenging. These challenges, consisting of traffic congestion, environmental impact, and diverse transportation needs, become of paramount concern, such that political campaigns run solely on these platforms.

Within this stock and flow model are the main variables that comprise transportation emissions within Durham Region. The urban transportation model highlights different modes of transportation in the form of stocks, which consist of public transit users (bus), fossil fuel car drivers, and EV car drivers, as well as the inflows and outflows that are influenced by various variables. 

All data used within this model was obtained from the various sources on the internet. The data used within the model is based off of estimate values. Data pertaining to population values and cities/towns that comprise Durham Region, ON obtained from the following source(s): 

Regional Municipality of Durham. (n.d.). Demographics and statistics. Durham Region Economic Development and Tourism. Retrieved November 24, 2024, from https://www.durham.ca/en/economic-development/invest-and-grow/demographics-and-statistics.aspx

Regional Municipality of Durham. (n.d.). Local municipalities. Durham Region. Retrieved from https://www.durham.ca/en/regional-government/local-municipalities.aspx

Group 8 Members: Leda Alizada (100821720), Pritika Lally (100867821), Mahad Rashid (100779108), Rileigh Rodych (100515185), Sami Siddique (100460897)

This model simulates a 100 acre organic strawberry farm. The net income of this farm is determined through several factors such as its yield and sales and its harvesting and packaging and labour as well. Adjusting the value of acres can increase farm size and see cost differences and net income differences.

The dynamics of moose (prey) and wolf (predator) populations

The dynamics of mouse (prey) and cat (predator) population