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

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. 

This model simulates the financial dynamics of an organic strawberry farm, illustrating how different variables impact the cumulative net income. Key inputs include the strawberry yield, sale price, worker hours, machine costs, and organic pesticide costs, all of which influence either the farm's revenue or expenses. The model allows adjustments to these variables, showing how factors like production levels, labor costs, and initial capital affect overall profitability. By visualizing these relationships, the model helps in exploring strategies to optimize income while managing costs effectively.

This model graphs the general idea of the inflow and outflow of energy in a small town in Ontario's power grid. The main sources of energy come from Nuclear being one of Ontario's biggest sources of energy, Wind, and Solar. The graph shows energy generation vs energy consumption and the demand in energy in the town.

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

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).

Relation between factors contributing to carbon emission and carbon absorption

This model simulates human population growth on both a global scale and a local scale for Ajax, Ontario. The global population starts at 8 billion and Ajax starts at 121,780 Carrying capacities are set to 12 billion for Earth and 150,000 for Ajax. The model demonstrates the growth, showing how populations grow and stabilize as they approach their environmental limits.

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

Brooklin is a small town that is developing quickly and has a younger average population than places like Whitby and Oshawa, therefore giving it a slightly higher birth rate. From when I was a child (Around 2008) it had a population of 15,000, and was estimated at >25,000 in 2018. This simple model is specifically for births and deaths, and doesn't take the fast development of neighbourhoods (i.e. people moving in from other places).

This model demonstrates how the population of trees fluctuates and changes when factors such as how much trees being planted and how many trees being harvested/torn down come into play! 

Star production was greater at the beginning of the universe, and has slowed down as lots of gases are now trapped in living stars. While this isn't true for every galaxy, this is a model simulating a galaxy where star formation rate is decreasing.

This model represents the population of ancient Rome in 200 BC with a density-dependent birth rate...the birth rate does not equal the death rate when the population is at carrying capacity because immigration and emigration are also factors. All values are based on historical estimates, as precise figures are not available.

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

This model displays how the population of the Earth changes. With a larger birth rate than death rate the population increases and heads towards K (carrying capacity). If the birth rate is lower than the death rate, the population will slowly diminish and will move away from carrying capacity.

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)

Tanjiopolis is a unique municipality located in northern Canada, known for its extreme seasonal climate where there is six months of very hot summers followed by six months of very cold winters, with no transitional seasons. This distinct environment has driven Tanjiopolis to innovate and thrive, harnessing its natural resources to achieve energy independence.

The municipality has invested heavily in a robust infrastructure of solar and wind generators, complemented by a few nuclear power facilities. The nuclear plants operate at only 10% of their maximum capacity during the summer, as the abundant solar energy meets the municipality's power needs. In contrast, during the winter, the nuclear facilities ramp up to 100% capacity to compensate for the reduced solar output due to limited sunlight.

Tanjiopolis takes pride in its commitment to sustainability, reinforced by a government-mandated policy that requires 2 solar panels per residential building, 4 solar panels per small business building, and 6 solar panels per large business building. This ensures that the municipality can sustain a population of 3 million people entirely through renewable energy sources, maintaining a self-sufficient power grid that operates independently from external systems.

This model simulates the global human population's growth and decline over a 50-year timeline, factoring in birth rates (density-dep), death rates, and Earth's carrying capacity (K). The model illustrates how population dynamics shift as the population approaches or moves away from K: when the population reaches K, the birth rate matches the death rate, causing no net change; when the population is below K, births exceed deaths, leading to growth; and when the population exceeds K, deaths surpass births, causing the population to shrink. Through this simulation, the model offers insights into possible population trends like stabilization, growth, or decline, and highlights the relationship between reproduction, mortality, and environmental limits. Clear units reflect these shifts per year.

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

This model simulates the growth of human populations at a global level and a local level (e.g., Oshawa) using logistic growth principles. It includes components for population size, birth rates, death rates, migration for local population, and carrying capacity. Each stock and flow is described with units and explanations.

The dynamics of homeless population in Toronto with constant homelessness and rehabilitation rates

The dynamics of a moose population with density-dependent birth rate...the birth rate equals the death rate when the population is at carrying capacity; the birth rate is greater than the death rate when the population is below carrying capacity; the birth rate is below the death rate when the population is above carrying capacity.