This document describes a group assignment to simulate a Biosawit experiment using Stella software. The experiment models the relationship between palm production, rat population, and owl population over a food chain. The group conducted 4 simulations varying the initial rat population from 5,000 to 30,000 while keeping the initial owl population at 5. All simulations showed palm production initially increasing as the rat population decreased due to owl predation, then fluctuating as the rat and owl populations fluctuated in response to each other over time.
This document provides information about a practical report on the STELLA simulation software. STELLA allows users to construct dynamic models that simulate biological systems using stocks, flows, converters and connectors. The report discusses three graphs generated using STELLA that model the relationship between rat and owl populations and palm oil production. The graphs demonstrate how increasing rat populations and decreasing owl populations can negatively impact palm oil yields over time. STELLA provides an engaging way for students to explore variables and conduct experiments to better understand complex systems.
This document discusses the use of simulation software Stella in education. It provides an overview of simulation theory and how Stella works. A case study is described where Stella was used to simulate predator-prey dynamics. Advantages of using simulation in education include motivating students through active learning and problem solving. Limitations include teachers lacking expertise and students not developing manual science skills. In conclusion, simulation can enhance learning if used properly to engage students in systems thinking.
This document discusses a simulation created using Stella software to model biological control in a palm oil ecosystem.
The simulation models the relationship between rat populations, owl populations as biological control agents, and palm oil production. The simulation showed that a high rat population with a low owl population leads to low palm production, while a low rat population with a high owl population leads to high palm production.
The simulation demonstrated that rat populations, owl populations, and palm oil production are interrelated through a food chain, with palm as the producer, rats as the primary consumer, and owls as the secondary consumer. The simulation helps to understand how changes in one part of the ecosystem can impact other parts.
This document discusses simulation as a learning tool. It provides examples of using simulations to model biological systems and explain complex relationships. Simulations allow students to interact with realistic scenarios, helping increase engagement and conceptual understanding. The document advocates for using simulations more in teaching, but cautions that teachers need to carefully consider how to integrate them and assess their effectiveness.
This document provides information about a practical report on the STELLA simulation software. STELLA allows users to construct dynamic models that simulate biological systems using stocks, flows, converters and connectors. The report discusses three graphs generated using STELLA that model the relationship between rat and owl populations and palm oil production. The graphs demonstrate how increasing rat populations and decreasing owl populations impact palm oil yields over time. STELLA provides an engaging learning tool but also has disadvantages like requiring computer access and training to use.
This document provides information about a practical report on the STELLA simulation software. STELLA allows users to construct dynamic models that simulate biological systems using stocks, flows, converters and connectors. The report discusses three graphs generated using STELLA that model the relationship between rat and owl populations and palm oil production. The graphs demonstrate how increasing rat populations and decreasing owl populations impact palm oil yields over time. STELLA provides an engaging learning tool but also has disadvantages like requiring computer access and training to use.
This document discusses the use of simulation software Stella in education. It provides an overview of simulation theory and describes how Stella allows users to create system dynamic models to visualize complex systems over time. The document also discusses the benefits of using simulations in education, including how they can motivate students and allow risk-free exploration. Predicting outcomes through simulation is described as an effective way for students to study trends by manipulating variables and observing results over multiple runs.
1) The document discusses using the Stella simulation software to model biological control in a palm oil ecosystem. It presents two graphs from a simulation showing the relationship between owl and rat populations over time under different conditions.
2) The advantages of using simulations discussed are that they enhance student motivation by allowing active learning, equip students with problem solving skills through interactive practice, and provide a risk-free environment for experimentation and exploration.
3) The disadvantages mentioned are that simulations may require purchasing a license, can only be accessed on computers, and some students may struggle without proper guidance.
This document provides information about a practical report on the STELLA simulation software. STELLA allows users to construct dynamic models that simulate biological systems using stocks, flows, converters and connectors. The report discusses three graphs generated using STELLA that model the relationship between rat and owl populations and palm oil production. The graphs demonstrate how increasing rat populations and decreasing owl populations can negatively impact palm oil yields over time. STELLA provides an engaging way for students to explore variables and conduct experiments to better understand complex systems.
This document discusses the use of simulation software Stella in education. It provides an overview of simulation theory and how Stella works. A case study is described where Stella was used to simulate predator-prey dynamics. Advantages of using simulation in education include motivating students through active learning and problem solving. Limitations include teachers lacking expertise and students not developing manual science skills. In conclusion, simulation can enhance learning if used properly to engage students in systems thinking.
This document discusses a simulation created using Stella software to model biological control in a palm oil ecosystem.
The simulation models the relationship between rat populations, owl populations as biological control agents, and palm oil production. The simulation showed that a high rat population with a low owl population leads to low palm production, while a low rat population with a high owl population leads to high palm production.
The simulation demonstrated that rat populations, owl populations, and palm oil production are interrelated through a food chain, with palm as the producer, rats as the primary consumer, and owls as the secondary consumer. The simulation helps to understand how changes in one part of the ecosystem can impact other parts.
This document discusses simulation as a learning tool. It provides examples of using simulations to model biological systems and explain complex relationships. Simulations allow students to interact with realistic scenarios, helping increase engagement and conceptual understanding. The document advocates for using simulations more in teaching, but cautions that teachers need to carefully consider how to integrate them and assess their effectiveness.
This document provides information about a practical report on the STELLA simulation software. STELLA allows users to construct dynamic models that simulate biological systems using stocks, flows, converters and connectors. The report discusses three graphs generated using STELLA that model the relationship between rat and owl populations and palm oil production. The graphs demonstrate how increasing rat populations and decreasing owl populations impact palm oil yields over time. STELLA provides an engaging learning tool but also has disadvantages like requiring computer access and training to use.
This document provides information about a practical report on the STELLA simulation software. STELLA allows users to construct dynamic models that simulate biological systems using stocks, flows, converters and connectors. The report discusses three graphs generated using STELLA that model the relationship between rat and owl populations and palm oil production. The graphs demonstrate how increasing rat populations and decreasing owl populations impact palm oil yields over time. STELLA provides an engaging learning tool but also has disadvantages like requiring computer access and training to use.
This document discusses the use of simulation software Stella in education. It provides an overview of simulation theory and describes how Stella allows users to create system dynamic models to visualize complex systems over time. The document also discusses the benefits of using simulations in education, including how they can motivate students and allow risk-free exploration. Predicting outcomes through simulation is described as an effective way for students to study trends by manipulating variables and observing results over multiple runs.
1) The document discusses using the Stella simulation software to model biological control in a palm oil ecosystem. It presents two graphs from a simulation showing the relationship between owl and rat populations over time under different conditions.
2) The advantages of using simulations discussed are that they enhance student motivation by allowing active learning, equip students with problem solving skills through interactive practice, and provide a risk-free environment for experimentation and exploration.
3) The disadvantages mentioned are that simulations may require purchasing a license, can only be accessed on computers, and some students may struggle without proper guidance.
This document discusses the benefits of using STELLA simulation software in education. It provides an example of a STELLA simulation involving the relationship between rat population, owl population, and palm oil production. The summary is:
1) STELLA simulation allows students to manipulate parameters and observe how outcomes change, helping students better understand relationships between variables.
2) Running simulations repeatedly increases student motivation and curiosity to explore topics.
3) Simulations help students make more accurate predictions about experiments compared to traditional methods, and enhance problem solving skills.
This document discusses simulation and modelling software called STELLA. It provides an overview of STELLA's features and how it can be used to simulate systems over time. It also describes the benefits and limitations of using simulations for education. Simulations can increase understanding, provide hands-on learning, and test designs without physical implementation. However, they may oversimplify details and require significant computing resources for complex simulations. Overall, simulations are a useful tool that can enhance the teaching and learning process.
This document discusses a simulation software called Stella that can be used to model relationships between variables over time. It then summarizes three graphs from a simulation on the relationship between owls, mice, and palm fruit production. The first graph shows owl and mice numbers changing directly with each other. The second shows what happens when the mice number changes - the owl population crashes to zero when the mice are reduced. The third shows the owl population recovering while mice and fruit numbers continue to change. The conclusion states that this simulation platform helps students learn by analyzing graphs and can increase their research skills.
The document discusses using the STELLA simulation software to model natural selection. It begins with an overview of simulation and modeling. It then discusses using STELLA specifically to model a rabbit population undergoing natural selection from fox predators. The model shows the average rabbit speed increasing over generations as slower rabbits are preyed upon more. The document analyzes the results and shows how they support Darwin's theory of natural selection. It concludes that STELLA is an effective teaching tool that can increase student understanding and motivation by allowing them to predict population changes over time.
This document discusses the use of STELLA simulation software to model various systems over time. It provides examples of using STELLA to model predator-prey relationships in an oil palm plantation and to predict climate change scenarios. The document also discusses how simulations like STELLA can encourage student interest and engagement in science learning by making experiments interactive, allowing hypothesis testing, and helping students understand real-world systems.
This document discusses using the STELLA simulation software to model predator-prey dynamics. It provides background on predator-prey theory and describes using STELLA to simulate the interaction between snowshoe hares and lynx over time. The simulation allows users to manipulate variables and observe their impact on predator and prey populations. It finds that STELLA engages students by allowing them to actively experiment with models and better understand complex systems through visualization of dynamic changes over many years.
The document discusses the benefits of simulation and modeling in education. It provides an example of using the STELLA software to model predator-prey dynamics between lynx and snowshoe hares. Adjusting the number of harvested lynx results in changes to both populations over time, demonstrating lag effects between the two. Simulation allows students to better understand complex systems and predict outcomes by experimenting with variables.
This document discusses a STELLA simulation of a population model. It begins with an introduction to computer simulation and population models. The objectives are to understand population using simulation and explore different outcome scenarios. Advantages of simulation include experimenting without impacting the real system, while disadvantages are lack of realism and potential errors. The STELLA population model analyzes the relationship between lifespan and birth rate under different parameter changes. It found that higher birth rates correspond to longer lifespans. In conclusion, population simulation is useful for analyzing concepts and predicting outcomes, though errors and credibility issues remain challenges.
This document discusses the use of Stella software for modelling and simulation in biology. It provides examples of using Stella to model relationships between owl population, rat population, and palm production over 24 years. Figures 1-4 show graphs of these relationships when rat population is 5,000, 10,000, 20,000, and 30,000. Table 1 further summarizes the data. The document also discusses benefits of modelling and simulation in the learning process, like accelerated and safe learning. While software costs and training requirements are disadvantages, overall simulation and modelling can help students learn deeply and boost motivation.
The document presents a simulation of the innovation process based on the "predator and prey" model. It discusses modeling emotions and feelings as important parts of social and economic models. It specifically models "hunger" using the predator-prey framework, where large corporations are predators and startups are prey. The simulation shows that taxing large corporations and providing subsidies to startups can boost innovation by increasing the population amplitudes of both groups over time. However, deviation from optimal taxation parameters may destabilize economic growth.
1) The document presents a simulation of innovation processes based on the "predator and prey" model. It discusses modeling emotions and feelings as important parts of social and economic models.
2) It specifically examines "hunger" as a feeling and simulates the satisfying of hunger using the predator and prey algorithm. Large companies are considered predators and startups are prey.
3) The simulation shows that taxing large companies and providing subsidies to startups can significantly increase innovation through population growth oscillations of predators and prey if parameters are within a narrow optimal range. Deviating from this range risks economic instability.
This document discusses using the STELLA simulation software to model predator-prey dynamics. It provides background on how STELLA can be used to conduct virtual experiments that represent real-world processes. As an example, it examines a predator-prey model simulating the population fluctuations between Canada lynx and snowshoe hares over time. The simulation allows students to adjust variables and make predictions about how the predator and prey populations would change.
1) The study aimed to determine environmental sources of variation in reproductive lifespan using genetically identical fruit fly lines. 2) While the lines were genetically identical, substantial variation was found between individuals' reproductive lifespans. 3) The study compared differences between treated and untreated lines, infected and cured lines, and results from different experimental sections, but no single environmental factor consistently explained the observed variation.
What Causes Economic Growth? A Breakdown of The Solow Growth ModelJaredBilberry1
The document summarizes an empirical study examining the Solow growth model and the augmented Solow model developed by Mankiw, Romer and Weil. The study uses data from 1960-1985 for non-oil producing countries to test the relationship between GDP per capita in 1985 and variables for investment, population growth, and secondary education. Descriptive statistics show average GDP increased from 1960 to 1985 while population and investment levels also rose. Correlation analysis found GDP correlated positively with investment and education, but negatively with population growth, supporting the models' predictions.
- Three experiments were conducted to study aphid population growth and predator-prey dynamics: a clip cage experiment to study aphid development rates, a feeding experiment to study predation rates of ladybird beetles, and a large cage experiment to study aphid population growth with and without predators.
- A stage-structured matrix population model was developed and validated based on results from the clip cage experiment. The model showed rapid exponential aphid population growth.
- Introduction of ladybird beetle predators in the large cage experiment led to inconsistent results, likely due to variability in predation behavior between adult ladybird beetles focused on mating versus feeding.
Mathematical Methods for Engineers 2 (MATH1064)Leslie matr.docxandreecapon
Mathematical Methods for Engineers 2 (MATH1064)
Leslie matrix Matlab group project
Due no later than 2 pm on Friday 10th October, 2014
Graduate Qualities: This project is designed to help the student achieve course objective 4: solve
simple applied problems using software such as Matlab , and to develop Graduate Qualities 1 & 3,
namely operating effectively with and upon a body of knowledge, and effective problem solving.
Assessment:
The assessment will take into account all of your documentation of the mathematical analysis of the
problem, your Matlab m-file(s), your Matlab output, the correctness of the final solutions and the
presentation of your whole report.
Groups should contain two or three people. It will be assumed that each member of the team
contributed equally and will be awarded individually the mark allocated to the report. If this is
not the case, then a lesser percentage for one or more members must be agreed by the team and
clearly indicated. This especially will apply to absences from the practical class or non-attendance
at agreed team meetings. The University policy on plagiarism will apply between different groups.
Students who wish to can submit a peer assessment form which can be found on the
course webpage.
How to divide the work: Each team member must participate in all aspects of the project: math-
ematical calculations, Matlab work and report writing.
Only one copy of your project report is required for each group.
Summary: In this project you will:
• Investigate the Leslie matrix model for a population
• Explain how a Leslie matrix can be used to calculate the population in each age class from time
to time
• Use Matlab to draw plots of age class populations evolving over time
• Use Matlab to study the long term behaviour of population numbers
Your report must be typed, and submitted through LearnOnline by one member of your
group. It should include:
• Written worked answers to all questions where this is required.
• Appropriately labeled figures where required.
• A listing of your Matlab script file should be included at the end of your report in an appendix.
• A coversheet is not needed but your report must have a title page that lists the names and
student identification numbers of all members of the group.
• The group’s .m file must be submitted as a saparate file via LearnOnline. Be sure to list all
group members at the top of the file; only one copy per group is required. There will be marks
awarded for submitting this file, so don’t forget. Your .m file may be run and checked during
the marking process.
1
Leslie Matrix Model
Invented by Patrick H. Leslie in the 1940s, the Leslie Matrix is a mathematical model of population
growth for a species. Time is divided into discrete periods, with individual memebers of the population
progressing through discrete age classes at given survival rates. Here is a simplified example:
The Central Australian Budgericoot (CAB) cannot live beyond five y ...
This document provides information about Adobe Photoshop, including its history, features, and procedures for editing images. Photoshop was created in 1987 by brothers Thomas and John Knoll and was purchased by Adobe Systems in 1988. It has become the industry standard for digital photo editing, allowing users to manipulate, crop, and correct images. The document also outlines the advantages of Photoshop such as improving photo quality and learning new skills, and the disadvantages including the learning curve, cost, and computer requirements.
This document describes a group assignment by three students to simulate a Biosawit experiment using Stella software. The experiment models the relationship between palm production, rat population, and owl population over a food chain. The students ran four simulations varying the initial rat population from 5,000 to 30,000 while keeping the initial owl population at 5. All simulations showed palm production initially increasing as the rat population decreased due to owl predation, then fluctuating as the rat and owl populations fluctuated in response to each other over the 25 year period.
1. The document discusses using data loggers in an experiment conducted by biology students to measure wavelength. It describes the components of a data logging system and benefits of using data loggers, such as collecting accurate measurements over long periods without human intervention.
2. An example experiment is described that uses data loggers to measure ultrasound attenuation in solids. The objectives, apparatus, procedures, and results are outlined.
3. Additional examples are provided of using ultrasound imaging to evaluate fetal development and diagnose issues during pregnancy examinations. Diagrams of ultrasound machine interfaces and applications in organ and fetal evaluations are displayed.
This document contains information about a course called Information & Communication Technology in Biology taken by four students - En. Azmi Bin Ibrahim, Nurafiqah Bt Hamzah, Nur Amalina Bt Che Ajid, and Nurulamirah Bt Rodzi. The course code is SBI3013 and appears to involve using Adobe Photoshop.
Challenging implementation ict in smart schoolnurulamirah001
Implementing ICT in schools faces several challenges, including passive students and parents who are too busy to be involved, the heavy investment required for facilities that have high maintenance costs, and a lack of technological infrastructure and teaching materials especially in rural areas with limited internet connections and outdated computer equipment. Teachers also struggle with time constraints in preparing lessons that incorporate technology, a lack of intensive ICT training, and negative attitudes from senior teachers accustomed to traditional teaching methods. Schools also lack sufficient technical support staff to handle inevitable technology problems.
This document discusses conducting an experiment to study the attenuation of ultrasound in solids. It involves using different ultrasound probes at various frequencies to transmit ultrasound waves through solid cylinders and measuring the reduction in intensity. The experiment aims to determine attenuation for different frequencies in reflection and transmission and compare the results to literature values. The document provides the objectives, apparatus needed, and step-by-step procedure to collect and analyze attenuation data for the solid cylinders using an ultrasonic echoscope and data logger system.
This document discusses the benefits of using STELLA simulation software in education. It provides an example of a STELLA simulation involving the relationship between rat population, owl population, and palm oil production. The summary is:
1) STELLA simulation allows students to manipulate parameters and observe how outcomes change, helping students better understand relationships between variables.
2) Running simulations repeatedly increases student motivation and curiosity to explore topics.
3) Simulations help students make more accurate predictions about experiments compared to traditional methods, and enhance problem solving skills.
This document discusses simulation and modelling software called STELLA. It provides an overview of STELLA's features and how it can be used to simulate systems over time. It also describes the benefits and limitations of using simulations for education. Simulations can increase understanding, provide hands-on learning, and test designs without physical implementation. However, they may oversimplify details and require significant computing resources for complex simulations. Overall, simulations are a useful tool that can enhance the teaching and learning process.
This document discusses a simulation software called Stella that can be used to model relationships between variables over time. It then summarizes three graphs from a simulation on the relationship between owls, mice, and palm fruit production. The first graph shows owl and mice numbers changing directly with each other. The second shows what happens when the mice number changes - the owl population crashes to zero when the mice are reduced. The third shows the owl population recovering while mice and fruit numbers continue to change. The conclusion states that this simulation platform helps students learn by analyzing graphs and can increase their research skills.
The document discusses using the STELLA simulation software to model natural selection. It begins with an overview of simulation and modeling. It then discusses using STELLA specifically to model a rabbit population undergoing natural selection from fox predators. The model shows the average rabbit speed increasing over generations as slower rabbits are preyed upon more. The document analyzes the results and shows how they support Darwin's theory of natural selection. It concludes that STELLA is an effective teaching tool that can increase student understanding and motivation by allowing them to predict population changes over time.
This document discusses the use of STELLA simulation software to model various systems over time. It provides examples of using STELLA to model predator-prey relationships in an oil palm plantation and to predict climate change scenarios. The document also discusses how simulations like STELLA can encourage student interest and engagement in science learning by making experiments interactive, allowing hypothesis testing, and helping students understand real-world systems.
This document discusses using the STELLA simulation software to model predator-prey dynamics. It provides background on predator-prey theory and describes using STELLA to simulate the interaction between snowshoe hares and lynx over time. The simulation allows users to manipulate variables and observe their impact on predator and prey populations. It finds that STELLA engages students by allowing them to actively experiment with models and better understand complex systems through visualization of dynamic changes over many years.
The document discusses the benefits of simulation and modeling in education. It provides an example of using the STELLA software to model predator-prey dynamics between lynx and snowshoe hares. Adjusting the number of harvested lynx results in changes to both populations over time, demonstrating lag effects between the two. Simulation allows students to better understand complex systems and predict outcomes by experimenting with variables.
This document discusses a STELLA simulation of a population model. It begins with an introduction to computer simulation and population models. The objectives are to understand population using simulation and explore different outcome scenarios. Advantages of simulation include experimenting without impacting the real system, while disadvantages are lack of realism and potential errors. The STELLA population model analyzes the relationship between lifespan and birth rate under different parameter changes. It found that higher birth rates correspond to longer lifespans. In conclusion, population simulation is useful for analyzing concepts and predicting outcomes, though errors and credibility issues remain challenges.
This document discusses the use of Stella software for modelling and simulation in biology. It provides examples of using Stella to model relationships between owl population, rat population, and palm production over 24 years. Figures 1-4 show graphs of these relationships when rat population is 5,000, 10,000, 20,000, and 30,000. Table 1 further summarizes the data. The document also discusses benefits of modelling and simulation in the learning process, like accelerated and safe learning. While software costs and training requirements are disadvantages, overall simulation and modelling can help students learn deeply and boost motivation.
The document presents a simulation of the innovation process based on the "predator and prey" model. It discusses modeling emotions and feelings as important parts of social and economic models. It specifically models "hunger" using the predator-prey framework, where large corporations are predators and startups are prey. The simulation shows that taxing large corporations and providing subsidies to startups can boost innovation by increasing the population amplitudes of both groups over time. However, deviation from optimal taxation parameters may destabilize economic growth.
1) The document presents a simulation of innovation processes based on the "predator and prey" model. It discusses modeling emotions and feelings as important parts of social and economic models.
2) It specifically examines "hunger" as a feeling and simulates the satisfying of hunger using the predator and prey algorithm. Large companies are considered predators and startups are prey.
3) The simulation shows that taxing large companies and providing subsidies to startups can significantly increase innovation through population growth oscillations of predators and prey if parameters are within a narrow optimal range. Deviating from this range risks economic instability.
This document discusses using the STELLA simulation software to model predator-prey dynamics. It provides background on how STELLA can be used to conduct virtual experiments that represent real-world processes. As an example, it examines a predator-prey model simulating the population fluctuations between Canada lynx and snowshoe hares over time. The simulation allows students to adjust variables and make predictions about how the predator and prey populations would change.
1) The study aimed to determine environmental sources of variation in reproductive lifespan using genetically identical fruit fly lines. 2) While the lines were genetically identical, substantial variation was found between individuals' reproductive lifespans. 3) The study compared differences between treated and untreated lines, infected and cured lines, and results from different experimental sections, but no single environmental factor consistently explained the observed variation.
What Causes Economic Growth? A Breakdown of The Solow Growth ModelJaredBilberry1
The document summarizes an empirical study examining the Solow growth model and the augmented Solow model developed by Mankiw, Romer and Weil. The study uses data from 1960-1985 for non-oil producing countries to test the relationship between GDP per capita in 1985 and variables for investment, population growth, and secondary education. Descriptive statistics show average GDP increased from 1960 to 1985 while population and investment levels also rose. Correlation analysis found GDP correlated positively with investment and education, but negatively with population growth, supporting the models' predictions.
- Three experiments were conducted to study aphid population growth and predator-prey dynamics: a clip cage experiment to study aphid development rates, a feeding experiment to study predation rates of ladybird beetles, and a large cage experiment to study aphid population growth with and without predators.
- A stage-structured matrix population model was developed and validated based on results from the clip cage experiment. The model showed rapid exponential aphid population growth.
- Introduction of ladybird beetle predators in the large cage experiment led to inconsistent results, likely due to variability in predation behavior between adult ladybird beetles focused on mating versus feeding.
Mathematical Methods for Engineers 2 (MATH1064)Leslie matr.docxandreecapon
Mathematical Methods for Engineers 2 (MATH1064)
Leslie matrix Matlab group project
Due no later than 2 pm on Friday 10th October, 2014
Graduate Qualities: This project is designed to help the student achieve course objective 4: solve
simple applied problems using software such as Matlab , and to develop Graduate Qualities 1 & 3,
namely operating effectively with and upon a body of knowledge, and effective problem solving.
Assessment:
The assessment will take into account all of your documentation of the mathematical analysis of the
problem, your Matlab m-file(s), your Matlab output, the correctness of the final solutions and the
presentation of your whole report.
Groups should contain two or three people. It will be assumed that each member of the team
contributed equally and will be awarded individually the mark allocated to the report. If this is
not the case, then a lesser percentage for one or more members must be agreed by the team and
clearly indicated. This especially will apply to absences from the practical class or non-attendance
at agreed team meetings. The University policy on plagiarism will apply between different groups.
Students who wish to can submit a peer assessment form which can be found on the
course webpage.
How to divide the work: Each team member must participate in all aspects of the project: math-
ematical calculations, Matlab work and report writing.
Only one copy of your project report is required for each group.
Summary: In this project you will:
• Investigate the Leslie matrix model for a population
• Explain how a Leslie matrix can be used to calculate the population in each age class from time
to time
• Use Matlab to draw plots of age class populations evolving over time
• Use Matlab to study the long term behaviour of population numbers
Your report must be typed, and submitted through LearnOnline by one member of your
group. It should include:
• Written worked answers to all questions where this is required.
• Appropriately labeled figures where required.
• A listing of your Matlab script file should be included at the end of your report in an appendix.
• A coversheet is not needed but your report must have a title page that lists the names and
student identification numbers of all members of the group.
• The group’s .m file must be submitted as a saparate file via LearnOnline. Be sure to list all
group members at the top of the file; only one copy per group is required. There will be marks
awarded for submitting this file, so don’t forget. Your .m file may be run and checked during
the marking process.
1
Leslie Matrix Model
Invented by Patrick H. Leslie in the 1940s, the Leslie Matrix is a mathematical model of population
growth for a species. Time is divided into discrete periods, with individual memebers of the population
progressing through discrete age classes at given survival rates. Here is a simplified example:
The Central Australian Budgericoot (CAB) cannot live beyond five y ...
This document provides information about Adobe Photoshop, including its history, features, and procedures for editing images. Photoshop was created in 1987 by brothers Thomas and John Knoll and was purchased by Adobe Systems in 1988. It has become the industry standard for digital photo editing, allowing users to manipulate, crop, and correct images. The document also outlines the advantages of Photoshop such as improving photo quality and learning new skills, and the disadvantages including the learning curve, cost, and computer requirements.
This document describes a group assignment by three students to simulate a Biosawit experiment using Stella software. The experiment models the relationship between palm production, rat population, and owl population over a food chain. The students ran four simulations varying the initial rat population from 5,000 to 30,000 while keeping the initial owl population at 5. All simulations showed palm production initially increasing as the rat population decreased due to owl predation, then fluctuating as the rat and owl populations fluctuated in response to each other over the 25 year period.
1. The document discusses using data loggers in an experiment conducted by biology students to measure wavelength. It describes the components of a data logging system and benefits of using data loggers, such as collecting accurate measurements over long periods without human intervention.
2. An example experiment is described that uses data loggers to measure ultrasound attenuation in solids. The objectives, apparatus, procedures, and results are outlined.
3. Additional examples are provided of using ultrasound imaging to evaluate fetal development and diagnose issues during pregnancy examinations. Diagrams of ultrasound machine interfaces and applications in organ and fetal evaluations are displayed.
This document contains information about a course called Information & Communication Technology in Biology taken by four students - En. Azmi Bin Ibrahim, Nurafiqah Bt Hamzah, Nur Amalina Bt Che Ajid, and Nurulamirah Bt Rodzi. The course code is SBI3013 and appears to involve using Adobe Photoshop.
Challenging implementation ict in smart schoolnurulamirah001
Implementing ICT in schools faces several challenges, including passive students and parents who are too busy to be involved, the heavy investment required for facilities that have high maintenance costs, and a lack of technological infrastructure and teaching materials especially in rural areas with limited internet connections and outdated computer equipment. Teachers also struggle with time constraints in preparing lessons that incorporate technology, a lack of intensive ICT training, and negative attitudes from senior teachers accustomed to traditional teaching methods. Schools also lack sufficient technical support staff to handle inevitable technology problems.
This document discusses conducting an experiment to study the attenuation of ultrasound in solids. It involves using different ultrasound probes at various frequencies to transmit ultrasound waves through solid cylinders and measuring the reduction in intensity. The experiment aims to determine attenuation for different frequencies in reflection and transmission and compare the results to literature values. The document provides the objectives, apparatus needed, and step-by-step procedure to collect and analyze attenuation data for the solid cylinders using an ultrasonic echoscope and data logger system.
LAND USE LAND COVER AND NDVI OF MIRZAPUR DISTRICT, UPRAHUL
This Dissertation explores the particular circumstances of Mirzapur, a region located in the
core of India. Mirzapur, with its varied terrains and abundant biodiversity, offers an optimal
environment for investigating the changes in vegetation cover dynamics. Our study utilizes
advanced technologies such as GIS (Geographic Information Systems) and Remote sensing to
analyze the transformations that have taken place over the course of a decade.
The complex relationship between human activities and the environment has been the focus
of extensive research and worry. As the global community grapples with swift urbanization,
population expansion, and economic progress, the effects on natural ecosystems are becoming
more evident. A crucial element of this impact is the alteration of vegetation cover, which plays a
significant role in maintaining the ecological equilibrium of our planet.Land serves as the foundation for all human activities and provides the necessary materials for
these activities. As the most crucial natural resource, its utilization by humans results in different
'Land uses,' which are determined by both human activities and the physical characteristics of the
land.
The utilization of land is impacted by human needs and environmental factors. In countries
like India, rapid population growth and the emphasis on extensive resource exploitation can lead
to significant land degradation, adversely affecting the region's land cover.
Therefore, human intervention has significantly influenced land use patterns over many
centuries, evolving its structure over time and space. In the present era, these changes have
accelerated due to factors such as agriculture and urbanization. Information regarding land use and
cover is essential for various planning and management tasks related to the Earth's surface,
providing crucial environmental data for scientific, resource management, policy purposes, and
diverse human activities.
Accurate understanding of land use and cover is imperative for the development planning
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it describes the bony anatomy including the femoral head , acetabulum, labrum . also discusses the capsule , ligaments . muscle that act on the hip joint and the range of motion are outlined. factors affecting hip joint stability and weight transmission through the joint are summarized.
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Exploiting Artificial Intelligence for Empowering Researchers and Faculty, In...Dr. Vinod Kumar Kanvaria
Exploiting Artificial Intelligence for Empowering Researchers and Faculty,
International FDP on Fundamentals of Research in Social Sciences
at Integral University, Lucknow, 06.06.2024
By Dr. Vinod Kumar Kanvaria
The simplified electron and muon model, Oscillating Spacetime: The Foundation...RitikBhardwaj56
Discover the Simplified Electron and Muon Model: A New Wave-Based Approach to Understanding Particles delves into a groundbreaking theory that presents electrons and muons as rotating soliton waves within oscillating spacetime. Geared towards students, researchers, and science buffs, this book breaks down complex ideas into simple explanations. It covers topics such as electron waves, temporal dynamics, and the implications of this model on particle physics. With clear illustrations and easy-to-follow explanations, readers will gain a new outlook on the universe's fundamental nature.
A workshop hosted by the South African Journal of Science aimed at postgraduate students and early career researchers with little or no experience in writing and publishing journal articles.
1. COURSE
SBI3013
INFORMATION & COMMUNICATION TECHNOLOGY IN BIOLOGY
GROUP ASSIGNMENT
SIMULATION AND MODELING
STELLA SOFTWARE
PREPARED BY:
NUR AMALINA BINTI CHE AJID
D20162076166
NURAFIQAH BINTI HAMZAH
D20162076167
NURUL AMIRAH BT RODZI
D20162076159
GROUP: A
PROGRAM
AT11 – ISMP BIOLOGY
CHECKED BY
EN AZMI IBRAHIM
FACULTY OF SCIENCE AND MATHEMATICS
UNIVERSITI PENDIDIKAN SULTAN IDRIS
2. INTRODUCTION
Modeling and Simulation
Model is a representation of an object, a system or an idea in some from other than that of
the entity itself. Two types of model are physical (scale model, prototype plants) and
mathematical (analytical queuing models, linear programs, simulation).
Simulation of system is the operation of a model, which is a representation of that
system. The model is amenable to manipulation which would be impossible, too expensive or too
impractical to perform on the system which it portrays. The operation of the model can be
studied and from these properties concerning the behavior of the actual system can be inferred.
Simulation modeling is the process of creating and analyzing a digital prototype of a
physical model to predict its performance in the real world. Simulation modeling is used to help
designers and engineers understand whether, under what conditions and which ways a part could
fail and what loads it can withstand.
Applications
1. Designing and analysis manufacturing systems.
2. Evaluating a new military weapons system tactics.
3. Determining ordering policies for an inventory system.
4. Designing communication systems and message protocol for them.
5. Designing and operating transportation facilities such a freeways, airports, subways or
ports.
6. Evaluating designs for service organizations such as hospitals, post offices or fast-food
restaurants.
7. Analyzing financial or economic systems.
3. Stella
STELLA (System Thinking Experiment Learning Laboratories with Animation) was
created by Robert Webb of Australia is a computer program that available in three versions
which are Great Stella, Small Stella d Stella4D. The programs contain a large library for
polyhedral which can be manipulated and altered in various way.
Stella is a flexible computer modeling package with an easy, intuitive interface that
allows users to construct dynamic models that realistically simulate biological systems. The
program, distributed by ISEE systems (formerly High Performance Systems) allows users to run
models created as graphical representations of a system using four fundamental building blocks.
Stella has been used in academic as a teaching tool and has been utilized in a variety of research
and business application. Stella software also used to predict the consequences of any action in
data details. This means, we can predict the result by just adjusting or clicking the related
variable and run the process the simulation software will directly predict the results. By using
stimulation we can save the time on collecting data and run any experiment or research.
In this case, the Stella software was used to manipulate data for Biosawit experiment to
see the relationship between palm production, rat and owl. This relationship between these three
are the using the concept of food chain where there is producer, primary consumer and secondary
consumer. The rats eat palms, the owl eat rats. When the population of rat decreases, the palms
produce is increases because there is small number of palms consumer.
4. RESULTS OF BIOSAWIT EXPERIMENT USING STELLA SOFTWARE
1. Graph 1
Figure 1: Graph shows the relationship between Owl: 5 and Rat: 5,000
Figure 1 show the simulation results in graph form for Biosawit experiment. In Figure 1, we
set the number of owl at five and the number of rat at 5,000 then, we run the simulation. This
simulation calculates the data of palm produced, number of owl, and number of rat within 25
years. As the year past, the production of palm increase according to the graph for just about 20
years. After 20 years the production of palm was recorded decreasing as the population of rats
increases. We take a look at the data details cart, in first half year the population of owl
drastically dropped from five to zero and from there the rat population begins to increase. As the
results the production of palms is ascending at first 20 years and began to drop years later. The
production of palms recorded at the first half year is 20,789,213.14 tan. However at the end of
year 25th the production of palm predicted is 554,810.14 tan which is decreasing 266%. The
5. number of rats is increase from 5000 rats to 1,955,082 rats in 25 years. In this experiment the
ratio of owl over rat is 1:100.
2. Graph 2
Figure 2: Graph shows the relationship between Owl: 5 and Rat: 10,000
In figure 2 show the simulation results in graph form for Biosawit experiment. In Figure 2,
we set the number of owl at five and the number of rat at 10,000 then, we run the simulation.
This simulation calculates the data of palm produced, number of owl, and number of rat within
25 years. As the year past, the production of palm constantly increase according to the graph in
25 years. Through all the years the production of palm was recorded increasing as the population
of rats increase and decrease throughout the years. The population of owl also went up and down
during all the years. We take a look at the data details cart, in first two years the population of
owl drastically dropped from five to three and from there the rat population begins to increase.
But when the rat population increases the owl population begins to increase and as the owl
6. population increases the population of rats decrease once again. As the results the palm
production is constantly increased. The pattern repeated approximately every five to six years
based on the graph. The production of palms recorded at the first half year is 20,787,614.14 tan.
However at the end of year 25th the production of palm predicted is 108,208,184.61 tan which is
increasing 520%. The number of rats is increase from 10,000 rats to 10,615 rats in 25 years. In
this experiment the ratio of owl over rat is 1:200. The relation we can conclude here is the owl
migrates to other places as the rat decreases because of the survival. When the owl migrate the
rat increases and the owl migrates to the palm estate again and the rat decreases again.
3. Graph 3
Figure 3: Graph shows the relationship between Owl: 5 and Rat: 20,000
figure 3 show the simulation results in graph form for Biosawit experiment. In Figure 3,
we set the number of owl at five and the number of rat at 20,000 then, we run the simulation.
This simulation calculates the data of palm produced, number of owl, and number of rat within
7. 25 years. As the year past, the production of palm constantly increase according to the graph in
25 years. Through all the years the production of palm was recorded increasing as the population
of rats increase and decrease throughout the years. The population of owl also went up and down
during all the years. We take a look at the data details cart, in first four to five years the
population of owl dropped from five to four and from there the rat population begins to increase.
But when the rat population increases the owl population begins to increase and as the owl
population increases the population of rats decrease once again. As the results the palm
production is constantly increased. The pattern repeated approximately first four years and the
pattern repeated around 12 years later based on the graph. The production of palms recorded at
the first half year is 20,789,213.14 tan. However at the end of year 25th the production of palm
predicted is 106,913,578.83 tan which is increasing 514%. The number of rats is decrease from
20,000 rats to 15,244 rats in 25 years. In this experiment the ratio of owl over rat is 1:400. The
relation we can conclude here is the owl migrates to other places as the rat decreases because of
the survival. When the owl migrate the rat increases and the owl migrates to the palm estate
again and the rat decreases again.
8. 4. Graph 4
Figure 4: Graph shows the relationship Owl: 5 and Rat: 30,000
In figure 4 show the simulation results in graph form for Biosawit experiment. In Figure 4,
we set the number of owl at five and the number of rat at 30,000 then, we run the simulation.
This simulation calculates the data of palm produced, number of owl, and number of rat within
25 years. As the year past, the production of palm constantly increase according to the graph in
25 years. Through all the years the production of palm was recorded increasing as the population
of rats increase and decrease throughout the years. The population of owl also went up and down
during all the years. We take a look at the data details cart, in first four to five years the
population of owl drastically dropped from five to three and from there the rat population begins
to increase. But when the rat population increases the owl population begins to increase and as
the owl population increases the population of rats decrease once again. As the results the palm
production is constantly increased. The pattern repeated approximately the first four to five years
and 14 years later based on the graph. The production of palms recorded at the first half year is
20,781,230.02 tan. However at the end of year 25th the production of palm predicted is
9. 106,112,952.00 tan which is increasing 510%. The number of rats is decrease from 30,000 rats to
17,446 rats in 25 years. In this experiment the ratio of owl over rat is 1:600. The relation we can
conclude here is the owl migrates to other places as the rat decreases because of the survival.
When the owl migrate the rat increases and the owl migrates to the palm estate again and the rat
decreases again.
For all four graphs we were fixed the number of owl but the number of rat was different.
From this simulation we can conclude that the population of rat affected the population of owl
and also the production of palms. In Figure 2, 3 and 4 shows the same pattern when the palms
production increased in percent because the presence of owl from first year to 25th years that still
can control the population of rat. Even though, there is still palm damaged but the number not as
much as good production of palm. However in Figure 1, we can see the palms production
decreased in percent because the absence of the owl at first year because the population of rat
decreased. The owl migrates to other place to survive. As the year past, the population of rat
increase but there is still no presence of owl. This makes the palm damaged and production
decreased.
5. Table 1
Year Number of Rat Number of Owl Palm Production
(tan)
Percentage
Palm
Production
½
24
5,000
1,955,082
5
0
20,789,213.14
554,810.14
Decreased 266%
1
24
10,000
10,615
5
3
20,787,614.14
108,208,184.61
Increased 520%
1
24
20,000
15,244
5
4
20,789,213.14
106,913,578.83
Increased 514%
1
24
30,000
17,446
5
3
20,781,230.02
106,112,952.00
Increased 510%
Table 1: Table shows the relationship between palm production, rat and owl for 24 years
10. Advantage and Disadvantage
The main advantages of using simulation in teaching and learning is the process becomes
more interesting and exciting as the learners can explore everything in various types of
experiments. If students and teachers have the software of simulation, they can explore it early
when they are not in the class. These can make them more inspiring to study and discover a new
thing. Besides, simulation also can help the teachers to teach the abstract content to the students.
It is meant that, the simulation can make the students easy to understand as they can use the
simulation and not just imagine on what have been taught by the teacher. Other advantages of
using simulation are very quick development of complex models, short learning cycles and no
programming is needed. So, only minimal errors will occur.
However, this simulation also can give disadvantages to the students that do not have
computer or limited availability of computers. They will not have the opportunity to explore after
they have learnt in the class. Their learning process will happen only in the class. In addition, this
simulation also can give a big problem to the users that do not know how to use computer. The
users need to do two things in one time, which are learning about the computer and also learning
about the simulation. So, may be the user will be lag behind the others. Besides, many
simulations require intensive pre simulation lesson preparation. So, it takes time if we want to
use the simulation in a short period. High cost of software also include as the disadvantage of the
simulation. Only the trial one can be used by the users. But after 30 days, it cannot be used
anymore. In addition, limited scope of applicability and also limited flexibility. This is because
the variation of the topics is not too many. Some of them may not fix with the users specific.
Conclusion
Simulation is suitable to be used in the school because it can motivate students in
learning. Simulation motivate students by keeping them actively engaged in the learning process
through requiring that problem solving and decision making skills be used to make learner is
involved. Thus, participant in simulations enables students to engage in systems thinking and
enhances their understanding of systems as well as of science concepts.
11. Simulation can be considered as a powerful tool in active learning experiences. It can
provide a kind of lab-like experiences. So, the students will become more exciting and inspiring
in study because most of the students like to do something rather than just hearing to something.
Finding a good simulation in teaching is a challenge for the teachers in order to integrate them
into the content and objectives of the course chosen. However, if the teachers know what their
students need, it is easier for them to choose a suitable simulation.
References
1. 20170331100320Sawitbio
2. Modeling and Simulation using Stella
https://www.slideshare.net/farhana25/modelling-and-simulation-using-stella