Models for a Multi-Agent System Based on Wasp-Like Behaviour for Distributed ...infopapers
D. Simian, F. Stoica, C. Simian, Models for a Multi-Agent System Based on Wasp-like Behaviour for Distributed Patients Repartition, Proceedings of the 9th WSEAS International Conference on Evolutionary Computing, Sofia, Bulgaria, ISBN 978-960-6766-58-9, ISSN 1790-5109, pp. 82-86, May 2008
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WOU Biology 100 Series Graphs Overview Making a graph is .docxericbrooks84875
WOU Biology 100 Series Graphs Overview
Making a graph is one of the easiest ways to get an idea of the patterns in your data.
Graphing is a fairly straightforward process, but there are a few things to keep in mind.
1. Type of graph. You should think carefully about the kind of data you have before you
decide what type of graph to produce. See Figure 1.
a. Line graphs are useful to show how a factor changes over time or in some other
gradual continuous increment (like temperature or ambient light).
b. Bar graphs are useful to show a total change or overall difference between
different discrete variables (like types of organisms or specific experimental
treatments).
Figure 1. Types of Graphs. The graph on the left is a line graph. The graph on the right is a bar graph.
2. Variables
a. The independent variable is the variable that you change or manipulate in the
experiment. This variable is usually placed along the x (horizontal) axis. In the
case of an experiment where you are observing something that changes over
time, time serves as an independent variable and is always listed on the x-axis. If,
in addition to time, there is a second independent variable (e.g. observing what
happens to two different treatments over time) this variable is usually graphed by
drawing multiple lines on the graph. See Figure 2.
b. The dependent variable is the response or what happens in response to the
independent variable. Typically, this variable is what you counted or measured
during the experiment. This variable is placed along the y (vertical) axis.
3. Titles and Labeling.
a. Every graph needs a concise and descriptive title that explains what phenomenon
the graph is attempting to visualize. If you averaged data from several different lab
groups before graphing, you should note in the title that your graph depicts
averaged data (like in the bar graph in Figure 1).
b. Each axis should be labeled, and the label should include the units in which the
data was recorded. Without units, your graph is meaningless.
WOU Biology 100 Series Graphs Overview
Table 1, below, shows an example of data collected during an experiment. The same data is
presented in Figure 2. Note how much easier it is to quickly examine the patterns of data
collected in the visual graph compared to the data table, as long as the graph is titled
properly, the axes are labeled (with units) and there is a key.
Table 1: Data table showing gas generation (viewed as movement of liquid up a tube) by Elodea
plants under different conditions. Note use of units in the table headings.
Movement of liquid in tube (in centimeters)
Time (minutes) Clear test tube Foil covered test tube
5 0.7 0
10 1.1 0.2
15 1.4 0.3
20 1.7 0.4
25 2.1 0.4
30 2.8 0.4
35 3.6 0.4
40 4.5 0.4
45 5.8 0.4
50 6.7 0.4
55 7.6 0.4
60 8.8 0.4
Figure 2: A line graph with title, labels (including units), and a key. This data is the same as .
Software Delivery At the Speed of AI: Inflectra Invests In AI-Powered QualityInflectra
In this insightful webinar, Inflectra explores how artificial intelligence (AI) is transforming software development and testing. Discover how AI-powered tools are revolutionizing every stage of the software development lifecycle (SDLC), from design and prototyping to testing, deployment, and monitoring.
Learn about:
• The Future of Testing: How AI is shifting testing towards verification, analysis, and higher-level skills, while reducing repetitive tasks.
• Test Automation: How AI-powered test case generation, optimization, and self-healing tests are making testing more efficient and effective.
• Visual Testing: Explore the emerging capabilities of AI in visual testing and how it's set to revolutionize UI verification.
• Inflectra's AI Solutions: See demonstrations of Inflectra's cutting-edge AI tools like the ChatGPT plugin and Azure Open AI platform, designed to streamline your testing process.
Whether you're a developer, tester, or QA professional, this webinar will give you valuable insights into how AI is shaping the future of software delivery.
Models for a Multi-Agent System Based on Wasp-Like Behaviour for Distributed ...infopapers
D. Simian, F. Stoica, C. Simian, Models for a Multi-Agent System Based on Wasp-like Behaviour for Distributed Patients Repartition, Proceedings of the 9th WSEAS International Conference on Evolutionary Computing, Sofia, Bulgaria, ISBN 978-960-6766-58-9, ISSN 1790-5109, pp. 82-86, May 2008
Advantages of the self organizing controller for high-pressure sterilization ...ISA Interchange
A study of a self-organizing controller is implemented in a way that response to controlled system follows the desired given by the model. The self-organizing controller has proven to be a valuable tool in sterilization equipment in order to verify the capacity of the response to any change in the pressure or temperature. Basically, this type of controller is based on the Self-Organizing Map (SOM) that is a neural network algorithm of unsupervised learning. The new ideas include clustering visualization, interactive training and one-dimension arrays.
WOU Biology 100 Series Graphs Overview Making a graph is .docxericbrooks84875
WOU Biology 100 Series Graphs Overview
Making a graph is one of the easiest ways to get an idea of the patterns in your data.
Graphing is a fairly straightforward process, but there are a few things to keep in mind.
1. Type of graph. You should think carefully about the kind of data you have before you
decide what type of graph to produce. See Figure 1.
a. Line graphs are useful to show how a factor changes over time or in some other
gradual continuous increment (like temperature or ambient light).
b. Bar graphs are useful to show a total change or overall difference between
different discrete variables (like types of organisms or specific experimental
treatments).
Figure 1. Types of Graphs. The graph on the left is a line graph. The graph on the right is a bar graph.
2. Variables
a. The independent variable is the variable that you change or manipulate in the
experiment. This variable is usually placed along the x (horizontal) axis. In the
case of an experiment where you are observing something that changes over
time, time serves as an independent variable and is always listed on the x-axis. If,
in addition to time, there is a second independent variable (e.g. observing what
happens to two different treatments over time) this variable is usually graphed by
drawing multiple lines on the graph. See Figure 2.
b. The dependent variable is the response or what happens in response to the
independent variable. Typically, this variable is what you counted or measured
during the experiment. This variable is placed along the y (vertical) axis.
3. Titles and Labeling.
a. Every graph needs a concise and descriptive title that explains what phenomenon
the graph is attempting to visualize. If you averaged data from several different lab
groups before graphing, you should note in the title that your graph depicts
averaged data (like in the bar graph in Figure 1).
b. Each axis should be labeled, and the label should include the units in which the
data was recorded. Without units, your graph is meaningless.
WOU Biology 100 Series Graphs Overview
Table 1, below, shows an example of data collected during an experiment. The same data is
presented in Figure 2. Note how much easier it is to quickly examine the patterns of data
collected in the visual graph compared to the data table, as long as the graph is titled
properly, the axes are labeled (with units) and there is a key.
Table 1: Data table showing gas generation (viewed as movement of liquid up a tube) by Elodea
plants under different conditions. Note use of units in the table headings.
Movement of liquid in tube (in centimeters)
Time (minutes) Clear test tube Foil covered test tube
5 0.7 0
10 1.1 0.2
15 1.4 0.3
20 1.7 0.4
25 2.1 0.4
30 2.8 0.4
35 3.6 0.4
40 4.5 0.4
45 5.8 0.4
50 6.7 0.4
55 7.6 0.4
60 8.8 0.4
Figure 2: A line graph with title, labels (including units), and a key. This data is the same as .
Software Delivery At the Speed of AI: Inflectra Invests In AI-Powered QualityInflectra
In this insightful webinar, Inflectra explores how artificial intelligence (AI) is transforming software development and testing. Discover how AI-powered tools are revolutionizing every stage of the software development lifecycle (SDLC), from design and prototyping to testing, deployment, and monitoring.
Learn about:
• The Future of Testing: How AI is shifting testing towards verification, analysis, and higher-level skills, while reducing repetitive tasks.
• Test Automation: How AI-powered test case generation, optimization, and self-healing tests are making testing more efficient and effective.
• Visual Testing: Explore the emerging capabilities of AI in visual testing and how it's set to revolutionize UI verification.
• Inflectra's AI Solutions: See demonstrations of Inflectra's cutting-edge AI tools like the ChatGPT plugin and Azure Open AI platform, designed to streamline your testing process.
Whether you're a developer, tester, or QA professional, this webinar will give you valuable insights into how AI is shaping the future of software delivery.
DevOps and Testing slides at DASA ConnectKari Kakkonen
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Welcome to UiPath Test Automation using UiPath Test Suite series part 3. In this session, we will cover desktop automation along with UI automation.
Topics covered:
UI automation Introduction,
UI automation Sample
Desktop automation flow
Pradeep Chinnala, Senior Consultant Automation Developer @WonderBotz and UiPath MVP
Deepak Rai, Automation Practice Lead, Boundaryless Group and UiPath MVP
Neuro-symbolic is not enough, we need neuro-*semantic*Frank van Harmelen
Neuro-symbolic (NeSy) AI is on the rise. However, simply machine learning on just any symbolic structure is not sufficient to really harvest the gains of NeSy. These will only be gained when the symbolic structures have an actual semantics. I give an operational definition of semantics as “predictable inference”.
All of this illustrated with link prediction over knowledge graphs, but the argument is general.
Transcript: Selling digital books in 2024: Insights from industry leaders - T...BookNet Canada
The publishing industry has been selling digital audiobooks and ebooks for over a decade and has found its groove. What’s changed? What has stayed the same? Where do we go from here? Join a group of leading sales peers from across the industry for a conversation about the lessons learned since the popularization of digital books, best practices, digital book supply chain management, and more.
Link to video recording: https://bnctechforum.ca/sessions/selling-digital-books-in-2024-insights-from-industry-leaders/
Presented by BookNet Canada on May 28, 2024, with support from the Department of Canadian Heritage.
Dev Dives: Train smarter, not harder – active learning and UiPath LLMs for do...UiPathCommunity
💥 Speed, accuracy, and scaling – discover the superpowers of GenAI in action with UiPath Document Understanding and Communications Mining™:
See how to accelerate model training and optimize model performance with active learning
Learn about the latest enhancements to out-of-the-box document processing – with little to no training required
Get an exclusive demo of the new family of UiPath LLMs – GenAI models specialized for processing different types of documents and messages
This is a hands-on session specifically designed for automation developers and AI enthusiasts seeking to enhance their knowledge in leveraging the latest intelligent document processing capabilities offered by UiPath.
Speakers:
👨🏫 Andras Palfi, Senior Product Manager, UiPath
👩🏫 Lenka Dulovicova, Product Program Manager, UiPath
Builder.ai Founder Sachin Dev Duggal's Strategic Approach to Create an Innova...Ramesh Iyer
In today's fast-changing business world, Companies that adapt and embrace new ideas often need help to keep up with the competition. However, fostering a culture of innovation takes much work. It takes vision, leadership and willingness to take risks in the right proportion. Sachin Dev Duggal, co-founder of Builder.ai, has perfected the art of this balance, creating a company culture where creativity and growth are nurtured at each stage.
GraphRAG is All You need? LLM & Knowledge GraphGuy Korland
Guy Korland, CEO and Co-founder of FalkorDB, will review two articles on the integration of language models with knowledge graphs.
1. Unifying Large Language Models and Knowledge Graphs: A Roadmap.
https://arxiv.org/abs/2306.08302
2. Microsoft Research's GraphRAG paper and a review paper on various uses of knowledge graphs:
https://www.microsoft.com/en-us/research/blog/graphrag-unlocking-llm-discovery-on-narrative-private-data/
Accelerate your Kubernetes clusters with Varnish CachingThijs Feryn
A presentation about the usage and availability of Varnish on Kubernetes. This talk explores the capabilities of Varnish caching and shows how to use the Varnish Helm chart to deploy it to Kubernetes.
This presentation was delivered at K8SUG Singapore. See https://feryn.eu/presentations/accelerate-your-kubernetes-clusters-with-varnish-caching-k8sug-singapore-28-2024 for more details.
Let's dive deeper into the world of ODC! Ricardo Alves (OutSystems) will join us to tell all about the new Data Fabric. After that, Sezen de Bruijn (OutSystems) will get into the details on how to best design a sturdy architecture within ODC.
1. JABATAN BIOLOGI
FAKULTI SAINS DAN MATEMATIK
UNIVERSITI PENDIDIKAN SULTAN IDRIS
TUGASAN 3
MODELING AND SIMULATION
SEM 1 SESI 2013/2014
KURSUS
:
IMFORMATION AND COMMUNICATION TEKNOLOGY
IN SCIENCE
KOD KURSUS
:
SSI 3013
Nama
:
RAHMAH BT SOID
No. Matrik
:
D20112052298
Kumpulan
:
UPSI01 ( A131PJJ)
Nama Pensyarah
:
DR. AZMI BIN IBRAHIM
PM-3, Level 2, Block 01,
Proton City Campus
2. ASSIGNMENT 3 - SSI 3013
MODELING AND SIMULATION
MODEL
A representation of an object, a system or an idea in some form other than of the
entity itself.
( Shannom)
SIMULATION
A Simulation of a 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 this properties concerning the behaviour of the actual system
can be inferred .
APPLICATIONS :a.
Designning and analyzing manufacturing system.
b.
Evaluating a new military weapons system or tactics
c.
Determining ordering policies for an inventory system.
d.
Designing communications systems and message protocols for them.
e.
Designing and operating transportation facilities such as freeways, airports
subways or ports.
f.
Evaluating designs for servicesorganizations such as hospital, post office or
fast food restaurants.
g.
Analyzing financial or economic system.
3. THE IMPORTANT OF SIMULATION IN EDUCATION :a.
Intructional simulations have the potential to engage students in “deep
learning” that empowers understanding
as opposed to “surface learning”
that requires only memorization.
b.
Using instructional simulations gives students concrete formats of what it
means to think like a scientist and do scientific work.
c.
Simulation allows students to change parameter values and see what happen.
d.
a feel for students develop what variables are important and the significance of
magnitude changes in parameters.
e.
Simulations help students understand that scientific knowledge rests on the
foundation of testable hypothesis.
WHAT IS STELLA?
STELLA stands for System Thinking for Education and Research. STELLA offers a
practical way to dynamically visualize and communicate how complex the system and ideas
really work.STELLA is used to stimulate a system over the time, jump the gap between
theory and the real world and also it enable students to creatively change the system.
STELLA teach students to look for a reletionships and also it create a clear communication
system inputs and outputs to demonstrate the outcomes.
There are many topics that’s can be contruct by using STELLA. For examples below :-
1. Amalgamated Industries
Think through the consequences associated with the development of a widget plant upstream
from th..
4. 2. Animal Temperature
Explore the laws of thermodynamics with this animal temperature model. As you
experiment, t..
3. Balloon Problem
This model illustrates how a simple interface can facilitate experimentation with a
mathematics m..
4. Distance and Time
Experiment with different velocities and find out how they impact the solution to this typical
di..
5. Extraverts and Introverts
This map (not a running simulation model) uses STELLA's storytelling feature to explore the
conce..
6. H1H1 Flu Outbreak
What is most effective in controlling the outbreak of a flu virus in schools? Is it better to vac..
5. 7. Limits to Growth
Growth processes have inherent limits to growth. Identifying these limits can help avoid pr..
8. Michaelis-Menten Dynamics
The Michaelis-Menten equations are taught in virtually any unit on enzyme kinetics. The
problem, ..
9. Mystery on the Island of Borneo
What do the bubonic plague, falling roof beams and dead fish have in common? Read this
story and ..
10. Natural Selection Pressure
In this model, a rabbit population comes under a natural selective pressure from a fox
population..
6. 11. Non-Seperable Differential Equations
Analytically daunting or deceptively simple? The model represents a classic problem—that
of mixin..
12. One- dimensional diffusion
Simulates the diffusion of heat through a one meter metal bar when the ends are held at a
constan..
13. Overturned Truck
You’re driving on the highway and around the curve is an overturned truck. Can you stop in
time? ..
14. Pendulum Story
Consider what happens when you connect a small ball (or bob) to the end of a string. When
t..
7. 15. Pharmacokinetics
Play the role of physician, trying to keep the drug level in a virtual patient’s bloodstream in t..
16. Plant Succesion Dynamics
See how plants in the ecosystems move through the successional stages of forb, grass, shrub,
pine..
17.Predator Prey Dynamics
As the manager of a small but thriving natural wilderness area, would you allow a one-time
harves..
18. President & Prime Minister
Whose coffee cools faster? This question quickly becomes relevant for the president and the
prime..
19.Reversible Reactions
Storytelling is used to present the basic structure of a reversible chemical reaction.
Experiment..
8. 20. Sustainable Shrimping
This Learning Environment should help you understand some basic population dynamics and
their rel..
21. Temperature Control
Conduct experiments and try to maintain a constant temperature in a house that uses a climate
con..
22. Virtual Bungee
Virtual bungee jumpers can experiment with different body weights and bungee cord
strengths. Then..
23. Virtual Hamlet
Use this virtual laboratory to explore the plot and character development in one of
Shakespeare's..
9. STELLA present four model building blocks that are used in the modelling process: Stocks,
flows,connectors and converter.SSSSSSSSS.
Stocks: The basic building block is the stock that is used to represent anything that
accumulates (populations, biomass, nutrients, water). These are tangible, countable,
physical accumulations. You can also use stocks to represent the degree of nonphysical accumulations such as knowledge or fear.
Flows: Flows are used to represent activities that lead to inputs and outputs to stocks. Flows
include births, migration and nutrient or biomass transport. These activities will
change the magnitude of stocks in the system.
Connectors: Connectors transmit information to regulate flows. Connectors can connect into
flows or converters but never into stocks. Only flows affect the magnitude of stocks.
However, connectors can affect both input and output flows.
Converters: Converters contain equations that generate an output value during each time
interval of a simulation. Converters often take in information and transform it for
use by another variable in the model. They are also handy for storing constant
values.
10. EXAMPLE :
TEMPERATURE CONTROL
How we can control the temperature in a house.
Find out in this simulation by exploring the dynamics of maintaining the desired temperature
in a house, using a climate control system.
11. EXPLORE THE MODEL
Based on the diagram below.
The model of this system consists of several balancing feedback loops linked to the
temperature of the house.
TOUR THE MODEL
1. The house temperature can go up or down depending on the outside temperature. If the
outside temperature is warmer the house will “gain” temperature. If the outside temperature
is colder, there will be a heat “loss”.
2. According to Newton’s Law of cooling this heat exchange is proportional to the difference
between the house temperature and the outside temperature.
3. The rate of heat exchange is determined by the insulation of the house. This constant
measures the time it takes for heat to transferbetween the house and it surrounding. The
greater the value of this constant , the better the insulation of the house.
4. As the house gain of loses heat to the outside its temperature move away from the
thermostat setting or target temperature.
12. 5. That is when the heating and cooling system kick in. The house heating/cooling system
uses a controller with two types of mechanisms for determining when to turn on the hearter or
air conditioner.
6. The first type is a “ proportional” controller. It reacts proportionally to the difference
between the house temperature and the thermostat. In this model we call that difference an “
error signal”.
7. The second type of mechanism is an “ intergral” controller. It operation is base on the
cumulative sum of errors over time. A fancier way to say the same thing is the intergral
controller operates according to the “ intergration of errors”. The resulting sum can be either
positive or negative.
8. Response time also plays a factor in the intergral controller as it does with the time it takes
the system to change the temperature of the house. A smaller time constant means the
heating / cooling system is capable of changing the temperature.
NEXT FOR SIMULATE
In order to run the simulation based on the graph below,
Firstly, we have to set ( based on graph number 1)
-
thermostat setting reading is 68
ambient temperature reading is 32
-
Temp loss time constant is 5
Resulting
-
Temp of house is 39
Next, based on the graph number 2
We adjust the reading of the number of
-
Thermostat setting is 53
Ambient temperature is 32
-
Temp loss time constant is 7
Resulting
-
Temp of house is 48.2 ( temp is increase)
13. Lastly, based on the graph number 3.
When we adjust the parameter to maximum reading, the temp of house is very hot.
Resulting
-
Temp of house is 70.6
Or we can adjust to another reading of parameter.
16. CONCLUSION
Simulation can be a powerful learning experiment. Using simulation in teaching and
learning have the potential to engage students in deep learning that will empowers
understanding about the whole topics. Since simulation required the students to be the
researchers and conduct the simulation by them this will engage students to their learning.
Simulation allow students to change parameter value and see what happen.
Therefore they will see clearly the relationship among variables. Simulation offer students
the opportunity to manipulate content knowledge and this will engages a variety of learning
styles. With simulation, we can use model to predict outcomes. It is easier for the students to
learn using simulation because as they change the parameter, they can predict what will
happen.
Furthermore, simulation help students understanding scientific knowledge by testing
hypothesis. This is due to the fact that simulation are very good at making clear the
complexities involved in issues. Also STELLA can increase the students motivation. I would
recommend others to use STELLA as well.
17. REFERENCES
Maria A. (2002). Introduction to modelling and simulation. Retriered December 1st 2012
From http://www-inf.utsfm.cl-hallende/download/
Weimer M. (2010). Simulation Deliver Real Benefits. Retriered December 1st 2012 from
http://www.teachingprofessor.com/articles/teaching-and-learning/simulation
http://www1.union.edu/rices/stella/stella_intro.html
http://home.ubalt.edu/ntsbarsh/simulation/sim.htm//introduction.Retriered on 7th
November.
RikMin. (2012) Advantages and Disadvantages of model-Driven Computer Simulation.
Retriered on November 17.2012 from http:// projects.edte-utwente.nl/pi/papers/sim
Adv.html
http://www.iseesystem.com/XMILE/index.php?-route=product
http://www.scientificsoftware-solution,com>simulation>visualization