This document discusses simulation modeling and its advantages and disadvantages. It describes the typical steps in a simulation study including problem definition, project planning, system definition, model formulation, input data collection and analysis, model translation, verification and validation, experimentation and analysis, and documentation. It provides examples of using simulation for circuit design. Simulation allows experimenting with designs efficiently before implementation and studying systems at different abstraction levels. However, simulations require significant computing resources and may introduce inaccuracies through simplifying assumptions. The document also discusses using simulations for teaching by allowing students to participate in simplified representations of real processes.

1. DEPARTMENT OF BIOLOGY
FACULTY OF SCIENCE & MATHEMATICS
UNIVERSITI PENDIDIKAN SULTAN IDRIS
INFORMATION AND COMMUNICATION TECHNOLOGY IN
BIOLOGY
(SBI3013)
DATA LOGGER:
BIOCHEMICAL OXYGEN DEMAND (B.O.D)
NAME MATRIC NO.
NOR AZWAHANIN BINTI ABU HASSAN D20152071976
NOR HIDAYAH BINTI ISMAIL D20152071972
AZIDA BINTIMD ZIN D20152071993
GROUP : A
LECTURER: ENCIKAZMI BIN IBRAHIM

2. STEP OF SIMULATION STUDY
1. Problem Definition
The initial step involves defining the goals of the study and determing what needs to be
solved. The problem is further defined through objective observations of the process to be
studied. Care should be taken to determine if simulation is the appropriate tool for the
problem under investigation.
2. Project Planning
The tasks for completing the project are broken down into work packages with a responsible
party assigned to each package. Milestones are indicated for tracking progress. This schedule
is necessary to determine if sufficient time and resources are available for completion.
3. System Definition
This step involves identifying the system components to be modeled and the preformance
measures to be analyzed. Often the system is very complex, thus defining the system requires
an experienced simulator who can find the appropriate level of detail and flexibility.
4. Model Formulation
Understanding how the actual system behaves and determining the basic requirements of the
model are necessary in developing the right model. Creating a flow chart of how the system
operates facilitates the understanding of what variables are involved and how these variables
interact.
5. Input Data Collection & Analysis
After formulating the model, the type of data to collect is determined. New data is collected
and/or existing data is gathered. Data is fitted to theoretical distributions. For example, the
arrival rate of a specific part to the manufacturing plant may follow a normal distribution
curve.

3. 6. Model Translation
The model is translated into programming language. Choices range from general purpose
languages such as fortran or simulation programs such as Arena.
7. Verification & Validation
Verification is the process of ensuring that the model behaves as intended, usually by
debugging or through animation. Verification is necessary but not sufficient for validation,
that is a model may be verified but not valid. Validation ensures that no significant difference
exists between the model and the real system and that the model reflects reality. Validation
can be achieved through statistical analysis. Additionally, face validity may be obtained by
having the model reviewed and supported by an expert.
8. Experimentation & Analysis
Experimentation involves developing the alternative model(s), executing the simulation runs,
and statistically comparing the alternative(s) system performance with that of the real system.
9. Documentation & Implementation
Documentation consists of the written report and/or presentation. The results and implications
of the study are discussed. The best course of action is identified, recommended and justified.

4. RESULT
Original graph
As for original graph, the value of mice and owl is decrease at the beginning of the
simulation. We set up the value of owl for is 10 while the value for mice is 13000. At the
beginning of simulation, the amount of owl is five but the amount drop drastically because of
insufficient of mice as the prey of the owl. When the amount of mice decrease as mice is
been eaten by the owl, the amount of palm fruit production increase because there is no mice
that will eat the palm fruit. Within time, when the amount of owl decrease to 0, the amount of
mice increase steadily. After approximately 17 years, the amount of palm fruit production
still increase within time because the little amount of mice.
Modifiedgraph(i)

5. For the modified graph (i), We set up the value of owl for is 6 while the value for mice is
25000. At the beginning of simulation, the value of mice is decrease and owl is increase at
the beginning of the simulation. The amount of mice 25000 but the amount drop drastically
because of mice is been eaten by the owl. The amount of owls also decrease because of
insufficient of mice as the prey of the owl. When the amount of mice decrease as the amount
of palm fruit production increase because there is no mice that will eat the palm fruit. Within
time, when the amount of owl decrease to 0, the amount of mice increase steadily. After
approximately 17 years, the amount of palm fruit production still increase within time
because the little amount of mice.
Modified graph (ii)
For the modified graph (i), We set up the value of owl for is 13 while the value for mice is
1000. At the beginning of simulation, the value of owl and mice is decrease at the beginning
of the simulation. The amount of owl is 13 but the amount drop drastically because of
insufficient of mice as the prey of the owl mice is been eaten by the owl. Within time, when
the amount of owl decrease to 0, the amount of mice increase steadily. When owl is
decrease, the amount of mice started to increase. After approximately 17 years, when the
amount of mice decrease as the amount of palm fruit production increase because there is no
mice that will eat the palm fruit. The amount of palm fruit production still increase within
time because the little amount of mice.

6. Advantage of stimulation
One of the primary advantages of simulators is that they are able to provide users with
practical feedback when designing real world systems. This allows the designer to determine
the correctness and efficiency of a design before the system is actually constructed.
Consequently, the user may explore the merits of alternative designs without actually
physically building the systems. By investigating the effects of specific design decisions
during the design phase rather than the construction phase, the overall cost of building the
system diminishes significantly. As an example, consider the design and fabrication of
integrated circuits. During the design phase, the designer is presented with a myriad of
decisions regarding such things as the placement of components and the routing of the
connecting wires. It would be very costly to actually fabricate all of the potential designs as a
means of evaluating their respective performance. Through the use of a simulator, however,
the user may investigate the relative superiority of each design without actually fabricating
the circuits themselves. By mimicking the behaviour of the designs, the circuit simulator is
able to provide the designer with information pertaining to the correctness and efficiency of
alternate designs. After carefully weighing the ramifications of each design, the best circuit
may then be fabricated.
Another benefit of simulators is that they permit system designers to study a problem at
several different levels of abstraction. By approaching a system at a higher level of
abstraction, the designer is better able to understand the behaviours and interactions of all the
high level components within the system and is therefore better equipped to counteract the
complexity of the overall system. This complexity may simply overwhelm the designer if the
problem had been approached from a lower level. As the designer better understands the
operation of the higher level components through the use of the simulator, the lower level
components may then be designed and subsequently simulated for verification and
performance evaluation. The entire system may be built based upon this ``top-down''
technique. This approach is often referred to as hierarchical decomposition and is essential in
any design tool and simulator which deals with the construction of complex systems. For
example, with respect to circuits, it is often useful to think of a microprocessor in terms of its
registers, arithmetic logic units, multiplexors and control units. A simulator which permits the
construction, interconnection and subsequent simulation of these higher level entities is much
more useful than a simulator which only lets the designer build and connects simple logic
gates. Working at a higher level abstraction also facilitates rapid prototyping in which

7. preliminary systems are designed quickly for the purpose of studying the feasibility and
practicality of the high-level design.
Thirdly, simulators can be used as an effective means for teaching or demonstrating concepts
to students. This is particularly true of simulators that make intelligent use of computer
graphics and animation. Such simulators dynamically show the behavior and relationship of
all the simulated system's components, thereby providing the user with a meaningful
understanding of the system's nature. Consider again, for example, a circuit simulator. By
showing the paths taken by signals as inputs are consumed by components and outputs are
produced over their respective fan out, the student can actually see what is happening within
the circuit and is therefore left with a better understanding for the dynamics of the circuit.
Such a simulator should also permit students to speed up, slow down, stop or even reverse a
simulation as a means of aiding understanding. This is particularly true when simulating
circuits which contain feedback loops or other operations which are not immediately intuitive
upon an initial investigation.
During the presentation of the design and implementation of the simulator in this report, it
will be shown how the above positive attributes have been or can be incorporated both in the
simulator engine and its user interface.
Disadvantage of stimulation
Despite the advantages of simulation presented above, simulators, like most tools, do have
their drawbacks. Many of these problems can be attributed to the computationally intensive
processing required by some simulators. As a consequence, the results of the simulation may
not be readily available after the simulation has started an event that may occur
instantaneously in the real world may actually take hours to mimic in a simulated
environment. The delays may be due to an exceedingly large number of entities being
simulated or due to the complex interactions that occur between the entities within the system
being simulated. Consequently, these simulators are restricted by limited hardware platforms
which cannot meet the computational demands of the simulator. However, as more powerful
platforms and improved simulation techniques become available, this problem is becoming
less of a concern.

8. One of the ways of combating the aforementioned complexity is to introduce simplifying
assumptions or heuristics into the simulator engine. While this technique can dramatically
reduce the simulation time, it may also give its users a false sense of security regarding the
accuracy of the simulation results. For example, consider a circuit simulator which makes the
simplifying assumption that a current passing through one wire does not adversely affect
current flowing in an adjacent wire. Such an assumption may indeed reduce the time required
for the circuit simulator to generate results. However, if the user places two wires of a circuit
too close together during the design, the circuit, when fabricated may fail to operate correctly
due to electromagnetic interference between the two wires. Even though the simulation may
have shown no anomalies in a design, the circuit may still have flaws.
Another means of dealing with the computational complexity is to employ the hierarchical
approach to design and simulation so as to permit the designer to operate at a higher level of
design. However, this technique may introduce its own problems as well. By operating at too
high an abstraction level, the designer may tend to oversimplify or even omit some of the
lower level details of the system. If the level of abstraction is too high, then it may be
impossible to actually build the device physically due to the lack of sufficiently detailed
information within the design. Actual construction of the system will not be able to occur
until the user provides low level information concerning the system's subcomponents. With
respect to circuit design and fabrication, work is currently on going in the field of silicon
compilers which are able to convert high level designs of circuits and translate them
accurately and efficiently into low level designs suitable for fabrication.
Function of simulation in future learning
 Simulations are learning experiences that enable students to participate in a simplified
representation of the social world.
 Simulations differ from classroom games. Games often involve activities in which
there is a competition to get correct answers. Examples of games include spelling bees
and competitive drill activities.
 Simulations, on the other hand, allow students to understand a process through
participation in that process. In most simulations, students take on roles and have

9. specific objectives to accomplish. In order to accomplish their goals, students use
resources provided and make decisions about how those resources should be used.
 Simulations are complex learning activities. Most research suggests that simulations
are about as effective as conventional classroom techniques in teaching subject
matter.
 Simulations are more effective in helping students retain knowledge learned as part of
the simulated experience.
 Research suggests that simulations are more effective than traditional methods in
developing positive attitudes toward academic goals.
 Simulations are also motivating for students. Frequently students express satisfaction
with participation in simulations and are excited about the learning that took place.
Students connect with simulations because the simulations deal with real questions
and issues.
Conclusion
Simulation is a powerful tool in assisting the teaching and learning process in school. They
provide interesting and exciting mode of learning to the new generation students. Not only to
attract the students’ interest, had simulation also promoted constructivism learning to
students. Through simulation, students can explore the content of the simulation. They have
the freedom to change any parameter in the simulation and observe the result. Students can
construct themselves to gain their knowledge when they try to understand and justify their
observation of the results. Through simulation too, the impossible to do experiment is also
made possible in simulation to give students better visualization and understanding. In shorts,
with the use of simulation, students can be motivated to learn and learning will be an
interesting and fun. Since there are still limitations in applying simulation in education, many
aspects should be considered before applying simulation in a class. Teachers need to be
trained, not only so that they can use simulation to aid their teaching process; but also to let
them know when to apply simulation wisely in teaching and learning session. Although the
technology is very important, some of manual skills are also not less important. Integrating
simulations in teaching and learning is supposed to give advantages, and complete what is
incomplete in the old method; not to suppress the benefit of old teaching and learning method
can provide to students.

10. Reference
https://ijair.org/administrator/components/com_jresearch/files/publications/IJAIR-
1202_final.pdf
Shiflet and George W 2006. Shiflet.System Dynamics Tool: STELLA Version 9 Tutorial 1
Introduction to Computational Science: Modeling and Simulation for the Sciences.Wofford
College.Princeton University Press
Lateef, F. (2010). Simulation-based learning: Just like the real thing. Journal of Emergencies
, Trauma and Shock, 3(4), 348–352. Retrieved 15 Nov. 2016
http://web.cs.mun.ca/~donald/msc/node6.html