Comparative Evaluation Between Mathematical Models For...

Comparative Evaluation Between Mathematical Models For...
COMPARATIVE EVALUATION BETWEEN MATHEMATICAL MODELS FOR DRUG
RELEASE OF CURCUMIN LOADED MULTIFUNCTIONAL ALBUMIN NANOPARTICLES
Abstract:
Curcumin loaded albumin nanoparticles were employed for intra–tumoral chemotherapy for
treatment of solid tumors1. Drug release study for Curcumin was monitored in–vitro using
dialysis1. The drug release data was fitted into 5 mathematical models such as zero order, first order,
Hixen–Crowell, Higuchi release and Korsmeyes–Peppas release kinetics model. R2 coefficient was
compared and was concluded that Higuchi release kinetics model is best suited for the drug release
kinetics for Curcumin.
Introduction and Significance:
Nanoparticle formulations have found extensive applications as drug delivery ... Show more content
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Analysis:
Drug release data for curcumin was fitted into 5 mathematical models and studied to determine the
best mathematical model. The model chosen were:
i. Zero Order Kinetics
The zero–order kinetics depends upon the initial concentration of the drug loaded on the
nanoparticles and refers to constant release of drug5,6. Zero order kinetics is represented as:
Qt=Qo+k*t
Qt = amount of drug dissolved in time t, Qo=initial amount of drug, k=zero order kinetics constant
ii. First Order Kinetics
The release of the drug which followed first order kinetics can be expressed by the equation7: log C
= log C0–Kt / 2.303
Co is the initial concentration of drug, k is the first order rate constant, and t is the time
iii. Hixon–Crowell Release Kinetics
Hixson and Crowell derived the equation: Q01/3–Qt1/3 = κ*t where Q0 is the initial amount of
drug, Qt is the remaining amount of drug at time t and κ (kappa) constant incorporates surface
volume relation. The equation describes the release from systems where there is a change in surface
area and diameter of particles or tablets9.
iv. Higuchi Release Kinetics

Higuchi drug release kinetics model was proposed Higuchi in 1961 based on a matrix system8. The
matrix model is based on initial drug concentration in the, instantaneous drug
... Get more on HelpWriting.net ...

A Study On My Colleague Career
During my colleague career, I had multiple statistical and model prediction courses that gave me the
required foundation to handle this project successfully. For instance, I had Probability Theory and
Stochastic Process,
Thuy Mai
CEO
Kyle Jordan
VP Marketing
Rachelle Edllund Account Manager
John Nguyen
CSO
Surya Sunkara Opearations Research Analyst
MIke Nedeau VP Sales Independent Study in EMGT (Course Topic Robustness in Reverse
Logistics) and Six–Sigma Quality courses that helped me to develop the required skill set. I also
reviewed some related standards and reference books in order to increase my knowledge.
Furthermore, I was proficient in Excel and Matlab programming that enabled me automates and
analyze the data professionally in this project. I had worked on similar analytical projects before and
due to my previous exposure to similar projects; my supervisor invited me to join the project and
help the team develop a customer churn prediction model.
CE1.7 In the first step, I attended multiple meetings with my supervisor and other team members to
understand the project goals fully. I quickly realized that there are a lot of steps and activities that I
have to do in order to complete the project in time. Therefore, I started creating a timetable for the
list the tasks I was responsible for. After I put together a draft of the activities, I scheduled a meeting
with my supervisor to make sure that my personal schedule and deadlines are in full compliance
with

Project Description Of A Mathematical Model
Project Description In many science and engineering applications, such as petroleum engineering,
aerospace engineer– ing and material sciences, inference based on a mathematical model and
available observations from the model has garnered importance in recent years. With the lack of the
analytical expres– sion, in most scenarios this solution involves numerical approximation. The
underlying system may contain unknown parameters which requires solving an inverse problem
based on the ob– served data. In many cases the underlying model may contain high dimensional
field which varies in multiple scales such as composite material, porous media etc. This high
dimensional solution can become computationally taxing even with the recent advent of ... Show
more content on Helpwriting.net ...
For example, in petroleum engineering the reservoir permeability may be unknown. From oil/water
pressure data from different well locations estimating the unknown κ is an inverse problem. 1
Figure 1: Left hand panel shows one dimensional basis at coarse level of discretization at grid
points 1,2,3,.... Basis corresponding grid point 2, φ2 is supported in [1,3] interval and zero otherwise
and linear in [1,2] and [2,3]. Right hand panel shows typical multiscale basis at two dimension,
which takes non zero value on coarse neighborhood of some coarse grid points but has high
resolution by solving a local problem. The solution u, the parameter κ can have oscillatory nature
(both in temporal and spatial scale) with multiple scales/periods. A numerical solution that captures
the local property of this solution requires capturing the local structure which involves solving a
homogenous version of (1) locally and use these solutions as basis to capture the global solution,
which is known as multiscale solution (Fish et al., 2012; Franca et al., 2005). A highly oscillatory
κ(x, t) = κ(x) is given for a two dimensional domain in Figure 2 . In numerical solution, the domain
is split into many small grids and basis corresponding to each grid, also known as fine scale basis,
can capture the oscillatory solution (see Figure 1). The linear pde system can be reduced into

Formal Mathematical Methods And Hardware Systems
Literature Review Formal mathematical methods are system designs that utilize specific rigorous
mathematical models in the production of software and hardware systems. The methods are unique
in comparison with other methods for they utilize mathematical proof as a complement to system
testing so as to ensure a perfect behavior. A formal approach to system designs ensures safety due to
the complicated nature of systems. The use of formal verification schemes creates the difference
between formal methods and other design systems. Hence, the primary principles of the system
must be proven to be right before they are adopted. Extensive testing has been used for long in
traditional system designs to ascertain behavior, but only finite conclusions are achieved. Testing
only reveals situations where a system would not fail, but the behavior outside the testing scenario is
not accounted for. If the result is positive after testing the theorem, then it remains true. An error in
design is impossible to fix via formal verification but could help in identification of errors in
reasoning which would otherwise be left unverified. The knowledge of mathematics results from
proofs that consist of valid and certain conclusions. Verification of mathematical statements is not
through experiments or social agreements but logical deductions from basic assumptions. The
method assures that the knowledge has universal application since mapping a mathematical
statement into physical reality holds

Adaptive Delta Causality Control With Prediction
Adaptive Delta–Causality Control with Prediction in Networked Real–Time Game Using Haptic
Media Haptic – "relating to the sense of touch, in particular relating to the perception and
manipulation of objects using the senses of touch and proprioception." MU – "The game is based on
a peer–to–peer (P2P) model. Each terminal refreshes a screen at 60 Hz and sends media units
(MUs), each of which includes information about the position, velocity, and time–stamp, to the
other terminal. We have two types of MUs: One has the position information of the mallet, and the
other has that of the puck." Networked Air Hockey using Haptic media Improve interactivity of the
air hockey game through applying prediction logic to adaptive delta–causality control. The value of
∆ is dynamically changed according to the network delay for MUs of the mallet, and is shared
among terminals Then, each terminal sends a changed value of ∆ to the other terminals. The
terminal selects the largest value from among the changed values as a new value of ∆. The adaptive
delta causality control scheme with prediction outputs each Media Unit by predicting the future
position later than the output time of the received MU by the prediction time Tpredict ( ≥ 0) ms to
keep the interactivity high. Measured through Quality of Experience assessment. Conclusion In this
paper, we proposed the adaptive ∆–causality control scheme with prediction and investigated the
effect of the proposed scheme by QoE assessment in a

Pharmaceutical Industry Case Study
Scheduling of Pharmaceutical Industry
Abstract
Scheduling of pharmaceutical industry is more challenging due to greater complexity of processes.
An overview on main time framework used in the basic structure of mathematical programming
formulations for the production scheduling. The models are based on continuous or discrete time
representation. In this paper I study only importance of these time representation and how to use for
the scheduling of processes. I also describe the regular and non–regular production scheduling for
the multipurpose batch plants.
Keywords: Process scheduling; Multipurpose plants; Discrete time model; Continuous time model;
Constraints.
Nomenclature
Indices
i/i^' order j machines k systems t time slots
Sets
I set of order, i ∈ I and ... Show more content on Helpwriting.net ...
Therefore it is important to improve production schedules in order to higher utilization of resources,
increased flexibility, reduced response time as well as cutting down the cost of production.
Literature Survey
I review mainly two Journal on the topic related to the scheduling of pharmaceutical industry as
given by "Discrete and continuous time representations and mathematical models for large
production scheduling problems: A case study from pharmaceutical industry".
In this literature, I found that the choice of time representation is an important parameter in the
structure and character of a mathematical model for scheduling. There are two types of time
representation – Discrete and continuous. I also learned about the Decomposition algorithm which is
used when the complexity of the problems are high. I understand and try to formulate the problem
given for continuous and discrete time formulation for the comparison of two models, results and

Determination Of Solubility Of Fenoprofen
RESULTS
Determination of solubility of fenoprofen The solubility data is shown in Table I and it is presented
in Figs.1, 2 and 3. The solubility of fenoprofen amongst various oils investigated was found to be
highest in oleic acid (198.93±2.91mg/ml) followed by triacetin, labrafil M1944, capryol 90. The
solubility of drug was insignificantly different between labrafac lipophil, IPM, and labrafac PG.
Amongst surfactants, labrasol showed maximum solubility (129.17±1.4mg/ml) followed by tween
80, and span 20. Propylene glycol showed highest solubility among the cosurfactants
(210.5±1.57mg/ml), followed by glycerol, plurol oleique, and lauroglycol 90.
Preliminary screening of surfactants for emulsification ability The % transmittance values of
dispersions are given in Table II. These studies indicated that tween 80 had good ability to emulsify
oleic acid followed by labrasol, while span 20 failed to emulsify oleic acid.
Preliminary screening of cosurfactants The % transmittance values of surfactant/cosurfactant
combinations are given in Table III. These studies revealed that no significant difference between
propylene glycol and glycerol ability to improve nanoemulsification of tween 80. However,
propylene glycol was selected because it showed better drug solubility than glycerol.
Construction of pseudo–ternary phase diagrams The transparent to translucent microemulsion
region for each investigated system are presented in phase diagram with one axis representing

Designing A Mathematical Model For Interpersonal...
Personally speaking, I think the definition of hobbies is that someone prefers do so than go to sleep
at night. In that case, I listed my hobbies as below.
One of them is doing math problems. It is a pleasure for me to solve those intriguing problems.
Every time I solve a problem that was challenging to me, I feel very happy for the self–
achievement. Even if it occurs at a deep night, I will get refreshed by the ecstasy. I was only four
when I first tasted the sweetness of math. At the high school, this interest has extended to the
enjoyment of modeling, because constructing a model is arduous too, whereas a good result brings
really a lot of happiness at the end. Much related to modeling, my research problem in 2015 is to
construct a ... Show more content on Helpwriting.net ...
With programming skills, I finished a number of projects, whose success facilitated me to continue
on my work. For instance, I celebrated my ten–year–old birthday party by successfully constructing
a program that could either produce or solve sudoku puzzles. On that day, the program gave me a
puzzle, and solved the puzzle itself. I considered this gift as more memorable than any other one. In
my high school, I made a software that helps us reciting English vocabularies by a technique similar
to flashcards. The advantage in efficiency makes it better than the latter. The software, for example,
supports data importing while flashcards do not. In addition to the improvement of manual
efficiency, the software expressed a strong enhancement of the users ' speed of reciting words, aka.,
the effectiveness. It was based on the forgetting curve proposed by Hermann Ebbinghaus, which
quantifies the interval between each repetition of memorizing process. I made a little adjustment to
the values, and set them as the time intervals between the repetitive appearances of a word. In this
sense, the word repeats again and again, and deepens the memorization every time. The
memorization becomes very deep using this strategy. I made a comparison between the efficiency of
reciting vocabularies using the traditional methodology and using this program. The result was that
both in one hour, the traditional way could only make me fully recall approximately 120 words,
whereas the program

What Links Disciplines Of Literature, Philosophy And...
What links the disciplines of literature, philosophy and mathematics? The answer is, quite simply,
fiction. Fiction has probably existed since the human brain was capable of imagination, either for
distraction or for the more functional purpose of envisioning hunt scenarios when our nomadic
ancestors would need to survive. Fiction can be defined as a literary tool that fools readers or theatre
goers into thinking that the author possesses certain knowledge about the world (Plato et al., 1963)
but is usually understood as a world or scenario that is created by human imagination. However, that
doesn 't necessarily make it untrue, as fiction can be based on reality and can become a useful
mechanism to convey important messages and help shape ... Show more content on Helpwriting.net
...
Numbers and equations simply to not concretely exist in our world. Maths is, after all, like any
written language, glorified scribbling that somehow resonates with meaning in our minds. Take as
an example '4 = 2 + 2 '. This string of symbols translates itself into what we understand is an
equation: the two sides of the '= ' sign are the same. However, neither '4 ' not '2 + 2 ' exist. They are
not visually identical. Yet somehow we accept that connecting them with the '= ' symbol indicates
that their numerical value is equivalent. Like fiction, maths merely requires us to suspend our
disbelief long enough for us to understand whatever story or message the author is trying to impart.
This philosophical position is called Mathematical Fictionalism. Its proponents believe that
mathematics are purely fictional but are still functional as long as its purpose is not meant to be a
mirror of reality (Leng, 2016). However, this position calls into question the validity of
mathematics. Fiction, if assumed completely truthful, becomes a lie. So if mathematics are purely a
human made language created to describe phenomena that cannot be described in another language,
how can we ascertain its precision and how much power should we give it in a discussion?
Mathematical entities do not exist concretely, yet we give them characteristics and magnitudes that
allow them to be ranked, ordered and even to interact amongst

Ss3150 Hwk Week 1
Webquest Handout Task 1: 1. The research methods knowledge database is a database of
generalized topics about performing social research. These vary from how to collect data, who to
collect data from, where to collect the data, etc. a. What is the difference between qualitative data
and quantitative data? How do you determine what type of data to collect? Can your topic be
represented by solid numbers, or is it based on opinion? 2. Quantitative because the data given is
concrete and generalizations like mean and mode can easily be identified. 3. Quantitative data is
easily compiled into something meaningful because it is based on concrete data. On the other hand,
qualitative data is presented in a raw form and needs ... Show more content on Helpwriting.net ...
The first section talks about first establishing the cause and effect relationship that you are going to
be testing. By determining the cause and effect relationship that you intend to test, so that an
outsider can test its validity based on your intended cause and effect, and the realized cause and
effect. The following section talks about single groups, more in particular about threats of a single
group to your cause and effect. It talks about 6 threats to single groups, including history threats,
maturation threats, testing threats, instrumentation threats, mortality threats and regression threats,
and gives good examples on how these threats may appear in your data. It then moves on to talk
about multiple group threats, and how it relates to selection bias or selection threats. It describes and
gives concrete examples of six types of multiple group threats, which include including selection–
history threats, selection–maturation threats, selection–testing threats, selecting–instrumentation
threats, selection–mortality threats and selection–regression threats. Lastly, this section talks about
social interaction threats as it relates to validity. It identifies and gives examples for four social
threats, which are diffusion or imitation treatment, compensatory rivalry, resentful demoralization,
and compensatory equalization of treatment. The reason that I

A Simple Mathematical Model Of The Underlying Economics
The current crisis is catalyzing an array of responses, including searching for causes, reworking
regulations, scapegoating and a massive capital injection. Without a clear understanding of the
cause, the remedies may do more harm than good, innocents may be scapegoated, and valuable
progress in financial tools may be lost. Worse, it will happen again. From a simple mathematical
model of the underlying economics, I first predicted this crisis in July of 2004. Economic dynamic
relating very low interest rates to the structure of the demand curve in the housing market made this
outcome foreseeable, indeed inevitable. The current crisis had a mathematical cause. There isn't
space here for full explanations; see ... Show more content on Helpwriting.net ...
The two central facts are these: for reasons we'll discuss, buyers buy a monthly payment, not a
house price, and buyers buy as much house as they can afford. This brings us to the heart of the
matter: mortgages that require no down payment, and only interest payments, alter the structure of
the demand curve for real estate, in a way that is harmless enough when interest rates are high, but
which drives a bubble at low interest rates. Specifically, they make housing prices inversely
proportional to the interest rate. If interest rates are cut in half, house prices double. When those
rates double, house prices are slashed in half. When interest rates are large, they are not likely to
double or halve, but when interest rates are small, a small adjustment can be a big percentage
change, and the danger of big swings in housing prices is appreciable, even inevitable. With no
down payment, no amortization and closing costs folded into the loan, the only issue in affording a
house is the monthly payment, which is the house price multiplied by the interest rate. If interest
rates are cut in half, the house you can buy with a given monthly payment costs twice as much. But
the same number of people with the same income distribution is competing for a fixed stock of
housing. The house price is bid up until the new monthly payment at the new interest rate matches
the old monthly payment at the old interest rate. The house price varies inversely

Strengths Of The Black-Scholes: Option Pricing Model
In 1973, Fischer Black and Myron Scholes developed the option pricing model called Black–
Scholes option pricing model. The model explains how to calculate the price of the option by using
present value of the asset's price, volatility, strike price, time to maturity, and the risk free interest
rate existed in the market. Time to maturity is usually expressed as the number of days. The Black–
Scholes option–pricing model can use for European call option, which pays no dividends at zero–
coupon risk–free interest rate before the option expire.
Majority of the market participants use the model for many reasons. Therefore, this paper will be
carefully studies the model with detailed analysis of the strength and weakness based on the
assumption of ... Show more content on Helpwriting.net ...
The inputs are more objective than other option pricing models.
The main strength of the model is its simplicity as other variables are easy to get from market. Once
the five variables are collected, the value of the option can be calculated easily. Therefore, this give
a benefit to market participants since they can compare market prices with different values based on
different inputs.
Although the model might seem as a complicated model for human calculation, the formula is
simple in mathematical terms. Therefore, high–tech computer programs are not need to compute and
it can also save time.
One of advantages of the model is that investors can use the model to analyze market volatility of
underlying assets. Results from the model are often useful in practice and minimize risk even
thought volatility is not constant. Then, investors will know whether the market value is rewarding
investment or not. Therefore, it acts as insurance and helps to reduce possible loss and expand
profits. Black–Scholes model is not only useful for estimating the value of the call option and
hedging of option but also enlarge the approach to other derivative

Examples Of Classical Job Shop Scheduling Problem
1. Introduction The classical job shop scheduling problem (JSP) is one of the major issues in
production planning that each operation is performed by the machine for finding completion time.
Machine is fixed and known in advance. The sequencing of all jobs is assigned on all machines.
This problem is developed method to find the answer more effective and widely. The flexible job
shop scheduling problem (FJSP) extended from the classical job shop scheduling problem (JSP) that
each operation can be processed by any machine from a set of available machines at that time and
effect to flexible in the machine selection for more efficiency in the usage of the machine and
without the delay of waiting for production. Generally, the FJSP can be separated two sub–problem;
1) a routing sub–problems that assignment each operation on any machine from a set of available
machines 2) a scheduling sub–problems that sequencing of operation on each machine in a feasible
schedule to minimize completion time or makespan. Therefore, FJSP makes the assignment of each
machine more complex than classical JSP. ... Show more content on Helpwriting.net ...
However, the constraints related to the direct usage of machines are not explicitly considered
sequence–dependent setup time, maintenance activities, transportation times of production, which
makes to assume mathematical models is closer to the real practice in industry. The sequence–
dependent setup time is a limitation of assumption and included in the processing time that set up
time of previous job is processed on each machine will affect the setup time of different job.
Because changing the type of job that makes waste of time, adjust the machine for operation on each
machine type. The complexity of the setup time is different the priority of the job. The operation for
same type with the same setup affects lowest completion time of the

Decision making is the most important aspect of any matter...
Decision making is the most important aspect of any matter one confronts in the physical world. The
success or failure of a project, business, operation, company etc. is directly proportional to the
logical reasoning carried out to reach a decision. Bad/ suboptimal decisions can ruin/damage
business, project, reputation and careers. Past few decades of research into the decision making
paradigm has highlighted that bad decisions are a byproduct of distortions and biases that
incapacitate our reasoning. Most of our decision making is heuristic in nature that serves perfectly
well in most of the situations. However, when the problem is large enough, one tends to ignore or
poorly identify the details that must be taken into account in order ... Show more content on
Helpwriting.net ...
The problem of finding an optimal solution under given constraints belongs to the discipline of
mathematical optimization that includes methods like linear programming, dynamic programming,
integer programming, Pontryagin's maximum principle and nonlinear programming. Linear
programming has been popular since its introduction and has helped solve many real world
problems yet it suffers from a grave limitation i.e. the real world is not linear which restricts its use
to linear optimization problems or to the ones that can be approximated as linear models. Problems
with strong nonlinearities will not give a feasible solution if approximated via linear models.
Nonlinear programming addresses these limitations but is far more difficult to implement and needs
more computational power to solve problems. In this paper we review the nonlinear programming
technique and the way it can help solve a complex optimization problem. The paper is organized as
follows. In section 2 we define the optimization problem and its mathematics. In section 3 we
discuss the nonlinear programming, its origin, uses and the software programs that can be used to
aid problem solving. Section 4 is an example of optimization problem and its solution via nonlinear
programming. In section 5, we give the conclusion.
2. Optimization Problem
Mathematically an optimization problem is defined as follows: where is called the objective or cost
function to be optimized subject to m inequality constraints

The Is The Applications Of Computers, Software, And...
Bioinformatics is the applications of computers, software, and mathematical models to the
management of biological information from large data sets. It is the storage and analysis of
biological information by the use of computers. Computers are used to gather, store, analyze, and
integrate information that can then be applied to gene–bases drug discovery and development. This
has become an essential part of biological research activity, and is rapidly becoming as ordinary in
biosciences as molecular biology has been since the 1980s. The science of Bioinformatics, which is
the melding of molecular biology with computer science, is essential to the use of genomic
information in understanding human diseases and in the identification of new molecular targets for
drug discovery (What is bioinformatics). The primary goal of bioinformatics is to increase the
understanding of biological processes. It is the "determination of the sequence of the entire human
genome (approximately three billion base pairs)" (What is bioinformatics). This is done so
differently from other approaches, however, due to its focus on developing and applying
computationally intensive techniques to achieve this goal. "With bioinformatics, scientists can
compare data from genetic material of a variety of living things, from tiny bacteria to large
organisms, such as humans (Bioinformatics, 2012). Bioinformatics has made it possible for
scientists to be able to analyze genomes and their functions.

The Importance Of Technology In Education
Technology has greatly revolutionized the healthcare industry. It has streamlined patient care and
opened networks to make information more attainable and concise which has allowed for the
improvement of patient care and drastically eliminating the chance for error. One of the most
influential ways technology has been changing the industry has been in education. More and more
schools in charge of molding the next leaders of the healthcare industry are using technologically
advanced tools to train the students in procedure and patient care. Simulations are becoming one of
those widely utilized tools in the education of healthcare professionals. They provide a safer
learning environment where students can explore a variety of scenarios and how to tackle them, and
can improve patient care and aid professionals in making better more educated decisions. Education
has grown throughout the ages with the advancement of technology. The earliest classrooms used
"wooden paddles with printed lessons, called Horn–Books." Which "were used to assist students in
learning verses."( The Evolution of Technology in the Classroom. (2017, August 01).) Flash forward
to today and it has become normalized to see people walking around with smart phones, laptop
computers are a staple in college classrooms, and some classes are even conducted online. The tools
of education have changed and one of those tools has been the use of simulators in the classroom.
According to the SSIH simulations are used as

A Mathematical Model Of Centripetal Force Essay
Kayla Ward
Formal lab report 1: lab 4
10/9/16
Centripetal Force
Abstract:
The idea of this experiment is to represent a mathematical model of centripetal force. This is
obtained using a rubber stopper and various hanging weights. Using these items one would attach
the weighted rubber stoppers to one end of the apparatus and a hanging mass on the opposite end.
The time and number of rotations were recorded for various weight scenarios and then used to graph
the square of the orbital period versus the Mass ratio of the swinging to hanging weight. This was
done to show accuracy of the theoretical model in real time. The data obtained from this experiment
is then calculated and plotted on a graph. This information was between a twenty percent confidence
interval due to human error. The results shown below proved the model to be accurate.
Introduction:
The goal of this experiment was to show the difference in centripetal velocity of the rubber stopper
as the weight changed between the rubber stopper, Msb, and the circular weight, MH. Centripetal
force is the net force pointing from the body toward a fixed center of curvature. In other words,
centripetal force can be defined as the net force that is central seeking and radial, causing a curved
motion to an object. The objective of this experiment is to study motion, with the concept of
centripetal force of a spinning object on the end of a string. As the string rotates around the
apparatus the string follows the

The Epidemic Of Ebola Hemorrhagic Fever
Introduction
Ebola hemorrhagic fever is a severe and often deadly illness named after a river in the Democratic
Republic of Congo (formerly Zaïre) where it was first identified in 1976 with a high case fatality
rate lying between 50 and 90%. Outbreaks between 1972 and 2007 are shown in Table 1. The
disease first came into the limelight in 1976 in Zaïre and Sudan in 1976 [17]. Its origin is still
unknown and it is widely believed that Ebola virus is transmitted to humans from discrete life cycles
in animals or insects, but regardless of the original source. Person–to–person transmission is the
means by which Ebola outbreaks and epidemics progress. Bioterrorism threats as well as emergence
of new pandemic and drug–resistant variants of known infections require development of the tools
that would adequately predict occurrence of epidemics, assess efficiency of countermeasures, and
optimize the efforts directed towards provision of biological safety.
Mathematical modeling has emerged as an important tool for gaining understanding of the dynamics
of the spread of infectious diseases. The need of accurate models describing the epidemic process
are vital, because infectious diseases outbreaks disturb the host population and has financial and
health consequences. There is also the need to use sound statistical analysis methods to test the fit of
such models to observed data to account for uncertainties by means of probabilistic models.
The optimization of the control of an

The Vs. Mathematical Model
V. MATHEMATICAL MODEL Although there are differences between the architectures discussed
so far, they however have 3 similar duties they perform [16] namely; 1) Infrastructure maintenance
of the peer–peer system: This task involves handling the periodic arrival and departure of peers to
and from the network. Prompt update of the indices of newly arriving peers and ensuring of
connectivity among peers is also important. 2) Query handling: This involves the use of efficient
mechanisms for propagating queries and returning responses. The target is effective search into the
network with minimum resource usage. 3) File transfer: This has to do with the reliability of the file
transfer between peers. In all the 3 architectures, once the requested file has been located, a direct
connection is set up between the two peers involved and the file download follows. These
similarities and differences are what have been captured by the mathematical model presented in
[16]. To capture the various characteristics, the peer–peer system was modelled as "a multiple class
closed queueing network" [16]. Each class comprises of a fixed number of peers, represented by N=
(N (1) .........N(c)), where C represents the number of classes. Workload is generated by peers online
when they generate queries or download files, they also increase the service capacity of the system
as more peers move from off–line

A Mathematical Model: An Idealization of a Real Problem
A mathematical model is an idealisation of a real problem. Mathematical models also allow
predictions to be made in studies. These models are usually expressed in functions. Graphs are then
plotted from these functions and will be analysed. The mathematical model that will be studied in
this investigation is mainly the cubic function.
Cubic functions are equations that are expressed in the form of f(x)=ax3+bx2+cx+d where a≠0 and
a,b,c and d are constants. There are usually two stationary points and an inflection point in cubic
functions. This inflection point can either be a stationary inflection point or a non–stationary
inflection point.
Cubic equations were known to the ancient Babylonians, Chinese, Greeks, Egyptians and Indians.
Physicians and mathematicians in the past such as Hippocrates , Diophantus , Menaechmus ,
Archimedes and many more have studied the cubic function.
The aim of this investigation is to investigate the relationship between the stationary points of a
cubic polynomial and the point of inflection of cubic functions .This investigation will also discuss
the contexts of applying cubic models to the real life situations.
Several items were used in completing this investigation. A TI–84 plus calculator from Texas
Instruments was used. A printer was also used in printing the papers in this investigation
In this task, a real life situation is that the electricity used in a house is charged into an electricity
bill. The electricity consumption in Australia

Tata Steel Operation Strategy
Book Reviews
BENJAMIN LEV
School of Management The University of Michigan–Dearborn 4901 Evergreen Road Dearborn,
Michigan 48128–1491
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The range of books reviewed is wide, covering theory and applications in operations research,
statistics, econometrics, mathematics, computers, and information systems (no software is
reviewed). In addition, we include books in other Helds that emphasize technical applications.
Publishers who wish to have their books and proceedings reviewed should send them to Professor
Benjamin Lev, School of Management, The University of Michigan–Dearborn, 4901 Evergreen
Road, Dearborn, Michigan 48128–1491, BLev@FOB–Fl.UMD.UMICH.EDU. We list the books
and proceedings ... Show more content on Helpwriting.net ...
Tata Steel Tata Steel is the largest steel producer in India. The main project described is the
development of a model for adjusting the mix of products to compensate for different levels of
power shortages at various times in the day. Prior to describing this project, the presenters show how
they used a productmix model to improve profitability, comparing its results to those of their
previous strategy of maximizing output. The electricity shortage model built on this one with the
added element of incorporating the fixed costs associated with the adjustments necessary to
compensate for a lack of power. The value in this tape is at several levels. The users of the model
make the point that they were skeptical that anyone in management science could tell metallurgists
how to improve their operation of a steel plant.

Juiy–August 1996
79
LEV
The speakers do a good presentation on the use of sensitivity analysis to determine how much scrap
to buy as a function of the price of scrap. Seeing this tape is as valuable for future managers in the
developed countries as it is for those in less developed countries: developed countries have no lock
on either knowledge or technology, and not using the available tools of management science will
hurt firms operating in highwage areas of the world when faced with competition that not only pays
lower wages but also uses sophisticated

Simulation Modelling Essay
Simulation modelling is the procedure of developing and analysing a prototype of a physical model
to predict its behaviour and performance in real–world or over time. A model represents the system
itself and the simulation represents the operation of the system over a specified time period. It can
be used to show the effects of possible conditions and the sequence of action. Modelling and
simulation helps obtaining information about how a system will perform without testing it in real
life. It shows any flaws in the design and reduces potential errors with the system itself, as well the
operation of it. Also, validates the safety and durability of a physical prototype. This reduces the
costs and increases the quality of products and systems.
Figure 1: Interaction between model, experiment, theory and the simulation of the system.
In civil engineering, a typical process of simulation modelling includes:
1. By using a 2 dimensional (2D) or 3 dimensional (3D) tool/application, develop a virtual model of
a system to represent ... Show more content on Helpwriting.net ...
Simulations can often be more realistic than traditional experiments, as they allow to change the
environment parameters at any stage of the design.
Analysis and simulations can be performed faster and more accurate than hand calculations and
experimental testing. This allows using them for efficient analyse of different alternatives. Also, it is
quicker to make decisions from using simulation applications.
Simulations set up a coherent environment that allows for integration of simulated systems in the
early analysis phase with initial components to a test environment for the final system. Then, this
environment can be transferred from development and testing field to training and education phases
for the system.

Designing A Representative Model Of Real World Environments
Most of the previous types of simulations and simulators referenced evaluating methods and
effective practices. In the distant past technological advancement in the form of medical training
mannequins or board games were the bulk of technology based simulators. With the growth of the
computer industry, we are naturally increasing our use of computerized technology. Simulation is
absolutely no exception to this. Computer simulations have become one of the greatest and most
common ways of creating a representative model of real world environments. Steadman, Coates,
Huang, Matevosian, Larmon, McCullough, and Ariel (2006) pointed out the spread of simulations in
the medical fields:
Fueled by the increasing emphasis on interactive medical education, the use of patient simulation
has spread from anesthesiology to a growing list of disciplines including emergency medicine,
radiology, pediatrics, neonatology, trauma, nursing, other allied health professions, and military and
disaster management. The applications of simulation range from routine skills and critical event
training to competency assessment. (p. 151)
The use of simulators has even seen an increase in entertainment circles with computer games such
as Flight Sim by Microsoft, Simcity originally by Electronic Arts and Maxis, and Sid Meier 's
Civilization originally by MicroProse. The value of these simulators lies in their ability to recreate
aircraft piloting, government administration, or historical society growth

Personal Statement In System Biology
Since the encounter to 'Network Motifs' by Uri Alon's book 'Design Principles of Biological
Networks' during my Integrated Masters at University of Hyderabad; systems biology has
immensely captivated me. Further in time; academic courses, workshops and projects guided me to
unearth this as the area that I want to pursue as my research. Although new, this field gives a holistic
perspective to the historically reductionist outlook of the biological systems.
While living organisms are interesting and exciting to think about, yet they have baffled scientists
since the beginning. Now interdisciplinary groups are working together address questions in these
systems. I want to work on complexity of biological systems using dynamical systems theory and
network analysis. These approaches in systems biology give an insights into intricate events such as
cell functions and diseases.
Though we have come a long ahead in 21st century, we still have before us an ordeal of the most
grievous kind: disease. Diseases affect not only the person physically, but also whole family
emotionally and economically. Brief suffering from reactive arthritis stimulated me to join the
search for an answer to the ... Show more content on Helpwriting.net ...
R. Ramaswamy, Nonlinear Dynamics Lab, Jawaharlal Nehru University. The current internship
under him has given me opportunities to look at some interesting mathematical models in diverse
fields. For instance, first project gives an insight into a portion of the current research being carried
out in dynamics of Complex Networks, in collaboration with Prof. B. Biswal, Delhi University. The
project involves construction of mathematical model of dynamics of social networks incorporating
Hebbian learning. With this, we are also working on multilayered network in ecological system with
Dr S. Prasad, JNU. Project aims at finding optimum time of Amla crop harvesting using agent based
model simulations of Amla–Deer–Human network on the top of field

Manual Sizing Of Analog Blocks
There is around 10 to 200 real valued parameters in an analog circuit, and value of these parameters
must be set in order to meet the specification .Circuit sizing is the process of setting these
parameters. for example a simple two stage operational amplifier has around 12 parameters ,which
includes width, length and passive component values of all transistors which have to be set to
achieve specifications such as CMRR(common mode rejection ratio), gain, power ,area ,bandwidth
,noise ,settling time ,offset value and slew rate . Figure 1 two stage operational amplifier
There are 6 mosfets in this figure, so we have two compute values of width and height of 6 mosfets
along with other parameters such as slew rate, bandwidth and noise margin.
A system on a chip (SOC) is an integrated circuit that integrates all components of a electronic
system into a single chip. SOC is made automated to a large extent. Manual sizing of analog blocks
is a bottleneck. There are several methods and techniques of sizing; circuit sizing is an optimization
problem. SPICE model is being considered the most accurate model as the final check–point is spice
correctness since the designer does not have access to the fabrication process so designer relies on
the simulator. Apart from spice there are circuit equations. These equations can be derived taking
some assumption with regard to the transistor behavior. There are three

Rabbits and Wolves Lab
"Rabbits and Wolves"
Introduction
I will be conducting a computer simulation called "Rabbits and Wolves". In this experiment I will be
interaction with a simple forest ecosystem model that will simply take into account three organisms:
rabbits, wolves, and grass.
As defined by "Environmental Science– 14th Edition", an ecosystem is "a set of organisms within a
defined area or volume that interact with one another and with their environment of nonliving
matter and energy." I will be using the computer simulation to alter the interactions of the organisms
in the ecosystem to see what changes occur.
Based on mathematical formulas and probability of certain events occurring, the model predicts the
changes in the populations of the three ... Show more content on Helpwriting.net ...
The grass growth rate is 1.
Graph 2
Evolution of the Rabbit Population without Predators–Wolves
Wolves have been removed from the parameters. The simulation was run for 200 iterations.
Graph 3
Evolution of the Rabbit Population with Default Predation and an Initial Decrease in the Grass Rate
Initial Grass Rate cut in half. The simulation was run for 201 iterations.
Graph 4
Increased Initial Population of Rabbits with Default Predation
The initial rabbit population was doubled from 20 to 40. The simulation was run for 200 iterations.
Discussion
My first objective, to view and interact with a simple scientific computer model, was most definitely
accomplished over the course of this simulation. I have done so by changing parameters, observing
the simulation, and analyzing the data that was brought about by the simulation. I also determined
through my use of the computer model that simple changes to one population in the ecosystem
created through this computer model does not change the outcome of the simulation in any major
way. On the other hand, if you completely get rid of one of the populations instead of raising or
lowering

Optimizing The Hypothalamic Hunger Regulation Mathematical...
OPTIMIZING AND VALIDATING THE HYPOTHALAMIC HUNGER REGULATION
MATHEMATICAL MODEL
Ms. Divya1, Dr. Saurabh Mukherjee2
1Research Scholar, 2Associate Professor, Department of Computer Science, AIM & ACT,
Banasthali University, Banasthali–304022, email: jangid.divya@gmail.com
Hypothalamus has a significant effect on the physiological functions of human body like Hunger
regulation, Energy balance etc. A mathematical model is being developed which mathematically
explains the functionality of Hunger Regulation. Some hormones also acts effectively during this
process plays as important role in this model. Hypothalamic Hunger RegulationMathematical Model
(HhRM). We are using statistical optimization tools to optimize and validate this Model. The ...
Show more content on Helpwriting.net ...
This Hunger Regulation process is simulated with the help of Hypothalamic Hunger Regulating
Mathematical Model (HhRM)[2]. HhRM is a mathematical approach for this homeostatic function
of human body.HhRM divided into five different steps. Each step represents the combination of
mathematical functions and variables. A simple binary function G (h) shows that whether the
hormones are secreted by internal organs or not. The hormonal signals explain by the random
numbers.Daubechies Wavelet function interprets the movement of Hormonal signals through Vegal
Nerve.The response to the Hormonal Signals is being generated by the hypothalamic receptors. For
this the concept of signal generation is used with scaling function with Entropy. The receptors
signals transferred to Central Nervous system.
The mathematical model HhRM is as follows: dH/dt = G^ ' (h)+f(h)D4^ ' (h)+ Em(s)Sc '(s)
WheredH/dtis the change in the processing of Hypothalamus, H with respect to Time t, G(h) is the
binary function f(h) is fractal function, D4'(h) is the Daubechies function, Em(s) is entropy measure
and Sc'(s) scaling function.
2. Objective
The objective of our study is to optimize the mathematical model HhRM. In previous version of
HhRM the simple scaling function was being used. Here our objective is to study

Essay On Engineering Service Systems
LCurrent technology–driven innovations in service systems tend to take the human server out of the
loop. That being the case, the substitution of human labor will potentially affect the United States
and other developed economies most, as the service sector in these countries is responsible for the
majority of employment. To improve this outlook, effective ways of integrating humans with
engineered service systems is needed. Instead of replacing human workers with machines, one could
think of an engineered partnership between both agents. For example, the necessary improvements
in the healthcare and education sectors will use people to do what people do best (e.g. creativity,
synthesis, improvisation, social skills), and machines to do ... Show more content on
Helpwriting.net ...
Hence, considering humans in the optimization of their designs. What is needed is convergent
research. Convergence is a research approach that cuts across fields to tackle societal problems that
require solutions at the interfaces of different disciplines. As stated by the National Academies, what
is needed is a "comprehensive synthetic framework" that melds the knowledge at the intersection of
these disciplines. But there are multiple difficulties to be overcome for the principles and models of
behavioral and cognitive science to converge with engineering and mathematics.
To overcome the challenges for convergence, languages and lingos need to be shared to guide
engineers to important human aspects that need to be represented mathematically. In turn, this space
might guide behavioral and cognitive scientists to research questions about humans that are
meaningful for engineers and vice versa. This middle ground could conceivably be the right meeting
space to foster the mathematical language that could incorporate randomness, improvisation and
other human characteristics that we need to model to achieve perfect cooperation between machines
and humans. This mathematical language or framework could be based on advances in the calculus
of finite differences, Markov chains, or a completely different paradigm. We are just beginning this
exploration of potential modeling approaches that

Computer Science Reflection
My undergraduate major is measurement, and I have spent the last three years learning about optics,
mechanics, electric circuits, and coding. Among those courses, and following three–years of training
including pieces of research and coursework, I have realized that I am enamored with coding the
most. First of all, I enjoy the process of coding and verifying and adjusting the program. Taking my
digital signal processor coursework as an example, after I finished writing the code, it appeared that
the code might not have been correct because I could not receive the right temperature. So I set the
program to a debug mode and run every single step of my code and observed the feedback value.
This was a very diversion for me. With the criticism comes about because of single–step
investigating, I made a couple of theories about the issue, for example, the time postponed is not
sufficiently exact, or I might be coding about the wrong sticks. So I checked these hypotheses one
by one, and for reasons unknown, the issue is the piece of my program concerning time deferring
because the segment I used to quantify temperature which is DS18B20 is extremely strict on timing
arrangement. Getting the right outcome is exceptionally fulfilling, so is the procedure because
through speculation autonomously I procure much involvement and capability. Another thing that
intrigues me regarding computer science comes from what I have learned through our research
requirement in theory and technology

Disadvantages Of Deep Learning
Deep Learning which developed as a Machine Learning approach has become very popular
nowadays. It helps in dealing with complex problem with a greater understanding. Traditional
Machine Learning model used to solve problems successfully where final output was a simple
function of input data, whereas Deep Learning can capture composite relations. Deep Learning is
basically learning data representations which is all about making things and presenting to real
audiences. In order to master the various aspects of Deep Learning one requires proficiency of core
academic content, critical thinking, analytical thinking and self–directed learning. It consists of
mathematical models, which can be termed as composition of same type of functions wherein some
of the functions can be changed so as to predict the final value. Human–computer interactions are
based on the belief of ... Show more content on Helpwriting.net ...
Some of these models need input parameter from the user, thus are heuristically designed and hence
performance might depend on characteristic of parameters and input image. Some of the new
learning–based methods overcome above problems and give solutions to complex problems. It is for
this reason that deep neural networks have recently seen an impressive comeback. CNN
(Convolutional Neural Network) used in Deep learning for image restoration, works by averaging
out the output of various trained network to the same input. Neural Networks have numerous
application in several areas of image processing. It is used for classifying the image and the
mathematical analysis of CNN operates feature extraction first and then give the results to trainable
classifier. This model works by training the network to reconstruct high quality images from
degraded or blurred input images. The model gives promising results from the learned set of
denoisers and also can be used for low level applications to deliver high performance

Particle Swarm Optimization And Its Range Of Operation
function, their numbers and intersection value determines accuracy of the tool and its range of
operation. If the membership function covers poles values from 0 to 4 then the maximum value for
pole is 4 and the least value is 0 and same thing happens for the constants. Each unit reduces an
order of two to order of one. If a transfer function of higher order is needed to be reduced the
operation is repeated several times. For example an order 8 to 2 function reduction will reduce the 8
poles into 4 poles then the 4 new poles into the final 2 poles. E. Particle Swarm Technique
Particle swarm optimization (PSO) is initialized with a group of random particles (solutions) and
then searches for optima by updating generations. In every iteration, each particle is updated by
following two "best" values. The first one is the best solution (fitness) each particle has achieved so
far, this value is called Pbest. Another "best" value that is tracked by the particle swarm optimizer is
the best value, obtained so far by any particle in the population. This best value is a global best and
called Gbest. Each particle consists of: Data representing a possible solution, a velocity value
indicating how much the Data can be changed, a personal best (Pbest) value indicating the closest
the particle 's Data has ever come to the Target.
The particles ' data could be anything. In the flocking birds' example above, the data would be the
X, Y, Z coordinates of each bird. The individual

I Agree With Reservations With The Title
I agree with reservations with the title. For the purposes of this essay, the word describe will be
defined as the active act of either explaining or representing an idea or concept within an area of
knowledge. The word transform will be defined as having a great impact the 'world'. Not to be
confused with 'change', here, transforming considers a degree of innovation, whilst 'change'
highlights a minor shift. The word seek is key, and will represent the idea that within the Area of
Knowledge, there can be the active intent to transform or describe.
One event can be interpreted as both transforming and describing, due to the room for subjective
judgement inherent in both of these two ideas. The concepts of description and transformation are
abstract in nature. The idea that one event has the capability of both describing and transforming the
world in different ways is evident in the area of knowledge of history. Here, the description of the
past has the potential to transform the present. The discovery of what happened behind China's
metaphorical walls would forever transform the way Mao and his regime was perceived. A specific
case of this is the French intellectuals involved in The Wind from the East: French Intellectuals, the
Cultural Revolution, and the Legacy of the 1960s which outlines first hand how a group of French
students were seduced by Mao's apparently pure ideology and the perceived cultural and intellectual
impact of the East during the 1960s. This is an

Description Of The Model Of 11 Submodels Essay
The model
The model consisted in 11 submodels, one equation was used to calculate a random population and
simulate the IAA to each individual for all treatment (level of methionine+cysteine). The FI and
MCI are describing according to the performance of the flock population, with the price of amino
acid is calculated the feed cost with the increase of IAA. The model preconized that the optimum
IAA is dependent on the economic variables, therefore the model defines the NR with the variation
of the amino acid prices and sale poultry. The optimum IAA is determined by the relationship
between IAA and NR.
The input of model is: 1 – μ of broiler; 2 – SD, 3 – parameters of the monomolecular model, 4 –
cost of standard diet, 5 – price of sale poultry, 6 – parameters to estimate energy, 7 – number of
animals that wish to simulate. The model output is the NR and the optimum amino acid intake.
Statistical approach and mathematical models
The model described simulates the potential performance and hence nutrient requirements at a flock
bird level, for this simulation needed of the µ and σ of the population, this parameter can be
acquired from experiments or literature data. Body weight gain (BWG.R) with random numbers
from a standardized normally distributed population were generated using the following generalized
equation (1):
BWG.R = ((SQRT (–2×LN(RUNIF( )))×SIN(2×PI( )×RUNIF( ))) × σ + µ, Equation 1.
BWG.R is the BWG for a random population, RUNIF() is a random number from a

Disadvantages Of Membrane Distillation
As time goes by, the fresh water supplement for living on the earth planet will become a serious
problem [1–3]. In addition, the Middle East is mentioned as one of the poorest regions in all over
the world in terms of water resources with less than 1% of the total available global freshwater per
capita [4, 5]. Therefore, one of the strategic options to satisfy the current and future request for fresh
water in countries established around Gulf is to build up desalination units. So, immediate need has
arisen to construct economic units for water desalination. Nowadays, it has been proved that
membrane separation processes have several advantages for desalination and water reuse of
contaminated resources in comparison to the conventional separation methods [1, 6–8]. ... Show
more content on Helpwriting.net ...
MD is a thermally driven separation process that utilizes a hydrophobic, membrane as a contactor
media between two fluids kept at different temperatures where the separation is obtained by the
mass transfer of the vapor phase. The driving force of the process is given by a partial vapor
pressure difference that caused from the temperature difference [13–17]. Two advantages of MD
are, the process performance is not highly affected by high feed salinity as see water, and has the
theoretical ability to reach 100% salt rejection [13, 18, 19]. There are commonly four types of
membranes arrangements for MD process: Direct Contact Membrane Distillation (DCMD), Vacuum
Membrane Distillation (VMD), Air Gap Membrane Distillation (AGMD), and the Sweeping Gas
Membrane Distillation (SGMD) [18, 20–22]. AGMD configuration is appropriated for water
desalination [23, 24], on the other hand water gap showed higher thermal efficiency and less internal
heat loss in comparison to AGMD [18, 25]. Therefore, these two arrangements are commonly used
for the see water

Prediction of Clean Coal Using Mathematical Models
The productivity of a plant depends on the variables presents and how they are controlled or
manipulated in order to achieve the desired results with minimum production cost. This can be
achieved by modeling mathematical equations to understand the behavior of the process and also
predict the behavior of the system if certain changes are introduced. In summary, engineers need to
model processes if they are going to design or develop those processes. In the practices of
engineering design, models are often applied to predict what will happen in a future situation.
However, the predictions are used in ways that have far different consequences than simply
anticipating the outcome of an experiment (What Is Mathematical Model). We obtain the response
of a system to the sum of the specific inputs by superposing the separate responses of the system to
each individual input. This principle is used to predict the response of a system to a complicated
input by breaking down the input into a set of simpler inputs that produce known system responses
or behaviors.
This research is aimed at finding possible ways of controlling different variables that affect the
production (in terms of quantity and quality) of a clean coal product through mathematical
equations. The main objectives are to determine the existence of a relationship between the input
and output operational variables based on the Plant information. The washability curve is also used
based on its accurate information about

A Mathematical Model Of Mechanical Seals
LITERATURE REVIEW
1. Etsion and Burstein[12] developed a mathematical model to allow performance prediction of all
non–contacting mechanical seals having a regular micro–surface structure in the form of
hemispherical pores. Seal performance such as equilibrium face separation, friction torque and
leakage across the seal are calculated for a range of seal pressures, pore sizes and pore ratio of the
ring surface area. An optimum pore size was found that depends on other variables and corresponds
to maximum axial stiffness and minimum friction torque.
2. Wang and Kato[13] presented their work on texturing Silicon Carbide (SiC) seals operating in
water for better performance. In this work, the stationary surface of the seal is textured using a
Reactive Ion Etching (RIE) process. Experiments were carried out to evaluate the effect of micro–
pits on the critical seizure load. The authors found that surface texturing is an effective way to
stabilize friction, reduce friction coefficient and to expand the low–friction range of SiC seals
working in water.
3. Siripuram[14] presents a numerical study of the effects of different shapes of micro–asperities in
sliding surface lubrication when hydrodynamic films are found. Positive and negative asperities of
constant height (depth) are considered with circular, square, diamond, hexagonal and triangular
cross–sections. The results indicate that triangular asperities giving the smallest leakage rate and
square asperities giving a largest

My Career Goals And Objectives Essay
Nine years ago when I first joined faculty of engineering at Alexandria University, I would have
never imagined that I would apply for a Management PhD program one day. My dream was to
become an electrical engineer but another thing was perfectly destined and planned for me. It has all
started during my last year in the college. I was majoring electrical engineering and I took a project
management course. This course has changed all my goals and objectives. I have become really
impressed by how it could be so important to simulate and optimize process, and how that can save
much money, time and resources. Unconsciously my career objectives have taken a twist during
studying that subject. Quantitative analysis, mathematical modelling and optimization of different
business processes have become my major interests.
In 2012, I completed the BSc degree in Electrical Engineering with an excellent cumulative grade
with degree of honor which is the highest possible grade in Egypt. My ranking was also the fourth
student among 196 students' class. After graduation, my vision was not so clear but I wanted to learn
more about business and its different areas. Therefore, I decided to enroll ina Master of Business
Administration program to pursue my goals. I applied to ESLSCA, a French business school which
has a campus in Alexandria, MBA program and fortunately I was accepted with a scholarship too. I
was a little bit afraid at the beginning of the program as I was the youngest and the

Simulations Essay
Simulations
"What I hear, I forget; what I see, I remember; what I do, I understand."
Confucius, 451 BC
General
As young children, we all learn by doing. Toddlers learn to walk on their own with their parents
standing by as guides, providing a safe environment that makes it possible for the child to learn.
When a child enters the "formal education" system, unfortunately this rich environment of learning
by doing often disappears. The education system is still stuck in the industrial age with classrooms
more often designed for teaching rather than for learning purposes.
To return to the learning by doing stage, enter simulations. A simulation is an instructional strategy
that offers the opportunity to learn in a ... Show more content on Helpwriting.net ...
A well–designed simulation meets all of John Keller's goals in the ARCS model (attention,
relevance, confidence and satisfaction). It presents a problem to be solved, allows the learner to
make choices, shows the consequences of those choices, and builds confidence in the learner when
the problem is mastered.
There are five major characteristics of simulations:
Simulations are problem–based units of learning that are set in motion by a particular task, issue,
policy, crisis, or problem. The problems to be addressed by the participant may be either implicit or
explicit, depending on the nature of the simulation. The issues inherent in the simulation are not
textbook problems or questions in which answers are cut and dried and determined quickly.
Participants carry out functions associated with their roles and the settings in which they find
themselves. The outcomes of the simulation are not determined by chance or luck. Instead,
participants experience consequences that follow from their own actions. Participants experience
reality of function to the extent that they fulfill their roles conscientiously and in a professional
manner, executing all the rights, privileges and responsibilities associated with the role.
(Grendler, 1994).
Debriefing after a simulation is one of the most

Impulse Generator and Lightning Characteristics Simulation...
Lightning as a phenomenon of electric discharge between the charged particles among the clouds or
between the clouds to earth is seen as a natural occurrence in our day to day life. The veracity and
wrath of this natures activity also has to be faced by the mankind in form of death, destruction and
total power outage. In today's world it is hard to imagine life without electricity. Also it incurs heavy
losses to the power distributors and consumers. The losses amounts from few hundred thousands to
millions like how the lightning storm caused power outage in Minnesota that lasted for 19 hours. [1]
It was Franklin in 1744–1750, who first carried out experiments on lightning [2]. From then on
significant studies have been made in the past ... Show more content on Helpwriting.net ...
The impulse generator comes into business here.Studying the lightning characteristics by creating
the similar impulse using the impulse generator helps in assessing the strength of the electrical
equipment's against transient and lightning impulse. This has been practiced by industrialists and
researchers for so many years. Massive and extremely large test apparatus are used for the impulse
wave generation. To ease down the process with reliable and accurate results, here for the design
4A, a GUI (Graphical User Interface) based software for impulse generator and lightning
characteristics simulation. This would be the only prototype standalone software which caters to the
impulse waveforms studies. The GUI will have the text entry fields to input the circuit parameters
and there will be plotting space where the impulse waveform will be plotted.
The framework of the software will be the mathematical model of the impulse generator circuit. For
different input values, the corresponding waveforms will be obtained. The main focus of this
research will be towards accessing waveforms and its characteristics that corroborates the industry
standard and laboratory experimental values with expected accuracy not lesser than 60%.This
software will be made available to work in cross platform operating systems such as Windows,
android, Mac etc. so that the user can download and install the application in their favourite media
device at

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