Personal Stories

Stories about how you have used Maple, MapleSim and Math in your life or work.

In the study of the Gödel spacetime model, a tetrad was suggested in the literature [1]. Alas, upon entering the tetrad in question, Maple's Tetrad's package complained that that matrix was not a tetrad! What went wrong? After an exchange with Edgardo S. Cheb-Terrab, Edgardo provided us with awfully useful comments regarding the use of the package and suggested that the problem together with its solution be presented in a post, as others may find it of some use for their work as well.

 

The Gödel spacetime solution to Einsten's equations is as follows.

 

Physics:-Version()

`The "Physics Updates" version in the MapleCloud is 858 and is the same as the version installed in this computer, created 2020, October 27, 10:19 hours Pacific Time.`

(1)

with(Physics); with(Tetrads)

_______________________________________________________

 

`Setting `*lowercaselatin_ah*` letters to represent `*tetrad*` indices`

 

((`Defined as tetrad tensors `*`see <a href='http://www.maplesoft.com/support/help/search.aspx?term=Physics,tetrads`*`,' target='_new'>?Physics,tetrads`*`,</a> `*`&efr;`[a, mu]*`, `)*eta[a, b]*`, `*gamma[a, b, c]*`, `)*lambda[a, b, c]

 

((`Defined as spacetime tensors representing the NP null vectors of the tetrad formalism `*`see <a href='http://www.maplesoft.com/support/help/search.aspx?term=Physics,tetrads`*`,' target='_new'>?Physics,tetrads`*`,</a> `*l[mu]*`, `)*n[mu]*`, `*m[mu]*`, `)*conjugate(m[mu])

 

_______________________________________________________

(2)

Working with Cartesian coordinates,

Coordinates(cartesian)

`Systems of spacetime coordinates are:`*{X = (x, y, z, t)}

 

{X}

(3)

the Gödel line element is

 

ds^2 = d_(t)^2-d_(x)^2-d_(y)^2+(1/2)*exp(2*q*y)*d_(z)^2+2*exp(q*y)*d_(z)*d_(t)

ds^2 = Physics:-d_(t)^2-Physics:-d_(x)^2-Physics:-d_(y)^2+(1/2)*exp(2*q*y)*Physics:-d_(z)^2+2*exp(q*y)*Physics:-d_(z)*Physics:-d_(t)

(4)

Setting the metric

Setup(metric = rhs(ds^2 = Physics[d_](t)^2-Physics[d_](x)^2-Physics[d_](y)^2+(1/2)*exp(2*q*y)*Physics[d_](z)^2+2*exp(q*y)*Physics[d_](z)*Physics[d_](t)))

_______________________________________________________

 

`Coordinates: `*[x, y, z, t]*`. Signature: `*`- - - +`

 

_______________________________________________________

 

Physics:-g_[mu, nu] = Matrix(%id = 18446744078354506566)

 

_______________________________________________________

 

`Setting `*lowercaselatin_is*` letters to represent `*space*` indices`

 

[metric = {(1, 1) = -1, (2, 2) = -1, (3, 3) = (1/2)*exp(2*q*y), (3, 4) = exp(q*y), (4, 4) = 1}, spaceindices = lowercaselatin_is]

(5)

The problem appeared upon entering the matrix M below supposedly representing the alleged tetrad.

interface(imaginaryunit = i)

M := Matrix([[1/sqrt(2), 0, 0, 1/sqrt(2)], [-1/sqrt(2), 0, 0, 1/sqrt(2)], [0, 1/sqrt(2), -I*exp(-q*y), I], [0, 1/sqrt(2), I*exp(-q*y), -I]])

Matrix(%id = 18446744078162949534)

(6)

Each of the rows of this matrix is supposed to be one of the null vectors [l, n, m, conjugate(m)]. Before setting this alleged tetrad, Maple was asked to settle the nature of it, and the answer was that M was not a tetrad! With the Physics Updates v.857, a more detailed message was issued:

IsTetrad(M)

`Warning, the given components form a`*null*`tetrad, `*`with a contravariant spacetime index`*`, only if you change the signature from `*`- - - +`*` to `*`+ - - -`*`. 
You can do that by entering (copy and paste): `*Setup(signature = "+ - - -")

 

false

(7)

So there were actually three problems:

1. 

The entered entity was a null tetrad, while the default of the Physics package is an orthonormal tetrad. This can be seen in the form of the tetrad metric, or using the library commands:

eta_[]

Physics:-Tetrads:-eta_[a, b] = Matrix(%id = 18446744078354552462)

(8)

Library:-IsOrthonormalTetradMetric()

true

(9)

Library:-IsNullTetradMetric()

false

(10)
2. 

The matrix M would only be a tetrad if the spacetime index is contravariant. On the other hand, the command IsTetrad will return true only when M represents a tetrad with both indices covariant. For  instance, if the command IsTetrad  is issued about the tetrad automatically computed by Maple, but is passed the matrix corresponding to "`&efr;`[a]^(mu)"  with the spacetime index contravariant,  false is returned:

"e_[a,~mu, matrix]"

Physics:-Tetrads:-e_[a, `~&mu;`] = Matrix(%id = 18446744078297840926)

(11)

"IsTetrad(rhs(?))"

Typesetting[delayDotProduct](`Warning, the given components form a`*orthonormal*`tetrad only if the spacetime index is contravariant. 
You can construct a tetrad with a covariant spacetime index by entering (copy and paste): `, Matrix(4, 4, {(1, 1) = 1, (1, 2) = 0, (1, 3) = 0, (1, 4) = 0, (2, 1) = 0, (2, 2) = 1, (2, 3) = 0, (2, 4) = 0, (3, 1) = 0, (3, 2) = 0, (3, 3) = sqrt(2)*exp(-q*y), (3, 4) = -sqrt(2), (4, 1) = 0, (4, 2) = 0, (4, 3) = 0, (4, 4) = 1}), true).rhs(g[])

 

false

(12)
3. 

The matrix M corresponds to a tetrad with different signature, (+---), instead of Maple's default (---+). Although these two signatures represent the same physics, they differ in the ordering of rows and columns: the timelike component is respectively in positions 1 and 4.

 

The issue, then, became how to correct the matrix M to be a valid tetrad: either change the setup, or change the matrix M. Below the two courses of action are provided.

 

First the simplest: change the settings. According to the message (7), setting the tetrad to be null, changing the signature to be (+---) and indicating that M represents a tetrad with its spacetime index contravariant would suffice:

Setup(tetradmetric = null, signature = "+---")

[signature = `+ - - -`, tetradmetric = {(1, 2) = 1, (3, 4) = -1}]

(13)

The null tetrad metric is now as in the reference used.

eta_[]

Physics:-Tetrads:-eta_[a, b] = Matrix(%id = 18446744078298386174)

(14)

Checking now with the spacetime index contravariant

e_[a, `~&mu;`] = M

Physics:-Tetrads:-e_[a, `~&mu;`] = Matrix(%id = 18446744078162949534)

(15)

At this point, the command IsTetrad  provided with the equation (15), where the left-hand side has the information that the spacetime index is contravariant

"IsTetrad(?)"

`Type of tetrad: `*null

 

true

(16)

Great! one can now set the tetrad M exactly as entered, without changing anything else. In the next line it will only be necessary to indicate that the spacetime index, mu, is contravariant.

Setup(e_[a, `~&mu;`] = M, quiet)

[tetrad = {(1, 1) = -(1/2)*2^(1/2), (1, 3) = (1/2)*2^(1/2)*exp(q*y), (1, 4) = (1/2)*2^(1/2), (2, 1) = (1/2)*2^(1/2), (2, 3) = (1/2)*2^(1/2)*exp(q*y), (2, 4) = (1/2)*2^(1/2), (3, 2) = -(1/2)*2^(1/2), (3, 3) = ((1/2)*I)*exp(q*y), (3, 4) = 0, (4, 2) = -(1/2)*2^(1/2), (4, 3) = -((1/2)*I)*exp(q*y), (4, 4) = 0}]

(17)

 

The tetrad is now the matrix M. In addition to checking this tetrad making use of the IsTetrad command, it is also possible to check the definitions of tetrads and null vectors using TensorArray.

e_[definition]

Physics:-Tetrads:-e_[a, `&mu;`]*Physics:-Tetrads:-e_[b, `~&mu;`] = Physics:-Tetrads:-eta_[a, b]

(18)

TensorArray(Physics:-Tetrads:-e_[a, `&mu;`]*Physics:-Tetrads:-e_[b, `~&mu;`] = Physics:-Tetrads:-eta_[a, b], simplifier = simplify)

Matrix(%id = 18446744078353048270)

(19)

For the null vectors:

l_[definition]

Physics:-Tetrads:-l_[mu]*Physics:-Tetrads:-l_[`~mu`] = 0, Physics:-Tetrads:-l_[mu]*Physics:-Tetrads:-n_[`~mu`] = 1, Physics:-Tetrads:-l_[mu]*Physics:-Tetrads:-m_[`~mu`] = 0, Physics:-Tetrads:-l_[mu]*Physics:-Tetrads:-mb_[`~mu`] = 0, Physics:-g_[mu, nu] = Physics:-Tetrads:-l_[mu]*Physics:-Tetrads:-n_[nu]+Physics:-Tetrads:-l_[nu]*Physics:-Tetrads:-n_[mu]-Physics:-Tetrads:-m_[mu]*Physics:-Tetrads:-mb_[nu]-Physics:-Tetrads:-m_[nu]*Physics:-Tetrads:-mb_[mu]

(20)

TensorArray([Physics:-Tetrads:-l_[mu]*Physics:-Tetrads:-l_[`~mu`] = 0, Physics:-Tetrads:-l_[mu]*Physics:-Tetrads:-n_[`~mu`] = 1, Physics:-Tetrads:-l_[mu]*Physics:-Tetrads:-m_[`~mu`] = 0, Physics:-Tetrads:-l_[mu]*Physics:-Tetrads:-mb_[`~mu`] = 0, Physics[g_][mu, nu] = Physics:-Tetrads:-l_[mu]*Physics:-Tetrads:-n_[nu]+Physics:-Tetrads:-l_[nu]*Physics:-Tetrads:-n_[mu]-Physics:-Tetrads:-m_[mu]*Physics:-Tetrads:-mb_[nu]-Physics:-Tetrads:-m_[nu]*Physics:-Tetrads:-mb_[mu]], simplifier = simplify)

[0 = 0, 1 = 1, 0 = 0, 0 = 0, Matrix(%id = 18446744078414241910)]

(21)

From its Weyl scalars, this tetrad is already in the canonical form for a spacetime of Petrov type "D": only `&Psi;__2` <> 0

PetrovType()

"D"

(22)

Weyl[scalars]

psi__0 = 0, psi__1 = 0, psi__2 = -(1/6)*q^2, psi__3 = 0, psi__4 = 0

(23)

Attempting to transform it into canonicalform returns the tetrad (17) itself

TransformTetrad(canonicalform)

Matrix(%id = 18446744078396685478)

(24)

Let's now obtain the correct tetrad without changing the signature as done in (13).

Start by changing the signature back to "(- - - +)"

Setup(signature = "---+")

[signature = `- - - +`]

(25)

So again, M is not a tetrad, even if the spacetime index is specified as contravariant.

IsTetrad(e_[a, `~&mu;`] = M)

`Warning, the given components form a`*null*`tetrad, `*`with a contravariant spacetime index`*`, only if you change the signature from `*`- - - +`*` to `*`+ - - -`*`. 
You can do that by entering (copy and paste): `*Setup(signature = "+ - - -")

 

false

(26)

By construction, the tetrad M has its rows formed by the null vectors with the ordering [l, n, m, conjugate(m)]. To understand what needs to be changed in M, define those vectors, independent of the null vectors [l_, n_, m_, mb_] (with underscore) that come with the Tetrads package.

Define(l[mu], n[mu], m[mu], mb[mu], quiet)

and set their components using the matrix M taking into account that its spacetime index is contravariant, and equating the rows of M  using the ordering [l, n, m, conjugate(m)]:

`~`[`=`]([l[`~&mu;`], n[`~&mu;`], m[`~&mu;`], mb[`~&mu;`]], [seq(M[j, 1 .. 4], j = 1 .. 4)])

[l[`~&mu;`] = Vector[row](%id = 18446744078368885086), n[`~&mu;`] = Vector[row](%id = 18446744078368885206), m[`~&mu;`] = Vector[row](%id = 18446744078368885326), mb[`~&mu;`] = Vector[row](%id = 18446744078368885446)]

(27)

"Define(op(?))"

`Defined objects with tensor properties`

 

{Physics:-D_[mu], Physics:-Dgamma[mu], Physics:-Psigma[mu], Physics:-Ricci[mu, nu], Physics:-Riemann[mu, nu, alpha, beta], Physics:-Weyl[mu, nu, alpha, beta], Physics:-d_[mu], Physics:-Tetrads:-e_[a, mu], Physics:-Tetrads:-eta_[a, b], Physics:-g_[mu, nu], Physics:-gamma_[i, j], Physics:-Tetrads:-gamma_[a, b, c], l[mu], Physics:-Tetrads:-l_[mu], Physics:-Tetrads:-lambda_[a, b, c], m[mu], Physics:-Tetrads:-m_[mu], mb[mu], Physics:-Tetrads:-mb_[mu], n[mu], Physics:-Tetrads:-n_[mu], Physics:-Christoffel[mu, nu, alpha], Physics:-Einstein[mu, nu], Physics:-LeviCivita[alpha, beta, mu, nu], Physics:-SpaceTimeVector[mu](X)}

(28)

Check the covariant components of these vectors towards comparing them with the lines of the Maple's tetrad `&efr;`[a, mu]

l[], n[], m[], mb[]

l[mu] = Array(%id = 18446744078298368710), n[mu] = Array(%id = 18446744078298365214), m[mu] = Array(%id = 18446744078298359558), mb[mu] = Array(%id = 18446744078298341734)

(29)

This shows the [l_, n_, m_, mb_] null vectors (with underscore) that come with Tetrads package

e_[nullvectors]

Physics:-Tetrads:-l_[mu] = Vector[row](%id = 18446744078354520414), Physics:-Tetrads:-n_[mu] = Vector[row](%id = 18446744078354520534), Physics:-Tetrads:-m_[mu] = Vector[row](%id = 18446744078354520654), Physics:-Tetrads:-mb_[mu] = Vector[row](%id = 18446744078354520774)

(30)

So (29) computed from M is the same as (30) computed from Maple's tetrad.

But, from (30) and the form of Maple's tetrad

e_[]

Physics:-Tetrads:-e_[a, mu] = Matrix(%id = 18446744078297844182)

(31)

for the current signature

Setup(signature)

[signature = `- - - +`]

(32)

we see the ordering of the null vectors is [n, m, mb, l], not [l, n, m, mb] used in [1] with the signature (+ - - -). So the adjustment required in  M, resulting in "M^( ')", consists of reordering M's rows to be [n, m, mb, l]

`#msup(mi("M"),mrow(mo("&InvisibleTimes;"),mo("&apos;")))` := simplify(Matrix(4, map(Library:-TensorComponents, [n[mu], m[mu], mb[mu], l[mu]])))

Matrix(%id = 18446744078414243230)

(33)

IsTetrad(`#msup(mi("M"),mrow(mo("&InvisibleTimes;"),mo("&apos;")))`)

`Type of tetrad: `*null

 

true

(34)

Comparing "M^( ')" with the tetrad `&efr;`[a, mu]computed by Maple ((24) and (31), they are actually the same.

References

[1]. Rainer Burghardt, "Constructing the Godel Universe", the arxiv gr-qc/0106070 2001.

[2]. Frank Grave and Michael Buser, "Visiting the Gödel Universe",  IEEE Trans Vis Comput GRAPH, 14(6):1563-70, 2008.


 

Download Godel_universe_and_Tedrads.mw

A few weeks ago a television station in Toronto asked me if I’d share some tips on how parents could help their kids stay engaged with remote learning. My initial reaction was to run for the hills – appearing on live TV is not my cup of tea. However my colleagues persuaded me to accept. You can see a clip of that segment here - I’ve included it in this post because otherwise someone on the marketing team would have ;-)

My tips are based on a wide variety of experiences. My role at Maplesoft requires me to speak with educators at all levels, and remote learning has been a hot topic of conversation lately, as you can imagine. As well, in my past life (i.e. life before kids) I was a high school math tutor, and now as a parent I’m in the thick of it helping my son navigate Kindergarten remotely.

So here are my 5 tips on how parents of elementary and high-school aged children can help their kids stay engaged with remote learning. If you have other tips, including suggestions for university students, feel free to leave them in the comments sections. And if these tips help you, please let me know. It will have made the stress of my appearance on TV worthwhile!

 

Tip 1: Look for the positives

These are unprecedented times for kids, parents and teachers. Over the course of the last 6-7 months, learning as we’ve grown to know it has changed radically. And while the change has been incredibility difficult for everyone, it’s helpful to look for the positives that remote learning can bring to our children:

  • Remote learning can help some kids focus on their work by minimizing the social pressures or distractions they may face at school.
  • Older kids are appreciating the flexibility that remote learning can offer with respect to when and how they complete their work.  
  • Younger kids are loving the experience of learning in the presence of mom and dad. My 4 year old thinks it’s awesome that I now know all the lyrics to the songs that he learns in school.
  • As many remote learning classrooms include students from across the school board, this can provide kids with the opportunity to connect with their peers from different socio-economic backgrounds living across the city.

 

Tip 2: Don’t shy away from your kid’s teacher

While some kids are thriving learning from home, we know that others are struggling.

If your high school student is struggling at school, do whatever it takes to convince them to connect with their teacher. If your child is younger, make the connection yourself.

In my role, I’ve had the opportunity to work with many teachers, and rest assured, many of them would welcome this engagement.  They want our kids to succeed, but without the face-to-face classroom interaction it’s becoming increasingly more difficult for them to rely on visual cues to see how your child is doing and if they are struggling with a concept.

So I encourage you to reach out to your kid’s teacher especially if you notice your child is having difficulty.

 

Tip 3: Get creative with learning

Another benefit of remote learning is that it presents us with a unique opportunity to get creative with learning.

Kids, especially those in middle school and high school, now have the time and opportunity to engage with a variety of different online learning resources. And when I say online learning resources, I mean more than just videos. Think interactive tools (such as Maple Learn), that help students visualize concepts from math and science, games that allow students to practice language skills, repositories of homework problems and practice questions that allow kids to practice concepts, the list goes on.

Best of all, many content providers and organizations, are offerings these resources and tools available for free or at a substantially reduced cost to help kids and parents during this time.

So if your child is having difficulty with a particular subject or if they are in need of a challenge, make sure to explore what is available online.

 

Tip 4: Embrace the tech

To be successful, remote learning requires children to learn a host of new digital skills, such as how to mute/unmute themselves, raise their hands electronically, turn on and off their webcam, toggle between applications to access class content and upload homework, keep track of their schedule via an electronic calendar, etc. This can be daunting for kids who are learning remotely for the first time.

As a parent you can help your child become more comfortable with remote learning by setting aside some time either before or after class to help them master these new tools. And since this is likely new to you, there are some great videos online that will show you how to use the system your school has mandated be it Microsoft Teams, Google Classroom or something else.  

 

Tip 5: It’s a skill

Remember that remote learning is a skill like any other skill, and it takes time and practice to become proficient.

So remember to be patient with yourself, your kids, and their teachers, as we embark on this new journey of learning. Everyone is trying their best and I truly believe a new rhythm will emerge as we progress through the school year.

We will find our way.

This is an animation of the spread of the COVID-19 over the U.S. in the first 150 days.  It was created in Maple 2020, making extensive use of DataFrames. 

 

https://www.youtube.com/watch?v=XHXeJKTeoRw

 

The animation of 150 Day history includes COVID-19 data published by the NY Times and geographic data assembled from other sources. Each cylinder represents a county or in two special cases New York City and Kansas City. The cross-sectional area of each cylinder is the area in square miles of the corresponding county. The height of each cylinder is on a logarithmic scale (in particular it is 100*log base 2 of the case count for the county. The argument of the logarithm function is the number of cases per county divided by the are in square miles-so an areal density.  Using a logarithmic scale facilitates showing super high density areas (e.g., NYC) along with lower density areas.  The heights are scaled by a prefactor of 100 for visualization.

Hi. My name is Eugenio and I’m a Professor at the Departamento de Didáctica de las Ciencias Experimentales, Sociales y Matemáticas at the Facultad de Educación of the Universidad Complutense de Madrid (UCM) and a member of the Instituto de Matemática Interdisciplinar (IMI) of the UCM.

I have a 14-year-old son. In the beginning of the pandemic, a confinement was ordered in Spain. It is not easy to make a kid understand that we shouldn't meet our friends and relatives for some time and that we should all stay at home in the first stage. So, I developed a simplified explanation of virus propagation for kids, firstly in Scratch and later in Maple, the latter using an implementation of turtle geometry that we developed long ago and has a much better graphic resolution (E. Roanes-Lozano and E. Roanes-Macías. An Implementation of “Turtle Graphics” in Maple V. MapleTech. Special Issue, 1994, 82-85). A video (in Spanish) of the Scratch version is available from the Instituto de Matemática Interdisciplinar (IMI) web page: https://www.ucm.es/imi/other-activities

Introduction

Surely you are uncomfortable being locked up at home, so I will try to justify that, although we are all looking forward going out, it is good not to meet your friends and family with whom you do not live.

I firstly need to mention a fractal is. A fractal is a geometric object whose structure is repeated at any scale. An example in nature is Romanesco broccoli, that you perhaps have eaten (you can search for images on the Internet). You can find a simple fractal in the following image (drawn with Maple):

Notice that each branch is divided into two branches, always forming the same angle and decreasing in size in the same proportion.

We can say that the tree in the previous image is of “depth 7” because there are 7 levels of branches.

It is quite easy to create this kind of drawing with the so called “turtle geometry” (with a recursive procedure, that is, a procedure that calls itself). Perhaps you have used Scratch programming language at school (or Logo, if you are older), which graphics are based in turtle geometry.

All drawings along these pages have been created with Maple. We can easily reform the code that generated the previous tree so that it has three, four, five,… branches at each level, instead of two.

But let’s begin with a tale that explains in a much simplified way how the spread of a disease works.

- o O o -

Let's suppose that a cat returns sick to Catland suffering from a very contagious disease and he meets his friends and family, since he has missed them so much.

We do not know very well how many cats each sick cat infects in average (before the order to STAY AT HOME arrives, as cats in Catland are very obedient and obey right away). Therefore, we’ll analyze different scenarios:

  1. Each sick cat infects two other cats.
  2. Each sick cat infects three other cats.
  3. Each sick cat infects five other cats

 

1. Each Sick Cat Infects Two Cats

In all the figures that follow, the cat initially sick is in the center of the image. The infected cats are represented by a red square.

· Before everyone gets confined at home, it only takes time for that first sick cat to see his friends, but then confinement is ordered (depth 1)

As you can see, with the cat meeting his friends and family, we already have 3 sick cats.

· Before all cats confine themselves at home, the first cat meets his friends, and these in turn have time to meet their friends (depth 2)

In this case, the number of sick cats is 7.

· Before every cat is confined at home, there is time for the initially sick cat to meet his friends, for these to meet their friends, and for the latter (friends of the friends of the first sick cat) to meet their friends (depth 3).

There are already 15 sick cats...

· Depth 4: 31 sick cats.

· Depth 5: 63 sick cats.

Next we’ll see what would happen if each sick cat infected three cats, instead of two.

 

2. Every Sick Cat Infects Three Cats

· Now we speed up, as you’ve got the idea.

The first sick cat has infected three friends or family before confining himself at home. So there are 4 infected cats.

· If each of the recently infected cats in the previous figure have in turn contact with their friends and family, we move on to the following situation, with 13 sick cats:

· And if each of these 13 infected cats lives a normal life, the disease spreads even more, and we already have 40!

· At the next step we have 121 sick cats:

· And, if they keep seeing friends and family, there will be 364 sick cats (the image reminds of what is called a Sierpinski triangle):

 

4. Every Sick Cat Infects Five Cats

· In this case already at depth 2 we already have 31 sick cats.

 

5. Conclusion

This is an example of exponential growth. And the higher the number of cats infected by each sick cat, the worse the situation is.

Therefore, avoiding meeting friends and relatives that do not live with you is hard, but good for stopping the infection. So, it is hard, but I stay at home at the first stage too!

 

POSSIBILITIES OF USING OF COMPUTER IN MATHEMATICS

AND OTHER APPLICATIONS IN INCLUSIVE EDUCATION

 

Alsu Gibadullina, math teacher, math teacher

Secondary and high school # 57, Kazan, Russia

 

In recent years Russia actively promoted and implemented the so-called inclusive education (IE). According to the materials of Alliance of human rights organizations “Save the children”: "Inclusive or included education is the term used to describe the process of teaching children with special needs in General (mass) schools. In the base of inclusive education is the ideology that ensures equal treatment for everybody, but creates special conditions for children with special educational needs. Experience shows that any of the rigid educational system some part of the children is eliminated because the system is not ready to meet the individual needs of these children in education. This ratio is 15 % of the total number of children in schools and so retired children become separated and excluded from the overall system. You need to understand that children do not fail but the system excludes children. Inclusive approaches can support such children in learning and achieving success. Inclusive education seeks to develop a methodology that recognizes that all children have different learning needs tries to develop a more flexible approach to teaching. If teaching and learning will become more effective as a result of the changes that introduces Inclusive Education, all children will win (not only children with special needs)."

There are many examples of schools that have developed their strategy implementation of IE, published many theoretical and practical benefits of inclusion today. All of them have common, immaterial character. There is no description of specific techniques implementing the principles of IO in the teaching of certain disciplines, particularly mathematics. In this paper we propose a methodology that can be successfully used as in “mathematical education for everyone", also for the development of scientific creativity of children at all age levels of the school in any discipline.

According to the author, one of the most effective methodological tools for education is a computer mathematics (SCM, SSM). Despite the fact that the SCM were created for solving problems of higher mathematics, their ability can successfully implement them in the school system. This opinion is confirmed by more than 10–year-old author's experience of using the package Maple in teaching mathematics. At first it was just learning the system and primitive using its. Then author’s interactive demonstrations, e-books, programs of analytical testing were created by the tools packages. The experience of using the system Maple in teaching inevitably led to the necessity to teach children to work with it. At first worked a club who has studied  the principles of the package’s work, which eventually turned into a research laboratory for the use of computer technology. Later on its basis there was created the scientific student society (SSS) “GEODROMhic" which operates to this day. The main idea and the ultimate goal of SSS – individual research activities on their interests with the creation of the author electronic scientific journals through the use of computer mathematics Maple. The field of application of the package was very diverse: from mathematics to psychology and cultural phenomena. SSS’s activity is very high: they are constantly and successfully participate in intellectual high-level activities (up to international). Obviously, not every SSS’s member reaches high end result. However, even basic experience in scientific analysis, modeling, intelligent  using of the computer teaches the critical thinking skills, evokes interest to new knowledge, allows you to experience their practical value, gives rise to the development of creative abilities. As a result, the research activity improves intellectual culture, self-esteem and confidence, resistance to external negative influence. It should be noted, however, that members of the scientific societies are not largely the so-called "gifted", than ordinary teenagers with different level of intellectual development and mathematical training. With all this especially valuable is that the student is dealing with mathematical signs and mathematical models, which contributes to the development of mathematical thinking.

From 2007 to 2012. our school (№. 57 of Kazan) was the experimental platform of the Republican study SKM (Maple) and other application software in the system of school mathematical education under the scientific management of Professor Yu. G. Ignatyev of Kazan state University (KF(P)U).

Practical adaptation of computer mathematics and other useful information technologies to the educational process of secondary schools passed and continues to work in the following areas:

  1. The creation of a demonstration support of different types of the lessons;
  2. The embedding of computing to the structure of practical trainings;
  3. In the form of additional courses - studying of computer applications through which you can conduct a research of the mathematical model and create animated presentation videos, web-pages, auto-run menu;
  4. Students’ working on individual creative projects:
    • construction of computer mathematical models;
    • creating author's programs with elements of scientific researches;
    • students create interactive computer-based tutorials;
    • creation of an electronic library of creative projects;
  5. The participation of students  in the annual competitions and scientific conferences for students;
  6. The accumulation and dissemination of new methodological experience.

Traditionally, the training system has the structure: explanation of a new →  the solution of tasks→ check, self-test and control → planning of the new unit  with using analysis. However, the main task types: 1) elementary, 2) basic, 3) combined, 4) integrated, 5) custom. With the increasing the level of training a number of basic tasks are growing and some integrated tasks become a class of basic. Thus, the library for basic operations is generated. The decision of the educational task occurs on the way of mastering the theoretical knowledge of mathematical modeling: 1) analysis of conditions (and construction drawing), 2) the search for methods of solution, 3) computation, 4) the researching.

To introduce computer mathematics in this training system, you can:

  • At demonstrations. For example, with Maplе facilities you can create a step-by-step interactive and animated images, which are essentially the exact graphic interpretation of mathematical models.
  • If we have centralized collective control.
  • If students have individual self-control.
  • In the analysis of the conditions of the problem, for the construction and visualization of its model, the study of this model.
  • In the computations.
  • In practical training of different forms.
  • In individual projects with elements of research.

In the learning process with the use of computer mathematics in the school a library of themed demonstrations, tasks of different levels and purpose, programs, analytical testing, research projects is generated. With all this especially valuable is that the student is dealing with mathematical signs and mathematical models. Addiction to them processed in the course of working with them it’s unobtrusive, naturally, organically.

Mathematical modelling (MM) is increasingly becoming an important component of scientific research. Today's powerful engineering tools allow to carry out numerous computer experiments, deep and full enough of exploring the object, without significant cost painless. Thus provided the advantages of theoretical approach, and experiment. The integration of information technology and MM method is effective, safe and economical. This explains its wide distribution and makes unavoidable component of scientific and technical progress.

Modeling is a natural process for people, it is present in any activity. The introduction with nature by man occurs through constant  modeling of situation, comparing with the basic models and past experience by them. Method for modeling, abstraction as a method of understanding the world is therefore  an effective method of learning. Training activities associated with the creative transformation of the subject. The main feature of educational training activities is the systematic solution of the educational problems. The connection of the principles of developmental education, mathematical modeling, neurophysiological mechanisms of the brain and experience with Maple leads to the following conclusions: the method of mathematical modeling is not only scientific research but also the way of development of thinking in general; computer and mathematical environment (Maple), which is a powerful tool for scientific simulation can be considered as the elementary analogue of the brain. These qualities of computer mathematics led to the idea of using it not only as an effective methodological tool but as a means of nurturing the thinking and development of mental functions of the brain. To study this effect the school psychologist conducted a test, which confirm the observations: the dynamics of intellectual options students  who working with Maple compares favorably with peers. In the process of doing computer math, in particular Maple, are involved in complex different mental functions. It is in the inclusion of all mental functions is the essence of integration of learning, its educational character. And this, in turn, contributes to the solution of moral problems.

Long-term work with computer mathematics led to the idea to use it as a tool for psychological testing. One of the projects focuses on the psychology and contains authors Maple–tests to identify the degree of development of different mental functions. Interactive mathematical environment  gives a wide variability and creative testing capabilities. Moreover, Maple–test can be used not only as diagnostic but also as educational, and corrective. This technology was tested in one of psycho neurological dispensaries a few years ago.

Currently, one of the author's students, the so-called "homeworkers", the second year is a young man with a diagnosis, categories F20, who does not speak and does not write independently. It was impossible to get feedback from him and have basic training until then author have begun to apply computer-based tools, including system Maple. Working with the computer tests and mathematical objects helps to see not only the mental and even the simplest thinking movement, but also emotional movement!

In general, the effectiveness of the implementation in the structure of educational process of secondary school of new organizational forms of the use of computers, based on the application of the symbolic mathematics package Maple, computer modeling and information technology, has many aspects, here are some of them:

  • goals of education and math in particular;
  • additional education;
  • methodical and professional opportunities;
  • theoretical education;
  • modeling;
  • scientific creativity;
  • logical language;
  • spatial thinking, the development of the imagination;
  • programming skills;
  • the specificity of technical translation;
  • differentiation and individualization of educational process;
  • prospective teaching, the continuity of higher and secondary mathematics education;
  • development of creative abilities, research skills;
  • analytical thinking;
  • mathematical thinking;
  • mental diagnosis;
  • mental correction.

       According to the author, the unique experience of the Kazan 57–th school suggests that computer mathematics (Maple) is the most effective also universal tool of new methods of inclusion. In recent decades, there are more children with a specific behavior, with a specific perception, not able to focus, with a poor memory, poor thinking processes. There are children, emotionally and intellectually healthy, or even ahead of their peers in one team together with them. High school should provide all the common core learning standards. It needed a variety of programs and techniques, as well as specialists who use them. Due to its remarkable features, computer mathematics, in particular Maple, can be used or be the basis of the variation of methods of physico-mathematical disciplines of inclusive education.

 

Research work

The fractal structure’s researching.

Modeling of the fractal sets in the Maple program.

Municipal Budget Educational Establishment “School # 57” of Kirov district of Kazan

    Author:  Ibragimova Evelina

    Scientific advisor:  Alsu Gibadullina - mathematics teacher

    Translator:  Aigul Gibadullina

In Russian

ИбрагимоваЭ_Фракталы.docx

In English

Fractals_researching.doc

 

     ( Images - in attached files )

Table of contents:

Introduction

I. Studying of principles of fractals construction

II. Applied meaning of fractals

III. Researching of computer programs of fractals construction

Conclusion

Introduction

We don’t usually think about main point of things, which we have to do with every day. Environmental systems are many-sided, ever–changing and compli­cated, but they are formed by a little number of rules. Fractals are apt example of this – they are complicated, but based on simple regulations. Self – similarity is the main attribute of them.  Just one fractal element contains genetically information about all system.  This information have a forming role for all system. But some­times self – similarity is partial.

Hypothesis of the research. Fractals and various elements of the Universe have general principles of structural organization. It is a reason why the theory of frac­tals is instrument for cognition of the world.

Purpose of the research. Studying  of genetic analogy  between fractals and alive and non-living Universe systems with computer-based mathematical mod­eling in the Mapel’s computer space.

Problems of the research. 

  1. studying of principles of fractal’s construction;
  2. Detection of  general fractal content of physical, biological and artificial sys­tems;
  3. Researching of applied meaning of fractals;
  4. Searching of computer programs which can generate all of known fractals;
  5. Researching of fractals witch was assigned by complex variables;
  6. Formation of innovative ideas of using of fractals in different spaces;

The object of research.  Fractal structures, nature and society objects.

The subject of research. Manifestation of fractality in different objects of the Universe.

Methods of the researching.

  1. Studying and analysis of literature of research’s problem;
  2. Searching of computer programs which can generate fractals and experimenta­tion with them;
  3. Comparative analysis of principles of generating of fractals and structural or­ganizations of physical, biological and artificial systems;
  4. Generation and formulation of innovation ways to applied significance of fractals.

Applied significance.

Researching of universality of fractals gives general academic way of cognition of nature and society.

 

I. Studying of principles of fractal construction

We can see fractal constructions everywhere – from crystals and different accu­mulations (clouds, rivers, mountains, stars etc.) to complex ecosystems and bio­logical objects like fern leafs or human brain. Actually, the idea that frac­tal principles are genetic code of our Universe has been discussed for about fifteen years. The first attempt of modeling of the process of the Universe construction was done by A.D. Linde. We also know that young Andrey Saharov had solved “fractal” calculation problem – it was already half a century ago.

Now therefore, fractal picture of the world was intuitively anticipated by human genius and it inevitably manifested in its activity.

Fractals are divided into four groups in the traditional way: geometric (constructive), algebraical (dynamical), stochastical and natural.

The first group of fractals is geometric. It is the most demonstrative type of fractals, because we can instantly observe the self-similarity in it. This type of fractals is constructed in the basis of original figure by her fragmentation and real­izing of different transformations. Geometrical fractals ensue on repeating of this procedure. They are using in computer-generated graphics for generating the pic­tures of leafs, bush, dimensional structures, etc.

The second large group of fractals -  algebraical. This fractals are constructed by iteration of nonlinear displays, which set by simple formulas. There are two types of algebraical fractals – linear and nonlinear. The first of them are determined by first order equates (linear equates), and the second by nonlinear equates, their na­ture significantly brighter, richer and more diverse than first order equates.

The third known group of fractals – stochastical. It is generated by method of random modification of options in iterative process. Therefore, we get an objects which is similar to nature fractals – asymmetrical trees, rugged coasts, mountain scenery etc. Such fractals are useful in modeling of land topography, sea–surface and electrolysis process etc.

The fourth group of fractals is nature, they are dominate in our life. The main difference of such fractals is that they can’t demonstrate infinite self-similarity. There is “physical fractals” term in the classification concept for nature fractals, this term notes their naturalness. These fractals are created with two simple opera­tions: copy and scaling. We can indefinitely list examples of nature fractals: hu­man’s circulatory system, crowns and leafs of trees, lungs, etc.  It is impossible to show all diversity of nature fractals.

 

II. Applied meaning of fractals

Fractals are having incredibly widespread application nowadays.

In the medicine. Human’s organism is consists from fractal structures: circulatory system, bronchus, muscle, neuron system, etc. So it’s naturally that fractal algorithms are useful in the medicine. For example, assessment of rhythm of fractal dimension while electric diagrams analyzing allows to make more infor­mative and accurate view on the beginning of specific illnesses. Also fractals are using for high–quality processing of  X–ray images (in the experimental way). There are designing of new methods in the gastroenterology which allows to ex­plore gastrointestinal tract organs qualitative and painlessly. Actually, there are discoveries of application of fractal methods for diagnosis and treatment of cancer.

In the science. There are no scientific and technical areas without fractal calcu­lations nowadays. It happens due to the fact that majority of nature objects have fractal structures and dimension: coasts of the continents; natural resources alloca­tion; magnetic field anomaly; dissemination of surges and vibrations in an elastic environments; porous, solid and fungal bodies; crystals; turbulence; dynamic of complicated systems in general, etc. Fractals are useful in geology, geophysics, in the oil sciences… It’s impossible to list all the spaces of adaptability.

Modeling of chaotic processes, particularly, in description of population models.

In telecommunications. It’s naturally that fractals are popular in this area too. Natan Coen is person, who had started to use fractal antennas. Fractal antenna has very compact form which provides high productivity. Due to this, such antennas are used in marine and air transport, in personal devises. The theory of fractal an­tennas has become an independent, well-developed apparatus of synthesis and analysis of electric small antenna (ESA) nowadays. There are developments of possibility of fractal compression of the traffic which is transmitted through the web. The goal of this is more effective transfer of information.

In the visual effects. The theory of fractals has penetrated area of formation of different kinds of visualizations and creation of special effects in the computer graphics soon. This theory are very useful in modeling of nature landscapes in computer games. The film industry also has not been without fractal geometry. All the special effects are based in fractal structure: mountain landscape, lava, flame, fog, large flows and the same. In the modern level of the cinema creation of the special effects is impossible without modeling of fractals.

In the economics. The Veirshtrass’s function is famous example of stochastic fractals. Analysis of graph of the function in interactive mathematic environment Maple allows to make sure in fractal structure of function by way of entry of dif­ferent ranges of graphic visualization. In any indefinitely small area of the part graph of the function absolutely looks like area of this part in the all . The property of function is used in analysis of stock markets.

In the architect. Notably, fractal structures have become useful in the architect more earlier than B. Mandelbrode had discovered them. S.B. Pomorov, Doctor of Architecture, Professor, member of Russian Architect Union, talks about applica­tion of fractal theory in the architect in his article. Let’s see on the part of this arti­cle:

“Fractal structures were found in configuration of African tribal villages, in an­cient Vavilon’s ziggurats, in iconic buildings of ancient India and China, in gothic temples of ancient Russia .

We can see the high fractal level in Malevich’s Architectons. But they were cre­ated long before emergence of the notion of fractals in the architect. People started to use fractal algorithms on the architect morphogenesis consciously after Mandel­brot’s publications. It was made possible to use fractal geometry for analyzing of architectural forms.

Fractals had become available to the majority of specialists due to the comput­erization.  They had been incredibly attractive for architectors, designers and town planners in aesthetic, philosophical and psychological way. Fractal theory was per­ceived on emotional, sensual level in the first phase. The constant repression lead­ing to loss of sensuality.

Application of fractal structures is effective on the microenvironment designing level: interior, household items and their elements. Fractal structures introduction allows creating a new surroundings for people with fractal properties on all levels. It corresponds to nesting spaces.

Fractal formations are not a panacea or a new era in the architect history. But it’s a new way to design architect forms which enriches the architectural theory and practice language. The understanding of na­ture fractal impacts on architectural view of urban environment. An attempt to de­velop the method of architectural designing which will base in an in-depth fractal forms is especially interesting. Will this method base only on mathematics? Will it be different methods and features symbiosis? The practice experiments and re­searches will show us. It’s safe to say that modern fractal approach can be useful not only for analysis, but also for harmonic order and nature’s chaos, architect which may be semantic dominant in nature and historic context.”

Computer systems. Fractal data compression is the most useful fractal applica­tion in the computer science. This kind of compression is based on the fact that it’s easy to describe the real world by fractal geometry. Nevertheless, pictures are compressed better than by other methods (like jpeg or gif). Another one advantage is that picture isn’t pixelateing while compressing. Often picture looks better after increase in fractal compressing.

Basic concept for fractal computer graphics is “Fractal triangle”. Also there are “Fractal figure”, “Fractal object”, “Fractal line”, “Fractal composition”, “Parent object” and “Heir object”. However, it should be noted that fractal computer graphics has recently received as a kind of computer graphics of 21th century.

 The opportunities of fractal computer graphics cannot be overemphasized. It allows creating abstract compositions where we can realize a lot of moves: hori­zontal and vertical, diagonal directions, symmetry and asymmetry etc. Only a few programmers from all over the world know about fractal graphics today.  To what can we compare fractal picture? For example, with complex structure of crystal or with snowflake, the elements of which line up in the one complex composition. This property of fractal object can be useful in ornament creating or designing of decorative composition. Algorithms of synthesis of fractal rates which allows to reproduce copy of any picture too close to the original are developed today.

 

III. Researching of computer programs of fractal construction

Strict algorithms of fractals are really good for programming. There are a lot of computer programs which introduce fractals nowadays. Computer mathematic systems are stand out from over programs, especially, Maple. Computer mathe­matics is mathematic modeling tool. So programming represents genetic structure of fractal in these systems and we can see precise submission of fractal structure in the picture while we enter a number of iterations . This is the reason why mathematic fractals should be studied with computer mathematics.  The last dis­covery in fractal geometry has been made possible by powerful, modern com­puters. Fractal property researching is almost completely based on computer cal­culations. It allows making computer experiments which reproduce processes and phenomenon which we can’t experiment in the real world with.

Our school has been worked with computer mathematics Maple package more than 10 years. So we have unique opportunity to experiment with mathematic fractals, thanks to that we can understand how initial values impact on outcome   (it is stochastic fractal). For example, we have understood the meaning of the fact that color is the fourth dimension: color changing leads to changing of physical char­acteristics. That is what astrophysics mean talking about “multicolored” of the Universe. While fractal constructing in interactive mathematic environment we re­ceived graphic models which was like A. D. Linde’s model of the Universe. Perhaps, it demonstrates that Universe has fractal structure.

 

Conclusion

Scientists and philosophers argue, can we talk about universality of fractals in recent years. There are two groups of two opposite positions. We agree with the fact that fractals are universal. Due to the fact that movement is inherent property of material also we always have fractals wherever we have movement.  

We are convinced that fractal is genetic property of the Universe, but it is not mean that all the Universe elements to the one fractal organization. In deployment process fractal structure is undergoing a lot of fluctuations (deviations) and a lot of points of bifurcation (branching) lead grate number of fractal development varie­ties.  

Therefore, we think that fractals are general academic method of real world re­searching. Fractals give the methodology of nature and community researching.

In transitional, chaotic period of society development social life become harder. Different social systems clash. Ancient values are exchanged for new values literally in all spaces. So it’s vitally important for science to develop behavior strategies which allow to avoid tragic mistakes. We think that fractals play important role in developing of such technologies. And – synergy is theory of evolving systems self- organization. But evolution happens on fractal principles, as we know now.

 

P.S.  Images - in attached files

 

Maple 2020 offers many improvements motivated and driven by our users.

Every single update in a new release has a story behind it. It might be a new function that a customer wants, a response to some feedback about usability, or an itch that a developer needs to scratch.

I’ll end this post with a story about acoustic guitars and how they drove improvements in signal and audio processing. But first, here are some of my personal favorites from Maple 2020.

Graph theory is a big focus of Maple 2020. The new features include more control over visualization, additional special graphs, new analysis functions, and even an interactive layout tool.

I’m particularly enamoured by these:

  • We’ve introduced new centrality measures - these help you determine the most influential vertices, based on their connections to other vertices
  • You now have more control over the styling of graphs – for example, you can vary the size or color of a nodebased on its centrality

I’ve used these two new features to identify the most influential MaplePrimes users. Get the worksheet here.

@Carl Love – looks like you’re the biggest mover and shaker on MaplePrimes (well, according to the eigenvector centrality of the MaplePrimes interaction graph).

We’ve also started using graph theory elsewhere in Maple. For example, you can generate static call graph to visualize dependencies between procedures calls in a procedure

You now get smoother edges for 3d surfaces with non-numeric values. Just look at the difference between Maple 2019 and 2020 for this plot.

Printing and PDF export has gotten a whole lot better.  We’ve put a lot of work into the proper handling of plots, tables, and interactive components, so the results look better than before.

For example, plots now maintain their aspect ratio when printed. So your carefully constructed psychrometric chart will not be squashed and stretched when exported to a PDF.

We’ve overhauled the start page to give it a cleaner, less cluttered look – this is much more digestible for new users (experienced users might find the new look attractive as well!). There’s a link to the Maple Portal, and an updated Maple Fundamentals guide that helps new users learn the product.

We’ve also linked to a guide that helps you choose between Document and Worksheet, and a link to a new movie.

New messages also guide new users away from some very common mistakes. For example, students often type “e” when referring to the exponential constant – a warning now appears if that is detected

We’re always tweaking existing functions to make them faster. For example, you can now compute the natural logarithm of large integers much more quickly and with less memory.

This calculation is about 50 times faster in Maple 2020 than in prior versions:

Many of our educators have asked for this – the linear algebra tutorials now return step by step solutions to the main document, so you have a record of what you did after the tutor is closed.

Continuing with this theme, the Student:-LinearAlgebra context menu features several new linear algebra visualizations to the Student:-LinearAlgebra Context Menu. This, for example, is an eigenvector plot.

Maple can now numerically evaluate various integral transforms.

The numerical inversion of integral transforms has application in many branches of science and engineering.

Maple is the world’s best tool for the symbolic solution of ODEs and PDEs, and in each release we push the boundary back further.

For example, Maple 2020 has improved tools for find hypergeometric solutions for linear PDEs.

This might seem like a minor improvement that’s barely worth mentions, but it’s one I now use all the time! You can now reorder worksheet tabs just by clicking and dragging.

The Hough transform lets you detect straight lines and line segments in images.

Hough transforms are widely used in automatic lane detection systems for autonomous driving. You can even detect the straight lines on a Sudoku grid!

The Physics package is always a pleasure to write about because it's something we do far better than the competition.

The new explore option in TensorArray combines two themes in Maple - Physics and interactive components. It's an intuitive solution to the real problem of viewing the contents of higher dimensional tensorial expressions.

There are many more updates to Physics in Maple 2020, including a completely rewritten FeynmanDiagrams command.

The Quantum Chemistry Toolbox has been updated with more analysis tools and curriculum material.

There’s more teaching content for general chemistry.

Among the many new analysis functions, you can now visualize transition orbitals.

I promised you a story about acoustic guitars and Maple 2020, didn’t I?

I often start a perfectly innocuous conversation about Maple that descends into several weeks of intense, feverish work.

The work is partly for me, but mostly for my colleagues. They don’t like me for that.

That conversation usually happens on a Friday afternoon, when we’re least prepared for it. On the plus side, this often means a user has planted a germ of an idea for a new feature or improvement, and we just have to will it into existence.

One Friday afternoon last year, I was speaking to a user about acoustic guitars. He wanted to synthetically generate guitar chords with reverb, and export the sound to a 32-bit Wave file. All of this, in Maple.

This started a chain of events that that involved least-square filters, frequency response curves, convolution, Karplus-Strong string synthesis and more. We’ll package up the results of this work, and hand it over to you – our users – over the next one or two releases.

Let me tell you what made it into Maple 2020.

Start by listening to this:

It’s a guitar chord played twice, the second time with reverb, both generated with Maple.

The reverb was simulated with convolving the artificially generated guitar chord with an impulse response. I had a choice of convolution functions in the SignalProcessing and AudioTools packages.

Both gave the same results, but we found that SignalProcessing:-Convolution was much faster than its AudioTools counterpart.

There’s no reason for the speed difference, so R&D modified AudioTools:-Convolution to leverage SignalProcessing:-Convolution for the instances for which their options are compatible. In this application, AudioTools:-Convolution is 25 times faster in Maple 2020 than Maple 2019!

We also discovered that the underlying library we use for the SignalProcessing package (the Intel IPP) gives two options for convolution that we were previously not using; a method which use an explicit formula and a “fast” method that uses FFTs. We modified SignalProcessing:-Convolution to accept both options (previously, we used just one of the methods),

That’s the story behind two new features in Maple 2020. Look at the entirety of what’s new in this release – there’s a tale for each new feature. I’d love to tell you more, but I’d run out of ink before I finish.

To read about everything that’s new in Maple 2020, go to the new features page.

Today we celebrated International Women's Day at Maplesoft. As part of our celebration, we had a panel of 5 successful women from within the community share their experiences and insights with us. 

Hearing these women speak has given me the courage to share my personal experience and advice to women in technology. If what I write here helps even one woman, then I will have accomplished something great today. 

-----

What do you do at Maplesoft?

My name is Karishma. I'm the Director, Product Management - Academic. 

 

Where did you grow up and where did you go to school (Diploma/degree)?

I was born and raised in Montreal to parents of Indian descent. Like most Indian parents, they “encouraged" me to pursue a career in either Law, Medicine, or Engineering, despite my true calling to pursue a career in theatre (at least that's what I believed it to be at the time)

Given that I had no siblings to break the ice, and that rebelling wasn't my Modus Operandi (that came much later), I did what any obedient teenager would do: I pursued a career in Electrical Engineering at McGill University. In my mind, this was the fastest way to landing a job and fleeing the proverbial nest. 

Electrical Engineering was far from glamorous, and after two years, I was ready to switch. It was due to the sheer insistence of my mother that I completed the degree. 

So how did I end up pursuing a graduate degree in Biomedical Engineering at McGill University? It wasn't the future I envisioned, but the economic downturn in 2001-2002 saw a massive decrease in hiring, and the job that I had held-out patiently for during those four years became a far-off dream. So I did the thing I never imagined I would: I accepted the offer to pursue a Master's and the very generous stipend that came with it. In case you are wondering, I only applied because my father nagged me into submission. (Insistence and nagging are two innate traits of Indian parents)

Contrary to what I expected, I loved my Master's degree! It gave me the freedom to immerse myself wholly in a topic I found exciting and allowed me to call the shots on my schedule, which led to my involvement in student government as VP Internal. But apart from the research and the independence, pursuing a master's degree opened doors to opportunities that I couldn't have imagined, such as an internship with the International Organization for Migration in Kenya, a job offer in Europe, and the chance to work at Maplesoft. (I guess my parents did know what was best for me.) 

 

What is the best part of your job?

It's figuring out how to solve problems our users have as well as the ones they might not realize they have. 

At Maplesoft, I work with some most brilliant minds I've ever encountered to build a product that makes math more accessible to our users, whether they be a student, researcher, scientist, or engineer. 

Some of the aspects of my role that I love the most include: 

  • speaking to and learning from our customers, 
  • interpreting the meaning behind their words, facial expressions, vocal intonations, and body language, and
  • collaborating with the sales, marketing, and development teams to turn what was 'said' into tangible actions that will enhance the product and user experience. 

Most nights, when I leave work, I do so with a sense of excitement because I know my actions and the actions of those I work with will help our users achieve their goals and ambitions. There's no better high. 

 

What advice do you have for young women interested in a career in your field? 
Throughout my career, I've had the privilege to work with some amazing women and men who've given me advice that I wish I had known when I was an undergraduate student. If you are a woman pursuing a STEM degree or starting your first job in a tech firm, here are three tips that may help you: 

1.   Don't be scared of the 'N' word. 
Don't be scared of NETWORKING. I know it can be intimidating, but it truly is the best way to land a job, advance your career, or meet the person you admire most. Remember that networking can take place anywhere - it's not exclusive to networking events. Some advice that I received that helped me overcome my fear of networking: 

  • Smile - Before you approach a person or enter a networking session, force yourself to smile. It will help you diffuse any tension you are holding and will make you appear more approachable. 
  • Research - Take the time to research the person(s) you would like to meet. Find out as much as you can about them and their company. Prepare some icebreaker questions and other questions to help carry the conversation forward ahead of time. Remember that people like to talk about themselves and their experiences. 
  • Don't take it personally - The person you approach may find networking equally tricky. So if they seem disinterested or aloof, don't take it personally. 
  • Just do it - Networking gets easier with practice. Don't let a failed attempt set you back. The worse thing that will happen is that you don't make a connection. 

 

 2.   It's ok to ask for help.
If you are a woman in an environment that is dominated by men, you might hesitate to ask for help. DON'T! There's nothing wrong with asking for help. That said, many women ask for help in a way that undermines their confidence and thus erodes others’ perception of them. Next time you need help, have a question or require clarification, take a moment to phrase your request, so you don't inadvertently put yourself down. 

 

3.   Play to your strengths
Don't think you need to know everything. Nobody expects it. If you landed a new job or co-op placement, and you are finding yourself doing things you've never done and don't come naturally to you yet, don't let your brain convince you that you don't deserve it. Remember that you earned it because of your qualities and strengths. 

Playing mini-golf recently, I realized that my protractor can only help me so far since it can't calculate the speed of the swing needed.  I decided a more sophisticated tool was needed and modeled a trick-shot in MapleSim.

To start, I laid out the obstacles, the ball and club, the ground, and some additional visualizations in the MapleSim environment.

 

When running the simulation, my first result wasn't even close to the hole (similar to when I play in real life!).

 

The model clearly needed to be optimized. I went to the Optimization app in MapleSim (this can be found under Add Apps or Templates  on the left hand side).

 

Inside the app I clicked "Load System" then selected the parameters I wanted to optimize.

 

For this case, I'm optimizing 's' (the speed of the club) and 'theta' (the angle of the club). For the Objective Function I added a Relative Translation Sensor to the model and attached a probe to the Vector Norm of the output.

 

Inside the app, I switched to the Objective Function section.  Selecting Probes, I added the new probe as the Objective Function by giving it a weight of 1.

 

 

Scrolling down to "Execute Parameter Optimization", I checked the "Use Global Optimization Toolbox" checkbox, and clicked Run Parameter Optimization.

 

Following a run time of 120 seconds, the app returns the graph of the objective function. 

 

Below the plot, optimal values for the parameters are given. Plugging these back into the parameter block for the simulation we see that the ball does in fact go into the hole. Success!

 

 

Mini_golf_Global_Optimization.msim

We recently had a question about using some of the plotting commands in Maple to draw things. We were feeling creative and thought why not take it a step further and draw something in 3D.

Using the geom3d, plottools, and plots packages we decided to make a gingerbread house.

To make the base of the house we decided to use 2 cubes, as these would give us additional lines and segments for the icing on the house.

point(p__1,[2,3,2]):
point(p__2,[3,3,2]):
cube(c1,p__1,2):
cube(c2,p__2,2):
base:=draw([c1,c2],color=tan);

Using the same cubes but changing the style to be wireframe and point we made some icing lines and decorations for the gingerbread house.

base_decor1:=draw([c1,c2],style=wireframe,thickness=3,color=red,transparency=0.2):
base_decor2:=draw([c1,c2],style=wireframe,thickness=10,color=green,linestyle=dot):
base_decor3:=draw([c1,c2],style=point,thickness=2,color="Silver",symbol=sphere):
base_decor:=display(base_decor1,base_decor2,base_decor3);