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Have you heard the news yet? Maple Learn has had a major update! You may be wondering what this means, and what all the shiny new features are. Let’s go through them together.

First, as with many updates, we’ve improved performance with Maple Learn. Longer documents will load and perform faster, requiring less computing power for operations, and as a result your browser will be more responsive. Performance on Chromebooks is also improved.

Operations that previously would have needed to be refreshed now automatically calculate. Up until now, if you performed a menu operation on an expression and then changed the value of the expression, the result would turn orange to warn you that the result was no longer valid. You would then have to refresh manually. Now, this is no longer the case, the orange refresh button has been removed from Maple Learn, and results are never out of date.

The plot window, inline plots, and the context panel are all resizable now. This means that, for example, if you’re presenting using Maple Learn, you can enlarge the plot window to be the focus of the presentation, and shrink the context panel out of the way. Take a look at the difference, with our animation of it in action!

Sliders are also more flexible now! Bounds for sliders can be expressed in terms of variables or symbols like π. As well, you can now animate sliders, animating the graph. This allows for more interactivity in documents. See the old view on the left, and the new view on the right! Make sure to take a look at an example of the animated slider below the views as well. 

   

You can also now snap groups to a grid, allow them to automatically adjust their position as other groups adjust. This ensures better alignment of groups. It also allows you to easily rearrange elements of your documents.

Next, Maple Learn could handle 3D plots before, but now Maple Learn supports 3D parametric plots!

Finally, Maple Learn now has printing! This means you can print out your Maple Learn documents, with two options: to print just the canvas, or to print just the plot. This was requested by many users.

Multiple selection is also possible, allowing you to select multiple cells in a group by holding down the Ctrl/Command key while clicking and dragging.

That’s all for the updates in this version, but keep an eye out for our other updates! For more details, please take a look at our What’s New In Maple Learn page. We hope you enjoy our new features, and let us know if there are any more features you’d like to see in Maple Learn below.

 This interactive electronic textbook, in the form of Maple worksheets, is released in its sixth edition, 2021 August.  This book has two major divisions, mathematics for chemistry -- the mathematics that any instructor of a course in chemistry would wish a student thereof to understand and to be able to implement, and mathematics of chemistry, in the sense of the classic volumes by Margenau and Murphy -- mathematical treatments of particular topics in chemistry from an introductory post-secondary level to a post-graduate level. The content, which includes not only chapters in previous editions that have been revised but also additional chapters on quantum mechanics, molecular spectrometry and advanced chemical kinetics, has been collected during two decades, with many contributions from other authors, acknowledged in particular locations.  Each chapter includes not only explanatory treatments but also illuminating examples and exercises with chemical applications where practicable.

 

Mathematics for chemistry      0  introduction to Maple commands

                                                 1  numbers, symbols and elementary functions

                                                 2  plotting, geometry, trigonometry and functions

                                                 3  differential calculus

                                                 4  integral calculus

                                                 5  multivariate calculus

                                                 6  linear algebra

                                                 7  differential and integral equations

                                                 8  probability, statistics, regression and optimisation

Mathematics of chemistry       9  chemical equilibrium

                                                10  group theory

                                                11  graph theory

                                                12  quantum mechanics in three parts -- models, atoms and molecules

                                                13  molecular spectrometry

                                                14  Fourier transforms

                                                15  advanced chemical kinetics

                                                16  dielectric and magnetic properties

The content freely available at https://www.maplesoft.com/applications/view.aspx?SID=154267 includes also a published report on teaching mathematics with symbolic software and an interactive periodic chart that yields information about particular chemical elements and their isotopic variants.

            The nature of this electronic interactive textbook makes it applicable with an instructor in a traditional setting, or computer laboratory, for which the material of mathematics for chemistry could be reasonably covered in three or four semesters, but even for self study.  The chapters on quantum mechanics and Fourier transforms are available as separate textbooks in the same format.

Mathematical visualizations are beautiful representations of technical phenomena.  From the visual “perfection” of the golden spiral to the pattern generation of fractals, so many works of art can be boiled down to formulas and equations.  Such is the case with N.G. de Bruijn’s medallion and frieze patterns.  Given two starting values, two lines of mathematical formulae produce a recursive sequence of complex numbers.  We can associate these numbers with the four cardinal directions, following the steps on a plot to produce beautiful patterns.  The patterns are of two different types, the closed medallion or repeating frieze, depending on the starting values.

When you need a complex math visualization, Maple is a perfect place to go.  A demonstration of medallion and frieze patterns is available in the Maple Application Center, in which you can vary the starting values and watch the outcome change, along with more detailed background information.  However, there’s an even simpler way to explore this program with the help of Maple Learn.  Maple Learn has the same computational power as Maple, streamlined into an easy-to-use notebook style.  

Maple Learn includes many core features, and anything missing can be ported in through Maple.  This is done using Maple’s DocumentTools:-Canvas package.  The package contains the necessary procedures to convert Maple code into a “canvas”, which can be opened as a Maple Learn sheet.  This makes the whole document look cleaner and allows for easy sharing with friends.

The medallion and frieze document, along with the additional contextual information, is now also available in Maple Learn’s Document Gallery, home to over one thousand example documents covering calculus, geometry, physics, and more.

Who else likes art?  I love art; doodling in my notebook between projects and classes is a great way to pass the time and keep my creativity sharp.  However, when I’m working in Maple Learn, I don’t need to get out my book; I can use the plot window as my canvas and get my drawing fix right then and there.

We’ve done a few blog posts on Maple Learn art, and we’re back at it again in even bigger and better ways.  Maple Learn’s recent update added some useful features that can be incorporated into art, including the ability to resize the plot window and animate using automatically-changing variables.

Even with all the previous posts, you may be thinking, “What’s all this?  How am I supposed to make art in a piece of math software?”  Well, there is a lot of beauty to mathematics.  Consider beautiful patterns and fractals, equations that produce surprisingly aesthetically interesting outputs, and the general use of mathematics to create technical art.  In Maple Learn, you don’t have to get that advanced (heck, unless you want to).  Art can be created by combining basic shapes and functions into any image you can imagine.  All of the images below were created in Maple Learn!

There are many ways you can harness artistic power in Maple Learn.  Here are the resources I recommend to get you started.

  1. I’ve recently made some YouTube videos (see the first one below) that provide a tutorial for Maple Learn art.  This series is less than 30 minutes in total, and covers - in three respective parts - the basics, some more advanced Learn techniques, and a full walkthrough of how I make my own art.
  2. Check out the Maple Learn document gallery art collection for some inspiration, the how-to documents for additional help, and the rest of the gallery to see even more Maple Learn in action!

Once you’re having fun and making art, consider submitting your art to the Maple Conference 2022 Maple Learn Art Showcase.  The due date for submission is October 14, 2022.  The Conference itself is on November 2-3, and is a free virtual event filled with presentations, discussions, and more.  Check it out!

 


 

New display of arbitrary constants and functions

 

When using computer algebra, first we want results. Right. And textbook-like typesetting was not fully developed 20+ years ago. So, in the name of getting those results, people somehow got used to the idea of "give up textbook-quality computer algebra display". But computers keep evolving, and nowadays textbook typesetting is fully developed, so we have better typesetting in place. For example, consider this differential equation:

 

Download New_arbitrary_constants_and_functions.mw

Edgardo S. Cheb-Terrab
Physics, Differential Equations and Mathematical Functions, Maplesoft

Problem statement:
Determine the relativistic uniformly accelerated motion, i.e. the rectilinear motion for which the acceleration w in the proper reference frame (at each instant of time) remains constant.

As an application of the post presented by Dr Cheb Terrab in MaplePrimes on the principle of relativity ( found here ), we solve the problem stated on page 24 of Landau & Lifshitz book [1], which makes use of the relativistic invariant condition of the constancy of a four-scalar, viz., `w__μ`*w^mu where w^mu is the four-acceleration. This little problem exemplify beautifully how to use invariance in relativity. This is the so-called hyperbolic motion and we explain why at the end of this worksheet.

NULL

let's introduce the coordinate system, X = (x, y, z, tau)with tau = c*t 

with(Physics)

Setup(coordinates = [X = (x, y, z, tau)])

[coordinatesystems = {X}]

(1)

%d_(s)^2 = g_[lineelement]

%d_(s)^2 = -Physics:-d_(x)^2-Physics:-d_(y)^2-Physics:-d_(z)^2+Physics:-d_(tau)^2

(2)

NULL

Four-velocity

 

The four-velocity is defined by  u^mu = dx^mu/ds and dx^mu/ds = dx^mu/(c*sqrt(1-v^2/c^2)*dt) 

Define this quantity as a tensor.

Define(u[mu], quiet)

The four velocity can therefore be computing using

u[`~mu`] = d_(X[`~mu`])/%d_(s(tau))

u[`~mu`] = Physics:-d_(Physics:-SpaceTimeVector[`~mu`](X))/%d_(s(tau))

(1.1)

NULL

As to the interval d(s(tau)), it is easily obtained from (2) . See Equation (4.1.5)  here with d(diff(tau(x), x)) = d(s(tau)) for in the moving reference frame we have that d(diff(x, x)) = d(diff(y(x), x)) and d(diff(y(x), x)) = d(diff(z(x), x)) and d(diff(z(x), x)) = 0.

 Thus, remembering that the velocity is a function of the time and hence of tau, set

%d_(s(tau)) = d(tau)*sqrt(1-v(tau)^2/c^2)

%d_(s(tau)) = Physics:-d_(tau)*(1-v(tau)^2/c^2)^(1/2)

(1.2)

subs(%d_(s(tau)) = Physics[d_](tau)*(1-v(tau)^2/c^2)^(1/2), u[`~mu`] = Physics[d_](Physics[SpaceTimeVector][`~mu`](X))/%d_(s(tau)))

u[`~mu`] = Physics:-d_(Physics:-SpaceTimeVector[`~mu`](X))/(Physics:-d_(tau)*(1-v(tau)^2/c^2)^(1/2))

(1.3)

Rewriting the right-hand side in components,

lhs(u[`~mu`] = Physics[d_](Physics[SpaceTimeVector][`~mu`](X))/(Physics[d_](tau)*(1-v(tau)^2/c^2)^(1/2))) = Library:-TensorComponents(rhs(u[`~mu`] = Physics[d_](Physics[SpaceTimeVector][`~mu`](X))/(Physics[d_](tau)*(1-v(tau)^2/c^2)^(1/2))))

u[`~mu`] = [Physics:-d_(x)/(Physics:-d_(tau)*(-(v(tau)^2-c^2)/c^2)^(1/2)), Physics:-d_(y)/(Physics:-d_(tau)*(-(v(tau)^2-c^2)/c^2)^(1/2)), Physics:-d_(z)/(Physics:-d_(tau)*(-(v(tau)^2-c^2)/c^2)^(1/2)), 1/(-(v(tau)^2-c^2)/c^2)^(1/2)]

(1.4)

Next we introduce explicitly the 3D velocity components while remembering that the moving reference frame travels along the positive x-axis

NULL

simplify(u[`~mu`] = [Physics[d_](x)/(Physics[d_](tau)*(-(v(tau)^2-c^2)/c^2)^(1/2)), Physics[d_](y)/(Physics[d_](tau)*(-(v(tau)^2-c^2)/c^2)^(1/2)), Physics[d_](z)/(Physics[d_](tau)*(-(v(tau)^2-c^2)/c^2)^(1/2)), 1/(-(v(tau)^2-c^2)/c^2)^(1/2)], {d_(x)/d_(tau) = v(tau)/c, d_(y)/d_(tau) = 0, d_(z)/d_(tau) = 0}, {d_(x), d_(y), d_(z)})

u[`~mu`] = [v(tau)/(c*((c^2-v(tau)^2)/c^2)^(1/2)), 0, 0, 1/(-(v(tau)^2-c^2)/c^2)^(1/2)]

(1.5)

Introduce now this explicit definition into the system

Define(u[`~mu`] = [v(tau)/(c*((c^2-v(tau)^2)/c^2)^(1/2)), 0, 0, 1/(-(v(tau)^2-c^2)/c^2)^(1/2)])

{Physics:-Dgamma[mu], Physics:-Psigma[mu], Physics:-d_[mu], Physics:-g_[mu, nu], u[mu], w[`~mu`], w__o[`~mu`], Physics:-LeviCivita[alpha, beta, mu, nu], Physics:-SpaceTimeVector[mu](X)}

(1.6)

NULL

Computing the four-acceleration

 

This quantity is defined by the second derivative w^mu = d^2*x^mu/ds^2 and d^2*x^mu/ds^2 = du^mu/ds and du^mu/ds = du^mu/(c*sqrt(1-v^2/c^2)*dt)

Define this quantity as a tensor.

Define(w[mu], quiet)

Applying the definition just given,

w[`~mu`] = d_(u[`~mu`])/%d_(s(tau))

w[`~mu`] = Physics:-d_[nu](u[`~mu`], [X])*Physics:-d_(Physics:-SpaceTimeVector[`~nu`](X))/%d_(s(tau))

(2.1)

Substituting for d_(s(tau))from (1.2) above

subs(%d_(s(tau)) = Physics[d_](tau)*(1-v(tau)^2/c^2)^(1/2), w[`~mu`] = Physics[d_][nu](u[`~mu`], [X])*Physics[d_](Physics[SpaceTimeVector][`~nu`](X))/%d_(s(tau)))

w[`~mu`] = Physics:-d_[nu](u[`~mu`], [X])*Physics:-d_(Physics:-SpaceTimeVector[`~nu`](X))/(Physics:-d_(tau)*(1-v(tau)^2/c^2)^(1/2))

(2.2)

Introducing now this definition (2.2)  into the system,

Define(w[`~mu`] = Physics[d_][nu](u[`~mu`], [X])*Physics[d_](Physics[SpaceTimeVector][`~nu`](X))/(Physics[d_](tau)*(1-v(tau)^2/c^2)^(1/2)), quiet)

lhs(w[`~mu`] = Physics[d_][nu](u[`~mu`], [X])*Physics[d_](Physics[SpaceTimeVector][`~nu`](X))/(Physics[d_](tau)*(1-v(tau)^2/c^2)^(1/2))) = TensorArray(rhs(w[`~mu`] = Physics[d_][nu](u[`~mu`], [X])*Physics[d_](Physics[SpaceTimeVector][`~nu`](X))/(Physics[d_](tau)*(1-v(tau)^2/c^2)^(1/2))))

w[`~mu`] = Array(%id = 36893488148327765764)

(2.3)

Recalling that tau = c*t, we get

"PDETools:-dchange([tau=c*t],?,[t],params=c)"

w[`~mu`] = Array(%id = 36893488148324030572)

(2.4)

Introducing anew this definition (2.4)  into the system,

"Define(w[~mu]=rhs(?),redo,quiet):"

NULL

In the proper referential, the velocity of the particle vanishes and the tridimensional acceleration is directed along the positive x-axis, denote its value by `#msub(mi("w"),mn("0"))`

Hence, proceeding to the relevant substitutions and introducing the corresponding definition into the system, the four-acceleration in the proper referential reads

  "Define(`w__o`[~mu]= subs(v(t)=`w__0`, v(t)=0,rhs(?)),quiet):"

w__o[`~mu`] = TensorArray(w__o[`~mu`])

w__o[`~mu`] = Array(%id = 36893488148076604940)

(2.5)

NULL

The differential equation solving the problem

 

NULL``

Everything is now set up for us to establish the differential equation that will solve our problem. It is at this juncture that we make use of the invariant condition stated in the introduction.

The relativistic invariant condition of uniform acceleration must lie in the constancy of a 4-scalar coinciding with `w__μ`*w^mu  in the proper reference frame.

We simply write the stated invariance of the four scalar (d*u^mu*(1/(d*s)))^2 thus:

w[mu]^2 = w__o[mu]^2

w[mu]*w[`~mu`] = w__o[mu]*w__o[`~mu`]

(3.1)

TensorArray(w[mu]*w[`~mu`] = w__o[mu]*w__o[`~mu`])

(diff(v(t), t))^2*c^2/(v(t)^2-c^2)^3 = -w__0^2/c^4

(3.2)

NULL

This gives us a first order differential equation for the velocity.

 

Solving the differential equation for the velocity and computation of the distance travelled

 

NULL

Assuming the proper reference frame is starting from rest, with its origin at that instant coinciding with the origin of the fixed reference frame, and travelling along the positive x-axis, we get successively,

NULL

dsolve({(diff(v(t), t))^2*c^2/(v(t)^2-c^2)^3 = -w__0^2/c^4, v(0) = 0})

v(t) = t*c*w__0/(t^2*w__0^2+c^2)^(1/2), v(t) = -t*c*w__0/(t^2*w__0^2+c^2)^(1/2)

(4.1)

NULL

As just explained, the motion being along the positive x-axis, we take the first expression.

[v(t) = t*c*w__0/(t^2*w__0^2+c^2)^(1/2), v(t) = -t*c*w__0/(t^2*w__0^2+c^2)^(1/2)][1]

v(t) = t*c*w__0/(t^2*w__0^2+c^2)^(1/2)

(4.2)

This can be rewritten thus

v(t) = w__0*t/sqrt(1+w__0^2*t^2/c^2)

v(t) = w__0*t/(1+w__0^2*t^2/c^2)^(1/2)

(4.3)

It is interesting to note that the ultimate speed reached is the speed of light, as it should be.

`assuming`([limit(v(t) = w__0*t/(1+w__0^2*t^2/c^2)^(1/2), t = infinity)], [w__0 > 0, c > 0])

limit(v(t), t = infinity) = c

(4.4)

NULL

The space travelled is simply

x(t) = Int(rhs(v(t) = w__0*t/(1+w__0^2*t^2/c^2)^(1/2)), t = 0 .. t)

x(t) = Int(w__0*t/(1+w__0^2*t^2/c^2)^(1/2), t = 0 .. t)

(4.5)

`assuming`([value(x(t) = Int(w__0*t/(1+w__0^2*t^2/c^2)^(1/2), t = 0 .. t))], [c > 0])

x(t) = c*((t^2*w__0^2+c^2)^(1/2)-c)/w__0

(4.6)

expand(x(t) = c*((t^2*w__0^2+c^2)^(1/2)-c)/w__0)

x(t) = c*(t^2*w__0^2+c^2)^(1/2)/w__0-c^2/w__0

(4.7)

This can be rewritten in the form

x(t) = c^2*(sqrt(1+w__0^2*t^2/c^2)-1)/w__0

x(t) = c^2*((1+w__0^2*t^2/c^2)^(1/2)-1)/w__0

(4.8)

NULL

The classical limit corresponds to an infinite velocity of light; this entails an instantaneous propagation of the interactions, as is conjectured in Newtonian mechanics.
The asymptotic development gives,

lhs(x(t) = c^2*((1+w__0^2*t^2/c^2)^(1/2)-1)/w__0) = asympt(rhs(x(t) = c^2*((1+w__0^2*t^2/c^2)^(1/2)-1)/w__0), c, 4)

x(t) = (1/2)*w__0*t^2+O(1/c^2)

(4.9)

As for the velocity, we get

lhs(v(t) = t*c*w__0/(t^2*w__0^2+c^2)^(1/2)) = asympt(rhs(v(t) = t*c*w__0/(t^2*w__0^2+c^2)^(1/2)), c, 2)

v(t) = t*w__0+O(1/c^2)

(4.10)

Thus, the classical laws are recovered.

NULL

Proper time

 

NULL

This quantity is given by "t'= ∫ dt sqrt(1-(v^(2))/(c^(2)))" the integral being  taken between the initial and final improper instants of time

Here the initial instant is the origin and we denote the final instant of time t.

NULL

`#mrow(mi("t"),mo("′"))` = Int(sqrt(1-rhs(v(t) = w__0*t/(1+w__0^2*t^2/c^2)^(1/2))^2/c^2), t = 0 .. t)

`#mrow(mi("t"),mo("′"))` = Int((1-w__0^2*t^2/((1+w__0^2*t^2/c^2)*c^2))^(1/2), t = 0 .. t)

(5.1)

Finally the proper time reads

`assuming`([value(`#mrow(mi("t"),mo("′"))` = Int((1-w__0^2*t^2/((1+w__0^2*t^2/c^2)*c^2))^(1/2), t = 0 .. t))], [w__0 > 0, c > 0, t > 0])

`#mrow(mi("t"),mo("′"))` = arcsinh(t*w__0/c)*c/w__0

(5.2)

When proc (t) options operator, arrow; infinity end proc, the proper time grows much more slowly than t according to the law

`assuming`([lhs(`#mrow(mi("t"),mo("′"))` = arcsinh(t*w__0/c)*c/w__0) = asympt(rhs(`#mrow(mi("t"),mo("′"))` = arcsinh(t*w__0/c)*c/w__0), t, 1)], [w__0 > 0, c > 0])

`#mrow(mi("t"),mo("′"))` = (ln(2*w__0/c)+ln(t))*c/w__0+O(1/t^2)

(5.3)

combine(`#mrow(mi("t"),mo("′"))` = (ln(2*w__0/c)+ln(t))*c/w__0+O(1/t^2), ln, symbolic)

`#mrow(mi("t"),mo("′"))` = ln(2*t*w__0/c)*c/w__0+O(1/t^2)

(5.4)

NULL

Evolution of the four-acceleration of the moving frame as observed from the fixed reference frame

 

NULL

To obtain the four-acceleration as a function of time, simply substitute for the 3-velocity (4.3)  in the 4-acceleration (2.4)

" simplify(subs(v(t) = w__0*t/(1+w__0^2*t^2/c^2)^(1/2),?),symbolic)"

w[`~mu`] = Array(%id = 36893488148142539108)

(6.1)

" w[t->infinity]^(  mu)=map(limit,rhs(?),t=infinity) assuming `w__0`>0,c>0"

`#msubsup(mi("w"),mrow(mi("t"),mo("→"),mo("∞")),mrow(mo("⁢"),mo("⁢"),mi("μ",fontstyle = "normal")))` = Array(%id = 36893488148142506460)

(6.2)

We observe that the non-vanishing components of the four-acceleration of the accelerating reference frame get infinite while those components in the moving reference frame keep their constant values . (2.5)

NULL

Evolution of the three-acceleration as observed from the fixed reference frame

 

NULL

This quantity is obtained simply by differentiating the velocity v(t)given by  with respect to the time t.

 

simplify(diff(v(t) = w__0*t/(1+w__0^2*t^2/c^2)^(1/2), t), size)

diff(v(t), t) = w__0/(1+w__0^2*t^2/c^2)^(3/2)

(7.1)

Here also, it is interesting to note that the three-acceleration tends to zero. This fact was somewhat unexpected.

map(limit, diff(v(t), t) = w__0/(1+w__0^2*t^2/c^2)^(3/2), t = infinity)

limit(diff(v(t), t), t = infinity) = 0

(7.2)

NULL

At the beginning of the motion, the acceleration should be w__0, as Newton's mechanics applies then

NULL

`assuming`([lhs(diff(v(t), t) = w__0/(1+w__0^2*t^2/c^2)^(3/2)) = series(rhs(diff(v(t), t) = w__0/(1+w__0^2*t^2/c^2)^(3/2)), t = 0, 2)], [c > 0])

diff(v(t), t) = series(w__0+O(t^2),t,2)

(7.3)

NULL

Justification of the name hyperbolic motion

 

NULL

Recall the expressions for x and diff(t(x), x)and obtain a parametric description of a curve, with diff(t(x), x)as parameter. This curve will turn out to be a hyperbola.

subs(x(t) = x, x(t) = c^2*((1+w__0^2*t^2/c^2)^(1/2)-1)/w__0)

x = c^2*((1+w__0^2*t^2/c^2)^(1/2)-1)/w__0

(8.1)

`#mrow(mi("t"),mo("′"))` = arcsinh(t*w__0/c)*c/w__0

`#mrow(mi("t"),mo("′"))` = arcsinh(t*w__0/c)*c/w__0

(8.2)

The idea is to express the variables x and t in terms of diff(t(x), x).

 

isolate(`#mrow(mi("t"),mo("′"))` = arcsinh(t*w__0/c)*c/w__0, t)

t = sinh(`#mrow(mi("t"),mo("′"))`*w__0/c)*c/w__0

(8.3)

subs(t = sinh(`#mrow(mi("t"),mo("′"))`*w__0/c)*c/w__0, x = c^2*((1+w__0^2*t^2/c^2)^(1/2)-1)/w__0)

x = c^2*((1+sinh(`#mrow(mi("t"),mo("′"))`*w__0/c)^2)^(1/2)-1)/w__0

(8.4)

`assuming`([simplify(x = c^2*((1+sinh(`#mrow(mi("t"),mo("′"))`*w__0/c)^2)^(1/2)-1)/w__0)], [positive])

x = c^2*(cosh(`#mrow(mi("t"),mo("′"))`*w__0/c)-1)/w__0

(8.5)

We now show that the equations (8.3) and (8.5) are parametric equations of a hyperbola with parameter the proper time diff(t(x), x)

 

Recall the hyperbolic trigonometric identity

cosh(`#mrow(mi("t"),mo("′"))`*w__0/c)^2-sinh(`#mrow(mi("t"),mo("′"))`*w__0/c)^2 = 1

cosh(`#mrow(mi("t"),mo("′"))`*w__0/c)^2-sinh(`#mrow(mi("t"),mo("′"))`*w__0/c)^2 = 1

(8.6)

Then isolating the sinh and the cosh from equations (8.3) and (8.5),

NULL

isolate(t = sinh(`#mrow(mi("t"),mo("′"))`*w__0/c)*c/w__0, sinh(`#mrow(mi("t"),mo("′"))`*w__0/c))

sinh(`#mrow(mi("t"),mo("′"))`*w__0/c) = t*w__0/c

(8.7)

isolate(x = c^2*(cosh(`#mrow(mi("t"),mo("′"))`*w__0/c)-1)/w__0, cosh(`#mrow(mi("t"),mo("′"))`*w__0/c))

cosh(`#mrow(mi("t"),mo("′"))`*w__0/c) = x*w__0/c^2+1

(8.8)

and substituting these in (8.6) , we get the looked-for Cartesian equation

 

subs(sinh(`#mrow(mi("t"),mo("′"))`*w__0/c) = t*w__0/c, cosh(`#mrow(mi("t"),mo("′"))`*w__0/c) = x*w__0/c^2+1, cosh(`#mrow(mi("t"),mo("′"))`*w__0/c)^2-sinh(`#mrow(mi("t"),mo("′"))`*w__0/c)^2 = 1)

(x*w__0/c^2+1)^2-w__0^2*t^2/c^2 = 1

(8.9)

NULL

This is the Cartesian equation of a hyperbola, hence the name hyperbolic motion

NULL

Reference

 

[1] Landau, L.D., and Lifshitz, E.M. The Classical Theory of Fields, Course of Theoretical Physics Volume 2, fourth revised English edition. Elsevier, 1975.

NULL

Download Uniformly_accelerated_motion.mw

This is a friendly reminder that the deadline for submissions for this year's Maple Conference Creative Works Exhibit is fast approaching!

If you are looking for inspiration, you can take a look at the writeup of the works that were featured last year in this write up in the most recent issue of Maple Transations.

Also, don't forget that you can also submit art made in Maple Learn for a special exhibit alongside the main gallery.

If you haven’t seen the posts already, the Maple Conference is coming up on the 2nd and 3rd of November! Last year’s art competition was very popular, so this year, not only are we holding the Maple Art and Creative Works Exhibit again, but we’ve decided to extend the art competition to include a Maple Learn Art Showcase!

You may be wondering what math art can be created in Maple Learn, and what the requirements are for the conference. Let’s address the first question first.

The best way to learn what kind of math art can be made is by taking a look at our Maple Learn Art document collection! This collection is in the Maple Learn document gallery, and includes art created by users with different levels of math and Maple Learn knowledge.

Many examples of art are shown in the collection, but take a look at this art piece, which shows a fun character made with functions!

                                          

We not only have static art, but animations as well. Take a look at this document, which shows an animated flower and bee, all created with math and Maple Learn.

 

Now for the conference requirements. The submission requirement date is October 14th 2022, and there’s only one criterion for submission:

  • Art must be created in Maple Learn, and submissions must include the Maple Learn document.

 

Feel free to include any extra information about yourself and your artwork directly in the document. You can share your submission by using the share icon in the top right of the Maple Learn UI. This will create a URL, which can be sent to gallery@maplesoft.com. Don’t forget to include your name in the emailed submission! Please contact us if you’re unsure about any of the criteria, or if you have any other questions!

It may seem overwhelming, but remember: submitting something gives you a chance to share your art with the world and not submitting removes that chance! If you'd like more information about the Maple Learn Art Showcase or the Maple Art and Creative Works Exhibit, please check out our page on submissions for the art gallery on the Maplesoft website, or check out this example submission. See you all next time!

Paulina Chin of Maplesoft and I are pleased to annouce Maple Transactions Volume 2 Issue 1, the Proceedings of the 2021 Maple Conference.

Articles can be found at mapletransactions.org

Some articles are written directly in Maple and are published via the Maple Cloud.  There is also a Demo Video by Michael Monagan of his new GCD code.  There are articles on Math Education, on Applications of Maple, on software, and on mathematics research.  We draw particular attention to the article by Veselin Jungic, 3M Teaching Fellow at SFU and a Fellow of the Canadian Math Society, on Indigenising mathematics.  We hope that, as members of the Maple Community, you find much of interest.

Best wishes,

Rob Corless, Editor-in-Chief

Maple Transactions

Forest of ethnomathematics and its root.  Colourful natural trees growing in a forest above ground while below ground is a colourful mathematical tree going to a single glowing root


 

New generalized unit vectors in Physics:-Vectors

 

The Physics:-Vectors package, written many years ago to teach Vector Analysis to 1st year undergrad students in Physics courses, introduces several things that are unique in computer algebra software. Briefly, this package has the ability to compute sums, dot and cross products, and differentiation with

• 

abstract vectors, like `#mover(mi("A"),mo("→"))` or `#mover(mi("A"),mo("→"))`(x, y, z), symbols or functions with an arrow on top that indicates to the system that they are vectors, not scalars;

• 

projected vectors of algebraic (non-matrix) type in any of the Cartesian, cylindrical or spherical basis and/or associated systems of coordinates, including for that purpose an implementation of the corresponding unit vectors of the three bases;

• 

abstract or projected differential operators that involve Nabla, Gradient, Divergence, Laplacian and Curl;

• 

inert vectors or vectorial differential operators, including related expansion of operations and simplification; 

• 

path, surface and volume vector integrals.

 

In addition to the above, the display is as in textbooks, the input resembles paper and pencil handwriting, and examples of the use of Physics:-Vectors in Vector Analysis are presented in the Physics,Examples page.

 

Download New_generalized_unit_vectors.mw

Edgardo S. Cheb-Terrab
Physics, Differential Equations and Mathematical Functions, Maplesoft

Einstein's principle of relativity

 

The main difference between Newtonian mechanics and the mechanics based on Einstein's principle of relativity is that in the latter the velocity of light, c, is the same in all inertial reference systems. Therefore, when comparing the velocity of an object measured in two reference systems 1 and 2 that are moving relative to each other, the Newtonian rule of addition of velocities, v__2_ = v__1_+v__R_, where v__R_ is the velocity of one system with respect to the other one, is not valid; if it were, the speed v__1_ and v__2_ of light in the systems 1 and 2 would not be the same. This introduces surprising conceptual consequences, and algebraic complications in the formulas relating the values of measurements, in the systems 1 and 2, of time, space and everything else that is related to that.

 

This post is thus about Einstein's principle of relativity and the consequences of the velocity of light being the same in all inertial reference systems. Although the topic is often considered advanced, the concepts, as shocking as they are, are easy to understand, and the algebra is still tractable in simple terms. The presentation, following Landau & Lifshitz [1], Chapter 1, is at a basic level, with no prerequisite expertise required, and illustrates well how to handle the basic algebraic aspects of special relativity using computer algebra.

 

Finally, it seems to me not useful to just present the algebra when the concepts behind Einstein's theory are straightforward and surprising. For that reason, the short sections 1 and 2 are all about these concepts, and the algebra only starts in section 3, with the Lorentz transformations (which was recently the topic of a Mapleprimes post at a more advanced level ). To reproduce the computations shown in this worksheet, please install the Maplesoft Physics Updates v.1314 or any subsequent version.

 

Download Einstens_principle_of_relativity.mw

Edgardo S. Cheb-Terrab
Physics, Differential Equations and Mathematical Functions, Maplesoft

 

Welcome back to another blog post, Maple Learn enthusiasts! Today we’re going to go through a concept and see what documents are available to help you learn the concept. What concept? Blood typing!

You may have gotten your blood tested before, but do you know the science behind blood types? Have you ever thought about it, even? Well, if not, you’re in the right place! Let’s take a look at some of the concepts you need to know before looking deeper into blood typing.

First, what are genotypes and phenotypes? Did you notice those terms had links attached to them? We have Maple Learn documents on this topic, shown below. Take a moment to read them over before we continue, but to summarize: A genotype is the genetic makeup within a trait, whereas a phenotype is the displayed trait. Another important term to recognize is allele – the specific variations of genes that are involved in the genotype.

The next thing to review is Punnett Squares, and the document is also shown below. Review this one too, to learn how to examine genetic combinations! Take a good look at the tables being used, as well, as an example of a creative use of a typically mathematical feature.

Now let’s finally dig into the blood types. Humans have 4 different blood types (excluding the Rhesus factor – but we won’t be talking about that today): A, B, AB, and O. A and B alleles are represented with an “I” with a superscript A or B, respectively. O is represented with “i”. Remember, a full genotype has two alleles, so someone with the blood type O would be represented as “ii” in their genotype. Can you read the Punnett Square below?

To extend your learning, take a look at our blood typing quiz! This quiz allows you to practice making Punnett Squares on paper, in order to figure out the likelihood of a phenotype (the blood type) given the genotype of the parents.

We hope you enjoyed the concept walkthrough! Please let us know if there are any other concepts you’d like to see explained through Maple Learn documents. Until next time!

Notation is one of the most important things to communicate with others in science. It is remarkable how many people use or do not use a computer algebra package just because of its notation. For those reasons, in the context of the Physics package, strong emphasis is put on using textbook notation as much as possible regarding input and output, including, for that purpose, as people here know, significant developments in Maple typesetting.

Still, for historical reasons, when using the Physics package, the labels used to refer to a coordinate system had been a single Capital Letter, as in X, Y, ...It was not possible to use, e.g. X', or x.

That has changed. Starting with the Maplesoft Physics Updates v.1308, any symbol can be used as a coordinate system label. The lines below demo this change.

 

Download new_coordinates_labels.mw

Edgardo S. Cheb-Terrab
Physics, Differential Equations and Mathematical Functions, Maplesoft

Welcome back to another document walkthrough! Today, I thought we’d take a look at a non-math example, like chemistry. The document we’ll be using is “Finding Average Atomic Mass”. Before we get too into it, I’d like to define some terms. Average atomic mass is defined as the weighted average mass of all isotopes of an element. An elemental isotope can be thought of as a “version” of the element – The same element at its core, but having different weight or other properties. This is due to having the same number of protons, but a different number of neutrons.

This document is, of course, about finding that average atomic mass. See the picture below for our problem, which states the element, the isotopes, and their separate masses and relative abundance.

The average atomic mass can then be calculated using sum notation. To calculate, take the weighted mean of the isotopes’ atomic masses, as shown in the overview section of the Average Atomic Mass document.

Once you’ve tried solving the problem yourself, take a look at the answer in group four, or one of the practice problems in group five. We have three examples on this topic (Average Atomic Mass Example 1, Average Atomic Mass Example 2, and Average Atomic Mass Example 3), so take a look at them all!

I hope you enjoyed learning just a bit of chemistry today, and let us know in the comments if there are any documents you’d specifically like to see explained, or any topics you’d like us to talk about!

 

Welcome back to another post on the Maple Learn Calculus collection! Previously on this series we looked at the Limit subcollection, and today we are going to look at the Derivative subcollection in the Maple Learn Document Gallery.

There are many different types of documents in this sub collection, so let’s take a look at one of them. We’ll start with the very first question people ask when learning about derivatives: What is a derivative?

 

This document starts us off with an example of f(x):=x2. The example provides the background information for the rest of the document, and a visualization with a slider.

Then, we define both the Geometric and Algebraic definition of a derivative. This allows us to understand the concept in two different ways, a very useful thing for students as they explore other topics within calculus.  

Finally, the document suggests two more documents for future learning: Derivatives: Notation, for more information on the notation used in derivatives, and the Formal Definition of a Derivative document, for more information on how derivatives are formally defined and derived. Make sure to check them out too!

Now, that’s just the start. We’ve got practice problems, definitions and visualizations of rules, information on points without derivatives, and much more. They’re useful for both new learning and as a refresher, so take a look!

We can’t wait to see you another time for when we dive into Derivative documents. Let us know after the Calculus collection showcase blog posts if there’s another collection you’d like to see showcased!

 

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