Ilias Kotsireas is a Professor and Director of the CARGO lab at Wilfrid Laurier University, in Waterloo Ontario.

Throughout my career as a teacher, I’ve had the opportunity to work with students from around the globe. I’ve been able to work with students in other countries, immersing myself in their culture and learning environment. This has allowed me to experience the differences in educational delivery first hand, and to assess how education is viewed in other parts of the world.

On more than one occasion, I’ve visited the city of Guangzhou, China, to teach summer and winter school courses, beginning in 2007 and most recently in 2015 and 2016. During this time, I have witnessed tremendous growth in the development of Chinese Universities, as well as Chinese culture as a whole.  For example, the two largest supercomputers in the world - according to the website www.top500.org  - are located in China.  Another indication of the scale of this extraordinary growth is the fact that China currently has more than 2,000 universities, government research facilities and laboratories.  Furthermore, China Central Television (CCTV) programs report that China is planning the creation of an additional 10 mega-cities, each comparable in size to Shanghai.

Ilias Kotserias stands with students and fellow professors at South China Normal University (SCNU) in Guangzhou, China.

Summer and winter school courses in China are incredibly intensive. Such courses can run for one or two weeks and include two lectures per day, one on the morning and one in the afternoon. A tremendous amount of material must be covered in a short amount of time to accommodate the entire course.

Overall, my experiences have shown that students in China are very enthusiastic about education. They are heavily engaged with the learning materials and often spend time with professors at the conclusion of a lecture to converse and ask questions about what they have just learned. Class sizes are significantly smaller and there is a lot of one-on-one time with students. Students in China take their studies very seriously; they are very focused and motivated to do well in their studies, and they bring a great deal of knowledge and curiosity to the classroom.  Professors cannot gloss over material or deliver a scripted lecture. Students hold professors accountable, and expect them to be knowledgeable and have a strong understanding of the material. They have a strong desire to learn and gain experiences and relevant skills that they can carry forward with them in their educational and professional careers.

Maplesoft graciously offered short term licenses to my students in China, so they could use Maple in the mathematics courses I was teaching. Using Maple allowed me to continue using an experimental approach to teaching that I use for my students back home in Canada.  This approach encourages students to start with simple experimentation that may also contain visual components, develop a plausible conjecture and subsequently attempt to solve it step by step.  This promotes a “learn by doing” paradigm that promotes active learning and helps students better understand key mathematical concepts. In a delightful episode, one Chinese student told me “I don’t understand your English accent,” but in teaching with Maple there are no accents I need to worry about!  Chinese students are inquisitive, respectful and conscientious; it is an absolutely gratifying experience working with them. 

I was first introduced to Maple when I was completing my Masters and Ph.D in France in the late 1990s. When I began teaching in 2001, I introduced Maple into my classrooms to improve my students’ ability to learn the materials, understand difficult concepts, and to create more sustainable engagement with them. Initially, it took some work to convince them to use Maple, as it was not among the conventional learning methods they were used to. Eventually students came to embrace Maple as a learning tool and I was able to use visual and interactive examples to engage them. With Maple, experimentation is at your fingertips and it allows me to incorporate an example-driven learning experience for my students.

It was a valuable experience to work with students from another culture and be able to engage them using the same method I use to engage my students back home. Maple is not only a powerful and convenient teaching tool, but it can also assist in bridging cultural gaps and creating a learning experience that is uniform across the globe.

At 3:00 PM EST on Thursday, December 15, Maplesoft hosted a momentous hour in my life, my "retirement party" ending my career at Maplesoft. It was a day I had planned some four years ago when I dropped to a lighter schedule, and a day my wife has been awaiting for six years.

Jim Cooper, CEO at Maplesoft, presented a very brief sketch of some milestones in my life, including my high school graduation in 1958, BA in 1963, MS in 1966, PhD in 1970, jobs at the University of Nebraska-Lincoln, Memorial University of Newfoundland, and the Rose-Hulman Institute of Technology. There was a picture of me taken from my high school graduation yearbook. There was a cake. There were kind words about my contributions to Maple, including "Clickable Calculus," the term and its meaning.

I was handed the microphone - I knew what I wanted to say. My wife was present in the gathering. I pointed to her and said that all the congratulations should go to her who had waited so patiently for my retirement for six years. I thanked Maplesoft and all its employees for nearly 14 of the best years of my life, for I have thoroughly enjoyed my return to Canada and my work (more like play) at Maplesoft. 

It's been a great opportunity to be part of the Maple experience, and now it's time for new ones. There'll be more woodworking in my basement woodshop where I make mostly noise and sawdust, some extra travel, more exercise and fresh air, long-delayed household projects, and whatever else my mate of 49 years asks.

But the best part of all is that I'll still have a connection to Maplesoft - I'll continue doing two webinars a month, will maintain and update much of the content I've created for Maple while at Maplesoft, and contribute additional content of relevance to the Maple community. 

Last week Michael Pisapia, Maplesoft European VP, attended the opening reception of Mathematics: The Winton Gallery at the Science Museum in London. Ahead of being open to the public on 8^th December, contributors and donors were invited to take a look behind the scenes of the new gallery, which explores how mathematicians, their tools and ideas have helped to shape the modern world over the last four hundred years.

The gallery is a spectacular space, designed by the world-renowned Zaha Hadid Architects, housing over a hundred artefacts of mathematical origin or significance. It is divided up into disciplines ranging from navigation to risk assessment, and gambling to architecture. Inspired by the Handley Page aircraft, the largest object on display, and suspended as the centrepiece, the gallery is laid out using principles of mathematics and physics. It follows the lines of airflow around it in a stunning display of imagined aerodynamics, brought to life using light and sculpture. You can learn more about its design in this video.

Guests at the reception enjoyed a specially commissioned piece of music from the Royal College of Music titled ‘Gugnunc’, named after the aircraft and inspired by the rhythms of Morse code and mathematical and mechanical processes, and performed at the centre of the gallery.

Of course any exhibit celebrating all things maths is of great interest to us here at Maplesoft, but this one especially so, since Mathematics: The Winton Gallery showcases the earliest available version of Maple.

A copy of Maple V, from 1997, sits in ‘The Power of Computers’ section of the Winton Gallery, in an exhibit which tells the story of the significant role played by mathematical software in improving the quality of mathematics education and research. Other objects in the section include a Calculating Machine from the Scientific Service circa 1939, a PDP-8 minicomputer from the 1960s, and part of Charles Babbage’s mid-19^th century analytical engine, intended as a high-powered mathematical calculator.

As many of you will remember, Maple V was a major milestone in the history of Maple, providing unparalleled interactivity, powerful symbolics and creative visualization in mathematical computation and modeling. For a walk down memory lane, check out Maple V: The Future of Mathematics (ca. 1994) on YouTube.

Seeing this copy of Maple finally in place in the exhibit marks the end of a long journey – and not just in the miles it travelled to arrive at the museum from its home in Canada. When we were first approached by the Science Museum for a donation of Maple, we launched a hunt to find not just the right copy of Maple with its box and manuals, but also artefacts that showcased the origin and history of Maple. It was a journey down memory lane for the inventors of Maple as well as the first few employees as they dug out old correspondences, photos, posters and other memorabilia that could be showcased. Today they can be proud of their contribution to this display at the Science Museum. 

Although the case of historic software packages is visually less impressive than many of the other items in the gallery, it certainly attracted plenty of attention as guests made their way in for the first time. 

For fans of Maple V - and there are many - it’s reassuring that the Science Museum are now entrusted with preserving not only the iconic packaging, but with telling the story of Maple’s history and marking its place in the evolution of mathematics and technology.

To learn more about Mathematics: The Winton Gallery, its highlights and architecture, visit http://www.sciencemuseum.org.uk/mathematics

To see the timeline of Maple’s evolution over the years, visit:  http://www.maplesoft.com/25anniversary/

On some platforms, my editor of choice has become the aptly named Sublime Text. Unfortunately, it does not seem to have built in syntax highlighting for the Maple programming language and so I set out to write some.  In the end, I wrote enough highlighting to keep me sane when looking at Maple source, but it could use a lot more work.  So in case anyone is interested I've put what I have in a Github repository: SublimeTextMaple

If you use Sublime Text, please download it and add your own enhacements and share in turn.

This MaplePrimes guest blog post is from Ian VanderBurgh, the Director of the Center for Education in Mathematics and Computing (CEMC) and a Lecturer in the Faculty of Mathematics at the University of Waterloo. He has been overseeing a project to develop online, interactive mathematics curriculum for high school students, and has been integral in the development of Möbius, Maplesoft's online courseware environment.

Start with one part interest in online education, add one part increased functionality for developing online content, and mix with one part increased focus in the media and elsewhere on mathematics education.  What does this produce?  The perfect time to create high-quality online resources to support learning and teaching in mathematics.

The Centre for Education in Mathematics and Computing (CEMC) at the University of Waterloo aims to increase interest, enjoyment, confidence, and ability in mathematics and computer science among learners and educators in Canada and internationally.  For more than fifty years, we have been working with teachers to support the important work that they do in the classroom.  When online courses rose to prominence several years ago, we felt that this gave us the perfect opportunity to create materials to better support the curriculum being taught across Canada and around the world.

The content for what we now call “Phase One” was planned: Advanced Functions (Pre-Calculus) as well as Calculus & Vectors.  These materials would support the education of students in their final year of secondary school, and also provide materials to reinforce concepts for students in STEM programs at the post-secondary level.

After deciding on the content, we needed a platform.  We knew that we needed one with exceptional mathematical capabilities.  Thus, we have been working hand-in-hand with Maplesoft ever since.

With content and platform established, the style began to take shape.  It is based around what one of my colleagues calls “the five Es”: Exposition (onscreen text with synchronized audio), Experimentation (worksheets where users can manipulate mathematical objects), Evaluation (re-generating quiz questions), Exercises (with answers and solutions), and Enrichment (application and extension problems and solutions).  Have a look at the materials and watch a video about the courseware.  After less than two years of “public life”, Phase One has received more than 2 million page views and usage is accelerating.

But why stop there?  Through the development of Phase One, all of the stakeholders realized that, while what we created was great, we needed better and more efficient development tools.  Thus, Möbius was born.  (In the meantime, the CEMC separately launched Phase Two of this ambitious initiative: resources in computer science to support the teaching and learning of programming concepts.)

Now, using the full capabilities of Möbius, we are developing Phase Three, a parallel set of resources to Phase One that will support mathematics at the Grade 7/8 level.  Why Grade 7/8?  We believe that these are very important years in education, that it is vital to future success in STEM disciplines that students flourish in these years, and that we should do whatever we can to support this.

What comes next?  Time will tell.  But, the CEMC will be there supporting mathematics and STEM education.  STEM disciplines will drive almost everything in the twenty-first century, and we have an obligation to do whatever we can to give young people every possible chance for success.

The 196 algorithm goes like this.  Start with an integer.  Reverse the digits.  Add the reversed number to the integer.  For most numbers, this eventually leads to a palendrome.  That is to say the number is equal to the reversed number.  I wrote a little Maple procedure to explore 196, the smallest number that will probrably never become a palendrome when put into the algorithm.

Let me know if you like my code.

Regards,
Matt

proc4.pdf

proc4.mw

http://mathworld.wolfram.com/196-Algorithm.html

The Saturday edition of our local newspaper (Waterloo Region Record) carries, as part of The PUZZLE Corner column, a weekly puzzle "STICKELERS" by Terry Stickels. Back on December 13, 2014, the puzzle was:

What number comes next?

1   4   18   96   600   4320   ?

The solution given was the number 35280, obtained by setting k = 1 in the general term k⋅k!.

On September 5, 2015, the column contained the puzzle:

What number comes next?

2  3  3  5  10  13  39  43  172  177  ?

The proposed solution was the number 885, obtained as a₁₀ from the recursion

where a₀ =2.

As a youngster, one of my uncles delighted in teasing me with a similar question for the sequence 36, 9, 50, 55, 62, 71, 79, 18, 20. Ignoring the fact that there is a missing entry between 9 and 50, the next member of the sequence is "Bay Parkway," which is what 22^nd Avenue is actually called in the Brooklyn neighborhood of my youth. These are subway stops on what was then called the West End line of the subway that went out to Stillwell Avenue in Coney Island.

Armed with the skepticism inspired by this provincial chestnut, I looked at both of these puzzles with the attitude that the "next number" could be anything I chose it to be. After all, a sequence is a mapping from the (nonnegative) integers to the reals, and unless the mapping is completely specified, the function values are not well defined.

Indeed, for the first puzzle, the polynomial f(x) interpolating the points

(0, 1), (1, 4), (2, 18), (3, 93), (4, 600), (5, 4320)

reproduces the first six members of the given sequence, and gives 18593 (not 35280) for f(7). In other words, the pattern k⋅k! is not a unique representation of the sequence, given just the first six members. The worksheet NextNumber derives the interpolating polynomial f and establishes that f(n) is an integer for every nonzero integer n.

Likewise, for the second puzzle, the polynomial g(x) interpolating the points

(1, 2) ,(2, 3) ,(3, 3) ,(4, 5) ,(5, 10) ,(6, 13) ,(7, 39) ,(8, 43), (9, 172) ,(10, 177)

reproduces the first ten members of the given sequence, and gives -7331(not 885) for g(11). Once again, the pattern proposed as the "solution" is not unique, given that the worksheet NextNumber contains both g(x) and a proof that for integer n, all values of g(n) are integers.

The upshot of these observations is that without some guarantee of uniqueness, questions like "what is the next number" are meaningless. It would be far better to pose such challenges with the words "Find a pattern for the given members of the following sequence" and warn that the function capturing that pattern might not be unique.

I leave it to the interested reader to prove or disprove the following conjecture: Interpolate the first n terms of either sequence. The interpolating polynomial p will reproduce these n terms, but for k>n, p(k) will differ from the corresponding member of the sequence determined by the stated patterns. (Results of limited numerical experiments are consistent with the truth of this conjecture.)

Attached: NextNumber.mw

Bruce Jenkins is President of Ora Research, an engineering research and advisory service. Maplesoft commissioned him to examine how systems-driven engineering practices are being integrated into the early stages of product development, the results of which are available in a free whitepaper entitled System-Level Physical Modeling and Simulation. In this series of blog posts, Mr. Jenkins discusses the results of his research.

This is the third entry in the series.

My last post, System-level physical modeling and simulation: Adoption drivers vs. adoption constraints, described my firm’s research project to investigate the contemporary state of adoption and application of systems modeling software technologies, and their attendant methods and work processes, in the engineering design of off-highway equipment and mining machinery.

In this project, I interviewed some half-dozen expert practitioners at leading manufacturers, including both engineering management and senior discipline leads, to identify key technological factors as well as business and competitive issues driving adoption and use of systems modeling at current levels.

After identifying present-day adoption drivers as well as current constraints on adoption, finally I sought to learn practitioners’ visions, strategies and best practices for accelerating and institutionalizing the implementation and usage of systems modeling tools and practices in their organizations.

I was strongly encouraged to find a wealth of avenues and opportunities for exploiting enterprise business drivers, current industry disruptions, and related internal realignments and change-management initiatives to help drive introduction—or proliferation—of these technologies and their associated new ways of working into engineering organizations:

• Systems modeling essential to compete by creating differentiated products
• Mechatronics revolution in off-highway equipment
• Industry downturns and disruptions create opportunities for disruptive innovation
• Opportunities to leverage change in underlying industry competitive dynamics
• Mining industry down-cycle creates opportunity to innovate, find new ways of working
• Some manufacturers are using current down-cycle in mining industry to change their product innovation strategy
• Strategies of manufacturers pursuing disruptive innovation
• Best odds are in companies with deep culture of continually inculcating new skills into their people, and rethinking methods and work processes
• Some managements willing to take radical corporate measures to replace old-thinking engineering staff with “systems thinkers”
• Downsizing in off-highway equipment manufacturers may push them to seek more systems-level value-add from their component suppliers
• New technology opportunities inside manufacturers ready to move more deeply into systems modeling
• Opportunities in new/emerging industries/companies without legacy investments in systems modeling tools and libraries
• Best practice for introducing systems modeling: start with work process, then bring in software
• Capitalizing on engineering’s leeway and autonomy in specifying systems modeling software compared with enterprise-standard CAD/PLM tools
• Systems modeling technology advances anticipated by practitioner advocates
• Improving software integration, interoperability, data interchange
• Improving co-simulation across domain tools
• Better, more complete FMI (Functional Mock-up Interface) implementation/compliance
• Higher-fidelity versions of FMI or similar

The white paper detailing the findings of this research is intended to offer guidance and advice for implementing change, as well as documentation to help convince colleagues, management and partners that new ways of working exist, and that the software technologies to support and enable them are available, accessible, and delivering payback and business advantage to forward-thinking engineering organizations today.

My hope is that this research finds utility as a practical, actionable aid for engineers and engineering management in helping their organizations to adopt and implement—or to strengthen and deepen—a simulation-led, systems-driven approach to product development.

You can download the full white paper reporting our findings here.

Bruce Jenkins, Ora Research
oraresearch.com

most effective built in operator code award goes to ppl that wrote the code for the union and intercect set operations for maple. Very important simple example below of  one of its applications.

When i work with algorithms, probably one of my most primary ports of enquiry (figuratively jeez skynet)  is to set up and if statement triggered to terminate the loop once the operations performed for any further cycles is INDEMPOTENT. this doesnt always mean your output is convergent in every case but it allows you to minimize the amount of time the cpu needs to collect data( ie the point at which it would produce that same set as it did in the last most loop)

Download idempotency.mwidempotency.mw

Walking into the big blue Maplesoft office on August 3^rd was a bit nerve wracking. I had no idea who anyone was, what to expect, or even what I would be doing. As I sat in the front hall waiting for someone to receive me, I remember thinking, “What have I gotten myself into?”. Despite my worries on that first day, interning at Maplesoft has been a great experience! I never knew that I would be able to learn so much about programming and working in a company in such a short amount of time. Although Maple was a programming language that was foreign to me a couple weeks ago, I feel like I’m relatively well versed in it now. Trying to learn a new language in this short timespan hasn’t been easy, but I think that I picked it up quickly, even if I’ve had my fair share of frustrations.

Chaos Game example on Rosetta Code

At Maplesoft, I’ve been contributing to the Rosetta Code project by writing short programs using Maple. The Rosetta Code project is dedicated to creating programming examples for many different tasks in different programming languages. My summer project has been to create solutions using Maple for as many tasks as possible and to post these to Rosetta Code; the goal being to have the list of tasks without Maple implementation shrink with each passing day. It’s nice to feel like I’m leaving a mark in this world, even if it is in such a small corner of the internet.

Flipping Bits example on Rosetta Code/MapleCloud

This internship, of course, came with its share of challenges. During my work on the Rosetta Code project, I posted solutions for a total of 38 tasks. Some of them were easy, but some of them took days to complete. On some days, I felt like I was on top of the world. Everything I made turned out great and I knew exactly how to tackle each problem. Other days were slower. I’ve spent ages just staring at a computer monitor trying to figure out just how on earth I was going to make this machine do what I wanted it to do! The 24 Game task was particularly hard, but also very educational. Through this task, I learned about modules, a concept previously unknown to me. I’m fairly sure that the 24 Game also took me the longest, whereas the Increment a numerical string task took me no time at all. Despite it being easy, the Increment a numerical string task wasn’t particularly fun; a bit of a challenge is required for something to be entertaining, after all. My personal favourite was the Fibonacci n-step number sequences task. It was the first really challenging task I encountered, and for after which the feeling of finally completing a task that I spent so long on, of finally overcoming that mountain, was extremely satisfying. Not all challenges end in satisfaction, however. I often found myself accidentally doing something that made the window freeze. I would close the program, then cry a bit on the inside when I realized I just lost the past half an hour’s worth of unsaved work. Nevertheless, I’m glad I got to face all these obstacles because they have made me more resilient and a better programmer.

The following is the code for the Fibonacci n-step number sequences task

numSequence := proc(initValues :: Array)

	local n, i, values;
	n := numelems(initValues);
	values := copy(initValues);
	for i from (n+1) to 15 do
		values(i) := add(values[i-n..i-1]);
	end do;
	return values;
end proc:
 
initValues := Array([1]):
for i from 2 to 10 do
	initValues(i) := add(initValues):
	printf ("nacci(%d): %a\n", i, convert(numSequence(initValues), list));
end do:
printf ("lucas: %a\n", convert(numSequence(Array([2, 1])), list));

Maple was a great software to program with and a fairly straightforward language to learn. Having previously programmed in Java, I found Maple similar enough that transitioning wasn’t too difficult. In fact, every once in a while when I didn`t know what to do for a task, I would take a look at the Java example in Rosetta Code and it would point me in a direction or give me some hints. While the two languages are similar, there are still many differences. For example, I liked the fact that in Maple, lists started at an index of 1 rather than 0 and arrays could an arbitrary starting index. Although it was different from what I was used to, I found that it made many things much less confusing. Another thing I liked was that the for loop syntax was very simple. I never once had to run through in my head how many times something would loop for. There were such a wide variety of commands in Maple. There was a command for practically anything, and if you knew that it existed and how to use it, then so much power could be at your fingertips. This is where the help system came in extremely handy. With a single search you might find that the solution to the exact problem you were trying to solve already existed as a Maple command. I always had a help window open when I was using Maple.

Multiplication Tables example on Rosetta Code

Spending my summer coding at Maplesoft has been fun, sometimes challenging, but an overall rewarding experience. Through contributing to the Rosetta Code project, I’ve learned so much about computer programming, and it certainly made the 45 minute drive out to Waterloo worth it!

Yili Xu,
Maplesoft SHAD Intern

Bruce Jenkins is President of Ora Research, an engineering research and advisory service. Maplesoft commissioned him to examine how systems-driven engineering practices are being integrated into the early stages of product development, the results of which are available in a free whitepaper entitled System-Level Physical Modeling and Simulation. In this series of blog posts, Mr. Jenkins discusses the results of his research.

This is the second entry in the series.

My last post, Strategies for accelerating the move to simulation-led, systems-driven engineering, described my firm’s research project to investigate the contemporary state of adoption and application of systems modeling software technologies, and their attendant methods and work processes, in the engineering design of off-highway equipment and mining machinery.

Adoption drivers

In this project, I conducted in-depth, structured but open-ended interviews with some half-dozen expert practitioners at leading manufacturers, including both engineering management and senior discipline leads. These interviews identified the following key technological factors as well as business and competitive issues driving adoption and use of systems modeling tools and methods at current levels:

• Fuel economy and emissions mandates, powertrain electrification and autonomous operation requirements
• Software’s ability to drive down product cost of ownership and delivery times
• Traditional development processes often fail to surface system-level issues until fabrication or assembly, or even until operational deployment
• Detailed analysis tools such as FEA and CFD focus on behaviors at the component level, and are not optimal for studies of the complete system
• Engineering departments/groups enjoy greater freedom in systems modeling software selection and purchase decisions than in enterprise-controlled CAD/PDM/PLM decisions
• Good C/VP-level visibility of systems modeling tools, especially in off-highway equipment

Adoption constraints

At the same time, there was widespread agreement among all the experts interviewed that these tools and methods are not being brought to bear with anywhere near the breadth or depth that practitioner advocates would like, and that they believe would be greatly beneficial to their organizations and industries.

In probing why this is, the interviews revealed an array of factors constraining broader adoption at present. These range from legacy engineering culture issues, through human resource limitations and constraints imposed by business models and corporate cultures, to entrenched shortcomings in how long-established systems modeling software toolsets have been deployed and applied to the product development process:

• Legacy engineering culture constraints
• Conservatism of mining machinery product development culture
• Engineering practices in long-standardized industries grounded in handbook formulas and rules of thumb
• Perceived lack of time in schedule to do systems modeling
• Human resource constraints
• Low availability of engineers with systems modeling skills
• Shortage of engineers trained in systems thinking
• Legacy engineering processes compound shortage of systems-thinking engineers
• Industry downturns put constraints on professional staff development
• Business-model and corporate-culture constraints
• Culture of seeking to mitigate cost and risk by staying with legacy designs instead of advancing and innovating the product
• Corporate awareness of need to innovate in mining machinery gets stifled at engineering level
• Low C/VP-level visibility of systems modeling tools in mining machinery
• Engineering organization constraints on innovating/modernizing their systems modeling technology infrastructure
• Power users wedded to legacy systems modeling tools
• Weak integration at many/most points of the engineering digital toolset chain
• Implementing systems modeling software as a sales configuration/costing aid seen as taking too much time

My next post will detail practitioners’ visions, strategies and best practices for accelerating and institutionalizing the implementation and usage of systems modeling tools and practices in their organizations.

You can download the full white paper reporting our findings here.

Bruce Jenkins, Ora Research
oraresearch.com

In order to change Maple for the better, I use to submit SCRs. However, as i was kindly
informed by Bryon (a copy of his e-letter on demand), MaplePrimes are under reconstruction and do not
work properly. At least my three messages sent through the Contact button were lost.
I have  unsuccessfully tried to reach beta.maplesoft.com (see the result of ping in the screen screen.29.08.16.docx).
Please, help me!

Bruce Jenkins is President of Ora Research, an engineering research and advisory service. Maplesoft commissioned him to examine how systems-driven engineering practices are being integrated into the early stages of product development, the results of which are available in a free whitepaper entitled System-Level Physical Modeling and Simulation. In the coming weeks, Mr. Jenkins will discuss the results of his research in a series of blog posts.

This is the first entry in the series.

Discussions of how to bring simulation to bear starting in the early stages of product development have become commonplace today. Driving these discussions, I believe, is growing recognition that engineering design in general, and conceptual and preliminary engineering in particular, face unprecedented pressures to move beyond the intuition-based, guess-and-correct methods that have long dominated product development practices in discrete manufacturing. To continue meeting their enterprises’ strategic business imperatives, engineering organizations must move more deeply into applying all the capabilities for systematic, rational, rapid design development, exploration and optimization available from today’s simulation software technologies.

Unfortunately, discussions of how to simulate early still fixate all too often on 3D CAE methods such as finite element analysis and computational fluid dynamics. This reveals a widespread dearth of awareness and understanding—compounded by some fear, intimidation and avoidance—of system-level physical modeling and simulation software. This technology empowers engineers and engineering teams to begin studying, exploring and optimizing designs in the beginning stages of projects—when product geometry is seldom available for 3D CAE, but when informed engineering decision-making can have its strongest impact and leverage on product development outcomes. Then, properly applied, systems modeling tools can help engineering teams maintain visibility and control at the subsystems, systems and whole-product levels as the design evolves through development, integration, optimization and validation.

As part of my ongoing research and reporting intended to help remedy the low awareness and substantial under-utilization of system-level physical modeling software in too many manufacturing industries today, earlier this year I produced a white paper, “System-Level Physical Modeling and Simulation: Strategies for Accelerating the Move to Simulation-Led, Systems-Driven Engineering in Off-Highway Equipment and Mining Machinery.” The project that resulted in this white paper originated during a technology briefing I received in late 2015 from Maplesoft. The company had noticed my commentary in industry and trade publications expressing the views set out above, and approached me to explore what they saw as shared perspectives.

From these discussions, I proposed that Maplesoft commission me to further investigate these issues through primary research among expert practitioners and engineering management, with emphasis on the off-highway equipment and mining machinery industries. In this research, focused not on software-brand-specific factors but instead on industry-wide issues, I interviewed users of a broad range of systems modeling software products including Dassault Systèmes’ Dymola, Maplesoft’s MapleSim, The MathWorks’ Simulink, Siemens PLM’s LMS Imagine.Lab Amesim, and the Modelica tools and libraries from various providers. Interviewees were drawn from manufacturers of off-highway equipment and mining machinery as well as some makers of materials handling machinery.

At the outset, I worked with Maplesoft to define the project methodology. My firm, Ora Research, then executed the interviews, analyzed the findings and developed the white paper independently of input from Maplesoft. That said, I believe the findings of this project strongly support and validate Maplesoft’s vision and strategy for what it calls model-driven innovation. You can download the white paper here.

Bruce Jenkins, Ora Research
oraresearch.com

Bruce Jenkins is President of Ora Research, an engineering research and advisory service. Maplesoft commissioned him to examine how systems-driven engineering practices are being integrated into the early stages of product development, the results of which are available in a free whitepaper entitled System-Level Physical Modeling and Simulation. In the coming weeks, Mr. Jenkins will discuss the results of his research in a series of blog posts.

This is the first entry in the series.

Discussions of how to bring simulation to bear starting in the early stages of product development have become commonplace today. Driving these discussions, I believe, is growing recognition that engineering design in general, and conceptual and preliminary engineering in particular, face unprecedented pressures to move beyond the intuition-based, guess-and-correct methods that have long dominated product development practices in discrete manufacturing. To continue meeting their enterprises’ strategic business imperatives, engineering organizations must move more deeply into applying all the capabilities for systematic, rational, rapid design development, exploration and optimization available from today’s simulation software technologies.

Unfortunately, discussions of how to simulate early still fixate all too often on 3D CAE methods such as finite element analysis and computational fluid dynamics. This reveals a widespread dearth of awareness and understanding—compounded by some fear, intimidation and avoidance—of system-level physical modeling and simulation software. This technology empowers engineers and engineering teams to begin studying, exploring and optimizing designs in the beginning stages of projects—when product geometry is seldom available for 3D CAE, but when informed engineering decision-making can have its strongest impact and leverage on product development outcomes. Then, properly applied, systems modeling tools can help engineering teams maintain visibility and control at the subsystems, systems and whole-product levels as the design evolves through development, integration, optimization and validation.

As part of my ongoing research and reporting intended to help remedy the low awareness and substantial under-utilization of system-level physical modeling software in too many manufacturing industries today, earlier this year I produced a white paper, “System-Level Physical Modeling and Simulation: Strategies for Accelerating the Move to Simulation-Led, Systems-Driven Engineering in Off-Highway Equipment and Mining Machinery.” The project that resulted in this white paper originated during a technology briefing I received in late 2015 from Maplesoft. The company had noticed my commentary in industry and trade publications expressing the views set out above, and approached me to explore what they saw as shared perspectives.

From these discussions, I proposed that Maplesoft commission me to further investigate these issues through primary research among expert practitioners and engineering management, with emphasis on the off-highway equipment and mining machinery industries. In this research, focused not on software-brand-specific factors but instead on industry-wide issues, I interviewed users of a broad range of systems modeling software products including Dassault Systèmes’ Dymola, Maplesoft’s MapleSim, The MathWorks’ Simulink, Siemens PLM’s LMS Imagine.Lab Amesim, and the Modelica tools and libraries from various providers. Interviewees were drawn from manufacturers of off-highway equipment and mining machinery as well as some makers of materials handling machinery.

At the outset, I worked with Maplesoft to define the project methodology. My firm, Ora Research, then executed the interviews, analyzed the findings and developed the white paper independently of input from Maplesoft. That said, I believe the findings of this project strongly support and validate Maplesoft’s vision and strategy for what it calls model-driven innovation. You can download the white paper here.

Bruce Jenkins, Ora Research
oraresearch.com

Download EXPONENTIAL_INTEGRAL_ODD_ZETA_SERIES_APPROXIMATION.mw