September 19, 2016 — Conrad Wolfram, Strategic Director
Today I’m pleased to announce Wolfram Enterprise Private Cloud (EPC), which takes the unique benefits of the Wolfram technology stack—ultimate computation, integrated language and deployment—and makes them available in a centralized, private, secure enterprise solution.
In essence, EPC enables you to put computation at the heart of your infrastructure and in turn deliver a complete enterprise computation solution for your organization.
September 16, 2016 — Greg Hurst, Kernel Developer, Mathematica Algorithm R&D
Thirty-nine students from seven different countries attended our camp at Bentley University this summer. Students arrived at camp with some programming experience, but most had little or no familiarity with the Wolfram Language. Despite this, in nine short days they were all able to complete amazing projects.
September 7, 2016 — Stephen Wolfram
The Computational Future
Computational thinking is going to be a defining feature of the future—and it’s an incredibly important thing to be teaching to kids today. There’s always lots of discussion (and concern) about how to teach mathematical thinking to kids. But looking to the future, this pales in comparison to the importance of teaching computational thinking. Yes, there’s a certain amount of mathematical thinking that’s needed in everyday life, and in many careers. But computational thinking is going to be needed everywhere. And doing it well is going to be a key to success in almost all future careers.
Doctors, lawyers, teachers, farmers, whatever. The future of all these professions will be full of computational thinking. Whether it’s sensor-based medicine, computational contracts, education analytics or computational agriculture—success is going to rely on being able to do computational thinking well.
I’ve noticed an interesting trend. Pick any field X, from archeology to zoology. There either is now a “computational X” or there soon will be. And it’s widely viewed as the future of the field.
September 1, 2016 — Håkan Wettergren, Applications Engineer, SystemModeler (MathCore)
Explore the contents of this article with a free Wolfram SystemModeler trial.Rolling bearings are one of the most common machine elements today. Almost all mechanisms with a rotational part, whether electrical toothbrushes, a computer hard drive or a washing machine, have one or more rolling bearings. In bicycles and especially in cars, there are a lot of rolling bearings, typically 100–150. Bearings are crucial—and their failure can be catastrophic—in development-pushing applications such as railroad wheelsets and, lately, large wind turbine generators. The Swedish bearing manufacturer SKF estimates that the global rolling bearing market volume in 2014 reached between 330 and 340 billion bearings.
Rolling bearings are named after their shapes—for instance, cylindrical roller bearings, tapered roller bearings and spherical roller bearings. Radial deep-groove ball bearings are the most common rolling bearing type, accounting for almost 30% of the world bearing demand. The most common roller bearing type (a subtype of a rolling bearing) is the tapered roller bearing, accounting for about 20% of the world bearing market.
With so many bearings installed every year, the calculations in the design process, manufacturing quality, operation environment, etc. have improved over time. Today, bearings often last as long as the product in which they are mounted. Not that long ago, you would have needed to change the bearings in a car’s gearbox or wheel bearing several times during that car’s lifetime. You might also have needed to change the bearings in a bicycle, kitchen fan or lawn mower.
For most applications, the basic traditional bearing design concept works fine. However, for more complex multidomain systems or more advanced loads, it may be necessary to use a more advanced design software. Wolfram SystemModeler has been used in advanced multidomain bearing investigations for more than 14 years. The accuracy of the rolling bearing element forces and Hertzian contact stresses are the same as the software from the largest bearing manufacturers. However, SystemModeler provides the possibilities to also model the dynamics of the nonlinear and multidomain surroundings, which give the understanding necessary for solving the problems of much more complex systems. The simulation time for models developed in SystemModeler is also shorter than comparable approaches.
May 6, 2016 — Silvia Hao, Consultant, Technical Communications and Strategy Group
Stippling is a kind of drawing style using only points to mimic lines, edges, and grayscale. The entire drawing consists only of dots on a white background. The density of the points gives the impression of grayscale shading.
Back in 1510, stippling was first invented as an engraving technique, and then became popular in many fields because it requires just one color of ink.
Here is a photo of a fine example taken from an exhibition of lithography and copperplate art (the Centenary of European Engraving Exhibition held at the Hubei Museum of Art in March 2015; in case you’re curious, here is the museum’s official page in English).
April 21, 2016 — Jofre Espigule-Pons, Consultant, Technical Communications and Strategy Group
Putting some color in Shakespeare’s tragedies with the Wolfram Language
After four hundred years, Shakespeare’s works are still highly present in our culture. He mastered the English language as never before, and he deeply understood the emotions of the human mind.
Have you ever explored Shakespeare’s texts from the perspective of a data scientist? Wolfram technologies can provide you with new insights into the semantics and statistical analysis of Shakespeare’s plays and the social networks of their characters.
William Shakespeare (April 26, 1564 (baptized)–April 23, 1616) is considered by many to be the greatest writer of the English language. He wrote 154 sonnets, 38 plays (divided into three main groups: comedy, history, and tragedy), and 4 long narrative poems.
April 15, 2016 — Eila Stiegler, Quality Analysis Manager, Wolfram|Alpha Quality Analysis
It’s four months into the new year. Spring is here. Well, so they say. And if the temperatures do not convince you, the influx of the number of runners on our roads definitely should. I have always loved running. Despite the fact that during each mile I complain about various combinations of the weather, the mileage, and my general state of mind, I met up with 37,000 other runners for the Chicago Marathon on October 11, 2015. As it turns out, this single event makes for a great example to explore what the Wolfram Language can do with larger datasets. The data we are using below is available on the Chicago Marathon results website.
This marathon is one of the six Abbott World Marathon Majors: the Tokyo, Boston, Virgin Money London, BMW Berlin, Bank of America Chicago, and TCS New York City marathons. If you are looking for things to add to your bucket list, I believe these are great candidates. Given the international appeal, let’s have a look at the runners’ nationalities and their travel paths. Our GeoGraphics functionality easily enables us to do so. Clearly many people traveled very far to participate:
March 2, 2016 — Michael Trott, Chief Scientist
An investigation of the golden ratio’s appearance in the position of human faces in paintings and photographs.
There is a vast amount of literature on the appearance of the golden ratio in nature, in physiology and psychology, and in human artifacts (see this page on the golden ratio; these articles on the golden ratio in art, in nature, and in the human body; and this paper on the structure of the creative process in science and art). In the past thirty years, there has been increasing skepticism about the prevalence of the golden ratio in these domains. Earlier studies have been revisited or redone. See, for example, Foutakis, Markowsky on Greek temples, Foster et al., Holland, Benjafield, and Svobodova et al. for human physiology.
In my last blog, I analyzed the aspect ratios of more than one million old and new paintings. Based on psychological experiments from the second half of the nineteenth century, especially by Fechner in the 1870s, one would expect many paintings to have a height-to-width ratio equal to the golden ratio or its inverse. But the large sets of paintings analyzed did not confirm such a conjecture.
While we did not find the expected prevalence of the golden ratio in external measurements of paintings, maybe looking “inside” will show signs of the golden ratio (or its inverse)?
In today’s blog, we will analyze collections of paintings, photographs, and magazine covers that feature human faces. We will also analyze where human faces appear in a few selected movies.
February 26, 2016 — Emily Suess, Technical Writer, Technical Communications and Strategy Group
Kip Thorne, physicist, New York Times bestselling author, and professor emeritus at Caltech, ignited fans’ passion for science through his work on the movie Interstellar. The sci-fi adventure won the 2015 Academy Award for Best Visual Effects, and the first cuts of some of those stunning visuals were created with Mathematica and the Wolfram Language.
“Mathematica was my way of testing whether or not I had the equations right,” says Thorne, whose computational approach to producing images led to publication in the American Journal of Physics and Classical and Quantum Gravity.
February 3, 2016 — Bernat Espigulé-Pons, Consultant, Technical Communications and Strategy Group
When I hear about something like January’s United States blizzard, I remember the day I was hit by the discovery of an infinitely large family of Koch-like snowflakes.
The Koch snowflake (shown below) is a popular mathematical curve and one of the earliest fractal curves to have been described. It’s easy to understand because you can construct it by starting with a regular hexagon, removing the inner third of each side, building an equilateral triangle at the location where the side was removed, and then repeating the process indefinitely:
If you isolate the hexagon’s lower side in the process above you’ll get the Koch curve, described in a 1904 paper by Helge von Koch (1870–1924). It has a long history that goes back way before the age of computer graphics. See, for example, this handmade drawing by the French mathematician Paul Lévy (1886–1971):