March 10, 2017 — Jeffrey Bryant, Research Programmer, Wolfram|Alpha Scientific Content
In Mathematica 10, we introduced support for anatomical structures in EntityValue, which included, among many other things, a “Graphics3D” property that returns a 3D model of the anatomical structure in question. We also styled the models and aligned them with the concepts in the Unified Medical Language System (UMLS).
March 2, 2017 — Håkan Wettergren, Applications Engineer, SystemModeler (MathCore)
Until now, it has been difficult for the average engineer to perform simple vibration analysis. The initial cost for simple equipment, including software, may be several thousand dollars—and it is not unusual for advanced equipment and software to cost ten times as much. Normally, a vibration specialist starts an investigation with a hammer impact test. An accelerometer is mounted on a structure, and a special impact hammer is used to excite the structure at several locations in the simplest and most common form of hammer impact testing. The accelerometer and hammer-force signals are recorded. Modal analysis is then used to get a preliminary understanding of the behavior of the system. The minimum equipment requirements for such a test are an accelerometer, an impact hammer, amplifiers, a signal recorder and analysis software.
I’ve figured out how to use the Wolfram Language on my smartphone to sample and analyze machine vibration and noise, and to perform surprisingly good vibration analysis. I’ll show you how, and give you some simple Wolfram Language code to get you started.
January 13, 2017 — Nick Lariviere, Kernel Developer, Core Mathematica Engineering
For the past couple of years, I’ve been playing with, collecting and analyzing data from used car auctions in my free time with an automotive journalist named Steve Lang to try and get an idea of what the used car market looks like in terms of long-term vehicle reliability. I figured it was about time that I showed off some of the ways that the Wolfram Language has allowed us to parse through information on over one million vehicles (and counting).
December 16, 2016 — Robert Cook, Senior Consultant, Wolfram Technical Services
The UK’s National Health Service (NHS) is in crisis. With a current budget of just over £100 billion, the NHS predicts a £30 billion funding gap by 2020 or 2021 unless there is radical action. A key part of this is addressing how the NHS can predict and prevent harm well in advance and deliver a “digital healthcare transformation” to their frontline services, utilizing vast quantities of data to make informed and insightful decisions.
This is where Wolfram comes in. Our UK-based Technical Services Team worked with the British NHS to help solve a specific problem facing the NHS—one many organizations will recognize: data sitting in siloed databases, with limited analysis algorithms on offer. They wanted to see if it was possible to pull together multiple data sources, combining off-the-shelf clinical databases with the hospital trusts’ bespoke offerings and mine them for signals. We set out to help them answer questions like “Can the number of slips, trips and falls in hospitals be reduced?”
November 9, 2016 — Christopher Carlson, Senior User Interface Developer, User Interfaces
Could you fit the code for a fully functional game of Pong into a single tweet? One that gives you more points the more you take your chances in letting the “ball” escape? Philip Maymin did, and took first prize with that submission in the One-Liner Competition held at this year’s Wolfram Technology Conference.
Participants in the competition submit 128 or fewer tweetable characters of Wolfram Language code to perform the most impressive computation they can dream up. We had a bumper crop of entries this year that showed the surprising power of the Wolfram Language. You might think that after decades of experience creating and developing with the Wolfram Language, we at Wolfram Research would have seen and thought of it all. But every year our conference attendees surprise us. Read on to see the amazing effects you can achieve with a tweet of Wolfram Language code.
Amy Friedman: “The Song Titles” (110 characters)
November 4, 2016 — Zach Littrell, Technical Content Writer, Technical Communications and Strategy Group
Here are just a handful of things I heard while attending my first Wolfram Technology Conference:
- “We had a nearly 4-billion-time speedup on this code example.”
- “We’ve worked together for over 9 years, and now we’re finally meeting!”
- “Coding in the Wolfram Language is like collaborating with 200 or 300 experts.”
- “You can turn financial data into rap music. Instead, how about we turn rap music into financial data?”
As a first-timer from the Wolfram Blog Team attending the Technology Conference, I wanted to share with you some of the highlights for me—making new friends, watching Wolfram Language experts code and seeing what the Wolfram family has been up to around the world this past year.
October 27, 2016 — John Moore, Marketing and Technical Content Team Lead
Software engineer and longtime Mathematica user Chad Slaughter uses the Wolfram Language to facilitate interdepartmental communication during software development. While most programming languages are designed to do one thing particularly well, developers like Slaughter often find that the Wolfram Language is more versatile: “With traditional C++, in order to develop a program, it’s going to take several hundred lines of code to do anything interesting. With Mathematica, I can do something interesting in less than five lines of code.”
August 26, 2016 — Zach Littrell, Technical Content Writer, Technical Communications and Strategy Group
We are constantly surprised by what fascinating applications and topics Wolfram Language experts are writing about, and we’re happy to again share with you some of these amazing authors’ works. With topics ranging from learning to use the Wolfram Language on a Raspberry Pi to a groundbreaking book with a novel approach to calculations, you are bound to find a publication perfect for your interests.
August 22, 2016 — Ishwarya Vardhani, Educational Partnerships
Are you a teacher who’s been asked “Why am I learning this?”, “How is this going to help me in real life?” and other variations of this question by your students? I know that I faced this when I was teaching, and it can be tough to provide a satisfactory response. However, being able to address this issue is critical in the classroom. We believe that Wolfram|Alpha provides one way to do so.
The Wolfram Knowledgebase, our ever-growing repository of curated computable data, gives you instant access to trillions of data elements across thousands of domains. With Wolfram|Alpha, you can query these data points using natural language (plain English) right in your classroom.
By using real-world data, students have the opportunity to direct their learning toward areas that they care about. In the economics classroom, you can discuss GDP using data about real countries, data that is current and citable. Explore Wolfram|Alpha’s trove of socioeconomic data that will open multiple areas of inquiry in the classroom. A wonderful side effect that I’ve found with using a tool like Alpha is that it also teaches you to pose queries intelligently. Being able to carefully construct a problem is an integral step in the process of thinking critically.
Join us for a special training event on August 24 to learn more about using Wolfram|Alpha in the classroom. This session in the Wolfram|Alpha for Educators: Webinar Training Series will focus on the economics classroom. Previous sessions in this series focused on calculus and physics classrooms, and you can watch our past event recordings online.
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: