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Data Analysis and Visualization

Computation & Analysis

Protecting NHS Patients with the Wolfram Language

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?"
Computation & Analysis

Edit Your NaNoWriMo Novel with the Wolfram Language

If you're like many of us at Wolfram, you probably know that November was National Novel Writing Month (NaNoWriMo). Maybe you even spent the past few weeks feverishly writing, pounding out that coming-of-age story about a lonely space dragon that you've been talking about for years. Congratulations! Now what? Revisions, of course! And we, the kindly Wolfram Blog Team, are here to get you through your revisions with a little help from the Wolfram Language.
Computation & Analysis

New in the Wolfram Language: FeatureExtraction

Two years ago, we introduced the first high-level machine learning functions of the Wolfram Language, Classify and Predict. Since then, we have been creating a set of automatic machine learning functionalities (ClusterClassify, DimensionReduction, etc.). Today, I am happy to present a new function called FeatureExtraction that deals with another important machine learning task: extracting features from data. Unlike Classify and Predict, which follow the supervised learning paradigm, FeatureExtraction belongs to the unsupervised learning paradigm, meaning that the data to learn from is given as a set of unlabeled examples (i.e. without an input -> output relation). The main goal of FeatureExtraction is to transform these examples into numeric vectors (often called feature vectors). For example, let's apply FeatureExtraction to a simple dataset:
Announcements & Events

Launching Wolfram Player for iOS

UPDATE:

Wolfram Player for iOS is out of beta! You can download it from the App Store today. Learn more in this blog post. It's been a long road. To some degree, we've been working on a Wolfram notebook front end for iOS for about six years now. And in the process, we've learned a lot about notebook front ends, a thing we already knew a lot about. Let's rewind the tape a bit and review.
Announcements & Events

Announcing Wolfram Enterprise Private Cloud

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.
Education & Academic

How to Teach Computational Thinking

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 traditional mathematical thinking to kids. But looking to the future, this pales in comparison to the importance of […]

Computation & Analysis

Rolling Bearings: Modeling and Analysis in Wolfram SystemModeler

Background

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.