The dynamic nature of air quality chemistry and transport makes it difficult to identify the mixture of air pollutants for a region. In this study of air quality in the Houston metropolitan area we apply dynamic principal component analysis (DPCA) to a normalized multivariate time series of daily concentration measurements of five pollutants (O3, CO, NO2, SO2, PM2.5) from January 1, 2009 through December 31, 2011 for each of the 24 hours in a day. The resulting dynamic components are examined by hour across days for the 3 year period. Diurnal and seasonal patterns are revealed underlining times when DPCA performs best and two principal components (PCs) explain most variability in the multivariate series. DPCA is shown to be superior to static principal component analysis (PCA) in discovery of linear relations among transformed pollutant measurements. DPCA captures the time-dependent correlation structure of the underlying pollutants recorded at up to 34 monitoring sites in the region. In winter mornings the first principal component (PC1) (mainly CO and NO2) explains up to 70% of variability. Augmenting with the second principal component (PC2) (mainly driven by SO2) the explained variability rises to 90%. In the afternoon, O3 gains prominence in the second principal component. The seasonal profile of PCs' contribution to variance loses its distinction in the afternoon, yet cumulatively PC1 and PC2 still explain up to 65% of variability in ambient air data. DPCA provides a strategy for identifying the changing air quality profile for the region studied.

The multidisciplinary project fits into the growing field of soft robotics that coalesced with the 2013 founding of the peer-reviewed Soft Robotics Journal, based at Tufts. The Boston region is a hub for research into the moving machines made of flexible materials that can change shape and size, making them useful for surgery and other complex environments.

For the past year, Tara Hutchinson has been trying to figure out what will happen to a tall building made from thin steel beams when "the big one" hits. To do that, she has erected a six-story tower that rises like a lime-green finger from atop a shrub-covered hill on the outskirts of San Diego, California. Hundreds of strain gauges and accelerometers fill the building, so sensitive they can detect wind gusts pressing against the walls. Now, Hutchinson just needs an earthquake. In most of the world, this would be a problem.

Mitsubishi Heavy Industries Ltd. on Tuesday unveiled a robot that can operate in the presence of flammable gases, such as after a gas leak following a disaster. A joint project with Chiba Institute of Technology, the Sakura No. 2 is the country's first mobile inspection unit that can operate in the presence of high concentrations of explosive gases such as methane and hydrogen. There is an increasing need for an inspection robot that is not a fire hazard as Japan steers toward becoming a hydrogen-based society, said Ken Onishi, a senior engineer in charge of the project for Mitsubishi Heavy. "There was a debate over whether to develop robots that can operate near hydrogen gas, as doing so requires an extremely high level of technology," Onishi said. "As we may encounter accidents such as collisions involving hydrogen cars or a truck loaded with hydrogen tanks rolling over inside a road tunnel, we decided to develop a robot that can deal with such situations."

Elsewhere, I have suggested that there are three junctures at which any data mining project may go the most wrong: 1. problem definition, 2. data acquisition and 3. model validation (see the Data Mining and Predictive Analytics Web log). Data acquisition is a superset of statistical sampling, and the text by Lohr is highly recommended for this topic. Model validation is well explained in the literature: see, for instance, Weiss and Kulikowski. Problem definition involves understanding the business problem and mapping an appropriate technical solution to it. This may not be as simple as it sounds, and it is easy to be naïve about the best way to construct a technical solution which most naturally solves the given problem.

Drowsy driving is notoriously tough to detect. There's no test to prove it, the way a breathalyzer can prove someone was driving drunk. But technology to detect drowsy driving is in the works. In commercial transport, one industry is leading the way: mining. The stakes are particularly high in this field since the enormous haul trucks used in mining are several times the height of a person.

Or at least a coin-sized cyborg stingray made from rat heart cells that can be controlled underwater using light. Designed by Kevin Kit Parker from Harvard University's Wyss Institute and his team, the 16-millimetre-long soft robot has a gold skeleton overlaid with a flexible polymer. Its muscles are made up of about 200,000 rat heart cells laid down in layers. "My building material is alive," says Parker. To get the tiny robot to move, the team tweaked the rat-cell genes to make them light-sensitive.

The successful candidates will join the Data Mining & Machine Learning Group and contribute to a new research project, ROCSAFE (see below) funded by the European Union's Horizon 2020 Programme. The research is likely to involve one of: (1) advances in temporal Bayesian reasoning for decision support; (2) routing of autonomous vehicles for optimal collection of multi-resolution image and sensor data; (3) context-aware decision support driven by sensor data analytics.

While developing countries are hungry for agricultural knowledge, the developed world is using millions of tons of pesticides and herbicides where it could have been avoided. A company called Blue River Technology came up with the solution and introduced its LettuceBot which looks like a typical tractor but in reality, is a machine-learning powered equipment. The bot can roll through a field and photograph up to 5,000 young plants every minute, using algorithms to identify plants as sprouts, weeds or lettuce. If it spots a lettuce that is not growing right, it will spray it too, to help create a more uniform and healthy crop. Considering that the only alternative is the traditional approach of spraying herbicides on everything, the method could be revolutionary.

But I wanted to make the community aware of an interesting new set of problems. How would you modify your favorite algorithm, if some of the choices you made were irrevocable? For example, writing the size of a mosquito to a memory location can be done an infinite number of times, but killing a mosquito with a laser can be done only once, we cannot bring the insect back to life. In a new paper we consider one such problem, irrevocable sampling from a stream.