Microbial fuel cells (MFCs) perform wastewater treatment and electricity production through the conversion of organic matter using microorganisms. For practical applications, it has been suggested that greater efficiency can be achieved by arranging multiple MFC units into physical stacks in a cascade with feedstock flowing sequentially between units. In this paper, we investigate the use of computational intelligence to physically explore and optimise (potentially) heterogeneous MFC designs in a cascade, i.e. without simulation. Conductive structures are 3-D printed and inserted into the anodic chamber of each MFC unit, augmenting a carbon fibre veil anode and affecting the hydrodynamics, including the feedstock volume and hydraulic retention time, as well as providing unique habitats for microbial colonisation. We show that it is possible to use design mining to identify new conductive inserts that increase both the cascade power output and power density.

The main focus here is on finding the point that minimizes the sum of the "distances" to n points in a d-dimensional space, called centroid or center, especially in the presence of outliers. We also discuss an interesting physics problem: finding the point of maximum or minimum light, sound, radioactivity, or heat intensity, in the presence of an energy field produced by n energy source points. Both problems are closely related and use the same algorithm to find solutions. The sum of "distances" between an arbitrary point (u, v) and a set S { (x(1), y(1)) ... (x(n), y(n)) } of n points is defined as follows: The function H has one parameter p called power, and when p 2, we are facing the traditional problem of finding the centroid of a cloud of points: in this case, the solution is the classic average of the n points. This solution is notoriously sensitive to outliers.

Bill Gates is often called Harvard's most famous dropout. He notably ditched the top university in 1975 to found Microsoft, and became the world's richest man. Looking back, Gates has said he was lucky that computers were a hobby and an obsession of his at a time when they were just starting to change the world. But speaking on Friday at another Ivy League university, Columbia University, along with fellow billionaire and famous investor Warren Buffett, Gates said that if he were to drop out of college today there's a limited chance he would end up in the computer industry, and likely not in developing operating software for companies. Roughly 1,000 people, mostly students, gathered on the campus to hear the two billionaires speak.

Artificial intelligence is coming to your patio thanks to the launch of the Sunflower, a solar powered smart patio umbrella that automatically adjusts itself to keep you in the shade. The umbrella is the first product from Los Angeles-based smart outdoor living manufacturer ShadeCraft. While its primary purpose is to keep you in the shade, the Sunflower also includes integrated security cameras, speakers, a microphone, and lights. These all operate wirelessly and are powered through four slim solar panels that run along the top of the frame of the umbrella itself. There should be plenty of power for the umbrella to operate: ShadeCraft says the Sunflower's batteries can hold up to 72 hours of operating charge -- good enough to get you through even the cloudiest of days.

Few frameworks have gained so much popularity as quickly as Apache Spark. The open source technology may not be ubiquitous yet in the analytics world, but it's fast approaching that point. Spark has certainly caught on among Web giants in the Silicon Valley, where it's often paired with Hadoop, Kafka, Cassandra and other open source tools to process big and fast-moving data. But the real mark for Spark may be how quickly it's been adopted by real-world companies. Among the firms using the in-memory technology are credit card company Capital One, the drug giant Roche, and DNV GL, an energy consulting firm.

In this article, we also discuss an interesting physics problem: finding the point of maximum or minimum light, sound, radioactivity, or heat intensity, in the presence of an energy field produced by n energy source points. However, the main focus here is on finding the point that minimizes the sum of the "distances" to n points in a d-dimensional space. Both problems are closely related and use the same algorithm to find solutions. The sum of "distances" between an arbitrary point (u, v) and a set S { (x(1), y(1)) ... (x(n), y(n)) } of n points is defined as follows: The function H has one parameter p called power, and when p 2, we are facing the traditional problem of finding the centroid of a cloud of points: in this case, the solution is the classic average of the n points. This solution is notoriously sensitive to outliers.

Perhaps the coolest thing about IBM's 9th "Five Innovations that will Help Change our Lives within Five Years" predictions is that none of them sound like science fiction. "With advances in artificial intelligence and nanotechnology, we aim to invent a new generation of scientific instruments that will make the complex invisible systems in our world today visible over the next five years," said Dario Gil, vice president of science & solutions at IBM Research in a statement. Among the five areas IBM sees as being key in the next five years include artificial intelligence, hyperimaging and small sensors. In five years, what we say and write will be used as indicators of our mental health and physical wellbeing. Patterns in our speech and writing analyzed by new cognitive systems will provide tell-tale signs of early-stage mental and neurological diseases that can help doctors and patients better predict, monitor and track these diseases.

FROM avoiding jaywalkers to emergency braking to eventually, perhaps, chauffeuring the vehicle itself, it is clear that artificial intelligence (AI) will be an important part of the cars of the future. But it is not only the driving of them that will benefit. AI will also permit such cars to use energy more sparingly. Cars have long had computerised engine-management that responds on the fly to changes in driving conditions. The introduction of electric power has, however, complicated matters.

The big data paradigm has changed how we make decisions. Armed with sophisticated machine learning and deep learning algorithms that can identify correlations hidden within huge data sets, big data has given us a powerful new tool to predict the future with uncanny accuracy and disrupt entire industries. What if some decisions can't be based just on data? That may sound like a heresy to people who have devoted themselves to the religion of data, to the business leaders who have declared their allegiance to making data-driven decisions. Sure, there may be obstacles to overcome, such as data cleanliness, governance, and security concerns.

A recent Conversation piece pointed out that the British electricity mix in 2016 was the cleanest in 60 years, with record capacity from renewable energy, mainly from wind and solar power. But one problem with this great expansion in renewables is they are intermittent, meaning they depend on weather conditions such as the wind blowing or sun shining. Unlike conventional power, this means they can't necessarily meet surges in demand. National Grid, the UK grid operator, has several ways of ensuring supply can always meet demand. For shorter gaps in generation, it asks electricity suppliers to run their conventional power stations at below maximum potential output and ramp up as needed.