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.

Much of the software now revolutionizing the financial services industry depends on algorithms that apply artificial intelligence (AI)--and increasingly, machine learning--to automate everything from simple, rote tasks to activities requiring sophisticated judgment. These algorithms and the analyses that undergird them have become progressively more sophisticated as the pool of potentially meaningful variables within the Big Data universe continues to proliferate. When properly implemented, algorithmic and AI systems increase processing speed, reduce mistakes due to human error and minimize labor costs, all while improving customer satisfaction rates. Creditscoring algorithms, for example, not only help financial institutions optimize default and prepayment rates, but also streamline the application process, allowing for leaner staffing and an enhanced customer experience. When effective, these algorithms enable lenders to tweak approval criteria quickly and continually, responding in real time to both market conditions and customer needs. Both lenders and borrowers stand to benefit. For decades, financial services companies have used different types of algorithms to trade securities, predict financial markets, identify prospective employees and assess potential customers. Although AIdriven algorithms seek to avoid the failures of rigid instructions-based models of the past--such as those linked to the 1987 "Black Monday" stock market crash or 2010's "Flash Crash"--these models continue to present potential financial, reputational and legal risks for financial services companies.

The transport ministry said Monday it will introduce safety standards for self-driving vehicles in Japan as early as this fall, including an alarm system that sounds 15 seconds after a driver takes his hands off the steering wheel while traveling on a highway. The introduction of the integrated standards is expected to spur the development of self-driving vehicles by Japanese automakers as well as information technology companies as they will make clear the technology necessary for such cars. The safety standards are in line with an agreement reached Friday by a U.N. working party tasked with creating a uniform system of regulations for vehicle design to facilitate international trade. Japanese automakers will be able to sell vehicles that pass domestic safety tests based on the new standards in the European market in the future as the same standards are expected to be introduced there. The Ministry of Land, Infrastructure, Transport and Tourism will revise relevant ministerial ordinances under the Road Traffic Act in line with the new regulations.

This list isn't just about what is new and innovative. It is about what will be adopted at scale. The beginning of a new year is usually the time to predict key trends for the year to come, and so it goes with the insurtech sector as well. Most lists focus on the latest sexy technologies and applications. But, after a year, we find these have hardly gained any traction and so cannot really be considered "trends" in our view.

For the last few years, technology vendors have been talking up the Internet of Things (IoT) and IT decision makers have taken notice. It can be hard to distinguish whether the hype over IoT is real, but as Piers Stobbs, chief data officer of British price comparison website Moneysupermarket.com "Much like big data, artificial intelligence, machine learning and blockchain, IoT has become important for a reason – there is something there. I think there is always a bit of hype around these technologies but they don't come about without some underlying gravitas". We're now at a stage where IT decision makers want clear use cases to be demonstrated at scale so that they can replicate these for their businesses.

You may have once seen a giant face in the clouds. Perhaps it took you aback, amused you, or maybe it prompted an "uncanny valley" kind of sensation--realness, but with a lingering unease. It's thought that a similar experience was shared by an early hominid approximately 3 million years ago. Researchers say a rock that bore resemblance to a face was carried, over some four kilometers from where it was probably found, to an Australopithecine home. Known as the Makapansgat pebble, it was found in 1925 in a South-African cave, in what may well have been a camp or dwelling.

A true genius, Alan Turing was played brilliantly by Benedict Cumberbatch inThe Imitation Game -- the movie about his life and role in ending WWII -- which introduced him to a whole new generation of admirers. It was Turing who predicted machine learning would play a big role in modern computing in his article the "Turing Test," way back in 1950. Indeed, Turing was way ahead of his time, which was a major theme in the movie, but now the world has caught up. The major advancements in readily accessible computing power, the quantity of data available, and algorithms that truly make machine learning possible are driving our ability to process data, analyze it, and act on it in ways that would make Mr. Turing proud. These advances have completely changed the machine learning game: The fundamental concept remains the same, but now it's far more sophisticated, efficient, and easily deployable. Beyond the big headline-grabbing examples of how machine learning will impact our lives -- such as through driverless cars -- it has exciting potential to put an end to the bland and sometimes ineffective customer experiences that many retailers are delivering to their customers.

Synechron Inc., the digital, business consulting and technology services provider, has today announced its predictions of top Financial Services Trends for 2017, supported by survey data from TABB Group. The survey of over 200 senior-level, global financial services business and IT decision-makers across the U.S. U.K. and Europe found 38% of respondents placed Regulation as their top priority for 2017, followed by Data Management (14.4%), These results clearly show that while digital innovation remains a long-term priority over the next five years, businesses need these programs to achieve pragmatic results for them in 2017. While digital innovation is at the heart of our business, financial services firms need immediate solutions to the problems they are facing today. Regulation, cost-pressure and out-dated technology systems and operations are part of almost every client conversation, so it is no surprise these items topped their 2017 priorities list.

An important step in data analysis is data exploration and representation. In this tutorial we will see how by combining a technique called Principal Component Analysis (PCA) together with Cluster Analysis we can represent in a two-dimensional space data defined in a higher dimensional one while, at the same time, being able to group this data in similar groups or clusters and find hidden relationships in our data. More concretely, PCA reduces data dimensionality by finding principal components. These are the directions of maximum variation in a dataset. By reducing a dataset original features or variables to a reduced set of new ones based on the principal components, we end up with the minimum number of variables that keep the maximum amount of variation or information about how the data is distributed. If we end up with just two of these new variables, we will be able to represent each sample in our data in a two-dimensional chart (e.g. a scatterplot). As an unsupervised data analysis technique, clustering organises data samples by proximity based on its variables.

Are you really ready to ride with just a robot at the wheel? To make self-driving cars safer, researchers from the École Polytechnique Fédérale de Lausanne (EPFL) in Switzerland want them to communicate both with each other and non-robotic vehicles to avoid any nasty surprises. As such, they rigged up three vehicles, including a robotic truck and non-autonomous vehicle, to function as one unit on a real road. By working in a team and using each others' sensors, the vehicles were able to anticipate each others' moves, making lane change maneuvers safer. In the EPFL's scheme, vehicles can travel together as a convoy that doesn't have any particular "leader."