Sheets of metal just two atoms thick can be produced by squashing molten droplets at great pressure between two sapphires. The researchers who developed the process say the unusual materials could have applications in industrial chemistry, optics and computers. Last year, scientists created a gold sheet that was a single atom thick, which they dubbed "goldene" after graphene, a material made of a single layer of carbon atoms. Such materials have been described as two-dimensional, as they are as thin as chemically possible. But making other 2D metals hadn't been possible until now. The new technique, developed by Luojun Du at the Chinese Academy of Sciences and his colleagues, can create 2D sheets of bismuth, gallium, indium, tin and lead that are as thin as their atomic bonds allow.

A training method based on a form of continuous spatially distributed optical error back-propagation is presented for an all optical network composed of nondiscrete neurons and weighted interconnections. The all optical network is feed-forward and is composed of thin layers of a Kerr(cid:173) type self focusing/defocusing nonlinear optical material. The training method is derived from a Lagrangian formulation of the constrained minimization of the network error at the output. This leads to a formulation that describes training as a calculation of the distributed error of the optical signal at the output which is then reflected back through the device to assign a spatially distributed error to the internal layers. This error is then used to modify the internal weighting values.

Moisture content (MC) estimation is important in the manufacturing process of drying bulky filter media products as it is the prerequisite for drying optimization. In this study, a dataset collected by performing 161 drying industrial experiments is described and a methodology for MC estimation in an non-destructive and online manner during industrial drying is presented. An artificial neural network (ANN) based method is compared to state-of-the-art MC estimation methods reported in the literature. Results of model fitting and training show that a three-layer Perceptron achieves the lowest error. Experimental results show that ANNs combined with oven settings data, drying time and product temperature can be used to reliably estimate the MC of bulky filter media products.

The world's smallest battery has been designed to power a computer the size of a grain of dust, that could be used as discrete sensors, or for medical implants. A team led by Chemnitz University of Technology in Germany say these microscopic batteries are needed to power the ongoing miniaturisation of electronics. Smart dust devices, including biocompatible sensor systems in the body, require computers to handle data at sizes smaller than a grain of dust, but while the devices are getting smaller, powering them has proved to be problematic. The current generation of microbatteries involve stacking films on a chip, but there is a limit to how small they can become before energy storage levels are too low. To solve this problem, the German team created a system that involved winding up strips of the same films used in current microbatteries, that can be released and re-coiled to generate and release enough tension to power a tiny computer.

Researchers at North Carolina State University have discovered a new law of physics that could enable improved friction and grip in robotics. It can be difficult for engineers to account for the friction that occurs when robots grip objects, particularly in wet environments. This is due to elastohydrodynamic lubrication (EHL), the friction that occurs when two solid surfaces come into contact with a thin layer of fluid between them. In humans, this friction occurs when fingertips are rubbed together, the fluid being the thin layer of naturally occurring oil on the skin. This could also apply to a robotic claw lifting an object that has been coated with oil, or to a surgical device being used inside the human body.

During the directional drilling, a bit may sometimes go to a nonproductive rock layer due to the gap about 20 m between the bit and high-fidelity rock type sensors. The only way to detect the lithotype changes in time is the usage of Measurements While Drilling (MWD) data. However, there are no mathematical modeling approaches that reconstruct the rock type based on MWD data with high accuracy. In this article, we present a data-driven procedure that utilizes MWD data for quick detection of changes in rock type. We propose the approach that combines traditional machine learning based on the solution of the rock type classification problem with change detection procedures rarely used before in Oil & Gas industry. The data come from a newly developed oilfield in the North of Western Siberia. The results suggest that we can detect a significant part of changes in rock type reducing the change detection delay from 20 to 2.6 m and the number of false positive alarms from 71 to 7 per well.

Imagine a robotic cell that can function on its own from carrying electric charge, studying the environment around it and changing shape constantly to adapt to it. Sounds like something right out of a fictional, futuristic world where robots are built from cells like humans and can do everything we can, but better. But now, physicists from Cornell University have done just this and given this device microscopic muscles too. The team has successfully created the world's first robot exoskeleton that can rapidly change its shape upon sensing chemical or thermal changes in its environment. The team has even given it electronic, photonic and chemical properties making it a functioning robotic microorganism.

The idea of a robot that can learn and function on its own, without the need for any human help, might sound like the plot to the latest science fiction blockbuster. But scientists have got one step closer to making this a reality with the creation of the first artificial brain connection that can learn autonomously. The groundbreaking study represents a big step toward intelligent machines that learn without the need for human input. Researchers have long-looked to the human brain for inspiration in creating an intelligent machine that can learn. The tiny electronic component consists of a thin layer of material sandwiched between two electrodes.

The work presented in this paper is part of a multidisciplinary team collaborating in the deployment of an autonomous oceanographic probe with the task of exploring marine regions and take phytoplankton samples for their subsequent analysis in a laboratory. We will describe an autonomous system that, from sensor data, is able to characterize phytoplankton structures. Because the system has to work inboard, a main goal of our approach is to dramatically reduce the dimensionality of the problem. Specifically, our development uses two AI techniques, namely Particle Swarm Optimization and Case-Based Reasoning. We report results of experiments performed with simulated environments.