With an operating footprint of up to 50km from the mining pit to iron ore carriers, it was easy for Citic Pacific Mining, Australia's largest magnetite mining company, to lose track of its assets, such as light vehicles, buses and service trucks. Find out how to draw up a battle plan for securing connected devices and the key areas to target. You forgot to provide an Email Address. This email address doesn't appear to be valid. This email address is already registered.

User interfaces provide an interactive window between physical and virtual environments. A new concept in the field of human-computer interaction is a soft user interface; a compliant surface that facilitates touch interaction through deformation. Despite the potential of these interfaces, they currently lack a signal processing framework that can efficiently extract information from their deformation. Here we present OrbTouch, a device that uses statistical learning algorithms, based on convolutional neural networks, to map deformations from human touch to categorical labels (i.e., gestures) and touch location using stretchable capacitor signals as inputs. We demonstrate this approach by using the device to control the popular game Tetris. OrbTouch provides a modular, robust framework to interpret deformation in soft media, laying a foundation for new modes of human computer interaction through shape changing solids.

Proteins are used in various applications by pharmaceutical, food, fuel, and many other industries and their usage is growing steadily (Kirk et al., 2002; Sanchez and Demain, 2010). Proteins have important advantages over chemical catalysts, as they are derived from renewable resources, are biodegradable and are often highly selective (Cherry and Fidantsef, 2003). Protein engineering is used to further improve the properties of proteins, for example to enhance their catalytic activity, modify their substrate specificity or to improve their thermostability (Rapley and Walker, 2000). Increasing the stability is an important aspect of protein engineering, as the proteins used in industry should be stable in the industrial process conditions, which often involve higher than ambient temperature and non-aqueous solvents (Bommarius et al., 2011). The properties of a protein are modified by introducing alterations to its amino acid sequence. Mutations in general tend to be destabilising, and if too many destabilising mutations are implemented, the protein may not remain functional without compensatory stabilising mutations (Tokuriki and Tawfik, 2009). The stability of a protein can be defined as the difference in Gibbs energy G between the folded and unfolded (or native and denaturated) state of the protein.

You've probably got at least one on your person right now. They're built to fit into smartwatches and smaller things, and that small size hampers how well they can sense changes. Engineers in Florida have now come up with a new take on the accelerometer that is as much as 1 million times as sensitive as a typical smartphone accelerometer, and it maintains that sensitivity up to a car-crash-scale 100 gs. That combination of high sensitivity and large dynamic range in a cube that's just 3 millimeters on a side should make the new accelerometer particularly useful in things that move quickly in three-dimensions, such as military drones, microrobots, and self-guided projectiles, according its inventors. Ordinary MEMS accelerometers are made up of a moveable plate and a stationary plate, oriented perpendicular to each dimension measured.

NPR's Mary Louise Kelly speaks with Pennsylvania Attorney General Josh Shapiro about his office's intent to look into how the data of 50 million Facebook users got into the hands of the political data-mining firm, Cambridge Analytica.

Robots have moved into factories, warehouses, stores and even our homes. Tech startups are developing self-driving bulldozers, drones to inspect work sites and robot bricklayers. In this photo taken Jan. 26, 2018, Mike Moy, an assistant plant manager for Lehigh Hanson Cement Group, inspects a Kespry drone he uses to survey inventories of rock, sand and other building materials at a mining plant in Sunol, California. Robots are coming to a construction site near you. Tech startups are developing self-driving bulldozers, survey drones and bricklaying robots to help the construction industry boost productivity, speed and safety as it struggles to find enough skilled workers.

Chemical databases store information in text representations, and the SMILES format is a universal standard used in many cheminformatics software. Encoded in each SMILES string is structural information that can be used to predict complex chemical properties. In this work, we develop SMILES2vec, a deep RNN that automatically learns features from SMILES to predict chemical properties, without the need for additional explicit feature engineering. Using Bayesian optimization methods to tune the network architecture, we show that an optimized SMILES2vec model can serve as a general-purpose neural network for predicting distinct chemical properties including toxicity, activity, solubility and solvation energy, while also outperforming contemporary MLP neural networks that uses engineered features. Furthermore, we demonstrate proof-of-concept of interpretability by developing an explanation mask that localizes on the most important characters used in making a prediction. When tested on the solubility dataset, it identified specific parts of a chemical that is consistent with established first-principles knowledge with an accuracy of 88%. Our work demonstrates that neural networks can learn technically accurate chemical concept and provide state-of-the-art accuracy, making interpretable deep neural networks a useful tool of relevance to the chemical industry.

This farm in Arkansas may soon be the most scientifically advanced farm in the world. There's a farm in Arkansas growing soybeans, corn, and rice that is aiming to be the most scientifically advanced farm in the world. Soil samples are run through powerful machines to have their microbes genetically sequenced, drones are flying overhead taking hyperspectral images of the crops, and soon supercomputers will be crunching the massive volumes of data collected. Scientists at the Department of Energy's Lawrence Berkeley National Laboratory (Berkeley Lab), working with the University of Arkansas and Glennoe Farms, hope this project, which brings together molecular biology, biogeochemistry, environmental sensing technologies, and machine learning, will revolutionize agriculture and create sustainable farming practices that benefit both the environment and farms. If successful, they envision being able to reduce the need for chemical fertilizers and enhance soil carbon uptake, thus improving the long-term viability of the land, while at the same time increasing crop yields.

Five simple soft sensor methodologies with two update conditions were compared on two experimentally-obtained datasets and one simulated dataset. The soft sensors investigated were moving window partial least squares regression (and a recursive variant), moving window random forest regression, the mean moving window of y, and a novel random forest partial least squares regression ensemble (RF-PLS), all of which can be used with small sample sizes so that they can be rapidly placed online. It was found that, on two of the datasets studied, small window sizes led to the lowest prediction errors for all of the moving window methods studied. On the majority of datasets studied, the RF-PLS calibration method offered the lowest onestep-ahead prediction errors compared to those of the other methods, and it demonstrated greater predictive stability at larger time delays than moving window PLS alone. It was found that both the random forest and RF-PLS methods most adequately modeled the datasets that did not feature purely monotonic increases in property values, but that both methods performed more poorly than moving window PLS models on one dataset with purely monotonic property values. Other data dependent findings are presented and discussed. Preprint submitted to Arxiv March 14, 2018 1. Introduction Soft sensors for regression tasks have found wide utility in process engineering and process analytical chemistry [1, 2, 3]. A soft sensor is effectively a calibration used on time-series data. Here, we consider a soft sensor to be any algorithm that can be used to estimate a property value from several readily available but indirect measurements. The goal of implementing a soft sensor is typically to avoid the use of a physical sensor for variables that may require extensive time or work up to measure [3]. In the context of industrial chemical processes, these algorithms should meet several specifications.

Data is shaping almost every area of our lives. Agriculture has been slow to embrace new technology but even here it's beginning to have a big impact. There are now hundreds of companies offering everything from farm management and precision tools to bots and drones. Some tractors have computing power that would have turned Nasa's moon-landing mission green with envy. What started in farm equipment is moving into the field – at least in the developed world.