Tue 13 Feb 2018 11.00 EST Last modified on Tue 13 Feb 2018 11.01 EST Drones invoke varying perceptions, from fun gadget to fly in the park to deadly military weapons. In the future, they may even be viewed as a handy tool in the battle to fight climate change. Greenhouse gas emissions from the tra...

A key phase in the bridge design process is the selection of the structural system. Due to budget and time constraints, engineers typically rely on engineering judgment and prior experience when selecting a structural system, often considering a limited range of design alternatives. The objective of this study was to explore the suitability of supervised machine learning as a preliminary design aid that provides guidance to engineers with regards to the statistically optimal bridge type to choose, ultimately improving the likelihood of optimized design, design standardization, and reduced maintenance costs. In order to devise this supervised learning system, data for over 600,000 bridges from the National Bridge Inventory database were analyzed. Key attributes for determining the bridge structure type were identified through three feature selection techniques. Potentially useful attributes like seismic intensity and historic data on the cost of materials (steel and concrete) were then added from the US Geological Survey (USGS) database and Engineering News Record. Decision tree, Bayes network and Support Vector Machines were used for predicting the bridge design type. Due to state-to-state variations in material availability, material costs, and design codes, supervised learning models based on the complete data set did not yield favorable results. Supervised learning models were then trained and tested using 10-fold cross validation on data for each state. Inclusion of seismic data improved the model performance noticeably. The data was then resampled to reduce the bias of the models towards more common design types, and the supervised learning models thus constructed showed further improvements in performance. The average recall and precision for the state models was 88.6% and 88.0% using Decision Trees, 84.0% and 83.7% using Bayesian Networks, and 80.8% and 75.6% using SVM.

The ability to instantly gather and analyse environmental and equipment data, and carry out real time risk and area assessments is a big benefit to large scale operations, particularly those like mining, when operatives are working in a compact, changing and potentially dangerous environment. From increasing automation and removing human operatives from dangerous environments, to real time atmospheric monitoring and safety alerts, to more efficient operations through equipment monitoring, new connected technology is having a massive impact on the future of the mining sectors. But are IoT and AI technologies really the beneficial platforms they appear to be? And what exactly can they do for mining operations? The mining industry is a large and diverse eco-system.

One of the primary activities that companies pursue with analytics and data is to plan and optimize operations; this has been a long-term focus of the "operations research" approach to analytics. It has always been done on a relatively small scale, however, using individual models with only a few variables. Cognitive tools--and machine learning in particular--can take this activity to the next level in breadth and depth. AI may not be known for its role in manufacturing and operations, but there is an opportunity to use these tools to dramatically improve the efficiency and effectiveness of these important industries. Take, for example, the steel manufacturing startup Big River Steel, which is attempting a major transformation in this most industrial of industries.

Edmonton, Alberta, Feb. 07, 2018 (GLOBE NEWSWIRE) -- OneSoft Solutions Inc. (the --Company-- or --OneSoft--) (TSX-V:OSS, OTC:OSSIF)--is pleased to announce that its wholly owned subsidiary, OneBridge Solutions, Inc. (--OneBridge--), has entered into a Pilot Program agreement with another U.S.-based, Fortune 500 natural gas, oil and petrochemical company (the --Client--). The Client, whose operations include natural gas gathering, treating, processing, transportation and storage, primarily in the United States, will evaluate OneBridge--s Cognitive Integrity ManagementTM (--CIM--) SaaS solution.

The classical view of a robot as a mechanical body with a central "brain" that controls its behavior could soon be on its way out. The authors of a recent article in Science Robotics argue that future robots will have intelligence distributed throughout their bodies. The concept, and the emerging discipline behind it, are variously referred to as "material robotics" or "robotic materials" and are essentially a synthesis of ideas from robotics and materials science. Proponents say advances in both fields are making it possible to create composite materials capable of combining sensing, actuation, computation, and communication and operating independently of a central processing unit. Much of the inspiration for the field comes from nature, with practitioners pointing to the adaptive camouflage of the cuttlefish's skin, the ability of bird wings to morph in response to different maneuvers, or the banyan tree's ability to grow roots above ground to support new branches.

IoT helps cities to predict accidents and crime as well as gives doctors real-time insight into information from pacemakers or biochips,

Power plants can deploy these innovative technologies today to more accurately predict the condition of assets and schedule appropriate maintenance to correct equipment problems before failure. Although the new administration in Washington has reversed the "war on coal," long-term trends in the U.S. are not promising. Most coal-fired capacity was built between 1950 and 1990, and the average coal plant is about 42 years old. With plant retirements expected to continue in 2018 and beyond, investment in new plants has come to a standstill. The confluence of regulatory issues and alternative energy sources is well known.

The 10 most popular things our readers bought on Amazon in January (Photo: Reviewed.com) If you make a purchase by clicking one of our links, we may earn a small share of the revenue. However, our picks and opinions are independent from USA TODAY's newsroom and any business incentives. If you think people take a break from shopping after the holidays, guess again. January was a productive month for our readers and their wallets.