Shear stress distribution prediction in open channels is of utmost importance in hydraulic structural engineering as it directly affects the design of stable channels. In this study, at first, a series of experimental tests were conducted to assess the shear stress distribution in prismatic compound channels. The shear stress values around the whole wetted perimeter were measured in the compound channel with different floodplain widths also in different flow depths in subcritical and supercritical conditions. A set of, data mining and machine learning models including Random Forest (RF), M5P, Random Committee (RC), KStar and Additive Regression Model (AR) implemented on attained data to predict the shear stress distribution in the compound channel. Results indicated among these five models, RF method indicated the most precise results with the highest R2 value of 0.9. Finally, the most powerful data mining method which studied in this research (RF) compared with two well-known analytical models of Shiono and Knight Method (SKM) and Shannon method to acquire the proposed model functioning in predicting the shear stress distribution. The results showed that the RF model has the best prediction performance compared to SKM and Shannon models.

WASHINGTON – The United States said new evidence and analysis of weapons debris recovered from an attack on Saudi oil facilities on Sept. 14 indicates the strike likely came from the north, reinforcing its earlier assessment that Iran was behind the offensive. In an interim report of its investigation -- seen by Reuters ahead of a presentation on Thursday to the United Nations Security Council -- Washington assessed that before hitting its targets, one of the drones traversed a location approximately 200 km (124 miles) to the northwest of the attack site. "This, in combination with the assessed 900 kilometer maximum range of the Unmanned Aerial Vehicle (UAV), indicates with high likelihood that the attack originated north of Abqaiq," the interim report said, referring to the location of one of the Saudi oil facilities that were hit. It added the United States had identified several similarities between the drones used in the raid and an Iranian designed and produced unmanned aircraft known as the IRN-05 UAV. However, the report noted that the analysis of the weapons debris did not definitely reveal the origin of the strike that initially knocked out half of Saudi Arabia's oil production.

According to McKinsey Global Institude study on impact of AI and automation, transportation and warehousing are one of the most automatable sectors of economy (3rd place), with 60% potential for automation. Predicting the future demands for production and supplies, improving transportation routines, or automating physical inspection and maintenance are some of vast possibilities to use data science in supply chain management. While self-driving cars seem to be a future of transport, we would like to focus on optimizing "here and now", without changing the market – just with smart, data-driven decisions. We would like to focus on a problem of choosing location of warehouse to minimise cost of both freight and warehouse maintenance. It is a complex Data Science problem, that is composed of various independent components that need to be optimised.

Faced with dire reports of looming global catastrophe due to the ongoing climate emergency, many of us are taking a long, hard look at the carbon footprint of our daily lives -- whether it's from the food we eat, how much we drive or how often we fly. But sometimes it's the most intangible of things that may actually be pumping out more carbon than we think -- namely, the surprisingly large carbon footprint that can be associated with creating machine learning models -- the same technology that underlies the apps on our smartphones, digital personal assistants and computers. While using such tech might not necessarily emit all that much carbon, the cause for concern lies behind the carbon impact of the computational processes that go into training AI -- and whether researchers and companies can be well-informed enough to choose less carbon-intensive options. Until now, artificial intelligence researchers have not really had an easily available method to quantify the carbon impact. But that's changing, thanks to a team from Canada's Montreal Institute for Learning Algorithms (MILA), Element AI and Polytechnique Montreal, which recently released a tool designed to help those working in the AI field estimate how much carbon is produced in training their machine learning models.

WIRE)--Bidgely today introduced the latest version of its UtilityAI Platform for delivering a personalized energy experience to utility customers as well as operational efficiencies for global utilities. As the energy industry's only artificial intelligence (AI) platform for hyper-personalization, Bidgely has developed the world's most accurate and actionable customer energy insights based on actual energy habits that are continuously improved and personalized with each interaction. To highlight the impacts of AI in 2019 and in the years to come, global utilities and energy retailers including NiSource, VSE-RWE, Hydro Ottawa and Origin Energy join Navigant Research and Smart Energy Consumer Collaborative in the video How AI Will Change the Utility Industry. "2019 has been a breakout year for UtilityAI," said Bidgely CEO Abhay Gupta. "Our AI-powered hyper-personalization for utilities is powered by actual insights gained from real world deployments, i.e. 15M homes from 30 utility partners in 15 countries. We continue to expand the Bidgely UtilityAI ecosystem with multiple, global customer engagements in key industry categories. This broadening of our solutions and deepening of each offering is only accelerating as our AI and machine learning algorithms become more powerful and reveal new value to be gained throughout an entire utility's operations."

IDAHO FALLS, Idaho, Dec. 5, 2019 – A powerful new supercomputer arrived this week at Idaho National Laboratory's Collaborative Computing Center. The machine has the power to run complex modeling and simulation applications, which are essential to developing next-generation nuclear technologies. Named after a central Idaho mountain range, Sawtooth arrives in December and will be available to users early next year. That is the highest ranking reached by an INL supercomputer. Of 102 new systems added to the list in the past six months, only three were faster than Sawtooth.

Robotic process automation (RPA) is gaining traction among enterprises, as RPA tools have proven they can streamline repetitive processes and save lots of time. But as more companies implement RPA, they're also finding they maximize ROI when they pair it with Intelligent Process Automation (IPA). RPA software revenue grew 63.1% in 2018 to $846 million, according to Gartner, making it the fastest-growing segment of the global enterprise software market. RPA tools are used in all industries, although Gartner says the biggest adopters are banks, insurance companies, telcos and utility companies. Such firms typically have many legacy systems and use RPA to help integrate data among them.

There are only a few ingredients needed for algae to take over: carbon dioxide, light, and water. The ancient microorganism is thriving thanks to record heat waves and fertilizers washed away into nearby waters. But what if a fourth ingredient -- artificial intelligence -- could transform the gooey sludge from a growing pest into a tool to fight climate change? A team of researchers at the AI technology company Hypergiant sees algae as a weapon that can be harnessed for our benefit. They recently built an AI-powered machine, the EOS bioreactor, that takes advantage of algae's ability to capture carbon dioxide through photosynthesis.

With all the buzz around digital currency and blockchain innovation, artificial intelligence (AI) has taken a lower priority somehow or another. In any case, this hasn't prevented the AI business from developing at an exponential rate. The artificial intelligence market is evaluated to develop a value to $191 billion by 2024 with a CAGR of 37%. Artificial intelligence is as yet a power. The capacity for machines to settle on decisions dependent on rationale, information, and data from the past is affecting various enterprises.

In its first year of operation, the Intelligent Towing Tank (ITT) conducted about 100,000 total experiments, essentially completing the equivalent of a PhD student's five years' worth of experiments in a matter of weeks. The automated experimental facility, developed in the MIT Sea Grant Hydrodynamics Laboratory, automatically and adaptively performs, analyzes, and designs experiments exploring vortex-induced vibrations (VIVs). Important for engineering offshore ocean structures like marine drilling risers that connect underwater oil wells to the surface, VIVs remain somewhat of a phenomenon to researchers due to the high number of parameters involved. Guided by active learning, the ITT conducts series of experiments wherein the parameters of each next experiment are selected by a computer. Using an "explore-and-exploit" methodology, the system dramatically reduces the number of experiments required to explore and map the complex forces governing VIVs.