Newswise -- Wood is a natural material that is lightweight and sustainable, with excellent physical properties, which make it an excellent choice for constructing a wide range of products with high quality requirements - for example for musical instruments and sports equipment. Unfortunately, as most natural products, wood has a very uneven material structure that extends over several length scales. Therefore, large safety margins are often required during processing, which limit the efficiency of material utilisation. With the help of science, this drawback could soon be resolved. A key technology for this is artificial intelligence.

Machine learning and artificial intelligence are becoming key components of mineral exploration programs as companies set exploration targets. Machine learning and artificial intelligence (AI) have the ability to solve two of the mining industry's biggest challenges: rising exploration costs and a lack of new discoveries. After a heavy downturn in the past few years, the mining and mineral exploration sector is finally starting to recover, but deep challenges remain. In an industry that thrives on new discoveries, today's resource companies are finding it harder and more expensive to locate new deposits. Gold provides one of the greatest examples of this dearth of new discoveries in the face of rising exploration costs.

Always in motion is the future." We've spread out towards the stars and colonized the solar system, from settlements orbiting the glittering rings of Saturn, to sprawling cities on the red hills of Mars built by nano insects invisible to the eyes. When their big bellies are filled to bursting, they rocket along invisible superhighways, delivering He3 to energy hungry fusion micro-reactors that power the interplanetary economy. Beyond the rings, deep space mining ships release clouds of drones like baby spiders into the wind and they digest asteroids hurtling in the endless void. The drones fuel an unprecedented building boom on nearly every planet circling the sun, as city after city goes up on barren rocks long hostile to organic life. The fastest transformations are taking place on Mars. The people who immigrated to Mars generations ago don't need oxygen at all.

By some estimates, the world generates 2.5 quintillion bytes of data every day. Yet only a sliver of that volume, much of it residing on enterprise servers, is fully leveraged to drive a deep understanding of the enterprise and how to improve it. What value lies untapped within all that data? As business leaders grapple with these questions, they rely increasingly on emerging cognitive technologies like artificial intelligence, machine learning and blockchain. When these technologies are coupled with cloud-based multi-enterprise networks, thought-leading companies are able to unearth, analyze and act upon critical insights across business lines and foster the emergence of intelligent enterprises.

Ever since the industrial chemist Leo Baekeland began synthesizing phenol and formaldehyde in 1907, the world has developed a love-hate relationship with the resulting polymer: plastic. While plastic is convenient, durable, and cheap, 50% of all plastics (about 150 million tons every year, worldwide) are used only once and then thrown away. Even for those who dutifully recycle our plastic water bottles and sandwich bags, we're only tackling a small part of the problem. "Considering the size of the problem, there's relatively limited infrastructure in place to capture and treat stormwater," says Tony Hale, program director for environmental informatics at the nonprofit San Francisco Estuary Institute (SFEI). That's where SFEI is looking to use research and data--and most recently, drones--to make a difference.

Ever since the industrial chemist Leo Baekeland began synthesizing phenol and formaldehyde in 1907, the world has developed a love-hate relationship with the resulting polymer: plastic. While plastic is convenient, durable, and cheap, 50% of all plastics (about 150 million tons every year, worldwide) are used only once and then thrown away. Even for those who dutifully recycle our plastic water bottles and sandwich bags, we're only tackling a small part of the problem. "Considering the size of the problem, there's relatively limited infrastructure in place to capture and treat stormwater," says Tony Hale, program director for environmental informatics at the nonprofit San Francisco Estuary Institute (SFEI). That's where SFEI is looking to use research and data--and most recently, drones--to make a difference.

A startup backed by billionaire Microsoft founder, Bill Gates, says a breakthrough in solar technology may revolutionize the way materials like steel and glass are created. The company, called Heliogen, says it uses artificial intelligence to help operate an array of mirrors capable of reflecting and focusing the sun's light and creating a type of solar oven. The system works so well that they report being able to create temperatures of 1,000 degrees Fahrenheit - about a quarter of the temperature found on the Sun's surface. That extreme heat is a first for solar-powered systems like Heliogens and, according to the company, could be used as an environmentally friendly way of creating crucial materials like cement, glass, and steel. As noted by CNN, Heliogen's system could drastically impact global emissions - roughly 7 percent of of C02 released into Earth's environment are from manufacturing cement alone according to the International Energy Agency.

Capturing the microscopic interactions that determine molecular reactivity poses a challenge across the physical sciences. Even a basic understanding of the underlying reaction mechanisms can substantially accelerate materials and compound design, including the development of new catalysts or drugs. Given the difficulties routinely faced by both experimental and theoretical investigations that aim to improve our mechanistic understanding of a reaction, recent advances have focused on data-driven routes to derive structure-property relationships directly from high-throughput screens. However, even these high-quality, high-volume data are noisy, sparse and biased -- placing them in a regime where machine-learning is extremely challenging. Here we show that a statistical approach based on deep filtering of nonlinear feature networks results in physicochemical models that are more robust, transparent and generalize better than standard machine-learning architectures. Using diligent descriptor design and data post-processing, we exemplify the approach using both literature and fresh data on asymmetric catalytic hydrogenation, Palladium-catalyzed cross-coupling reactions, and drug-drug synergy. We illustrate how the sparse models uncovered by the filtering help us formulate physicochemical reaction ``pharmacophores'', investigate experimental bias and derive strategies for mechanism detection and classification.

The intent of the current study is to analyze the LPG usage consumption and forecasting, by leveraging Time Series, the values to predict the LPG usage – by giving inputs area-wise, dealer-wise, and season-wise on a weekly, monthly, and yearly basis. This case study leverages AI and Machine Learning to predict LPG usage by using a concept mechanism like a trolley enabled with sensors. These trolleys capture the weights of the cylinders and transmit continuous updates on weight of the cylinder, gas leakage occurrences and ambient temperature to the dealers and manufacturers. Qualetics provides a solution that captures the above-mentioned data points continuously and allows the possibility of real-time streaming analytics of the LPG gas usage as well as advanced analytics on data captured over long periods of time. To know how Qualetics gives an effective solution, download the full usecase.

Machine learning (ML) methods reach ever deeper into quantum chemistry and materials simulation, delivering predictive models of interatomic potential energy surfaces1,2,3,4,5,6, molecular forces7,8, electron densities9, density functionals10, and molecular response properties such as polarisabilities11, and infrared spectra12. Large data sets of molecular properties calculated from quantum chemistry or measured from experiment are equally being used to construct predictive models to explore the vast chemical compound space13,14,15,16,17 to find new sustainable catalyst materials18, and to design new synthetic pathways19. Recent research has explored the potential role of machine learning in constructing approximate quantum chemical methods20, as well as predicting MP2 and coupled cluster energies from Hartree–Fock orbitals21,22. There have also been approaches that use neural networks as a basis representation of the wavefunction23,24,25. Most existing ML models have in common that they learn from quantum chemistry to describe molecular properties as scalar, vector, or tensor fields26,27.