Rapidly estimating the remaining wall thickness (RWT) is paramount for the non-destructive condition assessment evaluation of large critical metallic pipelines. A robotic vehicle with embedded magnetism-based sensors has been developed to traverse the inside of a pipeline and conduct inspections at the location of a break. However its sensing speed is constrained by the magnetic principle of operation, thus slowing down the overall operation in seeking dense RWT mapping. To ameliorate this drawback, this work proposes the partial scanning of the pipe and then employing Gaussian Processes (GPs) to infer RWT at the unseen pipe sections. Since GP prediction assumes to have normally distributed input data - which does correspond with real RWT measurements - Gaussian mixture (GM) models are proven in this work as fitting marginal distributions to effectively capture the probability of any RWT value in the inspected data. The effectiveness of the proposed approach is extensively validated from real-world data collected in collaboration with a water utility from a cast iron water main pipeline in Sydney, Australia.

Materials discovery is decisive for tackling urgent challenges related to energy, the environment, health care and many others. In chemistry, conventional methodologies for innovation usually rely on expensive and incremental strategies to optimize properties from molecular structures. On the other hand, inverse approaches map properties to structures, thus expediting the design of novel useful compounds. In this chapter, we examine the way in which current deep generative models are addressing the inverse chemical discovery paradigm. We begin by revisiting early inverse design algorithms. Then, we introduce generative models for molecular systems and categorize them according to their architecture and molecular representation. Using this classification, we review the evolution and performance of important molecular generation schemes reported in the literature. Finally, we conclude highlighting the prospects and challenges of generative models as cutting edge tools in materials discovery.

Exploring the effects a chemical compound has on a species takes a considerable experimental effort. Appropriate methods for estimating and suggesting new effects can dramatically reduce the work needed to be done by a laboratory. In this paper we explore the suitability of using a knowledge graph embedding approach for ecotoxicological effect prediction. A knowledge graph has been constructed from publicly available data sets, including a species taxonomy and chemical classification and similarity. The publicly available effect data is integrated to the knowledge graph using ontology alignment techniques. Our experimental results show that the knowledge graph based approach improves the selected baselines.

In 1863 five members of the Chōshū han in Japan made a secret journey to University College London in the UK to study. At the time of their departure, travel overseas was illegal in Japan, nonetheless all five students made an impact on the University that is commemorated to this day, and returned to establish institutions that augured a new era in their homeland, including the National Mint, the Japanese railways and the first Prime Minister. In the same spirit of international collaborations fostering pioneering innovations, materials and data scientists met at the Japanese Embassy in London on Friday 21st June during the "Season of Culture" to discuss "Global Trends in Research on Data-driven Discovery in Materials Science". The event was the 10th scholarly colloquium organized by the journal Science and Technology of Advanced Materials (STAM). Developments in data present an interesting example in science diplomacy where science and technology may facilitate a diplomatic agenda that in turn serves the interests of science.

Imagine a network of mine sites operated remotely--drilling, analysing core samples, collecting and interpreting data wirelessly from machine to machine, and transmitting real-time information into the cloud, absolutely without physical, human touch. In fact, it is fast becoming the reality in an industry that's increasingly powered by artificial intelligence a.k.a When we think of AI, we think of robots and machines capable of independent thought or autonomous movement. These are possibilities, and even realities, in today's world where practically anything can be automated. AI, however, goes beyond hardware, and its applications are farther-reaching than we can perhaps imagine.

An engineer working for Japanese carmaker Nissan has built a robot to help farmers reduce the use of herbicides and pesticides on their rice crops. The compact robot, called Aigamo, is designed to mimic the natural use of ducks that paddle around in flooded paddy fields. Ducks have been used as natural weed repellents for centuries to tear them up and feed on insects, with their manure even acting as an additional fertiliser. As it glides through the water, two mechanisms on the bottom muddy the water to prevent weeds from getting enough sunlight to grow. The technique was used in the late 20th century with live ducks, called'aigamo,' which would paddle the water with the same results and eat any insects they found along the way.

During a wide-ranging discussion at Amazon's re:MARS conference in Las Vegas, Naveen Rao, corporate vice president and general manager of AI at Intel, spoke about machine learning's rapid progress and the fields it might transform, in addition to the steps he believes must be taken to ensure it's not abused. Rao compared the advent of modern AI approaches with the iPhone. Like the iPhone, he said, machine learning -- a technique underlying systems from Amazon's Alexa to Google Lens -- wasn't the first form of AI, but it was nonetheless "exciting" and "consequential." He characterizes the coming AI revolution as the single largest transition the human species has ever encountered. "Few people anticipated the big-picture changes that smartphones would bring. No one foresaw that smartphones could make our work day substantially longer because we'd never get away from email," he said.

Machine learning advances chemistry and materials science by enabling large-scale exploration of chemical space based on quantum chemical calculations. While these models supply fast and accurate predictions of atomistic chemical properties, they do not explicitly capture the electronic degrees of freedom of a molecule, which limits their applicability for reactive chemistry and chemical analysis. Here we present a deep learning framework for the prediction of the quantum mechanical wavefunction in a local basis of atomic orbitals from which all other ground-state properties can be derived. This approach retains full access to the electronic structure via the wavefunction at force field-like efficiency and captures quantum mechanics in an analytically differentiable representation. On several examples, we demonstrate that this opens promising avenues to perform inverse design of molecular structures for target electronic property optimisation and a clear path towards increased synergy of machine learning and quantum chemistry.

AI in Oil & Gas market is projected to grow from an estimated USD 1.57 Billion in 2017 to USD 2.85 Billion by 2022, at a CAGR of 12.66% from 2017 to 2022. Northbrook, IL -- (SBWIRE) -- 06/20/2019 -- AI in Oil & Gas market is expected to grow from an estimated USD 1.57 Billion in 2017 to USD 2.85 Billion by 2022, at a CAGR of 12.66%, during the forecast period. The growth of AI in Oil & Gas market will be mainly driven by the rise in adoption of the big data technology in the Oil & Gas industry to augment E&P capabilities, a significant increase in venture capital investments, and growing need for automation in the Oil & Gas industry, and tremendous pressure to reduce production costs. Software in AI in Oil & Gas market is applicable in upstream Oil & Gas exploration and production activities. The hardware segment in AI in Oil & Gas market is expected to grow swiftly during the forecast period (2017 to 2022), mainly due to the increasing requirement for sophisticated hardware system configurations and components capable of handling massive data, including, but not limited to Tensor Processor Unit (TPU), Graphic Processing Unit (GPU), Resistive Processing Unit (RPU), Field Programmable Gate Array (FPGA), and Visual Processing Unit (VPU) to install software-based AI capabilities.

AI in Oil & Gas market is projected to grow from an estimated USD 1.57 Billion in 2017 to USD 2.85 Billion by 2022, at a CAGR of 12.66% from 2017 to 2022. Northbrook, IL -- (SBWIRE) -- 06/20/2019 -- AI in Oil & Gas market is expected to grow from an estimated USD 1.57 Billion in 2017 to USD 2.85 Billion by 2022, at a CAGR of 12.66%, during the forecast period. The growth of AI in Oil & Gas market will be mainly driven by the rise in adoption of the big data technology in the Oil & Gas industry to augment E&P capabilities, a significant increase in venture capital investments, and growing need for automation in the Oil & Gas industry, and tremendous pressure to reduce production costs. Software in AI in Oil & Gas market is applicable in upstream Oil & Gas exploration and production activities. The hardware segment in AI in Oil & Gas market is expected to grow swiftly during the forecast period (2017 to 2022), mainly due to the increasing requirement for sophisticated hardware system configurations and components capable of handling massive data, including, but not limited to Tensor Processor Unit (TPU), Graphic Processing Unit (GPU), Resistive Processing Unit (RPU), Field Programmable Gate Array (FPGA), and Visual Processing Unit (VPU) to install software-based AI capabilities.