In the last few years, many asset-intensive organizations, particularly in the mining, power and utilities, oil and gas, and chemicals industries, have turned to industrial Internet of Things (IIoT) and cognitive technologies to help improve a critical area of their business: equipment reliability.1 Asset performance management (APM) programs, which connect data and trigger actions via systems across the business, can play a major part in driving these improvements. According to a 2018 Deloitte survey, oil and gas leaders rated the big data derived from programs such as APM as the most likely to provide the greatest business value.2 However, when asked about how digital technology can be used most effectively within their companies, those same executives ranked APM below both cost reduction in maintenance and operations as well as improvements in safety.3 This seems to reveal a pervasive and narrow view of APM that may miss the connection between asset performance, broader maintenance and operations improvements, and safety. Merely implementing APM software and digitizing existing processes is not likely to improve core operations and obtain the financial results that executive leaders desire (and investors demand).

Norwegian chemical company Yara International has teamed up with tech giant IBM to transform the future of farming. The two companies together endeavour to build the "world's leading" digital farming platform which, they say, will provide holistic digital services and instant agronomic advice. Yara and IBM Services will jointly innovate and commercialise digital agricultural solutions that will help increase global food production. The collaboration will draw on Yara's agronomic knowledge – backed by more than 800 agronomists and a century of experience – and IBM's digital platforms, services and expertise in AI and data analytics. "Our collaboration centres around a common goal to make a real difference in agriculture," said Terje Knutsen, EVP Sales and Marketing in Yara.

The first chapter is the Introductory chapter. The second chapter aims to provide an update of the recent advances in the field of rational design of PDE inhibitors. The third chapter includes designing a series of peptidic inhibitors that possessed a substrate transition-state analog and evaluating the structure-activity relationship of the designed inhibitors, based on docking and scoring, using the docking simulation software Molecular Operating Environment. The aim of the forth chapter is to develop structure-property relationships for the qualitative and quantitative prediction of the reverse-phase liquid chromatographic retention times of chlorogenic acids.

Researchers from our Adsorption and Advanced Materials Group have used machine learning techniques to accurately predict the mechanical properties of metal organic frameworks (MOFs), materials which could be used to extract water from the air in the desert, store dangerous gases or power hydrogen-based cars. The researchers used their algorithm to predict the properties of more than 3000 existing MOFs, as well as MOFs which are yet to be synthesised in the laboratory. The results, published in the inaugural edition of the Cell Press journal Matter, could be used to significantly speed up the way materials are characterised and designed at the molecular scale. MOFs are self-assembling 3D compounds made of metallic and organic atoms connected together. Like plastics, they are highly versatile, and can be customised into millions of different combinations.

A machine learning algorithm that can predict the mechanical properties of metal–organic frameworks (MOFs) offers a way to overcome these highly varied and versatile materials' achilles heel – their instability.1 The team behind this work hope that this computational tool will speed up acceptance of these materials by industry. MOFs are a type of crystalline coordination polymers that form porous structures by combining metal clusters and organic ligands. 'Their "building block" nature allows chemists to easily tune their syntheses to tailor the pore size and surface chemistry for a specific application,' explains David Fairén-Jiménez at the University of Cambridge, UK. 'However, if you wish to use MOFs in real life, you need to shape them into pellets, and this densification may destroy their porosity, thus their functionality.'

The 2D Multi-Agent Path Finding (MAPF) problem aims at finding collision-free paths for a number of agents, from a set of start locations to a set of goal positions in a known 2D environment. MAPF has been studied in theoretical computer science, robotics, and artificial intelligence over several decades, due to its importance for robot navigation. It is currently experiencing significant scientific progress due to its relevance in automated warehousing (such as those operated by Amazon) and in other contemporary application areas. In this paper, we demonstrate that many recently developed MAPF algorithms apply more broadly than currently believed in the MAPF research community. In particular, we describe the 3D Pipe Routing (PR) problem, which aims at placing collision-free pipes from given start locations to given goal locations in a known 3D environment. The MAPF and PR problems are similar: a solution to a MAPF instance is a set of blocked cells in x-y-t space, while a solution to the corresponding PR instance is a set of blocked cells in x-y-z space. We show how to use this similarity to apply several recently developed MAPF algorithms to the PR problem, and discuss their performance on abstract PR instances. We also discuss further research necessary to tackle real-world pipe-routing instances of interest to industry today. This opens up a new direction of industrial relevance for the MAPF research community.

Concrete is the most widely used engineered material in the world with more than 10 billion tons produced annually. Unfortunately, with that scale comes a significant burden in terms of energy, water, and release of greenhouse gases and other pollutants. As such, there is interest in creating concrete formulas that minimize this environmental burden, while satisfying engineering performance requirements. Recent advances in artificial intelligence have enabled machines to generate highly plausible artifacts, such as images of realistic looking faces. Semi-supervised generative models allow generation of artifacts with specific, desired characteristics. In this work, we use Conditional Variational Autoencoders (CVAE), a type of semi-supervised generative model, to discover concrete formulas with desired properties. Our model is trained using open data from the UCI Machine Learning Repository joined with environmental impact data computed using a web-based tool. We demonstrate CVAEs can design concrete formulas with lower emissions and natural resource usage while meeting design requirements. To ensure fair comparison between extant and generated formulas, we also train regression models to predict the environmental impacts and strength of discovered formulas. With these results, a construction engineer may create a formula that meets structural needs and best addresses local environmental concerns.

This paper presents the results of an automated volatile organic compound (VOC) classification process implemented by embedding a machine learning algorithm into an Arduino Uno board. An electronic nose prototype is constructed to detect VOCs from three different fruits. The electronic nose is constructed using an array of five tin dioxide (SnO2) gas sensors, an Arduino Uno board used as a data acquisition section, as well as an intelligent classification module by embedding an approach function which receives data signals from the electronic nose. For the intelligent classification module, a training algorithm is also implemented to create the base of a portable, automated, fast-response, and economical electronic nose device. This solution proposes a portable system to identify and classify VOCs without using a personal computer (PC). Results show an acceptable precision for the embedded approach in comparison with the performance of a toolbox used in a PC. This constitutes an embedded solution able to recognize VOCs in a reliable way to create application products for a wide variety of industries, which are able to classify data acquired by an electronic nose, as VOCs. With this proposed and implemented algorithm, a precision of 99% for classification was achieved into the embedded solution.

While many fear the possibility of robots taking their job, a growing number of companies are putting AI-equipped machines to work in roles humans never wanted in the first place. This includes a broad range of applications, from tracking parasite bugs that pose a critical threat to forests to learning to identify risk in legal documents, according to Bloomberg. Swedish packaging company BillerudKorsnas has put robots in place in roles that involve repetitive tasks. Specifically, it's using AI systems to monitor massive amounts of data, in order to determine how long to cook wood chips before they turn into pulp. This would be an otherwise tedious tasks for humans, since they'd be charged with staring at diagrams all day.

There is a growing need for fast and accurate methods for testing developmental neurotoxicity across several chemical exposure sources. Current approaches, such as in vivo animal studies, and assays of animal and human primary cell cultures, suffer from challenges related to time, cost, and applicability to human physiology. We previously demonstrated success employing machine learning to predict developmental neurotoxicity using gene expression data collected from human 3D tissue models exposed to various compounds. The 3D model is biologically similar to developing neural structures, but its complexity necessitates extensive expertise and effort to employ. By instead focusing solely on constructing an assay of developmental neurotoxicity, we propose that a simpler 2D tissue model may prove sufficient. We thus compare the accuracy of predictive models trained on data from a 2D tissue model with those trained on data from a 3D tissue model, and find the 2D model to be substantially more accurate. Furthermore, we find the 2D model to be more robust under stringent gene set selection, whereas the 3D model suffers substantial accuracy degradation. While both approaches have advantages and disadvantages, we propose that our described 2D approach could be a valuable tool for decision makers when prioritizing neurotoxicity screening.