This work demonstrates the execution of a novel process model for knowledge discovery and data mining for metabolomics (MeKDDaM). It aims to illustrate MeKDDaM process model applicability using four different real-world applications and to highlight its strengths and unique features. The demonstrated applications provide coverage for metabolite profiling, target analysis, and metabolic fingerprinting. The data analysed in these applications were captured by chromatographic separation and mass spectrometry technique (LC-MS), Fourier transform infrared spectroscopy (FT-IR), and nuclear magnetic resonance spectroscopy (NMR) and involve the analysis of plant, animal, and human samples. The process was executed using both data-driven and hypothesis-driven data mining approaches in order to perform various data mining goals and tasks by applying a number of data mining techniques. The applications were selected to achieve a range of analytical goals and research questions and to provide coverage for metabolite profiling, target analysis, and metabolic fingerprinting using datasets that were captured by NMR, LC-MS, and FT-IR using samples of a plant, animal, and human origin. The process was applied using an implementation environment which was created in order to provide a computer-aided realisation of the process model execution.

This work presents MeKDDaM-SAGA, computer-aided automation software for implementing a novel knowledge discovery and data mining process model that was designed for performing justifiable, traceable and reproducible metabolomics data analysis. The process model focuses on achieving metabolomics analytical objectives and on considering the nature of its involved data. MeKDDaM-SAGA was successfully used for guiding the process model execution in a number of metabolomics applications. It satisfies the requirements of the proposed process model design and execution. The software realises the process model layout, structure and flow and it enables its execution externally using various data mining and machine learning tools or internally using a number of embedded facilities that were built for performing a number of automated activities such as data preprocessing, data exploration, data acclimatization, modelling, evaluation and visualization. MeKDDaM-SAGA was developed using object-oriented software engineering methodology and was constructed in Java. It consists of 241 design classes that were designed to implement 27 use-cases. The software uses an XML database to guarantee portability and uses a GUI interface to ensure its user-friendliness. It implements an internal embedded version control system that is used to realise and manage the process flow, feedback and iterations and to enable undoing and redoing the execution of the process phases, activities, and the internal tasks within its phases.

Materials design can be cast as an optimization problem with the goal of achieving desired properties, by varying material composition, microstructure morphology, and processing conditions. Existence of both qualitative and quantitative material design variables leads to disjointed regions in property space, making the search for optimal design challenging. Limited availability of experimental data and the high cost of simulations magnify the challenge. This situation calls for design methodologies that can extract useful information from existing data and guide the search for optimal designs efficiently. To this end, we present a data-centric, mixed-variable Bayesian Optimization framework that integrates data from literature, experiments, and simulations for knowledge discovery and computational materials design. Our framework pivots around the Latent Variable Gaussian Process (LVGP), a novel Gaussian Process technique which projects qualitative variables on a continuous latent space for covariance formulation, as the surrogate model to quantify "lack of data" uncertainty. Expected improvement, an acquisition criterion that balances exploration and exploitation, helps navigate a complex, nonlinear design space to locate the optimum design. The proposed framework is tested through a case study which seeks to concurrently identify the optimal composition and morphology for insulating polymer nanocomposites. We also present an extension of mixed-variable Bayesian Optimization for multiple objectives to identify the Pareto Frontier within tens of iterations. These findings project Bayesian Optimization as a powerful tool for design of engineered material systems.

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.