The Prediction of Self-Healing Capacity of Bacteria-Based Concrete Using Machine Learning Approaches

Advances in machine learning (ML) methods are important in industrial engineering and attract great attention in recent years. However, a comprehensive comparative study of the most advanced ML algorithms is lacking. Six integrated ML approaches for the crack repairing capacity of the bacteria-based self-healing concrete are proposed and compared. Six ML algorithms, including the Support Vector Regression (SVR), Decision Tree Regression (DTR), Gradient Boosting Regression (GBR), Artificial Neural Network (ANN), Bayesian Ridge Regression (BRR) and Kernel Ridge Regression (KRR), are adopted for the relationship modeling to predict crack closure percentage (CCP). Particle Swarm Optimization (PSO) is used for the hyper-parameters tuning. The importance of parameters is analyzed. It is demonstrated that integrated ML approaches have great potential to predict the CCP, and PSO is efficient in the hyper-parameter tuning. This research provides useful information for the design of the bacteria-based self-healing concrete and can contribute to the design in the rest of industrial engineering.     

Risk Governance of Emerging Technologies Demonstrated in Terms of its Applicability to Nanomaterials

Nanotechnologies have reached maturity and market penetration that require nano‐specific changes in legislation and harmonization among legislation domains, such as the amendments to REACH for nanomaterials (NMs) which came into force in 2020. Thus, an assessment of the components and regulatory boundaries of NMs risk governance is timely, alongside related methods and tools, as part of the global efforts to optimise nanosafety and integrate it into product design processes, via Safe(r)‐by‐Design (SbD) concepts. This paper provides an overview of the state‐of‐the‐art regarding risk governance of NMs and lays out the theoretical basis for the development and implementation of an effective, trustworthy and transparent risk governance framework for NMs. The proposed framework enables continuous integration of the evolving state of the science, leverages best practice from contiguous disciplines and facilitates responsive re‐thinking of nanosafety governance to meet future needs. To achieve and operationalise such framework, a science‐based Risk Governance Council (RGC) for NMs is being developed. The framework will provide a toolkit for independent NMs' risk governance and integrates needs and views of stakeholders. An extension of this framework to relevant advanced materials and emerging technologies is also envisaged, in view of future foundations of risk research in Europe and globally.     

Deep-Learning-Enabled Fast Optical Identification and Characterization of 2D Materials

Advanced microscopy and/or spectroscopy tools play indispensable roles in nanoscience and nanotechnology research, as they provide rich information about material processes and properties. However, the interpretation of imaging data heavily relies on the “intuition” of experienced researchers. As a result, many of the deep graphical features obtained through these tools are often unused because of difficulties in processing the data and finding the correlations. Such challenges can be well addressed by deep learning. In this work, the optical characterization of 2D materials is used as a case study, and a neural-network-based algorithm is demonstrated for the material and thickness identification of 2D materials with high prediction accuracy and real-time processing capability. Further analysis shows that the trained network can extract deep graphical features such as contrast, color, edges, shapes, flake sizes, and their distributions, based on which an ensemble approach is developed to predict the most relevant physical properties of 2D materials. Finally, a transfer learning technique is applied to adapt the pretrained network to other optical identification applications. This artificial-intelligence-based material characterization approach is a powerful tool that would speed up the preparation, initial characterization of 2D materials and other nanomaterials, and potentially accelerate new material discoveries.     

Rational design of high-entropy ceramics based on machine learning – A critical review

High-entropy materials provide a versatile platform for the rational design of novel candidates with exotic performances. Recently, it has been demonstrated that high-entropy ceramics (HECs), depending on their compositions, show great application potential because of their superior structural and functional properties. However, the immense phase space behind HECs significantly hinders the efficient design and exploitation of high-performance HECs through traditional trial-and-error experiments and expensive ab-initio calculations. Machine learning (ML), on the other hand, has become a popular approach to accelerate the discovery of HECs and screen HECs with exceptional properties. In this article, we review the recent progress of ML applications in discovering and designing novel HECs, including carbides, nitrides, borides, and oxides. We thoroughly discuss different ingredients that are involved in ML applications in HECs, including data collection, feature engineering, model refinement, and prediction performance improvement. We finally provide an outlook on the challenges and development directions of future ML models for HEC predictions.     

Artificial Intelligence in Material Engineering: A Review on Applications of Artificial Intelligence in Material Engineering

The role of artificial intelligence (AI) in material science and engineering (MSE) is becoming increasingly important as AI technology advances. The development of high-performance computing has made it possible to test deep learning (DL) models with significant parameters, providing an opportunity to overcome the limitation of traditional computational methods, such as density functional theory (DFT), in property prediction. Machine learning (ML)-based methods are faster and more accurate than DFT-based methods. Furthermore, the generative adversarial networks (GANs) have facilitated the generation of chemical compositions of inorganic materials without using crystal structure information. These developments have significantly impacted material engineering (ME) and research. Some of the latest developments in AI in ME herein are reviewed. First, the development of AI in the critical areas of ME, such as in material processing, the study of structure and material property, and measuring the performance of materials in various aspects, is discussed. Then, the significant methods of AI and their uses in MSE, such as graph neural network, generative models, transfer of learning, etc. are discussed. The use of AI to analyze the results from existing analytical instruments is also discussed. Finally, AI's advantages, disadvantages, and future in ME are discussed.     

Data Augmentation and Random Multi-Model Deep Learning for Data Classification

In the machine learning (ML) paradigm, data augmentation serves as a regularization approach for creating ML models. The increase in the diversification of training samples increases the generalization capabilities, which enhances the prediction performance of classifiers when tested on unseen examples. Deep learning (DL) models have a lot of parameters, and they frequently overfit. Effectively, to avoid overfitting, data plays a major role to augment the latest improvements in DL. Nevertheless, reliable data collection is a major limiting factor. Frequently, this problem is undertaken by combining augmentation of data, transfer learning, dropout, and methods of normalization in batches. In this paper, we introduce the application of data augmentation in the field of image classification using Random Multi-model Deep Learning (RMDL) which uses the association approaches of multiDL to yield random models for classification. We present a methodology for using Generative Adversarial Networks (GANs) to generate images for data augmenting. Through experiments, we discover that samples generated by GANs when fed into RMDL improve both accuracy and model efficiency. Experimenting across both MNIST and CIAFAR-10 datasets show that, error rate with proposed approach has been decreased with different random models.     

纳米高岭土的研究与应用

介绍了高岭土的结构、性质以及研究现状,并结合纳米基础理论和技术对高岭土的研究方向进行了探讨,综述了纳米高岭土的制备方法,总结了纳米高岭土的应用现状,同时对高岭土纳米化的前景与技术进行了展望.     

Machine learning in building energy management: A critical review and future directions

Over the past two decades, machine learning (ML) has elicited increasing attention in building energy management (BEM) research. However, the boundary of the ML-BEM research has not been clearly defined, and no thorough review of ML applications in BEM during the whole building life-cycle has been published. This study aims to address this gap by reviewing the ML-BEM papers to ascertain the status of this research area and identify future research directions. An integrated framework of ML-BEM, composed of four layers and a series of driving factors, is proposed. Then, based on the hype cycle model, this paper analyzes the current development status of ML-BEM and tries to predict its future development trend. Finally, five research directions are discussed: (1) the behavioral impact on BEM, (2) the integration management of renewable energy, (3) security concerns of ML-BEM, (4) extension to other building life-cycle phases, and (5) the focus on fault detection and diagnosis. The findings of this study are believed to provide useful references for future research on ML-BEM.     

Medical Diagnosis Using Machine Learning: A Statistical Review

Decision making in case of medical diagnosis is a complicated process. A large number of overlapping structures and cases, and distractions, tiredness, and limitations with the human visual system can lead to inappropriate diagnosis. Machine learning (ML) methods have been employed to assist clinicians in overcoming these limitations and in making informed and correct decisions in disease diagnosis. Many academic papers involving the use of machine learning for disease diagnosis have been increasingly getting published. Hence, to determine the use of ML to improve the diagnosis in varied medical disciplines, a systematic review is conducted in this study. To carry out the review, six different databases are selected. Inclusion and exclusion criteria are employed to limit the research. Further, the eligible articles are classified depending on publication year, authors, type of articles, research objective, inputs and outputs, problem and research gaps, and findings and results. Then the selected articles are analyzed to show the impact of ML methods in improving the disease diagnosis. The findings of this study show the most used ML methods and the most common diseases that are focused on by researchers. It also shows the increase in use of machine learning for disease diagnosis over the years. These results will help in focusing on those areas which are neglected and also to determine various ways in which ML methods could be employed to achieve desirable results.     

Development of Deep Learning Models for Predicting the Effects of Exposure to Engineered Nanomaterials on Daphnia magna

This study presents the results of applying deep learning methodologies within the ecotoxicology field, with the objective of training predictive models that can support hazard assessment and eventually the design of safer engineered nanomaterials (ENMs). A workflow applying two different deep learning architectures on microscopic images of Daphnia magna is proposed that can automatically detect possible malformations, such as effects on the length of the tail, and the overall size, and uncommon lipid concentrations and lipid deposit shapes, which are due to direct or parental exposure to ENMs. Next, classification models assign specific objects (heart, abdomen/claw) to classes that depend on lipid densities and compare the results with controls. The models are statistically validated in terms of their prediction accuracy on external D. magna images and illustrate that deep learning technologies can be useful in the nanoinformatics field, because they can automate time‐consuming manual procedures, accelerate the investigation of adverse effects of ENMs, and facilitate the process of designing safer nanostructures. It may even be possible in the future to predict impacts on subsequent generations from images of parental exposure, reducing the time and cost involved in long‐term reproductive toxicity assays over multiple generations.     

Machine Learning and Data-Driven Techniques for the Control of Smart Power Generation Systems: An Uncertainty Handling Perspective

Due to growing concerns regarding climate change and environmental protection, smart power generation has become essential for the economical and safe operation of both conventional thermal power plants and sustainable energy. Traditional first-principle model-based methods are becoming insufficient when faced with the ever-growing system scale and its various uncertainties. The burgeoning era of machine learning (ML) and data-driven control (DDC) techniques promises an improved alternative to these outdated methods. This paper reviews typical applications of ML and DDC at the level of monitoring, control, optimization, and fault detection of power generation systems, with a particular focus on uncovering how these methods can function in evaluating, counteracting, or withstanding the effects of the associated uncertainties. A holistic view is provided on the control techniques of smart power generation, from the regulation level to the planning level. The benefits of ML and DDC techniques are accordingly interpreted in terms of visibility, maneuverability, flexibility, profitability, and safety (abbreviated as the “5-TYs”), respectively. Finally, an outlook on future research and applications is presented.     

Nanoarchitectured Design of Porous Materials and Nanocomposites from Metal‐Organic Frameworks

The emergence of metal‐organic frameworks (MOFs) as a new class of crystalline porous materials is attracting considerable attention in many fields such as catalysis, energy storage and conversion, sensors, and environmental remediation due to their controllable composition, structure and pore size. MOFs are versatile precursors for the preparation of various forms of nanomaterials as well as new multifunctional nanocomposites/hybrids, which exhibit superior functional properties compared to the individual components assembling the composites. This review provides an overview of recent developments achieved in the fabrication of porous MOF‐derived nanostructures including carbons, metal oxides, metal chalcogenides (metal sulfides and selenides), metal carbides, metal phosphides and their composites. Finally, the challenges and future trends and prospects associated with the development of MOF‐derived nanomaterials are also examined.     

Semisupervised dimensionality reduction for hyperspectral images based on the combination of semisupervised learning and metric learning

This paper presents a semisupervised dimensionality reduction (DR) method based on the combination of semisupervised learning (SSL) and metric learning (ML) (CSSLML-DR) in order to overcome some existing limitations in HSIs analysis. Specifically, CSSML focuses on the difficulties of high dimensionality of hyperspectral images (HSIs) data, the insufficient number of labelled samples and inappropriate distance metric. CSSLML aims to learn a local metrics under which the similar samples are pushed as close as possible, and simultaneously, the different samples are pulled away as far as possible. CSSLML constructs two local-reweighted dynamic graphs in an iterative two-steps approach: L-step and V-step. In L-step, the local between-class and within-class graphs are updated. In V-step, the transformation matrix and the reduced space are updated. The algorithm is repeated until a stopping criterion is satisfied. Experimental results on two well-known hyperspectral image data sets demonstrate the superiority of CSSLML algorithm compared to some traditional DR methods.     

Nanomaterials: a Map for Their Selection in Food Packaging Applications

Even though research on nanotechnology has increased rapidly in the last decades, the application of nanotechnology in food and beverage packaging started to show an interest in the scientific community much more recently. Food safety, quality and improvements of properties compared with conventional materials make nanomaterials very attractive in the field of food and beverage packaging applications. Furthermore, in many cases, nanomaterials are used for both food packaging and the food contained, especially when we talk about nanomaterials for active and intelligent packaging. Oxygen scavengers, antimicrobial nanomaterials and nanobiosensors are some examples of current research efforts on nanomaterials for food packaging. This fact has led to a variety of nanoparticles and nanomaterials. The wide range of existing nanomaterials could make its selection for food packaging applications a challenge. Thus, building up a map based on the current state‐of‐the‐art nanoparticles and nanomaterials is required. Furthermore, there is a need to classify all the nanomaterials used specifically in food packaging, independently of their nature, the packaging material and the way they are integrated to this material. In this paper, a classification of the latest advances in this field was made accompanied by the use of Multi‐Criteria Decision Analysis in order to find which are the most relevant (and/or expected to be potentially exploited in the near future) nanomaterials in the area of food packaging. Copyright © 2014 John Wiley & Sons, Ltd.     

Small Silicon,Big Opportunities: The Development and Future of Colloidally‐Stable Monodisperse Silicon Nanocrystals

Nanomaterials are becoming increasingly widespread in consumer technologies, but there is global concern about the toxicity of nanomaterials to humans and the environment as they move rapidly from the research laboratory to the market place. With this in mind, it makes sense to intensify the nanochemistry community's global research effort on the synthesis and study of nanoparticles that are purportedly “green”. One potentially green nanoparticle that seems to be a most promising candidate in this context is silicon, whose appealing optical, optoelectronic, photonic, and biomedical attributes are recently gaining much attention. In this paper, we outline some of our recent contributions to the development of the growing field of silicon nanocrystals (ncSi) in order to stress the importance of continued study of ncSi as a green alternative to the archetypal semiconductor nanocrystals like CdSe, InAs, and PbS. While a variety of developments in synthetic methods, characterization techniques, and applications have been reported in recent years, the ability to prepare colloidally‐stable monodisperse ncSi samples may prove to have the largest impact on the field, as it opens the door to study and access the tunable size‐dependent properties of ncSi. Here, we summarize our recent contributions in size‐separation methods to achieve monodisperse samples, the characterization of size‐dependant property trends, the development of ncSi applications, and their potential impact on the promising future of ncSi.     

纳米材料及其技术研究进展

纳米科学技术是以纳米材料制备和应用技术为基础的科学技术,它为科学技术的研究与发展带来了机遇与挑战。介绍了纳米材料及其技术的国内外最新的研究进展,分析了纳米材料的研究现状,并对纳米材料未来的发展方向进行展望。     

纳米材料在油田开发中的应用

纳米材料作为一种高新技术产物,在生命科学、电子学、医药学等领域已展现出广阔的发展前景,在石油行业亦有着很大的应用潜力。将纳米材料引入石油工程领域,实现纳米科技和现有油田技术的有机结合,有助于为油田的高效可持续开发提供技术支撑。综述了纳米材料在油藏描述、钻井完井液、增产增注、提高采收率等领域的应用和研究进展,并对其未来发展提出了几点建议。     

Discovery of marageing steels:machine learning vs.physical metallurgical modelling

Physical metallurgical (PM) and data-driven approaches can be independently applied to alloy design.Steel technology is a field of physical metallurgy around which some of the most comprehensive under-standing has been developed,with vast models on the relationship between composition,processing,microstructure and properties.They have been applied to the design of new steel alloys in the pursuit of grades of improved properties.With the advent of rapid computing and low-cost data storage,a wealth of data has become available to a suite of modelling techniques referred to as machine learning (ML).ML is being emergingly applied in materials discovery while it requires data mining with its adoption being limited by insufficient high-quality datasets,often leading to unrealistic materials design predictions outside the boundaries of the intended properties.It is therefore required to appraise the strength and weaknesses of PM and ML approach,to assess the real design power of each towards designing novel steel grades.This work incorporates models and datasets from well-established literature on marageing steels.Combining genetic algorithm (GA) with PM models to optimise the parameters adopted for each dataset to maximise the prediction accuracy of PM models,and the results were compared with ML models.The results indicate that PM approaches provide a clearer picture of the overall composition-microstructure-properties relationship but are highly sensitive to the alloy system and hence lack on exploration ability of new domains.ML conversely provides little explicit physical insight whilst yielding a stronger pre-diction accuracy for large-scale data.Hybrid PM/ML approaches provide solutions maximising accuracy,while leading to a clearer physical picture and the desired properties.