Artificial Intelligence in Materials Modeling and Design

In recent decades, the use of artificial intelligence (AI) techniques in the field of materials modeling has received significant attention owing to their excellent ability to analyze a vast amount of data and reveal correlations between several complex interrelated phenomena. In this review paper, we summarize recent advances in the applications of AI techniques for numerical modeling of different types of materials. AI techniques such as machine learning and deep learning show great advantages and potential for predicting important mechanical properties of materials and reveal how changes in certain principal parameters affect the overall behavior of engineering materials. Furthermore, in this review, we show that the application of AI techniques can significantly help to improve the design and optimize the properties of future advanced engineering materials. Finally, a perspective on the challenges and prospects of the applications of AI techniques for material modeling is presented

Bioplastics made from upcycled food waste. Prospects for their use in the field of design

In recent years, the negative effects on the environment of the intensive use of synthetic, oil-derived plastics to make products, even those with a limited required duration, have given renewed impetus to the search for biodegradable and/or compostable materials obtained from renewable sources, particularly biopolymers derived from vegetable, animal or microbial matter that could prove a valid alternative in a number of applications: not only in the packaging industry, but also for making objects with a longer required duration. Indeed, as well as offering the possibility of being used as they are, immediately after having undergone traditional-type mechanical processing, it is also possible to mix, supplement and modify them both on a macro- and nanometric scale, allowing us to significantly increase their properties and performance and adapt them to a wide variety of needs. However, the real challenge is to create new materials from food waste and not from specially grown crops, whose production has, in any case, an environmental cost. This allows us to reduce the waste produced when processing foods, which is usually a practical problem and involves a considerable investment in economic terms. It also helps us address one of the worst problems of our time: that of the waste that sees a third of the food produced worldwide lost along the various steps of the food production chain. There is an enormous variety of vegetable, animal and microbial waste that can be used to create biopolymers: from the orange peels left over from fruit juice production to the grapes used to produce wine; from chocolate production waste to egg shells and prawns. We can extract the starches, cellulose, pectin, chitin, lactic acid, collagen, blood proteins and gelatin that form the basis of bioplastics from these materials, either extracting them directly or using mechanical or chemical processes. These are true ‘treasure troves’ of substances that can become useful materials thanks to processes of varying complexity. In recent years, the testing of substances made from food waste has increased significantly; the sheer abundance of raw materials that can be used to make them has encouraged institutional research, as well as an approach to project development that has been widely embraced by many young designers who craft these materials. Nevertheless, there is still no systematic record of the results achieved. This has slowed down their adoption, which in contrast offers enormous potential that is still almost entirely unexplored. This paper considers all aspects of these materials, starting with the most interesting experiments underway, and envisages possible future scenarios

A Cryogenic Silicon Interferometer for Gravitational-wave Detection

The detection of gravitational waves from compact binary mergers by LIGO has opened the era of gravitational wave astronomy, revealing a previously hidden side of the cosmos. To maximize the reach of the existing LIGO observatory facilities, we have designed a new instrument that will have 5 times the range of Advanced LIGO, or greater than 100 times the event rate. Observations with this new instrument will make possible dramatic steps toward understanding the physics of the nearby universe, as well as observing the universe out to cosmological distances by the detection of binary black hole coalescences. This article presents the instrument design and a quantitative analysis of the anticipated noise floor

JNER at 15 years: analysis of the state of neuroengineering and rehabilitation.

On JNER's 15th anniversary, this editorial analyzes the state of the field of neuroengineering and rehabilitation. I first discuss some ways that the nature of neurorehabilitation research has evolved in the past 15 years based on my perspective as editor-in-chief of JNER and a researcher in the field. I highlight increasing reliance on advanced technologies, improved rigor and openness of research, and three, related, new paradigms - wearable devices, the Cybathlon competition, and human augmentation studies - indicators that neurorehabilitation is squarely in the age of wearability. Then, I briefly speculate on how the field might make progress going forward, highlighting the need for new models of training and learning driven by big data, better personalization and targeting, and an increase in the quantity and quality of usability and uptake studies to improve translation

AI and OR in management of operations: history and trends

The last decade has seen a considerable growth in the use of Artificial Intelligence (AI) for operations management with the aim of finding solutions to problems that are increasing in complexity and scale. This paper begins by setting the context for the survey through a historical perspective of OR and AI. An extensive survey of applications of AI techniques for operations management, covering a total of over 1200 papers published from 1995 to 2004 is then presented. The survey utilizes Elsevier's ScienceDirect database as a source. Hence, the survey may not cover all the relevant journals but includes a sufficiently wide range of publications to make it representative of the research in the field. The papers are categorized into four areas of operations management: (a) design, (b) scheduling, (c) process planning and control and (d) quality, maintenance and fault diagnosis. Each of the four areas is categorized in terms of the AI techniques used: genetic algorithms, case-based reasoning, knowledge-based systems, fuzzy logic and hybrid techniques. The trends over the last decade are identified, discussed with respect to expected trends and directions for future work suggested