The skies overhead could soon be filled with constellations of commercial space stations occupying low earth orbit while human colonists settle the Moon with an eye on Mars, if today's robber barons have their way. But this won't result in the same freewheeling Wild West that we saw in the 19th century, unfortunately, as tomorrow's interplanetary settlers will be bringing their lawyers with them. In their new book, The End of Astronauts: Why Robots Are the Future of Exploration, renowned astrophysicist and science editor, Donald Goldsmith, and Martin Rees, the UK's Astronomer Royal, argue in favor of sending robotic scouts -- with their lack of weighty necessities like life support systems -- out into the void ahead of human explorers. But what happens after these synthetic astronauts discover an exploitable resource or some rich dork declares himself Emperor of Mars? In the excerpt below, Goldsmith and Rees discuss the challenges facing our emerging exoplanetary legal system.

This study examines the weak form of the efficient market hypothesis for Bitcoin using a feedforward neural network. Due to the increasing popularity of cryptocurrencies in recent years, the question has arisen, as to whether market inefficiencies could be exploited in Bitcoin. Several studies we refer to here discuss this topic in the context of Bitcoin using either statistical tests or machine learning methods, mostly relying exclusively on data from Bitcoin itself. Results regarding market efficiency vary from study to study. In this study, however, the focus is on applying various asset-related input features in a neural network. The aim is to investigate whether the prediction accuracy improves when adding equity stock indices (S&P 500, Russell 2000), currencies (EURUSD), 10 Year US Treasury Note Yield as well as Gold&Silver producers index (XAU), in addition to using Bitcoin returns as input feature. As expected, the results show that more features lead to higher training performance from 54.6% prediction accuracy with one feature to 61% with six features. On the test set, we observe that with our neural network methodology, adding additional asset classes, no increase in prediction accuracy is achieved. One feature set is able to partially outperform a buy-and-hold strategy, but the performance drops again as soon as another feature is added. This leads us to the partial conclusion that weak market inefficiencies for Bitcoin cannot be detected using neural networks and the given asset classes as input. Therefore, based on this study, we find evidence that the Bitcoin market is efficient in the sense of the efficient market hypothesis during the sample period. We encourage further research in this area, as much depends on the sample period chosen, the input features, the model architecture, and the hyperparameters.

Ionic liquids (ILs) are important solvents for sustainable processes and predicting activity coefficients (ACs) of solutes in ILs is needed. Recently, matrix completion methods (MCMs), transformers, and graph neural networks (GNNs) have shown high accuracy in predicting ACs of binary mixtures, superior to well-established models, e.g., COSMO-RS and UNIFAC. GNNs are particularly promising here as they learn a molecular graph-to-property relationship without pretraining, typically required for transformers, and are, unlike MCMs, applicable to molecules not included in training. For ILs, however, GNN applications are currently missing. Herein, we present a GNN to predict temperature-dependent infinite dilution ACs of solutes in ILs. We train the GNN on a database including more than 40,000 AC values and compare it to a state-of-the-art MCM. The GNN and MCM achieve similar high prediction performance, with the GNN additionally enabling high-quality predictions for ACs of solutions that contain ILs and solutes not considered during training.

Nowadays cross-industry ranging challenges include the reduction of greenhouse gas emission and enabling a circular economy. However, the production of paper from waste paper is still a highly resource intensive task, especially in terms of energy consumption. While paper machines produce a lot of data, we have identified a lack of utilization of it and implement a concept using an operator assistance system and state-of-the-art machine learning techniques, e.g., classification, forecasting and alarm flood handling algorithms, to support daily operator tasks. Our main objective is to provide situation-specific knowledge to machine operators utilizing available data. We expect this will result in better adjusted parameters and therefore a lower footprint of the paper machines.

A fish-shaped robot that'swims' around quickly picking up microplastics has been created by scientists. The tiny machine'wiggles' its body and'flaps' its tail fins to move through water, and could be used to help clear the oceans of plastic pollution. It measures just half-an-inch in length, meaning it can reach into tiny cracks and crevices to collect plastic pieces that would otherwise be inaccessible. Developed by a team at the Sichuan University in China, the robot has no power source, but moves thanks to flashes of near-infrared light. When the light is shone onto to the'fishtail' it bends away from the surface, and when the light is switched off it flops back, propelling the robot through the water.

The US Supreme Court on Tuesday declined an appeal from Bayer-owned Monsanto that aimed to challenge thousands of lawsuits claiming its weedkiller Roundup causes cancer -- a potentially costly ruling. The high court did not explain its decision not to take the case, which left intact a $25 million ruling in favor of a California man who alleged he developed cancer after using the chemical for years. The decision marks a major blow to the German conglomerate's legal fight against some 31,000 Roundup-related cases. "Bayer respectfully disagrees with the Supreme Court's decision," the company said in a statement. "The company believes that the decision undermines the ability of companies to rely on official actions taken by expert regulatory agencies," it added, referring to a 2020 federal finding that Roundup's active ingredient is not risky.

Computational catalysis and machine learning communities have made considerable progress in developing machine learning models for catalyst discovery and design. Yet, a general machine learning potential that spans the chemical space of catalysis is still out of reach. A significant hurdle is obtaining access to training data across a wide range of materials. One important class of materials where data is lacking are oxides, which inhibits models from studying the Oxygen Evolution Reaction and oxide electrocatalysis more generally. To address this we developed the Open Catalyst 2022(OC22) dataset, consisting of 62,521 Density Functional Theory (DFT) relaxations ( 9,884,504 single point calculations) across a range of oxide materials, coverages, and adsorbates (*H, *O, *N, *C, *OOH, *OH, *OH2, *O2, *CO).

Laser machining is a highly flexible non-contact manufacturing technique that has been employed widely across academia and industry. Due to nonlinear interactions between light and matter, simulation methods are extremely crucial, as they help enhance the machining quality by offering comprehension of the inter-relationships between the laser processing parameters. On the other hand, experimental processing parameter optimization recommends a systematic, and consequently time-consuming, investigation over the available processing parameter space. An intelligent strategy is to employ machine learning (ML) techniques to capture the relationship between picosecond laser machining parameters for finding proper parameter combinations to create the desired cuts on industrial-grade alumina ceramic with deep, smooth and defect-free patterns. Laser parameters such as beam amplitude and frequency, scanner passing speed and the number of passes over the surface, as well as the vertical distance of the scanner from the sample surface, are used for predicting the depth, top width, and bottom width of the engraved channels using ML models. Owing to the complex correlation between laser parameters, it is shown that Neural Networks (NN) are the most efficient in predicting the outputs. Equipped with an ML model that captures the interconnection between laser parameters and the engraved channel dimensions, one can predict the required input parameters to achieve a target channel geometry. This strategy significantly reduces the cost and effort of experimental laser machining during the development phase, without compromising accuracy or performance. The developed techniques can be applied to a wide range of ceramic laser machining processes.

Concrete, as the most widely used construction material, is inextricably connected with human development. Despite conceptual and methodological progress in concrete science, concrete formulation for target properties remains a challenging task due to the ever-increasing complexity of cementitious systems. With the ability to tackle complex tasks autonomously, machine learning (ML) has demonstrated its transformative potential in concrete research. Given the rapid adoption of ML for concrete mixture design, there is a need to understand methodological limitations and formulate best practices in this emerging computational field. Here, we review the areas in which ML has positively impacted concrete science, followed by a comprehensive discussion of the implementation, application, and interpretation of ML algorithms. We conclude by outlining future directions for the concrete community to fully exploit the capabilities of ML models.

No matter how conscientious the consumer, by the time the material gets to the end of the conveyor belt at the recycling plant, most plastics end up mixed together. Due to the rather rudimentary sorting techniques in use, only a small percentage of the plastic we try to recycle ends up getting recycled. "The ordinary consumer, with the best intentions--and also the correct procedure--puts everything in the plastic bin. We get it all," said Mogens Hinge, an associate professor in the department of biological and chemical engineering and process and materials engineering at Denmark's Aarhus University, and co-author of the paper "Plastic classification via in-line hyperspectral camera analysis and unsupervised machine learning," which appeared in Vibrational Spectroscopy this year. "Now we have a problem: we can wash it, but we can't unmix it. And plastic is not just plastic."