In many reacting flow systems, the thermo-chemical state-space is known or assumed to evolve close to a low-dimensional manifold (LDM). Various approaches are available to obtain those manifolds and subsequently express the original high-dimensional space with fewer parameterizing variables. Principal component analysis (PCA) is one of the dimensionality reduction methods that can be used to obtain LDMs. PCA does not make prior assumptions about the parameterizing variables and retrieves them empirically from the training data. In this paper, we show that PCA applied in local clusters of data (local PCA) is capable of detecting the intrinsic parameterization of the thermo-chemical state-space. We first demonstrate that utilizing three common combustion models of varying complexity: the Burke-Schumann model, the chemical equilibrium model and the homogeneous reactor. Parameterization of these models is known a priori which allows for benchmarking with the local PCA approach. We further extend the application of local PCA to a more challenging case of a turbulent non-premixed $n$-heptane/air jet flame for which the parameterization is no longer obvious. Our results suggest that meaningful parameterization can be obtained also for more complex datasets. We show that local PCA finds variables that can be linked to local stoichiometry, reaction progress and soot formation processes.

The UK is set to ease data mining laws in a move designed to further boost its flourishing AI industry. We all know that data is vital to AI development. Tech giants are in an advantageous position due to either having existing large datasets or the ability to fund/pay for the data required. Most startups rely on mining data to get started. Europe has notoriously strict data laws.

Roadheader is an engineering robot widely used in underground engineering and mining industry. Interactive dynamics simulation of roadheader is a fundamental problem in unmanned excavation and virtual reality training. However, current research is only based on traditional animation techniques or commercial game engines. There are few studies that apply real-time physical simulation of computer graphics to the field of roadheader robot. This paper aims to present an interactive physically-based simulation system of roadheader robot. To this end, an improved multibody simulation method based on generalized coordinates is proposed. First, our simulation method describes robot dynamics based on generalized coordinates. Compared to state-of-the-art methods, our method is more stable and accurate. Numerical simulation results showed that our method has significantly less error than the game engine in the same number of iterations. Second, we adopt the symplectic Euler integrator instead of the conventional fourth-order Runge-Kutta (RK4) method for dynamics iteration. Compared with other integrators, our method is more stable in energy drift during long-term simulation. The test results showed that our system achieved real-time interaction performance of 60 frames per second (fps). Furthermore, we propose a model format for geometric and robotics modeling of roadheaders to implement the system. Our interactive simulation system of roadheader meets the requirements of interactivity, accuracy and stability.

Artificial intelligence (AI) is often presented in binary terms in both popular culture and political analysis. Either it represents the key to a futuristic utopia defined by the integration of human intelligence and technological prowess, or it is the first step toward a dystopian rise of machines. This same binary thinking is practiced by academics, entrepreneurs, and even activists in relation to the application of AI in combating climate change. The technology industry's singular focus on AI's role in creating a new technological utopia obscures the ways that AI can exacerbate environmental degradation, often in ways that directly harm marginalized populations. In order to utilize AI in fighting climate change in a way that both embraces its technological promise and acknowledges its heavy energy use, the technology companies leading the AI charge need to explore solutions to the environmental impacts of AI.

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.