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Categories: Geoscience: Landslides, Mathematics: Modeling

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An interdisciplinary team of mathematicians, engineers, physicists, and medical scientists has uncovered an unexpected link between pure mathematics and genetics, that reveals key insights into the structure of neutral mutations and the evolution of organisms.

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Scientists have trained a machine learning model in outer space, on board a satellite. This achievement could revolutionize the capabilities of remote-sensing satellites by enabling real-time monitoring and decision making for a range of applications.

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A new machine-learning technique can efficiently learn to control a robot, leading to better performance with fewer data.

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Enzymes play a key role in cellular metabolic processes. To enable the quantitative assessment of these processes, researchers need to know the so-called 'turnover number' (for short: kcat) of the enzymes. A team of bioinformaticians now describes a tool for predicting this parameter for various enzymes using AI methods.

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Researchers find large methane emissions: 'Do not flood low-lying areas with freshwater'. Their studies find that freshwater lakes emit much more methane than saltwater lagoons, bogs and wet meadows.

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Scientists once assumed that flooding and mudslides after wildfires were linked to the waxy coating that builds up on charred soil, preventing water absorption. Researchers found that water flow came from absorbed water in both burnt and unburnt areas, suggesting that water was, in fact, being absorbed into burnt ground. The discovery provides valuable insights into where and when potential flooding and mudslides may occur and how landscapes recover after a wildfire.

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A team has combined economic theory with biology to understand how natural systems respond to change. The researchers noticed a similarity between consumers' shopping behavior and the behavior of metabolic systems, which convert food into energy in our bodies. The team focused on predicting how different metabolic systems might respond to environmental change by using an economic tool called the Slutsky equation. Their calculations indicated that very different metabolic systems actually share previously unknown universal properties, and can be understood using tools from other academic fields. Metabolic processes are used in drug development, bioengineering, food production and other industries, so being able to predict how such systems will respond to change can offer many benefits.

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A groundbreaking study reveals the critical yet severely understudied factor of salinity changes in ocean and coastlines caused by climate change.

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Using Japan's most powerful supercomputer, researchers reproduced cases of clear air turbulence around Tokyo. They simulated the fine vortices responsible for this dangerous phenomenon. The usefulness of the simulation in predicting turbulence was confirmed by comparing simulation data with data from aircraft recordings. This research should improve the forecasting of turbulence.

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Structural engineers are familiar with seventeenth-century scientist Robert Hooke's theory that a hanging chain will mirror the shape of an upstanding rigid arch. However, new research now shows that this common-held belief is incorrect because, regardless of the similarities, the hanging chain and the arch are two incompatible mechanical systems.

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Board games based on numbers, like Monopoly, Othello and Chutes and Ladders, make young children better at math, according to a comprehensive review of research published on the topic over the last 23 years.

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The arrangement of electrons in matter, known as the electronic structure, plays a crucial role in fundamental but also applied research such as drug design and energy storage. However, the lack of a simulation technique that offers both high fidelity and scalability across different time and length scales has long been a roadblock for the progress of these technologies. Researchers have now pioneered a machine learning-based simulation method that supersedes traditional electronic structure simulation techniques. Their Materials Learning Algorithms (MALA) software stack enables access to previously unattainable length scales.

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A solution for temporal asymmetry -- or entropy production -- in thermodynamics has been developed to further our understanding of the behavior of biological systems, machine learning, and AI tools. The researchers worked on the time-irreversible Ising model dynamics caused by asymmetric connections between neurons.

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A new tool helps scientists develop machine-learning models that generate richer, more detailed captions for charts, and vary the level of complexity of a caption based on the needs of users. This could help improve accessibility for people with visual disabilities.

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Researchers created a generic framework that enables an engineer or scientist to evaluate the effectiveness of defense schemes that seek to limit a hacker's ability to learn secret information by observing the behavior of a victim computer program.

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Imagine strapping on a virtual reality headset and 'walking' through a long-gone neighborhood in your city -- seeing the streets and buildings as they appeared decades ago. That's a very real possibility now that researchers have developed a method to create 3D digital models of historic neighborhoods using machine learning and historic Sanborn Fire Insurance maps.

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Many factors influence where a landslide will occur, including the shape of the terrain, its slope and drainage areas, the material properties of soil and bedrock, and environmental conditions like climate, rainfall, hydrology and ground motion resulting from earthquakes. Geologists have developed a new technique that uses artificial intelligence to better predict where and why landslides may occur could bolster efforts to protect lives and property in some of the world's most disaster-prone areas. The new method improves the accuracy and interpretability of AI-based machine-learning techniques, requires far less computing power and is more broadly applicable than traditional predictive models.

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In the space of a few months generative AI models, such as ChatGPT, Google's Bard and Midjourney, have been adopted by more and more people in a variety of professional and personal ways. But growing research is underlining that they are encoding biases and negative stereotypes in their users, as well as mass generating and spreading seemingly accurate but nonsensical information. Worryingly, marginalized groups are disproportionately affected by the fabrication of this nonsensical information.

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How do asset markets work? Which stocks behave similarly? Economists, physicists, and mathematicians work intensively to draw a picture but need to learn what is happening outside their discipline. A new paper now builds a bridge.

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Children who receive high-quality child care as babies, toddlers and preschoolers do better in science, technology, engineering and math through high school, and that link is stronger among children from low-income backgrounds, according to new research.