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Categories: Energy: Nuclear, Mathematics: Modeling

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Drosophila -- known as fruit flies -- are a valuable model for human heart pathophysiology, including cardiac aging and cardiomyopathy. However, a choke point in evaluating fruit fly hearts is the need for human intervention to measure the heart at moments of its largest expansion or its greatest contraction to calculate cardiac dynamics. Researchers now show a way to significantly cut the time needed for that analysis while utilizing more of the heart region, using deep learning and high-speed video microscopy.

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Physicists have developed a computer program incorporating machine learning that could help identify blobs of plasma in outer space known as plasmoids. In a novel twist, the program has been trained using simulated data.

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A team of researchers had already been working with electricity price data for years before Russia's invasion of Ukraine, exploring statistics and developing forecasting methods. Now they zero in on how prices in different countries relate and how countries were affected by the energy crisis and address the interdependencies of different markets. Their approach combines statistical physics and network science, identifying communities and the fundamental spatiotemporal patterns within the electricity price/time data from all countries. The researchers hope their work will strengthen the European perspective in the political debate about electricity markets and prices, because problems like this are best tackled via international cooperation.

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Raising the energy state of an atom's nucleus using a laser, or exciting it, would enable development of the most accurate atomic clocks ever to exist. This has been hard to do because electrons, which surround the nucleus, react easily with light, increasing the amount of light needed to reach the nucleus. By causing the electrons to bond with fluorine in a transparent crystal, UCLA physicists have finally succeeded in exciting the neutrons in a thorium atom's nucleus using a moderate amount of laser light. This accomplishment means that measurements of time, gravity and other fields that are currently performed using atomic electrons can be made with orders of magnitude higher accuracy.

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Physicists have made a significant breakthrough in solar cell technology by developing a new analytical model that improves the understanding and efficiency of thin-film photovoltaic (PV) devices.

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AI model finds the cancer clues at lightning speed. Researchers have developed an AI model that increases the potential for detecting cancer through sugar analyses. The AI model is faster and better at finding abnormalities than the current semi-manual method.

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Researchers have developed a novel deep learning algorithm that outperformed existing computer-based osteoporosis risk prediction methods, potentially leading to earlier diagnoses and better outcomes for patients with osteoporosis risk.

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Researchers have found that artificial intelligence models that are most accurate at predicting race and gender from X-ray images also show the biggest 'fairness gaps' -- that is, discrepancies in their ability to accurately diagnose images of people of different races or genders.

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Researchers have developed a deep-learning model, called PepFlow, that can predict all possible shapes of peptides -- chains of amino acids that are shorter than proteins, but perform similar biological functions. Peptides are known to be highly flexible, taking on a wide range of folding patterns, and are thus involved in many biological processes of interest to researchers in the development of therapeutics.

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Meet SQUID, a new computational tool. Compared with other genomic AI models, SQUID is more consistent, reduces background noise, and can yield better predictions regarding critical mutations. The new system aims to bring scientists closer to their findings' true medical implications.

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Behavioral analysis can provide a lot of information about the health status or motivations of a living being. A new technology makes it possible for a single deep learning model to detect animal motion across many species and environments. This 'foundational model', called SuperAnimal, can be used for animal conservation, biomedicine, and neuroscience research.

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Scientists have developed a new, more energy-efficient way for AI algorithms to process data. His model may become the basis for a new generation of AI that learns like we do. Notably, these findings may also lend support to neuroscience theories surrounding memory's role in learning.

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Scientists have proposed a new AI method in which systems of habitual and goal-directed behaviors learn to help each other. Through computer simulations that mimicked the exploration of a maze, the method quickly adapts to changing environments and also reproduced the behavior of humans and animals after they had been accustomed to a certain environment for a long time. The study not only paves the way for the development of systems that adapt quickly and reliably in the burgeoning field of AI, but also provides clues to how we make decisions in the fields of neuroscience and psychology.

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Researchers used theoretical calculations assessing electron orbital symmetry to synthesize new molecule designed to be both transparent and colorless while absorbing near-infrared light. This compound demonstrates the first systematic approach to producing such materials and have applications in advanced electronics. This compound also shows semiconducting properties.

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A technique can plan a trajectory for a robot using only language-based inputs. While it can't outperform vision-based approaches, it could be useful in settings that lack visual data to use for training.

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Researchers have experimentally generated high-density relativistic electron-positron pair-plasma beams by producing two to three orders of magnitude more pairs than previously reported.

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Data assimilation is an important mathematical discipline in earth sciences, particularly in numerical weather prediction (NWP). However, conventional data assimilation methods require significant computational resources. To address this, researchers developed a novel method to solve data assimilation on quantum computers, significantly reducing the computation time. The findings of the study have the potential to advance NWP systems and will inspire practical applications of quantum computers for advancing data assimilation.

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Complex metal alloys enter a new era of predictive design for aerospace and other high-temperature applications.

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The exhaust heat generated by a fusing plasma in a commercial-scale reactor may not be as damaging to the vessel's innards as once thought, according to new research about escaping plasma particles.

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Boys are good at math, girls not so much? A study has analyzed the social mechanisms that contribute to the gender gap in math confidence. While peer comparisons seem to play a crucial role for boys, girls' subjective evaluations are more likely to be based on objective performance.