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Categories: Energy: Fossil Fuels, Mathematics: Modeling

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Researchers recently published findings that could lay the groundwork for applying quantum computing methods to protein structure prediction.

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Researchers demonstrated a quantum algorithmic speedup with the quantum approximate optimization algorithm, laying the groundwork for advancements in telecommunications, financial modeling, materials science and more.

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Researchers have developed an assistive machine learning model that greatly improves the ability of medical professionals to read the electroencephalography (EEG) charts of intensive care patients. Because EEG readings are the only method for knowing when unconscious patients are in danger of suffering a seizure or are having seizure-like events, the computational tool could help save thousands of lives each year.

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Researchers developed a computer vision framework for posture estimation and identity tracking which they can use in indoor environments as well as in the wild. They have thus taken an important step towards markerless tracking of animals in the wild using computer vision and machine learning.

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With generative artificial intelligence (GenAI) transforming the social interaction landscape in recent years, large language models (LLMs), which use deep-learning algorithms to train GenAI platforms to process language, have been put in the spotlight. A recent study found that LLMs perform more like the human brain when being trained in more similar ways as humans process language, which has brought important insights to brain studies and the development of AI models.

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Researchers have outlined a way to manipulate water molecules to make CO2R more efficient, with the ultimate goal of creating a clean energy loop. Through their new method, the team was able to perform CO2R with nearly 100% efficiency under mildly acidic conditions, using either gold or zinc as catalysts.

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Researchers have unveiled a novel method to prepare quantum operations on a given quantum computer, using a machine learning generative model to find the appropriate sequence of quantum gates to execute a quantum operation. The study marks a significant step forward in unleashing the full extent of quantum computing.

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A team of geologists analyzed 3,500 samples taken in and around coal mines in Utah and Colorado. Their findings open the possibility that these mines could see a secondary resource stream in the form of rare earth metals used in renewable energy and numerous other high-tech applications.

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Large language model-based chatbots can't effectively recognize users' motivation when they are hesitant about making healthy behavior changes, but they can support those who are committed to take action, say researchers.

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Researchers used generative AI to develop a physics-informed technique to classify phase transitions in materials or physical systems that is much more efficient than existing machine-learning approaches.

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Researchers have determined that a protein prediction technology can yield accurate results in the hunt to efficiently find the best possible drug candidates for many conditions.

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Strongly interacting systems play an important role in quantum physics and quantum chemistry. Stochastic methods such as Monte Carlo simulations are a proven method for investigating such systems. However, these methods reach their limits when so-called sign oscillations occur. This problem has now been solved using the new method of wavefunction matching.

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By studying how bubbles form in a drop of biodiesel, researchers can help future engines get the most energy out of the fuel.

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A special review examines highly-cited papers in statistical ecology. The review, which covers a century of research, details how models and concepts have evolved alongside increasing computational power.

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Physicists and computer scientists have recently expanded the modern theory of the thermodynamics of computation. By combining approaches from statistical physics and computer science, the researchers introduce mathematical equations that reveal the minimum and maximum predicted energy cost of computational processes that depend on randomness, which is a powerful tool in modern computers.

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Coal phase-out is necessary to solve climate change, but can have negative impacts on workers and local communities dependent on coal for their livelihoods. Researchers have studied government plans for coal phase-out around the world and discovered that more than half of such plans include monetary compensation to affected parties. This planned compensation globally amounts to USD 200 billion, but it excludes China and India, the two largest users of coal that currently do not have phase-out plans. The study shows that if China and India decide to phase out coal as fast as needed to reach the Paris climate targets and pay similar compensation, it would cost upwards of USD 2 trillion.

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Systems controlled by next-generation computing algorithms could give rise to better and more efficient machine learning products, a new study suggests.

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Researchers present Tripath: new, deep learning models that can use 3D pathology datasets to make clinical outcome predictions. The research team imaged curated prostate cancer specimens, using two 3D high-resolution imaging techniques. The models were then trained to predict prostate cancer recurrence risk on volumetric human tissue biopsies. By comprehensively capturing 3D morphologies from the entire tissue volume, Tripath performed better than pathologists and outperformed deep learning models that rely on 2D morphology and thin tissue slices.

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Scientists introduce a large language model-based AI system that revolutionizes chemistry by integrating 18 advanced tools for tasks like organic synthesis and drug discovery.

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Emergency departments nationwide are overcrowded and overtaxed, but a new study suggests artificial intelligence (AI) could one day help prioritize which patients need treatment most urgently.