Revisiting phonon thermal transport in penta-graphene via a machine-learning potential-driven large-scale molecular dynamics simulation

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Alarm bells are ringing in the UK research community. Physics departments may close and researchers leave the UK. What is happening and why?

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Machine learning is also increasingly helpful for sifting through and categorising huge amounts of data. This can help to create early warnings about risks of fraudulent or unsafe food.

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The problem is compounded by APIs that implicitly create stream branches. Request.clone() and Response.clone() perform implicit tee() operations on the body stream – a detail that's easy to miss. Code that clones a request for logging or retry logic may unknowingly create branched streams that need independent consumption, multiplying the resource management burden.