Surface-informed active learning prediction of thermophysical properties for liquid refractory multicomponent alloy

· · 来源:nanning资讯

Throughout the development of Towerborne, we maintained our individual backend service codebases in various Azure DevOps (ADO) git repositories. For each service, we split out the codebase between a web and library project.

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The API recognizes that synchronous data sources are both necessary and common. The application should not be forced to always accept the performance cost of asynchronous scheduling simply because that's the only option provided. At the same time, mixing sync and async processing can be dangerous. Synchronous paths should always be an option and should always be explicit.

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