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Article analysis:

The article is generally reliable and trustworthy, as it provides a detailed description of how physical metallurgical principles can be used to predict yield strength in hot rolled steels using a small labeled dataset. The authors provide evidence for their claims, such as the strengthening mechanism-based compositions-microstructures-property linkage that was optimized by combining the labeled dataset and particle swarm optimization (PSO) algorithm, as well as the deep neural network (DNN) that was initially pre-trained by the big dataset labeled by calculated YS, and then fine-trained by the small dataset labeled by measured YS. Furthermore, they provide evidence for their results being in good agreement with experimental observations.

However, there are some potential biases that should be noted when considering this article. For example, there is no mention of any possible risks associated with using physical metallurgical principles to predict yield strength in hot rolled steels or any counterarguments to their claims. Additionally, there is no discussion of any other methods that could be used to predict yield strength or any comparison between these methods and the one proposed in this article. Finally, there is no mention of any potential limitations or drawbacks associated with using deep learning for this purpose.