The prediction of the glass-forming ability (GFA) by varying the composition of alloys is a challenging problem in glass physics, as well as a problem for industry, with enormous financial ramifications. Although different empirical guides for the prediction of GFA were established over decades, a comprehensive model or approach that is able to deal with as many variables as possible simultaneously for efficiently predicting good glass formers is still highly desirable. Here, by applying the support vector classification method, we develop models for predicting the GFA of binary metallic alloys from random compositions. The effect of different input descriptors on GFA were evaluated, and the best prediction model was selected, which shows that the information related to liquidus temperatures plays a key role in the GFA of alloys. On the basis of this model, good glass formers can be predicted with high efficiency. The prediction efficiency can be further enhanced by improving larger database and refined input descriptor selection. Our findings suggest that machine learning is very powerful and efficient and has great potential for discovering new metallic glasses with good GFA.

Hypothermia therapy is an old and important method of neuroprotection. Until now, many neurological diseases such as stroke, traumatic brain injury, intracranial pressure elevation, subarachnoid hemorrhage, spinal cord injury, hepatic encephalopathy, and neonatal peripartum encephalopathy have proven to be suppressed by therapeutic hypothermia. Beneficial effects of therapeutic hypothermia have also been discovered, and progress has been made toward improving the benefits of therapeutic hypothermia further through combination with other neuroprotective treatments and by probing the mechanism of hypothermia neuroprotection. In this review, we compare different hypothermia induction methods and provide a summarized account of the synergistic effect of hypothermia therapy with other neuroprotective treatments, along with an overview of hypothermia neuroprotection mechanisms and cold/hypothermia-induced proteins.

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The 3-D printing technique is of great value for predicting the precise fracture zone before, and during, personalized surgery, and can help surgeons achieve accurate anatomical reconstruction for repairs of blowout orbital fractures. Moreover, the simulated bone template produced by 3-D printing models allows for "true-to-original" orbital reconstruction, which can shorten the surgical duration and improve the accuracy and safety of the operation.

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