Due to thermal stability and excellent resistance to slag erosion,the used refractories can be recycled as the main raw material for some refractories.In this article the latest developments about used refractories in...Due to thermal stability and excellent resistance to slag erosion,the used refractories can be recycled as the main raw material for some refractories.In this article the latest developments about used refractories in metallurgical industry has been reviewed,focusing on the results reported in the past decade.The research and reutilization methods of used refractories were discussed.For the research of used refractories,the results of two aspects,the performance and surface corrosion characteristics,are summarized.Then,the advances in research on recycling technology of several main kinds of used refractories,such as MgO-C,Al2O3-SiC-C,Al2O3-MgO-C,magnesium-chrome,and corundum-spinel refractories were summarized and discussed in detail.Some results of the author’s group were reported等线accompanied by these comments.The microstructure and chemical composition were studied by scanning electron microscopy and energy dispersion spectra,and the properties of the refractories were analyzed.Afterwards,the scope of application of materials was determined according to the classification and analysis of refractories.Finally,the large-scale application of used refractories and an outlook is given to future developments of the entire recycling industry.展开更多
As more medical data become digitalized,machine learning is regarded as a promising tool for constructing medical decision support systems.Even with vast medical data volumes,machine learning is still not fully exploi...As more medical data become digitalized,machine learning is regarded as a promising tool for constructing medical decision support systems.Even with vast medical data volumes,machine learning is still not fully exploiting its potential because the data usually sits in data silos,and privacy and security regulations restrict their access and use.To address these issues,we built a secured and explainable machine learning framework,called explainable federated XGBoost(EXPERTS),which can share valuable information among different medical institutions to improve the learning results without sharing the patients’ data.It also reveals how the machine makes a decision through eigenvalues to offer a more insightful answer to medical professionals.To study the performance,we evaluate our approach by real-world datasets,and our approach outperforms the benchmark algorithms under both federated learning and non-federated learning frameworks.展开更多
基金National Key R&D Program of China(Grant No.2018YFC1901504)National Natural Science Foundation of China(Grant No.51872268).
文摘Due to thermal stability and excellent resistance to slag erosion,the used refractories can be recycled as the main raw material for some refractories.In this article the latest developments about used refractories in metallurgical industry has been reviewed,focusing on the results reported in the past decade.The research and reutilization methods of used refractories were discussed.For the research of used refractories,the results of two aspects,the performance and surface corrosion characteristics,are summarized.Then,the advances in research on recycling technology of several main kinds of used refractories,such as MgO-C,Al2O3-SiC-C,Al2O3-MgO-C,magnesium-chrome,and corundum-spinel refractories were summarized and discussed in detail.Some results of the author’s group were reported等线accompanied by these comments.The microstructure and chemical composition were studied by scanning electron microscopy and energy dispersion spectra,and the properties of the refractories were analyzed.Afterwards,the scope of application of materials was determined according to the classification and analysis of refractories.Finally,the large-scale application of used refractories and an outlook is given to future developments of the entire recycling industry.
文摘As more medical data become digitalized,machine learning is regarded as a promising tool for constructing medical decision support systems.Even with vast medical data volumes,machine learning is still not fully exploiting its potential because the data usually sits in data silos,and privacy and security regulations restrict their access and use.To address these issues,we built a secured and explainable machine learning framework,called explainable federated XGBoost(EXPERTS),which can share valuable information among different medical institutions to improve the learning results without sharing the patients’ data.It also reveals how the machine makes a decision through eigenvalues to offer a more insightful answer to medical professionals.To study the performance,we evaluate our approach by real-world datasets,and our approach outperforms the benchmark algorithms under both federated learning and non-federated learning frameworks.
