Phase classification has a clear guiding significance for the design of high entropy alloys.For mutually exclusive and non-mutually exclusive classifications,the composition descriptors,commonly used physical paramete...Phase classification has a clear guiding significance for the design of high entropy alloys.For mutually exclusive and non-mutually exclusive classifications,the composition descriptors,commonly used physical parameter descriptors,elemental-property descriptors,and descriptors extracted from the periodic table representation(PTR)by the convolutional neural network were collected.Appropriate selection among features with rich information is helpful for phase classification.Based on random forest,the accuracy of the four-label classification and balanced accuracy of the five-label classification were improved to be 0.907 and 0.876,respectively.The roles of the four important features were summarized by interpretability analysis,and a new important feature was found.The model extrapolation ability and the influence of Mo were demonstrated by phase prediction in(CoFeNiMn)_(1-x)Mo_(x).The phase information is helpful for the hardness prediction,the classification results were coupled with the PTR of hardness data,and the prediction error(the root mean square error)was reduced to 56.69.展开更多
Purpose–This study aims to enhance the accuracy of key entity extraction from railway accident report texts and address challenges such as complex domain-specific semantics,data sparsity and strong inter-sentence sem...Purpose–This study aims to enhance the accuracy of key entity extraction from railway accident report texts and address challenges such as complex domain-specific semantics,data sparsity and strong inter-sentence semantic dependencies.A robust entity extraction method tailored for accident texts is proposed.Design/methodology/approach–This method is implemented through a dual-branch multi-task mutual learning model named R-MLP,which jointly performs entity recognition and accident phase classification.The model leverages a shared BERT encoder to extract contextual features and incorporates a sentence span indexing module to align feature granularity.A cross-task mutual learning mechanism is also introduced to strengthen semantic representation.Findings–R-MLP effectively mitigates the impact of semantic complexity and data sparsity in domain entities and enhances the model’s ability to capture inter-sentence semantic dependencies.Experimental results show that R-MLP achieves a maximum F1-score of 0.736 in extracting six types of key railway accident entities,significantly outperforming baseline models such as RoBERTa and MacBERT.Originality/value–This demonstrates the proposed method’s superior generalization and accuracy in domainspecific entity extraction tasks,confirming its effectiveness and practical value.展开更多
Two dense pellicular agarose-glass matrices of different sizes and densities, i.e., AG-S and AG-L, have been characterized for their bed expansion behavior, flow hydrodynamics and particle classifications in an expand...Two dense pellicular agarose-glass matrices of different sizes and densities, i.e., AG-S and AG-L, have been characterized for their bed expansion behavior, flow hydrodynamics and particle classifications in an expanded bed system. A 26 mm ID column with side ports was used for sampling the liquid-solid suspension during expanded bed operations. Measurements of the collected solid phase at different column positions yielded the particle size and density distribution data. It was found that the composite matrices showed particle size as well as density classifications along the column axis, i.e., both the size and density of each matrix decreased with increasing the axial bed height. Their axial classifications were expressed by a correlation related to both the particle size and density as a function of the dimensionless axial bed height. The correlation was found to fairly describe the solid phase classifications in the expanded bed system. Moreover, it can also be applied to other two commercial solid matrices designed for expanded bed applications.展开更多
The main objective of this article is to study both dynamic and structural transitions of the Taylor-Couette flow, by using the dynamic transition theory and geometric theory of incompressible flows developed recently...The main objective of this article is to study both dynamic and structural transitions of the Taylor-Couette flow, by using the dynamic transition theory and geometric theory of incompressible flows developed recently by the authors. In particular, it is shown that as the Taylor number crosses the critical number, the system undergoes either a continuous or a jump dynamic transition, dictated by the sign of a computable, nondimensional parameter R. In addition, it is also shown that the new transition states have the Taylor vortex type of flow structure, which is structurally stable.展开更多
基金supported by the National Natural Science Foundation of China(Nos.51671075,51971086)the Natural Science Foundation of Heilongjiang Province,China(No.LH2022E081)。
文摘Phase classification has a clear guiding significance for the design of high entropy alloys.For mutually exclusive and non-mutually exclusive classifications,the composition descriptors,commonly used physical parameter descriptors,elemental-property descriptors,and descriptors extracted from the periodic table representation(PTR)by the convolutional neural network were collected.Appropriate selection among features with rich information is helpful for phase classification.Based on random forest,the accuracy of the four-label classification and balanced accuracy of the five-label classification were improved to be 0.907 and 0.876,respectively.The roles of the four important features were summarized by interpretability analysis,and a new important feature was found.The model extrapolation ability and the influence of Mo were demonstrated by phase prediction in(CoFeNiMn)_(1-x)Mo_(x).The phase information is helpful for the hardness prediction,the classification results were coupled with the PTR of hardness data,and the prediction error(the root mean square error)was reduced to 56.69.
基金funded by the Technology Research and Development Plan Program of China State Railway Group Co.,Ltd.(No.Q2024T001)the Foundation of China Academy of Railway Sciences Co.,Ltd.(No:2024YJ259).
文摘Purpose–This study aims to enhance the accuracy of key entity extraction from railway accident report texts and address challenges such as complex domain-specific semantics,data sparsity and strong inter-sentence semantic dependencies.A robust entity extraction method tailored for accident texts is proposed.Design/methodology/approach–This method is implemented through a dual-branch multi-task mutual learning model named R-MLP,which jointly performs entity recognition and accident phase classification.The model leverages a shared BERT encoder to extract contextual features and incorporates a sentence span indexing module to align feature granularity.A cross-task mutual learning mechanism is also introduced to strengthen semantic representation.Findings–R-MLP effectively mitigates the impact of semantic complexity and data sparsity in domain entities and enhances the model’s ability to capture inter-sentence semantic dependencies.Experimental results show that R-MLP achieves a maximum F1-score of 0.736 in extracting six types of key railway accident entities,significantly outperforming baseline models such as RoBERTa and MacBERT.Originality/value–This demonstrates the proposed method’s superior generalization and accuracy in domainspecific entity extraction tasks,confirming its effectiveness and practical value.
基金Supported by the National Natural Science Foundation of China (No. 20025617).
文摘Two dense pellicular agarose-glass matrices of different sizes and densities, i.e., AG-S and AG-L, have been characterized for their bed expansion behavior, flow hydrodynamics and particle classifications in an expanded bed system. A 26 mm ID column with side ports was used for sampling the liquid-solid suspension during expanded bed operations. Measurements of the collected solid phase at different column positions yielded the particle size and density distribution data. It was found that the composite matrices showed particle size as well as density classifications along the column axis, i.e., both the size and density of each matrix decreased with increasing the axial bed height. Their axial classifications were expressed by a correlation related to both the particle size and density as a function of the dimensionless axial bed height. The correlation was found to fairly describe the solid phase classifications in the expanded bed system. Moreover, it can also be applied to other two commercial solid matrices designed for expanded bed applications.
基金supported by the National Science Foundation, the Office of Naval Research and the National Natural Science Foundation of China
文摘The main objective of this article is to study both dynamic and structural transitions of the Taylor-Couette flow, by using the dynamic transition theory and geometric theory of incompressible flows developed recently by the authors. In particular, it is shown that as the Taylor number crosses the critical number, the system undergoes either a continuous or a jump dynamic transition, dictated by the sign of a computable, nondimensional parameter R. In addition, it is also shown that the new transition states have the Taylor vortex type of flow structure, which is structurally stable.
