Difficulty discrimination is an important step in autonomous design and interpreting teaching materials development, which is related to scientifi c nature of the materials, teaching effectiveness, and sequential teac...Difficulty discrimination is an important step in autonomous design and interpreting teaching materials development, which is related to scientifi c nature of the materials, teaching effectiveness, and sequential teaching progress. In this paper, we focus on the diffi culty discrimination of interpretation teaching materials on the basis of analytic hierarchy process and natural language processing. We analyze several factors which affect interpretation teaching materials, and we introduce theories of analytic hierarchy process and natural language processing which is intuitive and credible operation basis.展开更多
The rapid development of economy and tourism industry in Liaoning Province calls for more interpreting talents at primary and intermediate level for foreign exchange activities, escort tourism and business negotiation...The rapid development of economy and tourism industry in Liaoning Province calls for more interpreting talents at primary and intermediate level for foreign exchange activities, escort tourism and business negotiation. Due to the different demand from interpreting market, the cultivation of interpreters also need to be diversified. As a new emerging force, private application-oriented universities have set the goal as "To foster pragmatic and practical talents". But the available teaching materials and textbook can't satisfy the need for private application-oriented universities. Therefore, it is imperative to explore suitable teaching material based on the features and cultivation goals of private application-oriented universities, so that to better accomplish the teaching objective.展开更多
Predicting the material stability is essential for accelerating the discovery of advanced materials in renewable energy, aerospace, and catalysis. Traditional approaches, such as Density Functional Theory (DFT), are a...Predicting the material stability is essential for accelerating the discovery of advanced materials in renewable energy, aerospace, and catalysis. Traditional approaches, such as Density Functional Theory (DFT), are accurate but computationally expensive and unsuitable for high-throughput screening. This study introduces a machine learning (ML) framework trained on high-dimensional data from the Open Quantum Materials Database (OQMD) to predict formation energy, a key stability metric. Among the evaluated models, deep learning outperformed Gradient Boosting Machines and Random Forest, achieving up to 0.88 R2 prediction accuracy. Feature importance analysis identified thermodynamic, electronic, and structural properties as the primary drivers of stability, offering interpretable insights into material behavior. Compared to DFT, the proposed ML framework significantly reduces computational costs, enabling the rapid screening of thousands of compounds. These results highlight ML’s transformative potential in materials discovery, with direct applications in energy storage, semiconductors, and catalysis.展开更多
文摘Difficulty discrimination is an important step in autonomous design and interpreting teaching materials development, which is related to scientifi c nature of the materials, teaching effectiveness, and sequential teaching progress. In this paper, we focus on the diffi culty discrimination of interpretation teaching materials on the basis of analytic hierarchy process and natural language processing. We analyze several factors which affect interpretation teaching materials, and we introduce theories of analytic hierarchy process and natural language processing which is intuitive and credible operation basis.
文摘The rapid development of economy and tourism industry in Liaoning Province calls for more interpreting talents at primary and intermediate level for foreign exchange activities, escort tourism and business negotiation. Due to the different demand from interpreting market, the cultivation of interpreters also need to be diversified. As a new emerging force, private application-oriented universities have set the goal as "To foster pragmatic and practical talents". But the available teaching materials and textbook can't satisfy the need for private application-oriented universities. Therefore, it is imperative to explore suitable teaching material based on the features and cultivation goals of private application-oriented universities, so that to better accomplish the teaching objective.
文摘Predicting the material stability is essential for accelerating the discovery of advanced materials in renewable energy, aerospace, and catalysis. Traditional approaches, such as Density Functional Theory (DFT), are accurate but computationally expensive and unsuitable for high-throughput screening. This study introduces a machine learning (ML) framework trained on high-dimensional data from the Open Quantum Materials Database (OQMD) to predict formation energy, a key stability metric. Among the evaluated models, deep learning outperformed Gradient Boosting Machines and Random Forest, achieving up to 0.88 R2 prediction accuracy. Feature importance analysis identified thermodynamic, electronic, and structural properties as the primary drivers of stability, offering interpretable insights into material behavior. Compared to DFT, the proposed ML framework significantly reduces computational costs, enabling the rapid screening of thousands of compounds. These results highlight ML’s transformative potential in materials discovery, with direct applications in energy storage, semiconductors, and catalysis.
