This paper had developed and tested optimized content extraction algorithm using NLP method, TFIDF method for word of weight, VSM for information search, cosine method for similar quality calculation from learning doc...This paper had developed and tested optimized content extraction algorithm using NLP method, TFIDF method for word of weight, VSM for information search, cosine method for similar quality calculation from learning document at the distance learning system database. This test covered following things: 1) to parse word structure at the distance learning system database documents and Cyrillic Mongolian language documents at the section, to form new documents by algorithm for identifying word stem;2) to test optimized content extraction from text material based on e-test results (key word, correct answer, base form with affix and new form formed by word stem without affix) at distance learning system, also to search key word by automatically selecting using word extraction algorithm;3) to test Boolean and probabilistic retrieval method through extended vector space retrieval method. This chapter covers: to process document content extraction retrieval algorithm, to propose recommendations query through word stem, not depending on word position based on Cyrillic Mongolian language documents distinction.展开更多
Machine learning(ML)has become a powerful tool for accelerating the design and development of new materials.Among various traditional ML algorithms,decision tree-based ensemble learning methods are frequently chosen f...Machine learning(ML)has become a powerful tool for accelerating the design and development of new materials.Among various traditional ML algorithms,decision tree-based ensemble learning methods are frequently chosen for their strong predictive capabilities.However,decision trees are limited in regression tasks to interpolating within the data range of the training set,which restricts their usefulness for designing materials with enhanced properties.Herein,we focused on predicting and optimizing the L1_(2)-phase solvus temperature(T_(L12))and density,two critical properties for multi-principal-element superalloys(MPESAs).To achieve this,we employed the piecewise symbolic regression tree(PS-Tree),which demonstrates excellent extrapolation capability.Our model successfully predicted high T_(L12)values exceeding the training data range(1242℃),with four candidate alloys achieving TL12values of 1246,1249,1254,and 1274℃.Experimental validation confirmed the accuracy of these predictions,verifying the robust extrapolative capability of the PS-Tree method.Notably,one alloy exhibited a T_(L12)of 1267℃and a density of 7.94 g cm^(-3),outperforming most MPESAs.Additionally,another alloy exhibited a compressive yield strength of 897 MPa at 750℃,with a specific yield strength at this temperature higher than that of most L1_(2)-strengthened alloys and Co/Ni-based superalloys.Moreover,the model provided generalized insights,indicating that alloys with δ_(r)>5.3 and ΔH_(mix)<-12.8 J mol^(-1)K^(-1)tend to favor higher T_(L12).展开更多
The purpose of this study is to investigate the effect of Panoramic Virtual Reality(PVR)applied to online earth science classes on students’learning flow.To this end,a PVR learning material was made with a geology le...The purpose of this study is to investigate the effect of Panoramic Virtual Reality(PVR)applied to online earth science classes on students’learning flow.To this end,a PVR learning material was made with a geology learning site,which contains a core geologic concept contained in a high school curriculum in Korea.To this end,a PVR learning material was made at a geologic field site to provide an interactive and engaging way for students to grasp core geologic concepts according to the high school curriculum in Korea.The PVR was applied to online earth science classes with 45 high school students.In order to examine the effect of the PVR on students'learning flow,pre-post learning flow test papers were used,then matchedsample t-test analysis and students'responses were analyzed.The result shows online classes with PVR have positive effects on learning flow(p<0.05).And it was possible for the students to observe three-dimensional geologic structures effectively in online classes as in offline field trips.And the students'responded with positive feedbacks.These suggest that PVR in online classes can be used as an effective teaching method,which can improve students'flow and eventually understanding subjects.展开更多
With the help of Big Data and Citespace software, this research makes a statistical analysis of the journals anddissertations on College English teaching and learning materials collected by CNKI from 2011 to 2020. Thi...With the help of Big Data and Citespace software, this research makes a statistical analysis of the journals anddissertations on College English teaching and learning materials collected by CNKI from 2011 to 2020. This paper,based on the knowledge map drawn by the visualized analysis of literatures volume, authors, research institutions,and keywords clustering, analyzes the current research status and hotspots in the compilation of China’s CollegeEnglish textbooks.展开更多
文摘This paper had developed and tested optimized content extraction algorithm using NLP method, TFIDF method for word of weight, VSM for information search, cosine method for similar quality calculation from learning document at the distance learning system database. This test covered following things: 1) to parse word structure at the distance learning system database documents and Cyrillic Mongolian language documents at the section, to form new documents by algorithm for identifying word stem;2) to test optimized content extraction from text material based on e-test results (key word, correct answer, base form with affix and new form formed by word stem without affix) at distance learning system, also to search key word by automatically selecting using word extraction algorithm;3) to test Boolean and probabilistic retrieval method through extended vector space retrieval method. This chapter covers: to process document content extraction retrieval algorithm, to propose recommendations query through word stem, not depending on word position based on Cyrillic Mongolian language documents distinction.
基金financially supported by the National Natural Science Foundation of China(Nos.52371007 and 52301042)the National Key R&D Program of China(No.2020YFB0704503)+2 种基金Shenzhen Science and Technology Program(No.SGDX20210823104002016)Guangdong Basic and Applied Basic Research Foundation(No.2021B1515120071)Shenzhen Basic Research Project(No.JCYJ20241202123504007)
文摘Machine learning(ML)has become a powerful tool for accelerating the design and development of new materials.Among various traditional ML algorithms,decision tree-based ensemble learning methods are frequently chosen for their strong predictive capabilities.However,decision trees are limited in regression tasks to interpolating within the data range of the training set,which restricts their usefulness for designing materials with enhanced properties.Herein,we focused on predicting and optimizing the L1_(2)-phase solvus temperature(T_(L12))and density,two critical properties for multi-principal-element superalloys(MPESAs).To achieve this,we employed the piecewise symbolic regression tree(PS-Tree),which demonstrates excellent extrapolation capability.Our model successfully predicted high T_(L12)values exceeding the training data range(1242℃),with four candidate alloys achieving TL12values of 1246,1249,1254,and 1274℃.Experimental validation confirmed the accuracy of these predictions,verifying the robust extrapolative capability of the PS-Tree method.Notably,one alloy exhibited a T_(L12)of 1267℃and a density of 7.94 g cm^(-3),outperforming most MPESAs.Additionally,another alloy exhibited a compressive yield strength of 897 MPa at 750℃,with a specific yield strength at this temperature higher than that of most L1_(2)-strengthened alloys and Co/Ni-based superalloys.Moreover,the model provided generalized insights,indicating that alloys with δ_(r)>5.3 and ΔH_(mix)<-12.8 J mol^(-1)K^(-1)tend to favor higher T_(L12).
基金supported by the research grant of the Kongju National University in 2023.
文摘The purpose of this study is to investigate the effect of Panoramic Virtual Reality(PVR)applied to online earth science classes on students’learning flow.To this end,a PVR learning material was made with a geology learning site,which contains a core geologic concept contained in a high school curriculum in Korea.To this end,a PVR learning material was made at a geologic field site to provide an interactive and engaging way for students to grasp core geologic concepts according to the high school curriculum in Korea.The PVR was applied to online earth science classes with 45 high school students.In order to examine the effect of the PVR on students'learning flow,pre-post learning flow test papers were used,then matchedsample t-test analysis and students'responses were analyzed.The result shows online classes with PVR have positive effects on learning flow(p<0.05).And it was possible for the students to observe three-dimensional geologic structures effectively in online classes as in offline field trips.And the students'responded with positive feedbacks.These suggest that PVR in online classes can be used as an effective teaching method,which can improve students'flow and eventually understanding subjects.
基金This research is based on the“Construction of Chinese Academic Translation Team Project”(ZP1823105)of the Characteristic Humanities and Social Science Discipline Construction in 2018,and“2019-2020 Postgraduate Teaching Book Project Translation and International Communication”,East China University of Science and Technology.
文摘With the help of Big Data and Citespace software, this research makes a statistical analysis of the journals anddissertations on College English teaching and learning materials collected by CNKI from 2011 to 2020. This paper,based on the knowledge map drawn by the visualized analysis of literatures volume, authors, research institutions,and keywords clustering, analyzes the current research status and hotspots in the compilation of China’s CollegeEnglish textbooks.
