College English is not only an essential curriculum of language, but also is an extremely important curriculum of cultural in college education. As a result of various factors, there are individual differences in coll...College English is not only an essential curriculum of language, but also is an extremely important curriculum of cultural in college education. As a result of various factors, there are individual differences in college students＇ English study. Therefore, to gradually improve all the students＇ English, graded teaching becomes the best choice. In this thesis, graded teaching will be studied from several respects. And I hope it can give some slight help to some of college teachers.展开更多
The Federal Railroad Administration (FRA)’s Web Based Accident Prediction System (WBAPS) is used by federal, state and local agencies to get a preliminary idea on safety at a rail-highway grade crossing. It is an int...The Federal Railroad Administration (FRA)’s Web Based Accident Prediction System (WBAPS) is used by federal, state and local agencies to get a preliminary idea on safety at a rail-highway grade crossing. It is an interactive and user-friendly tool used to make funding decisions. WBAPS is almost three decades old and involves a three-step approach making it difficult to interpret the contribution of the variables included in the model. It also does not directly account for regional/local developments and technological advancements pertaining to signals and signs implemented at rail-highway grade crossings. Further, characteristics of a rail-highway grade crossing vary by track class which is not explicitly considered by WBAPS. This research, therefore, examines and develops a method and models to estimate crashes at rail-highway grade crossings by track class using regional/local level data. The method and models developed for each track class as well as considering all track classes together are based on data for the state of North Carolina. Linear, as well as count models based on Poisson and Negative Binomial (NB) distributions, was tested for applicability. Negative binomial models were found to be the best fit for the data used in this research. Models for each track class have better goodness of fit statistics compared to the model considering data for all track classes together. This is primarily because traffic, design, and operational characteristics at rail-highway grade crossings are different for each track class. The findings from statistical models in this research are supported by model validation.展开更多
Graded teaching divides students into classes in which students are at the same level.Graded teaching is the variant and development of traditional methods.This article makes some introduction of graded teaching,has a...Graded teaching divides students into classes in which students are at the same level.Graded teaching is the variant and development of traditional methods.This article makes some introduction of graded teaching,has a review of the theoretical development and tries to analyses its advantages and disadvantages.Lastly,we have some specific requirements for teachers.展开更多
Massive Open Online Course(MOOC)has become a popular way of online learning used across the world by millions of people.Meanwhile,a vast amount of information has been collected from the MOOC learners and institutions...Massive Open Online Course(MOOC)has become a popular way of online learning used across the world by millions of people.Meanwhile,a vast amount of information has been collected from the MOOC learners and institutions.Based on the educational data,a lot of researches have been investigated for the prediction of the MOOC learner’s final grade.However,there are still two problems in this research field.The first problem is how to select the most proper features to improve the prediction accuracy,and the second problem is how to use or modify the data mining algorithms for a better analysis of the MOOC data.In order to solve these two problems,an improved random forests method is proposed in this paper.First,a hybrid indicator is defined to measure the importance of the features,and a rule is further established for the feature selection;then,a Clustering-Synthetic Minority Over-sampling Technique(SMOTE)is embedded into the traditional random forests algorithm to solve the class imbalance problem.In experiment part,we verify the performance of the proposed method by using the Canvas Network Person-Course(CNPC)dataset.Furthermore,four well-known prediction methods have been applied for comparison,where the superiority of our method has been proved.展开更多
Lower-grade gliomas(including low-and intermediate-grade gliomas,World Health Organization grades II and III)are diffusely infiltrative neoplasms that arise most often in the cerebral hemispheres of adults and have tr...Lower-grade gliomas(including low-and intermediate-grade gliomas,World Health Organization grades II and III)are diffusely infiltrative neoplasms that arise most often in the cerebral hemispheres of adults and have traditionally been classified based on their presumed histogenesis as astrocytomas,oligodendrogliomas,or oligoastrocytomas.Although the histopathologic classification of lower-grade glioma has been the accepted standard for nearly a century,it suffers from high intra-and inter-observer variability and does not adequately predict clinical outcomes.Based on integrated analysis of multiplatform genomic data from The Cancer Genome Atlas,lower-grade gliomas have been found to segregate into three cohesive,clinically relevant molecular classes.Molecular classes were closely aligned with the status of isocitrate dehydrogenase(IDH)mutations,tumor protein 53 mutations and the co-deletion of chromosome arms 1 p and 19q,but were not closely aligned with histologic classes.These findings emphasize the potential for improved definition of clinically relevant disease subsets using integrated molecular approaches and highlight the importance of biomarkers for brain tumor classification.展开更多
文摘College English is not only an essential curriculum of language, but also is an extremely important curriculum of cultural in college education. As a result of various factors, there are individual differences in college students＇ English study. Therefore, to gradually improve all the students＇ English, graded teaching becomes the best choice. In this thesis, graded teaching will be studied from several respects. And I hope it can give some slight help to some of college teachers.
文摘The Federal Railroad Administration (FRA)’s Web Based Accident Prediction System (WBAPS) is used by federal, state and local agencies to get a preliminary idea on safety at a rail-highway grade crossing. It is an interactive and user-friendly tool used to make funding decisions. WBAPS is almost three decades old and involves a three-step approach making it difficult to interpret the contribution of the variables included in the model. It also does not directly account for regional/local developments and technological advancements pertaining to signals and signs implemented at rail-highway grade crossings. Further, characteristics of a rail-highway grade crossing vary by track class which is not explicitly considered by WBAPS. This research, therefore, examines and develops a method and models to estimate crashes at rail-highway grade crossings by track class using regional/local level data. The method and models developed for each track class as well as considering all track classes together are based on data for the state of North Carolina. Linear, as well as count models based on Poisson and Negative Binomial (NB) distributions, was tested for applicability. Negative binomial models were found to be the best fit for the data used in this research. Models for each track class have better goodness of fit statistics compared to the model considering data for all track classes together. This is primarily because traffic, design, and operational characteristics at rail-highway grade crossings are different for each track class. The findings from statistical models in this research are supported by model validation.
文摘Graded teaching divides students into classes in which students are at the same level.Graded teaching is the variant and development of traditional methods.This article makes some introduction of graded teaching,has a review of the theoretical development and tries to analyses its advantages and disadvantages.Lastly,we have some specific requirements for teachers.
基金supported by the National Natural Science Foundation of China under Grant No.61801222in part supported by the Fundamental Research Funds for the Central Universities under Grant No.30919011230in part supported by the Jiangsu Provincial Department of Education Degree and Graduate Education Research Fund under Grant No.JGZD18_012.
文摘Massive Open Online Course(MOOC)has become a popular way of online learning used across the world by millions of people.Meanwhile,a vast amount of information has been collected from the MOOC learners and institutions.Based on the educational data,a lot of researches have been investigated for the prediction of the MOOC learner’s final grade.However,there are still two problems in this research field.The first problem is how to select the most proper features to improve the prediction accuracy,and the second problem is how to use or modify the data mining algorithms for a better analysis of the MOOC data.In order to solve these two problems,an improved random forests method is proposed in this paper.First,a hybrid indicator is defined to measure the importance of the features,and a rule is further established for the feature selection;then,a Clustering-Synthetic Minority Over-sampling Technique(SMOTE)is embedded into the traditional random forests algorithm to solve the class imbalance problem.In experiment part,we verify the performance of the proposed method by using the Canvas Network Person-Course(CNPC)dataset.Furthermore,four well-known prediction methods have been applied for comparison,where the superiority of our method has been proved.
文摘Lower-grade gliomas(including low-and intermediate-grade gliomas,World Health Organization grades II and III)are diffusely infiltrative neoplasms that arise most often in the cerebral hemispheres of adults and have traditionally been classified based on their presumed histogenesis as astrocytomas,oligodendrogliomas,or oligoastrocytomas.Although the histopathologic classification of lower-grade glioma has been the accepted standard for nearly a century,it suffers from high intra-and inter-observer variability and does not adequately predict clinical outcomes.Based on integrated analysis of multiplatform genomic data from The Cancer Genome Atlas,lower-grade gliomas have been found to segregate into three cohesive,clinically relevant molecular classes.Molecular classes were closely aligned with the status of isocitrate dehydrogenase(IDH)mutations,tumor protein 53 mutations and the co-deletion of chromosome arms 1 p and 19q,but were not closely aligned with histologic classes.These findings emphasize the potential for improved definition of clinically relevant disease subsets using integrated molecular approaches and highlight the importance of biomarkers for brain tumor classification.
