Application of Grey Correlation Analysis in Physical Fitness Assessment of Higher Vocational Students

Taking students from Sanya Aviation and Tourism College as research objects, the relationship between physical course and physical health is discussed with the method of grey relational analysis. The index with the highest degree of correlation between female students and teaching assessment was the index related to the physical core competence exercise, while the indexes with the highest degree of correlation between male students and teaching assessment were the indexes related to the maintenance of strong and healthy body shape and physical core competence. Thus, in the physical education course setting of higher vocational colleges and in the process of the integrated development between their teaching and students’ physical health, their teaching mode should aim at students’ exercise needs and promoting the quality improvement of their physical education teaching.

Vehicle Travel Destination Prediction Method Based on Multi-source Data

Research on vehicle travel destinations mostly only consider vehicle trajectory data and ignore the influence of other multi-source data,such as weather,time,and points of interest(POI).This study proposes a destination prediction method based on multi-source data,and a multi-input neural network model is established.In terms of the coding of vehicle trajectory data,a GeoHash to vector(Geo2vec)model is proposed to realize the characterization of the trajectory.As for the coding of temporal features,a cyclic coding model is proposed based on trigonometric functions.For the coding of POI,an origin-destination POI matrix(OD-POI)model is proposed based on the state transition probability.Experimental results show that in terms of the average distance and root-mean-square distance deviations,Geo2vec reveals reductions of 4.51%and 5.63%compared to word to vector(Word2vec),and cyclic encoding shows reductions of 6.35%and 6.67%compared to label encoding;further,the method of OD-POI state transition probability is reduced by 5.85%and 6.4%,and the model based on multi-source data is 17.29%and 17.65%lower than the model based on trajectory data only.Finally,the cyclic encoding is reduced by 48.60%in the data dimension compared to one-hot encoding.Accurate destination prediction will help improve the efficiency of automotive human-computer interaction.

The 11th revision of the International Statistical Classification of Disease and Related Health Problems and Cholangiocarcinoma

The World Health Organisation(WHO)classifies clinical diagnoses with the International Statistical Classification of Disease and Related Health Problems(ICD).There have been various iterations of ICD since it is inception.For oncology,ICD-O was introduced in 1976 as a special adaption of ICD for cancers to facilitate greater coding detail of the topography,which describes the anatomical location of the tumour origin,and the morphology concerns the histology of a neoplasm.ICD-10 began in 1983 and was first used in 1994.At the same time,a code for cholangiocarcinoma(CCA)was also introduced(https://icd.who.int/browse10/2010/en).