Early stroke prediction is vital to prevent damage. A stroke happens when the blood flow to the brain is disrupted by a clot or bleeding, resulting in brain death or injury. However, early diagnosis and treatment redu...Early stroke prediction is vital to prevent damage. A stroke happens when the blood flow to the brain is disrupted by a clot or bleeding, resulting in brain death or injury. However, early diagnosis and treatment reduce long-term needs and lower health costs. We aim for this research to be a machine-learning method for forecasting early warning signs of stroke. The methodology we employed feature selection techniques and multiple algorithms. Utilizing the XGboost Algorithm, the research findings indicate that their proposed model achieved an accuracy rate of 96.45%. This research shows that machine learning can effectively predict early warning signs of stroke, which can help reduce long-term treatment and rehabilitation needs and lower health costs.展开更多
This study chose dominant tree species including Picea crassifolia,Pinus armandii and Pinus tabuliformis which are distributed in Qilian Mountains,Xiaolongshan Mountains,and Bailongjiang River.According to the differe...This study chose dominant tree species including Picea crassifolia,Pinus armandii and Pinus tabuliformis which are distributed in Qilian Mountains,Xiaolongshan Mountains,and Bailongjiang River.According to the different tree species,ages and components,we sampled leaves,branches,stems,and roots,and measured the contents of Nitrogen,Phosphorus,Potassium,along with soil fertility.The changes of N,P,and K contents in the different tree species were studied,and the relationship between nutrient content and environmental factors was analyzed.The results indicated that the content of P in all three species was the lowest(0.039–0.28 g kg),while N content was the highest(0.095–1.72 g kg).As the terminal organ of nutrient transport,the nutrient content of leaves was the highest.P.armandii(0.45 g kg) had a higher nutrient concentration than P.tabulaeformis(0.19 g kg) and P.crassifolia(0.29 g kg).The nutrient content of each species was highest in a young forest,but lowest in a mature forest.The nutrient content of all three tree species was significantly affected by soil nutrient content,and negatively correlated with available soil nutrients.展开更多
文摘Early stroke prediction is vital to prevent damage. A stroke happens when the blood flow to the brain is disrupted by a clot or bleeding, resulting in brain death or injury. However, early diagnosis and treatment reduce long-term needs and lower health costs. We aim for this research to be a machine-learning method for forecasting early warning signs of stroke. The methodology we employed feature selection techniques and multiple algorithms. Utilizing the XGboost Algorithm, the research findings indicate that their proposed model achieved an accuracy rate of 96.45%. This research shows that machine learning can effectively predict early warning signs of stroke, which can help reduce long-term treatment and rehabilitation needs and lower health costs.
基金supported by the Special Fund for Forestry Scientific Research in the Public Interest(No.201204101-4)National Natural Science Foundation of China(No.31260141)CFERN and GENE Award Funds on Ecological Papers
文摘This study chose dominant tree species including Picea crassifolia,Pinus armandii and Pinus tabuliformis which are distributed in Qilian Mountains,Xiaolongshan Mountains,and Bailongjiang River.According to the different tree species,ages and components,we sampled leaves,branches,stems,and roots,and measured the contents of Nitrogen,Phosphorus,Potassium,along with soil fertility.The changes of N,P,and K contents in the different tree species were studied,and the relationship between nutrient content and environmental factors was analyzed.The results indicated that the content of P in all three species was the lowest(0.039–0.28 g kg),while N content was the highest(0.095–1.72 g kg).As the terminal organ of nutrient transport,the nutrient content of leaves was the highest.P.armandii(0.45 g kg) had a higher nutrient concentration than P.tabulaeformis(0.19 g kg) and P.crassifolia(0.29 g kg).The nutrient content of each species was highest in a young forest,but lowest in a mature forest.The nutrient content of all three tree species was significantly affected by soil nutrient content,and negatively correlated with available soil nutrients.
