[Objectives] To survey the current using situation and price distribution of pharmaceutical preparations in medical institutions of Sichuan Province,to provide basis for pricing the pharmaceutical preparations in medi...[Objectives] To survey the current using situation and price distribution of pharmaceutical preparations in medical institutions of Sichuan Province,to provide basis for pricing the pharmaceutical preparations in medical institutions. [Methods] From June to September2016,15 sample hospitals were selected from Ⅱ-B class,Ⅱ-A class,Ⅲ-B class and Ⅲ-A class hospitals,and questionnaire survey was adopted to survey the current situation of pharmaceutical preparations in medical institutions,and 24 effective questionnaires were collected.SPASS17. 0 software was used to analyze the data of the questionnaires. [Results]The pharmaceutical preparations in medical institutions between 10 and 15 yuan accounted for 35%,which was the biggest percentage. The second was between 5 and 10 yuan,which accounted for 25%. [Conclusions] Most of the pharmaceutical preparations in medical institutions of Sichuan Province are in the low price,and floating rate price adjustment mechanism is suggested.展开更多
Ethereum has received increasing attention as the first blockchain platform to support smart contracts.Data mining has become an important tool for analyzing Ethereum transactions.However,existing methods have the dis...Ethereum has received increasing attention as the first blockchain platform to support smart contracts.Data mining has become an important tool for analyzing Ethereum transactions.However,existing methods have the disadvantage of covering partial transactions and being vulnerable to privacy-enhancing techniques.In this paper,we propose a scheme for transaction correlation with the node as an entity,which can cover all transactions while being resistant to privacy-enhancing techniques.Utilizing timestamps relayed from N fixed nodes to describe the network properties of transactions,we cluster transactions that enter the network from the same source node.Experimental results show that our method can determine with 97%precision whether two transactions enter the network from the same source node.展开更多
基金Supported by Sichuan Science and Technology Support Plan Program(2014-SZ0141)
文摘[Objectives] To survey the current using situation and price distribution of pharmaceutical preparations in medical institutions of Sichuan Province,to provide basis for pricing the pharmaceutical preparations in medical institutions. [Methods] From June to September2016,15 sample hospitals were selected from Ⅱ-B class,Ⅱ-A class,Ⅲ-B class and Ⅲ-A class hospitals,and questionnaire survey was adopted to survey the current situation of pharmaceutical preparations in medical institutions,and 24 effective questionnaires were collected.SPASS17. 0 software was used to analyze the data of the questionnaires. [Results]The pharmaceutical preparations in medical institutions between 10 and 15 yuan accounted for 35%,which was the biggest percentage. The second was between 5 and 10 yuan,which accounted for 25%. [Conclusions] Most of the pharmaceutical preparations in medical institutions of Sichuan Province are in the low price,and floating rate price adjustment mechanism is suggested.
基金supported by the National Key R&D Program of China with No.2020YFB1006100.
文摘Ethereum has received increasing attention as the first blockchain platform to support smart contracts.Data mining has become an important tool for analyzing Ethereum transactions.However,existing methods have the disadvantage of covering partial transactions and being vulnerable to privacy-enhancing techniques.In this paper,we propose a scheme for transaction correlation with the node as an entity,which can cover all transactions while being resistant to privacy-enhancing techniques.Utilizing timestamps relayed from N fixed nodes to describe the network properties of transactions,we cluster transactions that enter the network from the same source node.Experimental results show that our method can determine with 97%precision whether two transactions enter the network from the same source node.
