The advent of blockchain technology has transformed traditional business processes from centralized to decentralized. By eliminating the unnecessary intervention of middlemen, it can reduce the overall cost of patient medication by turning the drug supply chain entirely into a point-to-point decentralized business. This paper presents a reliable and encouraging P2P drug trading block chain technology and four related smart contracts. These contracts include consumer contracts and the supply, bidding and trading of drugs, which have been deployed on the Ethereum blockchain for the decentralized trading of drugs. We will use the Approach of Real cost descending (RCD) to achieve incentive transactions for suppliers and patients. This method provides P2P transactions and ensures the safety and transparency of drug data, as well as the anonymity of users in the transaction process. Finally, according to the requirements of Good Supplying Practice(GSP), the effectiveness of the proposed model is evaluated and analyzed.
In recent years, with the increase of world trade volume, multimodal transport has become the main mode of international trade transportation, and its development is facing a new stage. As an important part of multimodal transport, invoice affects the efficiency of multimodal transport. Electronic invoices are replacing traditional paper invoices as the main form of bill of lading. But due to the characteristics of paperless and intangible electronic, the electronic invoices, unlike traditional paper invoices, cannot be actually possessed. Considering the block chain technology possesses the characteristics of unchanged and traceability, it will apply into the electronic invoice. It provides shippers and carriers with freight information and pre-agreed service contract rates, enables shippers and carriers to conduct secure and transparent transactions, improves electronic invoice processing efficiency, and reduces shipper and carrier costs.
Cross-domain text classification has broad application prospects in the field of data mining. Since transfer learning can help target domain data to achieve the sharing and transfer of semantic information with the help of existing knowledge domains, transfer learning are generally used to achieve cross-domain text processing. Based on this, we propose a cross-domain text classification algorithm -MTrA. The algorithm is based on TrAdaBoost, taking into account the distribution differences between the source domain and the target domain. It uses the Maximum Mean Discrepancy(MMD) as the initial weight parameter of the two domain. MTrA adds a weight backfill factor that considers the accuracy of the source domain classification and balances the weight update method of the source domain data. Through the verification in the dataset 20 Newsgroups, Compared with the traditional TrAdaBoost algorithm, it improves the classification accuracy by 9.4% on average. it proves the effectiveness and advantages of the algorithm.
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