Blockchain, the underlying technology of digital currency, such as Bitcoin, has the characteristics of decentralization, stability, security, anonymity, and nontampering. Blockchain is being applied in an increasing number of fields. Supply chain finance is a financing model in which banks connect core enterprises with upstream and downstream enterprises to provide flexible financial products and services. In the traditional supply chain finance, the core enterprises with strong competitiveness and a large scale play an irreplaceable role in managing the supply chain information flow, logistics, and capital flow, which results in inequality and information asymmetry. Fraud in supply chain finance is also very serious. To solve the problems of the traditional supply chain finance, in this article, we build a new type of supply chain financial platform that uses blockchain technology to manage the whole process. This supply chain financial platform solves the problem of nontrust among the participants in the supply chain, improves the efficiency of the capital flow and information flow, reduces costs, and provides better financial services to the relevant parties in the supply chain. To protect users' privacy, we propose a new method of using homomorphic encryption in the blockchain to meet the needs of sensitive data privacy protection in supply chain financial scenarios.

Medical information is private, and medical data are valuable for medical research. Thus, medical information sharing is challenging because the data might be manipulated improperly and revealed during the operational process. The accuracy and integrity of medical information should be guaranteed throughout the sharing process. Medical institutions require shared information for scientific research and development; however, the issue of privacy inhibits the sharing process. In this article, we propose a new business process and a blockchain-based platform for medical information sharing. Our method exploits the advantages of blockchain in medical information recording and sharing. Information can be stored, shared, and credibly verified among parties in the distributed network. In addition, we propose a new consensus algorithm and a universal anonymous sharing model. These methods improve the efficiency and security of medical information sharing between users. In this way, both the information and the traces of the transaction can be stored in a distributed manner to prevent manipulation and fraud. Consequently, the value of medical information can be fully utilized.

Blockchain technology is an emerging distributed ledger technology that has exploratory applications in many areas. The consensus algorithm, as the core module of the blockchain, has an important impact on the security, scalability, and efficiency of the blockchain network. The consensus algorithm is also a popular topic in current blockchain technology research. In existing consortium blockchains and public blockchains, the blockchain has low efficiency or poor fault tolerance because of the limitations of the consensus algorithm. To both ensure the fault tolerance of the blockchain and improve the scalability and throughput, in this paper, we propose a new type of consensus algorithm: mixed Byzantine fault tolerance (MBFT). MBFT uses sharding and layered technology. MBFT functionally partitions the nodes that participate in the consensus process and improves the scalability and efficiency without sacrificing security. MBFT also introduces a random node selection mechanism and a credit mechanism to improve security and fault tolerance. We analyze the security and experiment on transaction throughput in a real network environment. The results prove that MBFT has good security and scalability and high throughput.

This paper draws attention to the specific aspect of institutional environments in international matchmaking for innovation. While technology is largely global this exploratory study of 18 cross-border cases between Europe and China points to the impact of distant institutional environments on the success and structure of cross-border collaborative innovation. Addressing differences in governance, market regulation, policy, IPR protection, language and culture like the Chinese guanxi are specific services based on dedicated capabilities of international innovation intermediaries.

The intelligent monitoring of tool wear status and wear prediction are important factors affecting the intelligent development of the modern machinery industry. Many scholars have used deep learning methods to achieve certain results in tool wear prediction. However, due to the instability and variability of the signal data, some neural network models may have gradient decay between layers. Most methods mainly focus on feature selection of the input data but ignore the influence degree of different features to tool wear. In order to solve these problems, this paper proposes a dual-stage attention model for tool wear prediction. A CNN-BiGRU-attention network model is designed, which introduces the self-attention to extract deep features and embody more important features. The IndyLSTM is used to construct a stable network to solve the gradient decay problem between layers. Moreover, the attention mechanism is added to the network to obtain the important information of output sequence, which can improve the accuracy of the prediction. Experimental study is carried out for tool wear prediction in a dry milling operation to demonstrate the viability of this method. Through the experimental comparison and analysis with regression prediction evaluation indexes, it proves the proposed method can effectively characterize the degree of tool wear, reduce the prediction errors, and achieve good prediction results.

Video highlights are welcomed by audiences, and are composed of interesting or meaningful shots, such as funny shots. However, video shots of highlights are currently edited manually by video editors, which is inconvenient and consumes an enormous amount of time. A way to help video editors locate video highlights more efficiently is essential. Since interesting or meaningful highlights in videos usually imply strong sentiments, a sentiment analysis model is proposed to automatically recognize sentiments of video highlights by time-sync comments. As the comments are synchronized with video playback time, the model detects sentiment information in time series of user comments. Moreover, in the model, a sentimental intensity calculation method is designed to compute sentiments of shots quantitatively. The experiments show that our approach improves the F1 score by 12.8% and overlapped number by 8.0% compared with the best existing method in extracting sentiments of highlights and obtaining sentimental intensities, which provides assistance for video editors in editing video highlights efficiently.