This paper constructs a DeFi Resilience Index (DRI) to assess the DeFi market’s capability to withstand risks and rebound from shocks. The DRI is developed through principal component analysis on four indicators relevant with the DeFi market, including the Total Value Locked, the Number of Active Addresses, the Volume of Fiat-backed Stablecoins, and the Volume of Trading. During the sample period from April 1, 2021 to October 25, 2023, the trend DRI aligns with the real actual state of the DeFi market. We identify 114 external shocks to the DeFi market to examine the effectiveness of the DRI, and find that higher DRI indicates the DeFi market’s stronger ability to recover from external shocks. We also observe that the DRI has a positive impact on the simultaneous and future 5-day performance of DeFi token prices. Further analysis indicates that DRI exhibits dynamic correlations with the performance of the cryptocurrency market, the stock market, and the option market. Regression results show that the prices of Bitcoin and Ether have positive and significant influences on the DRI, while the S&P 500 index and the VIX index have no significant impacts on the DRI. Our findings extend studies on market resilience from conventional financial markets to the emerging DeFi market, and highlight the interconnections between DeFi market resilience and other financial markets’ performance.
Glucagon-like peptide-1 (GLP-1) is a potential therapeutic agent for treating Type 2 diabetes, owing to its glucose-dependent capability to stimulate insulin secretion. Semaglutide is currently the best GLP-1 analogue; however, the Aib8 -Arg34 -GLP-1 (7-37) of semaglutide contains an unnatural amino acid at the eighth position (Aib: 2-aminoisobutyric acid), which hinders its fermentation process. Aib8 -Arg34 -GLP-1 (7-37) is mainly synthesised by solid-phase synthesis. However, solid-phase synthesis of Aib8 -Arg34 -GLP-1 (7-37) has many shortcomings: (i) The synthesis requires many organic solvents, (ii) the existence of deletion peptides impedes the subsequent purification process, (iii) the yield is low (approximately 16%), and (iv) it is not suitable for large-scale synthesis. However, the synthesis of Aib8 -Arg34 -GLP-1 (7-37) by liquid-phase fragment condensation of expressed and synthetic peptides (Arg34 -GLP-1 (9-37) and Boc-His (Boc)-Aib) has many advantages: (i) The synthesis process only requires a few organic reagents, (ii) the yield is high (approximately 60%), (iii) the purification conditions are simple and Aib8 -Arg34 -GLP-1 (7-37) with a purity of over 98% is obtained through one-step reverse-phase purification, and (iv) the raw materials are inexpensive and large-scale synthesis is possible. In conclusion, here, we developed an efficient method for synthesising Aib8 -Arg34 -GLP-1 (7-37).
One redundant wireless bridged network is proposed to transport the measuring data and control command for the remote launch system. For the importance, the high reliability is required in the process of system design. So the redundant technology and voting approach are used to improve the reliability of system. The master-slave double wireless links are built up, when the master link broken down, the data can be transported by slave link immediately. Based on the high performance wireless bridge, the network has >200Mbps bandwidth, 5~50km communication distance and high reliability, which can fulfils the requirements of remote launch system.
The network traffic data is interpreted differently by people in different sectors. This paper proposes a framework for hierarchically visualizing the network traffic data, and customizes a set of the classic approaches or algorithms to produce visualizations in different levels. Based on the framework, we developed a prototype system Hint Vis to support analyzing network traffic data in different levels by constructing layered semantic network traffic objects and producing hierarchical visualizations. The usability of Hint Vis is demonstrated by visualizing packets going through the gateway in a LAN. Depending on the hierarchical visualizations, analysts are able to semantically navigate in the network traffic data and concentrate on what they need.
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