A study on the influence mechanism of CBDC on monetary policy: An analysis based on e-CNY.

This paper attempts to introduce central bank digital currency (CBDC) into the analysis framework of monetary policy, and studies the influence mechanism of e-CNY, central bank digital currency in China, on the monetary policy of the central bank from the aspects of money demand, money supply and monetary policy transmission mechanism. The research finds that e-CNY will have significant impact on monetary policy: (1) E-CNY will change the structure of money demand, speed up currency circulation, make central bank reserves more controllable and money supply more intelligent; (2) E-CNY will increase the volatility and expansion effect of currency multiplier to a certain extent; (3) E-CNY will dredge the transmission channel of monetary policy so as to improve the transmission effect of existing monetary policy tools. At the same time, based on the organic combination with structural monetary policy tools, it will achieve precise implementation of medium-term lending facilities (MLF), pledged supplementary lending (PSL), and it may bring new monetary policy tools. (4) E-CNY will make the intermediate target of monetary policy more controllable and reliable, and have a positive impact on the target of monetary policy through the smooth transmission of monetary policy channels. Therefore, it is necessary to strengthen the research on CBDC, give full play to the positive role of e-CNY in monetary policy, and improve the effectiveness of monetary policy.

GFCNet: Utilizing graph feature collection networks for coronavirus knowledge graph embeddings.

In response to fighting COVID-19 pandemic, researchers in machine learning and artificial intelligence have constructed some medical knowledge graphs (KG) based on existing COVID-19 datasets, however, these KGs contain a considerable amount of semantic relations which are incomplete or missing. In this paper, we focus on the task of knowledge graph embedding (KGE), which serves an important solution to infer the missing relations. In the past, there have been a collection of knowledge graph embedding models with different scoring functions to learn entity and relation embeddings published. However, these models share the same problems of rarely taking important features of KG like attribute features, other than relation triples, into account, while dealing with the heterogeneous, complex and incomplete COVID-19 medical data. To address the above issue, we propose a graph feature collection network (GFCNet) for COVID-19 KGE task, which considers both neighbor and attribute features in KGs. The extensive experiments conducted on the COVID-19 drug KG dataset show promising results and prove the effectiveness and efficiency of our proposed model. In addition, we also explain the future directions of deepening the study on COVID-19 KGE task.

An evaluation method of fragile states index based on climate shock: A case of Bangladesh.

Fragile states index reflects a country's ability to maintain stability. The main objective of this study is to analyze how climate change influences fragile states index. Firstly, we aim to modify the fragile states index. We devise an index system of climate shocks (MCS), which measures not climate change but also governance capacity. Meanwhile, a three-class index system is formulated to measure fragility of states (MCFS). Afterwards, we utilize MCS to modify the initial index system based on multiplication model. Furthermore, the weights of MCS are obtained by Delphi method while the weights in the third level of MCFS are gotten by CRITIC Weighting Model. The weights in the second level of MCFS then are determined by Entropy Weighting Model and Group Making Method. Finally, the classification standard of measuring fragility of states is calculated through System Clustering Model. And then Bangladesh is chosen to show the variation tendency of fragility based on the data between 2000 and 2015. To further predict the fragility of Bangladesh, Cascaded Neural Network Model (CNN) is adopted to predict MCFS from 2016 to 2030. Eventually we determine and define tipping points into 2 types-amelioration tipping points and deterioration tipping points. The result show that Bangladesh reached the deterioration tipping points in 2016.

Fabrication and microstructure of quartz ceramics with orderly-arranged carbon filler.

Ceramics with tailored pore structure are showing potential applications in some special fields. For fabricating quartz ceramics with orderly-arranged carbon filler, a combination of 3D printing, vacuum suction filtration and sintering was explored to fabricate quartz ceramics with highly-ordered and well-connected big pore channels. The spatial lattice structure in the polylactic acid (PLA) template fabricated by 3D printing together with raw material ratio and sintering temperature has great effect on the properties and pore structure of the porous quartz ceramics. To demonstrate the technical feasibility for fabricating quartz ceramics with orderly-arranged filler, carbon powder was taken as an example and fully filled in the big pore channels of the porous quartz ceramics via vacuum impregnation method. By choosing the quartz ceramics with only highly-ordered and well-connected big pore channels as substrate, quartz ceramics with orderly-arranged carbon filler were successfully obtained.

PADFrag: A Database Built for the Exploration of Bioactive Fragment Space for Drug Discovery.

Structural analyses of drugs and pesticides can enable the identification of new bioactive compounds with novel and diverse scaffolds as well as improve our understanding of the bioactive fragment space. The Pesticide And Drug Fragments (PADFrag) database is a unique bioinformatic-cheminformatic cross-referencing resource that combines detailed bioactive fragment data and potential targets with a strong focus on quantitative, analytic, and molecular-scale information for the exploration of bioactive fragment space for drug discovery ( http://chemyang.ccnu.edu.cn/ccb/database/PADFrag/ ). The main applications of PADFrag are the analysis of the privileged structures within known bioactive molecules, ab initio molecule library design, and core fragment discovery for fragment-based drug design. Other potential applications include prediction of fragment interactions and general pharmaceutical research.