Corporate Credit Rating: A Survey

Corporate credit rating (CCR) plays a very important role in the process of contemporary economic and social development. How to use credit rating methods for enterprises has always been a problem worthy of discussion. Through reading and studying the relevant literature at home and abroad, this paper makes a systematic survey of CCR. This paper combs the context of the development of CCR methods from the three levels: statistical models, machine learning models and neural network models, summarizes the common databases of CCR, and deeply compares the advantages and disadvantages of the models. Finally, this paper summarizes the problems existing in the current research and prospects the future of CCR. Compared with the existing review of CCR, this paper expounds and analyzes the progress of neural network model in this field in recent years.Comment: 11 page

Word-Graph2vec: An efficient word embedding approach on word co-occurrence graph using random walk sampling

Word embedding has become ubiquitous and is widely used in various text mining and natural language processing (NLP) tasks, such as information retrieval, semantic analysis, and machine translation, among many others. Unfortunately, it is prohibitively expensive to train the word embedding in a relatively large corpus. We propose a graph-based word embedding algorithm, called Word-Graph2vec, which converts the large corpus into a word co-occurrence graph, then takes the word sequence samples from this graph by randomly traveling and trains the word embedding on this sampling corpus in the end. We posit that because of the stable vocabulary, relative idioms, and fixed expressions in English, the size and density of the word co-occurrence graph change slightly with the increase in the training corpus. So that Word-Graph2vec has stable runtime on the large scale data set, and its performance advantage becomes more and more obvious with the growth of the training corpus. Extensive experiments conducted on real-world datasets show that the proposed algorithm outperforms traditional Skip-Gram by four-five times in terms of efficiency, while the error generated by the random walk sampling is small

PoMC : An Efficient Blockchain Consensus Mechanism for the Agricultural Internet of Things

Blockchain-based agricultural IoT systems face key challenges such as high delay and low transaction throughput. Existing complicated consensus mechanisms can cause IoT devices work inefficiently due to the limited computing, storage and energy resources. Additionally, many message exchanges can lead to high latency in the consensus process, which hinders the real-time applications of the agricultural IoT. Therefore, we propose Proof-of-Multifactor-Capacity (PoMC), an efficient and secure consensus mechanism for the agricultural IoT. It uses the communication capacity and credibility of a node as the evidence for making consensus. Moreover, a senator node lottery algorithm based on a credit mechanism and a new distributed incentive mechanism are designed to enhance security and motivate nodes to actively maintain the system. This paper analyses the performance of PoMC theoretically, including security, latency and system throughput, and presents a comparison of its asymptotic complexity with some existing consensus mechanisms. The simulation results demonstate that the average transaction validation latency and average consensus latency of PoMC have decreased by 10% and 23%. In addition, PoMC outperforms SENATE, PoQF and PBFT by 56%, 60% and 64% in terms of the system throughput, respectively

Impact of Relaxation on the Performance of GeSe True Random Number Generator Based on Ovonic Threshold Switching

This work was supported in part by the National Natural Science Foundation of China under Grant 62104188; in part by the Major Key Project of Peng Cheng Laboratory (PCL) under Grant PCL2021A12; and in part by the Engineering and Physical Sciences Research Council (EPSRC), U.K., under Grant EP/M006727/1 and Grant EP/S000259/1

O2ATH: An OpenMP Offloading Toolkit for the Sunway Heterogeneous Manycore Platform

The next generation Sunway supercomputer employs the SW26010pro processor, which features a specialized on-chip heterogeneous architecture. Applications with significant hotspots can benefit from the great computation capacity improvement of Sunway many-core architectures by carefully making intensive manual many-core parallelization efforts. However, some legacy projects with large codebases, such as CESM, ROMS and WRF, contain numerous lines of code and do not have significant hotspots. The cost of manually porting such applications to the Sunway architecture is almost unaffordable. To overcome such a challenge, we have developed a toolkit named O2ATH. O2ATH forwards GNU OpenMP runtime library calls to Sunway's Athread library, which greatly simplifies the parallelization work on the Sunway architecture.O2ATH enables users to write both MPE and CPE code in a single file, and parallelization can be achieved by utilizing OpenMP directives and attributes. In practice, O2ATH has helped us to port two large projects, CESM and ROMS, to the CPEs of the next generation Sunway supercomputers via the OpenMP offload method. In the experiments, kernel speedups range from 3 to 15 times, resulting in 3 to 6 times whole application speedups.Furthermore, O2ATH requires significantly fewer code modifications compared to manually crafting CPE functions.This indicates that O2ATH can greatly enhance development efficiency when porting or optimizing large software projects on Sunway supercomputers.Comment: 15 pages, 6 figures, 5 tables

VP2 residue N142 of coxsackievirus A10 is critical for the interaction with KREMEN1 receptor and neutralizing antibodies and the pathogenicity in mice.

Coxsackievirus A10 (CVA10) has recently emerged as one of the major causative agents of hand, foot, and mouth disease. CVA10 may also cause a variety of complications. No approved vaccine or drug is currently available for CVA10. The residues of CVA10 critical for viral attachment, infectivity and in vivo pathogenicity have not been identified by experiment. Here, we report the identification of CVA10 residues important for binding to cellular receptor KREMEN1. We identified VP2 N142 as a key receptor-binding residue by screening of CVA10 mutants resistant to neutralization by soluble KREMEN1 protein. The receptor-binding residue N142 is exposed on the canyon rim but highly conserved in all naturally occurring CVA10 strains, which provides a counterexample to the canyon hypothesis. Residue N142 when mutated drastically reduced receptor-binding activity, resulting in decreased viral attachment and infection in cell culture. More importantly, residue N142 when mutated reduced viral replication in limb muscle and spinal cord of infected mice, leading to lower mortality and less severe clinical symptoms. Additionally, residue N142 when mutated could decrease viral binding affinity to anti-CVA10 polyclonal antibodies and a neutralizing monoclonal antibody and render CVA10 resistant to neutralization by the anti-CVA10 antibodies. Overall, our study highlights the essential role of VP2 residue N142 of CVA10 in the interactions with KREMEN1 receptor and neutralizing antibodies and viral virulence in mice, facilitating the understanding of the molecular mechanisms of CVA10 infection and immunity. Our study also provides important information for rational development of antibody-based treatment and vaccines against CVA10 infection

Neglected environmental health impacts of China's supply-side structural reform

“Supply-side structural reform” (SSSR) has been the most important ongoing economic reform in China since 2015, but its important environmental health effects have not been properly assessed. The present study addresses that gap by focusing on reduction of overcapacity in the coal, steel, and iron sectors, combined with reduction of emissions of sulfur dioxide (SO2), nitrogen oxide (NOx), and fine particulate matter (PM2.5), and projecting resultant effects on air quality and public health across cities and regions in China. Modeling results indicate that effects on air quality and public health are visible and distributed unevenly across the country. This assessment provides quantitative evidence supporting projections of the transregional distribution of such effects. Such uneven transregional distribution complicates management of air quality and health risks in China. The results challenge approaches that rely solely on cities to improve air quality. The article concludes with suggestions on how to integrate SSSR measures with cities’ air quality improvement attainment planning and management performance evaluation