In order to establish a new discipline specializing in accident science from the perspective of safety science. Under the guidance of the current research theories and methods of safety science, combined with the research paradigm of humanities and social medicine, this paper puts forward new viewpoints, new theories and new models about accident research. First of all, through literature retrieval, this paper analyzes the relevant research results of accidents at home and abroad, and expounds the existing problems and the basic trend of accident science research. Secondly, it puts forward eight kinds of attribute relations of the accident, and makes clear the characteristics and connotation of the accident. In the study of accident types, a hierarchical classification model based on accident cognition is created for the first time. It also points out the logical relevance of five levels of accident science research and the realistic relevance of three levels. At the same time, according to the thought of science of science, this paper puts forward a new definition of safety under the thinking of accident science and other basic concepts related to safety science, and explains the connotation. In addition, it creates and constructs the basic concept of accident science, establishes the conceptual model of accident science, and points out the “3-4-5” model of accident science research and its connotation. Thirdly, draw lessons from the interdisciplinary paradigm to study the relevant theoretical basis and discipline classification relationship of accident science, and construct the tree of accident science. Finally, the research contents of three main aspects of accident science are summarized. The results show that the research results in this paper not only play a fundamental role in the basic construction of accident science, but also further enrich and perfect the discipline system of safety science, which has a certain theoretical significance.

A novel coronavirus (2019-nCoV) was first identified in Wuhan, Hubei Province, and then spreads to the other Provinces of China. 2019-nCoV was reported to share the same receptor, Angiotensin-converting enzyme 2 (ACE2), with SARS-CoV. Previous studies have found ACE2 is abundantly present in humans in the epithelia of the lung and small intestines, and they found ACE2 expression in the basal layer of the non-keratinizing squamous epithelium in nasal and oral mucosa and the nasopharynx. Here based on the public single-cell RNA-Seq datasets, we analyzed the ACE2 expression in the nasal, mouth, lung, and colon tissues. We find that the number of ACE2-expressing cells in the nasal tissue and mouth is comparable to the number of ACE2-expressing cells in the lung tissue and colon. We also find that ACE2 tends to be co-expressed with HLA-DRB1, which plays a central role in the immune system by presenting peptides derived from extracellular proteins, in the nasal, mouth, lung, and colon tissues at single-cell resolution. We hope this provides valuable information for virus-prevention strategy and therapeutic strategy development.

Our image recognition system mainly judges whether the left upper limb in the image is the left upper limb or the right upper limb through our deep learning model in the image. The doctor then could give the correct surgical position. From the experimental results, it could be found that the precision rate and recall rate of the intelligent image recognition system proposed in this paper for preventing the upper limb dislocation surgery could reach 98% and 93%, respectively. It proved that our artificial intelligent image recognition system, AIIRS, could indeed assist orthopedic surgeons to prevent the occurrence of left and right dislocation in upper limb surgery. At the same time, this paper also completes the IRB application approval through the prototype experimental results and will conduct the second phase of human trials in the future. It showed that the research results of this paper will be of great benefit and research value to upper limb orthopedic surgery.

Our image recognition system mainly judges whether the left upper limb in the image is the left upper limb or the right upper limb through our deep learning model in the image. The doctor then could give the correct surgical position. From the experimental results, it could be found that the precision rate and recall rate of the intelligent image recognition system proposed in this paper for preventing the upper limb dislocation surgery could reach 98% and 93%, respectively. It proved that our intelligent image recognition system could indeed assist orthopedic surgeons to prevent the occurrence of left and right dislocation in upper limb surgery. At the same time, this paper also completes the IRB application approval through the prototype experimental results and will conduct the second phase of human trials in the future. It showed that the research results of this paper will be of great benefit and research value to upper limb orthopedic surgery.

Microgrid is one of the important ways to accommodate distributed PV generation. However, the optimal planning scheme of microgrids is closely related to solar resources, load characteristics, and the key components of microgrid systems. This paper uses HOMER software to conduct collaborative planning for grid-connected PV-storage microgrids with electric vehicles in multiple scenarios. Specifically, we construct a multi-scenario capacity optimization model for PV generation, energy storage, and converters, considering both the cleanliness and economic performance of microgrids. The performance is quantified using indices such as net present cost, levelized cost of electricity, and carbon dioxide emission under multiple scenarios. Finally, we conduct extensive case studies on a business park in Wuhan, China, to compare and discuss the planning performance under multiple scenarios, as well as sensitivity analysis with specific cases.

Members of the Ebolavirus genus demonstrate a marked differences in pathogenicity in humans with Ebola (EBOV) being the most pathogenic, Bundibugyo (BDBV) less pathogenic and Reston (RESTV) causing no severe disease in humans. The VP24 protein encoded by members of the Ebolavirus genus blocks type I interferon (IFN-I) signaling through interaction with host karyopherin alpha nuclear transporters, potentially contributing to virulence. Previously we demonstrated that BDBV VP24 (bVP24) binds with lower affinities to karyopherin alpha proteins relative to EBOV VP24 (eVP24), and this correlated with reduced inhibition of IFN-I signaling. We hypothesized that modification of eVP24-karyopherin alpha interface to make it similar to bVP24 would attenuate the ability to antagonize IFN-I response. We generated a panel of recombinant EBOVs containing single or combinations of point mutations in the eVP24-karyopherin alpha interface. Most of the viruses appeared to be attenuated in both IFN-I-competent 769-P and IFN-I-deficient Vero-E6 cells in the presence of IFNs. However, the R140A mutant grew at reduced levels even in the absence of IFNs in both cell lines, as well as in U3A STAT1 knockout cells. Both the R140A mutation and its combination with the N135A mutation caused almost complete inhibition of genome replication and transcription suggesting the role of these mutations in an IFN-I-independent attenuation. Additionally, we found that unlike eVP24, bVP24 does not inhibit interferon lambda 1 (IFN-λ1), which potentially explains the lower pathogenicity of BDBV relative to EBOV. Thus, the VP24 residues binding karyopherin alpha affect ebolavirus pathogenicity by IFN-I-dependent and independent mechanisms.