Abstract: The disclosure relates to a method, apparatus, electronic device and computer readable storage medium for document processing, including: in response to receiving a presenting operation for a document content, displaying at least one first document identification at a first predetermined position in a content page of a document; and in response to receiving an editing operation at a second predetermined position in the content page, replacing or hiding the first document identification, based on receiving the presenting operation on the document content, at least one first document identification is displayed at a first predetermined position of the content page of the document and displayed to the user, so that the user can directly select the required document from the displayed first document identification, which facilitates the user to find a suitable document template more quickly, optimizes the user's document usage links, and simplifies the user operations, thereby improving user experience.

Abstract: A nanozyme material and a preparation method thereof are provided. In the preparation method, Prussian blue (PB) is used to regulate the peroxidase activity of Fe,NA/CDs, and then gold nanoparticles are synthesized with Fe,NA/CDs as a reducing agent and a stabilizing agent. The nanozyme material has both a peroxidase activity and a Raman enhancement effect, which can be used for nanozyme-based surface-enhanced Raman spectroscopy (SERS). An embodiment of the present disclosure also provides a method for rapidly detection of a Pb2+ ion, which is based on a color change or an additional Raman SERS signal generation caused by nanozyme oxidation. Such sensing signals (colorimetric and SERS analysis) are derived from a catalytic oxidation reaction of ABTS or TMB, while the presence of Pb2+ specifically inhibits the oxidation, such that the rapid detection of Pb2+ can be achieved.

Abstract: A visual loop-mediated isothermal amplification (LAMP) method for a rapid test of tobacco includes: extracting a genomic DNA of the tobacco, designing primers, establishing a LAMP reaction system, and optimizing the LAMP reaction system. The present disclosure designs the LAMP primers according to a conserved region of a screened tobacco-specific Ntsp151 genomic sequence, and establishes the LAMP method based on color determination. The present disclosure can rapidly identify flue-cured tobacco leaves, finished cigarettes, and tea cigarettes, and achieves a detection limit of 200 copies per reaction. The test results of the LAMP method for non-tobacco samples are negative, indicating that the LAMP method has high specificity. Compared with the quantitative polymerase chain reaction (qPCR) method, the LAMP method has a coincidence rate of 96.7%, but the LAMP method only needs about 1 h to obtain the results.

Abstract: A microbial fermentation method for improving tobacco quality, comprising the following steps: (1) inoculating flue-cured tobacco as an inoculation source into a tobacco powder medium, and cultivating at 20-40° C. for 20-50 h; (2) inoculating microbial liquid in step (1) into a fresh tobacco powder medium, cultivating at 20-40° C. for 20-50 h, and circulating for 15-25 cycles to obtain stable flue-cured tobacco microbial flora; (3) inoculating the microbial flora obtained in step (2) into the fresh tobacco powder medium, and culturing at 20-40° C. for 20-50 h to obtain seed liquid of the microbial flora; (4) centrifuging the microbial liquid obtained in step (3) and collecting microbes; (5) washing the microbes collected in step (4); (6) resuspending the washed bacteria in step (5) to obtain a microbial suspension; and (7) spraying the microbial suspension in step (6) to the tobacco leaves for fermentation for 6-8 days.

Abstract: A system and method for safety testing a host autonomous vehicle (AV). This method includes: generating a trained machine learning (ML) agent and testing the host AV in an environment that includes one or more background vehicles configured to operate according to the trained ML agent. The ML agent is generated by: (i) obtaining a testing state model having non-safety-critical states and safety-critical states, (ii) editing the testing state model to obtain an edited testing state model that omits data concerning the non-safety-critical states, and (iii) training a ML agent using the edited state testing model so as to generate the trained ML agent.