Digital economy leads the integrated development of rural primary, secondary and tertiary industries

The rapid development of the digital economy has driven the digital and intelligent transformation and development of rural primary, secondary and tertiary industries, which has a revolutionary impact on the ways and paths of agricultural transformation iteration and rural industrial integration development. The integrated development of primary, secondary and tertiary industries in rural areas is a key measure to continuously promote rural revitalization. The digital economy still faces some challenges in leading the integrated development of rural industries, including high uncertainty in the macro policy environment, weak connection between the implementation and use of digital technology at the meso level and farmers, and the optimization of the interest linkage at the micro subject level. Based on the theory of industrial integration, the article attempts to analyze the empowering points of the digital economy from the perspectives of digital reconstruction of industrial factors, digital and intelligent transformation of the entire production process, and cultivation of new service models. It proposes ways to strengthen the construction of rural digital infrastructure, increase the effective supply of digital economy talents, and build a unified standard collaborative guarantee mechanism to assist in the development of rural industrial integration

Diagnosis of Roller Bearings Compound Fault Using Underdetermined Blind Source Separation Algorithm Based on Null-Space Pursuit

In order to solve the problem of underdetermined blind source separation (BSS) in the diagnosis of compound fault of roller bearings, an underdetermined BSS algorithm based on null-space pursuit (NSP) was proposed. In this algorithm, the signal model of faulty roller bearing is firstly used to construct an appropriate differential operator in null space. With the constructed differential operator, the mixed signals collected by the vibration sensor are decomposed into a series of stacks of narrow band signal containing the characteristics of faulty bearing. Finally, the underdetermined problem is transformed to an overdetermined problem by combining the narrow band signals and the original mixed signals into a new group of observed signals. In this way, the separation of the mixed signals can be realized. Experiments and engineering data analyses show that the problem of underdetermined BSS can be solved effectively by this approach, and then the compound fault of the roller bearing can be separated

Improvement on Meshing Stiffness Algorithms of Gear with Peeling

This paper investigates the effect of a gear tooth peeling on meshing stiffness of involute gears. The tooth of the gear wheel is symmetric about the axis, and its symmetry will change after the gear spalling, and its meshing stiffness will also change during the meshing process. On this basis, an analytical model was developed, and based on the energy method a meshing stiffness algorithm for the complete meshing process of single gear teeth with peeling gears was proposed. According to the influence of the change of meshing point relative to the peeling position on the meshing stiffness, this algorithm calculates its stiffness separately. The influence of the peeling sizes on mesh stiffness is studied by simulation analysis. As a very important parameter, the study of gear mesh stiffness is of great significance to the monitoring of working conditions and the prevention of sudden failure of the gear box system

Virtual Screening of Small Molecular Inhibitors against DprE1

Decaprenylphosphoryl-β-d-ribose oxidase (DprE1) is the flavoprotein subunit of decaprenylphosphoryl-d-ribose epimerase involved in cell wall synthesis in Mycobacterium tuberculosis and catalyzes the conversion of decaprenylphosphoryl ribose to decaprenylphosphoryl arabinose. DprE1 is a potential target against tuberculosis, including multidrug-resistant tuberculosis. We identified potential DprE1 inhibitors from the ChemDiv dataset through virtual screening based on pharmacophore and molecular docking. Thirty selected compounds were subjected to absorption, distribution, metabolism, excretion, and toxicity prediction with the Discovery Studio software package. Two compounds were obtained as hits for inhibiting DprE1 activity in M. tuberculosis and are suitable for further in vitro and in vivo evaluation

Quantitative Evaluation of in Vivo Target Efficacy of Anti-tumor Agents via an Immunofluorescence and EdU Labeling Strategy

Current methods used to evaluate in vivo target efficacy of selected compound include western blot to semi-quantitatively analyze protein expression. However, problems arise as it is difficult to compare in vivo target efficacy of anti-tumor agents with the same mode of action. It is therefore desirable to develop a protocol that can quantitatively display in vivo target efficacy while also providing other useful information. In this study EdU labeling was used to mark out the proliferating area. The tumor tissue was accordingly divided into proliferating and non-proliferating areas. Fifteen tumor related proteins were stained by immunofluorescence and were found to express in either the proliferating or non-proliferating areas. This allows the quantitative analysis of protein expressions within the precise area. With simple image analysis, our method gave precise percent changes of protein expression and cell proliferation between the drugs treated group and the control group. Additional information, such as, the status of protein expression can also be obtained. This method exhibits high sensitivity, and provides a quantitative approach for in vivo evaluation of target efficacy

Application of Composite Dictionary Multi-Atom Matching in Gear Fault Diagnosis

The sparse decomposition based on matching pursuit is an adaptive sparse expression method for signals. This paper proposes an idea concerning a composite dictionary multi-atom matching decomposition and reconstruction algorithm, and the introduction of threshold de-noising in the reconstruction algorithm. Based on the structural characteristics of gear fault signals, a composite dictionary combining the impulse time-frequency dictionary and the Fourier dictionary was constituted, and a genetic algorithm was applied to search for the best matching atom. The analysis results of gear fault simulation signals indicated the effectiveness of the hard threshold, and the impulse or harmonic characteristic components could be separately extracted. Meanwhile, the robustness of the composite dictionary multi-atom matching algorithm at different noise levels was investigated. Aiming at the effects of data lengths on the calculation efficiency of the algorithm, an improved segmented decomposition and reconstruction algorithm was proposed, and the calculation efficiency of the decomposition algorithm was significantly enhanced. In addition it is shown that the multi-atom matching algorithm was superior to the single-atom matching algorithm in both calculation efficiency and algorithm robustness. Finally, the above algorithm was applied to gear fault engineering signals, and achieved good results