The comprehensive evaluation of human resources (HR) quality is of greatly significance to improving the economic efficiency of financial enterprises. Based on deep neural network (DNN), this paper mainly proposes an evaluation model of comprehensive HR quality of financial enterprises, which dynamically identifies the HR quality that matches the posts at different layers. Firstly, a reasonable evaluation index system (EIS) was established, including 5 primary indices and 21 secondary indices. The evaluation problem was decomposed into multiple layers and indices. On this basis, an N-index convolutional neural network (CNN), i.e. the N-evaluation model, was established based on the Nevaluation model, which takes the improvement of the comprehensive HR quality into consideration. Finally, experiments were conducted to verify the effectiveness of the proposed model. The research results provide reference for the application of DNN in other evaluation fields.

It is demonstrated (hat SEM and TEM can be used for the identification of domain structures.

The significant reduction of the image quality caused by the magnetic field of samples is a major problem affecting the application of SEM (scanning electron microscopy) in the analysis of electronic devices. The main reason for this is that the electron trajectory is deflected by the Lorentz force. The usual solution to this problem is degaussing the sample at high temperatures. However, due to the poor heat resistance of some electronic components, it is imperative to find a method that can reduce the impact of magnetic field on the image quality and is straightforward and easy to operate without destroying the sample. In this paper, the influence of different magnetic field directions on the imaging quality was discussed by combining the experiment and software simulation. The principle of the method was studied, and the best observation direction was obtained.

TiO2 nanocrystals were prepared by a controlled hydrolysis procedure at room temperature. The effect of V-doping, N-doping and V/N codoping on the lattice parameters and magnetic properties of TiO2 nanocrystals was investigated by means of X-ray diffraction, high-resolution transmission electron microscopy, X-ray photoelectron spectroscopy and vibration sample magnetometry. Doping performed at room temperature causes the expansion of lattice parameters. Undoped and doped TiO2 nanocrystals show room-temperature ferromagnetism. A monotonic correlation between saturation magnetization and the ratio of the lattice parameter c to a (c/a) was observed. Saturation magnetization of TiO2 nanocrystals increases with the value of c/a.