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Analytic continuation maps imaginary-time Green’s functions obtained by various theoretical/numerical methods to real-time response functions that can be directly compared with experiments. Analytic continuation is an important bridge between many-body theories and experiments but is also a challenging problem because such mappings are ill-conditioned. In this work, we develop a neural network (NN)-based method for this problem. The training data is generated either using synthetic Gaussian-type spectral functions or from exactly solvable models where the analytic continuation can be obtained analytically. Then, we applied the trained NN to the testing data, either with synthetic noise or intrinsic noise in Monte Carlo simulations. We conclude that the best performance is always achieved when a proper amount of noise is added to the training data. Moreover, our method can successfully capture multi-peak structure in the resulting response function for the cases with the best performance. The method can be combined with Monte Carlo simulations to compare with experiments on real-time dynamics.

In the era of big data, science and technology continue to infiltrate into art, providing more new means of expression for art, and expanding the performance space for digital media art. The statistics and analysis of big data can also more clearly analyze the needs of users, which is both an opportunity and a challenge for the development of digital media art.

The origin of virtual reality technology can be traced back to the 1950s decades ago. In recent years, with the rapid improvement of computer graphics processing, computing ability and other technologies, virtual reality technology and equipment began to mature. Starting from the application and development of virtual reality technology in various fields, this paper analyzes the basic application process of virtual reality technology, and describes the value and content of its application in the intelligent system of cold chain logistics.

Direct current (DC) and radio frequency (RF) performances of InP-based high electron mobility transistors (HEMTs) are investigated by Sentaurus TCAD. The physical models including hydrodynamic transport model, Shockley–Read–Hall recombination, Auger recombination, radiative recombination, density gradient model and high field-dependent mobility are used to characterize the devices. The simulated results and measured results about DC and RF performances are compared, showing that they are well matched. However, the slight differences in channel current and pinch-off voltage may be accounted for by the surface defects resulting from oxidized InAlAs material in the gate-recess region. Moreover, the simulated frequency characteristics can be extrapolated beyond the test equipment limitation of 40 GHz, which gives a more accurate maximum oscillation frequency (f max) of 385 GHz.

Room-temperature negative differential resistance (NDR) has been observed in different types of organic materials. However, detailed study on the influence of the organic material on NDR performance is still scarce. In this work, room-temperature NDR is observed when CdSe quantum dot (QD) modified ITO is used as the electrode. Furthermore, material dependence of the NDR performance is observed by selecting materials with different charge transporting properties as the active layer, respectively. A peak-to-valley current ratio up to 9 is observed. It is demonstrated that the injection barrier between ITO and the organic active layer plays a decisive role for the device NDR performance. The influence of the aggregation state of CdSe QDs on the NDR performance is also studied, which indicates that the NDR is caused by the resonant tunneling process in the ITO/CdSe QD/organic active layer structure.

In this paper, based on elastic theory of different tension-compression moduli, formulas for calculation of stress and displacement are obtained for bending-compression/tension members under complex stress and subject to combined loadings. An example is given and the obtained analytical solution is compared with numerical results, showing high accuracy of the obtained analytic solution.