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Seismic data interpolation is an effective way to reconstruct missing seismic traces and to improve the quality of the seismic data set. In the field of deep learning, generative adversarial networks are capable of data generation and interpolation and have been widely used for high-quality image generations and image interpolations. In this letter, we propose a dual-domain conditional generative adversarial network (DD-CGAN) for seismic data interpolation. The DD-CGAN consists of a generator network and a discriminator network and uses the seismic data set and discrete Fourier transformed data set in the frequency domain as input vectors. The loss function of the DD-CGAN is defined by the generative-adversarial loss, the data loss function, and the total variation loss. Thus, the DD-CGAN can be trained more accurately. The discriminator is used to calculate the feature differences between the interpolated seismic data set and the complete seismic data set to drive the generator network for learning optimal parameters. The numerical results on the test data set and field seismic data set demonstrate the effectiveness of the proposed DD-CGAN. [ABSTRACT FROM AUTHOR]

AgSnO2, as the most potential material to replace AgCdO, is widely used in low-voltage electrical appliances. However, because of the high hardness and poor conductivity of SnO2, AgSnO2 has the problems of processing difficulties and large contact resistance. Based on the first principles of density functional theory, a method is proposed to calculate the electronic structure and mechanical properties of SnO2, SnO22Ce, SnO2–N, and SnO22–Ce–N. By analyzing the energy band, density of states, and elastic constants, the results show that Ce–N co-doped SnO2 has the highest relative conductivity, reduced hardness, and increased ductility and overcome processing difficulty. The doped AgSnO2 contact material was prepared by sol–gel method and powder metallurgy method, and the hardness and electrical properties of the sample were tested. The experimental results show that AgSnO2–Ce–N contacts have higher conductivity, lower contact resistance and arc energy, and lower hardness. The theory is consistent with the experiment, proving the feasibility of simulation. Therefore, Ce and N co-doping can effectively overcome the disadvantages of AgSnO2 contact materials, improve conductivity, and overcome processing difficulty. It provides a new idea and theoretical basis for the doping research of AgSnO2 contact materials. [ABSTRACT FROM AUTHOR]

Many remote sensing technologies were used to evaluate plant physioecology. However, few studies currently existed on the comparisons and the relationships among various remote sensing analyses methods. The objective of our study was to investigate the relationship of leaf image, chlorophyll fluorescence, reflectance with SPAD, and build the models from SPAD using the data obtained from rice and barley crops. Two pot experiments, one in 2009 and the other one in 2010, were conducted to study the relationship between these characteristics at a leaf scale. Three rice varieties (M17, M15, Xiushui 09) and three barley varieties (Hua 30, Zhepi 33, Zhexiu 12) were selected in our greenhouse experiment. The results showed that there was a highly significant relationship between R (red channel of the leaf image), G (green channel of the image), R550 (reflectance at 550 nm), and PRI (photochemical reflectance index), with SPAD (SPAD-502 readings) for rice and barley crops. Close linear correlation was found between leaf chlorophyll maximal fluorescence (${{\bf F}_{\bf m}}$) and ratio of variable to maximal chlorophyll fluorescence (${{\bf F}_{\bf v}}/{{\bf F}_{\bf m}}$) with SPAD reading for rice. However, no significant relationship between ${{\bf F}_{\bf m}}$ and ${{\bf F}_{\bf v}}/{{\bf F}_{\bf m}}$ with SPAD reading was found in barley. Linear and logarithmic equations could be used to describe the relationship between R, G, R550, PRI, ${{\bf F}_{\bf m}}$, and ${{\bf F}_{\bf v}}/{{\bf F}_{\bf m}}$ with SPAD reading. It suggested that leaf image analysis had stronger relationship with SPAD than reflectance or leaf chlorophyll fluorescence of the two crops of rice and barley. [ABSTRACT FROM PUBLISHER]

To better solve the passenger assignment problem, which is a subproblem of the transit network optimization problem, we build an artificial urban transit system (AUTS) and adopt a day-to-day learning mechanism to describe passengers' route and departure-time-choice behaviors. With the support of AUTS to handle the lower level assignment problem, we are able to solve the upper level transit network design problem. Compared with other bilevel models, our approach better accommodates passengers' dynamic learning behavior and their heterogeneity. Based on AUTS, we solve the frequency optimization problem and compare the results with an analytical method. We also perform some numerical experiments on AUTS and discover some interesting issues on the capacity of public transportation system and passengers' heterogeneity. [ABSTRACT FROM PUBLISHER]