Low-complexity full-field ultrafast nonlinear dynamics prediction by a convolutional feature separation modeling method

The modeling and prediction of the ultrafast nonlinear dynamics in the optical fiber are essential for the studies of laser design, experimental optimization, and other fundamental applications. The traditional propagation modeling method based on the nonlinear Schr\"odinger equation (NLSE) has long been regarded as extremely time-consuming, especially for designing and optimizing experiments. The recurrent neural network (RNN) has been implemented as an accurate intensity prediction tool with reduced complexity and good generalization capability. However, the complexity of long grid input points and the flexibility of neural network structure should be further optimized for broader applications. Here, we propose a convolutional feature separation modeling method to predict full-field ultrafast nonlinear dynamics with low complexity and high flexibility, where the linear effects are firstly modeled by NLSE-derived methods, then a convolutional deep learning method is implemented for nonlinearity modeling. With this method, the temporal relevance of nonlinear effects is substantially shortened, and the parameters and scale of neural networks can be greatly reduced. The running time achieves a 94% reduction versus NLSE and an 87% reduction versus RNN without accuracy deterioration. In addition, the input pulse conditions, including grid point numbers, durations, peak powers, and propagation distance, can be flexibly changed during the predicting process. The results represent a remarkable improvement in the ultrafast nonlinear dynamics prediction and this work also provides novel perspectives of the feature separation modeling method for quickly and flexibly studying the nonlinear characteristics in other fields.Comment: 15 pages,9 figure

Zeolite-cage-lock strategy for in situ synthesis of highly nitrogen-doped porous carbon for selective separation of carbon dioxide gas

Porous carbon structures doped with 18.14% nitrogen and prepared by a carbonizing organic template in ZSM-39 zeolitic cages show high CO2 adsorption capacity.</p

Completion of Eight Gynostemma BL. (Cucurbitaceae) Chloroplast Genomes: Characterization, Comparative Analysis, and Phylogenetic Relationships

Gynostemma BL., belonging to the family Cucurbitaceae, is a genus containing 17 creeping herbaceous species mainly distributed in East Asia. It can be divided into two subgenera based on different fruit morphology. Herein, we report eight complete chloroplast genome sequences of the genus Gynostemma, which were obtained by Illumina paired-end sequencing, assembly, and annotation. The length of the eight complete cp genomes ranged from 157,576 bp (G. pentaphyllum) to 158,273 bp (G. laxiflorum). Each encoded 133 genes, including 87 protein-coding genes, 37 tRNA genes, eight rRNA genes, and one pseudogene. The four types of repeated sequences had been discovered and indicated that the repeated structure for species in the Subgen. Triostellum was greater than that for species in the Subgen. Gynostemma. The percentage of variation of the eight cp genomes in different regions were calculated, which demonstrated that the coding and inverted repeats regions were highly conserved. Phylogenetic analysis based on Bayesian inference and maximum likelihood methods strongly supported the phylogenetic position of the genus Gynostemma as a member of family Cucurbitaceae. The phylogenetic relationships among the eight species were clearly resolved using the complete cp genome sequences in this study. It will also provide potential molecular markers and candidate DNA barcodes for future studies and enrich the valuable complete cp genome resources of Cucurbitaceae