Molten salt electrolytic synthesis of porous carbon from SiC and its application in supercapacitors.

Nanoscale porous carbide-derived carbon (CDC) microspheres were successfully synthesized the electrolysis etching of nano-SiC microsphere powder precursors with a particle diameter of 200 to 500 nm in molten CaCl. Electrolysis was conducted at 900 °C for 14 h in argon at an applied constant voltage of 3.2 V.

TaijiGNN: A New Cycle-Consistent Generative Neural Network for High-Quality Bidirectional Transformation between RGB and Multispectral Domains.

Since multispectral images (MSIs) and RGB images (RGBs) have significantly different definitions and severely imbalanced information entropies, the spectrum transformation between them, especially reconstructing MSIs from RGBs, is a big challenge. We propose a new approach, the Taiji Generative Neural Network (TaijiGNN), to address the above-mentioned problems. TaijiGNN consists of two generators, G_MSI, and G_RGB.

[Microbial Diversity and Influencing Factors in a Small Watershed in Winter].

The water quality of a small watershed will directly affect the water quality safety of the entire watershed. The Lidu small watershed of the Fulin District in Chongqing was selected for this study. The characteristics of runoff and the microbial community in the receiving water body were analyzed by high-throughput sequencing technology.

Hierarchical network modeling with multidimensional information for aquatic safety management in the cold chain.

The cold-chain information has characterized by the loss and dispersion according to the different collecting methods. The description for the quality decay factors of aquatic products can be defined as the multidimensional information. A series of nodes with multidimensional information are assembled to be hierarchies aiming at describing the environment conditions and locations in the supply chain.

Multi-matrices factorization with application to missing sensor data imputation.

We formulate a multi-matrices factorization model (MMF) for the missing sensor data estimation problem. The estimation problem is adequately transformed into a matrix completion one. With MMF, an n-by-t real matrix, R, is adopted to represent the data collected by mobile sensors from n areas at the time, T1, T2, .