Abstract: A method for optimizing a Polar-RNNA quantizer of MLC NAND flash based on deep learning comprises the following steps: Step S1: transforming an MLC flash detection task into a deep learning task, and obtaining three hard-decision read thresholds based on a neural network; Step S2: expanding six soft-decision read thresholds based on the three hard-decision read thresholds; Step S3: constructing an LLR mapping table, and obtaining new LLR information of MLC flash based on the LLR mapping table; Step S4: symmetrizing an MLC flash channel, and performing density evolution; and Step S5: optimizing the soft-decision read thresholds based on a genetic algorithm to obtain an optimal quantization interval. According to the invention, polar codes can be directly used for MLC flash channels without the arduous work of MLC flash channel modeling, so that the reliability of MLC flash is effectively improved.

Abstract: The present disclosure provides the use of 4-(Phenylethynyl) benzoic acid in preparing a plant growth regulator. 4-(Phenylethynyl) benzoic acid has a formula as C15H10O2, a molecular weight of 222.2390. The optimum concentration of 4-(Phenylethynyl) benzoic acid in the plant growth regulator is in a range of 10 ?M to 200 ?M. The plant growth regulator further contains a pesticidally acceptable carrier. The present disclosure further provides the use of 4-(Phenylethynyl) benzoic acid in regulating plant growth. In the use of 4-(Phenylethynyl) benzoic acid in preparing a plant growth regulator of the present disclosure, 4-(Phenylethynyl) benzoic acid has the ABA-like effects, and is more stable, easily available, cheaper, and environmentally friendly. Therefore, the present disclosure is suitable for large-scale popularization.