Abstract: The present invention relates to a molecular marker, a specific primer pair, and an identification method of the high-quality Ganoderma lucidum strain HMGIM-M624. The high-quality Ganoderma lucidum strain HMGIM-M624 was preserved in Guangdong Microbial Culture Collection Center (address: 5th Floor, No. 59 Building of No. 100 Yard, Mid. Xianlie Road, Guangzhou City) with the preservation number of GDMCC No: 60889 on Nov. 7, 2019. The molecular marker is an InDel molecular marker. The high-quality Ganoderma lucidum strain HMGIM-M624 has a base deletion of CATGCTGTA at the 246451th-246460th site of the chromosome sca34. The present invention provides reagents for detecting the molecular marker of the high-quality Ganoderma lucidum strain HMGIM-M624. The reagents can distinguish the high-quality Ganoderma lucidum strain HMGIM-M624 from the other 11 G. lucidum strains for commercial cultivation, thus specifically identifying the high-quality Ganoderma lucidum strain HMGIM-M624.

Abstract: A seismic quantitative prediction method for shale total organic carbon (TOC) based on sensitive parameter volumes is as follows. A target stratum for a TOC content to be measured is determined, logging curves with high correlations with TOC contents are analyzed, the logging curves are found as sensitive parameters; sample data are constructed using the sensitive parameters; a radial basis function (RBF) neural network is trained with the sample data as an input and the TOC content at a depth corresponding to the sample data as an output to obtain a RBF neural network prediction model; sensitive parameter volumes are obtained by using the sensitive parameters and post stack three-dimension seismic data to invert; prediction samples are constructed using the sensitive parameter volumes; the predicted samples are input to the RBF neural network prediction model to calculate corresponding TOC values, thereby the TOC content of the target stratum is predicted.

Abstract: An apparatus, such as a network element, comprises a receiver to receive a plurality of packets. A memory stores instructions and forms a first and second set of virtual queues to store the plurality of packets. A processor having one or more cores with one or more packet classifiers provides a classification of a packet in the plurality of packets. The processor in communication with the memory executes instructions to transfer the packet from the receiver to a virtual queue in the first set of virtual queues based on the classification. The processor also transfers the packet from the virtual queue to a transmitter based on a demand rate value and supply rate value associated with the virtual queue.

Abstract: An apparatus, such as a network element, comprises a receiver to receive a plurality of packets. A memory stores instructions and forms a first and second set of virtual queues to store the plurality of packets. A processor having one or more cores with one or more packet classifiers provides a classification of a packet in the plurality of packets. The processor in communication with the memory executes instructions to transfer the packet from the receiver to a virtual queue in the first set of virtual queues based on the classification. The processor also transfers the packet from the virtual queue to a transmitter based on a demand rate value and supply rate value associated with the virtual queue.