Abstract: Methods, systems, and devices for entity token segregation are described. A custodial token platform (e.g., via a ledger service or manager) monitors a set of actions by a first set of user profiles at a custodial token platform based on the first set of user profiles being associated with a first segregated entity for which a rebalancing rule is configured. The custodial token platform determines, based on the rebalancing rule, a net change in token amounts attributed to the first set of user profiles associated with the first segregated entity during a first duration of time. The custodial token platform executes a transfer of a first amount of one or more crypto tokens between a first blockchain address associated with the first segregated entity and a second blockchain address associated with the custodial token platform, where the first amount is based on the rebalancing rule and the net change.

Abstract: A short-term traffic flow prediction method based on a causal gated-low-pass graph convolutional network can include constructing a causal gated-low-pass graph convolutional network, where the causal gated-low-pass graph convolutional network includes a causal gated-low-pass convolutional block. The causal gated-low-pass convolutional block is connected to a fully-connected output layer, the causal gated-low-pass convolutional block includes two causal gated linear units and a low-pass graph convolutional block, and the low-pass graph convolutional block is set between the causal gated linear units. The method can further include obtaining a traffic flow network diagram and a traffic flow value based on traffic flow data, using the traffic flow network diagram as input, and performing short-term traffic flow prediction by using the causal gated-low-pass graph convolutional network. The method can predict short-term traffic flow with high accuracy.

Abstract: A short-term traffic flow prediction method based on a causal gated-low-pass graph convolutional network can include constructing a causal gated-low-pass graph convolutional network, where the causal gated-low-pass graph convolutional network includes a causal gated-low-pass convolutional block. The causal gated-low-pass convolutional block is connected to a fully-connected output layer, the causal gated-low-pass convolutional block includes two causal gated linear units and a low-pass graph convolutional block, and the low-pass graph convolutional block is set between the causal gated linear units. The method can further include obtaining a traffic flow network diagram and a traffic flow value based on traffic flow data, using the traffic flow network diagram as input, and performing short-term traffic flow prediction by using the causal gated-low-pass graph convolutional network. The method can predict short-term traffic flow with high accuracy.

Abstract: The present invention relates to a synchronous sampling and measuring system and a method thereof for flue gas partition. The system includes a detection device arranged at an SCR outlet for simultaneous detection of flue gas from the pipelines in different areas, wherein the detection device is connected to a speed measurement device for measuring the speed of flue gas, the speed measurement device is connected to a central control unit and one end of a valve group, respectively, the other end of the valve group is connected to an air extracting device and a dilution unit, respectively, the control end of the valve group and the control end of the air extracting device are connected to a central control unit, respectively, the dilution unit is connected to a CEMS analyzer.