Abstract: Provided are a display substrate and a display device. The display substrate includes a display region and a border region located at the periphery of the display region. The border region includes a border sealant, and a plurality of normal shift register units and a plurality of dummy shift register units that are arranged in sequence. The display substrate also includes a plurality of gate lines, at least part of the gate lines is located in the display region; at least one of the dummy shift register units is staggered from the normal shift register units in an extending direction of the gate line, and the at least one of the dummy shift register units is staggered from the normal shift register units in a direction away from the border sealant.

Abstract: Biometric information is obtained from a user. A determination is made that the obtained biometric information matches at least one entry in a biometric database. In response to determining that the obtained biometric information matches the at least one entry in the biometric database, non-biometric verification information is obtained. A determination is made as to whether at least a portion of the non-biometric verification information matches at least a portion of preset non-biometric verification information associated with the user. In response to determining that the at least a portion of the non-biometric verification information matches, a determination is made that an identity of the user is verified. In response to determining that the at least a portion of the non-biometric verification information does not match, a determination is made that the identity of the user is not verified.

Abstract: A method of predicting the operation reliability of a distribution network, the method being based on an ARIMA model. By establishing an ARIMA model predict a user monthly power outage count; convert a non-smooth element outage count time series to a smooth time series; perform regression on only lagged values of a dependent variable, and on current values and lagged values of a stochastic error term, so as to establish a user monthly power outage count model; according to a prediction result sample an outage point, and at the same time, taking into account a real time load operating state, establish a failure mode effects table based on TLOC criteria and PLOC criteria; and calculate a system recovery time for each instance of device outage, and finally obtain a whole-year reliability index.

Abstract: An embodiment provides a multi-time-scale Digital/Analog (D/A) hybrid simulation system and simulation method therefor for a complex power distribution network and a computer storage medium, and is intended to solve existing problems about multi-time-scale modeling and simulation of the complex power distribution network and simulation accuracy and efficiency of the complex power distribution network.

Abstract: An embodiment provides a multi-time-scale Digital/Analog (D/A) hybrid simulation system and simulation method therefor for a complex power distribution network and a computer storage medium, and is intended to solve existing problems about multi-time-scale modeling and simulation of the complex power distribution network and simulation accuracy and efficiency of the complex power distribution network.

Abstract: A method of predicting the operation reliability of a distribution network, the method being based on an ARIMA model. By establishing an ARIMA model predict a user monthly power outage count; convert a non-smooth element outage count time series to a smooth time series; perform regression on only lagged values of a dependent variable, and on current values and lagged values of a stochastic error term, so as to establish a user monthly power outage count model; according to a prediction result sample an outage point, and at the same time, taking into account a real time load operating state, establish a failure mode effects table based on TLOC criteria and PLOC criteria; and calculate a system recovery time for each instance of device outage, and finally obtain a whole-year reliability index.