Abstract: An antenna structure for directing light is disclosed. The antenna structure includes a reflector having a reflective surface and a ring-shaped dielectric grating arranged at the reflective surface and extending concentrically along a center axis perpendicular to the reflective surface and forming an omnidirectional reflector surrounding a low-index center portion of the ring-shaped dielectric grating. The antenna structure is configured to outcouple light emitted inside the low-index center portion through an upper end of the dielectric grating along the center axis with a Gaussian beam profile projection efficiency ? of at least 65%. A light emitting device and a method for designing an antenna structure are also disclosed.

Abstract: Systems and methods are provided for estimating travel time and distance. Such method may comprise obtaining a vehicle trip dataset comprising an origin, a destination, a time-of-day, a trip time, and a trip distance associated with each of a plurality of trips, and training a neural network model with the vehicle trip dataset to obtain a trained model. The neural network model may comprise a first module and a second module, the first module may comprise a first number of neuron layers, the first module may be configured to obtain the origin and the destination as first inputs to estimate a travel distance, the second module may comprise a second number of neuron layers, and the second module may be configured to obtain the information of a last layer of the first module and the time-of-day as second inputs to estimate a travel time.

Abstract: Disclosed is a method for maize cultivation in the black soil area of northeast China, including the following steps that can be conducted in any order: A. conservation tillage with straw returning: after a crop is harvested in autumn, raking and concentrating straws in rows; B. when sowing, disposing a front-loaded returning device on a sowing machine for a second concentration; C. when sowing, increasing the number of plants through a wide- and narrow-row effect; D. additional application of an organic bacterial fertilizer: where, the organic bacterial fertilizer includes a base fertilizer and a seed fertilizer; the base fertilizer refers to 500 kg of a 40% organic-inorganic mixed slow-release fertilizer; the seed fertilizer refers to 80 kg of an organic bacterial fertilizer; E. weeding with a herbicide treated by a secondary dilution method; and F. subsoiling to break a plow pan in summer: subsoiling flat land parcels to 40 cm.

Abstract: Disclosed is a method for maize cultivation in the black soil area of northeast China, including the following steps that can be conducted in any order: A. conservation tillage with straw returning: after a crop is harvested in autumn, raking and concentrating straws in rows; B. when sowing, disposing a front-loaded returning device on a sowing machine for a second concentration; C. when sowing, increasing the number of plants through a wide- and narrow-row effect; D. additional application of an organic bacterial fertilizer: where, the organic bacterial fertilizer includes a base fertilizer and a seed fertilizer; the base fertilizer refers to 500 kg of a 40% organic-inorganic mixed slow-release fertilizer; the seed fertilizer refers to 80 kg of an organic bacterial fertilizer; E. weeding with a herbicide treated by a secondary dilution method; and F. subsoiling to break a plow pan in summer: subsoiling flat land parcels to 40 cm.

Abstract: Systems and methods are provided for estimating travel time and distance. Such method may comprise obtaining a vehicle trip dataset comprising an origin, a destination, a time-of-day, a trip time, and a trip distance associated with each of a plurality of trips, and training a neural network model with the vehicle trip dataset to obtain a trained model. The neural network model may comprise a first module and a second module, the first module may comprise a first number of neuron layers, the first module may be configured to obtain the origin and the destination as first inputs to estimate a travel distance, the second module may comprise a second number of neuron layers, and the second module may be configured to obtain the information of a last layer of the first module and the time-of-day as second inputs to estimate a travel time.

Abstract: A method may comprise recursively performing: (1) providing one or more states of a simulation environment to a simulated vehicle, and the states comprise a first current time and a first current location of the simulated vehicle; (2) obtaining an action by the simulated vehicle when the simulated vehicle has reached a milestone, wherein: the action is selected from: waiting at the first current location of the simulated vehicle, picking up a passenger group A at an origin of passenger group A's transportation, and dropping off a passenger group B at a destination of passenger group B's transportation, and the milestone is an origin or a destination of any passenger group's transportation; (3) determining a reward to the simulated vehicle for the action; and (4) updating the one or more states based on the action to obtain one or more updated states for providing to the simulated vehicle.

Abstract: The present disclosure provides a bracket and a scooter storage box having same. The bracket is used for storing an electric scooter. The bracket includes: a chassis, the chassis being provided with a recess portion for receiving a wheel of the electric scooter, the chassis being further provided with a carrying surface urging against the lower surface of a scooter body of the electric scooter; and a charging portion, electrically connected to the electric scooter to charge the electric scooter. The electric scooter may be limited and carried by the cooperation of the recess portion on the chassis of the bracket and the carrying surface, so that the electric scooter may be steadily stored. Furthermore, the electric scooter may also be charged by the charging potion to meet the charging needs of the electric scooter.

Abstract: Systems and methods for allocating service requests are provided. The method may include receiving a service request, determining an estimated value of the service request, and determining at least one candidate service provider for the service request. The method may further include, for each of the at least one candidate service provider, obtaining historical order parameters of the candidate service provider, receiving expected order parameters of the candidate service provider, and determining an order allocation weight of the service request based on the estimated value of the service request, the historical order parameters, and the expected order parameters of the service provider. The method may further include determining a target service provider based on at least one order allocation weight of the service request with respect to the at least one candidate service provider.

Abstract: The disclosure provides an electric scooter storage method based on a scooter storage box. The scooter storage box is configured to store at least one electric scooter, The scooter storage box includes a box body, a bracket arranged in the box body and used for storing the electric scooter, an access port, and an interaction device. The method further includes: if the interaction device detects a scooter taking-out instruction, verifying identity information of a scooter taking-out user; if the verification is passed, conveying the electric scooter to the access port, and opening the access port; and if it is detected that the electric scooter is taken out from the bracket, recording renting information of the electric scooter, the renting information at least including: user information of a scooter taking-out user who takes out the electric scooter, and identification information and renting time of the electric scooter.

Abstract: A method for providing vehicle navigation simulation environment may comprise recursively performing steps (1)-(4) for a time period: (1) providing one or more states of a simulation environment to a simulated agent, wherein: the simulated agent comprises a simulated vehicle, and the states comprise a first current time and a first current location of the simulated vehicle; (2) obtaining an action by the simulated vehicle when the simulated vehicle has no passenger, wherein the action is selected from: waiting at the first current location of the simulated vehicle, and transporting M passenger groups; (3) determining a reward to the simulated vehicle for the action; and (4) updating the one or more states based on the action to obtain one or more updated states for providing to the simulated vehicle, wherein: the updated states comprise a second current time and a second current location of the simulated vehicle.

Abstract: The disclosure provides an electric scooter storage method based on a scooter storage box. The scooter storage box is configured to store at least one electric scooter, The scooter storage box includes a box body, a bracket arranged in the box body and used for storing the electric scooter, an access port, and an interaction device. The method further includes: if the interaction device detects a scooter taking-out instruction, verifying identity information of a scooter taking-out user; if the verification is passed, conveying the electric scooter to the access port, and opening the access port; and if it is detected that the electric scooter is taken out from the bracket, recording renting information of the electric scooter, the renting information at least including: user information of a scooter taking-out user who takes out the electric scooter, and identification information and renting time of the electric scooter.

Abstract: The present disclosure provides a bracket and a scooter storage box having same. The bracket is used for storing an electric scooter. The bracket includes: a chassis, the chassis being provided with a recess portion for receiving a wheel of the electric scooter, the chassis being further provided with a carrying surface urging against the lower surface of a scooter body of the electric scooter; and a charging portion, electrically connected to the electric scooter to charge the electric scooter. The electric scooter may be limited and carried by the cooperation of the recess portion on the chassis of the bracket and the carrying surface, so that the electric scooter may be steadily stored. Furthermore, the electric scooter may also be charged by the charging potion to meet the charging needs of the electric scooter.

Abstract: A method for operating a ride-share-enabled vehicle includes determining a target location of the ride-share-enabled vehicle, determining a ride-sharing policy algorithm to determine a behavior of the ride-share-enabled vehicle including whether to accept a multiple shared ride or maintain a single shared ride and a route of the multiple shared ride, if any, based on the determined target location of the ride-share-enabled vehicle, determining a behavior of the ride-share-enabled vehicle based on a current location of the ride-share-enabled vehicle and the determined ride-sharing policy algorithm, and causing the ride-share-enabled vehicle to be operated according to the determined behavior of the ride-share-enabled vehicle.

Abstract: A method for providing vehicle navigation simulation environment may comprise recursively performing steps (1)-(4) for a time period: (1) providing one or more states of a simulation environment to a simulated agent, wherein: the simulated agent comprises a simulated vehicle, and the states comprise a first current time and a first current location of the simulated vehicle; (2) obtaining an action by the simulated vehicle when the simulated vehicle has no passenger, wherein the action is selected from: waiting at the first current location of the simulated vehicle, and transporting M passenger groups; (3) determining a reward to the simulated vehicle for the action; and (4) updating the one or more states based on the action to obtain one or more updated states for providing to the simulated vehicle, wherein: the updated states comprise a second current time and a second current location of the simulated vehicle.

Abstract: A method may comprise recursively performing: (1) providing one or more states of a simulation environment to a simulated vehicle, and the states comprise a first current time and a first current location of the simulated vehicle; (2) obtaining an action by the simulated vehicle when the simulated vehicle has reached a milestone, wherein: the action is selected from: waiting at the first current location of the simulated vehicle, picking up a passenger group A at an origin of passenger group A's transportation, and dropping off a passenger group B at a destination of passenger group B's transportation, and the milestone is an origin or a destination of any passenger group's transportation; (3) determining a reward to the simulated vehicle for the action; and (4) updating the one or more states based on the action to obtain one or more updated states for providing to the simulated vehicle.

Abstract: A method of classifying high-dimensional data to facilitate subsequent processing of the data. Generally characterized, the method includes determining a statistical distribution function for a first dataset; determining a statistical distribution function for a second dataset; identifying a characterizing feature of the first and second datasets; determining a probability of distinction based on the first and second statistical distribution functions, with the probability of distinction being the probability that the characterizing feature can be used to distinguish the first dataset from the second dataset; and identifying a subject of the first dataset based on the probability of distinction. Once the subject has been identified, the first dataset can be further analyzed using processing techniques appropriate to the subject.

Abstract: A method of classifying high-dimensional data to facilitate subsequent processing of the data. Generally characterized, the method includes determining a statistical distribution function for a first dataset; determining a statistical distribution function for a second dataset; identifying a characterizing feature of the first and second datasets; determining a probability of distinction based on the first and second statistical distribution functions, with the probability of distinction being the probability that the characterizing feature can be used to distinguish the first dataset from the second dataset; and identifying a subject of the first dataset based on the probability of distinction. Once the subject has been identified, the first dataset can be further analyzed using processing techniques appropriate to the subject.