Abstract: Provided are systems and processes for optimizing assignments of deliveries for perishable goods. In one aspect, a method is provided for pairing a set of created orders with a set of available couriers. The set of created orders may include orders confirmed by the merchant and the set of available couriers include couriers that are online with an active status. Feasible pairings are generated between each courier and each created order. Infeasible pairings are eliminated based on factors such as transportation mode. Possible routes for each pairing are generated and scored based on weighted factors. The scores are optimized to achieve a set of routes with a maximum score. The routes are then offered to the corresponding courier if the courier will arrive at or after the created order is completed by the merchant. A neural network may be implemented to recognize the optimal score for a given duration.

Abstract: Provided are an antibody fusion protein, a preparation method thereof and an application thereof. The antibody fusion protein is high in expression quantity, and the transient expression quantity in mammalian cells 293E is 100-150 mg/L; the antibody fusion protein is high in assembly rate, and the correct assembly rate exceeds 95%; the antibody fusion protein has a high affinity, and a single-sided antibody/fusion protein and antigen binding KD value is equivalent to a positive control monoclonal antibody/fusion protein and antigen binding KD value; the antibody fusion protein is convenient to purify, and the purity can reach more than 95% in one-step purification by using Protein A or Protein L, and the tumor inhibition rate in a pharmacodynamic experiment animal can reach up to 92%.

Abstract: Provided are a bivalent bispecific antibody and a preparation method thereof, a coding gene, a host cell and a composition. The bivalent bispecific antibody comprises: a) a single-chain variable fragment scFv, a flexible peptide, a heavy chain IgG1 constant region CH1 and a hinge region partial sequence of the antibody that specifically binds to a first antigen, and b) a single-chain variable fragment scFv, and a light chain constant region CL, that is, scFv1-CL or CL-scFv1, of the antibody that specifically binds to a second antigen; or comprises: c) a light chain and a heavy chain of the antibody that specifically binds to the first antigen, and d) a light chain and a heavy chain of the antibody that specifically binds to the second antigen.

Abstract: Described are systems and processes for generating dynamic estimated time of arrival predictive updates for delivery of perishable goods. In one aspect a system is configured for generating dynamic estimated time of arrival (ETA) predictive updates between a series of successive events for real-time delivery of orders. For each order, a plurality of delivery events and corresponding timestamps are received from devices operated by customers, restaurants, and couriers. Based on the timestamps, the system generates a plurality of ETA time predictions for one or more of the delivery events with trained predictive models that use weighted factors including historical restaurant data and historical courier performance. As additional timestamps are received for a delivery event, the trained predictive models dynamically update the ETA time predictions for successive events. The predictive models may be continuously trained by updating the weighted factors based on the received timestamps.

Abstract: Described are systems and processes for generating dynamic estimated time of arrival predictive updates for delivery of perishable goods. In one aspect a system is configured for generating dynamic estimated time of arrival (ETA) predictive updates between a series of successive events for real-time delivery of orders. For each order, a plurality of delivery events and corresponding timestamps are received from devices operated by customers, restaurants, and couriers. Based on the timestamps, the system generates a plurality of ETA time predictions for one or more of the delivery events with trained predictive models that use weighted factors including historical restaurant data and historical courier performance. As additional timestamps are received for a delivery event, the trained predictive models dynamically update the ETA time predictions for successive events. The predictive models may be continuously trained by updating the weighted factors based on the received timestamps.

Abstract: Provided are various systems and processes for improving last-mile delivery of real-time, on-demand orders for perishable goods. In one aspect, a method is provided for aggregating on-demand deliveries using a depot dispatch protocol which may implement automated order transport and retrieval systems. The method comprises dispatching merchant couriers to transport on-demand orders from merchants to a merchant depot where the orders are aggregated and batched based on optimized delivery routes and destination proximities. Batches of orders are then transported to a customer depot corresponding to an area of delivery destinations. Orders are then assigned to delivery couriers for completion of delivery to customers. Such delivery routing systems and processes may be implemented alongside a delivery tracking system for generating estimated time of arrival predictive updates for real-time delivery of perishable goods.

Abstract: Provided are various systems and processes for improving last-mile delivery of real-time, on-demand orders for perishable goods. In one aspect, a method is provided for aggregating on-demand deliveries using a depot dispatch protocol which may implement automated order transport and retrieval systems. The method comprises dispatching merchant couriers to transport on-demand orders from merchants to a merchant depot where the orders are aggregated and batched based on optimized delivery routes and destination proximities. Batches of orders are then transported to a customer depot corresponding to an area of delivery destinations. Orders are then assigned to delivery couriers for completion of delivery to customers. Such delivery routing systems and processes may be implemented alongside a delivery tracking system for generating estimated time of arrival predictive updates for real-time delivery of perishable goods.

Abstract: Provided are various mechanisms and processes for optimizing real-time, on-demand delivery service for perishable goods. In one aspect, a method is provided for aggregating on-demand deliveries using a depot dispatch protocol. The method comprises dispatching runners to pick up on-demand orders for drop off at a depot where the orders are aggregated and batched based on optimized delivery routes and destination proximities. Batches of orders are then assigned to couriers who may arrive at the depot to receive the batched orders without having to navigate through areas of high congestion. Such delivery routing system may be implemented alongside a delivery tracking system for generating estimated time of arrival predictive updates for real-time delivery of perishable goods.

Abstract: Provided are various mechanisms and processes for optimizing real-time, on-demand deliveries of perishable goods. In one aspect, a method is provided for aggregating on-demand deliveries using a depot dispatch protocol. The method comprises dispatching runners to pick up on-demand orders for drop off at a depot where the orders are aggregated and batched based on optimized delivery routes and destination proximities. Batches of orders are then assigned to couriers who may arrive at the depot to receive the batched orders without having to navigate through areas of high congestion. Such delivery routing system may be implemented alongside a delivery tracking system for generating estimated time of arrival predictive updates for real-time delivery of perishable goods.

Abstract: Provided are systems and processes for optimizing assignments of deliveries for perishable goods. In one aspect, a method is provided for pairing a set of created orders with a set of available couriers. The set of created orders may include orders confirmed by the merchant and the set of available couriers include couriers that are online with an active status. Feasible pairings are generated between each courier and each created order. Infeasible pairings are eliminated based on factors such as transportation mode. Possible routes for each pairing are generated and scored based on weighted factors. The scores are optimized to achieve a set of routes with a maximum score. The routes are then offered to the corresponding courier if the courier will arrive at or after the created order is completed by the merchant. A neural network may be implemented to recognize the optimal score for a given duration.

Abstract: Provided are systems and processes for optimizing assignments of deliveries for perishable goods. In one aspect, a method is provided for pairing a set of created orders with a set of available couriers. The set of created orders may include orders confirmed by the merchant and the set of available couriers include couriers that are online with an active status. Feasible pairings are generated between each courier and each created order. Infeasible pairings are eliminated based on factors such as transportation mode. Possible routes for each pairing are generated and scored based on weighted factors. The scores are optimized to achieve a set of routes with a maximum score. The routes are then offered to the corresponding courier if the courier will arrive at or after the created order is completed by the merchant. A neural network may be implemented to recognize the optimal score for a given duration.

Abstract: Provided are various systems and processes for improving last-mile delivery of real-time, on-demand orders for perishable goods. In one aspect, an automated vehicle (AV) comprises a body including a storage compartment for storing perishable goods. The storage compartment is accessible by a user upon authentication of the user. The AV further comprises a sensor module for receiving data for navigating the AV. The sensor module is positioned above the body on a support structure at a predetermined height above the ground, such as three to five feet. The data includes one or more of the following: audio data, video data, radio waves, and backscattered light waves. The AV further comprises an onboard computer system configured to process the data to navigate the AV along motor vehicle routes and pedestrian routes. The AV may be configured to interface with an automated locker system to retrieve or deposit the perishable goods.

Abstract: Provided are various systems and processes for improving last-mile delivery of real-time, on-demand orders for perishable goods. In one aspect, a method is provided for aggregating on-demand deliveries using a depot dispatch protocol which may implement automated order transport and retrieval systems. The method comprises dispatching merchant couriers to transport on-demand orders from merchants to a merchant depot where the orders are aggregated and batched based on optimized delivery routes and destination proximities. Batches of orders are then transported to a customer depot corresponding to an area of delivery destinations. Orders are then assigned to delivery couriers for completion of delivery to customers. Such delivery routing systems and processes may be implemented alongside a delivery tracking system for generating estimated time of arrival predictive updates for real-time delivery of perishable goods.

Abstract: Provided are systems and processes for optimizing assignments of deliveries for perishable goods. In one aspect, a method is provided for pairing a set of created orders with a set of available couriers. The set of created orders may include orders confirmed by the merchant and the set of available couriers include couriers that are online with an active status. Feasible pairings are generated between each courier and each created order. Infeasible pairings are eliminated based on factors such as transportation mode. Possible routes for each pairing are generated and scored based on weighted factors. The scores are optimized to achieve a set of routes with a maximum score. The routes are then offered to the corresponding courier if the courier will arrive at or after the created order is completed by the merchant. A neural network may be implemented to recognize the optimal score for a given duration.

Abstract: Provided are various mechanisms and processes for optimizing real-time, on-demand deliveries of perishable goods. In one aspect, a method is provided for aggregating on-demand deliveries using a depot dispatch protocol. The method comprises dispatching runners to pick up on-demand orders for drop off at a depot where the orders are aggregated and batched based on optimized delivery routes and destination proximities. Batches of orders are then assigned to couriers who may arrive at the depot to receive the batched orders without having to navigate through areas of high congestion. Such delivery routing system may be implemented alongside a delivery tracking system for generating estimated time of arrival predictive updates for real-time delivery of perishable goods.

Abstract: Described are systems and processes for generating dynamic estimated time of arrival predictive updates for delivery of perishable goods. In one aspect a system is configured for generating dynamic estimated time of arrival (ETA) predictive updates between a series of successive events for real-time delivery of orders. For each order, a plurality of delivery events and corresponding timestamps are received from devices operated by customers, restaurants, and couriers. Based on the timestamps, the system generates a plurality of ETA time predictions for one or more of the delivery events with trained predictive models that use weighted factors including historical restaurant data and historical courier performance. As additional timestamps are received for a delivery event, the trained predictive models dynamically update the ETA time predictions for successive events. The predictive models may be continuously trained by updating the weighted factors based on the received timestamps.

Abstract: An optimizing design method for a chassis structure of electronic equipment is disclosed, including: investigating from the point of view of mechanical, electric and thermal three-field coupling, determining the preliminary design size of the chassis, performing a mechanical analysis by using a mechanical analysis software such as ANSYS; converting the mesh model among the three-fields, obtaining the mesh model used for the electromagnetic and thermal analyses; setting the thermal analysis parameters, performing the thermal analysis by using an electromagnetic analysis software such as ICEPAK; determining a resonance frequency of the chassis and an electric parameter of the absorbing material, performing an electromagnetic analysis by using a thermal analysis software such as FEKO; correcting the analysis result by sample testing; determining whether the chassis satisfies the design requirement, if it satisfies the requirement, the optimizing design will be finished, otherwise, modifying the preliminary compu

Abstract: An optimizing design method for a chassis structure of electronic equipment is disclosed, including: investigating from the point of view of mechanical, electric and thermal three-field coupling, determining the preliminary design size of the chassis, performing a mechanical analysis by using the software Ansys; converting the mesh model among the three-fields, obtaining the mesh model used for the electromagnetic and thermal analyses; setting the thermal analysis parameters, performing the thermal analysis by using the software IcePak; determining a resonance frequency of the chassis and an electric parameter of the absorbing material, performing an electromagnetic analysis by using the software FeKo; correcting the analysis result by sample testing; determining whether the chassis satisfies the design requirement, if it satisfies the requirement, the optimizing design will be finished, otherwise, modifying the preliminary CAD model, the electromagnetic analysis parameter and the thermal analysis parameter,

Abstract: A foldable mobile electronic device includes a body, a top cover and a hinge. The body includes a first sliding mechanism. The top cover includes a second sliding mechanism moving between a first position and a second position relative to the body. The hinge includes a first protrusion and a second protrusion. The first protrusion corresponds to the first sliding mechanism and the second protrusion corresponds to the second sliding mechanism. When the top cover is in the first position, the hinge extends from the body and the top cover to be exposed, and when the top cover is in the second position, the hinge is accommodated in the foldable mobile electronic device.

Abstract: A foldable mobile electronic device includes a body, a top cover and a hinge. The body includes a first sliding mechanism. The top cover includes a second sliding mechanism moving between a first position and a second position relative to the body. The hinge includes a first protrusion and a second protrusion. The first protrusion corresponds to the first sliding mechanism and the second protrusion corresponds to the second sliding mechanism. When the top cover is in the first position, the hinge extends from the body and the top cover to be exposed, and when the top cover is in the second position, the hinge is accommodated in the foldable mobile electronic device.