Abstract: Disclosed is a polymer valve material, which includes a scaffold, prepared with a first polyurethane as a raw material by electrospinning; and a composite layer, prepared with second polyurethane as a raw material and composited onto the scaffold. The present disclosure further discloses a method for preparing a polymer valve material, including: a spinning step, electrostatic spinning a first polyurethane solution to obtain a prefabricated film; a compositing step, compositing second polyurethane and the prefabricated film to obtain the polymer valve material; and an adjustment step, heat pressing the prefabricated film before or during compositing. The valve material of the present disclosure can improve the creep resistance and edge tear resistance of the valve, as well as extends its service life.

Abstract: Disclosed are a polymer valve leaflet material, a preparation method for integrally molding the same, and use thereof. The preparation method includes: a spinning step, electrospinning a first polyurethane solution to a receiving device to obtain a prefabricated film on the receiving device; and a compositing step, compositing second polyurethane with the prefabricated film to obtain the polymer valve leaflet material. The present disclosure solves the problems of uneven thickness of various parts of the leaflets, weak points inside the leaflets, and easy tearing of the leaflets at weak points in the existing impregnation process, thereby addressing the issues of easy creep, relatively low overall strength and poor tear resistance of the existing leaflets.

Abstract: Systems and methods are provided herein for generating a synthetic training data set that can be used to train a machine-learning model to identify when two addresses match (e.g., when a user-defined address and an authoritative address match). The addresses may each be tokenized. Each candidate address can be scored based on a number of common tokens it shares with the user-defined address. The highest-scored candidate address may be selected as a matching address for the user-defined address. In some embodiments, a number of the remaining candidate address can be selected as negative examples (e.g., candidate addresses that do not match the user-defined address) based on, for example, historical delivery information associated with the corresponding addresses. In this manner, an expansive training data set may be generated using addresses associated with user profiles of an online service provider and a set of authoritative addresses obtained from an authoritative source.

Abstract: Respective labels are generated automatically for a plurality of record pairs, with a label for a given pair indicating a relationship detected between the records of the pair. One or more machine learning models are trained using the labeled record pairs. The trained versions of the models are stored.

Abstract: Systems and methods are disclosed to implement a machine learned system to determine the comparative relationship between item package quantity (IPQ) information indicated in two item descriptions. In embodiments, the system employs a neural network that includes a token encoding layer, an attribute summarizing layer, and a comparison layer. The token encoding layer accepts an item description as a token sequence and encodes the tokens with token attributes that are relevant to IPQ extraction. The attribute summarizing layer uses a convolutional neural network to generate a set of fixed-size feature vectors for each encoded token sequence. All feature vectors for both item descriptions are then provided to the comparison layer to generate the IPQ comparison result. Advantageously, the disclosed neural network model can be trained to make accurate predictions about the IPQ relationship of the two item descriptions using a small set of token-level attributes as input signals.

Abstract: Respective text feature sets and non-text feature sets are generated corresponding to individual pairs of a plurality of record pairs. At least one text feature is based on whether a text token exists in both records of a pair. Perceptual hash values are used for non-text feature sets. A machine learning model is trained, using the text and non-text feature sets, to generate relationship scores for record pairs. The model includes a text sub-model and a non-text sub-model.

Abstract: A WAN-based remote mobile monitoring method and device of electrophysiological data, including microprocessor circuit, electrophysiological signal sampling processing circuit, data storage circuit, image liquid crystal circuit, real-time clock, emergent calling circuit, working power supply management circuit, wireless network interface circuit, USB interface circuit, etc, has the in-built binding IP address of the remote electrophysiological data monitoring server, TCP/IP protocol, PPP protocol and BlueTooth protocol. Application controls self-adaptive analysis computing, exception event warning and alarming privilege graded setting, event data package combination, network digital communication, data storage circuit area management, safety information data storage management and remote emergent calling of the remote mobile monitoring devices.