Abstract: A microspheric ionomer having a cross-linked structure, a preparation method therefor, applications thereof, and a preparation system thereof. The ionomer comprises structure units A represented by formula (1), structure units B represented by formula (2), and a cross-linking structure provided by a cross-linking agent, M being separately selected from H, a metal cation, and a straight chain, a saturated alkyl of branched or ring-shaped C1-C20, R being H or a methyl; and metal cations are introduced to part of structure units A in the ionomer. The ionomer shows an outstanding effect on nucleation of PET, serves as a nucleating agent for PET modification, so as to obtain a corresponding PET composition.

Abstract: A liquid injection device includes: an accommodating cavity for detachable mounting of an electronic vaporization device; a liquid storage tank; a liquid supply mechanism; and a control assembly. The control assembly is connected to the liquid supply mechanism and detects whether the electronic vaporization device is mounted in the accommodating cavity. The liquid supply mechanism is in communication with the liquid storage tank to: be connected to the electronic vaporization device and supply liquid to the electronic vaporization device when the electronic vaporization device is mounted in the accommodating cavity, and be disconnected from the electronic vaporization device and stop supplying liquid to the electronic vaporization device when the electronic vaporization device is removed from the accommodating cavity.

Abstract: A domestic waste incineration fly ash treatment system is provided, suitable for clean and efficient treatment of municipal solid waste incineration fly ash, and includes a three-stage water washing subsystem, a filtrate post-treatment subsystem, an MVR evaporation salt production subsystem, a pressure filtration subsystem, and a filter cake post-treatment subsystem.

Abstract: A novel composite biological agent based on a porous frame material, comprising porous frame materials and biomolecules. The porous frame materials cover a biological product, wherein the porous frame materials are metal-organic frame material (MOFs), covalent organic frame materials (COFs), and hydrogen-bonding organic frame materials (HOFs), and the biomolecules are any one or a combination of antibodies, enzymes, peptides, vaccines, nucleotides, and virus species. The composite biological agent uses the porous frame materials and biomolecules to form a porous frame material/biomolecule complex, and the biomolecules are coated to achieve the protection effect. Under the premise of remaining biomolecule activity, the system can achieve efficient separation and recovery of the porous materials and the biomolecules, so that the technical problems of synthesis, storage, release, etc. are solved, a good technical effect is achieved, and the biomolecules are effectively protected.

Abstract: A chiral stationary phase comprises a porous framework material and biomolecules. The porous framework material includes one of the metal-organic framework (MOF) material, the covalent organic framework (COF) material and the hydrogen-bonded organic framework (HOF) material. The biomolecules are biological chiral resolving agents. A pore size of the porous framework material is 0.2-15 nm. The porous framework material serves as a solid carrier. The biomolecules are loaded into the porous framework material. The porous framework material is modified with one or more of carboxyl, hydroxyl, amino, aldehyde, double bonds and mercapto groups.

Abstract: Machine-learned model scoring techniques for reducing model invocations are provided. In one technique, first values for non-pair-specific interaction features that are associated with a first entity are identified. Second values for pair-specific interaction features that are associated with (a) the first entity and (b) a second entity that is different than the first entity are identified. A machine-learned model that has been trained based on the non-pair-specific interaction features and the pair-specific interaction features generates a first score based on the first values. The machine-learned model also generates a second score based on the first values and the second values. A final score for the first entity-second entity pair is computed based on the first score and the second score. Based on the final score, data about the first entity is transmitted over a computer network to a computing device associated with the second entity.

Abstract: The novel porous framework materials (such as metal organic frameworks or covalent organic frameworks) having a wide range of applications, which was designed and developed in an inventive manner to resolve issues with respect to a carrier material in a stationary phase of a conventional chiral chromatographic column in which the carrier material has poor stability, a chiral resolving agent has a low loading rate, and the chiral resolving agent is prone to loss or is applied in a restricted manner. The porous framework material efficiently loads a chiral resolving agent (such as proteins, enzymes, or macrocyclic antibiotics) by means of covalent bonding, adsorption, embedding, and crosslinking, such that a variety of efficient and durable chiral stationary phases are prepared to serve as a novel high-performance chromatographic column filler used for chromatographic chiral separation (such as high-performance liquid chromatography or capillary chromatography).

Abstract: A novel composite biological agent based on a porous frame material, comprising porous frame materials and biomolecules. The porous frame materials cover a biological product, wherein the porous frame materials are metal-organic frame material (MOFs), covalent organic frame materials (COFs), and hydrogen-bonding organic frame materials (HOFs), and the biomolecules are any one or a combination of antibodies, enzymes, peptides, vaccines, nucleotides, and virus species. The composite biological agent uses the porous frame materials and biomolecules to form a porous frame material/biomolecule complex, and the biomolecules are coated to achieve the protection effect. Under the premise of remaining biomolecule activity, the system can achieve efficient separation and recovery of the porous materials and the biomolecules, so that the technical problems of synthesis, storage, release, etc. are solved, a good technical effect is achieved, and the biomolecules are effectively protected.

Abstract: A block chain-based multi-chain management method comprises: constructing a virtual node for each mechanism node, the virtual node comprising node clusters generated based on block chains in which each mechanism node participates for processing different affairs and/or flows; constructing a block chain identity management module and a block chain transaction agent module for each constructed virtual node; connecting identity information of each mechanism node to a block chain identity management system corresponding to block chains in each virtual node by means of the block chain identity management module; and acquiring the identity information corresponding to each transaction by means of the block chain identity management module, packaging the acquired identity information and corresponding transaction parameters to generate packaging information, and sending the packaging information to a target block chain executing the corresponding transaction for transaction execution, by the block chain transaction

Abstract: One embodiment of the present invention sets forth a technique for managing machine learning. The technique includes organizing a set of reusable components for performing machine learning under a framework. The technique also includes representing, within the framework, a machine learning model as a graph-based structure that includes nodes representing a subset of the reusable components and edges representing input-output relationships between pairs of the nodes. The technique further includes validating the machine learning model based on inputs and outputs associated with the nodes and the input-output relationships represented by the edges in the graph-based structure. Finally, the technique includes generating the machine learning model according to the graph-based structure and configurations for the subset of the reusable components.

Abstract: One embodiment of the present invention sets forth a technique for processing training data for a machine learning model. The technique includes training the machine learning model using training data comprising a set of features and a set of original labels associated with the set of features. The technique also includes generating multiple groupings of the training data based on internal representations of the training data in the machine learning model. The technique further includes replacing, in a first subset of groupings of the training data, a first subset of the original labels with updated labels based at least on occurrences of values for the original labels in the first subset of groupings.

Abstract: Disclosed herein, inter alia, are polymerized metal organic framework compositions and polymer compositions, and methods of making and using the same.

Abstract: Disclosed herein, inter alia, are polymerized metal organic framework compositions and polymer compositions, and methods of making and using the same.

Abstract: A process for the preparation of a heterocyclic macrocycle-templated supramolecular metal organic material, the process comprising preparing a reaction mixture containing a metal, a heterocyclic macrocycle, and organic ligands and forming, in the reaction mixture, a heterocyclic macrocycle-templated metal organic material comprising the metal, the heterocyclic macrocycle and the ligands by template-directed synthesis with the heterocyclic macrocycle serving as the template and being encapsulated within a cage of the template metal organic material.