Abstract: Systems and methods for obtaining product information via a conversational user interface. The communication channel receives communication from a user, the intent and entities of which are deduced by the NLP. These are communicated by the fulfillment API to the knowledge engine which retrieves information that fulfills the intent. The information is communicated to the fulfillment API, which converts the intent into a response, which in turn is forwarded by the NLP to the communication channel, and back to the user.

Abstract: Machine learning segmentation methods and systems that perform segmentation quickly, efficiently, cheaply, and optionally provides an interactive feature that allows a user to alter the segmentation until a desired result is obtained. The automated machine learning segmentation tool receives all potentially important attributes and provides segmentation of items. It also receives information about important features of the data and finds how best to differentiate between groups using cluster-based machine learning algorithms. In addition, visualization of the segmentation explains to a user how the segmentation was obtained.

Abstract: Systems and methods for embedding a computational notebook within an enterprise application software. A computational notebook editor embedded is embedded within a software client interface which is in communication with the software client interface. The application server comprises a reverse proxy server that is embedded within the application server. A container management system is in communication with the application server and comprises a multi-user server, a notebook interactive development environment, and a notebook execution tool.

Abstract: Provided is a high-voltage UPS battery system. The system includes a battery casing, a cell mounted within the battery casing, and a heating base mounted within the battery casing and at a bottom end of the cell. The heating base is configured to selectively heat the cell.

Abstract: A method and system for a machine learning cluster analysis of historical lead time data, which is augmented by one or more features. The data can also be divided into groups, based on time-density of the data, with clustering performed on each group. Furthermore, clustering can also be projected onto two dimensions. In addition, the historical lead time data is separated into a plurality of tolerance zones based on tolerance criteria. The clusters are separated in accordance with a tolerance zone of each group; and further separated according to one or more lead time identifiers to provide one or more separated clusters.

Abstract: The disclosure provides methods of analyzing an analyte of interest in a biological sample using fluorescent agents and macroconjugates which comprise a core containing a cross-linked polymer or protein, tags, specific binding members or fragments thereof, and optionally carrier proteins. Also provided are methods of analyzing two or more analytes of interest in a biological sample in a single assay using microparticles and detection conjugates comprising different fluorophore labels, acquiring transmitted light and fluorescent images of the microparticles, and using a customized image analysis process to analyze the acquired images.

Abstract: A dynamic supply chain planning system for analysis of historical lead time data that uses machine learning algorithms to forecast future lead times based on historical lead time data, weather data and financial data related to locations and dates within the supply chain.

Abstract: Machine learning segmentation methods and systems that perform segmentation quickly, efficiently, cheaply, and optionally provides an interactive feature that allows a user to alter the segmentation until a desired result is obtained. The automated machine learning segmentation tool receives all potentially important attributes and provides segmentation of items. It also receives information about important features of the data and finds how best to differentiate between groups using cluster-based machine learning algorithms. In addition, visualization of the segmentation explains to a user how the segmentation was obtained.

Abstract: Systems and methods for embedding a computational notebook within an enterprise application software. A computational notebook editor embedded is embedded within a software client interface which is in communication with the software client interface. The application server comprises a reverse proxy server that is embedded within the application server. A container management system is in communication with the application server and comprises a multi-user server, a notebook interactive development environment, and a notebook execution tool.

Abstract: The present invention discloses a lithium ion battery with high capacity, which comprises an cathode material, a anode material and an electrolyte. The cathode material has a chemical formula LiNi(1-x)MexO, wherein x is 10?6 to 10?1, and Me is a third metal other than Li and Ni. Said material has the advantages of high purity, high density and high delithiation capacity. The preparation method of the cathode material comprises: subjecting a nickel salt and an additive to chemical co-precipitation, calcining, and inducing of a crack structure with an inducing environment, an inducing chemical, or a combination thereof to give a precursor, which is mixed with Li2O, sintered and crushed to give the cathode material.

Abstract: A method and system for a machine learning duster analysis of historical lead time data, which is augmented by one or more features. The data can also be divided into groups, based on time-density of the data, with clustering performed on each group. Furthermore, clustering can also be projected onto two dimensions. In addition, the historical lead time data is separated into a plurality of tolerance zones based on tolerance criteria. The clusters are separated in accordance with a tolerance zone of each group; and further separated according to one or more lead time identifiers to provide one or more separated clusters.

Abstract: A dynamic supply chain planning system for analysis of historical lead time data that uses machine learning algorithms to forecast future lead times based on historical lead time data, weather data and financial data related to locations and dates within the supply chain.

Abstract: This application relates to the technical field of lithium-ion batteries, in particular to a lithium-ion battery electrolyte solution, a secondary battery, a battery module, a battery pack, and an electrical device. The lithium-ion battery electrolyte solution includes a lithium salt, an organic solvent and an additive. The additive includes a compound represented by Formula I: where R1 to R4 are each independently selected from a hydrogen atom, a halogen atom, a nitrate ester group, a nitrite ester group, a substituted or unsubstituted C1 to C12 alkyl, and a substituted or unsubstituted C1 to C12 alkoxy, provided that at least one of R1 to R4 is a nitrate ester group. This application solves the problems of poor solubility of existing additives and low conductivity of a formed solid electrolyte interphase (SEI).

Abstract: In certain embodiments, a computer-implemented method includes: receiving, by a caching system plugin, a request to create a persistent volume for a container application instance; configuring, by the caching system plugin, a local cache volume on a host computing device; configuring, by the caching system plugin, a remote storage volume on a remote storage device; selecting, by a policy manager of the caching system plugin, a cache policy for the container application instance; creating, by the caching system plugin and from a cache manager, a virtual block device associated with the local cache volume, the remote storage volume, and the cache policy; and providing the virtual block device for use by the container application instance as the persistent volume.

Abstract: Systems and methods for dynamic demand sensing in a supply chain in which constantly-updated data is used to select a machine learning model or retrain a pre-selected machine learning model, for forecasting sales of a product at a specific location. The updated data includes product information and geographic information. Also disclosed are systems and methods relating to demand forecasting and readjusting forecasts based on forecast error.

Abstract: This application provides a positive electrode plate and a preparation method thereof, an electrode assembly, a battery cell, and a battery and pertains to the field of battery technologies. The method for preparing a positive electrode plate includes: providing an adhesive liquid on at least one surface of at least partial to-be-bent position of a positive electrode current collector and curing it to form an adhesive layer, and forming a positive electrode active material layer on at least one surface of the positive electrode current collector having the adhesive layer. The adhesive liquid is first provided on at least one surface of the to-be-bent position of the positive electrode current collector and cured to form the adhesive layer, and then the positive electrode active material layer is formed.

Abstract: The disclosure relates to the technical filed of organic synthesis, in particular to a method for preparing veratrole. In the disclosure, veratrole is prepared by using lithium hydroxide instead of sodium hydroxide, catechol as raw material, and dimethyl sulfate as methylating agent. Comparing with the existing method of adding sodium hydroxide solution, the method according to the disclosure allows reduced reaction temperature, shortened reaction time, and thereby improves the stability and safety of the reaction and the product yield. The experimental data of the examples in the disclosure shows a veratrole yield up to 95%. Furthermore, the preparation method according to the disclosure does not need to be carried out under a reflux condition, the operation of which is simpler, thereby further improving the safety and stability of the reaction.

Abstract: Systems and methods for features engineering, in which internal and external signals are received and fused. The fusing is based on meta-data of each of the one or more internal signals and each of the one or more external signals. A set of features is generated based on one or more valid combinations that match a transformation input, the transformation forming part of library of transformations. Finally, a set of one or more features is selected from the plurality of features, based on a predictive strength of each feature. The set of selected features can be used to train and select a machine learning model.

Abstract: In certain embodiments, a computer-implemented method includes: receiving, by a caching system plugin, a request to create a persistent volume for a container application instance; configuring, by the caching system plugin, a local cache volume on a host computing device; configuring, by the caching system plugin, a remote storage volume on a remote storage device; selecting, by a policy manager of the caching system plugin, a cache policy for the container application instance; creating, by the caching system plugin and from a cache manager, a virtual block device associated with the local cache volume, the remote storage volume, and the cache policy; and providing the virtual block device for use by the container application instance as the persistent volume.