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: 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: A process for polymerization of vinyl chloride may include contacting vinyl chloride, demineralized water, at least one stabilizer agent, at least one dispersing agent, an initiator and optionally one or more comonomers; continuously feeding the vinyl chloride, demineralized water, at least one stabilizer agent, at least one dispersing agent, an initiator and optionally one or more comonomers into a tubular reactor having a length of at least 20 times an internal diameter of the tubular reactor; and continuously polymerizing the vinyl chloride, and optionally the one or more comonomers.

Abstract: A business analytics conversational tool comprising: a device comprising a communication channel, a natural language processor (NLP), a fulfillment application program interface (F-API), a database application program interface (D-API), and a business management database; wherein: the NLP receives a user-input from a user through the communication channel; the NLP deduces an intent of the user-input; the NLP communicates the intent to the F-API; the F-API communicates a request for data associated with the intent to the database via the D-API; the D-API communicates the data associated with the intent to the F-API; the F-API converts the data associated with the intent to conversational form and sends the conversational form for voice output through the communication channel.

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: A business analytics conversational tool comprising: a device comprising a communication channel, a natural language processor (NLP), a fulfillment application program interface (F-API), a database application program interface (D-API), and a business management database; wherein: the NLP receives a user-input from a user through the communication channel; the NLP deduces an intent of the user-input; the NLP communicates the intent to the F-API; the F-API communicates a request for data associated with the intent to the database via the D-API; the D-API communicates the data associated with the intent to the F-API; the F-API converts the data associated with the intent to conversational form and sends the conversational form for voice output through the communication channel.

Abstract: A business analytics conversational tool comprising: a device comprising a communication channel, a natural language processor (NLP), a fulfillment application program interface (F-API), a database application program interface (D-API), and a business management database; wherein: the NLP receives a user-input from a user through the communication channel; the NLP deduces an intent of the user-input; the NLP communicates the intent to the F-API; the F-API communicates a request for data associated with the intent to the database via the D-API; the D-API communicates the data associated with the intent to the F-API; the F-API converts the data associated with the intent to conversational form and sends the conversational form for voice output through the communication channel.

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: 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: 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 business analytics conversational tool comprising: a device comprising a communication channel, a natural language processor (NLP), a fulfillment application program interface (F-API), a database application program interface (D-API), and a business management database; wherein: the NLP receives a user-input from a user through the communication channel; the NLP deduces an intent of the user-input; the NLP communicates the intent to the F-API; the F-API communicates a request for data associated with the intent to the database via the D-API; the D-API communicates the data associated with the intent to the F-API; the F-API converts the data associated with the intent to conversational form and sends the conversational form for voice output through the communication channel.

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: 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: 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: A wheel disc adapted for use in a fabricated vehicle wheel includes a steel wheel disc defining an axis and includes a center mounting portion, an outer rim connecting flange, a plurality of radially outwardly extending spokes connecting the center mounting portion to the outer rim connecting flange and a ventilation opening provided between each pair of adjacent spokes. A width defined by a center wall of the spoke is tapered inwardly along a substantial portion of a radial length thereof as the center wall of the spoke extends generally from the center mounting portion to the outer rim connecting flange or wherein a width defined by outer walls of the spoke is tapered inwardly along a substantial portion of a radial length thereof as the outer walls of the spoke extends generally from the center mounting portion to the outer rim connecting flange.

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: A business analytics conversational tool comprising: a device comprising a communication channel, a natural language processor (NLP), a fulfillment application program interface (F-API), a database application program interface (D-API), and a business management database; wherein: the NLP receives a user-input from a user through the communication channel; the NLP deduces an intent of the user-input; the NLP communicates the intent to the F-API; the F-API communicates a request for data associated with the intent to the database via the D-API; the D-API communicates the data associated with the intent to the F-API; the F-API converts the data associated with the intent to conversational form and sends the conversational form for voice output through the communication channel.

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: A business analytics conversational tool comprising: a device comprising a communication channel, a natural language processor (NLP), a fulfillment application program interface (F-API), a database application program interface (D-API), and a business management database; wherein: the NLP receives a user-input from a user through the communication channel; the NLP deduces an intent of the user-input; the NLP communicates the intent to the F-API; the F-API communicates a request for data associated with the intent to the database via the D-API; the D-API communicates the data associated with the intent to the F-API; the F-API converts the data associated with the intent to conversational form and sends the conversational form for voice output through the communication channel.