Abstract: A computer system includes a transceiver that receives over a data communications network different types of input data from multiple source nodes and a processing system that defines for each of multiple data categories, a set of groups of data objects for the data category based on the different types of input data. Predictive machine learning model(s) predict a selection score for each group of data objects in the set of groups of data objects for the data category for a predetermined time period. Control machine learning model(s) determine how many data objects are permitted for each group of data objects based on the selection score. Decision-making machine learning model(s) prioritize the permitted data objects based on one or more predetermined priority criteria. Subsequent activities of the computer system are monitored to calculate performance metrics for each group of data objects and for data objects actually selected during the predetermined time period.

Abstract: A computer system is provided for monitoring and detecting changes in a data generating processes, which may be under a multi-dimensional and unsupervised setting. A target dataset is split into paired subgroups by a separator and one or more detectors are applied to detect changes, anomalies, inconsistencies, and the like between the paired subgroups. Metrics may be generated by the detector(s), which are then passed to an evaluating system.

Abstract: Systems and processes for recording operational activities in a traceable manner are provided herein. Event data for events associated with operational activities may be received at a system, the event data including, for each of the events, a time parameter and a subject of the respective event. The events may be grouped and processed according to the time parameter or the subject of each of the events, and one or more database tables of a model may be populated and/or updated with event entries based on the event data. The model may include a first part corresponding to processing of the operational activities, and a second part corresponding to results of the processing of the operational activities, where event entries stored in the first and second parts of the model are linked by respective process dates for the event entries. The tables may be output for presentation to a user.

Abstract: A computer system includes a transceiver that receives over a data communications network different types of input data from multiple source nodes and a processing system that defines for each of multiple data categories, a set of groups of data objects for the data category based on the different types of input data. Predictive machine learning model(s) predict a selection score for each group of data objects in the set of groups of data objects for the data category for a predetermined time period. Control machine learning model(s) determine how many data objects are permitted for each group of data objects based on the selection score. Decision-making machine learning model(s) prioritize the permitted data objects based on one or more predetermined priority criteria. Subsequent activities of the computer system are monitored to calculate performance metrics for each group of data objects and for data objects actually selected during the predetermined time period.

Abstract: A computer system includes a transceiver that receives over a data communications network different types of input data from multiple source nodes and a processing system that defines for each of multiple data categories, a set of groups of data objects for the data category based on the different types of input data. Predictive machine learning model(s) predict a selection score for each group of data objects in the set of groups of data objects for the data category for a predetermined time period. Control machine learning model(s) determine how many data objects are permitted for each group of data objects based on the selection score. Decision-making machine learning model(s) prioritize the permitted data objects based on one or more predetermined priority criteria. Subsequent activities of the computer system are monitored to calculate performance metrics for each group of data objects and for data objects actually selected during the predetermined time period.

Abstract: A computer system is provided for monitoring and detecting changes in a data generating processes, which may be under a multi-dimensional and unsupervised setting. A target dataset is split into paired subgroups by a separator and one or more detectors are applied to detect changes, anomalies, inconsistencies, and the like between the paired subgroups. Metrics may be generated by the detector(s), which are then passed to an evaluating system.

Abstract: A computer system is provided for monitoring and detecting changes in a data generating processes, which may be under a multi-dimensional and unsupervised setting. A target dataset is split into paired subgroups by a separator and one or more detectors are applied to detect changes, anomalies, inconsistencies, and the like between the paired subgroups. Metrics may be generated by the detector(s), which are then passed to an evaluating system.

Abstract: A computer system is provided for monitoring and detecting changes in a data generating processes, which may be under a multi-dimensional and unsupervised setting. A target dataset is split into paired subgroups by a separator and one or more detectors are applied to detect changes, anomalies, inconsistencies, and the like between the paired subgroups. Metrics may be generated by the detector(s), which are then passed to an evaluating system.

Abstract: A computer system is provided for monitoring and detecting changes in a data generating processes, which may be under a multi-dimensional and unsupervised setting. A target dataset is split into paired subgroups by a separator and one or more detectors are applied to detect changes, anomalies, inconsistencies, and the like between the paired subgroups. Metrics may be generated by the detector(s), which are then passed to an evaluating system.

Abstract: A computer system includes a transceiver that receives over a data communications network different types of input data from multiple source nodes and a processing system that defines for each of multiple data categories, a set of groups of data objects for the data category based on the different types of input data. Predictive machine learning model(s) predict a selection score for each group of data objects in the set of groups of data objects for the data category for a predetermined time period. Control machine learning model(s) determine how many data objects are permitted for each group of data objects based on the selection score. Decision-making machine learning model(s) prioritize the permitted data objects based on one or more predetermined priority criteria. Subsequent activities of the computer system are monitored to calculate performance metrics for each group of data objects and for data objects actually selected during the predetermined time period.

Abstract: Systems and processes for recording operational activities in a traceable manner are provided herein. Event data for events associated with operational activities may be received at a system, the event data including, for each of the events, a time parameter and a subject of the respective event. The events may be grouped and processed according to the time parameter or the subject of each of the events, and one or more database tables of a model may be populated and/or updated with event entries based on the event data. The model may include a first part corresponding to processing of the operational activities, and a second part corresponding to results of the processing of the operational activities, where event entries stored in the first and second parts of the model are linked by respective process dates for the event entries. The tables may be output for presentation to a user.

Abstract: Provided is a spiral soft tissue biopsy needle, comprising a puncture needle rod (1), a sampling needle rod (2), and a hand shank (3) for fixing the puncture needle rod (1) and the sampling needle rod (2), wherein the sampling needle rod (2) comprises a spiral needle rod (21) for fixing the sampling site and a cutting needle rod (22) for separating the sample from the sampling site, the spiral needle rod (21) has a hollow structure, the front end of the spiral needle rod (21) is provided with a spring spiral tube (211), and the front end of the cutting needle rod (22) is provided with a cutting edge (221). The spiral soft tissue biopsy needle of the present invention can improve the success rate of one-time sampling and achieve a multi-point sampling in different positions.

Abstract: A computer system is provided for monitoring and detecting changes in a data generating processes, which may be under a multi-dimensional and unsupervised setting. A target dataset is split into paired subgroups by a separator and one or more detectors are applied to detect changes, anomalies, inconsistencies, and the like between the paired subgroups. Metrics may be generated by the detector(s), which are then passed to an evaluating system.

Abstract: Provided is a spiral soft tissue biopsy needle, comprising a puncture needle rod (1), a sampling needle rod (2), and a hand shank (3) for fixing the puncture needle rod (1) and the sampling needle rod (2), wherein the sampling needle rod (2) comprises a spiral needle rod (21) for fixing the sampling site and a cutting needle rod (22) for separating the sample from the sampling site, the spiral needle rod (21) has a hollow structure, the front end of the spiral needle rod (21) is provided with a spring spiral tube (211), and the front end of the cutting needle rod (22) is provided with a cutting edge (221). The spiral soft tissue biopsy needle of the present invention can improve the success rate of one-time sampling and achieve a multi-point sampling in different positions.