Abstract: A valve comprising a valve body including a first end and a second end spaced apart along a longitudinal axis, a central portion disposed between the first end and the second end, wherein the first end and the second end define a first flow passageway and a second flow passageway, respectively, and wherein the central portion defines an interior chamber, wherein the first end of the valve body includes a bore, and a removable seat disposed in the bore and defining at least in part the first flow passageway.

Abstract: A unibody valve comprising a valve body including a first end and a second end spaced apart along a longitudinal axis, a central portion disposed between the first end and the second end, wherein the first end and the second end define a first flow passageway and a second flow passageway, respectively, and wherein the central portion defines an interior chamber, and a disc disposed within the interior chamber, wherein the interior chamber includes an enlarged side portion positioned outside of the at least one of the first flow passageway and the second flow passageway projected along the longitudinal axis, wherein the disc is disposed in the enlarged side portion when in an open position.

Abstract: A method includes creating a table with a plurality of cells indicative of intersections of tracks and wedges on a data storage disc surface. The method also includes storing disc pending command information for different disc pending commands in corresponding different cells of the plurality of cells according to locations for the different disc pending commands on the data storage disc surface. The method further includes searching the table for one or more next disc pending commands of the different disc pending commands to schedule for execution.

Abstract: A data storage device can have one or more rotating data media with data tracks that are radially disposed from a central spindle. The data tracks may be logically divided into multiple regions while a write strategy is generated with a region module to set a sequence of different regions for future data writes. Receipt of a data write request to the data storage media from a host can prompt the region module to classify the data write request as a sequential or random write in order to intelligently select a region to satisfy the data write request based on the write strategy to maximize data writing consistency for data associated with the data write request.

Abstract: Methods and systems include identifying an abnormal condition in a PFC circuit comprising an input configured to be coupled to a 3-phase power source and to receive input 3-phase power from the 3-phase power source, a bus having a plurality of bus lines, each bus line configured to be coupled to the input and to carry one phase of the input 3-phase power, a PFC leg including a contactor configured to selectively couple a capacitor bank included in the PFC leg to the bus. In response to identifying the abnormal condition, the contactor is controlled to decouple the capacitor bank from the bus, and after a reset button has been activated, the contactor is recoupled to the capacitor bank to resume operating the PFC leg to provide power factor correction to the input 3-phase power.

Abstract: A data storage device can have one or more rotating data media with data tracks that are radially disposed from a central spindle. The data tracks may be logically divided into multiple regions while a write strategy is generated with a region module to set a sequence of different regions for future data writes. Receipt of a data write request to the data storage media from a host can prompt the region module to classify the data write request as a sequential or random write in order to intelligently select a region to satisfy the data write request based on the write strategy to maximize data writing consistency for data associated with the data write request.

Abstract: The present disclosure provides systems and methods for classifying incidents based on determining an odds ratio that represents a likelihood of an incident being related to the problem, classifying incidents based on determining a decision tree that forms branches based on whether a feature is present in the incident, and predicting whether a new incident is related to a problem. Features may be extracted from a set of incidents (e.g., that are reported over a certain time period) that include incidents related to a problem and incidents not related to the problem. The incidents related to the problem and a portion of the incidents not related to the problem may be used to train a logistic regression model or generate a decision tree. The trained logistic regression model may be used to determine the odds ratios or predict whether a new incident is related to a problem.

Abstract: ECC is used to for controlling errors in blocks of data by including a measure of redundancy within the data for recovering one or more unreadable portions of the data. A codeword includes at least a message and one or more additional ECC blocks. In the event of an unreadable sector, the ECC is decoded and used to recover the erroneous or missing portion(s) of the message. With disc access parallelism, ECCs can now be distributed across data storage surfaces and/or multiple storage platters. Distribution of ECCs increases the likelihood of recovering data from a head failure or from burst errors on a data storage surface and adds the option to decode parity after only 1/n revolutions.

Abstract: The present disclosure provides systems and methods for classifying incidents based on determining an odds ratio that represents a likelihood of an incident being related to the problem, classifying incidents based on determining a decision tree that forms branches based on whether a feature is present in the incident, and predicting whether a new incident is related to a problem. Features may be extracted from a set of incidents (e.g., that are reported over a certain time period) that include incidents related to a problem and incidents not related to the problem. The incidents related to the problem and a portion of the incidents not related to the problem may be used to train a logistic regression model or generate a decision tree. The trained logistic regression model may be used to determine the odds ratios or predict whether a new incident is related to a problem.

Abstract: Methods and systems include identifying an abnormal condition in a PFC circuit comprising an input configured to be coupled to a 3-phase power source and to receive input 3-phase power from the 3-phase power source, a bus having a plurality of bus lines, each bus line configured to be coupled to the input and to carry one phase of the input 3-phase power, a PFC leg including a contactor configured to selectively couple a capacitor bank included in the PFC leg to the bus. In response to identifying the abnormal condition, the contactor is controlled to decouple the capacitor bank from the bus, and after a reset button has been activated, the contactor is recoupled to the capacitor bank to resume operating the PFC leg to provide power factor correction to the input 3-phase power.

Abstract: A system includes a read/write controller removably coupled to a storage drive. Responsive to detection of a coupling between the read/write controller and the storage drive, the read/write controller retrieves key information from the storage drive, uses the key information to locate adaptives associated with the primary storage medium, and loads the adaptives into volatile memory to configure read/write settings for access to the primary storage medium.

Abstract: A system includes a read/write controller removably coupled to a storage drive. Responsive to detection of a coupling between the read/write controller and the storage drive, the read/write controller retrieves key information from the storage drive, uses the key information to locate adaptives associated with the primary storage medium, and loads the adaptives into volatile memory to configure read/write settings for access to the primary storage medium.

Abstract: Methods, software programs, and systems for behavioral analysis of computer applications, and, more particularly, for the prediction of application behavior within and across heterogeneous environments, using machine learning techniques, are disclosed. A method according to certain of these embodiments includes retrieving merged configuration/utilization data and generating predicted application behavior information. The merged configuration/utilization data includes at least a portion of application configuration information and at least a portion of environment configuration information. The application configuration information is information regarding a configuration of an application. The environment configuration information is information regarding a configuration of an environment in which the application is executed. The predicted application behavior information is generated using a machine learning model, which receives the merged configuration/utilization data as one or more inputs.

Abstract: A method is used in evaluating a test subject in computing environments. A first machine learning system generates test subject features. A second machine learning system analyzes the test subject to detect distinguishing features of the test subject. A third machine learning system performs natural language processing on the test subject features to create evaluation information associated with the test subject. A test subject evaluation system provides an evaluation of the test subject based on the distinguishing features and the evaluation information.

Abstract: A system includes a read/write controller removably coupled to a storage drive. Responsive to detection of a coupling between the read/write controller and the storage drive, the read/write controller retrieves key information from the storage drive, uses the key information to locate adaptives associated with the primary storage medium, and loads the adaptives into volatile memory to configure read/write settings for access to the primary storage medium.

Abstract: The present disclosure provides systems and methods for classifying incidents based on determining an odds ratio that represents a likelihood of an incident being related to the problem, classifying incidents based on determining a decision tree that forms branches based on whether a feature is present in the incident, and predicting whether a new incident is related to a problem. Features may be extracted from a set of incidents (e.g., that are reported over a certain time period) that include incidents related to a problem and incidents not related to the problem. The incidents related to the problem and a portion of the incidents not related to the problem may be used to train a logistic regression model or generate a decision tree. The trained logistic regression model may be used to determine the odds ratios or predict whether a new incident is related to a problem.

Abstract: Implementations disclosed herein provide for detecting an expected or unexpected power loss event on a storage device. Responsive to the detection of the power loss event, a plurality of potentially affected data blocks are identified and/or marked, such as by a processor, to indicate that data stored therein is potentially corrupted.

Abstract: A method is used in evaluating a test subject in computing environments. A first machine learning system generates test subject features. A second machine learning system analyzes the test subject to detect distinguishing features of the test subject. A third machine learning system performs natural language processing on the test subject features to create evaluation information associated with the test subject. A test subject evaluation system provides an evaluation of the test subject based on the distinguishing features and the evaluation information.

Abstract: A storage system such as a hard disc drive (HDD), solid-state drive (SSD), hybrid drive (SSHD), storage rack, set of storage racks, JBOD, array of discs, etc. may include a variety of storage media. Failures may be detected in the storage media of the storage system. Such a failure may affect the physical capacity of the storage system. A storage controller of the storage system initiates a media conversion that converts a portion of the storage media from media storing data according to a first recording scheme to a media storing data according to a second recording scheme. The second recording scheme stores data at a higher density compared to the first recording scheme.

Abstract: A storage system such as a hard disc drive (HDD), solid-state drive (SSD), hybrid drive (SSHD), storage rack, set of storage racks, JBOD, array of discs, etc. may include a variety of storage media. Failures may be detected in the storage media of the storage system. Such a failure may affect the physical capacity of the storage system. A storage controller of the storage system initiates a media conversion that converts a portion of the storage media from media storing data according to a first recording scheme to a media storing data according to a second recording scheme. The second recording scheme stores data at a higher density compared to the first recording scheme.