Abstract: An example method comprises receiving historical wind turbine failure data and asset data from SCADA systems, receiving first historical sensor data, determining healthy assets of the renewable energy assets by comparing signals to known healthy operating signals, training at least one machine learning model to indicate assets that may potentially fail and to a second set of assets that are operating within a healthy threshold, receiving first current sensor data of a second time period, applying a machine learning model to the current sensor data to generate a first failure prediction a failure and generate a list of assets that are operating within a healthy threshold, comparing the first failure prediction to a trigger criteria, generating and transmitting a first alert if comparing the first failure prediction to the trigger criteria indicates a failure prediction, and updating a list of assets to perform surveillance if within a healthy threshold.

Abstract: A mechanism is provided in a data processing system comprising at least one processor and at least one memory, the at least one memory comprising instructions executed by the at least one processor to cause the at least one processor to implement a healthcare analytics management system. A healthcare analytics development sub-system of the healthcare analytics management system develops an analytics pipeline of a set of analytics assets for a selected healthcare based on a set of business needs for a healthcare analytics client and a healthcare analytics model based on the set of analytics assets and the set of business needs. The healthcare analytics model links to the analytics pipeline. A model deployment module of a healthcare analytics operation sub-system of the healthcare analytics management system deploys the healthcare analytics model on a set of computing devices of the selected healthcare consumer.

Abstract: An automatic righting system includes a discrimination calibration module, a righting module, and a controller. The discrimination calibration module includes a distance sensing unit. The distance sensing unit is located at the front end of the glasses and configured to acquire the distance between the distance sensing unit and a first feature position. The righting module includes a push-up air cushion. The push-up air cushion is mounted at the front end of the glasses. The push-up air cushion is configured to be inflated to push the glasses to move upward. The controller is electrically connected to the distance sensing unit and the push-up air cushion. The controller is configured to control the push-up air cushion to start or stop to right the glasses according to a distance signal of the distance sensing unit.

Abstract: An example method utilizing different pipelines of a prediction system, comprises receiving event and alarm data from event logs, failure data, and asset data from SCADA system(s), retrieve patterns of events, receiving historical sensor data from sensors of components of wind turbines, training a set of models to predict faults for each component using the patterns of events and historical sensor data, each model of a set having different observation time windows and lead time windows, evaluating each model of a set using standardized metrics, comparing evaluations of each model of a set to select a model with preferred lead time and accuracy, receive current sensor data from the sensors of the components, apply the selected model(s) to the current sensor data to generate a component failure prediction, compare the component failure prediction to a threshold, and generate an alert and report based on the comparison to the threshold.

Abstract: A method for sleep monitoring is provided, including: detecting whether a sleep assistance command is received; lighting up a breathing light at a preset frequency in response to receiving the sleep assistance command; monitoring physical sign information of a user in response to receiving a monitoring command; detecting whether the user enters into a sleep state according to the physical sign information; gradually reducing a brightness of the breathing light within a first preset duration until the breathing light is extinguished in a case where the user enters into the sleep state, and determining sleep quality information of the user according to the physical sign information of the user. The physical sign information includes at least breathing information, which is determined according to at least one of: oronasal airflow information, thoracoabdominal motion information, or pressure sensing information.

Abstract: An example method utilizing different pipelines of a prediction system, comprises receiving event and alarm data from event logs, failure data, and asset data from SCADA system(s), retrieve patterns of events, receiving historical sensor data from sensors of components of wind turbines, training a set of models to predict faults for each component using the patterns of events and historical sensor data, each model of a set having different observation time windows and lead time windows, evaluating each model of a set using standardized metrics, comparing evaluations of each model of a set to select a model with preferred lead time and accuracy, receive current sensor data from the sensors of the components, apply the selected model(s) to the current sensor data to generate a component failure prediction, compare the component failure prediction to a threshold, and generate an alert and report based on the comparison to the threshold.

Abstract: An example method comprises receiving historical sensor data from sensors of components of wind turbines, training a set of models to predict faults for each component using the historical sensor data, each model of a set having different observation time windows and lead time windows, evaluating each model of a set using standardized metrics, comparing evaluations of each model of a set to select a model with preferred lead time and accuracy, receive current sensor data from the sensors of the components, apply the selected model(s) to the current sensor data to generate a component failure prediction, compare the component failure prediction to a threshold, and generate an alert and report based on the comparison to the threshold.

Abstract: The present disclosure discloses a method, device, apparatus, and storage medium for sleep monitoring. The method includes: monitoring physical sign information of a user in response to receiving a monitoring command; determining sleep quality information of the user according to the physical sign information of the user, wherein the physical sign information comprises at least breathing information.

Abstract: An example method comprises receiving historical sensor data from sensors of components of wind turbines, training a set of models to predict faults for each component using the historical sensor data, each model of a set having different observation time windows and lead time windows, evaluating each model of a set using standardized metrics, comparing evaluations of each model of a set to select a model with preferred lead time and accuracy, receive current sensor data from the sensors of the components, apply the selected model(s) to the current sensor data to generate a component failure prediction, compare the component failure prediction to a threshold, and generate an alert and report based on the comparison to the threshold.

Abstract: An example method comprises receiving historical wind turbine failure data and asset data from SCADA systems, receiving first historical sensor data, determining healthy assets of the renewable energy assets by comparing signals to known healthy operating signals, training at least one machine learning model to indicate assets that may potentially fail and to a second set of assets that are operating within a healthy threshold, receiving first current sensor data of a second time period, applying a machine learning model to the current sensor data to generate a first failure prediction a failure and generate a list of assets that are operating within a healthy threshold, comparing the first failure prediction to a trigger criteria, generating and transmitting a first alert if comparing the first failure prediction to the trigger criteria indicates a failure prediction, and updating a list of assets to perform surveillance if within a healthy threshold.

Abstract: Disclosed is a use of a liposomal pharmaceutical preparation of mitoxantrone in the preparation of a medicament for treating lymphoma, wherein the lymphoma is preferably non-Hodgkin's lymphoma, further preferably aggressive non-Hodgkin's lymphoma, more preferably diffuse large B-cell lymphoma or peripheral T-cell lymphoma, and more further preferably relapsed or refractory diffuse large B-cell lymphoma or peripheral T-cell lymphoma. The mitoxantrone liposomes are used as single anti-tumor therapeutic agent without being combined with other anti-tumor agents.

Abstract: A system, method, and computer program product for passively informed mental health risk prediction. The system may include receiving mental health risk input signals from a blood glucometer and other devices. The mental health risk signals may include glucometer data, demographic data, and other data. The glucometer data for the subject may include at least one blood glucose value. The mental health risk input signals are input into a machine learning system. The machine learning system has been previously trained with mental health risk input signals and mental health status data for a plurality of subjects. The machine learning system outputs a prediction of mental health risk for the subject. The machine learning system may comprise a neural network.

Abstract: An example method comprises receiving historical wind turbine failure data and asset data from SCADA systems, receiving first historical sensor data, determining healthy assets of the renewable energy assets by comparing signals to known healthy operating signals, training at least one machine learning model to indicate assets that may potentially fail and to a second set of assets that are operating within a healthy threshold, receiving first current sensor data of a second time period, applying a machine learning model to the current sensor data to generate a first failure prediction a failure and generate a list of assets that are operating within a healthy threshold, comparing the first failure prediction to a trigger criteria, generating and transmitting a first alert if comparing the first failure prediction to the trigger criteria indicates a failure prediction, and updating a list of assets to perform surveillance if within a healthy threshold.

Abstract: A mechanism is provided in a data processing system comprising at least one processor and at least one memory, the at least one memory comprising instructions executed by the at least one processor to cause the at least one processor to implement a healthcare analytics management system. A healthcare analytics development sub-system of the healthcare analytics management system develops an analytics pipeline of a set of analytics assets for a selected healthcare based on a set of business needs for a healthcare analytics client and a healthcare analytics model based on the set of analytics assets and the set of business needs. The healthcare analytics model links to the analytics pipeline. A model deployment module of a healthcare analytics operation sub-system of the healthcare analytics management system deploys the healthcare analytics model on a set of computing devices of the selected healthcare consumer.

Abstract: A mechanism is provided to implement a healthcare analytics management system. A healthcare analytics development sub-system develops an analytics pipeline of a set of analytics assets for a selected healthcare based on a set of business needs for a healthcare analytics client and a healthcare analytics model based on the set of analytics assets and the set of business needs. A model deployment module of a healthcare analytics operation sub-system deploys the healthcare analytics model on a set of computing devices of the selected healthcare consumer. Responsive to detecting a performance deviation of the deployed healthcare analytics model from the set of business needs, a model feedback module determines improvement needs for the healthcare analytics model. The model feedback module feeds the improvement needs back to the healthcare analytics development sub-system. The healthcare analytics development sub-system customizes the healthcare analytics model based on the improvement needs.

Abstract: An example method comprises receiving event and alarm data from event logs, failure data, and asset data from SCADA system(s), retrieve patterns of events from the SCADA data, receiving historical sensor data from sensors of components of wind turbines, training a set of models to predict faults for each component using the patterns of events and historical sensor data, each model of a set having different observation time windows and lead time windows, evaluating each model of a set using standardized metrics, comparing evaluations of each model of a set to select a model with preferred lead time and accuracy, receive current sensor data from the sensors of the components, apply the selected model(s) to the current sensor data to generate a component failure prediction, compare the component failure prediction to a threshold, and generate an alert and report based on the comparison to the threshold.

Abstract: A mechanism is provided to implement a healthcare analytics management system. A healthcare analytics development sub-system develops an analytics pipeline of a set of analytics assets for a selected healthcare based on a set of business needs for a healthcare analytics client and a healthcare analytics model based on the set of analytics assets and the set of business needs. A model deployment module of a healthcare analytics operation sub-system deploys the healthcare analytics model on a set of computing devices of the selected healthcare consumer. Responsive to detecting a performance deviation of the deployed healthcare analytics model from the set of business needs, a model feedback module determines improvement needs for the healthcare analytics model. The model feedback module feeds the improvement needs back to the healthcare analytics development sub-system. The healthcare analytics development sub-system customizes the healthcare analytics model based on the improvement needs.

Abstract: The present invention discloses a method for lowering an oil pipe in a gas well without well-killing, a soluble bridge plug and a material preparation method thereof, wherein, the method comprises the steps of: lowering a bridge plug in a wellbore such that the bridge plug blocks the wellbore at a predetermined location in the wellbore; injecting water in the wellbore after the pressure in the wellbore has been relieved so as to replace gases in the wellbore; and lowering an oil pipe in the wellbore to the location of the bridge plug. The method for lowering an oil pipe in a gas well without well-killing, the soluble bridge plug and the material preparation method thereof provided in the present invention successfully solve the problem of high cost for lowering an oil pipe under pressure after a fracturing fluid has been injected into the casing.

Abstract: There is provided an electronic device for detecting apnea and a computer-readable storage medium. The electronic device includes: at least one of an electroencephalographic sensor configured to detect electroencephalographic data of a user or a nasal airflow sensor configured to detect nasal airflow data of the user; and a controller communicatively coupled to the at least one of the electroencephalographic sensor or the nasal airflow sensor. The controller is configured to determine a candidate apnea event based on at least the electroencephalographic data or the nasal airflow data.

Abstract: An example method comprises receiving event and alarm data from event logs, failure data, and asset data from SCADA system(s), retrieve patterns of events from the SCADA data, receiving historical sensor data from sensors of components of wind turbines, training a set of models to predict faults for each component using the patterns of events and historical sensor data, each model of a set having different observation time windows and lead time windows, evaluating each model of a set using standardized metrics, comparing evaluations of each model of a set to select a model with preferred lead time and accuracy, receive current sensor data from the sensors of the components, apply the selected model(s) to the current sensor data to generate a component failure prediction, compare the component failure prediction to a threshold, and generate an alert and report based on the comparison to the threshold.