Embodiments discussed herein facilitate determination of risk of recurrence of atrial fibrillation (AF) after ablation based on fractal features. One example embodiment is configured to generate a binary mask of at least a portion of a CT scan of a heart of a patient with AF; compute one or more radiomic fractal-based features from at least one of the binary mask or the portion of the CT scan; provide the one or more radiomic fractal-based features to a trained machine learning (ML) classifier; and receive a prediction from the trained ML classifier of whether or not the AF will recur after AF ablation, wherein the prediction is based at least in part on the one or more radiomic fractal-based features.

Models that employ both measurable engine parameters as well as predictable engine parameters may be used to determine when a thermostat is malfunctioning before the thermostat malfunction results in an engine breakdown. Particular models may be used to provide an estimated coolant temperature and an estimated thermostat position. The estimated coolant temperature can be compared to an actual measured engine coolant temperature. The estimated thermostat position can be evaluated with respect to what the thermostat position should be given a particular engine coolant temperature. In some cases, comparison between a healthy model and a faulty model may be used to ascertain thermostat health.

Models that employ both measurable engine parameters as well as predictable engine parameters may be used to determine when a thermostat is malfunctioning before the thermostat malfunction results in an engine breakdown. Particular models may be used to provide an estimated coolant temperature and an estimated thermostat position. The estimated coolant temperature can be compared to an actual measured engine coolant temperature. The estimated thermostat position can be evaluated with respect to what the thermostat position should be given a particular engine coolant temperature. In some cases, comparison between a healthy model and a faulty model may be used to ascertain thermostat health.

A method and device for suppressing range ambiguity and a computer readable storage medium are provided. The method includes: determining a pulse timing relationship of a transmission signal; determining orthogonal nonlinear frequency modulation signals; modulating the transmission signal by using the orthogonal nonlinear frequency modulation signals; transmitting the modulated transmission signal according to the pulse timing relationship, and determining echo data of the modulated transmission signal; and generating an image according to a polarization scattering matrix for the echo data of the modulated transmission signal.

A method and device for suppressing range ambiguity and a computer readable storage medium are provided. The method includes: determining a pulse timing relationship of a transmission signal; determining orthogonal nonlinear frequency modulation signals; modulating the transmission signal by using the orthogonal nonlinear frequency modulation signals; transmitting the modulated transmission signal according to the pulse timing relationship, and determining echo data of the modulated transmission signal; and generating an image according to a polarization scattering matrix for the echo data of the modulated transmission signal.

Models that employ both measurable engine parameters as well as predictable engine parameters may be used to determine when a thermostat is malfunctioning before the thermostat malfunction results in an engine breakdown. Particular models may be used to provide an estimated coolant temperature and an estimated thermostat position. The estimated coolant temperature can be compared to an actual measured engine coolant temperature. The estimated thermostat position can be evaluated with respect to what the thermostat position should be given a particular engine coolant temperature. In some cases, comparison between a healthy model and a faulty model may be used to ascertain thermostat health.

Models that employ both measurable engine parameters as well as predictable engine parameters may be used to determine when a thermostat is malfunctioning before the thermostat malfunction results in an engine breakdown. Particular models may be used to provide an estimated coolant temperature and an estimated thermostat position. The estimated coolant temperature can be compared to an actual measured engine coolant temperature. The estimated thermostat position can be evaluated with respect to what the thermostat position should be given a particular engine coolant temperature. In some cases, comparison between a healthy model and a faulty model may be used to ascertain thermostat health.

A system monitors an individual for conditions indicating a possibility of occurrence of irregular heart events. A database includes a plurality of combinations of at least a first signature and a second signature. A first portion of the plurality of combinations is associated with a normal heartbeat and a second portion of the plurality of combinations is associated with an irregular heart event. A wearable heart monitor that is worn on a body of the patient includes a heart sensor for generating a heart signal responsive to monitoring a beating of a heart of the individual. The monitor further includes a processor for receiving the heart signal from the heart sensor. The processor is configured to analyze the heart signal using a plurality of different processes. Each of the plurality of different processes generates at least one of the first signature and the second signature. The plurality of different processes provide a unique combination including at least the first signature and the second signature for the generated heart signal. The processor compares the unique combination with the plurality of combinations in the database, locates a combination of the plurality of combinations that substantially matches the unique combination and generates a first indication if the unique combination substantially matches one of the first portion of the plurality of combinations and a second indication if the unique combination substantially matches one of the second portion of the plurality of combinations.

A system monitors an individual for conditions indicating a possibility of occurrence of irregular heart events. A database includes a plurality of combinations of at least a first signature and a second signature. A first portion of the plurality of combinations is associated with a normal heartbeat and a second portion of the plurality of combinations is associated with an irregular heart event. A wearable heart monitor that is worn on a body of the patient includes a heart sensor for generating a heart signal responsive to monitoring a beating of a heart of the individual. The monitor further includes a processor for receiving the heart signal from the heart sensor. The processor is configured to analyze the heart signal using a plurality of different processes. Each of the plurality of different processes generates at least one of the first signature and the second signature. The plurality of different processes provide a unique combination including at least the first signature and the second signature for the generated heart signal. The processor compares the unique combination with the plurality of combinations in the database, locates a combination of the plurality of combinations that substantially matches the unique combination and generates a first indication if the unique combination substantially matches one of the first portion of the plurality of combinations and a second indication if the unique combination substantially matches one of the second portion of the plurality of combinations.

An online system presents content to its users, in which the content includes links that launch a messaging application and initiate conversations via the application. The system receives information indicating that negative experiences occurred during the conversations and may use this information and attributes of entities participating in the conversations to train a model to predict a likelihood of an occurrence of a negative experience. Upon determining an opportunity to present a user with a link that launches the application and initiates a potential conversation with an entity via the application, the system applies the model to predict a likelihood of an occurrence of the negative experience by the user during the potential conversation based on the entity's attributes. Based on the predicted likelihood, the system adjusts a value associated with presenting the link and passes the adjusted value to a process that selects content for presentation to the user.