Abstract: Abnormal behavior detection for industrial machines component(s) using large language models based on sequence(s) of log file messages recorded by logger(s) at initial time(s). The log file message sequence may be transformed into an anomaly severity sequence comprising log tokens encoding, times, and/or associated anomaly severity levels. The anomaly severity sequence may be input into the large language model. The large language model may output an anomaly severity histogram predicting M probabilities that the logger(s) will record log file messages encoded by log keys associated with M respective anomaly severity levels at the subsequent time(s). Abnormal behavior may be predicted based on the M probability patterns of the anomaly severity histogram. in the one or more components of the industrial machine logged by the one or more loggers at the one or more subsequent times. A control command may trigger automatically altering the component(s) operation to prevent the abnormal behavior.

Abstract: There is provided a method of differentiating between a strain and a contraction of the pelvic floor muscles (PFM) of a subject. The method comprises receiving data generated by an orientation sensor provided within a vaginal probe device that is located within the vaginal canal of the subject; utilizing a processor to process data generated by the orientation sensor to determine a direction of rotation of the vaginal probe device during a measurement period. When the processor determines that the vaginal probe device has rotated in the cranial-ventral direction relative to the subject, an output is generated indicating that there has been a contraction of the PFM during the measurement period; and when the processor determines that the vaginal probe device has rotated in the caudal-dorsal direction relative to the subject, an output is generated indicating that there has been a strain of the PFM during the measurement period.

Abstract: Methods, computer program products and nozzle monitoring modules are provided, to monitor nozzles in 2D and/or 3D inkjet printing systems. Nozzle monitoring comprises registering an image of a printed product with respect to a corresponding raster file and evaluating nozzle performance and managing nozzles by applying a neural network (NN) trained on a plurality of registered images of the printed product and corresponding raster files. The disclosed NN approach may apply a deep learning model, and has been shown to improve performance over manual analysis of images of printed products (which is the current method of monitoring nozzles). Disclosed methods and modules may be implemented in 2D inkjet printing system and/or in 3D additive manufacturing (AM) inkjet printing system, monitoring nozzles that deposit layers of the 3D product.

Abstract: There is provided a method of differentiating between a strain and a contraction of the pelvic floor muscles (PFM) of a subject. The method comprises receiving data generated by an orientation sensor provided within a vaginal probe device that is located within the vaginal canal of the subject; utilizing a processor to process data generated by the orientation sensor to determine a direction of rotation of the vaginal probe device during a measurement period. When the processor determines that the vaginal probe device has rotated in the cranial-ventral direction relative to the subject, an output is generated indicating that there has been a contraction of the PFM during the measurement period; and when the processor determines that the vaginal probe device has rotated in the caudal-dorsal direction relative to the subject, an output is generated indicating that there has been a strain of the PFM during the measurement period.

Abstract: A method of differentiating between a strain and a contraction of the pelvic floor muscles (PFM) of a subject includes receiving data generated by an orientation sensor provided within a vaginal probe device that is located within the vaginal canal of the subject and utilizing a processor to process data generated by the orientation sensor to determine a direction of rotation of the vaginal probe device during a measurement period. When the processor determines that the vaginal probe device has rotated in the cranial-ventral direction relative to the subject, an output is generated indicating that there has been a contraction of the PFM during the measurement period; and when the processor determines that the vaginal probe device has rotated in the caudal-dorsal direction relative to the subject, an output is generated indicating that there has been a strain of the PFM during the measurement period.

Abstract: The present disclosure provides method and system determining an optimal set of operating parameters for desired clinical outcome. The method comprises to receive treatment or target skin data comprising skin characteristics associated with skin to be treated with an aesthetic treatment by the aesthetic skin treatment unit and preset operating parameters for performing the aesthetic treatment. Further, the treatment data is analyzed using plurality of trained models to predict plurality of sets of operating parameters for the aesthetic skin treatment unit to perform the aesthetic treatment. Using the plurality of sets of operating parameters, an optimal set of operating parameters is determined for performing the aesthetic treatment by the using the aesthetic skin treatment unit. By proposed system and method, accurate set of operation parameters may be predicted to achieve desired clinical outcomes, without human intervention.

Abstract: A method of differentiating between a strain and a contraction of the pelvic floor muscles (PFM) of a subject includes receiving data generated by an orientation sensor provided within a vaginal probe device that is located within the vaginal canal of the subject and utilizing a processor to process data generated by the orientation sensor to determine a direction of rotation of the vaginal probe device during a measurement period. When the processor determines that the vaginal probe device has rotated in the cranial-ventral direction relative to the subject, an output is generated indicating that there has been a contraction of the PFM during the measurement period; and when the processor determines that the vaginal probe device has rotated in the caudal-dorsal direction relative to the subject, an output is generated indicating that there has been a strain of the PFM during the measurement period.