Abstract: Machine learning-based methods are disclosed to intelligently generate dynamic workflows, such as dynamic call lists and priority levels of alarm notifications. To generate a dynamic call list based on an alarm notification, the system may identify electronic devices present in an area using emitted passive electromagnetic signals (e.g., RF signals such as Bluetooth, WiFi, and/or cellular). The identification of the electronic devices may be associated with known and/or unknown individuals on the premises. The RF signal data may be processed using at least one ML model to determine the severity of the alarm. Based on the processed data, the system may assign a priority level to the alarm, where the priority level may range from level 0 (no action) to level 4 (dispatch law enforcement immediately). The disclosed methods use trained machine learning models to generate a dynamic call list and identify false alarms with increased accuracy.

Abstract: Machine learning-based methods are disclosed to intelligently generate dynamic workflows, such as dynamic call lists and priority levels of alarm notifications. To generate a dynamic call list based on an alarm notification, the system may identify electronic devices present in an area using emitted passive electromagnetic signals (e.g., RF signals such as Bluetooth, WiFi, and/or cellular). The identification of the electronic devices may be associated with known and/or unknown individuals on the premises. The RF signal data may be processed using at least one ML model to determine the severity of the alarm. Based on the processed data, the system may assign a priority level to the alarm, where the priority level may range from level 0 (no action) to level 4 (dispatch law enforcement immediately). The disclosed methods use trained machine learning models to generate a dynamic call list and identify false alarms with increased accuracy.

Abstract: An illustrative example porous fuel cell component includes a plurality of fibers, a plurality of first pores defined by spaces between the fibers, and a plurality of second pores defined by an interior space in at least some of the fibers. Another illustrative example porous fuel cell component includes a plurality of first fibers and a plurality of second fibers that are different than the first fibers. The second fibers are hollow.

Abstract: Topical compositions for the transdermal delivery of cannabinoids. In some embodiments, the topical formulation and medium chain triglycerides and one or more cannabinoids. The topical compositions can be used for the treatment of pain, muscle soreness, and sexual dysfunction.

Abstract: The present invention relates to a continuous production system for producing carbon-containing compounds, comprising a genetically modified photosynthetic microorganism, such as a Cyanobacterium, that contains one or more mutations or deletions in a glycogen biosynthesis or storage pathway. The system and methods provided herein facilitate the production of carbon-containing compounds under reduced growth conditions, including biofuels, lipids, and other specialty chemicals, such as fatty acids, alkanes, alkenes, wax esters, and triglycerides.