Abstract: A fan and motor assembly for a blower is provided. The fan and motor assembly includes a fan extending between a first end and a second end opposite the first end, a rotor coupled to the fan, and a ring coupled to the second end of the fan and the rotor for securing the rotor to the fan. The fan includes a fan hub adjacent the first end and a plurality of blades adjacent the second end. The fan hub and the plurality of blades of the fan are integrally formed together. The fan at least partially surrounds the rotor.

Abstract: A blower is provided, the blower including a conduit extending along a flowpath extending between an inlet portion and an outlet portion. An airflow generation assembly is positioned between the inlet portion and the outlet portion. The airflow generation assembly is configured to induce a flow of air along the flowpath from the inlet portion through the outlet portion. A muffler assembly is coupled to the inlet portion and includes a muffler wall and a mount member extending from the inlet portion to the muffler wall to form an inlet gap therebetween.

Abstract: Techniques are provided for machine learning-based user sentiment prediction using audio and video sentiment analysis. One method comprises obtaining audio sensor data and video sensor from at least one sensor associated with a user; applying the audio sensor data to a first machine learning model that analyzes an audio sentiment of the user to provide an audio sentiment score; applying the video sensor data to a second machine learning model that analyzes a video sentiment of the user to provide a video sentiment score; applying the audio sentiment score and the video sentiment score to an ensemble model that determines an aggregate sentiment score based on the audio sentiment score and the video sentiment score; and initiating an automated remedial action based on the aggregate sentiment score. An output of the ensemble model can be applied to a feedback agent that updates the first and/or second machine learning models.

Abstract: In one embodiment, activity of a plurality of applications in a computer network is monitored, and a plurality of individual business transactions occurring within the plurality of applications may be identified. Additionally network traffic details associated with each particular business transaction of the plurality of individual business transactions may be determined. In response to detecting a network-based threat on a particular network flow within the computer network, the techniques herein may correlate the particular network flow to a corresponding business transaction of the plurality of individual business transactions based on the associated network traffic details of the corresponding business transaction. Accordingly, threat mitigation may be initiated specific to the corresponding business transaction in response to the detected network-based threat being correlated to the corresponding business transaction.

Abstract: In one embodiment, activity of a plurality of applications in a computer network is monitored, and a plurality of individual business transactions occurring within the plurality of applications may be identified. Additionally network traffic details associated with each particular business transaction of the plurality of individual business transactions may be determined. In response to detecting a network-based threat on a particular network flow within the computer network, the techniques herein may correlate the particular network flow to a corresponding business transaction of the plurality of individual business transactions based on the associated network traffic details of the corresponding business transaction. Accordingly, threat mitigation may be initiated specific to the corresponding business transaction in response to the detected network-based threat being correlated to the corresponding business transaction.