Abstract: An electric power take-off system includes a motor configured to convert electrical power received from a battery into hydraulic power, an inverter configured to provide electrical power to the motor from the battery, a heat dissipation device in thermal communication with the inverter, wherein the heat dissipation device includes a thermal fluid pump configured to pump cooling fluid through a plurality of conduits, a flow meter configured determine a flow rate through the plurality of conduits, and a controller configured to receive data from the flow meter and provide operating parameters to the heat dissipation device, wherein the controller is further configured to determine if the data from the flow meter is less than a critical operating condition and decrease the hydraulic power provided by the electric power take-off system in response to determining that the data from the flow meter is less than the critical operating condition.

Abstract: A refuse vehicle includes a chassis, an energy storage device supported by the chassis and configured to provide electrical power to a prime mover, wherein activation of the prime mover selectively drives the refuse vehicle, a body for storing refuse therein supported by the chassis, a first hydraulic pump configured to convert electrical power into hydraulic power, a second first hydraulic pump configured to convert electrical power into hydraulic power, and a motor coupled to at least one of the body or the chassis and configured to drive the first hydraulic pump and drive the second hydraulic pump.

Abstract: A method for machine learning-based detection of an automated fraud or abuse attack includes: identifying, via a computer network, a digital event associated with a suspected automated fraud or abuse attack; composing, via one or more computers, a digital activity signature of the suspected automated fraud or abuse attack based on digital activity associated with the suspected automated fraud or abuse attack; computing, via a machine learning model, an encoded representation of the digital activity signature; searching, via the one or more computers, an automated fraud or abuse signature registry based on the encoded representation of the digital activity signature; determining a likely origin of the digital event based on the searching of the automated fraud or abuse signature registry; and selectively implementing one or more automated threat mitigation actions based on the likely origin of the digital event.

Abstract: A method for machine learning-based detection of an automated fraud or abuse attack includes: identifying, via a computer network, a digital event associated with a suspected automated fraud or abuse attack; composing, via one or more computers, a digital activity signature of the suspected automated fraud or abuse attack based on digital activity associated with the suspected automated fraud or abuse attack; computing, via a machine learning model, an encoded representation of the digital activity signature; searching, via the one or more computers, an automated fraud or abuse signature registry based on the encoded representation of the digital activity signature; determining a likely origin of the digital event based on the searching of the automated fraud or abuse signature registry; and selectively implementing one or more automated threat mitigation actions based on the likely origin of the digital event.

Abstract: A method for machine learning-based detection of an automated fraud or abuse attack includes: identifying, via a computer network, a digital event associated with a suspected automated fraud or abuse attack; composing, via one or more computers, a digital activity signature of the suspected automated fraud or abuse attack based on digital activity associated with the suspected automated fraud or abuse attack; computing, via a machine learning model, an encoded representation of the digital activity signature; searching, via the one or more computers, an automated fraud or abuse signature registry based on the encoded representation of the digital activity signature; determining a likely origin of the digital event based on the searching of the automated fraud or abuse signature registry; and selectively implementing one or more automated threat mitigation actions based on the likely origin of the digital event.

Abstract: The embodiments herein provide a fireproof barrier shield comprising the panels combined together to form a fireproof blanket placed on the building structure to protect the structure from flying embers, radiant heat and fire accidents. The panel comprises an inner heat retardant layer an outer heat reflective layer secured together using a heat resistant adhesive glue or thread. Each panel has several slots for receiving a fastener for combining the panels. The straps comprising a buckle and a belt are provided for securing the fireproof blanket to the ground using screw type anchors or stakes. The buckle is made of heat conductive material and the strap is made of fireproof threads. The perforated channels with brass or aluminum hose fittings are sewn to the inner layer to allow the water flow. The shield comprises a third layer arranged as an outer layer or an inner layer.

Abstract: The embodiments herein provide a fireproof barrier shield comprising the panels combined together to form a fireproof blanket placed on the building structure to protect the structure from flying embers, radiant heat and fire accidents. The panel comprises an inner heat retardant layer an outer heat reflective layer secured together using a heat resistant adhesive glue or thread. Each panel has several slots for receiving a fastener for combining the panels. The straps comprising a buckle and a belt are provided for securing the fireproof blanket to the ground using screw type anchors or stakes. The buckle is made of heat conductive material and the strap is made of fireproof threads. The perforated channels with brass or aluminum hose fittings are sewn to the inner layer to allow the water flow. The shield comprises a third layer arranged as an outer layer or an inner layer.