Abstract: A gas turbine engine includes a fan located at a forward portion of the gas turbine engine, and a compressor section and a turbine section arranged in serial flow order. The compressor section and the turbine section together define a core airflow path. A rotary member is rotatable with the fan and with a low pressure turbine of the turbine section. The low pressure turbine includes a rotating drum to which a first airfoil structure is connected and extends radially inward toward the rotary member. A torque frame connects the rotating drum to the rotary member and transfers torque from the first airfoil structure mounted to the rotating drum to the rotary member. The torque frame includes an inner disk mounted to the rotary member, an outer ring and a second airfoil structure formed separately from the outer ring and connected thereto by a releasable connecting structure. The second airfoil structure extends radially inward from the outer ring toward the inner disk.

Abstract: Methods and apparatus to remove liquid from a housing are disclosed. An example system includes a pump including a chamber, a shaft positioned at least partially in the chamber, and a bearing to support the shaft, the chamber including a chamber inlet and a chamber outlet, the chamber to hold a fluid in a first state, a first conduit to carry the fluid in a second state of the fluid, the first conduit fluidly coupled to the chamber inlet, a second conduit to carry the fluid in the first state of the fluid, the second conduit fluidly coupled to the chamber outlet, at least one jet pump to deliver a mixture of the fluid in the first state of the fluid and the second state of the fluid to a third conduit, and a heat exchanger coupled to the first conduit upstream of the first inlet.

Abstract: Methods, apparatus, systems and articles of manufacture to monitor health of a closed loop system in a turbine engine are disclosed. An example apparatus includes non-linear solver circuitry to: iteratively determine a volume of a gas in a system of a turbine engine based on a pressure measurement, a temperature measurement and a constant that corresponds to the gas, the non-linear solver circuitry to iteratively determine the volume of the gas using a thermodynamic state model; and determine a mass of the gas based on the volume of the gas and a volume of the system; optimization circuitry to iteratively determine a mass loss of the gas based on the mass of the gas using a system of equations; and component control circuitry to adjust use of the system of the gas based on the mass loss of the gas.

Abstract: A gas turbine engine includes a fan located at a forward portion of the gas turbine engine, and a compressor section and a turbine section arranged in serial flow order. The compressor section and the turbine section together define a core airflow path. A rotary member is rotatable with the fan and with a low pressure turbine of the turbine section. The low pressure turbine includes a rotating drum to which a first airfoil structure is connected and extends radially inward toward the rotary member. A torque frame connects the rotating drum to the rotary member and transfers torque from the first airfoil structure mounted to the rotating drum to the rotary member. The torque frame includes an inner disk mounted to the rotary member, an outer ring and a second airfoil structure formed separately from the outer ring and connected thereto by a releasable connecting structure. The second airfoil structure extends radially inward from the outer ring toward the inner disk.

Abstract: A pump system for pressurizing a fluid within a closed loop supercritical transport bus is disclosed herein. The examples of the pump system disclosed herein include an impeller coupled to an impeller shaft; an axial flux electric motor including rotors and a stator; a rotor shaft connected to the rotors, wherein the rotor shaft is coupled to the impeller shaft; a seal to prevent the fluid from contacting the axial flux electric motor; a first housing to frame the impeller shaft; and a second housing to frame the axial flux electric motor, wherein the second housing is separate from the first housing.

Abstract: A pump system to pressurize a fluid within a closed loop transport bus is disclosed herein. The example of the pump system disclosed herein includes a pump, including an impeller, to increase kinetic energy of a fluid flowing through the impeller; an electric motor, including a rotor shaft connected to the impeller, to provide torque to the rotor shaft; a first bearing to support the rotor shaft at a first operational speed range, the first bearing coupled to an inner race; a second bearing to support the rotor shaft at a second operational speed range, the rotor shaft coupled to an outer race; and one or more sprag elements configured to: engage the inner race with the outer race at the first operational speed range; and disengage the inner race from the outer race at the second operational speed range.

Abstract: Oil lubricated supercritical fluid pumps with oil separators are disclosed herein. An example pump system to pressurize a fluid within a closed loop thermal transport bus disclosed herein includes a pump housing, a duct fluidly coupled to the pump housing, a first portion of the duct to include a mixture of an oil and the supercritical fluid, a second portion of the duct to include the supercritical fluid, and a separator positioned in a third portion of the duct between the first portion of the duct and the second portion of the duct, the separator to separate the oil in the mixture from the supercritical fluid.

Abstract: In one embodiment, a method includes training a target machine-learning model iteratively by accessing training data of content objects, training an intermediate machine-learning model that outputs contextual evaluation measurements based on the training data, generating state-indications associated with the training data, wherein the state-indications comprise user-intents, system actions, and user actions, training the target machine-learning model based on the contextual evaluation measurements, the state-indications, and an action set comprising possible system actions, extracting rules based on the target machine-learning model by a sequential pattern-mining model, generating synthetic training data based on the rules, updating the training data by adding the synthetic training data to the training data, determining if a completion condition is reached for the training, and if the completion condition is reached returning the target machine-learning model, else repeating the iterative training of the tar

Abstract: An input transformation function that transforms input data for a second machine learning system is learned using a first machine learning system, the learning being based on minimizing a summation of a task loss and a post-activation density loss. The input data is transformed using the learned input transformation function to alter the post-activation density to reduce an amount of energy consumed for an inferencing task and the inferencing task is carried out on the transformed input data using the second machine learning system.

Abstract: In one embodiment, a method includes receiving a user request from a client system associated with a user, generating a response to the user request which references one or more entities, generating a personalized recommendation based on the user request and the response, wherein the personalized recommendation references one or more of the entities of the response, and sending instructions for presenting the response and the personalized recommendation to the client system.

Abstract: In one embodiment, a method includes receiving a user input at a client system, wherein the user input is associated with one or more intents and one or more slots, generating one or more first dialog acts based on the user input, calculating a task-confidence score based on one or more intent-confidence scores associated with the one or more intents, respectively, and one or more slot-confidence scores associated with the one or more slots, respectively, generating one or more second dialog acts modifying the one or more first dialog acts responsive to the task-confidence score being less than a threshold score, and presenting a response to the user input at the client system, wherein the response is based on one or more of the first dialog acts or the second dialog acts.

Abstract: In one embodiment, a method includes receiving a query from a user from a client system associated with the user, determining one or more initial memory slots based on the query, accessing a memory graph associated with the user which comprises a plurality of nodes and a plurality of edges connecting the nodes, and wherein one or more of the nodes correspond to one or more episodic memories of the user, respectively, and wherein each edge corresponds to a relationship between the connected nodes, selecting one or more candidate nodes from the memory graph by one or more machine-learning models based on the initial memory slots, generating a response based on the initial memory slots and episodic memories corresponding to the selected candidate nodes, and sending instructions for presenting the response to the client system in response to the query.

Abstract: In one embodiment, a method includes training a target machine-learning model iteratively by accessing training data of content objects, training an intermediate machine-learning model that outputs contextual evaluation measurements based on the training data, generating state-indications associated with the training data, wherein the state-indications comprise user-intents, system actions, and user actions, training the target machine-learning model based on the contextual evaluation measurements, the state-indications, and an action set comprising possible system actions, extracting rules based on the target machine-learning model by a sequential pattern-mining model, generating synthetic training data based on the rules, updating the training data by adding the synthetic training data to the training data, determining if a completion condition is reached for the training, and if the completion condition is reached returning the target machine-learning model, else repeating the iterative training of the tar

Abstract: In one embodiment, a method includes receiving a user request from a client system associated with a user, generating a response to the user request which references one or more entities, generating a personalized recommendation based on the user request and the response, wherein the personalized recommendation references one or more of the entities of the response, and sending instructions for presenting the response and the personalized recommendation to the client system.

Abstract: In one embodiment, a method includes receiving a user request from a client system associated with a user, generating a response to the user request which references one or more entities, generating a personalized recommendation based on the user request and the response, wherein the personalized recommendation references one or more of the entities of the response, and sending instructions for presenting the response and the personalized recommendation to the client system.

Abstract: In one embodiment, a method includes receiving a user request from a client system associated with a user, generating a response to the user request which references one or more entities, generating a personalized recommendation based on the user request and the response, wherein the personalized recommendation references one or more of the entities of the response, and sending instructions for presenting the response and the personalized recommendation to the client system.

Abstract: The present invention relates to a process for preparing chickpea flour having an increased function of the chickpea protein wherein the process efficiently removes the compounds responsible for the off flavor or nutty flavor of the chickpea flour without deteriorating its functionality. The process for preparation of the chickpea flour includes steps such as: preparing the chickpeas to obtain broken chickpeas; treating the broken chickpeas to obtain chickpea flakes; deoiling the chickpea flakes using a predefined solvent to extract compounds responsible for off-flavor; generating chickpea flour from the deoiled chickpea flakes; and recycling the solvent used for deoiling. The process imparts different functional properties to the chickpea flour that are desired in baking applications and to use it as vegan food ingredient in vegan egg replacer and vegan dairy replacement.

Abstract: In one embodiment, a method includes receiving a query from a user from a client system associated with the user, accessing a knowledge graph comprising a plurality of nodes and edges connecting the nodes, wherein each node corresponds to an entity and each edge corresponds to a relationship between the entities corresponding to the connected nodes, determining one or more initial entities associated with the query based on the query, selecting one or more candidate nodes by a conversational reasoning model from the knowledge graph corresponding to one or more candidate entities, respectively, wherein each candidate node is selected based on the nodes corresponding to the initial entities, dialog states associated with the query, and a context associated with the query, generating a response based on the initial entities and the candidate entities, and sending instructions for presenting the response to the client system in response to the query.

Abstract: In one embodiment, a method includes receiving a user input from a client system associated with a user, determining a task based on the user input and a confidence score associated with the task, generating one or more first dialog acts based on a task policy which specifies dialog acts associated with the task, generating one or more second dialog acts based on an override policy responsive to the confidence score being less than a threshold score, wherein the override policy specifies dialog acts that modify dialog acts specified by the task policy; and sending instructions for presenting a response to the user input to the client system, wherein the response is based on one or more of the first dialog acts or the second dialog acts.