Abstract: Techniques, systems, and methods are disclosed to generate messaging to view content on media devices based on predictive factors. Information may be received to trigger one or more predictive factors and then generate a candidate set of offers to view content at a media device based on the information. Based on the one or more predictive factors, confidence values may be determined for each offer in the candidate set of offers. The candidate set of offers may be ranked based on the associated confidence values. Subsequently, presentation of at least one offer of the candidate set of offers may be caused to display in a user interface screen on the media device based on the ranking.

Abstract: A turbomachine engine includes a core engine having one or more compressor sections, one or more turbine sections that includes a power turbine, and a combustion chamber in flow communication with the compressor sections and turbine sections. The turbomachine engine also includes a shaft coupled to the power turbine and characterized by a midshaft rating (MSR) between two hundred (ft/sec)1/2 and three hundred (ft/sec)1/2. In one aspect, the shaft has a redline speed between fifty and two hundred fifty feet per second (ft/sec). In another aspect, the shaft has a length L, an outer diameter D, and a ratio of L/D between twelve and thirty-seven.

Abstract: Systems and methods for dynamic open activity response assessment provide for: receiving an open activity response from a client device of a user; in response to the open activity response, providing the open activity response to multiple machine learning models to process multiple open response assessments in real time; receiving multiple assessment scores from the multiple machine learning models; and providing multiple assessment results to the client device of the user based on the multiple assessment scores corresponding to the multiple open response assessments associated with the open activity response.

Abstract: A turbomachine engine includes a core engine having one or more compressor sections, one or more turbine sections that includes a power turbine, and a combustion chamber in flow communication with the compressor sections and turbine sections. The turbomachine engine also includes a shaft coupled to the power turbine and characterized by a midshaft rating (MSR) between two hundred (ft/sec)1/2 and three hundred (ft/sec)1/2. In one aspect, the shaft has a redline speed between fifty and two hundred fifty feet per second (ft/sec). In another aspect, the shaft has a length L, an outer diameter D, and a ratio of L/D between twelve and thirty-seven.

Abstract: The present disclosure is directed to a gas turbine engine assembly having a compressor configured to increase pressure of incoming air, a combustion chamber, at least one turbine coupled to a generator, a torsional damper, and a controller. The combustion chamber is configured to receive a pressurized air stream from the compressor. Further, fuel is injected into the pressurized air in the combustion chamber and ignited so as to raise a temperature and energy level of the pressurized air. The turbine is operatively coupled to the combustion chamber so as to receive combustion products that flow from the combustion chamber. The generator is coupled to the turbine via a shaft. Thus, the torsional damper is configured to dampen torsional oscillations of the generator. Moreover, the controller is configured to provide additional damping control to the generator.

Abstract: Systems and methods for automated custom training of a scoring model are disclosed herein. The method include: receiving a plurality of responses received from a plurality of students in response to providing of a prompt; identifying an evaluation model relevant to the provided prompt, which evaluation model can be a machine learning model trained to output a score relevant to at least portions of a response; generating a training indicator that provides a graphical depiction of the degree to which the identified evaluation model is trained; determining a training status of the model; receiving at least one evaluation input when the model is identified as insufficiently trained; updating training of the evaluation model based on the at least one received evaluation input; and controlling the training indicator to reflect the degree to which the evaluation model is trained subsequent to the updating of the training of the evaluation model.

Abstract: Systems and methods for automated custom training of a scoring model are disclosed herein. The method include: receiving a plurality of responses received from a plurality of students in response to providing of a prompt; identifying an evaluation model relevant to the provided prompt, which evaluation model can be a machine learning model trained to output a score relevant to at least portions of a response; generating a training indicator that provides a graphical depiction of the degree to which the identified evaluation model is trained; determining a training status of the model; receiving at least one evaluation input when the model is identified as insufficiently trained; updating training of the evaluation model based on the at least one received evaluation input; and controlling the training indicator to reflect the degree to which the evaluation model is trained subsequent to the updating of the training of the evaluation model.

Abstract: The present disclosure is directed to a gas turbine engine assembly having a compressor configured to increase pressure of incoming air, a combustion chamber, at least one turbine coupled to a generator, a torsional damper, and a controller. The combustion chamber is configured to receive a pressurized air stream from the compressor. Further, fuel is injected into the pressurized air in the combustion chamber and ignited so as to raise a temperature and energy level of the pressurized air. The turbine is operatively coupled to the combustion chamber so as to receive combustion products that flow from the combustion chamber. The generator is coupled to the turbine via a shaft. Thus, the torsional damper is configured to dampen torsional oscillations of the generator. Moreover, the controller is configured to provide additional damping control to the generator.

Abstract: A turbomachine engine includes a core engine having one or more compressor sections, one or more turbine sections that includes a power turbine, and a combustion chamber in flow communication with the compressor sections and turbine sections. The turbomachine engine also includes a shaft coupled to the power turbine and characterized by a midshaft rating (MSR) between two hundred (ft/sec)1/2 and three hundred (ft/sec)1/2. In one aspect, the shaft has a redline speed between fifty and two hundred fifty feet per second (ft/sec). In another aspect, the shaft has a length L, an outer diameter D, and a ratio of L/D between twelve and thirty-seven.

Abstract: Systems and methods for automated custom training of a scoring model are disclosed herein. The method include: receiving a plurality of responses received from a plurality of students in response to providing of a prompt; identifying an evaluation model relevant to the provided prompt, which evaluation model can be a machine learning model trained to output a score relevant to at least portions of a response; generating a training indicator that provides a graphical depiction of the degree to which the identified evaluation model is trained; determining a training status of the model; receiving at least one evaluation input when the model is identified as insufficiently trained; updating training of the evaluation model based on the at least one received evaluation input; and controlling the training indicator to reflect the degree to which the evaluation model is trained subsequent to the updating of the training of the evaluation model.

Abstract: A turbomachine engine includes a core engine having one or more compressor sections, one or more turbine sections that includes a power turbine, and a combustion chamber in flow communication with the compressor sections and turbine sections. The turbomachine engine also includes a shaft coupled to the power turbine and characterized by a midshaft rating (MSR) between two hundred (ft/sec)1/2 and three hundred (ft/sec)1/2. In one aspect, the shaft has a redline speed between fifty and two hundred fifty feet per second (ft/sec). In another aspect, the shaft has a length L, an outer diameter D, and a ratio of L/D between twelve and thirty-seven.

Abstract: A turbomachine engine includes a core engine having one or more compressor sections, one or more turbine sections that includes a power turbine, and a combustion chamber in flow communication with the compressor sections and turbine sections. The turbomachine engine also includes a shaft coupled to the power turbine and characterized by a midshaft rating (MSR) between two hundred (ft/sec)1/2 and three hundred (ft/sec)1/2. In one aspect, the shaft has a redline speed between fifty and two hundred fifty feet per second (ft/sec). In another aspect, the shaft has a length L, an outer diameter D, and a ratio of L/D between twelve and thirty-seven.

Abstract: An un-ducted turbofan engine defining a radial direction and an axial direction that includes a core engine, a fan, a plurality of variable outlet guide vanes, and a pitch change mechanism. Each of the plurality of variable outlet guide vanes are attached in a rotatable manner to the core engine of the un-ducted turbofan engine. The pitch change mechanism is positioned radially between the engine air flowpath and the plurality of variable outlet guide vanes and coupled to at least one variable outlet guide vane of the plurality of variable outlet guide vanes for changing a pitch of the at least one variable outlet guide vane.

Abstract: A turbomachine engine includes a core engine having one or more compressor sections, one or more turbine sections that includes a power turbine, and a combustion chamber in flow communication with the compressor sections and turbine sections. The turbomachine engine also includes a shaft coupled to the power turbine and characterized by a midshaft rating (MSR) between two hundred (ft/sec)1/2 and three hundred (ft/sec)1/2. In one aspect, the shaft has a redline speed between fifty and two hundred fifty feet per second (ft/sec). In another aspect, the shaft has a length L, an outer diameter D, and a ratio of L/D between twelve and thirty-seven.

Abstract: A turbomachine engine includes a core engine having one or more compressor sections, one or more turbine sections that includes a power turbine, and a combustion chamber in flow communication with the compressor sections and turbine sections. The turbomachine engine also includes a shaft coupled to the power turbine and characterized by a midshaft rating (MSR) between two hundred (ft/sec)1/2 and three hundred (ft/sec)1/2. In one aspect, the shaft has a redline speed between fifty and two hundred fifty feet per second (ft/sec). In another aspect, the shaft has a length L, an outer diameter D, and a ratio of L/D between twelve and thirty-seven.

Abstract: Systems and methods for automated custom training of a scoring model are disclosed herein. The method include: receiving a plurality of responses received from a plurality of students in response to providing of a prompt; identifying an evaluation model relevant to the provided prompt, which evaluation model can be a machine learning model trained to output a score relevant to at least portions of a response; generating a training indicator that provides a graphical depiction of the degree to which the identified evaluation model is trained; determining a training status of the model; receiving at least one evaluation input when the model is identified as insufficiently trained; updating training of the evaluation model based on the at least one received evaluation input; and controlling the training indicator to reflect the degree to which the evaluation model is trained subsequent to the updating of the training of the evaluation model.

Abstract: An un-ducted turbofan engine defining a radial direction and an axial direction that includes a core engine, a fan, a plurality of variable outlet guide vanes, and a pitch change mechanism. Each of the plurality of variable outlet guide vanes are attached in a rotatable manner to the core engine of the un-ducted turbofan engine. The pitch change mechanism is positioned radially between the engine air flowpath and the plurality of variable outlet guide vanes and coupled to at least one variable outlet guide vane of the plurality of variable outlet guide vanes for changing a pitch of the at least one variable outlet guide vane.

Abstract: An aircraft including a fuselage and an aft engine is provided. The fuselage extends from a forward end of the aircraft towards an aft end of the aircraft. The aft engine is mounted to the fuselage proximate the aft end of the aircraft and includes a fan and a nacelle. The fan is rotatable about a central axis of the aft engine and includes a plurality of fan blades. The nacelle of the aft engine surrounds the plurality of fan blades and defines a bottom portion having a forward end. Additionally, the nacelle defines a curved surface at the forward end of the bottom portion, the curved surface including a reference point where the curved surface defines the smallest radius of curvature. The nacelle further defines a normal reference line extending normal from the reference point. The normal reference line defines an angle with the central axis of the aft engine greater than zero to, e.g., allow for a maximum amount of airflow into the aft engine.

Abstract: Systems and methods for automated custom training of a scoring model are disclosed herein. The method include: receiving a plurality of responses received from a plurality of students in response to providing of a prompt; identifying an evaluation model relevant to the provided prompt, which evaluation model can be a machine learning model trained to output a score relevant to at least portions of a response; generating a training indicator that provides a graphical depiction of the degree to which the identified evaluation model is trained; determining a training status of the model; receiving at least one evaluation input when the model is identified as insufficiently trained; updating training of the evaluation model based on the at least one received evaluation input; and controlling the training indicator to reflect the degree to which the evaluation model is trained subsequent to the updating of the training of the evaluation model.

Abstract: Systems and methods for automated custom training of a scoring model are disclosed herein. The method include: receiving a plurality of responses received from a plurality of students in response to providing of a prompt; identifying an evaluation model relevant to the provided prompt, which evaluation model can be a machine learning model trained to output a score relevant to at least portions of a response; generating a training indicator that provides a graphical depiction of the degree to which the identified evaluation model is trained; determining a training status of the model; receiving at least one evaluation input when the model is identified as insufficiently trained; updating training of the evaluation model based on the at least one received evaluation input; and controlling the training indicator to reflect the degree to which the evaluation model is trained subsequent to the updating of the training of the evaluation model.