Abstract: An agricultural system includes a header with a feed roller configured to engage a crop and to drive material other than grain (MOG) of the crop toward a field. The agricultural system also includes a chopper configured to cut the MOG driven toward the field by the feed roller for discharge from the agricultural system and a control system configured to output a control signal to adjust a speed of the chopper relative to a speed of the feed roller based on a size of the MOG discharged from the agricultural system.

Abstract: An agricultural system includes a header having a row unit with a feed roller configured to engage a crop and to pull a stalk of the crop toward the field. The header also includes a conveyor configured to receive a desirable crop material of the crop from the row unit and to direct the desirable crop material of the crop toward an inlet of the agricultural system. The header further includes a sensor system configured to detect presence of material other than grain (MOG) proximate to or at the conveyor.

Abstract: An impact sensor system for a header of an agricultural system includes a first sensor vibrationally connected to a deck plate of the header, wherein the first sensor is configured to generate first signals in response to detection of vibrations due to initial contact between a crop and the deck plate of the header. The impact sensor system also includes a second sensor vibrationally connected to the deck plate of the header and positioned forward of the first sensor, wherein the second sensor is configured to generate second signals in response to detection of the vibrations due to the initial contact between the crop and the deck plate of the header.

Abstract: A back light unit includes a plurality of light emitting diodes positioned on a substrate and a plurality of flat lenses. A respective one of the plurality of flat lens are positioned a distance above a respective one of the plurality of light emitting diodes. Each of the plurality of flat lenses includes a plurality of annular segments having a common center. Each of the plurality of annular segments includes a prism shape microstructure having a first facet configured to refract the light in a desired direction and a second facet oriented in a plane that is parallel to a plane of an emitting surface of respective ones of the plurality of light emitting diode.

Abstract: An insert for an arthropod trapping device. The insert comprising a substrate and a frame for supporting the substrate, where a surface of the substrate has an adhesive disposed thereon, an optional mounting bracket spaced apart from the adhesive surface of the insert and located at a first end of the insert, and an optional graspable tab extending from the frame at a second end of the insert.

Abstract: Particulate additives that may be useful in mitigating fluid loss and/or as bridging agents or diverting agents in subterranean treatment fluids such as drilling fluids. In some embodiments, the particulate additives include a polymeric material and calcium carbonate disposed on at least a portion of the outer surface of the polymer core wherein the particulates have a particle size (d50) of from about 2 ?m to about 1600 ?m in diameter. In some embodiments, the methods include providing a treatment fluid that includes the base fluid and the particulate additives and introducing the treatment fluid into at least a portion of a subterranean formation.

Abstract: The computing device obtains a training data set related to a plurality of historic user inputs associated with preferences of one or more services or items from an entity. For each of the one or more services or items, the computing device executes operations to train a plurality of models using the training data set to generate a plurality of recommended models, apply a validation data set to generate a plurality of predictions from the plurality of recommended models, obtain a weight of each metric of a plurality of metrics from the entity, obtain user inputs associated with user preferences, and determine a relevancy score for each metric. The computing device selects a recommended model based on the relevancy score of the selected metric or a combination of selected metrics, generates one or more recommendations for the users, and outputs the one or more generated recommendations to the users.

Abstract: A computing system detects a defective object. An image is received of a manufacturing line that includes objects in a process of being manufactured. Each pixel included in the image is classified as a background pixel class, a non-defective object class, or a defective object class using a trained neural network model. The pixels included in the image that were classified as the non-defective object class or the defective object class are grouped into polygons. Each polygon is defined by a contiguous group of pixels classified as the non-defective object class or the defective object class. Each polygon is classified in the non-defective object class or in the defective object class based on a number of pixels included in a respective polygon that are classified in the non-defective object class relative to a number of pixels included in the respective polygon that are classified in the defective object class.

Abstract: The computing device obtains a training data set related to a plurality of historic user inputs associated with preferences of one or more services or items from an entity. For each of the one or more services or items, the computing device executes operations to train a plurality of models using the training data set to generate a plurality of recommended models, apply a validation data set to generate a plurality of predictions from the plurality of recommended models, obtain a weight of each metric of a plurality of metrics from the entity, obtain user inputs associated with user preferences, and determine a relevancy score for each metric. The computing device selects a recommended model based on the relevancy score of the selected metric or a combination of selected metrics, generates one or more recommendations for the users, and outputs the one or more generated recommendations to the users.

Abstract: This disclosure relates to N-acetylserotonin derivatives that have neuroprotective properties for uses in treating or preventing traumatic brain injury, vision loss, neuronal cell ischemia, and retinal degenerative diseases. In certain embodiments, this disclosure relates to pharmaceutical compositions comprising the N-acetylserotonin derivative and a pharmaceutically acceptable excipient.

Abstract: A computing device trains a machine state predictive model. A generative adversarial network with an autoencoder is trained using a first plurality of observation vectors. Each observation vector of the first plurality of observation vectors includes state variable values for state variables and an action variable value for an action variable. The state variables define a machine state, wherein the action variable defines a next action taken in response to the machine state. The first plurality of observation vectors successively defines sequential machine states to manufacture a product. A second plurality of observation vectors is generated using the trained generative adversarial network with the autoencoder. A machine state machine learning model is trained to predict a subsequent machine state using the first plurality of observation vectors and the generated second plurality of observation vectors. A description of the machine state machine learning model is output.

Abstract: A computing device trains a machine state predictive model. A generative adversarial network with an autoencoder is trained using a first plurality of observation vectors. Each observation vector of the first plurality of observation vectors includes state variable values for state variables and an action variable value for an action variable. The state variables define a machine state, wherein the action variable defines a next action taken in response to the machine state. The first plurality of observation vectors successively defines sequential machine states to manufacture a product. A second plurality of observation vectors is generated using the trained generative adversarial network with the autoencoder. A machine state machine learning model is trained to predict a subsequent machine state using the first plurality of observation vectors and the generated second plurality of observation vectors. A description of the machine state machine learning model is output.

Abstract: A stalk roller assembly for an agricultural system includes a stalk roller having a hollow shaft. The hollow shaft has a first end having a first shape, such that the first end engages a guide element. Furthermore, the hollow shaft has a second end having a second shape that engages a drive shaft, such that the first shape is different than the second shape. In addition, the hollow shaft is formed from a single piece of material.

Abstract: A secure DNS query may be made by establishing a secure connection with a specific DNS server to determine an address for a hostname. A client device may have a database that may contain a record of a secure DNS server for one or more hostnames. When a DNS request contains one of the specified hostnames, an authenticated session may be created with the designated secure DNS server and a network address for the hostname is returned using the session. The authenticated session may authenticate a client device to the server as well as authenticate the server to the client. In some embodiments, the secure DNS server may accept connections from authenticated clients and may disregard connection requests from non authenticated clients.

Abstract: A computing system obtains image data representing images. Each of the images is captured at different time points of a physical environment. The physical environment comprises a first object and a second object. The computing system executes a control system to augment the physical environment. The control system detects a group forming in the images. The control system tracks an aspect of a movement, of a given object, in the group. The control system simulates the physical environment and the movement, of the given object, in the group in a simulated environment. The control system evaluates simulated actions in the simulated environment for a predefined objective for the physical environment. The predefined objective is related to an interaction between objects in the group. The control system generates based on evaluated simulated actions and autonomously from involvement by any user of the control system, an indication to augment the physical environment.

Abstract: A computing system responsive to obtaining original image data, detects a set of data point(s), in the original image data, that indicates an object. The system determines, based on the set of data point(s), a set of pixels associated with the object in the original image data. The system generates an alternative visual identifier for the object that provides a unique identifier for the set of pixels absent in the original image data. The system generates, autonomously from intervention by any user of the computing system, pixel information to conceal feature(s) of the object. The system obtains modified image data comprising the alternative visual identifier. The modified image data further comprises the feature(s) of the object in the original image data visually concealed in the modified image data according to the pixel information. The system outputs an image representation of a trajectory of the object through the modified image data.

Abstract: A stalk roller assembly for an agricultural system includes a stalk roller having a hollow shaft. The hollow shaft has a first end having a first shape, such that the first end engages a guide element. Furthermore, the hollow shaft has a second end having a second shape that engages a drive shaft, such that the first shape is different than the second shape. In addition, the hollow shaft is formed from a single piece of material.

Abstract: A combine feeder assembly having conveyors and slats pivotally connected to the conveyors to rotate about respective slat pivot axes. Each slat has a paddle that is movable between a first position in which the paddle extends outside the travel path of the conveyor by a first distance, and a second position in which the paddle does not extend outside the travel path or extends outside the travel path by a second distance that is less than the first distance. A cam surface moves the slats to the first slat position along a delivery path of the conveyors. A first housing wall extends over the delivery path and is spaced from the travel path by at least the first distance. A second housing wall extends over a return path. The second housing wall is spaced from the travel path by less than the first distance.

Abstract: A computing system receives historical data. The historical data comprises physical actions taken in an experiment in a physical environment. The experiment comprises user-defined stages. The historical data comprises a recorded outcome, according to user-defined performance indicator(s) related to the user-defined stages, for each physical action taken in the experiment. The system generates, by a discrete event simulator, a computing representation of a simulated environment of the physical environment. The simulated environment comprises processing stages. The system obtains simulation data. The simulation data comprises simulated actions taken by the discrete event simulator. The simulation data comprises a predicted outcome, according to user-defined performance indicator(s) related to the processing stages, for each simulated action taken by the discrete event simulator. The system validates accuracy of the discrete event simulator at predicting the recorded outcome in the experiment.

Abstract: A computing system receives historical data. The historical data comprises physical actions taken in an experiment in a physical environment. The experiment comprises user-defined stages. The historical data comprises a recorded outcome, according to user-defined performance indicator(s) related to the user-defined stages, for each physical action taken in the experiment. The system generates, by a discrete event simulator, a computing representation of a simulated environment of the physical environment. The simulated environment comprises processing stages. The system obtains simulation data. The simulation data comprises simulated actions taken by the discrete event simulator. The simulation data comprises a predicted outcome, according to user-defined performance indicator(s) related to the processing stages, for each simulated action taken by the discrete event simulator. The system validates accuracy of the discrete event simulator at predicting the recorded outcome in the experiment.