Abstract: Techniques are described for cleaning sensors of a vehicle. An attribute of the surrounding environment (e.g., type of precipitation, intensity of precipitation, etc.) is determined along with orientations and/or attributes of the sensors (e.g. fields-of-view, focal lengths, spectral ranges, etc). Cleaning frequencies are then be determined based, at least in part, on the attribute of the environment together with the orientations and/or attributes of the sensors. Cleaning elements are then activated at the determined frequencies to clean the sensors. In some examples, the cleaning frequencies may be determined based additionally on an attribute of travel of the vehicle (e.g. direction of travel of the vehicle, speed of the vehicle, etc.).

Abstract: An apparatus for generating a digital assistant is disclosed. The apparatus include at least a processor and a memory communicatively connected to the at least a processor. The memory instructs the processor to receive at least one user query from a user. The memory instructs the processor to extract a plurality of background data and a plurality of contextual data from the user dataset. The memory instructs the processor to receive at least one user query from a user. The memory instructs the processor to generate a query response as a function of the at least one user query and the contextual data using the digital assistant. The memory instructs the processor to display the query response using the digital assistant on a display device.

Abstract: Compositions and methods of treating a gastrointestinal dysbiosis using a limited number of defined bacterial species are provided.

Abstract: Various embodiments of the present disclosure provide predictive modeling techniques for generating predictive classifications from a plurality of continuous sensor measurements. The techniques may include identifying change points from sensor measurements for an input data object, determining data spikes from the sensor measurements based on the change points, and generating a predictive classification for the input data object based on the data spikes. The predictive classification may correspond to an evaluation time period with one or more sub-time periods. The techniques may include determining a sub-time period classification for each of the sub-time periods of the evaluation time period. The predictive classification may be derived from the sub-time period classifications. Using the predictive classification, an action output may be generated and provided for the input data object.

Abstract: A computer-implemented process for medical sensing, the process including the steps of: accessing scattering data representing successive sets of measurements of electromagnetic wave scattering by internal features of a body part of a living subject, each said measurement representing scattering of electromagnetic waves emitted by a corresponding antenna of an array of antennas disposed about the body part as measured by a corresponding antenna of the array of antennas at a corresponding time, wherein the successive sets of measurements are temporally spaced apart; processing each of the measurements to generate corresponding spectral data representing intensities measured by the corresponding antenna at the corresponding time as a function of frequency; and processing the spectral data of each antenna for successive times to generate corresponding pulsatility data representing successive pulsations within a corresponding spatially localized region within the body part.

Abstract: An apparatus and method for generating a compiled artificial intelligence (AI) model. The apparatus incudes a processor that is configured to receive data sets from user devices. The processor is further configured to convert the data sets using a machine-learning model into a cleansed data format and generate an accumulated model using the converted data sets as training data.

Abstract: An apparatus and method for fracking optimization, wherein the apparatus includes at least a processor, and a memory, wherein the memory containing instructions configuring the at least a processor to receive a reservoir datum from at least a sensing device, generate a production training data include a plurality of reservoir datums as input correlated to a plurality of optimal production parameters as output, train a fracking optimization machine-learning model using the production training data, determine an optimal production parameter as a function of the fracking optimization machine-learning model, and generating an optimal production plan as a function of the optimal production parameter.

Abstract: Disclosed are various approaches for determining an animal feed formulation. In some implementations, a system may obtain data that provides characteristics of an animal population located at a site. The system may identify a target growth schedule for the animal population. The system may use a data processing model to predict nutrition requirements for tissue growth in the animal population, using the target growth schedule and the characteristics of the animal population, as the model predicts nutrition requirements based on projected nitrogenous energy requirements for the tissue growth. The system may identify a feed formulation producible from a combination of animal feed ingredients to satisfy the nutrition requirements for tissue growth. The system may generate a data output for use of the feed formulation with the animal population, the data output indicating the identified feed formulation producible from the combination of animal feed ingredients.

Abstract: The invention provides compositions and methods for use in the treatment and prevention of cholestatic diseases.

Abstract: An apparatus for multi-stage fracking, wherein the apparatus includes a pump configured to pump a fracking fluid into a rock region comprises a plurality of rock zones, and a computing device communicatively connected to the pump, wherein the computing device includes at least a processor, and a memory communicatively connected to the at least a processor containing instructions configuring the at least a processor to receive reservoir data, determine an optimal fracking stimulation parameter as a function of the reservoir data, identify a fracking stage as a function of the optimal fracking stimulation parameter, and adjust a pump configuration of the pump as a function of the fracking stage.

Abstract: At least one processing device, operating upon a body of text in a document, identifies occurrences of at least one content type in the body of text. The at least one processing device thereafter generates a user interface that includes portions of text from the body of text that are representative of at least some of the occurrences of the at least one content type in the document. For each content type, the occurrences corresponding to that content type can be grouped together to provide grouped content type occurrences that are subsequently collocated in the user interface. Those portions of text corresponding to the grouped content type occurrences may be arranged in alphanumeric order. The user interface may comprise at least a portion of the body of text as well as indicia indicating instances of the occurrences within the portion of the body of text.

Abstract: In an aspect, an apparatus for predicting downhole conditions is disclosed. The apparatus comprises at least a processor and a memory communicatively connected to the at least a processor. The memory containing instructions configuring the at least a processor to receive a condition datum. The memory additionally contains instructions configuring the at least a processor to produce a measured downhole condition using at least a sensor. The memory instructs the processor to convert the condition datum and the measured downhole conditions into a cleansed data format using a data conversion module. The processor is instructed by the memory to identify a flagged data as a function of the cleansed condition datum and the cleansed measured downhole conditions using a downhole machine learning model. The memory instructs the processor to generate a predicted downhole condition as a function of the flagged data.

Abstract: An apparatus for generating a digital assistant is disclosed. The apparatus include at least a processor and a memory communicatively connected to the at least a processor. The memory instructs the processor to receive at least one user query from a user. The memory instructs the processor to extract a plurality of background data and a plurality of contextual data from the user dataset. The memory instructs the processor to receive at least one user query from a user. The memory instructs the processor to generate a query response as a function of the at least one user query and the contextual data using the digital assistant. The memory instructs the processor to display the query response using the digital assistant on a display device.

Abstract: An apparatus and method for fracking optimization, wherein the apparatus includes at least a processor, and a memory, wherein the memory containing instructions configuring the at least a processor to receive a reservoir datum from at least a sensing device, generate a production training data include a plurality of reservoir datums as input correlated to a plurality of optimal production parameters as output, train a fracking optimization machine-learning model using the production training data, determine an optimal production parameter as a function of the fracking optimization machine-learning model, and generating an optimal production plan as a function of the optimal production parameter.

Abstract: An apparatus and method for fracking optimization, wherein the apparatus includes at least a processor, and a memory, wherein the memory containing instructions configuring the at least a processor to receive a reservoir datum from at least a sensing device, generate a production training data include a plurality of reservoir datums as input correlated to a plurality of optimal production parameters as output, train a fracking optimization machine-learning model using the production training data, determine an optimal production parameter as a function of the fracking optimization machine-learning model, and generating an optimal production plan as a function of the optimal production parameter.

Abstract: An apparatus for multi-stage fracking, wherein the apparatus includes a pump configured to pump a fracking fluid into a rock region comprises a plurality of rock zones, and a computing device communicatively connected to the pump, wherein the computing device includes at least a processor, and a memory communicatively connected to the at least a processor containing instructions configuring the at least a processor to receive reservoir data, determine an optimal fracking stimulation parameter as a function of the reservoir data, identify a fracking stage as a function of the optimal fracking stimulation parameter, and adjust a pump configuration of the pump as a function of the fracking stage.

Abstract: A ladar system capable of operating in a ladar mode and an optical communication mode is disclosed. When operating in the ladar mode, a ladar transmitter and ladar receiver cooperate to transmit ladar pulses that are used for ranging to targets in a field of view. When operating in an optical communication mode, ladar pulses are used to transmit data messages to locations in the field of view that are determined to have devices that are capable of receiving the optically communicated data messages.

Abstract: An apparatus and method for generating a compiled artificial intelligence (AI) model. The apparatus incudes a processor that is configured to receive data sets from user devices. The processor is further configured to convert the data sets using a machine-learning model into a cleansed data format and generate an accumulated model using the converted data sets as training data.

Abstract: Disclosed herein is a compact beam scanner assembly that includes an ellipsoidal reimaging mirror.

Abstract: Disclosed herein are a number of example embodiments that employ controllable delays between successive ladar pulses in order to discriminate between “own” ladar pulse reflections and “interfering” ladar pulses reflections by a receiver. Example embodiments include designs where a sparse delay sum circuit is used at the receiver and where a funnel filter is used at the receiver. Also, disclosed are techniques for selecting codes to use for the controllable delays as well as techniques for identifying and tracking interfering ladar pulses and their corresponding delay codes. The use of a ladar system with pulse deconfliction is also disclosed as part of an optical data communication system.