Abstract: A cartridge for a conducted electrical weapon may comprise a body having a first end opposite a second end and an outer surface opposite an inner surface. A projectile may be disposed within the body. The inner surface of the body may define a plurality of grooves extending from the first end of the body to a location within the body.

Abstract: A cartridge for a conducted electrical weapon may comprise a body having a first end opposite a second end and an outer surface opposite an inner surface. A projectile may be disposed within the body. The inner surface of the body may define a plurality of grooves extending from the first end of the body to a location within the body.

Abstract: A cartridge for a conducted electrical weapon may comprise a body having a first end opposite a second end and an outer surface opposite an inner surface. A projectile may be disposed within the body. The inner surface of the body may define a plurality of grooves extending from the first end of the body to a location within the body.

Abstract: The present invention relates to lipase variants. The present invention also relates to polynucleotides encoding the variants; nucleic acid constructs, vectors, and host cells comprising the polynucleotides; and methods of using the variants.

Abstract: A computer-implemented method for securing a user device is disclosed. A signed device authentication key is requested and received from a user application executing on the user device. The signed device authentication key is obtained via a software module installed on the user device and associated with a secure data processing provider. A device setup request is transmitted from the user device to the secure data processing system using the signed device authentication key. The device setup request comprises the signed device authentication key. The authenticity of the device setup request is verified at the secure data processing system based on the signed device authentication key.

Abstract: Disclosed are systems and methods for obtaining input data comprising properties associated with at least one parent well and a child well associated with the at least one parent well, dividing the input data into a training data subset, a validation data subset, and a test data subset, selecting at least one machine learning model using the training data subset, the validation data subset, and the test data subset based on k-fold cross validation, tuning hyper parameters for each of the at least one machine learning model, and generating a learning output using the at least one machine learning model and the hyper parameters for each of the at least one machine learning model, the learning output indicating a test root-mean-square error (RMSE) and a training RMSE.

Abstract: Disclosed are systems and methods for receiving historical production data associated with at least one hydraulic fracturing well, receiving time-series data associated with the at least one hydraulic fracturing well, the time-series data representing at least one type of data, receiving non-temporal data associated with the at least one hydraulic fracturing well, generating a machine learning model based on the historical production data, the time-series data associated with the at least one hydraulic fracturing well and based on an original job design during a first stage of the job at a particular hydraulic fracturing well, and the non-temporal data, determining an optimized job design for the particular hydraulic fracturing well having an objective function using a prediction based on the machine learning model, and implementing the optimized job design for the particular hydraulic fracturing well.

Abstract: A wellbore trajectory survey, having an ordered plurality of survey points, is acquired. Each of the plurality of survey points has a measured depth, an inclination, and a geographic location of the point in the wellbore corresponding to the survey point. The wellbore trajectory survey is ordered on measured depth. The processor identifies a minimum lateral measured depth (min_LMD), a maximum lateral measured depth (max_LMD). The processor identifies a mid-lateral point (mid_LMP) in the plurality of survey points whose measured depth (mid_LMD) is greater than min_LMD and less than max_LMD.

Abstract: The present invention relates to lipase variants. The present invention also relates to polynucleotides encoding the variants; nucleic acid constructs, vectors, and host cells comprising the polynucleotides; and methods of using the variants.

Abstract: A wellbore trajectory survey includes an ordered plurality of survey points, each of which has a measured depth, an inclination, and a geographic location. The wellbore trajectory survey is ordered on measured depth. A minimum lateral measured depth (min_LMD) as a measured depth of a first point in the ordered plurality of survey points for which the inclination exceeds an inclination threshold angle is identified. A maximum lateral measured depth (max_LMD) as a measured depth of a last point in the ordered plurality of survey points for which the inclination exceeds the inclination threshold angle is identified. The processor determines from the wellbore trajectory survey that the wellbore at measured depths between the min_LMD and the max_LMD exhibits porpoising into and out of a formation.

Abstract: The disclosure is directed to methods to design and revise hydraulic fracturing (HF) job plans. The methods can utilize one or more data sources from public, proprietary, confidential, and historical sources. The methods can build mathematical, statistical, machine learning, neural network, and deep learning models to predict production outcomes based on the data source inputs. In some aspects, the data sources are processed, quality checked, and combined into composite data sources. In some aspects, ensemble modeling techniques can be applied to combine multiple data sources and multiple models. In some aspects, response features can be utilized as data inputs into the modeling process. In some aspects, time-series extracted features can be utilized as data inputs into the modeling process. In some aspects, the methods can be used to build a HF job plan prior to the start of work at a well site.

Abstract: The disclosure is directed to methods to design and revise hydraulic fracturing (HF) job plans. The methods can utilize one or more data sources from public, proprietary, confidential, and historical sources. The methods can build mathematical, statistical, machine learning, neural network, and deep learning models to predict production outcomes based on the data source inputs. In some aspects, the data sources are processed, quality checked, and combined into composite data sources. In some aspects, ensemble modeling techniques can be applied to combine multiple data sources and multiple models. In some aspects, response features can be utilized as data inputs into the modeling process. In some aspects, time-series extracted features can be utilized as data inputs into the modeling process. In some aspects, the methods can be used to build a HF job plan prior to the start of work at a well site.

Abstract: The disclosure is directed to methods to design and revise hydraulic fracturing (HF) job plans. The methods can utilize one or more data sources from public, proprietary, confidential, and historical sources. The methods can build mathematical, statistical, machine learning, neural network, and deep learning models to predict production outcomes based on the data source inputs. In some aspects, the data sources are processed, quality checked, and combined into composite data sources. In some aspects, ensemble modeling techniques can be applied to combine multiple data sources and multiple models. In some aspects, response features can be utilized as data inputs into the modeling process. In some aspects, time-series extracted features can be utilized as data inputs into the modeling process. In some aspects, the methods can be used to build a HF job plan prior to the start of work at a well site.

Abstract: A wellbore trajectory survey includes an ordered plurality of survey points, each of which has a measured depth, an inclination, and a geographic location. The wellbore trajectory survey is ordered on measured depth. A minimum lateral measured depth (min_LMD) as a measured depth of a first point in the ordered plurality of survey points for which the inclination exceeds an inclination threshold angle is identified. A maximum lateral measured depth (max_LMD) as a measured depth of a last point in the ordered plurality of survey points for which the inclination exceeds the inclination threshold angle is identified. The processor determines from the wellbore trajectory survey that the wellbore at measured depths between the min_LMD and the max_LMD exhibits porpoising into and out of a formation.

Abstract: Disclosed are systems and methods for receiving historical production data associated with at least one hydraulic fracturing well, receiving time-series data associated with the at least one hydraulic fracturing well, the time-series data representing at least one type of data, receiving non-temporal data associated with the at least one hydraulic fracturing well, generating a machine learning model based on the historical production data, the time-series data associated with the at least one hydraulic fracturing well and based on an original job design during a first stage of the job at a particular hydraulic fracturing well, and the non-temporal data, determining an optimized job design for the particular hydraulic fracturing well having an objective function using a prediction based on the machine learning model, and implementing the optimized job design for the particular hydraulic fracturing well.

Abstract: The present invention relates to lipase variants of the parent lipase shown as SEQ ID NO: 2 with improved wash performance and/or Benefit Risk Factor (BRF). The invention also relates to polynucleotides encoding the lipase variants of the invention, compositions comprising a lipase variant of the invention as well as methods of producing such lipase variants of the invention and the use of the lipase variants or compositions thereof.

Abstract: The present invention relates to lipase variants of the parent lipase shown as SEQ ID NO: 2 with improved stability during use in a wash process (IWS). The invention also relates to polynucleotides encoding the lipase variants of the invention, compositions comprising a lipase variant of the invention as well as methods of producing such lipase variants of the invention and the use of the lipase variants or compositions thereof.

Abstract: The present invention relates to lipase variants of the parent lipase shown as SEQ ID NO: 2 with improved wash performance and/or Benefit Risk Factor (BRF). The invention also relates to polynucleotides encoding the lipase variants of the invention, compositions comprising a lipase variant of the invention as well as methods of producing such lipase variants of the invention and the use of the lipase variants or compositions thereof.

Abstract: Method and system for recording data describing a first entity, the data endorsed by a second entity comprising the second entity validating data describing the first entity, wherein an identifier is associated with the data, the identifier being generated from a public key of the first entity. Cryptographically signing data corresponding with the data describing the first entity using at least a private key of the second entity. Posting a transaction to a block chain including the cryptographically signed data. Method and system for obtaining data describing a first entity the data endorsed by a second entity comprising. Receiving an identifier of data describing the first entity. Retrieving an entry from a block chain based on the received identifier. Authenticating the entry using a public key of the second entity. Extracting the data describing the first entity from the retrieved entry.

Abstract: Disclosed are systems and methods for obtaining input data comprising properties associated with at least one parent well and a child well associated with the at least one parent well, dividing the input data into a training data subset, a validation data subset, and a test data subset, selecting at least one machine learning model using the training data subset, the validation data subset, and the test data subset based on k-fold cross validation, tuning hyper parameters for each of the at least one machine learning model, and generating a learning output using the at least one machine learning model and the hyper parameters for each of the at least one machine learning model, the learning output indicating a test root-mean-square error (RMSE) and a training RMSE.