Abstract: The application describes bioreactors comprising gas permeable membranes disposed on the housing covering at least partially a transfer opening. The bioreactor is also provided with a mixing element and ports adapted to receive sensors measuring values of parameters related to the process implemented in the bioreactor. The bioreactor comprises at least one opening in one of the walls. The at least one opening is covered with a gas permeable membrane.

Abstract: A cap tap seal puncturing and removal device is disclosed that is a manual device for puncturing, piercing, and/or removing safety seals from a container. The cap tap seal puncturing and removal device comprises a cylindrical device that has a container capping component at one side or end and a puncturing, piercing, and/or removal component at an opposing side or end. The cap seal puncture and removal component comprises a protruding point or cone and a barb or seal removal feature. The device is employed by hand via a twisting motion to puncture and completely remove a safety seal from a container.

Abstract: Disclosed is an approach that applies a fuzzy logic model that may involve fuzzy-matching a plurality of address fields to determine a common physical address, and determining a number of communiques directed to that address with reference to a threshold that may determine an excessive number of communiques. The plurality of address fields may also be fuzzy-matched to information in a fraud-risk database which may comprise a fraud-risk address. One or more matches may be presented to a user who may adjust the views of the various matches, track various trends within the data, and harmonize the various address fields relating to a physical address.

Abstract: Disclosed are techniques for using machine learning models to more reliably predict likelihoods of application failure. A model is trained to identify and display events that may cause high severity application failures. Logistic regression may be used to fit the model such that application features are mapped to a high severity event flag. Significant features of applications that relate to the high severity flag may be selected using stepwise regression. The identified applications may be displayed on a graphical user interface for review and reprioritization. Information may be ranked and displayed according to multiple different ranking criteria, such as one ranking generated by a first model, and another determined by one or more users. The multiple ranking criteria may be used to inform steps taken, and/or to retrain or tune the parameters of the model for subsequent predictions or classifications.

Abstract: A computing system receives issue data for a plurality of issues from a risk management system. The issue data for each respective issue of the plurality of issues includes an issue identifier and a due date for resolution of the respective issue. The computing system generates due date revision risk scores for the plurality of issues. A due date revision risk score for the respective issue indicates a predicted probability that the due date for resolution of the respective issue will need revision within a fixed period of time. The computing system generates a warning message for at least the respective issue of the plurality of issues based on the due date revision risk score for the respective issue being greater than a threshold. The computing system revises parameters of the at least one machine learning model based on due date revision outcomes associated with the plurality of issues.

Abstract: A computing system receives issue data for a plurality of issues from a risk management system. The issue data for each respective issue of the plurality of issues includes an issue identifier. The computing system generates issue validation risk scores for the plurality of issues. An issue validation risk score for the respective issue indicates a predicted probability that a resolution of the respective issue will fail validation within a fixed period of time. The computing system generates a warning message for at least the respective issue of the plurality of issues based on the issue validation risk score for the respective issue being greater than a threshold. The computing system revises parameters of the at least one machine learning model based on validation failure outcomes associated with the plurality of issues.

Abstract: Disclosed are techniques for using machine learning models to more reliably predict likelihoods of application failure. A model is trained to identify and display events that may cause high severity application failures. Logistic regression may be used to fit the model such that application features are mapped to a high severity event flag. Significant features of applications that relate to the high severity flag may be selected using stepwise regression. The identified applications may be displayed on a graphical user interface for review and reprioritization. Information may be ranked and displayed according to multiple different ranking criteria, such as one ranking generated by a first model, and another determined by one or more users. The multiple ranking criteria may be used to inform steps taken, and/or to retrain or tune the parameters of the model for subsequent predictions or classifications.

Abstract: Systems, apparatuses, methods, and computer program products are disclosed for automating incident severity classification. An example method includes: receiving a historical incident dataset; receiving a new incident dataset including information regarding a set of new incidents; generating a set of predicted severity classifications for the set of new incidents using a machine learning model, the historical incident dataset, and the new incident dataset; determining that a severity classification mismatch between at least one assigned severity classification and a predicted severity classification of the set of predicted severity classifications exists for the new incident dataset; and in response to determining that the severity classification mismatch exists, prompting further investigation of the severity classification mismatch through outputting an indication of the severity classification mismatch.

Abstract: Systems, apparatuses, methods, and computer program products are disclosed for automating incident severity classification. An example method includes: receiving a historical incident dataset; receiving a new incident dataset including information regarding a set of new incidents; generating a set of predicted severity classifications for the set of new incidents using a machine learning model, the historical incident dataset, and the new incident dataset; determining that a severity classification mismatch between at least one assigned severity classification and a predicted severity classification of the set of predicted severity classifications exists for the new incident dataset; and in response to determining that the severity classification mismatch exists, prompting further investigation of the severity classification mismatch through outputting an indication of the severity classification mismatch.

Abstract: Disclosed is an approach that applies a fuzzy logic model that may involve fuzzy-matching a plurality of address fields to determine a common physical address, and determining a number of communiques directed to that address with reference to a threshold that may determine an excessive number of communiques. The plurality of address fields may also be fuzzy-matched to information in a fraud-risk database which may comprise a fraud-risk address. One or more matches may be presented to a user who may adjust the views of the various matches, track various trends within the data, and harmonize the various address fields relating to a physical address.

Abstract: Systems, apparatuses, methods, and computer program products are disclosed for automating incident severity classification. An example method includes receiving, by communications circuitry, a historical incident dataset, the historical incident dataset including information regarding a set of historical incidents and an assigned severity classification for each historical incident in the set of historical incidents. The example method further includes training, by a model generator and using the historical incident dataset, a machine learning model to classify incident severity. The example method further includes receiving, by the communications circuitry, a new incident dataset, generating, by a prediction engine and using the trained machine learning model and the new incident dataset, a set of predicted severity classifications for the set of new incidents, and outputting, by the communications circuitry, the predicted set of severity classifications for the set of new incidents.

Abstract: Disclosed is an approach that applies a fuzzy logic model that may involve fuzzy-matching a plurality of address fields to determine a common physical address, and determining a number of communiques directed to that address with reference to a threshold that may determine an excessive number of communiques. The plurality of address fields may also be fuzzy-matched to information in a fraud-risk database which may comprise a fraud-risk address. One or more matches may be presented to a user who may adjust the views of the various matches, track various trends within the data, and harmonize the various address fields relating to a physical address.

Abstract: Disclosed is an approach that applies a fuzzy logic model that may involve fuzzy-matching a plurality of address fields to determine a common physical address, and determining a number of communiques directed to that address with reference to a threshold that may determine an excessive number of communiques. The plurality of address fields may also be fuzzy-matched to information in a fraud-risk database which may comprise a fraud-risk address. One or more matches may be presented to a user who may adjust the views of the various matches, track various trends within the data, and harmonize the various address fields relating to a physical address.

Abstract: The disclosure features mass spectrometry systems and methods that include an ion source, an ion trap, a detector subsystem featuring first and second detector elements, and a controller electrically connected to the ion source, the ion trap, and the detector subsystem and configured so that during operation of the system, the controller: applies an electrical signal to the ion source to generate positively and negatively charged particles from sample particles in the system; applies an electrical signal to the ion trap to eject a plurality of particles from the ion trap through a common aperture of the ion trap, and determines information about the sample particles based on first and second electrical signals generated by the ejected particles.

Abstract: Systems and methods for power management systems are disclosed. One embodiment of the invention includes a power management system for a vehicle locating unit, the vehicle locating unit having a receiver for receiving at an assigned message frame an intermittent transmission from a communications source, wherein each intermittent transmission includes at least one assignable message frame, the power management system including a processing device for controlling wake and sleep modes of the receiver and a memory for storing a plurality of memory bit patterns for comparison with a pattern of bits contained in some portion of an assigned message frame, wherein the processing device is configured to perform the following steps: index to the receiver's assigned message frame in the intermittent transmission, re-enter wake mode after indexing, and subject the receiver to a constant average current draw.

Abstract: The disclosure features mass spectrometry systems and methods that include an ion source, an ion trap, a detector subsystem featuring first and second detector elements, and a controller electrically connected to the ion source, the ion trap, and the detector subsystem and configured so that during operation of the system, the controller: applies an electrical signal to the ion source to generate positively and negatively charged particles from sample particles in the system; applies an electrical signal to the ion trap to eject a plurality of particles from the ion trap through a common aperture of the ion trap, and determines information about the sample particles based on first and second electrical signals generated by the ejected particles.

Abstract: Mass spectrometry systems include a core featuring an ion source, an ion trap, and an ion detector connected along a gas path, a pressure regulation subsystem connected to the gas path and configured to regulate a gas pressure in the gas path, a sample pre-concentrator connected to the gas path, where the sample pre-concentrator includes an adsorbent material, and a controller connected to the sample pre-concentrator, where during operation of the system, the controller is configured to heat sample particles adsorbed on the adsorbent material to desorb the particles from the adsorbent material and introduce the desorbed particles into the gas path, and a pressure difference between a gas pressure in the sample pre-concentrator and a gas pressure in at least one of the ion source, the ion trap, and the ion detector when the desorbed particles are introduced into the gas path is 50 mTorr or less.

Abstract: The disclosure features mass spectrometry systems and methods that include an ion source, an ion trap, a detector subsystem featuring first and second detector elements, and a controller electrically connected to the ion source, the ion trap, and the detector subsystem and configured so that during operation of the system, the controller: applies an electrical signal to the ion source to generate positively and negatively charged particles from sample particles in the system; applies an electrical signal to the ion trap to eject a plurality of particles from the ion trap through a common aperture of the ion trap, and determines information about the sample particles based on first and second electrical signals generated by the ejected particles.

Abstract: Mass separators are provided that can include at least one electrode component having a surface, in one cross section, defining at least two runs associated via at least one rise, the rise being orthogonally related to the runs. Mass selective detectors are provided that can include at least a first pair of opposing electrodes with each of the opposing electrodes having a complimentary surface, in one cross section, defining at least two runs associated via a rise. Methods for optimizing mass separation within a mass selective detector are also provided, including providing mass separation parameters; providing one set electrodes within the separator having a surface operatively aligned within the separator, the surface, in one cross section, defining at least two runs associated via a rise, the rise being orthogonally related to the runs; and modifying one or both of the rise and/or runs to achieve the mass separation parameters.

Abstract: Systems and methods for power management systems are disclosed. One embodiment of the invention includes a power management system for a vehicle locating unit, the vehicle locating unit having a receiver for receiving at an assigned message frame an intermittent transmission from a communications source, wherein each intermittent transmission includes at least one assignable message frame, the power management system including a processing device for controlling wake and sleep modes of the receiver and a memory for storing a plurality of memory bit patterns for comparison with a pattern of bits contained in some portion of an assigned message frame, wherein the processing device is configured to perform the following steps: index to the receiver's assigned message frame in the intermittent transmission, re-enter wake mode after indexing, and subject the receiver to a constant average current draw.