Abstract: A load shed module configured to be connected in series between a power supply and a load is disclosed. A separate load shed module is connected in series between each load and the power supply. The load shed module determines the frequency of the voltage supplied from the power supply. Based on the frequency, the load shed module determines if utility power is connected or if a generator is connected. If the generator is connected and the frequency of the voltage goes outside of a desired operating range for a preset time, the load shed module disconnects the load from the power supply. Each load shed module includes a priority setting and reconnects its corresponding load after a predetermined time corresponding to the priority setting.

Abstract: An adjustable stand for an electronic device that is operable for stable movement in two directions with the use of a single hand of a user. An ergonomic design leverages a natural and intuitive movement of a user's hand to maintain an orientation of the electronic device throughout at least 180 degrees of movement.

Abstract: A system and method for recovering high-quality biomethane (RNG) from biogas sources is provided. The gas stream is compressed and liquids are separated from the gas stream at elevated pressure and reduced temperature. The compressing is performed using a plurality of compressors operating in parallel with common control set points. The system and method improve upon conventional practices and yield a biomethane product which meets strict gas pipeline quality specifications. Additionally, the system and method are an improvement to the overall methane recovery efficiency for biogas processing facilities.

Abstract: Systems and methods for automated real estate property matching across disparate data sources are disclosed. A method may include: retrieving first property data and second property data; normalizing the first property data and the second property data; executing a first phase of a configurable systemic multi-step matching process to match a property in the first property data to the property in the second property data, the first phase outputting a first score; executing a second phase of the configurable systemic multi-step matching process, the second phase outputting a second score; determining that the first score to the second score are within a predetermined amount indicating an exact match; populating an entry in a unified property database with matching data from the first property data and the second property data; and storing the entry for the property in the unified property database.

Abstract: A method and system for predicting the likelihood that a patient will suffer from a cardiac event is provided. The method includes receiving electrocardiogram data associated with the patient, providing at least a portion of the electrocardiogram data to a trained model, receiving a risk score indicative of the likelihood the patient will suffer from the cardiac event within a predetermined period of time from when the electrocardiogram data was generated, and outputting the risk score to at least one of a memory or a display for viewing by a medical practitioner or healthcare administrator. The system includes at least one processor executing instructions to carry out the steps of the method.

Abstract: A method for determining cardiology disease risk from electrocardiogram trace data and clinical data includes receiving electrocardiogram trace data associated with a patient, receiving the patient's clinical data, providing both sets of data to a trained machine learning composite model that is trained to evaluate the data with respect to each disease of a set of cardiology diseases including three or more of cardiac amyloidosis, aortic stenosis, aortic regurgitation, mitral stenosis, mitral regurgitation, tricuspid regurgitation, abnormal reduced ejection fraction, or abnormal interventricular septal thickness, generating, by the model and based on the evaluation, a composite risk score reflecting a likelihood of the patient being diagnosed with one or more of the cardiology diseases within a predetermined period of time from when the electrocardiogram trace data was generated, and outputting the composite risk score to at least one of a memory or a display.

Abstract: A method for determining cardiology disease risk from electrocardiogram trace data and clinical data includes receiving electrocardiogram trace data associated with a patient, receiving the patient's clinical data, providing both sets of data to a trained machine learning composite model that is trained to evaluate the data with respect to each disease of a set of cardiology diseases including three or more of cardiac amyloidosis, aortic stenosis, aortic regurgitation, mitral stenosis, mitral regurgitation, tricuspid regurgitation, abnormal reduced ejection fraction, or abnormal interventricular septal thickness, generating, by the model and based on the evaluation, a composite risk score reflecting a likelihood of the patient being diagnosed with one or more of the cardiology diseases within a predetermined period of time from when the electrocardiogram trace data was generated, and outputting the composite risk score to at least one of a memory or a display.

Abstract: A method and system for determining cardiac disease risk from electrocardiogram trace data is provided. The method includes receiving electrocardiogram trace data associated with a patient, the electrocardiogram trace data having an electrocardiogram configuration including a plurality of leads. One or more leads of the plurality of leads that are derivable from a combination of other leads of the plurality of leads are identified, and a portion of the electrocardiogram trace data does not include electrocardiogram trace data of the one or more leads. The portion of the electrocardiogram data is provided to a trained machine learning model, to evaluate the portion of the electrocardiogram trace data with respect to one or more cardiac disease states. A risk score reflecting a likelihood of the patient being diagnosed with a cardiac disease state within a predetermined period of time is generated by the trained machine learning model based on the evaluation.

Abstract: A method and system for predicting the likelihood that a patient will suffer from a cardiac event is provided. The method includes receiving electrocardiogram data associated with the patient, providing at least a portion of the electrocardiogram data to a trained model, receiving a risk score indicative of the likelihood the patient will suffer from the cardiac event within a predetermined period of time from when the electrocardiogram data was generated, and outputting the risk score to at least one of a memory or a display for viewing by a medical practitioner or healthcare administrator. The system includes at least one processor executing instructions to carry out the steps of the method.

Abstract: A method and system for predicting the likelihood that a patient will suffer from a cardiac event is provided. The method includes receiving electrocardiogram data associated with the patient, providing at least a portion of the electrocardiogram data to a trained model, receiving a risk score indicative of the likelihood the patient will suffer from the cardiac event within a predetermined period of time from when the electrocardiogram data was generated, and outputting the risk score to at least one of a memory or a display for viewing by a medical practitioner or healthcare administrator. The system includes at least one processor executing instructions to carry out the steps of the method.

Abstract: A method includes the step of receiving electrocardiogram (ECG) data associated with a plurality of patients and an electrocardiogram configuration including a plurality of leads and a time interval. The electrocardiogram data includes, for each lead included in the plurality of leads, voltage data associated with at least a portion of the time interval. The method also includes training an artificial intelligence model on the ECG data, tuning the artificial intelligence model using data from a device having fewer leads than the plurality of leads, and evaluating the artificial intelligence model on additional data received from the ECG data.

Abstract: A method for determining cardiology disease risk from electrocardiogram trace data and clinical data includes receiving electrocardiogram trace data associated with a patient, receiving the patient's clinical data, providing both sets of data to a trained machine learning composite model that is trained to evaluate the data with respect to each disease of a set of cardiology diseases including three or more of cardiac amyloidosis, aortic stenosis, aortic regurgitation, mitral stenosis, mitral regurgitation, tricuspid regurgitation, abnormal reduced ejection fraction, or abnormal interventricular septal thickness, generating, by the model and based on the evaluation, a composite risk score reflecting a likelihood of the patient being diagnosed with one or more of the cardiology diseases within a predetermined period of time from when the electrocardiogram trace data was generated, and outputting the composite risk score to at least one of a memory or a display.

Abstract: A respirator mask is described that includes a facepiece defining a hinge region. The facepiece is configured to create a seal with a face of a user. First and second mask substrates are spaced apart from one another and positioned on opposite sides of the hinge region. First and second filters are positioned adjacent the first and second mask substrates, respectively. First and second filter caps are configured to secure the first and second filters, respectively, to the facepiece.

Abstract: A shower door sweep includes a bottom sweep element which defines an outer trough and an inner trough. The outer trough is sized to receive the bottom edge of a shower door. A drain channel element is inserted into the inner trough of the bottom sweep element and collects water droplets sliding down along the door wall and routes them to the hinge-end of the shower door even when the door is in the open position.

Abstract: A shower door sweep includes a bottom sweep element which defines an outer trough and an inner trough. The outer trough is sized to receive the bottom edge of a shower door. A drain channel element is inserted into the inner trough of the bottom sweep element and collects water droplets sliding down along the door wall and routes them to the hinge-end of the shower door even when the door is in the open position.

Abstract: A laminated glass header for supporting a shower door. The header may have a taper at one or both of its ends to provide an automatic full open or full close feature of the door.

Abstract: An assembly, method, apparatus, and system for supplying power to security, life safety, information technology, and/or automation system components is disclosed. Some embodiments may include a power supply with a projecting terminal and a cable having a first end with an electrical contact surface for contacting a power transmitting surface of the projecting terminal to establish an electrical connection, so that a first end housing portion surrounds the electrical contact surface and at least partially covers the projecting terminal. The electrical contact surface may be adjustable using a spring, which may also assist in maintaining contact with the power transmitting surface located at the projecting terminal distal end. A cable second end may connect with an electronic device, and may be customizable according to the electronic device. Embodiments may also include keying features for ensuring connection with appropriate power supplies.

Abstract: A laminated glass header for supporting a shower door. The header may have a taper at one or both of its ends to provide an automatic full open or full close feature of the door.

Abstract: The present disclosure describes carriers for carrying products, connected with reusable stickiness (adhesive) to grab and release different surfaces (products) and also includes the gecko conveyor, gecko cylinder, mechanical gecko arm etc. While the gecko's “glue” holds the product on the carrier strongly in one direction, one can easily pull the product out to the other direction and no extra motor or vacuum is needed, hence saving on costs and on extra parts that can be damaged. The shape of the product can be changed and is not necessarily symmetric. The carrier can come as a complete new product or the gecko adhesive can be as an add-on a belt of an existing carrier and includes a carrier that has reusable adhesive made out of carbon nanotubes mimicking a gecko foot. The present invention is directed to all carriers such as in factories, construction, cranes and conveyor belts in airports.

Abstract: A loop memory cell (LMC) includes a minimum of one optical loop coupled by a minimum of one input armlet and on output armlet. The input armlet(s) can couple only in one direction, from the input armlet(s) into the optical loop, and not back. The output armlet(s) can couple or not, according to the refractive index changer, from the optical loop into the output armlet(s). The LMC is configured to collect the input data and store the date in the optical loop until needed. Changing the refractive index the LMC can act as a memory cell or modulator. The LMC overcomes the energy loss of conventional techniques, allowing the creation of variety of building blocks and complex processing blocks for different applications and algorithms. The LMC has increased information storing efficiency, increased data processing speeds, and can modulate data thereby reducing processing complexity and increasing speeds.