Abstract: The present disclosure relates to methods for biosensing using one-dimensional conducting polymer systems. The present disclosure also relates to one-dimensional sensors having substrates coated with conducting polymers such as polyaniline. The geometry of the substrates is configured to maximize the geometrical probability of detecting pathogens, aerosols, etc.

Abstract: Systems for performing an automated, in situ calibration of one or more sensors of internet of things (IoT) systems include one or more emulators capable of generating calibration set points that are applied to the sensors during the calibration process. The systems also include one or more computing devices configured to store the data necessary for the calibrations. The computing devices are further configured to monitor the sensor outputs during normal operation of the IoT systems to check for a loss of calibration or compromised data integrity; execute an automated calibration of a upon the detection of a loss of calibration or data-integrity issue; and validate the calibration results.

Abstract: Systems are provided for measuring the conductivity of a liquid medium or a gel-like solid or semi-solid medium in which airborne viruses have been forced to fall; and monitoring the resulting changes in the capacitive and resistive properties of the medium due to colloidal electrochemistry of the viral particles using a range of frequency detection techniques. Various control parameters can be implemented to facilitate the estimation of very low viral concentration levels in the medium. The control parameters can include, for example, the level of the medium, the temperature of the medium, statistical classification parameters, and the addition of a buffer to the medium to amplify the signal.

Abstract: Biosensors are provided for the detection of pathogens such as viruses. The sensors can includes a substrate, and a film disposed on the substrate. The film can include an electrically-conducting polymer, and an aromatic dialdehyde such as terephthaldehyde. The sensors experience a fast and repeatable decrease in electrical conductivity in the presence of certain pathogens, including the SARS-Cov-2 pseudo virus.

Abstract: Systems for determining the moisture level in non-polar materials, such as polymers, include an electrical circuit including a capacitive sensor, and a signal generator that the provides the electrical circuit with an electrical input of varying frequency. The systems also include a computing device the determines the moisture level in the non-polar material based on a relationship between the moisture level, and a response of the electrical circuit to the electrical input while the capacitive sensor is in contact with the non-polar material.

Abstract: Systems and methods are provided for reliably detecting aerosolized virus particles using electrochemical characteristics of the virus, and its interaction with ?-conjugated conducting solid-state substrates.

Abstract: Systems and methods are provided for detecting potentially fatal, and non-fatal diseases in an non-invasive, low-cost, and reliable manner by detecting the trace presence of volatile organic compounds (VOCs) in the human breath. The systems and methods can be home-based, non-invasive systems and methods for diagnosing CLD (chronic liver disease), CKD (chronic kidney disease), and other diseases using lifestyle-based, repetitive detection of VOCs in the human breath and an adaptive machine learning algorithm.

Abstract: Systems for detecting aerosolized viral loads in an airspace include a first and, optionally, a second volume of a liquid. The first volume includes particles sampled from the airspace, and the second volume is free of virus particles. A first conductivity probe is immersed in the first liquid, and a second conductivity probe is immersed in the second liquid. An alternating voltage is applied to each of the first and second conductivity probes. The difference between the resulting alternating currents through the first and second conductivity probes is determined, and is used to estimate the viral load in the first volume, and in the airspace.

Abstract: A system for rule management, predictive maintenance and quality assurance of a process using automatic rule formation comprising a plurality of sensors capable of being attached to at least one machine for measuring at least one information about the process and machine operation. The system comprises a server connected to the sensors over a wireless communication network and running a reconfigurable rule management program for identifying and processing the particular process and machine information related to at least one process received from the plurality of sensors. A controller in communication with the server capable of controlling the process based on a rule set by the rule engine. The rule engine automatically detects the normal process data, classifies the received data based on the dynamic rule formed by the rule engine and finds anomalies in the process or machine operation for predictive maintenance and process quality assurance.

Abstract: A field learning system comprising a system of feedback using a user interface in a web based and mobile application to overcome the difficulty and infeasibility of supervised machine learning systems used for modeling failure states of machines.

Abstract: Air sensing and purification systems include interconnected components that can both sense and cleanse an airborne viral load quickly and efficiently, minimizing the probability of human infection in the presence of a virus such as COVID-19. The systems can include a biosensing or viral load sensing system configured to measure an airborne viral load and probability of infection from the viral load; and an air purification device configured to effectively neutralize the virus in the adjacent space. The system also can include an alarm hub. The alarm hub can be a local server/hub that is wirelessly connected to the viral load sensing system and the air purification device; and can house strong visual and/or audible alarms to make the user aware of any breach of the social distancing protocol, and/or a high airborne viral load.

Abstract: A machine learning method and system for predictive maintenance of a dryer. The method includes obtaining over a communication network, an information associated with the dryer and receiving measurements of a vibration level of one of a process blower, a cassette motor and a regeneration blower associated with the dryer. Further, an anomaly is determined based on at least one of a back pressure and a fault and balance of at least one of the process blower and the regeneration blower is tracked. An alarm for maintenance is raised when one of an anomaly and an off-balance is detected.

Abstract: A machine learning method and system for predictive maintenance of a dryer. The method includes obtaining over a communication network, an information associated with the dryer and receiving measurements of a vibration level of one of a process blower, a cassette motor and a regeneration blower associated with the dryer. Further, an anomaly is determined based on at least one of a back pressure and a fault and balance of at least one of the process blower and the regeneration blower is tracked. An alarm for maintenance is raised when one of an anomaly and an off-balance is detected.

Abstract: The present disclosure is directed to a carbogenic nanoparticle polymer inks including a conducting polymer, such as those made of CQD-PPy and/or R-GO-PPy, methods of making the inks, and moisture and VOC sensors made therefrom.

Abstract: A method and system of detecting faults in rotor driven equipment includes generating data from one or more vibration sensors communicatively coupled to the rotor driven equipment. The data from the one or more machine wearable sensors is collected onto a mobile data collector. The data is sampled at random to estimate a maximum value. Further, a sampling error may be controlled under a predefined value. The data may be analyzed through a combination of Cartesian to Spherical transformation, statistics of the entity extraction (such as variance of azimuthal angle), big data analytics engine and a machine learning engine. A fault is displayed on a user interface associated with the rotor driven equipment.

Abstract: Systems and processes are provided for managing epidemic and pandemic disease populations by individually scanning persons in a pre-selected retention space, to identify persons having body temperatures in excess of a predetermined level. Such persons are prompted to register, and are instructed to self-quarantine at the address provided during the registration process. The location of the registered person subsequently is tracked to ensure that the person remains at the registered location during the quarantine period. The registration information can be provided to health and law enforcement authorities to help control the spread of the disease population.

Abstract: A method and system of a predictive maintenance IoT system comprises receiving a plurality of sensor data over a communications network and determining one or more clusters from the sensor data based on a pre-determined rule set. Further, the sensor data is classified through a machine learning engine and the sensor data is further base-lined through a combination of database architecture, data training architecture, and a base-lining algorithm. Intensity or degree of fault state is mapped to a fuel gauge to be depicted on a user interface and a predictive maintenance state is predicted through a regression model and appropriate alarm is raised for user action.

Abstract: Systems for performing an automated, in situ calibration of one or more sensors of internet of things (IoT) systems include one or more emulators capable of generating calibration set points that are applied to the sensors during the calibration process. The systems also include one or more computing devices configured to store the data necessary for the calibrations. The computing devices are further configured to monitor the sensor outputs during normal operation of the IoT systems to check for a loss of calibration or compromised data integrity; execute an automated calibration of a upon the detection of a loss of calibration or data-integrity issue; and validate the calibration results.

Abstract: A method and system of a machine learning architecture for predictive and preventive maintenance of vacuum pumps. The method includes receiving one of a motor sensor data and a blower sensor data over a communications network. The motor sensor data is classified into one of a vacuum state sensor data and break state sensor data. The vacuum state sensor data is analyzed to detect an operating vacuum level and an alarm is raised when the vacuum state sensor data exceeds a pre-defined safety range. Vacuum break data is classified into one of a clean filter category and clogged filter category and an alarm is raised if an entry under the clogged filter category is detected. The blower sensor data in association with the motor sensor data is analyzed based on machine learning to detect one of a deficient oil level and a deficient oil structure.

Abstract: A method for accurately measuring the real-time valid dew-point value of a material and determining the total moisture content of the material by using an algorithm during the material drying process. The algorithm estimates the valid dew-point value of the material and the total moisture content of the material by analyzing sensor data received on a server. The algorithm determines a valid dew-point value by estimating an inflection point of the moisture content versus time friction/curve for the material, and the total moisture content of the material is determined within the valid dew-point value.