Abstract: A method, computer program product, and system for identifying a surface area size of a biosensing structure, for use in a biosensor device, based on a plurality of nucleotides structures under test. A first set of properties are determined comprising: reaction coordinate values, and potential of mean force (PMF) values, for the plurality of nucleotide structures based on a first set of testing conditions comprising a first surface area material, a first surface area pattern, and a first surface area size. A second set of properties is determined comprising reaction coordinate values, and PMF values, for the plurality of nucleotide structures based on a second set of testing conditions comprising a second surface area material, a second surface area pattern, a second surface area size, or a combination thereof and a target population of nucleotide structures among the plurality of nucleotide structures are identified.

Abstract: Sensory input data signal is communicated to an AI platform and translated into a corresponding scenario. The translation is directed at an associated force and application of the force to a selected or identified object and environment. Real-time analysis of the force is applied, which includes modeling an expected behavior. An assessment response is received and compared to a corpus. A solution in the corpus proximal to the assessment response is identified, and a reaction of proximity of the response to the identified solution is created. Proximity data is converted to a sensory output signal. Receipt of the sensory output signal by a corresponding sensory output device creates a physical manifestation of generated feedback of the reaction data to a sensory medium. Embodiments are directed at both learning and assessment, and leverage a corpus with the AI platform in support of an interactive learning experience.

Abstract: Embodiments relate to a system, program product, and method for use with an intelligent (AI) computer platform to explore real-world modeling. A sensory input data signal is communicated to the AI platform and translated into a corresponding scenario. The translation is directed at an associated force and application of the force to a selected or identified object and environment. A reaction to the application is created, and reaction data is converted to a sensory output signal. Receipt of the sensory output signal by a corresponding sensory output device creates a physical manifestation of generated feedback of the reaction data to a sensory medium. The embodiments are directed at both learning and assessment, and leverage a corpus with the AI platform in support of an interactive learning experience.

Abstract: A method and system of removing artifacts in electroencephalogram (EEG) signals is provided. Digital EEG data, based on a digitizing of analog EEG signals of a patient from a first electrode, is received. Digital sweat sensor data, based on a digitizing of analog sweat sensor signals of the patient that are contemporaneous with the analog EEG signals, is received. A transform is applied to the digital sweat sensor data based on a predetermined sweat stress profile of the patient. The digital EEG data is adaptively adjusted by subtracting the transformed sweat sensor data from the digital EEG data.

Abstract: Techniques that facilitate monitoring learning performance using neurofeedback are described. In one embodiment, a system is provided that comprises a memory that stores computer-executable components and a processor that executes computer-executable components stored in the memory. In one implementation, the computer-executable components comprise a feedback component that receives first feedback information regarding mental function of a user in association with participation in a learning experience, wherein the first feedback information is captured via a NIRS spectroscopy sensor worn by the user.

Abstract: A method, a system and devices for dispensing a reward are provided. A reward dispensing machine (RDM) may include one or more reward reservoirs each storing a respective kind of pre-customized reward items or material. The RDM may include a customization unit, as well as a communication unit configured to receive a reward request associated with a recipient and comprising a description of an intended reward. The RDM may include a processor and memory storing a customization script that causes the processor to control the customization unit to produce the intended reward by receiving one of the pre-customized reward items or material from one of the one or more reward reservoirs and customizing the one of the pre-customized reward items or material into the intended reward based on the description of the intended reward. The RDM may include a dispensing unit configured to dispense the intended reward to the recipient.

Abstract: The present invention relates generally to the field of microelectronics, and more particularly to a structure and method of forming a biosensor having a nucleotide attracting surface tailored to reduce false detection of nucleotides and enabling optical detection of nucleotides. The biosensor may include an analyte-affinity layer on an upper surface of a dielectric layer. The analyte-affinity layer may include a plurality of cylindrical gold portions with dimensions tailored for a target analyte. A distance between adjacent portions of the plurality of portions may range from approximately 50% of a length of a target analyte to approximately 300% of a length of a target analyte. The plurality of portions of the analyte-affinity layer have an upper surface with a diameter ranging from approximately 3 nm to approximately 20 nm.

Abstract: The present invention relates generally to the field of microelectronics, and more particularly to a structure and method of forming a biosensor having a nucleotide attracting surface formed to reduce false detection of nucleotides and enabling electrical detection of nucleotides. The biosensor may include an analyte-affinity layer on an upper surface of a substrate. A conductive layer may extend a length of the substrate below and in contact with the analyte-affinity layer. The conductive layer may be electrically connected to one or more transistors. The analyte-affinity layer may have dimensions tailored for a target analyte. A distance between a first analyte-affinity layer and a second analyte-affinity layer may range from approximately 50% of a length of a target analyte to approximately 300% of a length of a target analyte. The analyte-affinity layer may have an upper surface with a diameter ranging from approximately 3 nm to approximately 20 nm.

Abstract: A method, computer program product, and system for identifying a surface area size of a biosensing structure, for use in a biosensor device, based on a plurality of nucleotides structures under test. A first set of properties are determined comprising: reaction coordinate values, and potential of mean force (PMF) values, for the plurality of nucleotide structures based on a first set of testing conditions comprising a first surface area material, a first surface area pattern, and a first surface area size. A second set of properties is determined comprising reaction coordinate values, and PMF values, for the plurality of nucleotide structures based on a second set of testing conditions comprising a second surface area material, a second surface area pattern, a second surface area size, or a combination thereof and a target population of nucleotide structures among the plurality of nucleotide structures are identified.