Abstract: Disclosed is a method for classifying monitoring results from an analytical sensor system 20, by allowing 100 a first set of analyte sample solutions to interact with a ligand 3 and acquiring 101 a set of response data, extracting 102 at least one interaction parameter from the response data, and for each analyte sample solution providing 103 a trained machine learning algorithm with the interaction parameter(s). The trained machine learning algorithm classifies 104 each analyte sample solution based on the interaction parameter(s) into at least one quality classification group indicative of the interaction of the analyte sample solution with the ligand 3. The machine learning algorithm is trained 200 using a set of interaction parameters extracted from response data obtained from interactions between a second set of analyte sample solutions with at least one ligand 3, and at least one quality classification group indicative of the interaction of the analyte sample solution with the ligand 3.

Abstract: Disclosed is a method for classifying monitoring results from an analytical sensor system (20), by allowing (100) a first set of analyte sample solutions to interact with a ligand (3) and acquiring (101) a set of response data, extracting (102) at least one interaction parameter from the response data, and for each analyte sample solution providing (103) a trained machine learning algorithm with the interaction parameter(s). The trained machine learning algorithm classifies (104) each analyte sample solution based on the interaction parameter(s) into at least one quality classification group indicative of the interaction of the analyte sample solution with the ligand (3).

Abstract: Disclosed is a method for classifying monitoring results from an analytical sensor system (20), by allowing (100) a first set of analyte sample solutions to interact with a ligand (3) and acquiring (101) a set of response data, extracting (102) at least one interaction parameter from the response data, and for each analyte sample solution providing (103) a trained machine learning algorithm with the interaction parameter(s). The trained machine learning algorithm classifies (104) each analyte sample solution based on the interaction parameter(s) into at least one quality classification group indicative of the interaction of the analyte sample solution with the ligand (3).

Abstract: A remote control for the control of AC power uses the AC power wave of an existing AC power line for the transmitting control signals from a control location to a remote location. A transmitter at the control location provides a predetermined number of control pulses to the AC power line and includes at least one breakdown device and at least one capacitor in series across the AC power line.