Abstract: A method for identifying sensor drift can include: setting an autocorrelation threshold for a sensor in a long-short term memory (LSTM) model developed based on historical process measurements from an analogous sensor to a sensor; collecting measured data from the sensor; applying the LSTM model to the measured data from the sensor, wherein applying the LSTM model comprises: applying the LSTM model to the measured data from the sensor to yield LSTM predicted data; calculating key performance indicators (KPIs) of the LSTM data based on an accumulated slow drift error (ASDE) model, wherein the KPIs comprise an error, an accumulated prediction error, an accumulated slow-drift error, and an estimated autocorrelation; and identifying sensor drift when the estimated autocorrelation violates the autocorrelation threshold.

Abstract: A method for operating a processing/manufacturing facility (e.g., processing/manufacturing facility) may comprise: collecting operational conditions in a processing/manufacturing facility from one or more automated systems and/or sensors; communicating the operational conditions to one or more of a plurality of agents that comprise an adaptive analytical model, wherein the plurality of agents comprises an agent selected from the group consisting of: a software sensor agent, a base control stability agent, an alarm agent, a transient process agent, an advanced control optimization agent, a computer vision agent, a display agent, an integrator/orchestrator agent, and any combination thereof; and deriving data, instructions for changing operational parameters of a process of the processing/manufacturing facility, and/or recommendations for changing operational parameters of the process of the processing/manufacturing facility using the adaptive analytical models of the agents.

Abstract: A method for identifying sensor drift can include: setting an autocorrelation threshold for a sensor in a long-short term memory (LSTM) model developed based on historical process measurements from an analogous sensor to a sensor; collecting measured data from the sensor; applying the LSTM model to the measured data from the sensor, wherein applying the LSTM model comprises: applying the LSTM model to the measured data from the sensor to yield LSTM predicted data; calculating key performance indicators (KPIs) of the LSTM data based on an accumulated slow drift error (ASDE) model, wherein the KPIs comprise an error, an accumulated prediction error, an accumulated slow-drift error, and an estimated autocorrelation; and identifying sensor drift when the estimated autocorrelation violates the autocorrelation threshold.

Abstract: A dynamic quality control method for a chemical manufacturing process to maintain one or more products within quality and/or technical specification(s) can include measuring properties of feed-stock, intermediates, and the products to yield respective quality vectors; assigning a node specification to each of the feed-stocks, the intermediates, and the products, wherein the node specifications for the feed-stocks and the intermediates are dynamic node specifications; calculating conversion tensors to correlate any pairs of node specifications; comparing the quality vectors to the corresponding dynamic node specification(s); identifying a quality deficit and/or a quality surplus associated with each of the quality vectors based on the comparison to the corresponding dynamic node specification(s); and compensating for the quality deficit and/or the quality surplus by (A) adjusting conditions of the manufacturing process and/or (B) compensating the quality deficits with the quality surpluses via material transfe

Abstract: The disclosure describes compstatin analogs and methods of using such analogs for the treatment of complement-related disease and disorder.

Abstract: The disclosure describes compstatin analogs and methods of using such analogs for the treatment of complement-related disease and disorder.