Abstract: A method for predicting outcome of a process used for manufacturing a sample in a bioreactor, the process belonging to a category. The method comprises selecting a process model based on the category; accessing historic data related to past process runs for manufacturing the sample; accessing current data obtained from a current process run of the process. The obtained current data, which is based on the selected process model, comprises: process strategy data, bioreactor instrument data, data from online sensors and/or data from offline sensors. The method further comprises predicting an outcome of at least one selected parameter of the current process run for manufacturing the sample based on the accessed historic data and current data.

Abstract: The present invention relates to a method for predicting outcome of a process used for manufacturing a sample in a bioreactor, the process belonging to a category. The method comprises selecting (51) a process model based on the category; accessing (53) historic data related to past process runs for manufacturing the sample; accessing (53) current data obtained (54) from a current process run of the process. The obtained current data, which is based on the selected process model, comprises: process strategy data, bioreactor instrument data, data from online sensors and/or data from offline sensors. The method further comprises predicting (62) an outcome of at least one selected parameter of the current process run for manufacturing the sample based on the accessed historic data and current data. The present invention also relates to a method for modelling a process and a control system (10) for controlling a process.

Abstract: The present discussion relates to generating power generation forecasts both on-site and remote to a wind farm, or other intermittent power generation asset, so as to increase the reliability of providing a forecast to interested parties, such as regulatory authorities. Forecasts may be separately generated at both the on-site and remote locations and, if both are available, one is selected for transmission to interested parties, such as regulatory authorities. If, due to circumstances, one forecast is unavailable, the other forecast may be used in its place locally and remotely, communications permitting.

Abstract: The present invention relates to a method for predicting outcome of a process used for manufacturing a sample in a bioreactor, the process belonging to a category. The method comprises selecting (51) a process model based on the category; accessing (53) historic data related to past process runs for manufacturing the sample; accessing (53) current data obtained (54) from a current process run of the process. The obtained current data, which is based on the selected process model, comprises: process strategy data, bioreactor instrument data, data from online sensors and/or data from offline sensors. The method further comprises predicting (62) an outcome of at least one selected parameter of the current process run for manufacturing the sample based on the accessed historic data and current data. The present invention also relates to a method for modelling a process and a control system (10) for controlling a process.

Abstract: A system and method including receiving vibration spectrum data from a plurality of different assets; determining, based on a shape of the vibration spectrum data for each of the plurality of assets, clusters for the plurality of assets, assets being grouped in a same cluster having vibration spectrum data of a similar spectral shape; determining for each of the clusters, based on an application of domain derived pattern recognition rules for the vibration spectrum data, one of a plurality of fault classifications; generating an output including an association of each of the plurality of assets with the fault classification of the cluster in which the particular asset is grouped; and saving a record of the output.

Abstract: A wind power generation system includes one or both of a memory or storage device storing one or more processor-executable executable routines, and one or more processors configured to execute the one or more executable routines which, when executed, cause acts to be performed. The acts include receiving weather data, wind turbine system data, or a combination thereof; transforming the weather data, the wind turbine system data, or the combination thereof, into a data subset, wherein the data subset comprises a first time period data; selecting one or more wind power system models from a plurality of models; transforming the one or more wind power system models into one or more trained models at least partially based on the data subset; and executing the one or more trained models to derive a forecast, wherein the forecast comprises a predicted electrical power production for the wind power system.

Abstract: In various example embodiments, a method and system for assessment of wind-induced train blow-over risk are presented.

Abstract: The present discussion relates to generating power generation forecasts both on-site and remote to a wind farm, or other intermittent power generation asset, so as to increase the reliability of providing a forecast to interested parties, such as regulatory authorities. Forecasts may be separately generated at both the on-site and remote locations and, if both are available, one is selected for transmission to interested parties, such as regulatory authorities. If, due to circumstances, one forecast is unavailable, the other forecast may be used in its place locally and remotely, communications permitting.

Abstract: A wind power generation system includes one or both of a memory or storage device storing one or more processor-executable executable routines, and one or more processors configured to execute the one or more executable routines which, when executed, cause acts to be performed. The acts include receiving weather data, wind turbine system data, or a combination thereof; transforming the weather data, the wind turbine system data, or the combination thereof, into a data subset, wherein the data subset comprises a first time period data; selecting one or more wind power system models from a plurality of models; transforming the one or more wind power system models into one or more trained models at least partially based on the data subset; and executing the one or more trained models to derive a forecast, wherein the forecast comprises a predicted electrical power production for the wind power system.

Abstract: Embodiments allow data cleaning of industrial data gathered from at least one sensor. The data cleaning utilizes a workflow that defines at least one cleaning step to be performed. Each cleaning step comprises detecting defects based on at least one constraint such as various models and/or statistics. Potential defects are presented to a user for feedback. The data is cleaned based on the feedback. Multiple copies of the data are stored to track all the various cleaning choices. All choices can be rolled back at will so that cleaning decisions made can be eliminated and different choices applied. Intermediate data is captured to allow reporting and auditing of the cleaning process.

Abstract: A method for classifying a tissue sample of a biopsy specimen into one of a plurality of classes is presented. The method includes receiving a light from at least one location of the tissue sample including a plurality of chromophores, wherein the received light comprises at least one of an attenuated illumination light and a re-emitted light. The method further includes processing a spectrum of the received light to determine a feature for each of the chromophores in the at least one location of the tissue sample. In addition, the method includes estimating a Z-score for each of the chromophores based on the determined feature. Also, the method includes classifying the tissue sample into one of the plurality of classes based on the estimated Z-score for each of the chromophores.

Abstract: A method for classifying a tissue sample of a biopsy specimen into one of a plurality of classes is presented. The method includes receiving a light from at least one location of the tissue sample including a plurality of chromophores, wherein the received light comprises at least one of an attenuated illumination light and a re-emitted light. The method further includes processing a spectrum of the received light to determine a feature for each of the chromophores in the at least one location of the tissue sample. In addition, the method includes estimating a Z-score for each of the chromophores based on the determined feature. Also, the method includes classifying the tissue sample into one of the plurality of classes based on the estimated Z-score for each of the chromophores.

Abstract: Disclosed is a method for making flexible circuits in which portions of a tie layer are removed by etching the underlying polymer. Also disclosed are flexible circuits made by this method.