Abstract: A device, computer program, and computer-implemented method for determining a variable of a technical system. An input variable is determined for a first model for determining the variable at a first temporal resolution. A first time series is provided, at the first temporal resolution, including values which characterize an operating variable of the technical system. A second time series is provided. at a second temporal resolution, including values which characterize the operating variable of the technical system, the first and second temporal resolutions being different. The second time series is mapped using a second model for determining a first prediction for the variable of the technical system at the second temporal resolution on the first prediction. Parameters of a second model are determined, using the second time series, which are mapped on parameters of a third model at the first temporal resolution.

Abstract: A method for aggregating a dataset, which respectively assigns an output variable value to a plurality of input variable vectors, into a function term. In the method, one or more elementary function expression(s) from an alphabet is/are sampled using a neural transform network. The elementary function expressions are assembled to form one or more candidate function term(s). When the candidate function term(s) is/are complete, the input variables are mapped to associated candidate output variable values using each candidate function term. A deviation between candidate output variable values and corresponding output variable values of the dataset is evaluated using a predefined metric. It is checked whether a predefined abort condition is satisfied. If the abort condition has not been satisfied, parameters which characterize the behavior of the transformer network are updated and branching back for sampling elementary function expressions using the transformer network takes place.

Abstract: A method for creating a model of a technical system as a function of measured sensor data of the technical system. The method includes the following steps: initializing a symbolic regression problem. A list of mathematical functions is established, including at least one linear and/or non-linear function and/or at least a one-dimensional parameterizable characteristic curve. The at least one-dimensional characteristic curve is implemented by a Smoothed Grid Regression (SGR) model. Solving the symbolic regression problem with the aid of a genetic algorithm.

Abstract: A reagent container for use in an automatic fluidic system, the automatic fluidic system, and a method for transferring reagent to the automatic fluidic system are provided. The reagent container includes a portable reservoir configured to hold a discrete volume of a reagent therein. The reagent container further includes a needleless valve removably secured to the portable reservoir. The needleless valve has a valve housing, a valve portion, and a cap member. The valve housing has first and second interior regions and a first opening disposed between the first and second interior regions. The second interior region fluidly communicates with an interior region of the portable reservoir. The valve portion has a closed operational position for preventing reagent flow through the first opening from the portable reservoir. The valve portion has an open operational position for allowing reagent flow through the first opening from the portable reservoir.

Abstract: Disclosed is an analytical composition comprising a carrier coupled to an analytical transducer nanoparticle and a selector. Disclosed also are methods to make the analytical compositions. The analytical compositions are useful in detection and/or quantification of analytes, such as chemical compounds, biological species, and the like. The selector renders the analytical composition specific to the analyte in question. The detection and/or quantification of the analytical compositions, thus the analytes, occur by virtue of the analytical transducer nanoparticle.

Abstract: A lateral flow device is disclosed. The lateral flow device includes a substrate having a flow path and a detection zone disposed along the flow path. The detection zone includes an immobilized target-binding moiety directed against a target of a Raman-active complex. Also disclosed is a method of conducting a lateral flow assay and detection system. The method includes i) defining a flow path having a detection zone; ii) flowing a sample down the flow path; and iii) immobilizing a Raman-active complex if present, at the detection zone. The sample includes a Raman-active complex or a Raman-active tag.