Abstract: The present disclosure relates to a method for determining at least one process variable of a medium. The method includes steps of recording a first value for the process variable by means of a first method for determining the process variable and recording a second value for the process variable by means of a second method for determining the process variable. The method also includes steps of selecting at least one of the detected values for the process variable by means of a classifier and outputting the selected value for the process variable. The present disclosure further relates to a computer program designed for executing a method according to the present disclosure, and to a computer program product having a computer program according to the present disclosure.

Abstract: A method for automated in-line detection of deviations of an actual state of a fluid from a reference state is disclosed wherein measured values captured at the same time are evaluated in a combined manner with respect to at least three measurement variables that are different measurement quantities of the fluid and/or a measurement quantity of the fluid measured at different measuring points. The method includes creating a reference data set, wherein reference measured values are mapped to a reference vector of a vector space using a neural network; in-line measurement, wherein measured values at all times are mapped to a measurement vector using the neural network; comparing the measurement vector with the reference vectors using a kernel density estimator of a predefinable window width; and creating an assessment with respect to a deviation of the actual state from the reference state on the basis of the kernel density estimator.

Abstract: The present disclosure relates to a method for determining at least one process variable of a medium, including steps of recording a sensor signal from a field device and determining a selected model from a set of at least two different models by means of a classifier. Each of the models is used for determining the process variable based at least on the sensor signal. The classifier is designed to select the selected model. The method also includes a step of determining the process variable based at least on the selected model and the sensor signal.

Abstract: The present disclosure relates to a method for determining at least one process variable of a medium. The method includes steps of recording a first value for the process variable by means of a first method for determining the process variable and recording a second value for the process variable by means of a second method for determining the process variable. The method also includes steps of selecting at least one of the detected values for the process variable by means of a classifier and outputting the selected value for the process variable. The present disclosure further relates to a computer program designed for executing a method according to the present disclosure, and to a computer program product having a computer program according to the present disclosure.

Abstract: A method for automated in-line detection of deviations of an actual state of a fluid from a reference state is disclosed wherein measured values captured at the same time are evaluated in a combined manner with respect to at least three measurement variables that are different measurement quantities of the fluid and/or a measurement quantity of the fluid measured at different measuring points. The method includes creating a reference data set, wherein reference measured values are mapped to a reference vector of a vector space using a neural network; in-line measurement, wherein measured values at all times are mapped to a measurement vector using the neural network; comparing the measurement vector with the reference vectors using a kernel density estimator of a predefinable window width; and creating an assessment with respect to a deviation of the actual state from the reference state on the basis of the kernel density estimator.

Abstract: Systems and methods for improving search results using social routing are provided. In example embodiments, an affinity metric and match metric are determined for a plurality of users. The affinity metric indicates an interaction level between a given user and the match metric indicates how well the given user matches parameters of a search string. Based on a correlation between the affinity metric and the match metric, one or more items of content may be transmitted to one or more selected users. Improved search results can then be generated based on interactions with the one or more items of content.

Abstract: Systems and methods for improving search results using social routing are provided. In example embodiments, an affinity metric and match metric are determined for a plurality of users. The affinity metric indicates an interaction level between a given user and the match metric indicates how well the given user matches parameters of a search string. Based on a correlation between the affinity metric and the match metric, one or more items of content may be transmitted to one or more selected users. Improved search results can then be generated based on interactions with the one or more items of content.

Abstract: Therapeutic compositions for treatment of protein serine/threonine phosphatase-related diseases are obtained by engineering amino acid sequences that disrupt interaction between the protein serine/threonine phosphatase and a protein inhibitor and are provided herein. Calcineurin and PPI are examples of protein serine/threonine phosphatases. RCAN1 is an inhibitor of calcineurin and is overexpressed in patients with serious diseases, such as Down syndrome and Alzheimer's disease. Molecules that bind RCAN1 at regions that interact with calcineurin selectively modulate functions of calcineurin to treat these diseases. Methods of treating a subject for a protein serine/threonine phosphatase-related disease by administering a molecule having an amino acid sequence selected from the group of SEQ ID NOs: 1-19 are further provided.

Abstract: Cancer, obesity, and diabetes are examples of PTP1B-related diseases. The invention herein provides embodiments of therapeutic methods and compositions for treating PTP1B related diseases by engineering peptides that bind to the functional spine of PTP1B to alter the conformation of the protein and inhibit a function of PTP1B. Molecules bind to amino acid residues of the functional spine of PTP1B, regulate the catalytic cycle, and treat PTP1B-related diseases. For example, molecules bind to at least one of Tyr152, Tyr153, His175, Thr178, Pro185, Phe191, Leu192, Asn193, Cys215, Gly218, Phe280, Val287, Trp291, Lys292, Leu294, Ser295, and Glu297 to reduce at least one symptom of a PTP1B related disease.

Abstract: Therapeutic compositions for treatment of protein serine/threonine phosphatase-related diseases are obtained by engineering amino acid sequences that disrupt interaction between the protein serine/threonine phosphatase and a protein inhibitor and are provided herein. Calcineurin and PPI are examples of protein serine/threonine phosphatases. RCAN1 is an inhibitor of calcineurin and is overexpressed in patients with serious diseases, such as Down syndrome and Alzheimer's disease. Molecules that bind RCAN1 at regions that interact with calcineurin selectively modulate functions of calcineurin to treat these diseases. Methods of treating a subject for a protein serine/threonine phosphatase-related disease by administering a molecule having an amino acid sequence selected from the group of SEQ ID NOs: 1-19 are further provided.

Abstract: A turf reinforcement fabric and a method for producing such a fabric by weaving a plurality of filaments in a predetermined pattern to form a three-dimensional structure formed to have a loft thickness without requiring the application of heat to heat shrink the fibers, and/or formed with one or more fibers of increased thickness at peaks and valleys of the woven structure, and/or incorporating flame retardant and/or UV stabilizing fiber additives.

Abstract: A turf reinforcement fabric and a method for producing such a fabric by weaving a plurality of filaments in a predetermined pattern to form a three-dimensional structure formed to have a loft thickness without requiring the application of heat to heat shrink the fibers, and/or formed with one or more fibers of increased thickness at peaks and valleys of the woven structure, and/or incorporating flame retardant and/or UV stabilizing fiber additives.

Abstract: A turf reinforcement fabric and a method for producing such a fabric by weaving a plurality of filaments in a predetermined pattern to form a three-dimensional structure formed to have a loft thickness without the application of heat to heat shrink the fibers, and/or formed with one or more fibers of increased thickness at peaks and valleys of the woven structure, and/or incorporating flame retardant and/or UV stabilizing fiber additives.

Abstract: A turf reinforcement fabric and a method for producing such a fabric by weaving a plurality of filaments in a predetermined pattern to form a three-dimensional structure formed to have a loft thickness without the application of heat to heat shrink the fibers.

Abstract: A turf reinforcement fabric and a method for producing such a fabric by weaving a plurality of filaments in a predetermined pattern to form a three-dimensional structure formed to have a loft thickness without the application of heat to heat shrink the fibers.