Abstract: A minimally invasive penetrating microelectrode array is used to generate localized electric field “hotspots” for delivering biomolecules, such as nucleic acid or protein molecules, into cells located in the epidermal or dermal layer of the skin via transient membrane permeabilization. The “hotspots” can be controlled by selectively insulating the penetrating microelectrodes at specific regions. The portion of microelectrodes that are not covered with insulation coating can be coated with nucleic acid or protein vaccine vector, or other biomolecules to be delivered. Upon insertion into the skin, an anchor microelectrode region mechanically anchors the penetrating microelectrode to position the target tissue microelectrode region, so as to selectively align the biomolecule coating with cells located in the tissue location. The biomolecule coating will dissolve when in contact with surrounding tissue. By applying an electrical pulse, the biomolecules can be delivered into surrounding cells.

Abstract: The present invention provides in one aspect inexpensive and scalable methods of fabricating porous membranes comprising vertically aligned carbon nanotubes.

Abstract: Disclosed herein are boron-nitride nanoparticle membranes and methods of manufacturing boron-nitride nanoparticle membranes. In an embodiment, a boron-nitride nanoparticle membrane includes a matrix and a plurality of one-dimensional boron-nitride nanoparticles disposed within the matrix, where he plurality of boron-nitride nanoparticles are configured for selective molecular transport through each of the plurality of one-dimensional boron-nitride nanoparticles.

Abstract: Illustrated is a system and method for modeling and predicting call center service calls to plan staffing needs based upon historical call center volume data and product life cycle data for the product being serviced by the call center. It includes identifying adjusted historical monthly call volume data. Additionally, the system and method includes transforming the adjusted historical monthly call volume data, using a forecasting algorithm, into a long-term trend and season effect forecast. Further, the system and method includes transforming the adjusted historical monthly call volume data, using a regression algorithm, into a long-term product life cycle based forecast. Additionally, the system and method includes adjusting a point prediction, based on the long-term product life cycle based forecast to create a long-term adjusted monthly forecast, the point prediction adjusted where it exceeds a data point in a bound forecast generated from the long-term trend and season effect forecast.

Abstract: A method for presenting data comprises receiving the data; and deriving a multi-level dynamic hierarchical structure for the data based on drilldown sequences input from a user, wherein the drilldown sequences automatically compute a graphical visual comparison of the data and comprise: deriving a multi-pixel bar chart to display an aggregated data paradigm; and deriving a graphical illustration to display a data distribution paradigm.

Abstract: A novel and non-obvious method, system and apparatus for determining respiratory volume flow rate of a subject and associated parameters such as tidal volume, minute volume, and respiratory rate. The method for determining respiratory volume flow rate of a subject can include selecting an airway cavity of the subject, measuring delivery volume flow rate of respiratory gas delivered to the airway cavity, measuring pressure within the airway cavity and calculating a respiratory volume flow rate of the subject using the measured delivery volume flow rate of respiratory gas delivered to the airway cavity and the measured pressure within the airway cavity. The method further can include generating a warning signal selected from the group consisting of an indicator that a respiratory volume flow value is outside of an expected value for the subject and an indicator that an airway cavity measurement value does not conform to an expected value.

Abstract: A method and system is used to construct a forecast error confidence interval. The predication interval provides a range of error for a current forecast value to any desired confidence level. The method and system involve running a forecast method on a set of historical data. For each historical period, a forecast is obtained at each time point in the period. The forecasts are compared to the target value of interest in each period. The comparison of target values to forecast values is used to build an error series for each historical period. The error values within each error series are pooled to form an error distribution series. The error distribution series can be used to provide a confidence interval for the current forecast.

Abstract: Disclosed herein are systems and methods for profile-based forecasting with dynamic profile selection. Some method embodiments may comprise determining a reference set of profiles from a source set of profiles, and using the reference set of profiles to generate a forecast. The reference set determination comprises at least comparing a current, partial profile to each profile in the source set to obtain a similarity measurement for each profile in the source set.

Abstract: Disclosed herein are systems and methods for forecasting with model-based PDF (probability density function) estimates. Some method embodiments may comprise: estimating model parameters for a time series, calculating a PDF for the time series, and generating a forecast from the PDF. The model parameters may comprise a variance for a hidden noise source, and the PDF for the time series may be based at least in part on an estimated variance for the hidden noise source.

Abstract: Systems and methods are disclosed for identifying recurrent patterns. In one embodiment, a method comprises: estimating a power spectral density of a time series; determining a duration-level associated with a peak in the power spectral density; and aggregating the time series at the duration-level to obtain a recurrent pattern.

Abstract: Systems and methods are disclosed for cumulative attribute forecasting using a probability density function of a current-to-future value ratio. Some method embodiments may comprise determining an unconditional probability density function for a ratio of a cumulative attribute's current value to an associated future value for the cumulative attribute, and determining an unconditional PDF for a future value of the cumulative attribute. The unconditional probability functions are combined with a current value of the cumulative attribute in a manner that yields a conditional probability density function for the future value. The conditional probability density function may then be used to determine forecasts such as a most likely future value, a mean value, a median value, a confidence interval for the future value, and a probability of achieving a future value within some specified range.

Abstract: A method for presenting data comprises receiving the data; and deriving a multi-level dynamic hierarchical structure for the data based on drilldown sequences input from a user, wherein the drilldown sequences automatically compute a graphical visual comparison of the data and comprise: deriving a multi-pixel bar chart to display an aggregated data paradigm; and deriving a graphical illustration to display a data distribution paradigm.

Abstract: Systems and methods for detecting an interesting event in a data stream. More specifically, a monitoring system is configured to monitor a data stream and establish a sensitivity parameter based on sequences generated from values in a first portion of the data stream. A detector may be trained using the sensitivity parameter to detect the occurrence of an interesting event in the data stream.