Abstract: A stemless prosthetic shoulder joint may include a prosthetic humeral head and a stemless base. The stemless base may include a collar and an anchor extending from the collar intended to anchor the base into the proximal humerus. The anchor may include various features to enhance the fixation of the base, including hooks, threads, and/or expandable members that may be transitioned from a contracted insertion condition to an expanded implanted condition once the base is positioned in the bone. The anchor and/or collar may also include additional features to enhance fixation, such as geometries and surface features to enhance fixation to bone. The anchor may include a plurality of chisel slots to facilitate removal of bone during a revision surgery.

Abstract: The present invention relates to generating hiPSC-derived cardiomyocytes and embryoid bodies that recapitulate the disease phenotype of Long QT Syndrome and their use in developing pharmacological treatments thereof. The present invention also includes the use of a compound which inhibits the ubiquitin-proteasome pathway for the preparation of a medicament for the prophylaxis or treatment of a disease associated with prolonged ventricular repolarization (cardiac arrhythmia) caused by one or more mutations in the amino acid sequence of the hERG potassium channel.

Abstract: The present invention relates to generating hiPSC-derived cardiomyocytes and embryoid bodies that recapitulate the disease phenotype of Long QT Syndrome and their use in developing pharmacological treatments thereof. The present invention also includes the use of a compound which inhibits the ubiquitin-proteasome pathway for the preparation of a medicament for the prophylaxis or treatment of a disease associated with prolonged ventricular repolarization (cardiac arrhythmia) caused by one or more mutations in the amino acid sequence of the hERG potassium channel.

Abstract: In some embodiments, an update server receives an update request for an instance of a client application executed at a computing system. The update request includes context data describing an attribute of the computing system. If an update for the client application modifies a feature of the instance of client application associated with the described attribute, the update server provides the update to the computing system. The update server also receives an update request for an additional instance of the client application executed at another computing system. The additional update request includes context data describing an attribute of the additional computing system. If an available update modifies a feature of the additional instance of the client application that is associated with the described attribute, the update server provides the update to the additional computing system. If not, the update server notifies the additional computing system that no updates are available.

Abstract: The present invention relates to a method for inducing cardiomyocyte differentiation using a combination and/or composition of factors. The present invention also includes the combination and/or composition of factors. For example, the factors comprise at least one p38-MAPK modulator, at least one immunomodulator, at least one Wnt modulator and/or at least one ROS modulator. In particular, the factors comprise at least one p38-MAPK inhibitor, at least one immunosuppressant, at least one Wnt modulator and/or at least one ROS activator.

Abstract: The present invention relates to a method for screening an agent as a drug candidate using induced pluripotent stem cell-derived cardiomyocyte-like cells (iPSC-CMs). The method is useful for screening cardiovascular agents and/or agents affecting cardiac function. The method is also useful for assessing the cardiotoxicity of agents. The present invention also relates to a method for predicting risk of and/or predisposition to cardiac arrhythmia in a subject.

Abstract: The various embodiments of the present disclosure provide a method and system for managing a plurality of advertisements. The method comprises registering a plurality of users for an advertisement management application, retrieving information on one or more advertisements from the user, downloading an application tool from an application server; installing the application tool on a user device, accessing one or more advertisement accounts of at least one user, displaying a plurality of details of the one or more advertisements, providing a plurality of suggestions for the one or more advertisements and performing one or more actions on the advertisements from the plurality of suggestions based on a user requirement.

Abstract: A device receives, from a customer, a request for an end-to-end path through a network, determines parameters of a query based on the request and path criteria, and executes the query on a database of network elements capable of being included in the end-to-end path. The device also selects one or more of the network elements provided in the database based on results of the query, and reserves, in the database, the one or more selected network elements for the end-to-end path.

Abstract: A method of managing a process model history having process models stored therein, includes organizing the process models according to first and second priority criteria, wherein each process model is represented according to a combination of a value in connection with the first and second priority criteria. The representation may be coordinate values in a multi-dimensional space having dimensions corresponding to the first and second priority criteria. A degree of separation or relationship to a common point of reference is calculated for each process model, where the point of reference is a value in connection with the first and second priority criteria. A process model may be removed or selected for deletion based on the degree of separation or proximity in relation to the point of reference, subject to the total number of process models identified for the same control routine, and the total number of process models identified for the same operational region.

Abstract: A process control system integrates the collection and analysis of process control data used to perform certain computationally expensive process control functions, like adaptive model generation and tuning parameter generation, in the same control device in which one or more of the process control routines are implemented, to thereby provide for faster and more efficient support of the process control routines. This system replaces a layered approach using multiple processing devices by integrating an analytical server which performs computationally expensive analyses used by one or more control routines directly into the real-time control device in which the one or more control routines are located. This integration provides the ability to analyze large quantities of data for multiple process loops controlled by a particular device in a fast and efficient manner.

Abstract: A controller includes a control module to control operation of a process in response to control data, a plug-in module coupled to the control module as a non-layered, integrated extension thereof, and a model identification engine. The plug-in detects a change in the control data, and a collects the control data and data in connection with a condition of the process in response to the detected change. The model identification engine executes a plurality of model parameter identification cycles. Each cycle includes simulations of the process each having different simulation parameter values and each using the control data as an input, an estimation error calculation for each simulation based on an output of the simulation and based on the operating condition data, and a calculation of a model parameter value based on the estimation errors and simulation parameter values used in the simulation corresponding to each of the estimation errors.

Abstract: A robust method of creating process models for use in controller generation, such as in MPC controller generation, adds noise to the process data collected and used in the model generation process. In particular, a robust method of creating a parametric process model first collects process outputs based on known test input signals or sequences, adds random noise to the collected process data and then uses a standard or known technique to determine a process model from the collected process data. Unlike existing techniques for noise removal that focus on clean up of non-random noise prior to generating a process model, the addition of random, zero-mean noise to the process data enables, in many cases, the generation of an acceptable parametric process model in situations where no process model parameter convergence was otherwise obtained.

Abstract: A process control system integrates the collection and analysis of process control data used to perform certain computationally expensive process control functions, like adaptive model generation and tuning parameter generation, in the same control device in which one or more of the process control routines are implemented, to thereby provide for faster and more efficient support of the process control routines. This system replaces a layered approach using multiple processing devices by integrating an analytical server which performs computationally expensive analyses used by one or more control routines directly into the real-time control device in which the one or more control routines are located. This integration provides the ability to analyze large quantities of data for multiple process loops controlled by a particular device in a fast and efficient manner.

Abstract: A process control system integrates the collection and analysis of process control data used to perform certain computationally expensive process control functions, like adaptive model generation and tuning parameter generation, in the same control device in which one or more of the process control routines are implemented, to thereby provide for faster and more efficient support of the process control routines. This system replaces a layered approach using multiple processing devices by integrating an analytical server which performs computationally expensive analyses used by one or more control routines directly into the real-time control device in which the one or more control routines are located. This integration provides the ability to analyze large quantities of data for multiple process loops controlled by a particular device in a fast and efficient manner.

Abstract: A user utilizing a user interface inputs debt information, income information and savings information. The user also inputs retirement information which includes aspects of the user's retirement plans (e.g., age of retirement, current age, etc.). The debt information, the income information, and the savings information are assigned to categories. The categories are prioritized to generate a savings plan that includes a savings prioritization (e.g., save into these types of accounts in this order) and/or a spending prioritization (e.g., spend from these types of accounts in this order). The savings plan and/or a quantified benefit of its use is displayed to the user.

Abstract: A device receives, from a customer, a request for an end-to-end path through a network, determines parameters of a query based on the request and path criteria, and executes the query on a database of network elements capable of being included in the end-to-end path. The device also selects one or more of the network elements provided in the database based on results of the query, and reserves, in the database, the one or more selected network elements for the end-to-end path.

Abstract: A controller includes a control module to control operation of a process in response to control data, a plug-in module coupled to the control module as a non-layered, integrated extension thereof, and a model identification engine. The plug-in detects a change in the control data, and a collects the control data and data in connection with a condition of the process in response to the detected change. The model identification engine executes a plurality of model parameter identification cycles. Each cycle includes simulations of the process each having different simulation parameter values and each using the control data as an input, an estimation error calculation for each simulation based on an output of the simulation and based on the operating condition data, and a calculation of a model parameter value based on the estimation errors and simulation parameter values used in the simulation corresponding to each of the estimation errors.

Abstract: A method of managing a process model history having process models stored therein, includes organizing the process models according to first and second priority criteria, wherein each process model is represented according to a combination of a value in connection with the first and second priority criteria. The representation may be coordinate values in a multi-dimensional space having dimensions corresponding to the first and second priority criteria. A degree of separation or relationship to a common point of reference is calculated for each process model, where the point of reference is a value in connection with the first and second priority criteria. A process model may be removed or selected for deletion based on the degree of separation or proximity in relation to the point of reference, subject to the total number of process models identified for the same control routine, and the total number of process models identified for the same operational region.

Abstract: A robust method of creating process models for use in controller generation, such as in MPC controller generation, adds noise to the process data collected and used in the model generation process. In particular, a robust method of creating a parametric process model first collects process outputs based on known test input signals or sequences, adds random noise to the collected process data and then uses a standard or known technique to determine a process model from the collected process data. Unlike existing techniques for noise removal that focus on clean up of non-random noise prior to generating a process model, the addition of random, zero-mean noise to the process data enables, in many cases, the generation of an acceptable parametric process model in situations where no process model parameter convergence was otherwise obtained.

Abstract: A process controller that may be used to control a process having a set of process outputs effected by a set of process control input signals includes a multiple-input/multiple output controller that uses the process outputs to develop the set of process control input signals and a process model, which may be a non-linear process model, that receives the set of process control input signals to produce a prediction signal for one or more of the process outputs. The multiple-input/multiple-output control element includes another process model, which may be a standard linear process model, to develop a prediction vector for each of the process outputs and includes a correction unit that modifies the prediction vector for the one or more of the process outputs using the prediction signal for the one or more of the process outputs to thereby compensate for the non-linearities of the process.