Abstract: The present disclosure provides a multi-queue multi-cluster task scheduling method and system. The method includes: constructing a training data set; training and optimizing a plurality of parallel deep neural networks (DNN) by using the training data set to obtain a plurality of trained and optimized parallel DNNs; setting a reward function, where the reward function minimizes the sum of a task delay and energy consumption by adjusting a reward value proportion of the task delay and a reward value proportion of the energy consumption; inputting a to-be-scheduled state space into the plurality of trained and optimized parallel DNNs to obtain a plurality of to-be-scheduled action decisions; determining an optimal action decision among the plurality of to-be-scheduled action decisions based on the reward function for output; and scheduling the plurality of task attribute groups to a plurality of clusters based on the optimal action decision.

Abstract: A method of producing an artificial neural network capable of predicting the survivability of a patient, including: storing in an electronic database patient health data comprising a plurality of sets of data, each set having at least one of a first parameter relating to heart rate variability data and a second parameter relating to vital sign data, each set further having a third parameter relating to patient survivability; providing a network of nodes interconnected to form an artificial neural network, the nodes comprising a plurality of artificial neurons, each artificial neuron having at least one input with an associated weight; and training the artificial neural network using the patient health data such that the associated weight of the at least one input of each artificial neuron is adjusted in response to respective first, second and third parameters of different sets of data from the patient health data.

Abstract: Loading resources is disclosed including sending, using a first thread, a resource loading request to a second thread, the resource loading request including a request for a resource, the first thread and the second thread being located in one process; and the first thread running on a dynamic language runtime platform, receiving, using the first thread, an instruction sent back by the second thread in response to the resource loading request, and based on the instruction and the resource preloaded by the process, loading, using the first thread, the resource included in the resource loading request, the resource being preloaded by the process comprises a web engine.

Abstract: A method of producing an artificial neural network capable of predicting the survivability of a patient, including: storing in an electronic database patient health data comprising a plurality of sets of data, each set having at least one of a first parameter relating to heart rate variability data and a second parameter relating to vital sign data, each set further having a third parameter relating to patient survivability; providing a network of nodes interconnected to form an artificial neural network, the nodes comprising a plurality of artificial neurons, each artificial neuron having at least one input with an associated weight; and training the artificial neural network using the patient health data such that the associated weight of the at least one input of each artificial neuron is adjusted in response to respective first, second and third parameters of different sets of data from the patient health data.

Abstract: A system for health condition monitoring includes a wearable device, a portable device and a server. The portable device is capable of communicating between the wearable device and the server. The system further includes a non-contact ECG acquisition module for capturing ECG signals from a user wearing the wearable device, a non-contact audio acquisition module for capturing a respiratory sound signal and a heart sound signal from the user wearing the wearable device, a first signal processing and analysis module for receiving and processing the ECG signals, the respiratory sound signal and the heart sound signal to perform QRS detection, HR calculation and ECG derived RR determination, and a second signal processing and analysis module for receiving and processing the ECG signals, the respiratory sound signal and the heart sound signal to perform heart sound localization, heart sound cancellation, respiratory sound restoration, and sound based RR determination.

Abstract: A method of producing an artificial neural network capable of predicting the survivability of a patient, including: storing in an electronic database patient health data comprising a plurality of sets of data, each set having at least one of a first parameter relating to heart rate variability data and a second parameter relating to vital sign data, each set further having a third parameter relating to patient survivability; providing a network of nodes interconnected to form an artificial neural network, the nodes comprising a plurality of artificial neurons, each artificial neuron having at least one input with an associated weight; and training the artificial neural network using the patient health data such that the associated weight of the at least one input of each artificial neuron is adjusted in response to respective first, second and third parameters of different sets of data from the patient health data.

Abstract: Loading resources is disclosed including sending, using a first thread, a resource loading request to a second thread, the resource loading request including a request for a resource, the first thread and the second thread being located in one process; and the first thread running on a dynamic language runtime platform, receiving, using the first thread, an instruction sent back by the second thread in response to the resource loading request, and based on the instruction and the resource preloaded by the process, loading, using the first thread, the resource included in the resource loading request, the resource being preloaded by the process comprises a web engine.

Abstract: A method of producing an artificial neural network capable of predicting the survivability of a patient, including: storing in an electronic database patient health data comprising a plurality of sets of data, each set having at least one of a first parameter relating to heart rate variability data and a second parameter relating to vital sign data, each set further having a third parameter relating to patient survivability; providing a network of nodes interconnected to form an artificial neural network, the nodes comprising a plurality of artificial neurons, each artificial neuron having at least one input with an associated weight; and training the artificial neural network using the patient health data such that the associated weight of the at least one input of each artificial neuron is adjusted in response to respective first, second and third parameters of different sets of data from the patient health data.

Abstract: A system for health condition monitoring includes a wearable device, a portable device and a server. The portable device is capable of communicating between the wearable device and the server. The system further includes a non-contact ECG acquisition module for capturing ECG signals from a user wearing the wearable device, a non-contact audio acquisition module for capturing a respiratory sound signal and a heart sound signal from the user wearing the wearable device, a first signal processing and analysis module for receiving and processing the ECG signals, the respiratory sound signal and the heart sound signal to perform QRS detection, HR calculation and ECG derived RR determination, and a second signal processing and analysis module for receiving and processing the ECG signals, the respiratory sound signal and the heart sound signal to perform heart sound localization, heart sound cancellation, respiratory sound restoration, and sound based RR determination.

Abstract: A method of producing an artificial neural network capable of predicting the survivability of a patient, including: storing in an electronic database patient health data comprising a plurality of sets of data, each set having at least one of a first parameter relating to heart rate variability data and a second parameter relating to vital sign data, each set further having a third parameter relating to patient survivability; providing a network of nodes interconnected to form an artificial neural network, the nodes comprising a plurality of artificial neurons, each artificial neuron having at least one input with an associated weight; and training the artificial neural network using the patient health data such that the associated weight of the at least one input of each artificial neuron is adjusted in response to respective first, second and third parameters of different sets of data from the patient health data.

Abstract: A method of producing an artificial neural network capable of predicting the survivability of a patient, including: storing in an electronic database patient health data comprising a plurality of sets of data, each set having at least one of a first parameter relating to heart rate variability data and a second parameter relating to vital sign data, each set further having a third parameter relating to patient survivability; providing a network of nodes interconnected to form an artificial neural network, the nodes comprising a plurality of artificial neurons, each artificial neuron having at least one input with an associated weight; and training the artificial neural network using the patient health data such that the associated weight of the at least one input of each artificial neuron is adjusted in response to respective first, second and third parameters of different sets of data from the patient health data.

Abstract: A method of producing an artificial neural network capable of predicting the survivability of a patient, including: storing in an electronic database patient health data comprising a plurality of sets of data, each set having at least one of a first parameter relating to heart rate variability data and a second parameter relating to vital sign data, each set further having a third parameter relating to patient survivability; providing a network of nodes interconnected to form an artificial neural network, the nodes comprising a plurality of artificial neurons, each artificial neuron having at least one input with an associated weight; and training the artificial neural network using the patient health data such that the associated weight of the at least one input of each artificial neuron is adjusted in response to respective first, second and third parameters of different sets of data from the patient health data.

Abstract: A second service component-oriented information entity sent by a first service component is sent to a second service component that processes the information entity, so that interaction between the service components based on the information entity is realized, and the association between the service components is also realized. A service component in an operating system can execute a specific function or provide a specific service. The function or service can be provided by the system or an application program. The information entity can transmitted among multiple service components to perform a function or service.

Abstract: A method of producing an artificial neural network capable of predicting the survivability of a patient, including: storing in an electronic database patient health data comprising a plurality of sets of data, each set having at least one of a first parameter relating to heart rate variability data and a second parameter relating to vital sign data, each set further having a third parameter relating to patient survivability; providing a network of nodes interconnected to form an artificial neural network, the nodes comprising a plurality of artificial neurons, each artificial neuron having at least one input with an associated weight; and training the artificial neural network using the patient health data such that the associated weight of the at least one input of each artificial neuron is adjusted in response to respective first, second and third parameters of different sets of data from the patient health data.

Abstract: A method of producing an artificial neural network capable of predicting the survivability of a patient, including: storing in an electronic database patient health data comprising a plurality of sets of data, each set having at least one of a first parameter relating to heart rate variability data and a second parameter relating to vital sign data, each set further having a third parameter relating to patient survivability; providing a network of nodes interconnected to form an artificial neural network, the nodes comprising a plurality of artificial neurons, each artificial neuron having at least one input with an associated weight; and training the artificial neural network using the patient health data such that the associated weight of the at least one input of each artificial neuron is adjusted in response to respective first, second and third parameters of different sets of data from the patient health data.

Abstract: A method of predicting survivability of a patient. The method includes storing in an electronic database patient health data comprising a plurality of sets of data, each set having a first parameter relating to heart rate variability data including at least one of ST segment elevation and depression, a second parameter relating to vital sign data, and a third parameter relating to patient survivability; providing a network of nodes interconnected to form an artificial neural network, the nodes comprising a plurality of neurons, each having at least one input with an associated weight; and training the neural network using the patient health data such that the associated weight of the at least one input of each neuron is adjusted in response to respective first, second and third parameters of different sets of data from the patient health data, such that the neural network is trained to produce a prediction on the survivability of a patient within the next 72 hours.

Abstract: A system for providing casting design advisement for designing a cast component is provided. The system includes an integrated design environment, the integrated design environment receiving a concept design for the cast component, the concept design being user-modifiable. The system also includes a plurality of design advisory modules, the plurality of design advisory modules determining physics models associated with the concept design for the cast component and providing advisement analysis for the concept design based on the physics models. The system includes a design rule logic solver for receiving concept design features associated with the concept design and comparing the concept design features with stored casting design rules to determine a geometry modification guide.

Abstract: A method of predicting survivability of a patient. The method includes storing in an electronic database patient health data comprising a plurality of sets of data, each set having a first parameter relating to heart rate variability data including at least one of ST segment elevation and depression, a second parameter relating to vital sign data, and a third parameter relating to patient survivability; providing a network of nodes interconnected to form an artificial neural network, the nodes comprising a plurality of neurons, each having at least one input with an associated weight; and training the neural network using the patient health data such that the associated weight of the at least one input of each neuron is adjusted in response to respective first, second and third parameters of different sets of data from the patient health data, such that the neural network is trained to produce a prediction on the survivability of a patient within the next 72 hours.

Abstract: A method includes increasing EXPA1 gene expression or increasing the activity of EXPA1 polypeptide to produce a plant with an improved trait. The method may further include increasing the expression of RDL1 gene or the activity of RDL1 kpolypeptide. expression of GhRDL1 and GhEXPA1 Co-expression of EXPA1 and RDL genes can improve plant traits. These methods have applications in crops and flower productions.

Abstract: A method of producing an artificial neural network capable of predicting the survivability of a patient, including: storing in an electronic database patient health data comprising a plurality of sets of data, each set having at least one of a first parameter relating to heart rate variability data and a second parameter relating to vital sign data, each set further having a third parameter relating to patient survivability; providing a network of nodes interconnected to form an artificial neural network, the nodes comprising a plurality of artificial neurons, each artificial neuron having at least one input with an associated weight; and training the artificial neural network using the patient health data such that the associated weight of the at least one input of each artificial neuron is adjusted in response to respective first, second and third parameters of different sets of data from the patient health data.