Abstract: The present disclosure provides a kit and method for constructing a long-acting depression animal model. The present disclosure adopts Bacille Calmette-Guerin (BCG) (or Mycobacterium bovis (M bovis)) and low-dose pertussis toxin (PTX) for combined induction, which greatly reduces modeling cost. A mouse model constructed by the method of the present disclosure can clearly distinguish a time interval of pathological behaviors from a time interval of depression-like behaviors and has prominent face validity and predictive validity. The method of the present disclosure is simple and convenient and does not require a large number of tedious daily operations as the depression model construction by chronic unpredictable stress, chronic restraint stress, social defeat, mother-child separation, or the like. The mouse model of the present disclosure can provide a valuable long-term intervention window for research on antidepressant therapy.

Abstract: A method includes simulating a process, with computer-based software, to produce virtual data about the process; identifying process parameters for a real-world version of the process; providing a real-world sensor to sense a parameter associated with the real-world version of the process; receiving sensor readings from the real-world sensor while the real-world version is being performed; and training a machine-learning software model to predict a behavior of the real-world sensor based on the virtual data about the process, the process parameters, and the sensor readings.

Abstract: A time-aware application task scheduling system for a green data center (GDC) that includes a task scheduling processor coupled to one or more queue processors and an energy collecting processor connected to one or more renewable energy sources and a grid power source. The systems is capable of determining a service rate for a plurality of servers to process a plurality of application tasks in the GDC and scheduling, via processing circuitry, one or more of the application tasks to be executed in one or more of the servers at a rate according to a difference in an accumulated arriving rate for the plurality of application tasks into the one or more queues and a removal rate for the plurality of application tasks from the one or more queues. The system is further capable of removing the one or more application tasks from their associated queues for execution in the scheduled one or more servers.

Abstract: A method includes identifying machine process parameters for an additive manufacturing process to produce a part, providing a real-world sensor to sense a characteristic associated with a real-world version of the additive manufacturing process, receiving sensor readings from the real-world sensor while the machine is performing the real-world version of the additive manufacturing process, generating, with a computer-based processor, physics-based features associated with the additive manufacturing process, and training a machine-learning software model based at least in part on the machine process parameters, the sensor readings, and the physics-based features to predict a behavior of the real-world sensor.

Abstract: A method of scheduling tasks includes receiving a plurality of application tasks into one or more queues of a green data center (GDC), and determining a service rate for a plurality of servers to process the plurality of application tasks in the GDC, via processing circuitry. The method also includes scheduling, via the processing circuitry, one or more of the application tasks to be executed in one or more of the servers at a rate according to a difference in an accumulated arriving rate for the plurality of application tasks into the one or more queues and a removal rate for the plurality of application tasks from the one or more queues; and removing the one or more application tasks from their associated queues for execution in the scheduled one or more servers.

Abstract: A time-aware method of scheduling application tasks in a green data center (GDC) using a task scheduling processor and an energy collecting processor connected to one or more renewable energy sources and a grid power source. The method includes receiving energy data regarding available energy from renewable energy resources such that the renewable energy source is used first then receiving the application tasks and determining a service rate for servers to process the application tasks by using a service rate obtained by solving a profit maximization problem for the GDC by prioritizing and immediately scheduling or removing certain application tasks. The method also includes determining an initial fitness level of each representative application task based on total revenue of the GDC, an energy cost of the GDC, and a penalty associated with violated constraints.

Abstract: A method includes simulating a process, with computer-based software, to produce virtual data about the process; identifying process parameters for a real-world version of the process; providing a real-world sensor to sense a parameter associated with the real-world version of the process; receiving sensor readings from the real-world sensor while the real-world version is being performed; and training a machine-learning software model to predict a behavior of the real-world sensor based on the virtual data about the process, the process parameters, and the sensor readings.

Abstract: A method includes identifying machine process parameters for an additive manufacturing process to produce a part, providing a real-world sensor to sense a characteristic associated with a real-world version of the additive manufacturing process, receiving sensor readings from the real-world sensor while the machine is performing the real-world version of the additive manufacturing process, generating, with a computer-based processor, physics-based features associated with the additive manufacturing process, and training a machine-learning software model based at least in part on the machine process parameters, the sensor readings, and the physics-based features to predict a behavior of the real-world sensor.

Abstract: A system and method of scheduling tasks include receiving inputted data task variables for a private CDC and a plurality of public clouds; initializing parameters for a plurality of representative entities in a cluster of entities; determining a fitness level of each representative entity in the cluster of entities; updating one or more task scheduling parameters for a given number of time slots based on the parameters for the plurality of representative entities and the fitness level of each representative entity; determining a total number of data tasks to be dispatched to the private CDC and the plurality of public clouds based on an iteration result of a final time slot for the given number of time slots for a global best position; and updating the data task variables using the total number of data tasks to be dispatched.

Abstract: Tandem electro-optic devices and active materials for electro-optic devices are disclosed. Tandem devices include p-type and n-type layers between the active layers, which are doped to achieve carrier tunneling. Low bandgap conjugated polymers are also disclosed.

Abstract: A system and method of scheduling tasks include receiving inputted data task variables for a private CDC and a plurality of public clouds; initializing parameters for a plurality of representative entities in a cluster of entities; determining a fitness level of each representative entity in the cluster of entities; updating one or more task scheduling parameters for a given number of time slots based on the parameters for the plurality of representative entities and the fitness level of each representative entity; determining a total number of data tasks to be dispatched to the private CDC and the plurality of public clouds based on an iteration result of a final time slot for the given number of time slots for a global best position; and updating the data task variables using the total number of data tasks to be dispatched.

Abstract: Tandem electro-optic devices and active materials for electro-optic devices are disclosed. Tandem devices include p-type and n-type layers between the active layers, which are doped to achieve carrier tunneling. Low bandgap conjugated polymers are also disclosed.

Abstract: Tandem electro-optic devices and active materials for electro-optic devices are disclosed. Tandem devices include p-type and n-type layers between the active layers, which are doped to achieve carrier tunneling. Low bandgap conjugated polymers are also disclosed.

Abstract: Tandem electro-optic devices and active materials for electro-optic devices are disclosed. Tandem devices include p-type and n-type layers between the active layers, which are doped to achieve carrier tunneling. Low bandgap conjugated polymers are also disclosed.