Abstract: Described are techniques for predictive microservice activation. The techniques include training a machine learning model using a plurality of sequences of coordinates, where the plurality of sequences of coordinates are respectively based upon a corresponding plurality of series of vectors generated from historical usage data for an application and its associated microservices. The techniques further include inputting a new sequence of coordinates representing a series of application operations to the machine learning model. The techniques further include identifying a predicted microservice for future utilization based on an output vector generated by the machine learning model. The techniques further include activating the predicted microservice prior to the predicted microservice being called by the application.

Abstract: In an approach for smart test data workload generation, a processor receives a plurality of expected image frames for a user interface application to be tested. The plurality of expected image frames is pre-defined and represents a series of workflows and operations of the user interface application to be expected based on a design requirement. A processor calculates a first set of hash-values for each corresponding expected image frame. A processor samples the user interface application with a frequency to a plurality of testing image frames during a test run on the user interface application. A processor calculates a second set of hash-values for each sampled testing image frame. A processor compares the first set of hash-values to the second set of hash-values. A processor verifies that the second set of hash-values matches the first set of hash-values.

Abstract: A negative electrode active material for a secondary battery includes a negative electrode active material particle that includes a first object including a silicon oxide, and a second object including at least one of copper, a copper compound, tungsten, or a molybdenum oxide and attached to a surface of the first object. A Raman spectrum of the negative electrode active material particle has a maximum peak within a range of greater than or equal to 470 cm?1 and less than or equal to 490 cm?1. An XRD spectrum thereof has a peak within a range of 37°±1° and a peak within a range of 44°±1°, has a peak within a range of 40°±1°, or has a peak within any one of a range of 23°±1°, a range of 25°±1°, a range of 37°±1°, a range of 41°±1°, a range of 54°±1°, or a range of 60°±10.

Abstract: A secondary battery includes a positive electrode, a negative electrode including a negative electrode active material, and an electrolytic solution. The negative electrode active material includes a lithium-silicon-containing oxide that includes lithium and silicon as constituent elements and includes magnesium present on a surface layer of the lithium-silicon-containing oxide. The lithium-silicon-containing oxide includes a phase including silicon and a phase including at least one kind of lithium silicate represented by Formula (1). A range in which magnesium is present is within a range of greater than or equal to 10 nm and less than or equal to 3000 nm from a surface of the lithium-silicon-containing oxide in a depth direction. Magnesium forms at least one kind of magnesium silicate represented by Formula (2). A ratio of a number of moles of magnesium to a number of moles of lithium is greater than or equal to 0.

Abstract: In an approach for smart test data workload generation, a processor receives a plurality of expected image frames for a user interface application to be tested. The plurality of expected image frames is pre-defined and represents a series of workflows and operations of the user interface application to be expected based on a design requirement. A processor calculates a first set of hash-values for each corresponding expected image frame. A processor samples the user interface application with a frequency to a plurality of testing image frames during a test run on the user interface application. A processor calculates a second set of hash-values for each sampled testing image frame. A processor compares the first set of hash-values to the second set of hash-values. A processor verifies that the second set of hash-values matches the first set of hash-values.

Abstract: A secondary battery includes a positive electrode; a negative electrode including a negative electrode current collector and a negative electrode active material layer which is provided on the negative electrode current collector and contains a negative electrode active material, and an electrolytic solution. The negative electrode active material includes a carbon-containing material and a silicon-containing material, and a spreading resistance distribution in the negative electrode active material layer is 1.03 or more and 10 or less as measured using a scanning spreading resistance microscope.

Abstract: Described are techniques for predictive microservice activation. The techniques include training a machine learning model using a plurality of sequences of coordinates, where the plurality of sequences of coordinates are respectively based upon a corresponding plurality of series of vectors generated from historical usage data for an application and its associated microservices. The techniques further include inputting a new sequence of coordinates representing a series of application operations to the machine learning model. The techniques further include identifying a predicted microservice for future utilization based on an output vector generated by the machine learning model. The techniques further include activating the predicted microservice prior to the predicted microservice being called by the application.

Abstract: A secondary battery includes a positive electrode; a negative electrode including a negative electrode current collector and a negative electrode active material layer which is provided on the negative electrode current collector and contains a negative electrode active material, and an electrolytic solution. The negative electrode active material includes a carbon-containing material and a silicon-containing material, and a spreading resistance distribution in the negative electrode active material layer is 1.03 or more and 10 or less as measured using a scanning spreading resistance microscope.