Abstract: A device may receive a machine learning model, training data, and test data, and may perform a unit test on the machine learning model to generate unit test results. The device may perform regression tests on the machine learning model, with the training data and the test data, to calculate model scores, create graphs, determine inference delays, and identify missing points for the machine learning model. The device may perform scale and longevity tests on the machine learning model, with the training data and the test data, to identify additional missing points and calculate a resource utilization for the machine learning model. The device may update the machine learning model, to generate an updated machine learning model, based on the unit test results, the model scores, the graphs, the inference delays, the missing points, the additional missing points, or the resource utilization.

Abstract: In some implementations, a monitoring device may obtain a plurality of time-series data streams respectively associated with a plurality of resources. The monitoring device may generate, using a plurality of machine learning models and based on the plurality of time-series data streams, a plurality of sets of multi-step forecast values, wherein each set of multi-step forecast values is associated with the plurality of resources. The monitoring device may determine, based on the plurality of sets of multi-step forecast values, a set of particular multi-step forecast values associated with the plurality of resources. The monitoring device may cause, based on the set of particular multi-step forecast values, one or more actions to be performed. In some implementations, the monitoring device may determine, based on the plurality of time-series data streams and the plurality of sets of multi-step forecast values, that a correlation exists between a first resource and a second resource.

Abstract: A vehicle exhaust system includes a first exhaust gas path and a second exhaust gas path. At least one valve is positioned within the first exhaust gas path and an active noise control system is associated with the second exhaust gas path. An example method includes, providing the first exhaust gas path with a first tailpipe having a first outlet and the second exhaust gas path with a second tailpipe having a second outlet separate from the first outlet. The valve and the active noise control system are controlled simultaneously to control noise generated by the vehicle exhaust system.

Abstract: A process for the continuous production of hydrogen from methane and/or natural gas and/or methane-rich hydrocarbons and steam at low temperature, using a solid catalyst comprising group VIII metal oxide(s) in two parallel reactors, comprising reducing the solid catalyst in both the reactors by contacting the catalyst with a gaseous feed comprising a reducing agent, contacting a first gaseous feed comprising methane and/or natural gas and/or methane rich hydrocarbons, simultaneously contacting a second gaseous feed comprising steam called Feed-B with the solid catalyst reduced in step-i in a second reactor. while regularly switching over the two feeds, Feed-A arid Feed-B, between the two parallel reactors to obtain a mixed product stream comprising hydrogen from the two reactors.

Abstract: A process for the activation of perovskite-type oxide by increasing its surface area and/or catalytic activity, which comprises: (i) treating perovskite-type oxide hydrothermally with liquid water with water/perovskite-type oxide ratio of above 0.1 at temperature of 50°-500° C. and period of 0.1-100 h under autogeneous pressure and drying the resulting mass or treating perovskite-type oxide hydrothermally with water vapors with or without any inert gas at space velocity of above 100 h−1, temperature of 200°-1000° C. and a period of 0.1-100 h and (ii) calcining the hydrothermally treated perovskite-type oxide in air or inert gas or under vacuum at temperature of 300°-1000° C. for a period of 0.1-100 h.