Abstract: A durability test method a phase change material includes: placing a material to be tested in a solid-liquid combination test component for detecting the material to be tested, and executing a preset centrifugal rotation detection control scheme; performing cycling control on a temperature regulation semiconductor provided on the solid-liquid combination test component according to a preset temperature single-cycle regulation scheme; obtaining a temperature detection information of the material to be tested; and analyzing and processing the temperature detection information of the material to be tested to generate a durability information of the material to be tested according to a preset durability analysis method of the material to be tested.

Abstract: A method and system are provided for non-destructive testing of a tunnel lining, and relate to the field of tunnel quality testing technologies. The method includes: obtaining tunnel design information; extracting rebar arrangement information and lining information; determining theoretical arrangement interval information; determining location information of a signal conducting bar group; pre-embedding signal conducting bars in sequence based on the location information of the signal conducting bar group; and manufacturing the lining, mounting an interface slot based on a location of the signal conducting bar, and marking a location of each interface slot as input point location information or receiving point location information, wherein the input point location information indicates a location corresponding to the signal input connection point, and the receiving point location information indicates a location corresponding to the signal receiving point.

Abstract: A computer system has a first machine learning module configured to predict a probability of a respective option being selected by a particular user if presented to that user via a computer app. A second machine learning module is configured to determine a respective confidence value associated with the probability. A third module uses the predicted probabilities and confidence values to determine at least one option to be presented to the particular user.

Abstract: A computer system has a first machine learning module configured to predict a probability of a respective option being selected by a particular user if presented to that user via a computer app. A second machine learning module is configured to determine a respective confidence value associated with the probability. A third module uses the predicted probabilities and confidence values to determine at least one option to be presented to the particular user.

Abstract: A computer system has a first machine learning module configured to predict a probability of a respective option being selected by a particular user if presented to that user via a computer app. A second machine learning module is configured to determine a respective confidence value associated with the probability. A third module uses the predicted probabilities and confidence values to determine at least one option to be presented to the particular user.

Abstract: A computer system has a first machine learning module configured to predict a probability of a respective option being selected by a particular user if presented to that user via a computer app. A second machine learning module is configured to determine a respective confidence value associated with the probability. A third module uses the predicted probabilities and confidence values to determine at least one option to be presented to the particular user.