Abstract: A thermal management component is provided, including a first substrate (111) and a second substrate (112). The first substrate (111) is provided with at least seven interfaces. The first substrate (111) is provided with at least seven flow channels. The at least seven flow channels communicate with the at least seven interfaces. The first substrate (111) is provided with a control valve. The control valve is configured to control communication between the at least seven flow channels to control communication between the at least seven interfaces. The second substrate (112) is disposed on a first side of the first substrate (111). The second substrate (112) is provided with through holes corresponding to the interfaces disposed on the first side of the first substrate (111). The interfaces disposed on the first side of the first substrate (111) respectively pass through the corresponding through holes. A thermal management system and a vehicle are further disclosed.

Abstract: The invention pertains to an early detection method for network unreliable information using ensemble learning, within the field of early detection technology for unreliable network data. It involves the following steps: (1) converting input text sequences into word vector sequences; (2) inputting these word vectors into three base models—Transformer, Bi-SATT-CAPS, and BiTCN—for classifying unreliable information; (3) training and predicting with these models to generate new training and test data sets; (4) weighting and merging these new data sets to create a new training set for the meta-learner SVM; (5) training the new set with the meta-learner SVM to obtain the final classification result. This method retains the text's grammatical and structural features, using only blog posts and early comments to accurately detect unreliable information. By employing an improved weight fusion strategy, the method leverages the strengths of the three base models to enhance early detection effectiveness.