Experimental environment and parameter configuration
The training and development of this experiment is based on Pytorch framework under Linux server, using Ubuntu system, the specific experimental environment is shown in Table 1.

The training and development of this experiment is based on Pytorch framework under Linux server, using Ubuntu system, the specific experimental environment is shown in Table 1.

The experimental framework employs five benchmark datasets for comprehensive evaluation, as detailed in Section 3.1. Section 3.2 subsequently presents the architectural details and implementation specifics of the proposed ST-CFI methodology.

The experiments in this study were all carried out on the test set, which is provided by the Kaggle platform and can be obtained through https://cryoetdataportal.czscience.com/datasets/10445 The hybrid model proposed in…

In this section, we applied the multi-condition optimization method based on the CFD non-equilibrium condensation flow calculation method to optimize the design of a Dykas planar cascade29. The optimal values of the design parameters of the Dykas…

In this section, we will discuss the basic operational laws for a Cartesian form of CFS. Moreover, we have discussed the notion of the score function and accuracy function that can help us manage the ranking of two CFNs. Moreover, we have…

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The diffusion process of gangue slurry was studied under working conditions with roughness spectral exponents of 2.8, 2.3, 1.8, 1.3, and 0.8. The…

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This segment reviews existing research on secure and energy-efficient routing in WSNs, essential in various critical applications. A major challenge in WSNs is achieving a balance between conserving energy and maintaining secure communication,…

The generation of optimal learning paths requires balancing two potentially competing objectives: maximizing knowledge acquisition while maintaining appropriate cognitive load levels. We formulate this as a…