In this paper, we mainly discuss the uniqueness problem when an entire function shares 0 CM and nonzero complex constant a IM with its difference operator. We also consider the general case where they share two distinct complex constants a ∗ $a^{*}$ CM and a IM under some additional condition and give some further discussions.

We study the uniqueness problems on entire functions and their difference operators or shifts. Our main result is a difference analogue of a result of Jank‐Mues‐Volkmann, which is concerned with the uniqueness of the entire function sharing one finite value with its derivatives. Two relative results are proved, and examples are provided for our results.

In this article, we investigate the uniqueness problems of difference operators of two meromorphic functions. Uniqueness of a meromorphic function f n and its difference operator with the same 1-points and poles is also proved. 2000 Mathematics Subject Classification: Primary 30D35; Secondary 39B32.

Accurate prediction of sea surface temperature (SST) is critical in marine sciences and related disciplines. The task is rendered challenging by complex environmental factors and the inherent non-linearity of time. Current studies typically concentrate on either the structural aspect of sensor networks or their temporal correlations, often overlooking the interplay between these dimensions. To address this issue, this paper proposes a Graph Spatiotemporal Sampling Aggregation Method (GSSA), This method comprises two primary components: spatial information aggregation and temporal information convolution. Designed to simultaneously address spatial structural correlations and temporal variations, GSSA offers a comprehensive solution for SST prediction. The method utilizes neighbor sampling to endow the model with inductive capabilities, leveraging spatial information aggregation to capture spatial details and temporal information convolution to grasp temporal dynamics. Significantly improving upon the SAGE aggregation’s ability to capture temporal correlations among node sequences. Experiments on SST data from the South China Sea and the Bohai Sea demonstrate that this method surpasses traditional machine learning methods in two different regions and at different prediction scales in the short and medium term.