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A new eight-direction algorithm (iFAD8) for the simulation of drainage directions on grid digital elevation models (DEMs) is presented. In this algorithm, a flexible triangular facet construction technique (ND∞) is developed to provide local drainage directions ranging continuously from 0°to 360°. Subsequently, a flow aggregation technique using global deviations of local drainage directions is proposed to simplify the flow paths into a nondispersive format. Another algorithm (FAD8) accompanying the iFAD8 is also presented, which uses the D∞ directions as the local drainage directions. Then FAD8, iFAD8, and three existing algorithms are compared on 10 abstract terrains including nine basic terrains and a Himmelblau terrain. The algorithms are tested to reproduce exact slope lines and specific catchment areas derived from the terrain functions. The results show that iFAD8 has better performance than FAD8. Both iFAD8 and FAD8 outperform existing algorithms in most cases. The flow aggregation technique is shown to be an excellent choice for nondispersive drainage direction simulation based on infinite directions. The ND∞ direction based on flexible triangular facets is also an improvement to the local infinite direction of D∞. We conclude that the iFAD8 algorithm can provide better definitions of drainage directions.Plain-language Summary Overland movement of water and soil plays an important role in hydrological cycle and geomorphologic evolution. Analysis and modeling of these processes rely on drainage direction information. Through digitalizing and discretizing a continuous terrain into a mass of grid cells, drainage direction can be assigned to each cell. The accuracy of drainage directions may have a tremendous impact on the hydrologic and geomorphologic modeling. So an advanced algorithm for determination of drainage direction is required. Many algorithms have been proposed for this purpose. But there are some shortcomings in the existing algorithms. Here we present a new algorithm named iFAD8 to provide single drainage direction for each topographic cell. Two newly developed techniques are adopted in iFAD8 to improve both local drainage directions and global flow paths. iFAD8 partly optimizes the shortcomings of existing algorithms in theory and has a better performance on various terrains. Finally, iFAD8 will benefit hydrologic and geomorphologic modeling.

Four-dimensional (4D) data-independent acquisition (DIA)-based proteomics is a promising technology. However, its full performance is restricted by the time-consuming building and limited coverage of a project-specific experimental library. Herein, we developed a versatile multifunctional deep learning model Deep4D based on self-attention that could predict the collisional cross section, retention time, fragment ion intensity, and charge state with high accuracies for both the unmodified and phosphorylated peptides and thus established the complete workflows for high-coverage 4D DIA proteomics and phosphoproteomics based on multidimensional predictions. A 4D predicted library containing ∼2 million peptides was established that could realize experimental library-free DIA analysis, and 33% more proteins were identified than using an experimental library of single-shot measurement in the example of HeLa cells. These results show the great values of the convenient high-coverage 4D DIA proteomics methods.