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Abstract Background: In bioinformatics, network alignment algorithms have been applied to protein-protein interaction (PPI) networks to discover evolutionary conserved substructures at the system level. However, most previous methods aim to maximize the similarity of aligned proteins in pairwise networks, while concerning little about the feature of connectivity in these substructures, such as the protein complexes. Results: In this paper, we identify the problem of finding conserved protein complexes, which requires the aligned proteins in a PPI network to form a connected subnetwork. By taking the feature of connectivity into consideration, we propose ConnectedAlign, an efficient method to find conserved protein complexes from multiple PPI networks. The proposed method improves the coverage significantly without compromising of the consistency in the aligned results. In this way, the knowledge of protein complexes in well-studied species can be extended to that of poor-studied species. Conclusions: We conducted extensive experiments on real PPI networks of four species, including human, yeast, fruit fly and worm. The experimental results demonstrate dominant benefits of the proposed method in finding protein complexes across multiple species. 

Accurately predicting bare‐soil evaporation requires the proper characterization of the near‐surface atmospheric conditions. These conditions, dependent on factors such as surface microtopography and wind velocity, vary greatly and therefore require high‐resolution datasets to be fully incorporated into evaporation models. These factors are oftentimes parameterized in models through the aerodynamic resistance (r_a), in which the vapor roughness length (z_0v) and the momentum roughness length (z_0m) are two crucial parameters that describe the transport near the soil‐atmosphere interface. Typically, when evaluating bare‐soil evaporation, these two characteristic lengths are assumed equal, although differences are likely to occur especially in turbulent flows over undulating surfaces. Thus, this study aims to investigate the relationship betweenz_0vandz_0mabove undulating surfaces to ultimately improve accuracy in estimating evaporation rate. To achieve this goal, four uniquely designed wind tunnel—soil tank experiments were conducted considering different wind speeds and undulation spacings. Particle image velocimetry (PIV) was used to measure the velocity field above the undulating surface in high resolution. Using the high‐fidelity data set, the logarithmic ratio ofz_0vtoz_0mis determined and used to estimater_a. Results confirm that these lengths differ significantly, with the logarithmic ratio roughly ranging from −15 to −5 under the conditions tested. PIV‐measured results demonstrate this ratio is closely tied to the mass and momentum transport behaviors influenced by surface undulations. Using the data‐integrated formulation ofr_a, predictions of evaporation rate were prepared for both the laboratory and lysimeter experiments, demonstrating the efficacy of the proposed approach in this study.