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An experiment was conducted on the Qingtu Lake Observation Array (QLOA) to measure wind and dust information at various wall-normal heights during the sand and dust storm (SDS) process. According to the indicators of the non-stationary features in the flow field, the SDS process can be divided into three stages: ascending, stabilizing, and descending. Based on this division, the Hilbert–Huang transform (HHT) is employed to extract dominant flow structures, which carries a significant portion of the turbulent kinetic energy. Moreover, the HHT spectrum of stream-wise velocity component reveals that the scales of the dominant structures are approximately hundreds of meters in the horizontal direction, and hence suggests the presence of large and very large-scale coherence during the SDS. The hypotheses of Townsend [The Structure of Turbulent Shear Flow (Cambridge University Press, 1976)] and Davenport ["The spectrum of horizontal gustiness near the ground in high winds," Q. J. R. Meteorol. Soc. 87, 194–211 (1961)] are utilized to demonstrate the vertical coherence of turbulence, which suggests the wall-similarity and evolution of inner/outer interactions for coherent structures during the SDS. Finally, the coherence spectrum [ γ L 2 = exp (− 2 c 1 Δ z / λ x) ] and the linear transfer kernel [ | H L 2 | = exp (d 1 − d 2 Δ z / λ x) ] are parameterized, where c 1 , d 1 , d 2 are fitting parameters, Δ z is wall-normal offset, and λx refers to streamwise wavelength, to illustrate the evolution of the interactions between near-wall and outer regions during the SDS, which highlights the strong connections during the stabilizing stage. In general, the present study analyzed horizontal and wall-normal structures for a comprehensive SDS process, and thus, these findings present abundant features of wall-attached eddies which further be used to improve/enrich existing near-wall models. [ABSTRACT FROM AUTHOR]

With the recent advances in machine learning, strategies based on data can be used to augment wall modeling in the turbulent boundary layer. Combined with the attached eddy hypothesis, the present work applies extreme gradient boosting (XGBoost) to predict the large-scale wall-attached structures at a range of wall-normal locations based on a near-wall reference position ( z R + ≈ 4) spanning a Reynolds-number range R e τ ∼ O (10 3) − O (10 5). The input and output signals are selected as the large-scale structures; here, the input signals are set as in the fixed near-wall reference position by a series of streamwise velocity ({ X − N , ... , X − 1 , X 0 , X 1 , ... , X N }), and the output signal Y 0 is set directly above X 0 . Within each dataset, the large-scale wall-attached structures are identified from the prediction modeled by XGBoost between the turbulence in the upper region and at the near-wall reference position, resulting in a successful prediction of the large-scale structures inclination angles. Along the wall-normal offset Δ z and streamwise offset L x (distance between X i and X 0 ), the slope of the feature importance (represented by contour levels) is exactly equal to the degree of inclination of large-scale structures, indicating the turbulent inner and outer connection inferred by the machine learning input and output interactions perspective. This study shows that there is a great opportunity in machine learning for wall-bounded turbulence modeling by connecting the flow interactions between near-wall and outer regions. [ABSTRACT FROM AUTHOR]

The auxin/indole-3-acetic acid (Aux/IAA) and auxin response factor (ARF) genes are two crucial gene families in the plant auxin signaling pathway. Nonetheless, there is limited knowledge regarding the Aux/IAA and ARF gene families in Populus simonii. In this study, we first identified 33 putative PsIAAs and 35 PsARFs in the Populus simonii genome. Analysis of chromosomal location showed that the PsIAAs and PsARFs were distributed unevenly across 17 chromosomes, with the greatest abundance observed on chromosomes 2. Furthermore, based on the homology of PsIAAs and PsARFs, two phylogenetic trees were constructed, classifying 33 PsIAAs and 35 PsARFs into three subgroups each. Five pairs of PsIAA genes were identified as the outcome of tandem duplication, but no tandem repeat gene pairs were found in the PsARF family. The expression profiling of PsIAAs and PsARFs revealed that several genes exhibited upregulation in different tissues and under various stress conditions, indicating their potential key roles in plant development and stress responses. The variance in expression patterns of specific PsIAAs and PsARFs was corroborated through RT-qPCR analysis. Most importantly, we instituted that the PsIAA7 gene, functioning as a central hub, exhibits interactions with numerous Aux/IAA and ARF proteins. Furthermore, subcellular localization findings indicate that PsIAA7 functions as a protein localized within the nucleus. To conclude, the in-depth analysis provided in this study will contribute significantly to advancing our knowledge of the roles played by PsIAA and PsARF families in both the development of P. simonii tissue and its responses to stress. The insights gained will serve as a valuable asset for further inquiries into the biological functions of these gene families. [ABSTRACT FROM AUTHOR]