Spatiotemporal Variations of Vegetation Net Primary Productivity and Its Response to Meteorological Factors Across the Yellow River Basin During the Period 1981–2020

Based on trend analysis, partial correlation analysis, and Mann–Kendall test, we analyzed the spatiotemporal variations of net primary productivity (NPP) in the Yellow River Basin and their responses to meteorological factors during the period 1981–2020. The results revealed that NPP had high values in the mid-south part but low values in the northwestern part of the Yellow River Basin. The average NPP was 195.3 g C·m−2·a−1 from 1981 to 2020, and the inter-annual fluctuation of NPP showed a significant increasing trend with an increasing rate of 2.35 g C·m−2·a−2 (p < 0.01). The annual mean temperature showed a positive correlation with NPP in 99.6% of the basin, and 91.4% of which passed the 0.01 significant test. NPP and annual precipitation positively correlated in 87.1% of the basin, and 41.7% of which passed the 0.01 significant test. In 75.2% of the basin, NPP was related negatively with annual sunshine hours, and 17.6% of which of which passed the 0.01 significant test

Discovering explicit Reynolds-averaged turbulence closures for turbulent separated flows through deep learning-based symbolic regression with non-linear corrections

This work introduces a novel data-driven framework to formulate explicit algebraic Reynolds-averaged Navier-Stokes (RANS) turbulence closures. Recent years have witnessed a blossom in applying machine learning (ML) methods to revolutionize the paradigm of turbulence modeling. However, due to the black-box essence of most ML methods, it is currently hard to extract interpretable information and knowledge from data-driven models. To address this critical limitation, this work leverages deep learning with symbolic regression methods to discover hidden governing equations of Reynolds stress models. Specifically, the Reynolds stress tensor is decomposed into linear and non-linear parts. While the linear part is taken as the regular linear eddy viscosity model, a long short-term memory neural network is employed to generate symbolic terms on which tractable mathematical expressions for the non-linear counterpart are built. A novel reinforcement learning algorithm is employed to train the neural network to produce best-fitted symbolic expressions. Within the proposed framework, the Reynolds stress closure is explicitly expressed in algebraic forms, thus allowing for direct functional inference. On the other hand, the Galilean and rotational invariance are craftily respected by constructing the training feature space with independent invariants and tensor basis functions. The performance of the present methodology is validated through numerical simulations of three different canonical flows that deviate in geometrical configurations. The results demonstrate promising accuracy improvements over traditional RANS models, showing the generalization ability of the proposed method. Moreover, with the given explicit model equations, it can be easier to interpret the influence of input features on generated models

Metrics in Master Planning Low Impact Development for Grand Rapids Michigan

Planners, designers, citizens, and governmental agencies are interested in measuring and assessing urban design treatments that are environmentally sensitive across numerous environmental design issues such as stormwater, adapting to climate change, wildlife suitability, visual quality, and maintaining soil productivity. This chapter examines a case study in the Grand Rapids Michigan, exploring design ideas for the extension of a medical campus and adjoining areas. The results of the case study present newly derived equations to assess soil productivity. The results of the soil equation development indicate that the soil productivity of an area has two primary dimensions, forming an annual plant preference cluster, a woody plant preference cluster, and a wetland plant preference cluster, where each soil setting requires a different soil profile. The equations explain between 90 and 97% of the variance and are definitive (p-value<.001). The environmental variables examined in the study, including the soil productivity, indicate that the developed master plan for the site is significantly better than traditional approaches and the existing site characteristics (p-value < 0.05)

Treatment of vulval condyloma with a combination of paiteling and cryotherapy, and its effect on late recurrence

Purpose: To study the clinical effectiveness of a combination of Paiteling and cryotherapy in the treatment of vulval condyloma acuminatum (VCA), and its effect on late recurrence. Methods: Eighty-six VCA patients were chosen as research subjects, and were randomized into group A and group B. Group A patients were treated with combination of Paiteling and cryotherapy, while group B patients received cryotherapy only. The clinical effects of the two treatment methods on VCA were evaluated by measuring area of damaged skin, levels of interleukin-6 (IL-6) and C-reactive protein (CRP), as well as degree of recurrence of VCA in the two groups, before and after treatment. Results: Total clinical treatment effectiveness in group A was significantly higher compared with group B (p &lt; 0.05). After treatment, the area of damaged skin, and levels of IL-6 and CRP were markedly lower in group A than in group B (p &lt; 0.001). After 6 months of treatment, disease control was higher in group A than in group B (p &lt; 0.05). There was also a lower incidence of adverse reactions in group A than in group B (p &lt; 0.05). Conclusion: These results indicate that the combination of Paiteling and cryotherapy is more effective than cryotherapy alone in improving treatment effectiveness and reducing late recurrence of VCA. Therefore, the combined treatment has potentials clinical application in the management of VCA

Changes in global climate heterogeneity under the 21st century global warming

Publisher Copyright: © 2021 The Author(s)Variations in climate types are commonly used to describe changes in natural vegetation cover in response to global climate change. However, few attempts have been made to quantify the heterogeneous dynamics of climate types. In this study, based on the Coupled Model Intercomparison Project phase 5 (CMIP5) historical and representative concentration pathway (RCP) runs from 18 global climate models, we used Shannon's Diversity Index (SHDI) and Simpson's Diversity Index (SIDI) to characterise of global climate heterogeneity from a morphological perspective. Our results show that global climate heterogeneity calculated by the SHDI/SIDI indices decreased from 1901 to 2095 at a significance level of 0.01. As radiative forcing intensified from RCP 2.6 to 8.5, the SHDI/SIDI decreased significantly. Furthermore, we observed that the spatial distribution of global climate heterogeneity was significantly reduced, with a pronounced latitudinal trend. Sensitivity analysis indicated that the temperature increase played a more significant role in reducing global climate heterogeneity than precipitation under the three warming scenarios, which is possibly attributed to anthropogenic forcing. Our findings suggest that the dynamics of global climate heterogeneity can be an effective means of quantifying global biodiversity loss.Peer reviewe