The large-scale strike-slip Tan–Lu Fault Zone (TLFZ) and Xiangfan–Guangji Fault Zone (XGFZ) both terminate in the southeastern corner of the Dabie Orogen at an angle of almost 90°, and this corner therefore provides a very good natural laboratory for understanding the mechanism by which large-scale strike-slip faults terminate. We present new geochronological and structural data for the southeastern tip of the XGFZ and the southwestern tip of the TLFZ. The NW–SE-striking XGFZ records ductile shearing in its northwestern segment, characterized by discrete dextral shear zones that formed at temperatures of 350–400°C, as indicated by quartz c-axes fabrics and microstructures. In the southeastern segment of the XGFZ, WNW–ESE-trending thrusts are displayed. The NE–SW-striking TLFZ is characterized by discrete NE–SW-trending sinistral ductile shear zones in the Qianshan–Tongcheng segment, brittle left-lateral strike-slip faults in the Taihu–Qianshan segment, and thrusts to the south of Taihu. The trends of these thrusts change progressively southward from NE–SW to ENE–WSW and E–W. New zircon U–Pb dating results and previous cooling biotite 40Ar/39Ar ages constrain the timing of shearing in the XGFZ to 112–102 Ma (late Early Cretaceous), which is the same as the age of faulting in the TLFZ (110–102 Ma). The large-scale strike-slip TLFZ and XGFZ both terminate at their tips as thrusts. We suggest that interactions between the two faults led to the kinematic changing from strike-slip to thrusting, with this playing an important role in controlling the termination of these two large-scale intersecting strike-slip faults. The dextral shearing of the NW–SE-trending XGFZ, the sinistral shearing of the NE–SW-trending TLFZ, and the nearly E–W-trending thrust faults all indicate continental-scale N–S compression in eastern China during the late Early Cretaceous. This compression resulted from rapid NNW-ward oblique subduction of the Paleo-Pacific Plate.
Collaborative robots face challenges in achieving high accuracy using existing parameter identification methods, especially in applications like dragging, teaching, and collision detection. To address the issue of robot dynamics identification accuracy in scenarios with unknown parameters, this paper introduces a method for robot joint parameter identification based on the Cuckoo Search-backpropagation neural network (CS-BPNN). Initially, a kinetic model incorporating Coulomb viscous friction is presented. Subsequently, a BPNN is employed to approximate and fit the nonlinear function, while the CS algorithm is integrated into the BPNN model to optimize its parameters. Collected data undergo third-order Butterworth filtering, and a loss function compares predicted values with actual values to ascertain the accuracy of the proposed method. Experimental results demonstrate that the CS-BPNN approach proposed herein can converge the mean square error (MSE) to [Formula: see text] N⋅m when robot dynamics parameters are unknown. This method boasts of a lower MSE and superior discrimination accuracy compared to traditional methods like least squares (LS), genetic algorithm (GA), and BPNN. Consequently, the method presented in this study not only enhances the accuracy of parameter identification but also offers a fresh perspective for the parameter identification of robot joint dynamics models.
Changes in species composition across communities, i.e., β-diversity, is a central focus of ecology. Compared to macroorganisms, the β-diversity of soil microbes and its drivers are less studied. Whether the determinants of soil microbial β-diversity are consistent between soil depths and between abundant and rare microorganisms remains controversial. Here, using the 16S-rRNA of soil bacteria and archaea sampled at different soil depths (0–10 and 30–50 cm) from 32 sites along an aridity gradient of 1500 km in the temperate grasslands in northern China, we compared the effects of deterministic and stochastic processes on the taxonomic and phylogenetic β-diversity of soil microbes. Using variation partitioning and null models, we found that the taxonomic β-diversity of the overall bacterial communities was more strongly determined by deterministic processes in both soil layers (the explanatory power of environmental distance in topsoil: 25.4%; subsoil: 47.4%), while their phylogenetic counterpart was more strongly determined by stochastic processes (the explanatory power of spatial distance in topsoil: 42.1; subsoil 24.7%). However, in terms of abundance, both the taxonomic and phylogenetic β-diversity of the abundant bacteria in both soil layers was more strongly determined by deterministic processes, while those of rare bacteria were more strongly determined by stochastic processes. In comparison with bacteria, both the taxonomic and phylogenetic β-diversity of the overall abundant and rare archaea were strongly determined by deterministic processes. Among the variables representing deterministic processes, contemporary and historical climate and aboveground vegetation dominated the microbial β-diversity of the overall and abundant microbes of both domains in topsoils, but soil geochemistry dominated in subsoils. This study presents a comprehensive understanding on the β-diversity of soil microbial communities in the temperate grasslands in northern China. Our findings highlight the importance of soil depth, phylogenetic turnover, and species abundance in the assembly processes of soil microbial communities.
Exploring the biogeographic patterns of soil microbial diversity is critical for understanding mechanisms underlying the response of soil processes to climate change. Using top- and subsoils from an ∼1,500-km temperate grassland transect, we find divergent patterns of microbial diversity and its determinants in the topsoil versus the subsoil. Furthermore, we find important and direct legacy effects of historical climate change on the microbial diversity of subsoil yet indirect effects on topsoil. Our findings challenge the conventional assumption of similar geographic patterns of soil microbial diversity along soil profiles and help to improve our understanding of how soil microbial communities may respond to future climate change in different regions with various climate histories.
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