► The Xuefengshan Belt is an Early Mesozoic intracontinental orogen. ► This belt can be divided into a Western Outer Zone and an Eastern Zone. ► The bulk architecture of the whole belt results from a ...polyphase deformation. ► This orogen was originated as a far-field effect of the Paleo-Pacific subduction.
Intracontinental orogens remain less understood than accretionary or collisional orogens that are related to plate margin interactions. In the center of the South China block, the Xuefengshan Belt provides a well-exposed example of such an intracontinental orogen of Early Mesozoic age. Detailed field tectonic observations indicate that the Xuefengshan Belt can be divided into a Western Outer Zone characterized by km-scale box-fold structures, and an Eastern Zone, separated from the Western Outer Zone by the SE-dipping Main Xuefengshan Thrust. In the Eastern Zone, NW verging folds coeval with a pervasive slaty cleavage and a NW–SE trending lineation are the dominant structures. From west to east, the dip of the cleavage surface exhibits a fan-like pattern. The bulk architecture of the Xuefengshan Belt results from polyphase deformation: D1 is characterized by a top-to-the-NW ductile shearing; D2 corresponds to SE-directed back thrusting and folding; D3 consists of upright folds with vertical cleavage and lineation. At depth, a high strain zone characterized by greenschist facies metamorphic rocks and a top-to-the-NW ductile shearing corresponds to a ductile décollement zone that accommodated the deformation of the Neoproterozoic to Early Triassic sedimentary series. Kinematic compatibility suggests that the synmetamorphic ductile shearing was coeval with the D1 event in the sedimentary cover. The Xuefengshan Belt is interpreted as an Early Mesozoic intracontinental orogen, which possibly originated from the SE-directed continental subduction of a piece of the South China block in response to northwestwards subduction of the Pacific plate.
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Deep computation, as an advanced machine learning model, has achieved the state-of-the-art performance for feature learning on big data in industrial Internet of Things (IoT). However, the current ...deep computation model usually suffers from overfitting due to the lack of public available labeled training samples, limiting its performance for big data feature learning. Motivated by the idea of active learning, an adaptive dropout deep computation model (ADDCM) with crowdsourcing to cloud is proposed for industrial IoT big data feature learning in this paper. First, a distribution function is designed to set the dropout rate for each hidden layer to prevent overfitting for the deep computation model. Furthermore, the outsourcing selection algorithm based on the maximum entropy is employed to choose appropriate samples from the training set to crowdsource on the cloud platform. Finally, an improved supervised learning from multiple experts scheme is presented to aggregate answers given by human workers and to update the parameters of the ADDCM simultaneously. Extensive experiments are conducted to evaluate the performance of the presented model by comparing with the dropout deep computation model and other state-of-the-art crowdsourcing algorithms. The results demonstrate that the proposed model can prevent overfitting effectively and aggregate the labeled samples to train the parameters of the deep computation model with crowdsouring for industrial IoT big data feature learning.
Deep learning has been successfully applied to feature learning in speech recognition, image classification and language processing. However, current deep learning models work in the vector space, ...resulting in the failure to learn features for big data since a vector cannot model the highly non-linear distribution of big data, especially heterogeneous data. This paper proposes a deep computation model for feature learning on big data, which uses a tensor to model the complex correlations of heterogeneous data. To fully learn the underlying data distribution, the proposed model uses the tensor distance as the average sum-of-squares error term of the reconstruction error in the output layer. To train the parameters of the proposed model, the paper designs a high-order back-propagation algorithm (HBP) by extending the conventional back-propagation algorithm from the vector space to the high-order tensor space. To evaluate the performance of the proposed model, we carried out the experiments on four representative datasets by comparison with stacking auto-encoders and multimodal deep learning models. Experimental results clearly demonstrate that the proposed model is efficient to perform feature learning when evaluated using the STL-10, CUAVE, SANE and INEX datasets.
•Petrography and geochemistry data are applied in this research.•The Dulate arc is the primary source area.•REE mixing of provenance modelling method is used to determine the source rocks.•The ...sedimentary rocks deposit in a back-arc basin.•The back-arc basin formed by the southward subduction of the Zaysan–Erqis Ocean.
Northeast Junggar occupies an important position that links East Junggar and Chinese Altai. Numerous magmatic and sedimentary rocks of the Paleozoic in this area recorded the final amalgamation processes of East Junggar and Chinese Altai. This study analyzes the petrological and geochemical characteristics of sandstones and mudstones from the Early Carboniferous Nanmingshui formation in Northeast Junggar. The provenance and tectonic setting of these clastic rocks are discussed. Petrography indicates that the composition and texture maturity of the sandstones are low. The components of the sandstones are mainly volcanic fragments (61–87%), feldspars (9–30%), and monocrystalline quartz (2–18%), with a few polycrystalline quartz and other minerals. Slice observation indicates that the majority of the volcanic fragments of sandstones are basic-intermediate volcanic rocks with a few dacite and felsic plutonic fragments. The detrital modes of the sandstones reflect that these sandstones are derived from undissected arcs. A low to moderate chemical index of alteration and the Al2O3–CaO*+Na2O–K2O diagram reflect a low to moderate weathering degree in the source area. Trace and rare earth element (e.g., La, Th, Hf, Sc, Cr, Co, and Eu) contents and their ratios suggest that the source rocks of the clastic rocks are intermediate-basic rocks with some felsic rocks. Compared with sandstones, the source rocks for mudstones are more felsic. The petrography and geochemistry characteristics of the clastic rocks suggest that the proximal Dulate arc should be the primary source area. Mixing calculations based on REE data suggest that approximately two-thirds of the sandstone fragments are intermediate-basic volcanic rocks. The contents of the major and trace elements, as well as the stratum features, of the clastic rocks manifest that these clastic rocks resemble sedimentary rocks in a back-arc basin. The formation of this back-arc basin is caused by the southward subduction of the Zaysan–Erqis Ocean.
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Major challenges in vaccine development include rapidly selecting or designing immunogens for raising cross-protective immunity against different intra- or inter-subtypic pathogens, especially for ...the newly emerging varieties. Here we propose a computational method, Conformational Epitope (CE)-BLAST, for calculating the antigenic similarity among different pathogens with stable and high performance, which is independent of the prior binding-assay information, unlike the currently available models that heavily rely on the historical experimental data. Tool validation incorporates influenza-related experimental data sufficient for stability and reliability determination. Application to dengue-related data demonstrates high harmonization between the computed clusters and the experimental serological data, undetectable by classical grouping. CE-BLAST identifies the potential cross-reactive epitope between the recent zika pathogen and the dengue virus, precisely corroborated by experimental data. The high performance of the pathogens without the experimental binding data suggests the potential utility of CE-BLAST to rapidly design cross-protective vaccines or promptly determine the efficacy of the currently marketed vaccine against emerging pathogens, which are the critical factors for containing emerging disease outbreaks.
Reducing energy consumption is a vital and challenging problem for the edge computing devices since they are always energy-limited. To tackle this problem, a deep Q-learning model with multiple DVFS ...(dynamic voltage and frequency scaling) algorithms was proposed for energy-efficient scheduling (DQL-EES). However, DQL-EES is highly unstable when using a single stacked auto-encoder to approximate the Q-function. Additionally, it cannot distinguish the continuous system states well since it depends on a Q-table to generate the target values for training parameters. In this paper, a double deep Q-learning model is proposed for energy-efficient edge scheduling (DDQ-EES). Specially, the proposed double deep Q-learning model includes a generated network for producing the Q-value for each DVFS algorithm and a target network for producing the target Q-values to train the parameters. Furthermore, the rectified linear units (ReLU) function is used as the activation function in the double deep Q-learning model, instead of the Sigmoid function in QDL-EES, to avoid gradient vanishing. Finally, a learning algorithm based on experience replay is developed to train the parameters of the proposed model. The proposed model is compared with DQL-EES on EdgeCloudSim in terms of energy saving and training time. Results indicate that our proposed model can save average 2%-2.4% energy and achieve a higher training efficiency than QQL-EES, proving its potential for energy-efficient edge scheduling.
The Xuefengshan Belt, characterized by large-scale fold and thrust structures and widespread granites, is a key area to decipher the tectonic evolution of the South China block. In this belt, two ...magmatic episodes are recorded by Early Paleozoic and Early Mesozoic granites. In this paper, we carried out precise SIMS zircon UPb dating and in situ LuHf isotopes measurements on these granitic plutons. Our study indicates that the Early Paleozoic and the Early Mesozoic granites are late-orogenic products of the Early Paleozoic orogen and the Middle Triassic Xuefengshan orogen, respectively. In the Xuefengshan Belt, the Early Paleozoic event is poorly registered, since this area corresponds to the outer zone of the early Paleozoic orogen. The Silurian–Early Devonian granites are late-orogenic plutons emplaced after the main tectonic and metamorphic stage in the Wuyi–Baiyun–Yunkai belt, coeval with a widespread, subsequent extensional event in eastern and southern South China. On the other hand, Triassic granites are formed in an intracontinental environment with weakly to strongly peraluminous signatures. Zircon UPb ages presented here, associated with a summary of newly acquired data in the same region, suggest that the emplacement of anatectic granites occurred around 225–215Ma, not in a rather wide range of ca. 245–200Ma. In situ zircon εHf(t) values indicate a crust-derived source without a mantle-derived input for the two generations of granites. Combining our data with recent studies, we infer that the central area of the South China block has experienced two tectonothermal events: the Early Paleozoic magmatism developed as a result of the collapse of the Early Paleozoic orogen, while these Early Mesozoic granites can be the late-orogenic products of the intracontinental Xuefengshan orogen, most likely manifesting the far field effect by the subduction of the Paleo-Pacific ocean plate at the southeastern margin of the South China block.
► Precise SIMS zircon UPb ages are presented from the Xuefengshan Belt. ► Early Paleozoic late-orogenic magmatism was widespread in the South China block. ► The Middle Triassic Xuefengshan orogeny ended with late-orogenic granites. ► The Triassic magmatism of this belt is precisely constrained during 225–215Ma.
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In recent years, metallopolymers have attracted much attention as precursors to generate magnetic metal/metal alloy nanoparticles (NPs) through pyrolysis or photolysis because they offer the ...advantages of ease of solution processability, atomic level mixing and stoichiometric control over composition. The as-generated NPs usually possess narrow size distributions with precise control of composition and density per unit area. Moreover, patterned NPs can be achieved on various substrates in this way owing to the good film-forming property of metallopolymers and such work is important for many applications based on metal nanostructures. By combining the merits of both the solution processability of metallopolymers and nanoimprint lithography (NIL), a new platform can be created for fabricating bit-patterned media (BPM) and the next-generation of nanoscale ultra-high-density magnetic data storage devices. Furthermore, most of these metallopolymers can be used directly as a negative-tone resist to generate magnetic metallic nanostructures by electron-beam lithography and UV photolithography. Self-assembly and subsequent pyrolysis of metalloblock copolymers can also afford well-patterned magnetic metal or metal alloy NPs
in situ
with periodicity down to dozens of nanometers. In this review, we highlight the use of metallopolymer precursors for the synthesis of magnetic metal/metal alloy NPs and their nanostructures and the related applications.
This tutorial review summarizes the strategies of using metallopolymers as precursors for generating functional magnetic metal/metal alloy NPs and other metal nanostructures.
As one important technique of fuzzy clustering in data mining and pattern recognition, the possibilistic c-means algorithm (PCM) has been widely used in image analysis and knowledge discovery. ...However, it is difficult for PCM to produce a good result for clustering big data, especially for heterogenous data, since it is initially designed for only small structured dataset. To tackle this problem, the paper proposes a high-order PCM algorithm (HOPCM) for big data clustering by optimizing the objective function in the tensor space. Further, we design a distributed HOPCM method based on MapReduce for very large amounts of heterogeneous data. Finally, we devise a privacy-preserving HOPCM algorithm (PPHOPCM) to protect the private data on cloud by applying the BGV encryption scheme to HOPCM, In PPHOPCM, the functions for updating the membership matrix and clustering centers are approximated as polynomial functions to support the secure computing of the BGV scheme. Experimental results indicate that PPHOPCM can effectively cluster a large number of heterogeneous data using cloud computing without disclosure of private data.
MYB transcription factors play important roles in plant responses to biotic and abiotic stress. In this study,
, a R2R3-MYB gene, was cloned from wheat (
L.). TaODORANT1 was localized in the nucleus ...and functioned as a transcriptional activator.
was up-regulated in wheat under PEG6000, NaCl, ABA, and H
O
treatments.
-overexpressing transgenic tobacco plants exhibited higher relative water content and lower water loss rate under drought stress, as well as lower Na
accumulation in leaves under salt stress. The transgenic plants showed higher CAT activity but lower ion leakage, H
O
and malondialdehyde contents under drought and salt stresses. Besides, the transgenic plants also exhibited higher SOD activity under drought stress. Our results also revealed that
overexpression up-regulated the expression of several ROS- and stress-related genes in response to both drought and salt stresses, thus enhancing transgenic tobacco plants tolerance. Our studies demonstrate that TaODORANT1 positively regulates plant tolerance to drought and salt stresses.
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