51 research outputs found

    Trans‐gender things: Objects and the materiality of trans‐femininity in Ming‐Qing China

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    This article demonstrates that objects, more specifically the trans usage of objects that disrupted and rearticulated the normative alignment of objects, sexed bodies and gender embodiments, served a formative role in helping male‐assigned individuals to cross gender boundaries and achieve trans‐femininity in Ming‐Qing China. The examined objects include the foot‐binding cloth for the feminine bodily image of bound feet, the embroidery needle for ‘womanly work’ and concealing underwear for feminine, penetrated sexual acts. This object‐oriented heuristic offers a new culturally specific approach to trans history beyond identarian frameworks and foregrounds the material multiplicity of trans formations and embodiments in Ming‐Qing China

    4,4′-Bipyridine–dimethyl­glyoxime (1/1)

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    In the title compound, C10H8N2·C4H8N2O2, both the dimethyl­glyoxime and the 4,4′-bipyridine mol­ecules have crystallographic C i symmetry. The mol­ecules stack along the a-axis direction with a dihedral angle of 20.4 (8)° between their planes. In the crystal, the components are linked by O—H⋯N hydrogen bonds into alternating chains along [120] and [10]

    Thermal Sensor Boiler Monitoring based on Wireless Sensing

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    To solve the problems of traditional wiring monitoring methods, such as difficulty in wiring, high temperature, and premature aging of the lines, the development status and trend of ZigBee technology were analyzed. A ZigBee-based online gas leakage monitoring system for power plant boilers was designed to avoid gas leakage in these boilers. ZigBee short-range wireless communication technology was used instead of the wired method to complete online monitoring of power plant boilers. Results showed that the system timely monitored the gas leakage and revealed the operating status of the power plant boiler in real time. In addition, the next moment of gas leakage was predicted, which ensured the safe and stable operation of the power plant boiler. In summary, gray system theory provides powerful theoretical support for the leakage status assessment and gas leakage prediction of the boiler. The proposed system ensures the safe, stable, and efficient operation of the power plant boiler

    Thermal Sensor Boiler Monitoring based on Wireless Sensing

    No full text
    To solve the problems of traditional wiring monitoring methods, such as difficulty in wiring, high temperature, and premature aging of the lines, the development status and trend of ZigBee technology were analyzed. A ZigBee-based online gas leakage monitoring system for power plant boilers was designed to avoid gas leakage in these boilers. ZigBee short-range wireless communication technology was used instead of the wired method to complete online monitoring of power plant boilers. Results showed that the system timely monitored the gas leakage and revealed the operating status of the power plant boiler in real time. In addition, the next moment of gas leakage was predicted, which ensured the safe and stable operation of the power plant boiler. In summary, gray system theory provides powerful theoretical support for the leakage status assessment and gas leakage prediction of the boiler. The proposed system ensures the safe, stable, and efficient operation of the power plant boiler

    TAE-Net: Task-Adaptive Embedding Network for Few-Shot Remote Sensing Scene Classification

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    Recently, approaches based on deep learning are quite prevalent in the area of remote sensing scene classification. Though significant success has been achieved, these approaches are still subject to an excess of parameters and extremely dependent on a large quantity of labeled data. In this study, few-shot learning is used for remote sensing scene classification tasks. The goal of few-shot learning is to recognize unseen scene categories given extremely limited labeled samples. For this purpose, a novel task-adaptive embedding network is proposed to facilitate few-shot scene classification of remote sensing images, referred to as TAE-Net. A feature encoder is first trained on the base set to learn embedding features of input images in the pre-training phase. Then in the meta-training phase, a new task-adaptive attention module is designed to yield the task-specific attention, which can adaptively select informative embedding features among the whole task. In the end, in the meta-testing phase, the query image derived from the novel set is predicted by the meta-trained model with limited support images. Extensive experiments are carried out on three public remote sensing scene datasets: UC Merced, WHU-RS19, and NWPU-RESISC45. The experimental results illustrate that our proposed TAE-Net achieves new state-of-the-art performance for few-shot remote sensing scene classification

    Few-Shot Remote Sensing Image Scene Classification Based on Metric Learning and Local Descriptors

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    Scene classification is a critical technology to solve the challenges of image search and image recognition. It has become an indispensable and challenging research topic in the field of remote sensing. At present, most scene classifications are solved by deep neural networks. However, existing methods require large-scale training samples and are not suitable for actual scenarios with only a few samples. For this reason, a framework based on metric learning and local descriptors (MLLD) is proposed to enhance the classification effect of remote sensing scenes on the basis of few-shot. Specifically, MLLD adopts task-level training that is carried out through meta-learning, and meta-knowledge is learned to improve the model’s ability to recognize different categories. Moreover, Manifold Mixup is introduced by MLLD as a feature processor for the hidden layer of deep neural networks to increase the low confidence space for smoother decision boundaries and simpler hidden layer representations. In the end, a learnable metric is introduced; the nearest category of the image is matched by measuring the similarity of local descriptors. Experiments are conducted on three public datasets: UC Merced, WHU-RS19, and NWPU-RESISC45. Experimental results show that the proposed scene classification method can achieve the most advanced results on limited datasets

    Few-Shot Remote Sensing Image Scene Classification Based on Metric Learning and Local Descriptors

    No full text
    Scene classification is a critical technology to solve the challenges of image search and image recognition. It has become an indispensable and challenging research topic in the field of remote sensing. At present, most scene classifications are solved by deep neural networks. However, existing methods require large-scale training samples and are not suitable for actual scenarios with only a few samples. For this reason, a framework based on metric learning and local descriptors (MLLD) is proposed to enhance the classification effect of remote sensing scenes on the basis of few-shot. Specifically, MLLD adopts task-level training that is carried out through meta-learning, and meta-knowledge is learned to improve the model’s ability to recognize different categories. Moreover, Manifold Mixup is introduced by MLLD as a feature processor for the hidden layer of deep neural networks to increase the low confidence space for smoother decision boundaries and simpler hidden layer representations. In the end, a learnable metric is introduced; the nearest category of the image is matched by measuring the similarity of local descriptors. Experiments are conducted on three public datasets: UC Merced, WHU-RS19, and NWPU-RESISC45. Experimental results show that the proposed scene classification method can achieve the most advanced results on limited datasets

    Determination of Aroma Composition of Santalum album linn by Solid-phase Microextraction-Gas Chromatography-Mass Spectrometry

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    [Objectives] This study aimed to determine the volatile components in Santalum album linn and gradually clarify the aroma composition of S. album linn. [Methods] Solid-phase microextraction method was used to obtain the volatile components of S. album linn. The aroma components were analyzed by gas chromatography-mass spectrometry and their relative contents were calculated using the area normalization method. [Results] In a dry state at room temperature, 39 chemical components were identified from S. album linn, mainly olefins (91.15%), alkanes (3.00%), alcohols (2.56%), esters (2.19%), ketones (0.55%), aldehydes (0.41%) and heterocyclics (0.14%). [Conclusions] This method has the advantages of low sample consumption, easy operation, rapid identification of aroma components and high sensitivity, and can effectively separate and determine volatile components in S. album linn, realizing the rapid identification of different S. album linn varieties and providing technical support for further research on Chinese medicinal materials

    Novel Bacterial Cellulose/Gelatin Hydrogels as 3D Scaffolds for Tumor Cell Culture

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    Three-dimensional (3D) cells in vitro culture are becoming increasingly popular in cancer research because some important signals are lost when cells are cultured in a two-dimensional (2D) substrate. In this work, bacterial cellulose (BC)/gelatin hydrogels were successfully synthesized and were investigated as scaffolds for cancer cells in vitro culture to simulate tumor microenvironment. Their properties and ability to support normal growth of cancer cells were evaluated. In particular, the human breast cancer cell line (MDA-MD-231) was seeded into BC/gelatin scaffolds to investigate their potential in 3D cell in vitro culture. MTT proliferation assay, scanning electron microscopy, hematoxylin and eosin staining and immunofluorescence were used to determine cell proliferation, morphology, adhesion, infiltration, and receptor expression. The in vitro MDA-MD-231 cell culture results demonstrated that cells cultured on the BC/gelatin scaffolds had significant adhesion, proliferation, ingrowth and differentiation. More importantly, MDA-MD-231 cells cultured in BC/gelatin scaffolds retained triple-negative receptor expression, demonstrating that BC/gelatin scaffolds could be used as ideal in vitro culture scaffolds for tumor cells
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