المؤلفون: WANG Zheng, GOU Junli, XU Shihao, SHAN Jianqiang

المصدر: Yuanzineng kexue jishu, Vol 58, Iss 1, Pp 84-92 (2024)

مصطلحات موضوعية: heat pipe cooled reactor, startup characteristic, high-temperature heat pipe, equivalent network model, Nuclear engineering. Atomic power, TK9001-9401, Nuclear and particle physics. Atomic energy. Radioactivity, QC770-798

الوصف: Heat pipe cooled reactors (HPRs) have been considered as one of the most promising candidates for deep space and deep-sea missions due to their advantages of simple structure, high power density and high reliability, etc. To investigate the transient characteristics of such heat pipe cooled reactors, including startup, shutdown, power transients and accident conditions, it is necessary to develop suitable and efficient models for describing the core, the heat pipe and the power conversion system. Especially, for the startup process, an accurate and efficient model for the simulation of high-temperature heat pipe startup from the frozen state is indispensable. In this study, two transient models based on the dusty gas model (DGM) were developed. The first model (model 1) solved the mass and momentum equations of vapor flow, while the second model (model 2) simplified the vapor flow as a 1D steady-state heat conduction problem using an equivalent network model. The models considered the evaporation/condensation flux at the vapor/liquid interface using the kinetic theory of gases. The wick and wall were modeled using an improved network model, which took into account the phase transition of the working fluid in the wick. Different methods were used to solve these models in this paper. For the model considering the vapor flow, the finite-difference discretization scheme and the SIMPLEC algorithm were used to solve the governing equations. For the equivalent network model, a loosely coupled numerical method is employed. The solution of wick and wall equations was in a transient state, while the equivalent heat conduction equation of the vapor flow was solved in a steady state mode. The alternating direction implicit (ADI) was adopted to solve the equations for the wick and wall regions. The startup experiments of high-temperature heat pipes with different working fluids are simulated to validate the accuracy of these models. The results indicate that the simulation results agree well with the experimental data. Compared with the flat-front startup model, the temperature distribution calculated by model 2 is more accurate, and the description of startup is more plausible. Meanwhile, model 2 gives quite reasonable results although it is less accurate than the model 1. The calculation efficiency of the model 2 is significantly improved compared to model 1. Consequently, in the feasibility study stage of an HPR system, the simplified equivalent network model (model 2) with considering both accuracy and efficiency is suitable for the simulation of heat pipe startup.

وصف الملف: electronic resource

العلاقة: https://doaj.org/toc/1000-6931Test

الوصول الحر: https://doaj.org/article/606342853e764b5c976fb3d1c015a819Test

المؤلفون: Rao, Haocong, Xu, Shihao, Hu, Xiping, Cheng, Jun, Hu, Bin

المصدر: In IJCAI, 2021

مصطلحات موضوعية: Computer Science - Computer Vision and Pattern Recognition

الوصف: Skeleton-based person re-identification (Re-ID) is an emerging open topic providing great value for safety-critical applications. Existing methods typically extract hand-crafted features or model skeleton dynamics from the trajectory of body joints, while they rarely explore valuable relation information contained in body structure or motion. To fully explore body relations, we construct graphs to model human skeletons from different levels, and for the first time propose a Multi-level Graph encoding approach with Structural-Collaborative Relation learning (MG-SCR) to encode discriminative graph features for person Re-ID. Specifically, considering that structurally-connected body components are highly correlated in a skeleton, we first propose a multi-head structural relation layer to learn different relations of neighbor body-component nodes in graphs, which helps aggregate key correlative features for effective node representations. Second, inspired by the fact that body-component collaboration in walking usually carries recognizable patterns, we propose a cross-level collaborative relation layer to infer collaboration between different level components, so as to capture more discriminative skeleton graph features. Finally, to enhance graph dynamics encoding, we propose a novel self-supervised sparse sequential prediction task for model pre-training, which facilitates encoding high-level graph semantics for person Re-ID. MG-SCR outperforms state-of-the-art skeleton-based methods, and it achieves superior performance to many multi-modal methods that utilize extra RGB or depth features. Our codes are available at https://github.com/Kali-Hac/MG-SCRTest.
Comment: Accepted at IJCAI 2021 Main Track. Sole copyright holder is IJCAI. Codes are available at https://github.com/Kali-Hac/MG-SCRTest

الوصول الحر: http://arxiv.org/abs/2106.03069Test

المؤلفون: Zhang, Qipeng, Xu, Shihao, Zhang, Lanpeng, Yang, Liang, Jiang, Changlong

المساهمون: National Natural Science Foundation of China

المصدر: Advanced Science ; ISSN 2198-3844 2198-3844

الوصف: Advancing a metal‐free room temperature phosphorescent (RTP) material that exhibits multicolor emission, remarkable RTP lifetime, and high quantum yield still faces the challenge of achieving intersystem crossing between singly and triplet excited states, as well as the rapid decay of triplet excited states due to nonradiative losses. In this study, a novel strategy is proposed to address these limitations by incorporating o‐phenylenediamine, which generates multiple luminescent centers, and long‐chain polyacrylic acid to synthesize carbonized polymer dots (CPDs). These CPDs are then embedded in a rigid B 2 O 3 matrix, effectively limiting nonradiative losses through the synergistic effects of polymer cross‐linking and the rigid matrix. The resulting CPD‐based materials exhibit remarkable ultralong phosphorescence in shades of blue and lime green, with a visible lifetime of up to 49 s and a high phosphorescence quantum yield. Simultaneously, this study demonstrates the practical applicability of these excellent material properties in anti‐counterfeiting and information encryption.

الإتاحة: https://doi.org/10.1002/advs.202400781Test

المؤلفون: Wang, Luhui, Zhu, Haiyan, Shi, Zhehao, Chen, Bo, Huang, Huirong, Lin, Ganglian, Li, Jiacheng, Yu, Haitao, Xu, Shihao, Chen, Gang, Ou, Rongying, Dai, Chunxiu

المصدر: Theranostics ; volume 14, issue 1, page 75-95 ; ISSN 1838-7640

مصطلحات موضوعية: Pharmacology, Toxicology and Pharmaceutics (miscellaneous), Medicine (miscellaneous)

الإتاحة: https://doi.org/10.7150/thno.83051Test
https://www.thno.org/v14p0075.htmTest

المؤلفون: XU, Shihao, WEI, Changzhu, ZHANG, Litao, MU, Rongjun

المساهمون: National Natural Science Foundation of China

المصدر: Chinese Journal of Aeronautics ; volume 37, issue 4, page 421-435 ; ISSN 1000-9361

مصطلحات موضوعية: Mechanical Engineering, Aerospace Engineering

الإتاحة: https://doi.org/10.1016/j.cja.2023.12.020Test
https://api.elsevier.com/content/article/PII:S1000936123004351?httpAccept=text/xmlTest
https://api.elsevier.com/content/article/PII:S1000936123004351?httpAccept=text/plainTest

المؤلفون: Xu, Shihao, Rao, Haocong, Hu, Xiping, Hu, Bin

مصطلحات موضوعية: Computer Science - Computer Vision and Pattern Recognition

الوصف: In this paper, we focus on unsupervised representation learning for skeleton-based action recognition. Existing approaches usually learn action representations by sequential prediction but they suffer from the inability to fully learn semantic information. To address this limitation, we propose a novel framework named Prototypical Contrast and Reverse Prediction (PCRP), which not only creates reverse sequential prediction to learn low-level information (e.g., body posture at every frame) and high-level pattern (e.g., motion order), but also devises action prototypes to implicitly encode semantic similarity shared among sequences. In general, we regard action prototypes as latent variables and formulate PCRP as an expectation-maximization task. Specifically, PCRP iteratively runs (1) E-step as determining the distribution of prototypes by clustering action encoding from the encoder, and (2) M-step as optimizing the encoder by minimizing the proposed ProtoMAE loss, which helps simultaneously pull the action encoding closer to its assigned prototype and perform reverse prediction task. Extensive experiments on N-UCLA, NTU 60, and NTU 120 dataset present that PCRP outperforms state-of-the-art unsupervised methods and even achieves superior performance over some of supervised methods. Codes are available at https://github.com/Mikexu007/PCRPTest.
Comment: Codes are available at https://github.com/Mikexu007/PCRPTest

الوصول الحر: http://arxiv.org/abs/2011.07236Test

المؤلفون: Rao, Haocong, Xu, Shihao, Hu, Xiping, Cheng, Jun, Hu, Bin

مصطلحات موضوعية: Computer Science - Computer Vision and Pattern Recognition

الوصف: Action recognition via 3D skeleton data is an emerging important topic in these years. Most existing methods either extract hand-crafted descriptors or learn action representations by supervised learning paradigms that require massive labeled data. In this paper, we for the first time propose a contrastive action learning paradigm named AS-CAL that can leverage different augmentations of unlabeled skeleton data to learn action representations in an unsupervised manner. Specifically, we first propose to contrast similarity between augmented instances (query and key) of the input skeleton sequence, which are transformed by multiple novel augmentation strategies, to learn inherent action patterns ("pattern-invariance") of different skeleton transformations. Second, to encourage learning the pattern-invariance with more consistent action representations, we propose a momentum LSTM, which is implemented as the momentum-based moving average of LSTM based query encoder, to encode long-term action dynamics of the key sequence. Third, we introduce a queue to store the encoded keys, which allows our model to flexibly reuse proceeding keys and build a more consistent dictionary to improve contrastive learning. Last, by temporally averaging the hidden states of action learned by the query encoder, a novel representation named Contrastive Action Encoding (CAE) is proposed to represent human's action effectively. Extensive experiments show that our approach typically improves existing hand-crafted methods by 10-50% top-1 accuracy, and it can achieve comparable or even superior performance to numerous supervised learning methods.
Comment: Accepted by Information Sciences. Our codes are available at https://github.com/Mikexu007/AS-CALTest

الوصول الحر: http://arxiv.org/abs/2008.00188Test

المؤلفون: Xu, Shihao, Fang, Jing, Hu, Xiping, Ngai, Edith, Wang, Wei, Guo, Yi, Leung, Victor C. M.

مصطلحات موضوعية: Computer Science - Human-Computer Interaction, Computer Science - Machine Learning, Statistics - Machine Learning

الوصف: Human gait refers to a daily motion that represents not only mobility, but it can also be used to identify the walker by either human observers or computers. Recent studies reveal that gait even conveys information about the walker's emotion. Individuals in different emotion states may show different gait patterns. The mapping between various emotions and gait patterns provides a new source for automated emotion recognition. Compared to traditional emotion detection biometrics, such as facial expression, speech and physiological parameters, gait is remotely observable, more difficult to imitate, and requires less cooperation from the subject. These advantages make gait a promising source for emotion detection. This article reviews current research on gait-based emotion detection, particularly on how gait parameters can be affected by different emotion states and how the emotion states can be recognized through distinct gait patterns. We focus on the detailed methods and techniques applied in the whole process of emotion recognition: data collection, preprocessing, and classification. At last, we discuss possible future developments of efficient and effective gait-based emotion recognition using the state of the art techniques on intelligent computation and big data.

الوصول الحر: http://arxiv.org/abs/2003.11461Test

المؤلفون: Xu, Shihao

المصدر: Proceedings of the 2nd International Conference on Culture, Design and Social Development (CDSD 2022) ; page 393-399 ; ISBN 9782384760176 9782384760183

الإتاحة: https://doi.org/10.2991/978-2-38476-018-3_45Test

المؤلفون: Chen, Juntao, Xu, Cuidi, Yang, Kun, Gao, Rifeng, Cao, Yirui, Liang, Lifei, Chen, Siyue, Xu, Shihao, Rong, Ruiming, Wang, Jina, Zhu, Tongyu

المصدر: Nature Communications ; volume 14, issue 1 ; ISSN 2041-1723

مصطلحات موضوعية: General Physics and Astronomy, General Biochemistry, Genetics and Molecular Biology, General Chemistry, Multidisciplinary

الوصف: Ischemia reperfusion injury (IRI) is a common cause of acute kidney injury (AKI). The role of N 6- methyladenosine (m6A) modification in AKI remains unclear. Here, we characterize the role of AlkB homolog 5 (ALKBH5) and m6A modification in an I/R-induced renal injury model in male mice. Alkbh5 -knockout mice exhibit milder pathological damage and better renal function than wild-type mice post-IRI, whereas Alkbh5 -knockin mice show contrary results. Also conditional knockout of Alkbh5 in the tubular epithelial cells alleviates I/R-induced AKI and fibrosis. CCL28 is identified as a target of ALKBH5. Furthermore, Ccl28 mRNA stability increases with Alkbh5 deficiency, mediating by the binding of insulin-like growth factor 2 binding protein 2. Treg recruitment is upregulated and inflammatory cells are inhibited by the increased CCL28 level in IRI- Alkbh5 fl/fl Ksp Cre mice. The ALKBH5 inhibitor IOX1 exhibits protective effects against I/R-induced AKI. In summary, inhibition of ALKBH5 promotes the m6A modifications of Ccl 28 mRNA, enhancing its stability, and regulating the Treg/inflammatory cell axis. ALKBH5 and this axis is a potential AKI treatment target.

الإتاحة: https://doi.org/10.1038/s41467-023-36747-yTest
https://www.nature.com/articles/s41467-023-36747-y.pdfTest
https://www.nature.com/articles/s41467-023-36747-yTest