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1. STCF Conceptual Design Report: Volume 1 -- Physics & Detector

Author

Achasov, M., Ai, X. C., Aliberti, R., An, L. P., An, Q., Bai, X. Z., Bai, Y., Bakina, O., Barnyakov, A., Blinov, V., Bobrovnikov, V., Bodrov, D., Bogomyagkov, A., Bondar, A., Boyko, I., Bu, Z. H., Cai, F. M., Cai, H., Cao, J. J., Cao, Q. H., Cao, Z., Chang, Q., Chao, K. T., Chen, D. Y., Chen, H., Chen, H. X., Chen, J. F., Chen, K., Chen, L. L., Chen, P., Chen, S. L., Chen, S. M., Chen, S., Chen, S. P., Chen, W., Chen, X. F., Chen, X., Chen, Y., Chen, Y. Q., Cheng, H. Y., Cheng, J., Cheng, S., Dai, J. P., Dai, L. Y., Dai, X. C., Dedovich, D., Denig, A., Denisenko, I., Ding, D. Z., Dong, L. Y., Dong, W. H., Druzhinin, V., Du, D. S., Du, Y. J., Du, Z. G., Duan, L. M., Epifanov, D., Fan, Y. L., Fang, S. S., Fang, Z. J., Fedotovich, G., Feng, C. Q., Feng, X., Feng, Y. T., Fu, J. L., Gao, J., Ge, P. S., Geng, C. Q., Geng, L. S., Gilman, A., Gong, L., Gong, T., Gradl, W., Gu, J. L., Escalante, A. G., Gui, L. C., Guo, F. K., Guo, J. C., Guo, J., Guo, Y. P., Guo, Z. H., Guskov, A., Han, K. L., Han, L., Han, M., Hao, X. Q., He, J. B., He, S. Q., He, X. G., He, Y. L., He, Z. B., Heng, Z. X., Hou, B. L., Hou, T. J., Hou, Y. R., Hu, C. Y., Hu, H. M., Hu, K., Hu, R. J., Hu, X. H., Hu, Y. C., Hua, J., Huang, G. S., Huang, J. S., Huang, M., Huang, Q. Y., Huang, W. Q., Huang, X. T., Huang, X. J., Huang, Y. B., Huang, Y. S., Hüsken, N., Ivanov, V., Ji, Q. P., Jia, J. J., Jia, S., Jia, Z. K., Jiang, H. B., Jiang, J., Jiang, S. Z., Jiao, J. B., Jiao, Z., Jing, H. J., Kang, X. L., Kang, X. S., Ke, B. C., Kenzie, M., Khoukaz, A., Koop, I., Kravchenko, E., Kuzmin, A., Lei, Y., Levichev, E., Li, C. H., Li, C., Li, D. Y., Li, F., Li, G., Li, H. B., Li, H., Li, H. N., Li, H. J., Li, H. L., Li, J. M., Li, J., Li, L., Li, L. Y., Li, N., Li, P. R., Li, R. H., Li, S., Li, T., Li, W. J., Li, X. H., Li, X. Q., Li, Y., Li, Y. Y., Li, Z. J., Liang, H., Liang, J. H., Liao, G. R., Liao, L. Z., Liao, Y., Lin, C. X., Lin, X. S., Liu, B. J., Liu, C. W., Liu, D., Liu, F., Liu, G. M., Liu, H. B., Liu, J., Liu, J. J., Liu, J. B., Liu, K., Liu, K. Y., Liu, L., Liu, Q., Liu, S. B., Liu, T., Liu, X., Liu, Y. W., Liu, Y., Liu, Y. L., Liu, Z. Q., Liu, Z. Y., Liu, Z. W., Logashenko, I., Long, Y., Lu, C. G., Lu, N., Lü, Q. F., Lu, Y., Lv, Z., Lukin, P., Luo, F. J., Luo, T., Luo, X. F., Lyu, H. J., Lyu, X. R., Ma, J. P., Ma, P., Ma, Y., Maas, F., Malde, S., Matvienko, D., Meng, Z. X., Mitchell, R., Dias, J. M., Nefediev, A., Nefedov, Y., Olsen, S. L., Ouyang, Q., Pakhlov, P., Pakhlova, G., Pan, X., Pan, Y., Passemar, E., Pei, Y. P., Peng, H. P., Peng, L., Peng, X. Y., Peng, X. J., Peters, K., Pivovarov, S., Pyata, E., Qi, B. B., Qi, Y. Q., Qian, W. B., Qian, Y., Qiao, C. F., Qin, J. J., Qin, L. Q., Qin, X. S., Qiu, T. L., Rademacker, J., Redmer, C. F., Sang, H. Y., Saur, M., Shan, W., Shan, X. Y., Shang, L. L., Shao, M., Shekhtman, L., Shen, C. P., Shen, J. M., Shen, Z. T., Shi, H. C., Shi, X. D., Shwartz, B., Sokolov, A., Song, J. J., Song, W. M., Song, Y., Song, Y. X., Sukharev, A., Sun, J. F., Sun, L., Sun, X. M., Sun, Y. J., Sun, Z. P., Tang, J., Tang, S. S., Tang, Z. B., Tian, C. H., Tian, J. S., Tikhonov, Y., Todyshev, K., Uglov, T., Vorobyev, V., Wan, B. D., Wang, B. L., Wang, B., Wang, D. Y., Wang, G. Y., Wang, G. L., Wang, H. L., Wang, J., Wang, J. H., Wang, J. C., Wang, M. L., Wang, R., Wang, S. B., Wang, W., Wang, W. P., Wang, X. C., Wang, X. D., Wang, X. L., Wang, X. P., Wang, X. F., Wang, Y. D., Wang, Y. P., Wang, Y. Q., Wang, Y. L., Wang, Y. G., Wang, Z. Y., Wang, Z. L., Wang, Z. G., Wei, D. H., Wei, X. L., Wei, X. M., Wen, Q. G., Wen, X. J., Wilkinson, G., Wu, B., Wu, J. J., Wu, L., Wu, P. W., Wu, T. W., Wu, Y. S., Xia, L., Xiang, T., Xiao, C. W., Xiao, D., Xiao, M., Xie, Y. H., Xing, Y., Xing, Z. Z., Xiong, X. N., Xu, F. R., Xu, J., Xu, L. L., Xu, Q. N., Xu, X. C., Xu, X. P., Xu, Y. C., Xu, Y. P., Xu, Y., Xu, Z. Z., Xuan, D. W., Xue, F. F., Yan, L., Yan, M. J., Yan, W. B., Yan, W. C., Yan, X. S., Yang, B. F., Yang, C., Yang, H. J., Yang, H. R., Yang, H. T., Yang, J. F., Yang, S. L., Yang, Y. D., Yang, Y. H., Yang, Y. S., Yang, Y. L., Yang, Z. Y., Yao, D. L., Yin, H., Yin, X. H., Yokozaki, N., You, S. Y., You, Z. Y., Yu, C. X., Yu, F. S., Yu, G. L., Yu, H. L., Yu, J. S., Yu, J. Q., Yuan, L., Yuan, X. B., Yue, Y. F., Zeng, M., Zeng, S., Zhang, A. L., Zhang, B. W., Zhang, G. Y., Zhang, G. Q., Zhang, H. J., Zhang, H. B., Zhang, J. Y., Zhang, J. L., Zhang, J., Zhang, L., Zhang, L. M., Zhang, R., Zhang, S. L., Zhang, T., Zhang, X., Zhang, Y., Zhang, Y. X., Zhang, Y. T., Zhang, Y. F., Zhang, Y. C., Zhang, Y. M., Zhang, Y. L., Zhang, Z. H., Zhang, Z. Y., Zhao, H. Y., Zhao, J., Zhao, L., Zhao, M. G., Zhao, Q., Zhao, R. G., Zhao, R. P., Zhao, Z. G., Zhao, Z. X., Zhemchugov, A., Zheng, B., Zheng, L., Zheng, Q. B., Zheng, R., Zheng, Y. H., Zhong, X. H., Zhou, H. J., Zhou, H. Q., Zhou, H., Zhou, S. H., Zhou, X., Zhou, X. K., Zhou, X. R., Zhou, Y. L., Zhou, Y., Zhou, Y. X., Zhou, Z. Y., Zhu, J. Y., Zhu, K., Zhu, R. D., Zhu, R. L., Zhu, S. H., Zhu, Y. C., Zhu, Z. A., Zhukova, V., Zhulanov, V., Zou, B. S., and Zuo, Y. B.

Subjects
High Energy Physics - Experiment, High Energy Physics - Phenomenology, Physics - Instrumentation and Detectors
Abstract

The Super $\tau$-Charm facility (STCF) is an electron-positron collider proposed by the Chinese particle physics community. It is designed to operate in a center-of-mass energy range from 2 to 7 GeV with a peak luminosity of $0.5\times 10^{35}{\rm cm}^{-2}{\rm s}^{-1}$ or higher. The STCF will produce a data sample about a factor of 100 larger than that by the present $\tau$-Charm factory -- the BEPCII, providing a unique platform for exploring the asymmetry of matter-antimatter (charge-parity violation), in-depth studies of the internal structure of hadrons and the nature of non-perturbative strong interactions, as well as searching for exotic hadrons and physics beyond the Standard Model. The STCF project in China is under development with an extensive R\&D program. This document presents the physics opportunities at the STCF, describes conceptual designs of the STCF detector system, and discusses future plans for detector R\&D and physics case studies.

Published
2023
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2. $^{197}$Au($\gamma,\,xn;\,x\,=\,1\thicksim9$) Reaction Cross Section Measurements using Laser-Driven Ultra-Intense $\gamma$-Ray Source

Author

Wu, D., Lan, H. Y., Zhang, J. Y., Liu, J. X., Lu, H. G., Lv, J. F., Wu, X. Z., Zhang, H., Cai, J., Ma, Q. Y., Xia, Y. H., Wang, Z. N., Wang, M. Z., Yang, Z. Y., Xu, X. L., Geng, Y. X., Zhao, Y. Y., Lin, C., Ma, W. J., Yu, J. Q., Wang, H. R., Liu, F. L., He, C. Y., Guo, B., Zhu, P., Zhang, G. Q., Wang, N. Y., Ma, Y. G., and Yan, X. Q.

Subjects
Nuclear Experiment, Physics - Plasma Physics
Abstract

We present a new method for the measurements of photonuclear reaction flux-weighted average cross sections and isomeric ratios using a laser-driven bremsstrahlung $\gamma$-ray source. An ultra-bright ultra-fast 60$\,\thicksim\,$250 MeV bremsstrahlung $\gamma$-ray source was established using the 200 TW laser facility in the Compact Laser Plasma Accelerator Laboratory, Peking University, which could cover the energy range from knocking out neutrons to producing pions. Stable quasi-monoenergetic electron beams were generated via laser wakefield acceleration with a charge of 300$\,\thicksim\,$600 pC per shot. The averaged $\gamma$-ray intensities ($\geqslant$8 MeV) were higher than 10$^{8}$ per shot and the instantaneous intensities can reach above 10$^{19}$ s$^{-1}$ with a duration time about 6.7 ps. $^{65}$Cu($\gamma,\,n$)$^{64}$Cu and $^{27}$Al($\gamma,\,x$)$^{24}$Na reactions were used as $\gamma$-ray flux monitors in the experiments. The flux-weighted average cross sections and isomeric ratios of $^{197}$Au($\gamma,\,xn;\,x\,=\,1\thicksim9$) reactions were analyzed through activation measurements. The results showed good agreement with previous works and proved this method to be accurate. The $^{197}$Au($\gamma,\,xn;\,x\,=\,7\thicksim\,9$) reaction cross sections were first achieved with the highest threshold energy of 71.410 MeV. Theoretical cross sections of TALYS 1.9 were calculated to compare with experiment results. This method offered a unique way of gaining insight into photonuclear reaction research, especially for short-lived isomers which extremely lack experimental data.

Published
2022

3. Automatic Velocity Picking Using a Multi-Information Fusion Deep Semantic Segmentation Network

Author

Wang, H. T., Zhang, J. S., Zhao, Z. X., Zhang, C. X., Li, L., Yang, Z. Y., and Geng, W. F.

Subjects
Computer Science - Computer Vision and Pattern Recognition, Electrical Engineering and Systems Science - Image and Video Processing
Abstract

Velocity picking, a critical step in seismic data processing, has been studied for decades. Although manual picking can produce accurate normal moveout (NMO) velocities from the velocity spectra of prestack gathers, it is time-consuming and becomes infeasible with the emergence of large amount of seismic data. Numerous automatic velocity picking methods have thus been developed. In recent years, deep learning (DL) methods have produced good results on the seismic data with medium and high signal-to-noise ratios (SNR). Unfortunately, it still lacks a picking method to automatically generate accurate velocities in the situations of low SNR. In this paper, we propose a multi-information fusion network (MIFN) to estimate stacking velocity from the fusion information of velocity spectra and stack gather segments (SGS). In particular, we transform the velocity picking problem into a semantic segmentation problem based on the velocity spectrum images. Meanwhile, the information provided by SGS is used as a prior in the network to assist segmentation. The experimental results on two field datasets show that the picking results of MIFN are stable and accurate for the scenarios with medium and high SNR, and it also performs well in low SNR scenarios.

Published
2022
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4. STCF conceptual design report (Volume 1): Physics & detector

Author

Achasov, M., Ai, X. C., An, L. P., Aliberti, R., An, Q., Bai, X. Z., Bai, Y., Bakina, O., Barnyakov, A., Blinov, V., Bobrovnikov, V., Bodrov, D., Bogomyagkov, A., Bondar, A., Boyko, I., Bu, Z. H., Cai, F. M., Cai, H., Cao, J. J., Cao, Q. H., Cao, X., Cao, Z., Chang, Q., Chao, K. T., Chen, D. Y., Chen, H., Chen, H. X., Chen, J. F., Chen, K., Chen, L. L., Chen, P., Chen, S. L., Chen, S. M., Chen, S., Chen, S. P., Chen, W., Chen, X., Chen, X. F., Chen, X. R., Chen, Y., Chen, Y. Q., Cheng, H. Y., Cheng, J., Cheng, S., Cheng, T. G., Dai, J. P., Dai, L. Y., Dai, X. C., Dedovich, D., Denig, A., Denisenko, I., Dias, J. M., Ding, D. Z., Dong, L. Y., Dong, W. H., Druzhinin, V., Du, D. S., Du, Y. J., Du, Z. G., Duan, L. M., Epifanov, D., Fan, Y. L., Fang, S. S., Fang, Z. J., Fedotovich, G., Feng, C. Q., Feng, X., Feng, Y. T., Fu, J. L., Gao, J., Gao, Y. N., Ge, P. S., Geng, C. Q., Geng, L. S., Gilman, A., Gong, L., Gong, T., Gou, B., Gradl, W., Gu, J. L., Guevara, A., Gui, L. C., Guo, A. Q., Guo, F. K., Guo, J. C., Guo, J., Guo, Y. P., Guo, Z. H., Guskov, A., Han, K. L., Han, L., Han, M., Hao, X. Q., He, J. B., He, S. Q., He, X. G., He, Y. L., He, Z. B., Heng, Z. X., Hou, B. L., Hou, T. J., Hou, Y. R., Hu, C. Y., Hu, H. M., Hu, K., Hu, R. J., Hu, W. H., Hu, X. H., Hu, Y. C., Hua, J., Huang, G. S., Huang, J. S., Huang, M., Huang, Q. Y., Huang, W. Q., Huang, X. T., Huang, X. J., Huang, Y. B., Huang, Y. S., Hüsken, N., Ivanov, V., Ji, Q. P., Jia, J. J., Jia, S., Jia, Z. K., Jiang, H. B., Jiang, J., Jiang, S. Z., Jiao, J. B., Jiao, Z., Jing, H. J., Kang, X. L., Kang, X. S., Ke, B. C., Kenzie, M., Khoukaz, A., Koop, I., Kravchenko, E., Kuzmin, A., Lei, Y., Levichev, E., Li, C. H., Li, C., Li, D. Y., Li, F., Li, G., Li, G., Li, H. B., Li, H., Li, H. N., Li, H. J., Li, H. L., Li, J. M., Li, J., Li, L., Li, L., Li, L. Y., Li, N., Li, P. R., Li, R. H., Li, S., Li, T., Li, W. J., Li, X., Li, X. H., Li, X. Q., Li, X. H., Li, Y., Li, Y. Y., Li, Z. J., Liang, H., Liang, J. H., Liang, Y. T., Liao, G. R., Liao, L. Z., Liao, Y., Lin, C. X., Lin, D. X., Lin, X. S., Liu, B. J., Liu, C. W., Liu, D., Liu, F., Liu, G. M., Liu, H. B., Liu, J., Liu, J. J., Liu, J. B., Liu, K., Liu, K. Y., Liu, K., Liu, L., Liu, Q., Liu, S. B., Liu, T., Liu, X., Liu, Y. W., Liu, Y., Liu, Y. L., Liu, Z. Q., Liu, Z. Y., Liu, Z. W., Logashenko, I., Long, Y., Lu, C. G., Lu, J. X., Lu, N., Lü, Q. F., Lu, Y., Lu, Y., Lu, Z., Lukin, P., Luo, F. J., Luo, T., Luo, X. F., Lyu, H. J., Lyu, X. R., Ma, J. P., Ma, P., Ma, Y., Ma, Y. M., Maas, F., Malde, S., Matvienko, D., Meng, Z. X., Mitchell, R., Nefediev, A., Nefedov, Y., Olsen, S. L., Ouyang, Q., Pakhlov, P., Pakhlova, G., Pan, X., Pan, Y., Passemar, E., Pei, Y. P., Peng, H. P., Peng, L., Peng, X. Y., Peng, X. J., Peters, K., Pivovarov, S., Pyata, E., Qi, B. B., Qi, Y. Q., Qian, W. B., Qian, Y., Qiao, C. F., Qin, J. J., Qin, J. J., Qin, L. Q., Qin, X. S., Qiu, T. L., Rademacker, J., Redmer, C. F., Sang, H. Y., Saur, M., Shan, W., Shan, X. Y., Shang, L. L., Shao, M., Shekhtman, L., Shen, C. P., Shen, J. M., Shen, Z. T., Shi, H. C., Shi, X. D., Shwartz, B., Sokolov, A., Song, J. J., Song, W. M., Song, Y., Song, Y. X., Sukharev, A., Sun, J. F., Sun, L., Sun, X. M., Sun, Y. J., Sun, Z. P., Tang, J., Tang, S. S., Tang, Z. B., Tian, C. H., Tian, J. S., Tian, Y., Tikhonov, Y., Todyshev, K., Uglov, T., Vorobyev, V., Wan, B. D., Wang, B. L., Wang, B., Wang, D. Y., Wang, G. Y., Wang, G. L., Wang, H. L., Wang, J., Wang, J. H., Wang, J. C., Wang, M. L., Wang, R., Wang, R., Wang, S. B., Wang, W., Wang, W. P., Wang, X. C., Wang, X. D., Wang, X. L., Wang, X. L., Wang, X. P., Wang, X. F., Wang, Y. D., Wang, Y. P., Wang, Y. Q., Wang, Y. L., Wang, Y. G., Wang, Z. Y., Wang, Z. Y., Wang, Z. L., Wang, Z. G., Wei, D. H., Wei, X. L., Wei, X. M., Wen, Q. G., Wen, X. J., Wilkinson, G., Wu, B., Wu, J. J., Wu, L., Wu, P., Wu, T. W., Wu, Y. S., Xia, L., Xiang, T., Xiao, C. W., Xiao, D., Xiao, M., Xie, K. P., Xie, Y. H., Xing, Y., Xing, Z. Z., Xiong, X. N., Xu, F. R., Xu, J., Xu, L. L., Xu, Q. N., Xu, X. C., Xu, X. P., Xu, Y. C., Xu, Y. P., Xu, Y., Xu, Z. Z., Xuan, D. W., Xue, F. F., Yan, L., Yan, M. J., Yan, W. B., Yan, W. C., Yan, X. S., Yang, B. F., Yang, C., Yang, H. J., Yang, H. R., Yang, H. T., Yang, J. F., Yang, S. L., Yang, Y. D., Yang, Y. H., Yang, Y. S., Yang, Y. L., Yang, Z. W., Yang, Z. Y., Yao, D. L., Yin, H., Yin, X. H., Yokozaki, N., You, S. Y., You, Z. Y., Yu, C. X., Yu, F. S., Yu, G. L., Yu, H. L., Yu, J. S., Yu, J. Q., Yuan, L., Yuan, X. B., Yuan, Z. Y., Yue, Y. F., Zeng, M., Zeng, S., Zhang, A. L., Zhang, B. W., Zhang, G. Y., Zhang, G. Q., Zhang, H. J., Zhang, H. B., Zhang, J. Y., Zhang, J. L., Zhang, J., Zhang, L., Zhang, L. M., Zhang, Q. A., Zhang, R., Zhang, S. L., Zhang, T., Zhang, X., Zhang, Y., Zhang, Y. J., Zhang, Y. X., Zhang, Y. T., Zhang, Y. F., Zhang, Y. C., Zhang, Y., Zhang, Y., Zhang, Y. M., Zhang, Y. L., Zhang, Z. H., Zhang, Z. Y., Zhang, Z. Y., Zhao, H. Y., Zhao, J., Zhao, L., Zhao, M. G., Zhao, Q., Zhao, R. G., Zhao, R. P., Zhao, Y. X., Zhao, Z. G., Zhao, Z. X., Zhemchugov, A., Zheng, B., Zheng, L., Zheng, Q. B., Zheng, R., Zheng, Y. H., Zhong, X. H., Zhou, H. J., Zhou, H. Q., Zhou, H., Zhou, S. H., Zhou, X., Zhou, X. K., Zhou, X. P., Zhou, X. R., Zhou, Y. L., Zhou, Y., Zhou, Y. X., Zhou, Z. Y., Zhu, J. Y., Zhu, K., Zhu, R. D., Zhu, R. L., Zhu, S. H., Zhu, Y. C., Zhu, Z. A., Zhukova, V., Zhulanov, V., Zou, B. S., and Zuo, Y. B.

Published
2024
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6. Dirac Monopoles in Rogue Waves

Author

Zhao, L. -C., Meng, L. -Z., Qin, Y. -H., Yang, Z. -Y., and Liu, J.

Subjects
Nonlinear Sciences - Pattern Formation and Solitons
Abstract

We for the first time demonstrate that the widely existed nonlinear waves such as rogue waves, contain Dirac monopoles. We find that the density zeros of these nonlinear waves on an extended complex plane can constitute the Dirac virtual magnetic monopole fields with a quantized flux of elementary $\pi$. We then can explain the exotic property of ``appearing from nowhere and disappearing without a trace" of rogue waves by means of a Dirac monopole collision mechanism. The maximum amplification ratio and multiple phase steps of high-order rogue waves are found to be closely related to the number of their contained monopoles. Important implications of the intrinsic virtual magnetic monopole fields are discussed.

Published
2021

7. AC Oscillation of a Spin Soliton Driven by a Constant Force

Author

Zhao, L-C, Wang, W., Tang, Q., Yang, Z-Y, Yang, W-L, and Liu, J

Subjects
Condensed Matter - Quantum Gases
Abstract

The phenomena of AC oscillation generated by a DC drive, such as the famous Josephson AC effect in superconductors and Bloch oscillation in solid physics, are of great interest in physics. Here we report another example of such counter-intuitive phenomenon that a spin soliton in a two-component Bose-Einstein condensate is driven by a constant force: The initially static spin soliton first moves in a direction opposite to the force and then changes direction, showing an extraordinary AC oscillation in a long term. In sharp contrast to the Josephson AC effect and Bloch oscillation, we find that the nonlinear interactions play important roles and the spin soliton can exhibit a periodic transition between negative and positive inertial mass even in the absence of periodic potentials. We then develop an explicit quasiparticle model that can account for this extraordinary oscillation satisfactorily. Important implications and possible applications of our finding are discussed., Comment: 9 pages, 6 figures

Published
2019
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9. Several Localized Waves Induced by Linear Interference between a Nonlinear Plane Wave and Bright Solitons

Author

Qin, Y. H., Zhao, L. C., Yang, Z. Y., and Yang, W. L.

Subjects
Condensed Matter - Quantum Gases, Nonlinear Sciences - Pattern Formation and Solitons, Nonlinear Sciences - Exactly Solvable and Integrable Systems, Quantum Physics
Abstract

We investigate linear interference effects between a nonlinear plane wave and bright solitons, which are admitted by pair-transition coupled two-component Bose-Einstein condensate. We demonstrate the interference effects can induce several localized waves possessing distinctive wave structures, mainly including anti-dark soliton, W-shaped soliton, multi-peak soliton, Kuznetsov-Ma like breather, and multi-peak breather. Especially, the explicit conditions for them are clarified by a phase diagram based on the linear interference properties. Furthermore, the interactions between these localized waves are discussed. The detailed analysis indicate that soliton-soliton interaction induced phase shift brings the collision between these localized waves be inelastic for soliton involving collision, and be elastic for breathers. These characters come from that the profile of solitons depend on relative phase between bright soliton and plane wave, and the profile of breathers do not depend on the relative phase. These results would motivate more discussions on linear interference between other nonlinear waves. Especially, the solitons or breathers obtained here are not related with modulational instability. The underlying reasons are discussed in detail., Comment: 8 pages, 7 figures

Published
2017
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10. The Mechanism of Kuznetsov-Ma Breather

Author

Zhao, L. C., Ling, L. M., and Yang, Z. Y.

Subjects
Nonlinear Sciences - Pattern Formation and Solitons, Nonlinear Sciences - Exactly Solvable and Integrable Systems
Abstract

We discuss how to understand the dynamical process of Kuznetsov-Ma breather, based on some basic physical mechanisms. It is shown that dynamical process of Kuznetsov-Ma breather involves at least two distinctive mechanisms: modulational instability, and the interference effects between a bright soliton and a plane wave background. Our analysis indicates that modulational instability plays dominant roles in mechanism of Kuznetsov-Ma breather admitting weak perturbations, and the interference effect plays dominant role for the Kuznetsov-Ma breather admitting strong perturbations. For intermediate cases, the two mechanisms are both involved greatly. These characters provide a possible way to understand the evolution of strong perturbations on a plane wave background., Comment: 5 pages, 4 figures

Published
2017
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13. STCF conceptual design report (Volume 1): Physics & detector

Author

Achasov, M., primary, Ai, X. C., additional, An, L. P., additional, Aliberti, R., additional, An, Q., additional, Bai, X. Z., additional, Bai, Y., additional, Bakina, O., additional, Barnyakov, A., additional, Blinov, V., additional, Bobrovnikov, V., additional, Bodrov, D., additional, Bogomyagkov, A., additional, Bondar, A., additional, Boyko, I., additional, Bu, Z. H., additional, Cai, F. M., additional, Cai, H., additional, Cao, J. J., additional, Cao, Q. H., additional, Cao, X., additional, Cao, Z., additional, Chang, Q., additional, Chao, K. T., additional, Chen, D. Y., additional, Chen, H., additional, Chen, H. X., additional, Chen, J. F., additional, Chen, K., additional, Chen, L. L., additional, Chen, P., additional, Chen, S. L., additional, Chen, S. M., additional, Chen, S., additional, Chen, S. P., additional, Chen, W., additional, Chen, X., additional, Chen, X. F., additional, Chen, X. R., additional, Chen, Y., additional, Chen, Y. Q., additional, Cheng, H. Y., additional, Cheng, J., additional, Cheng, S., additional, Cheng, T. G., additional, Dai, J. P., additional, Dai, L. Y., additional, Dai, X. C., additional, Dedovich, D., additional, Denig, A., additional, Denisenko, I., additional, Dias, J. M., additional, Ding, D. Z., additional, Dong, L. Y., additional, Dong, W. H., additional, Druzhinin, V., additional, Du, D. S., additional, Du, Y. J., additional, Du, Z. G., additional, Duan, L. M., additional, Epifanov, D., additional, Fan, Y. L., additional, Fang, S. S., additional, Fang, Z. J., additional, Fedotovich, G., additional, Feng, C. Q., additional, Feng, X., additional, Feng, Y. T., additional, Fu, J. L., additional, Gao, J., additional, Gao, Y. N., additional, Ge, P. S., additional, Geng, C. Q., additional, Geng, L. S., additional, Gilman, A., additional, Gong, L., additional, Gong, T., additional, Gou, B., additional, Gradl, W., additional, Gu, J. L., additional, Guevara, A., additional, Gui, L. C., additional, Guo, A. 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Published
2023
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19. Purtscher-like retinopathy induced by diffuse cutaneous systemic scleroderma.

Author

Yang, Z -Y, Chen, Y -X, and Zhao, X -Y

Subjects
*SYSTEMIC scleroderma, *AUTOIMMUNE diseases, *SCLERODERMA (Disease), *OPTICAL coherence tomography, *RETINAL artery
Abstract

This article discusses a case of Purtscher-like retinopathy (PLR) in a 61-year-old woman with diffuse cutaneous systemic scleroderma. The patient presented with slightly blurred vision and was diagnosed with bilateral PLR upon ophthalmologic examination. Fundus photography and ultra-widefield OCT-angiography revealed multiple cotton-wool spots, Purtscher flecken, and intraretinal hemorrhages. The patient received plasma exchange and symptomatic therapy but unfortunately passed away 10 days later due to refractory heart failure and cardiogenic shock. PLR is a rare occlusive microangiopathy associated with systemic conditions, and this case adds to the limited number of reported cases associated with systemic scleroderma. [Extracted from the article]

Published
2024
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28. Hemorrhagic risk factors after rt-PA thrombolysis in acute cerebral infarction.

Author

LIU, L., LUO, G.-Q., LIU, Q., and YANG, Z.-Y.

Abstract

OBJECTIVE: This study aims to investigate the risk factors of hemorrhagic transformation (HT) after thrombolysis with recombinant tissue plasminogen activator (rt-PA) in patients with acute cerebral infarction (ACI) and establish a logistic regression equation and the risk prediction model. PATIENTS AND METHODS: One hundred and ninety patients with ACI were divided into the HT group (n=20) and non-HT group (n=170) according to whether HT occurred within 24 hours after rt-PA thrombolysis. The clinical data were collected for analyzing the influencing factors, and a logistic regression analysis model was then established. Besides, patients in the HT group were further grouped into symptomatic hemorrhage (n=7) and non-symptomatic hemorrhage (n=13) according to the type of hemorrhage. The clinical diagnostic value of risk factors in symptomatic hemorrhage after thrombolysis in ACI was analyzed using the ROC curve. RESULTS: We found that history of atrial fibrillation, time from onset to thrombolysis, pre-thrombolytic glucose, pre-thrombolytic National Institute of Health Stroke Scale (NIHSS) score, 24-hour post-thrombolytic NIHSS score, and proportion of patients with large cerebral infarction were all the influencing factors of HT risk after rt-PA thrombolysis in patients with ACI (p<0.05). Logistic regression analysis model was established with an accuracy of 88.42% (168/190), a sensitivity of 75.00% (15/20), and a specificity of 90.00% (153/170). The time from onset to thrombolysis, pre-thrombolytic glucose, and 24-hour post-thrombolytic NIHSS score had higher clinical value in predicting the risk of HT after rt-PA thrombolysis, with the AUCs of 0.874, 0.815 and 0.881, respectively. Blood glucose and pre-thrombolytic NIHSS score were independent risk factors related to symptomatic hemorrhage after thrombolysis in ACI (p<0.05). The AUCs for predicting symptomatic hemorrhage alone and in combination were 0.813, 0.835, and 0.907, respectively, with sensitivities of 85.70%, 87.50% and 90.00%, and specificities of 62.50%, 60.00%, and 75.42% respectively. CONCLUSIONS: The establishment of a prediction model based on the risk factors of HT after rt-PA thrombolysis had a good predictive value in patients with ACI. This model was helpful in guiding clinical judgment and improving the safety of intravenous thrombolysis. Early identification of symptomatic bleeding risk factors provided a reference for clinical treatment and prognostic measures of patients with ACI. [ABSTRACT FROM AUTHOR]

Published
2023

36. Particle Dynamic Characteristics of the Vertical Tubes in a Horizontal-Vertical Pneumatic Conveying System with Different Curvature Radius Bends.

Author

Yan, F., Yang, Z. Y., Tu, P. P., and Zhu, R.

Subjects
PNEUMATIC-tube transportation, PNEUMATICS, CURVATURE, PRESSURE drop (Fluid dynamics), GRANULAR flow, FLUCTUATIONS (Physics), RADIUS (Geometry)
Abstract

To examine the flow characteristics of particles in the vertical tube corresponding to different curvature radius bends, the particle velocity is measured at the Minimum Pressure Drop (MPD) using a high-speed Particle Image Velocimeter (PIV). This experiment explores the effects of particle flow characteristics at different curvature radius bends and their corresponding vertical tubes with pressure drop, power consumption, the intensity of particle fluctuation velocity, power spectrum and time-frequency characteristics. It is observed that the pressure drop and power consumption can be reduced with the help of a large curvature radius bend. Besides, the reduction of particle velocity in the large curvature radius bend and its corresponding vertical tube is less, and the particle possesses a larger intensity of fluctuation velocity in its corresponding vertical tube. The particles in the vertical tube corresponding to the large curvature radius bend lead to large peaks of the power spectrum in the low-frequency region, which is closely linked to the pressure drop. Eventually, the dynamics of particles in a vertical tube are revealed from the perspective of time-frequency analysis by using a continuous wavelet transform. [ABSTRACT FROM AUTHOR]

Published
2022
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37. Abnormal hyperplasia of chondrocytes in a rat model of glucocorticoid-induced osteonecrosis of the femoral head.

Author

WANG, Q.-R., YANG, Z.-Y., ZHANG, W.-L., LI, Q.-H., and KANG, P.-D.

Abstract

OBJECTIVE: The aim of our study was to determine whether abnormal hyperplasia of chondrocytes occurs in glucocorticoid-induced osteonecrosis of the femoral head (GC-ONFH) using a well-established rat model. MATERIALS AND METHODS: Rats were injected with lipopolysaccharide and methylprednisolone to induce GC-ONFH, while control animals were injected with saline (12 animals per group). Establishment of the disease model was confirmed using micro-computed tomography and hematoxylin-eosin (HE) staining of femoral head tissue sections. Chondrocyte hyperplasia was detected using HE staining and semi-quantitated using toluidine blue and saffron O staining. Expression of the autophagy marker LC3B was assessed in cartilage tissues of femoral head using immunohistochemistry. RESULTS: G C-ONFH a nimals s howed s ignificantly greater area of abnormal chondrocyte hyperplasia in femoral head tissue sections than control animals. They also showed significantly higher expression of LC3B in articular cartilage of the femoral head. CONCLUSIONS: GC-ONFH may be associated with abnormal chondrocyte hyperplasia in articular surface cartilage, which may be related to glucocorticoid-induced overactivation of autophagy. [ABSTRACT FROM AUTHOR]

Published
2022

41. Disruption prediction and model analysis using LightGBM on J-TEXT and HL-2A

Author

Zhong, Y, primary, Zheng, W, additional, Chen, Z Y, additional, Xia, F, additional, Yu, L M, additional, Wu, Q Q, additional, Ai, X K, additional, Shen, C S, additional, Yang, Z Y, additional, Yan, W, additional, Ding, Y H, additional, Liang, Y F, additional, Chen, Z P, additional, Tong, R H, additional, Bai, W, additional, Fang, J G, additional, and Li, F, additional

Published
2021
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