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1,278 results on '"QIAO, Bin"'

1. A response to commenter Ke Lan's comment on our paper published in Nature Communications (2023)14:5782 by J. Yan et al

Author

Yan, Ji, Li, Jiwei, He, X. T., Wang, Lifeng, Chen, Yaohua, Wang, Feng, Han, Xiaoying, Pan, Kaiqiang, Liang, Juxi, Li, Yulong, Guan, Zanyang, Liu, Xiangming, Che, Xingsen, Chen, Zhongjing, Zhang, Xing, Xu, Yan, Li, Bin, He, Minging, Cai, Hongbo, Hao, Liang., Liu, Zhanjun, Zheng, Chunyang, Dai, Zhensheng, Fan, Zhengfeng, Qiao, Bin, Li, Fuquan, Jiang, Shaoen, Yu, M. Y., and Zhu, Shaoping

Subjects
Physics - Plasma Physics
Abstract

A response to commenter Ke Lan's comment on our paper published in Nature Communications (2023)14:5782 by J. Yan et al

Published
2024

2. Formation Mechanism of Laser-Driven Magnetized 'Pillars of Creation'

Author

Lei, Zhu, Wang, Lifeng, Li, Jiwei, Zou, Shiyang, Wu, Junfeng, Zhao, Zhonghai, Sun, Wei, Yuan, Wenqiang, Li, Longxing, Yan, Zheng, Li, Jun, Ye, Wenhua, He, Xiantu, and Qiao, Bin

Subjects
Physics - Plasma Physics, Astrophysics - Solar and Stellar Astrophysics
Abstract

Pillars of Creation, one of the most recognized objects in the sky, are believed to be associated with the formation of young stars. However, so far, the formation and maintenance mechanism for the pillars are still not fully understood due to the complexity of the nonlinear radiation magneto-hydrodynamics (RMHD). Here, assuming laboratory laser-driven conditions, we studied the self-consistent dynamics of pillar structures in magnetic fields by means of two-dimensional (2D) and three-dimensional (3D) RMHD simulations, and these results also support our proposed experimental scheme. We find only when the magnetic pressure and ablation pressure are comparable, the magnetic field can significantly alter the plasma hydrodynamics. For medium magnetized cases ($\beta_{initial} \approx 3.5$), {the initial magnetic fields undergo compression and amplification. This amplification results in the magnetic pressure inside the pillar becoming large enough to support the sides of the pillar against radial collapse due to pressure from the surrounding hot plasma. This effect is particularly pronounced for the parallel component ($B_y$), which is consistent with observational results.} In contrast, a strong perpendicular ($B_x, B_z$) magnetic field ($\beta_{initial} < 1$) almost remains its initial distribution and significantly suppresses the expansion of blow-off gas plasma, leading to the inability to form pillar-like structures. The 3D simulations suggest that the bending at the head of `Column \uppercase\expandafter{\romannumeral1}' in pillars of creation may be due to the non-parallel magnetic fields. After similarity scaling transformation, our results can be applied to explain the formation and maintenance mechanism of the pillars, and can also provide useful information for future experimental designs.

Published
2024
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9. Phase-dependent bubble hosing and resonant amplification of betatron oscillation in few-cycle laser wakefield accelerator

Author

Lei, Bifeng, Seipt, Daniel, Liu, Bin, Zepf, Matt, and Qiao, Bin

Subjects
Physics - Accelerator Physics, Physics - Plasma Physics
Abstract

Betatron oscillation of trapped electrons in laser-driven long-distance propagating plasma bubble has been investigated with the help of particle-in-cell simulations and theoretical analysis. Parametric oscillation of the trapped electron beam is identified as a result of bubble breathing which develops with the steepening and depletion of the laser pulse. It leads to parametric amplification of the betatron oscillation of the electron beam with the exponential growth of its amplitude. It results in severe degradation of the qualities of the electron beam and thus is important for long-scale laser wakefield acceleration.

Published
2023

17. Experimental confirmation of driving pressure boosting and smoothing for hybrid-drive inertial fusion at the 100-kJ laser facility

Author

Yan, Ji, Li, Jiwei, He, X. T., Wang, Lifeng, Chen, Yaohua, Wang, Feng, Han, Xiaoying, Pan, Kaiqiang, Liang, Juxi, Li, Yulong, Guan, Zanyang, Liu, Xiangming, Che, Xingsen, Chen, Zhongjing, Zhang, Xing, Xu, Yan, Li, Bin, He, Minqing, Cai, Hongbo, Hao, Liang, Liu, Zhanjun, Zheng, Chunyang, Dai, Zhensheng, Fan, Zhengfeng, Qiao, Bin, Li, Fuquan, Jiang, Shaoen, Yu, M. Y., and Zhu, Shaoping

Published
2023
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25. Artificial Neural Network Spectral Light Curve Template for Type Ia Supernovae and its Cosmological Constraints

Author

Cheng, Qiao-Bin, Feng, Chao-Jun, Zhai, Xiang-Hua, and Li, Xin-Zhou

Subjects
Astrophysics - Cosmology and Nongalactic Astrophysics, Astrophysics - High Energy Astrophysical Phenomena, General Relativity and Quantum Cosmology
Abstract

The spectral energy distribution (SED) sequence for type Ia supernovae (SN Ia) is modeled by an artificial neural network. The SN Ia luminosity is characterized as a function of phase, wavelength, a color parameter and a decline rate parameter. After training and testing the neural network, the SED sequence could give both the spectrum with wavelength range from 3000\AA~to 8000\AA~ and the light curve with phase from 20 days before to 50 days after the maximum luminosity for the supernovae with different colors and decline rates. Therefore, we call this the Artificial Neural Network Spectral Light Curve Template (ANNSLCT) model. We retrain the Joint Light-curve Analysis (JLA) supernova sample by using the ANNSLCT model and obtain the parameters for each supernova to make a constraint on the cosmological $\Lambda$CDM model. We find that the best fitting values of these parameters are almost the same as those from the JLA sample trained with the Spectral Adaptive Lightcurve Template 2 (SALT2) model. So we believe that the ANNSLCT model could be used to analyze a large number of SN Ia multi-color light curves measured in the current and future observational projects., Comment: 8 pages two columns, 17 figures

Published
2020
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37. Artificial Neural Network for Constructing Type Ia Supernovae Spectrum Evolution Model

Author

Cheng, Qiao-Bin, Feng, Chao-Jun, Zhai, Xiang-Hua, and Li, Xin-Zhou

Subjects
Astrophysics - Cosmology and Nongalactic Astrophysics, Astrophysics - Instrumentation and Methods for Astrophysics
Abstract

We construct and train an artificial neural network called the back-propagation neural network to describe the evolution of the type Ia supernova spectrum by using the data from the CfA Supernova Program. This network method has many attractive features, and one of them is that the constructed model is differentiable. Benefitting from this, we calculate the absorption velocity and its variation. The model we constructed can well describe not only the spectrum of SNe Ia with wavelength range from $3500\AA$ to $8000\AA$, but also the light-curve evolution with phase time from $-15$ to $50$ with different colors. Moreover, the number of parameters needed during the training process is much less than the usual methods., Comment: v2 refs added, two columns, 7pages, 12 figures

Published
2018
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50. Machine Learning Model Based on Prognostic Nutritional Index for Predicting Long‐Term Outcomes in Patients With HCC Undergoing Ablation.

Author

Zhang, Nan, Lin, Ke, Qiao, Bin, Yan, Liwei, Jin, Dongdong, Yang, Daopeng, Yang, Yue, Xie, Xiaohua, Xie, Xiaoyan, and Zhuang, Bowen

Abstract

Aims: To develop multiple machine learning (ML) models based on the prognostic nutritional index (PNI) and determine the optimal model for predicting long‐term survival outcomes in hepatocellular carcinoma (HCC) patients after local ablation. Methods: From January 2009 to December 2019, we analyzed data from 848 primary HCC patients who underwent local ablation. ML models were constructed and evaluated using the concordance index (C‐index), concordance‐discordance area under curve (C/D AUC), and Brier scores. The optimal ML model was interpreted using the partial dependence plot (PDP) and SHapley Additive exPlanations (SHAP) framework. Additionally, the prognostic performance of our model was compared with other models. Results: Alkaline phosphatase, preoperation alpha‐fetoprotein level, PNI, tumor number, and tumor size were identified as independent prognostic factors for ML model construction. Among the 19 ML algorithms tested, the Aorsf model showed superior performance in both the training cohort (C/D AUC: 0.733; C‐index: 0.736; Brier score: 0.133) and validation cohort (C/D AUC: 0.713; C‐index: 0.793; Brier score: 0.117). The time‐dependent AUC of the Aorsf model for predicting overall survival was as follows: 1‐, 3‐, 5‐, 7‐, and 9‐year were 0.828, 0.765, 0.781, 0.817, and 0.812 in the training cohort, 0.846, 0.859, 0.824, 0.845, and 0.874 in the validation cohort, respectively. The PDP and SHAP algorithms were employed for visual interpretation. Furthermore, time‐AUC and decision curve analysis demonstrated that the Aorsf model provided superior clinical benefits compared to other models. Conclusion: The PNI‐based Aorsf model effectively predicts long‐term survival outcomes after ablation therapy, making a significant contribution to HCC research by improving surveillance, prevention, and treatment strategies. [ABSTRACT FROM AUTHOR]

Published
2024
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