Author: "Chen Q." - Searchworks@Jio Institute Digital Library Search Results

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45,010 results on '"Chen Q."'

1. Broadband $\gamma$-ray spectrum of supernova remnant Cassiopeia A

Author: Cao, Zhen, Aharonian, F., Bai, Y. X., Bao, Y. W., Bastieri, D., Bi, X. J., Bi, Y. J., Bian, W., Bukevich, A. V., Cai, C. M., Cao, W. Y., Cao, Zhe, Chang, J., Chang, J. F., Chen, A. M., Chen, E. S., Chen, H. X., Chen, Liang, Chen, Long, Chen, M. J., Chen, M. L., Chen, Q. H., Chen, S., Chen, S. H., Chen, S. Z., Chen, T. L., Chen, X. B., Chen, X. J., Chen, Y., Cheng, N., Cheng, Y. D., Chu, M. C., Cui, M. Y., Cui, S. W., Cui, X. H., Cui, Y. D., Dai, B. Z., Dai, H. L., Dai, Z. G., Danzengluobu, Diao, Y. X., Dong, X. Q., Duan, K. K., Fan, J. H., Fan, Y. Z., Fang, J., Fang, J. H., Fang, K., Feng, C. F., Feng, H., Feng, L., Feng, S. H., Feng, X. T., Feng, Y., Feng, Y. L., Gabici, S., Gao, B., Gao, C. D., Gao, Q., Gao, W., Gao, W. K., Ge, M. M., Ge, T. T., Geng, L. S., Giacinti, G., Gong, G. H., Gou, Q. B., Gu, M. H., Guo, F. L., Guo, J., Guo, X. L., Guo, Y. Q., Guo, Y. Y., Han, Y. A., Hannuksela, O. A., Hasan, M., He, H. H., He, H. N., He, J. Y., He, X. Y., He, Y., Hernández-Cadena, S., Hor, Y. K., Hou, B. W., Hou, C., Hou, X., Hu, H. B., Hu, S. C., Huang, C., Huang, D. H., Huang, J. J., Huang, T. Q., Huang, W. J., Huang, X. T., Huang, X. Y., Huang, Y., Huang, Y. Y., Ji, X. L., Jia, H. Y., Jia, K., Jiang, H. B., Jiang, K., Jiang, X. W., Jiang, Z. J., Jin, M., Kaci, S., Kang, M. M., Karpikov, I., Khangulyan, D., Kuleshov, D., Kurinov, K., Li, B. B., Li, Cheng, Li, Cong, Li, D., Li, F., Li, H. B., Li, H. C., Li, Jian, Li, Jie, Li, K., Li, L., Li, R. L., Li, S. D., Li, T. Y., Li, W. L., Li, X. R., Li, Xin, Li, Y. Z., Li, Zhe, Li, Zhuo, Liang, E. W., Liang, Y. F., Lin, S. J., Liu, B., Liu, C., Liu, D., Liu, D. B., Liu, H., Liu, H. D., Liu, J., Liu, J. L., Liu, J. R., Liu, M. Y., Liu, R. Y., Liu, S. M., Liu, W., Liu, X., Liu, Y., Liu, Y. N., Lou, Y. Q., Luo, Q., Luo, Y., Lv, H. K., Ma, B. Q., Ma, L. L., Ma, X. H., Mao, J. R., Min, Z., Mitthumsiri, W., Mou, G. B., Mu, H. J., Nan, Y. C., Neronov, A., Ng, K. C. Y., Ni, M. Y., Nie, L., Ou, L. J., Pattarakijwanich, P., Pei, Z. Y., Qi, J. C., Qi, M. Y., Qin, J. J., Raza, A., Ren, C. Y., Ruffolo, D., Sáiz, A., Saeed, M., Semikoz, D., Shao, L., Shchegolev, O., Shen, Y. Z., Sheng, X. D., Shi, Z. D., Shu, F. W., Song, H. C., Stenkin, Yu. V., Stepanov, V., Su, Y., Sun, D. X., Sun, H., Sun, Q. N., Sun, X. N., Sun, Z. B., Tabasam, N. H., Takata, J., Tam, P. H. T., Tan, H. B., Tang, Q. W., Tang, R., Tang, Z. B., Tian, W. W., Tong, C. N., Wan, L. H., Wang, C., Wang, G. W., Wang, H. G., Wang, H. H., Wang, J. C., Wang, K., Wang, Kai, Wang, L. P., Wang, L. Y., Wang, R., Wang, W., Wang, X. G., Wang, X. J., Wang, X. Y., Wang, Y., Wang, Y. D., Wang, Z. H., Wang, Z. X., Wang, Zheng, Wei, D. M., Wei, J. J., Wei, Y. J., Wen, T., Weng, S. S., Wu, C. Y., Wu, H. R., Wu, Q. W., Wu, S., Wu, X. F., Wu, Y. S., Xi, S. Q., Xia, J., Xia, J. J., Xiang, G. M., Xiao, D. X., Xiao, G., Xin, Y. L., Xing, Y., Xiong, D. R., Xiong, Z., Xu, D. L., Xu, R. F., Xu, R. X., Xu, W. L., Xue, L., Yan, D. H., Yan, J. Z., Yan, T., Yang, C. W., Yang, C. Y., Yang, F. F., Yang, L. L., Yang, M. J., Yang, R. Z., Yang, W. X., Yao, Y. H., Yao, Z. G., Ye, X. A., Yin, L. Q., Yin, N., You, X. H., You, Z. Y., Yu, Y. H., Yuan, Q., Yue, H., Zeng, H. D., Zeng, T. X., Zeng, W., Zha, M., Zhang, B. B., Zhang, B. T., Zhang, F., Zhang, H., Zhang, H. M., Zhang, H. Y., Zhang, J. L., Zhang, Li, Zhang, P. F., Zhang, P. P., Zhang, R., Zhang, S. R., Zhang, S. S., Zhang, W. Y., Zhang, X., Zhang, X. P., Zhang, Yi, Zhang, Yong, Zhang, Z. P., Zhao, J., Zhao, L., Zhao, L. Z., Zhao, S. P., Zhao, X. H., Zhao, Z. H., Zheng, F., Zhong, W. J., Zhou, B., Zhou, H., Zhou, J. N., Zhou, M., Zhou, P., Zhou, R., Zhou, X. X., Zhu, B. Y., Zhu, C. G., Zhu, F. R., Zhu, H., Zhu, K. J., Zou, Y. C., and Zuo, X.
Subjects: Astrophysics - High Energy Astrophysical Phenomena
Abstract: The core-collapse supernova remnant (SNR) Cassiopeia A (Cas A) is one of the brightest galactic radio sources with an angular radius of $\sim$ 2.5 $\arcmin$. Although no extension of this source has been detected in the $\gamma$-ray band, using more than 1000 days of LHAASO data above $\sim 0.8$ TeV, we find that its spectrum is significantly softer than those obtained with Imaging Air Cherenkov Telescopes (IACTs) and its flux near $\sim 1$ TeV is about two times higher. In combination with analyses of more than 16 years of \textit{Fermi}-LAT data covering $0.1 \, \mathrm{GeV} - 1 \, \mathrm{TeV}$, we find that the spectrum above 30 GeV deviates significantly from a single power-law, and is best described by a smoothly broken power-law with a spectral index of $1.90 \pm 0.15_\mathrm{stat}$ ($3.41 \pm 0.19_\mathrm{stat}$) below (above) a break energy of $0.63 \pm 0.21_\mathrm{stat} \, \mathrm{TeV}$. Given differences in the angular resolution of LHAASO-WCDA and IACTs, TeV $\gamma$-ray emission detected with LHAASO may have a significant contribution from regions surrounding the SNR illuminated by particles accelerated earlier, which, however, are treated as background by IACTs. Detailed modelling can be used to constrain acceleration processes of TeV particles in the early stage of SNR evolution.
Published: 2025

2. Exploring the determinants on massive open online courses continuance learning intention in business toward accounting context

Author: Shang, D., Chen, Q., Guo, X., Jin, H., Ke, S., and Li, M.
Subjects: Computer Science - Computers and Society
Abstract: Massive open online courses (MOOC) have become important in the learning journey of college students and have been extensively implemented in higher education. However, there are few studies that investigated the willingness to continue using Massive open online courses (MOOC) in the field of business in higher education. Therefore, this paper proposes a comprehensive theoretical research framework based on the Theory of Planned Behavior (TPB). In the field of business, a representative accounting course is taken as an example. We adopt the questionnaire survey method and use the partial least squares structural equation model to analyze the collected feedback data from college students and test the hypotheses. This paper focuses on the potential influencing factors and mechanisms of the willingness to continuously use Massive open online courses (MOOC) in accounting. The results show that interface convenience (IC) and interface design aesthetics (IDA) have positive effects on user attitude (ATT). User attitude (ATT), perceived behavioral control (PBC), and subjective norms (SN) have positive effects on the continuance learning intention. In addition, academic self-efficacy (EF) not only significantly affects continuance learning intention (CI) but also moderates the relationship between the Theory of Planned Behavior (user attitude, perceived behavior control, subjective norms) and the continuance learning intention of accounting MOOC. Therefore, the Theory of Planned Behavior(TPB) is extended in social science accounting Massive open online courses environment. Based on these findings, this paper provides several theoretical and practical implications for researchers and practitioners of MOOC, accounting, and the design of learning systems in higher education contexts., Comment: 15 pages,2 figures
Published: 2024

3. Detection of two TeV gamma-ray outbursts from NGC 1275 by LHAASO

Author: Cao, Zhen, Aharonian, F., Axikegu, Bai, Y. X., Bao, Y. W., Bastieri, D., Bi, X. J., Bi, Y. J., Cai, J. T., Cao, Q., Cao, W. Y., Cao, Zhe, Chang, J., Chang, J. F., Chen, A. M., Chen, E. S., Chen, Liang, Chen, Lin, Chen, Long, Chen, M. J., Chen, M. L., Chen, Q. H., Chen, S. H., Chen, S. Z., Chen, T. L., Chen, Y., Cheng, N., Cheng, Y. D., Cui, M. Y., Cui, S. W., Cui, X. H., Cui, Y. D., Dai, B. Z., Dai, H. L., Dai, Z. G., Danzengluobu, della Volpe, D., Dong, X. Q., Duan, K. K., Fan, J. H., Fan, Y. Z., Fang, J., Fang, K., Feng, C. F., Feng, L., Feng, S. H., Feng, X. T., Feng, Y. L., Gabici, S., Gao, B., Gao, C. D., Gao, L. Q., Gao, Q., Gao, W., Gao, W. K., Ge, M. M., Geng, L. S., Giacinti, G., Gong, G. H., Gou, Q. B., Gu, M. H., Guo, F. L., Guo, X. L., Guo, Y. Q., Guo, Y. Y., Han, Y. A., He, H. H., He, H. N., He, J. Y., He, X. B., He, Y., Heller, M., Hor, Y. K., Hou, B. W., Hou, C., Hou, X., Hu, H. B., Hu, Q., Hu, S. C., Huang, D. H., Huang, T. Q., Huang, W. J., Huang, X. T., Huang, X. Y., Huang, Y., Huang, Z. C., Ji, X. L., Jia, H. Y., Jia, K., Jiang, K., Jiang, X. W., Jiang, Z. J., Jin, M., Kang, M. M., Ke, T., Kuleshov, D., Kurinov, K., Li, B. B., Li, Cheng, Li, Cong, Li, D., Li, F., Li, H. B., Li, H. C., Li, H. Y., Li, J., Li, Jian, Li, Jie, Li, K., Li, W. L., Li, X. R., Li, Xin, Li, Y. Z., Li, Zhe, Li, Zhuo, Liang, E. W., Liang, Y. F., Lin, S. J., Liu, B., Liu, C., Liu, D., Liu, H., Liu, H. D., Liu, J., Liu, J. L., Liu, J. Y., Liu, M. Y., Liu, R. Y., Liu, S. M., Liu, W., Liu, Y., Liu, Y. N., Lu, R., Luo, Q., Lv, H. K., Ma, B. Q., Ma, L. L., Ma, X. H., Mao, J. R., Min, Z., Mitthumsiri, W., Mu, H. J., Nan, Y. C., Neronov, A., Ou, Z. W., Pang, B. Y., Pattarakijwanich, P., Pei, Z. Y., Qi, M. Y., Qi, Y. Q., Qiao, B. Q., Qin, J. J., Ruffolo, D., Sáiz, A., Semikoz, D., Shao, C. Y., Shao, L., Shchegolev, O., Sheng, X. D., Shu, F. W., Song, H. C., Stenkin, Yu. V., Stepanov, V., Su, Y., Sun, Q. N., Sun, X. N., Sun, Z. B., Tam, P. H. T., Tang, Q. W., Tang, Z. B., Tian, W. W., Wang, C., Wang, C. B., Wang, G. W., Wang, H. G., Wang, H. H., Wang, J. C., Wang, K., Wang, L. P., Wang, L. Y., Wang, P. H., Wang, R., Wang, W., Wang, X. G., Wang, X. Y., Wang, Y., Wang, Y. D., Wang, Y. J., Wang, Z. H., Wang, Z. X., Wang, Zhen, Wang, Zheng, Wei, D. M., Wei, J. J., Wei, Y. J., Wen, T., Wu, C. Y., Wu, H. R., Wu, S., Wu, X. F., Wu, Y. S., Xi, S. Q., Xia, J., Xia, J. J., Xiang, G. M., Xiao, D. X., Xiao, G., Xin, G. G., Xin, Y. L., Xing, Y., Xiong, Z., Xu, D. L., Xu, R. F., Xu, R. X., Xu, W. L., Xue, L., Yan, D. H., Yan, J. Z., Yan, T., Yang, C. W., Yang, F., Yang, F. F., Yang, H. W., Yang, J. Y., Yang, L. L., Yang, M. J., Yang, R. Z., Yang, S. B., Yao, Y. H., Yao, Z. G., Ye, Y. M., Yin, L. Q., Yin, N., You, X. H., You, Z. Y., Yu, Y. H., Yuan, Q., Yue, H., Zeng, H. D., Zeng, T. X., Zeng, W., Zha, M., Zhang, B. B., Zhang, F., Zhang, H. M., Zhang, H. Y., Zhang, J. L., Zhang, L. X., Zhang, Li, Zhang, P. F., Zhang, P. P., Zhang, R., Zhang, S. B., Zhang, S. R., Zhang, S. S., Zhang, X., Zhang, X. P., Zhang, Y. F., Zhang, Yi, Zhang, Yong, Zhao, B., Zhao, J., Zhao, L., Zhao, L. Z., Zhao, S. P., Zheng, F., Zhou, B., Zhou, H., Zhou, J. N., Zhou, M., Zhou, P., Zhou, R., Zhou, X. X., Zhu, C. G., Zhu, F. R., Zhu, H., Zhu, K. J., and Zuo., X.
Subjects: Astrophysics - High Energy Astrophysical Phenomena
Abstract: The Water Cherenkov Detector Array (WCDA) is one of the components of Large High Altitude Air Shower Observatory (LHAASO) and can monitor any sources over two-thirds of the sky for up to 7 hours per day with >98\% duty cycle. In this work, we report the detection of two outbursts of the Fanaroff-Riley I radio galaxy NGC 1275 that were detected by LHAASO-WCDA between November 2022 and January 2023 with statistical significance of 5.2~$\sigma$ and 8.3~$\sigma$. The observed spectral energy distribution in the range from 500 GeV to 3 TeV is fitted by a power-law with a best-fit spectral index of $\alpha=-3.37\pm0.52$ and $-3.35\pm0.29$, respectively. The outburst flux above 0.5~TeV was ($4.55\pm 4.21)\times~10^{-11}~\rm cm^{-2}~s^{-1}$ and ($3.45\pm 1.78)\times~10^{-11}~\rm cm^{-2}~s^{-1}$, corresponding to 60\%, 45\% of Crab Nebula flux. Variation analysis reveals the variability time-scale of days at the TeV energy band. A simple test by one-zone synchrotron self-Compton model reproduces the data in the gamma-ray band well., Comment: 11 pages, 8 figures, 3 tables
Published: 2024

4. Entanglement and coherence of the wobbling mode

Author: Chen, Q. B. and Frauendorf, S.
Subjects: Nuclear Theory, Nuclear Experiment
Abstract: The entanglement and coherence of the wobbling mode are studied in the framework of the particle plus triaxial rotor model for the one-quasiparticle nucleus $^{135}$Pr and the two-quasiparticles nucleus $^{130}$Ba. The focus lies on the coupling between the total and the particle angular momenta. Using the Schmidt decomposing, it is quantified in terms of the von Neumann entropy of the respective sub-systems, which measures their mutual entanglement. The entropy and the entanglement increase with spin $I$ and number of wobbling quanta $n$. The coherence of the wobbling mode is studied by means of the eigenstate decomposition of its reduced density matrix. To a good approximation, the probability distributions of the total angular momentum can be interpreted as the incoherent combination of the coherent contributions from the first two pairs of eigenvectors with the largest weight of the reduced density matrix. Decoherence measures are defined, which, in accordance, scatter between 0.1 to 0.2 at low spin and between 0.1 and 0.3 at high spin. Entanglement in the framework of the adiabatic approximation is further analyzed. In general, the coherent eigenstates of the effective collective Hamiltonian approximate the reduced density matrix with the limited accuracy of its pair of eigenstates with the largest weight. As the adiabatic approximation becomes more accurate with decreasing excitation energy, the probability distribution of the angle of the total angular momentum around a principal axis approaches the one of the full reduced density matrix. The $E2$ transition probabilities and spectroscopic quadrupole moments reflect this trend., Comment: 26 pages, 24 figures
Published: 2024

5. LHAASO detection of very-high-energy gamma-ray emission surrounding PSR J0248+6021

Author: Cao, Zhen, Aharonian, F., An, Q., Axikegu, Bai, Y. X., Bao, Y. W., Bastieri, D., Bi, X. J., Bi, Y. J., Cai, J. T., Cao, Q., Cao, W. Y., Cao, Zhe, Chang, J., Chang, J. F., Chen, A. M., Chen, E. S., Chen, Liang, Chen, Lin, Chen, Long, Chen, M. J., Chen, M. L., Chen, Q. H., Chen, S. H., Chen, S. Z., Chen, T. L., Chen, Y., Cheng, N., Cheng, Y. D., Cui, M. Y., Cui, S. W., Cui, X. H., Cui, Y. D., Dai, B. Z., Dai, H. L., Dai, Z. G., Danzengluobu, Dong, X. Q., Duan, K. K., Fan, J. H., Fan, Y. Z., Fang, J., Fang, K., Feng, C. F., Feng, L., Feng, S. H., Feng, X. T., Feng, Y. L., Gabici, S., Gao, B., Gao, C. D., Gao, L. Q., Gao, Q., Gao, W., Gao, W. K., Ge, M. M., Geng, L. S., Giacinti, G., Gong, G. H., Gou, Q. B., Gu, M. H., Guo, F. L., Guo, X. L., Guo, Y. Q., Guo, Y. Y., Han, Y. A., He, H. H., He, H. N., He, J. Y., He, X. B., He, Y., Hor, Y. K., Hou, B. W., Hou, C., Hou, X., Hu, H. B., Hu, Q., Hu, S. C., Huang, D. H., Huang, T. Q., Huang, W. J., Huang, X. T., Huang, X. Y., Huang, Y., Huang, Z. C., Ji, X. L., Jia, H. Y., Jia, K., Jiang, K., Jiang, X. W., Jiang, Z. J., Jin, M., Kang, M. M., Ke, T., Kuleshov, D., Kurinov, K., Li, B. B., Li, Cheng, Li, Cong, Li, D., Li, F., Li, H. B., Li, H. C., Li, H. Y., Li, J., Li, Jian, Li, Jie, Li, K., Li, W. L., Li, X. R., Li, Xin, Li, Y. Z., Li, Zhe, Li, Zhuo, Liang, E. W., Liang, Y. F., Lin, J., Liu, B., Liu, C., Liu, D., Liu, H., Liu, H. D., Liu, J., Liu, J. L., Liu, J. Y., Liu, M. Y., Liu, R. Y., Liu, S. M., Liu, W., Liu, Y., Liu, Y. N., Lu, R., Luo, Q., Lv, H. K., Ma, B. Q., Ma, L. L., Ma, X. H., Mao, J. R., Min, Z., Mitthumsiri, W., Mu, H. J., Nan, Y. C., Neronov, A., Ou, Z. W., Pang, B. Y., Pattarakijwanich, P., Pei, Z. Y., Qi, M. Y., Qi, Y. Q., Qiao, B. Q., Qin, J. J., Ruffolo, D., Sáiz, A., Semikoz, D., Shao, C. Y., Shao, L., Shchegolev, O., Sheng, X. D., Shu, F. W., Song, H. C., Stenkin, Yu. V., Stepanov, V., Su, Y., Sun, Q. N., Sun, X. N., Sun, Z. B., Tam, P. H. T., Tang, Q. W., Tang, Z. B., Tian, W. W., Wang, C., Wang, C. B., Wang, G. W., Wang, H. G., Wang, H. H., Wang, J. C., Wang, K., Wang, L. P., Wang, L. Y., Wang, P. H., Wang, R., Wang, W., Wang, X. G., Wang, X. Y., Wang, Y., Wang, Y. D., Wang, Y. J., Wang, Z. H., Wang, Z. X., Wang, Zhen, Wang, Zheng, Wei, D. M., Wei, J. J., Wei, Y. J., Wen, T., Wu, C. Y., Wu, H. R., Wu, S., Wu, X. F., Wu, Y. S., Xi, S. Q., Xia, J., Xia, J. J., Xiang, G. M., Xiao, D. X., Xiao, G., Xin, G. G., Xin, Y. L., Xing, Y., Xiong, Z., Xu, D. L., Xu, R. F., Xu, R. X., Xu, W. L., Xue, L., Yan, D. H., Yan, J. Z., Yan, T., Yang, C. W., Yang, F., Yang, F. F., Yang, H. W., Yang, J. Y., Yang, L. L., Yang, M. J., Yang, R. Z., Yang, S. B., Yao, Y. H., Yao, Z. G., Ye, Y. M., Yin, L. Q., Yin, N., You, X. H., You, Z. Y., Yu, Y. H., Yuan, Q., Yue, H., Zeng, H. D., Zeng, T. X., Zeng, W., Zha, M., Zhang, B. B., Zhang, F., Zhang, H. M., Zhang, H. Y., Zhang, J. L., Zhang, L. X., Zhang, Li, Zhang, P. F., Zhang, P. P., Zhang, R., Zhang, S. B., Zhang, S. R., Zhang, S. S., Zhang, X., Zhang, X. P., Zhang, Y. F., Zhang, Yi, Zhang, Yong, Zhao, B., Zhao, J., Zhao, L., Zhao, L. Z., Zhao, S. P., Zheng, F., Zheng, J. H., Zhou, B., Zhou, H., Zhou, J. N., Zhou, M., Zhou, P., Zhou, R., Zhou, X. X., Zhu, C. G., Zhu, F. R., Zhu, H., Zhu, K. J., Zou, Y. C., and Zuo, X.
Subjects: Astrophysics - High Energy Astrophysical Phenomena
Abstract: We report the detection of an extended very-high-energy (VHE) gamma-ray source coincident with the location of middle-aged (62.4~\rm kyr) pulsar PSR J0248+6021, by using the LHAASO-WCDA data of live 796 days and LHAASO-KM2A data of live 1216 days. A significant excess of \gray induced showers is observed both by WCDA in energy bands of 1-25~\rm TeV and KM2A in energy bands of $>$ 25~\rm TeV with 7.3 $\sigma$ and 13.5 $\sigma$, respectively. The best-fit position derived through WCDA data is R.A. = 42.06$^\circ \pm$ 0.12$^\circ$ and Dec. = 60.24$^\circ \pm $ 0.13$^\circ$ with an extension of 0.69$^\circ\pm$0.15$^\circ$ and that of the KM2A data is R.A.= 42.29$^\circ \pm $ 0.13$^\circ$ and Dec. = 60.38$^\circ \pm$ 0.07$^\circ$ with an extension of 0.37$^\circ\pm$0.07$^\circ$. No clear extended multiwavelength counterpart of this LHAASO source has been found from the radio band to the GeV band. The most plausible explanation of the VHE \gray emission is the inverse Compton process of highly relativistic electrons and positrons injected by the pulsar. These electrons/positrons are hypothesized to be either confined within the pulsar wind nebula or to have already escaped into the interstellar medium, forming a pulsar halo., Comment: 12 pages, 10 figures, Accepted by Sci. China-Phys. Mech. Astron
Published: 2024

6. Universal and robust dynamic decoupling controls for zero-field magnetometry by using molecular clock sensors

Author: Jiang, Jiawen and Chen, Q.
Subjects: Quantum Physics
Abstract: Color centers in diamond and silicon carbide (SiC), and molecular spins through a host matrix control are promising for nanoscale quantum sensing because they can be optically addressable, coherently controllable, and placed proximate to the targets. However, large transverse zero-field splitting (ZFS) is often inevitable due to their intrinsic symmetry and/or the high local strains of the host matrix. Although spin coherence can be extended due to magnetic noise-insensitive clock transitions at a vanishing magnetic field, the eigenstates of these sensors are not sensitive to weak magnetic signals in the linear order. We address this challenge by employing a combination of radio-frequency (RF) field driving along the NV orientation and microwave (MW) dynamic decoupling pulse sequences. RF driving can effectively mitigate the transverse ZFS effect and enhance the NV center's sensitivity to AC magnetic field signals. This combination not only suppresses environmental noise but also enables quantum frequency mixing between the transverse ZFS and the signal. It also offers the potential to detect weak AC signals at intermediate and high frequencies with high resolution, a task difficult to achieve using conventional methods.
Published: 2024

7. Transition signatures for electron-positron pair creation in space-time inhomogeneous electric field

Author: Li, C. K., Zhou, X. X., Chen, Q., An, B., Li, Y. J., Lin, N. S., and Wan, Y.
Subjects: High Energy Physics - Phenomenology, Physics - Plasma Physics
Abstract: The process of electron-positron pair creation through multi-photon absorption in a space-time dependent electric field is analyzed using computational quantum field theory. Our findings reveal two distinct pair creation channels: the symmetric and asymmetric transition channels. We propose that the asymmetric transition channel arises from the inherent spatial inhomogeneity of intense laser pulses. By mapping the field-theoretical model of laser-assisted multi-photon pair creation onto a quantum-mechanical time-dependent framework, a semi-analytical solution that captures the asymmetric transition signatures of vacuum decay is derived. Additionally, it is demonstrated that neglecting spatial inhomogeneity leads to erroneous transition amplitudes and incorrect identification of pair creation channels, even when the dipole approximation holds.
Published: 2024

8. Shape coexistence in Ne isotopes and hyperon impurity effect on low-lying states

Author: Xue, Huai-Tong, Cui, Ji-Wei, Chen, Q. B., Zhou, Xian-Rong, and Sagawa, Hiroyuki
Subjects: Nuclear Theory
Abstract: Based on the beyond-mean-field Skyrme-Hartree-Fock model, we investigate the shape coexistence in Ne isotopes and the effect of $\la$ hyperon on the energy level structure in the nuclei. The up-to-date Skyrme-type $N\la$ interaction SLL4 and the $NN$ interaction SGII are employed. Low-lying energy spectra of $^{20,22,24,26,28,30,32,34}$Ne, including the low-lying states with $J\leq 6$, are predicted, discussed in detail, and found in good agreement with experimental results. The electric quadrupole transition rate is also examined. The coexistences of a ground state rotational band and a $\be$ vibrational band are revealed in $^{20,22,24}$Ne. Unlike the previously discovered shrinkage effect of $\la_{s}$ on the ground state nuclei, it is found that the $\la_{s}$ may alter the excitation mode of the second band by affecting the distribution of the collective wave function, thereby causing the $\be$ vibrational band transitions to a vibrational band with equidistant energy levels., Comment: 12 pages, 6 figures
Published: 2024

9. Proton drip line of deformed hypernuclei

Author: Liu, Yi-Xiu, Xue, Huai-Tong, Chen, Q. B., Zhou, Xian-Rong, and Schulze, H. -J.
Subjects: Nuclear Theory
Abstract: The proton drip line of (hyper)nuclei is examined within the framework of the deformed Skyrme-Hartree Fock approach by adjusting the nuclear force parameters to exactly reproduce the core binding energies. The impact of adding a {\Lambda} hyperon in a s or p state is studied, and it is found that in some cases the deformation effect facilitates the extension of the drip line by an added p-state hyperon. However, no extension of the drip line is found for s-state hypernuclei., Comment: 7 pages, 2 Figures
Published: 2024

10. Practical identifiability and parameter estimation of compartmental epidemiological models

Author: Chen, Q. Y., Rapti, Z., Drossinos, Y., Cuevas-Maraver, J., Kevrekidis, G. A., and Kevrekidis, P. G.
Subjects: Statistics - Methodology
Abstract: Practical parameter identifiability in ODE-based epidemiological models is a known issue, yet one that merits further study. It is essentially ubiquitous due to noise and errors in real data. In this study, to avoid uncertainty stemming from data of unknown quality, simulated data with added noise are used to investigate practical identifiability in two distinct epidemiological models. Particular emphasis is placed on the role of initial conditions, which are assumed unknown, except those that are directly measured. Instead of just focusing on one method of estimation, we use and compare results from various broadly used methods, including maximum likelihood and Markov Chain Monte Carlo (MCMC) estimation. Among other findings, our analysis revealed that the MCMC estimator is overall more robust than the point estimators considered. Its estimates and predictions are improved when the initial conditions of certain compartments are fixed so that the model becomes globally identifiable. For the point estimators, whether fixing or fitting the that are not directly measured improves parameter estimates is model-dependent. Specifically, in the standard SEIR model, fixing the initial condition for the susceptible population S(0) improved parameter estimates, while this was not true when fixing the initial condition of the asymptomatic population in a more involved model. Our study corroborates the change in quality of parameter estimates upon usage of pre-peak or post-peak time-series under consideration. Finally, our examples suggest that in the presence of significantly noisy data, the value of structural identifiability is moot.
Published: 2024

11. Constraints on Ultra Heavy Dark Matter Properties from Dwarf Spheroidal Galaxies with LHAASO Observations

Author: Cao, Zhen, Aharonian, F., An, Q., Axikegu, Bai, Y. X., Bao, Y. W., Bastieri, D., Bi, X. J., Bi, Y. J., Cai, J. T., Cao, Q., Cao, W. Y., Cao, Zhe, Chang, J., Chang, J. F., Chen, A. M., Chen, E. S., Chen, Liang, Chen, Lin, Chen, Long, Chen, M. J., Chen, M. L., Chen, Q. H., Chen, S. H., Chen, S. Z., Chen, T. L., Chen, Y., Cheng, N., Cheng, Y. D., Cui, M. Y., Cui, S. W., Cui, X. H., Cui, Y. D., Dai, B. Z., Dai, H. L., Dai, Z. G., Danzengluobu, della Volpe, D., Dong, X. Q., Duan, K. K., Fan, J. H., Fan, Y. Z., Fang, J., Fang, K., Feng, C. F., Feng, L., Feng, S. H., Feng, X. T., Feng, Y. L., Gabici, S., Gao, B., Gao, C. D., Gao, L. Q., Gao, Q., Gao, W., Gao, W. K., Ge, M. M., Geng, L. S., Giacinti, G., Gong, G. H., Gou, Q. B., Gu, M. H., Guo, F. L., Guo, X. L., Guo, Y. Q., Guo, Y. Y., Han, Y. A., He, H. H., He, H. N., He, J. Y., He, X. B., He, Y., Heller, M., Hor, Y. K., Hou, B. W., Hou, C., Hou, X., Hu, H. B., Hu, Q., Hu, S. C., Huang, D. H., Huang, T. Q., Huang, W. J., Huang, X. T., Huang, X. Y., Huang, Y., Huang, Z. C., Ji, X. L., Jia, H. Y., Jia, K., Jiang, K., Jiang, X. W., Jiang, Z. J., Jin, M., Kang, M. M., Ke, T., Kuleshov, D., Kurinov, K., Li, B. B., Li, Cheng, Li, Cong, Li, D., Li, F., Li, H. B., Li, H. C., Li, H. Y., Li, J., Li, Jian, Li, Jie, Li, K., Li, W. L., Li, X. R., Li, Xin, Li, Y. Z., Li, Zhe, Li, Zhuo, Liang, E. W., Liang, Y. F., Lin, S. J., Liu, B., Liu, C., Liu, D., Liu, H., Liu, H. D., Liu, J., Liu, J. L., Liu, J. Y., Liu, M. Y., Liu, R. Y., Liu, S. M., Liu, W., Liu, Y., Liu, Y. N., Lu, R., Luo, Q., Lv, H. K., Ma, B. Q., Ma, L. L., Ma, X. H., Mao, J. R., Min, Z., Mitthumsiri, W., Mu, H. J., Nan, Y. C., Neronov, A., Ou, Z. W., Pang, B. Y., Pattarakijwanich, P., Pei, Z. Y., Qi, M. Y., Qi, Y. Q., Qiao, B. Q., Qin, J. J., Ruffolo, D., Saiz, A., Semikoz, D., Shao, C. Y., Shao, L., Shchegolev, O., Sheng, X. D., Shu, F. W., Song, H. C., Stenkin, Yu. V., Stepanov, V., Su, Y., Sun, Q. N., Sun, X. N., Sun, Z. B., Tam, P. H. T., Tang, Q. W., Tang, Z. B., Tian, W. W., Wang, C., Wang, C. B., Wang, G. W., Wang, H. G., Wang, H. H., Wang, J. C., Wang, K., Wang, L. P., Wang, L. Y., Wang, P. H., Wang, R., Wang, W., Wang, X. G., Wang, X. Y., Wang, Y., Wang, Y. D., Wang, Y. J., Wang, Z. H., Wang, Z. X., Wang, Zhen, Wang, Zheng, Wei, D. M., Wei, J. J., Wei, Y. J., Wen, T., Wu, C. Y., Wu, H. R., Wu, S., Wu, X. F., Wu, Y. S., Xi, S. Q., Xia, J., Xia, J. J., Xiang, G. M., Xiao, D. X., Xiao, G., Xin, G. G., Xin, Y. L., Xing, Y., Xiong, Z., Xu, D. L., Xu, R. F., Xu, R. X., Xu, W. L., Xue, L., Yan, D. H., Yan, J. Z., Yan, T., Yang, C. W., Yang, F., Yang, F. F., Yang, H. W., Yang, J. Y., Yang, L. L., Yang, M. J., Yang, R. Z., Yang, S. B., Yao, Y. H., Yao, Z. G., Ye, Y. M., Yin, L. Q., Yin, N., You, X. H., You, Z. Y., Yu, Y. H., Yuan, Q., Yue, H., Zeng, H. D., Zeng, T. X., Zeng, W., Zha, M., Zhang, B. B., Zhang, F., Zhang, H. M., Zhang, H. Y., Zhang, J. L., Zhang, L. X., Zhang, Li, Zhang, P. F., Zhang, P. P., Zhang, R., Zhang, S. B., Zhang, S. R., Zhang, S. S., Zhang, X., Zhang, X. P., Zhang, Y. F., Zhang, Yi, Zhang, Yong, Zhao, B., Zhao, J., Zhao, L., Zhao, L. Z., Zhao, S. P., Zheng, F., Zhou, B., Zhou, H., Zhou, J. N., Zhou, M., Zhou, P., Zhou, R., Zhou, X. X., Zhu, C. G., Zhu, F. R., Zhu, H., Zhu, K. J., and Zuo, X.
Subjects: Astrophysics - High Energy Astrophysical Phenomena, High Energy Physics - Phenomenology
Abstract: In this work we try to search for signals generated by ultra-heavy dark matter at the Large High Altitude Air Shower Observatory (LHAASO) data. We look for possible gamma-ray by dark matter annihilation or decay from 16 dwarf spheroidal galaxies in the field of view of LHAASO. Dwarf spheroidal galaxies are among the most promising targets for indirect detection of dark matter which have low fluxes of astrophysical $\gamma$-ray background while large amount of dark matter. By analyzing more than 700 days observational data at LHAASO, no significant dark matter signal from 1 TeV to 1 EeV is detected. Accordingly we derive the most stringent constraints on the ultra-heavy dark matter annihilation cross-section up to EeV. The constraints on the lifetime of dark matter in decay mode are also derived., Comment: 17 pages, 12 figures, accepted by PRL
Published: 2024

12. Research on Control System of Truss Manipulator and Inspection of Various Motion Mechanisms

Author: Wang, X., Chen, Q. S., Yu, H., and Liu, H.
Published: 2024
Full Text: View/download PDF

13. Data quality control system and long-term performance monitor of the LHAASO-KM2A

Author: Cao, Zhen, Aharonian, F., Axikegu, Bai, Y. X., Bao, Y. W., Bastieri, D., Bi, X. J., Bi, Y. J., Bian, W., Bukevich, A. V., Cao, Q., Cao, W. Y., Cao, Zhe, Chang, J., Chang, J. F., Chen, A. M., Chen, E. S., Chen, H. X., Chen, Liang, Chen, Lin, Chen, Long, Chen, M. J., Chen, M. L., Chen, Q. H., Chen, S., Chen, S. H., Chen, S. Z., Chen, T. L., Chen, Y., Cheng, N., Cheng, Y. D., Cui, M. Y., Cui, S. W., Cui, X. H., Cui, Y. D., Dai, B. Z., Dai, H. L., Dai, Z. G., Danzengluobu, Dong, X. Q., Duan, K. K., Fan, J. H., Fan, Y. Z., Fang, J., Fang, J. H., Fang, K., Feng, C. F., Feng, H., Feng, L., Feng, S. H., Feng, X. T., Feng, Y., Feng, Y. L., Gabici, S., Gao, B., Gao, C. D., Gao, Q., Gao, W., Gao, W. K., Ge, M. M., Geng, L. S., Giacinti, G., Gong, G. H., Gou, Q. B., Gu, M. H., Guo, F. L., Guo, X. L., Guo, Y. Q., Guo, Y. Y., Han, Y. A., Hasan, M., He, H. H., He, H. N., He, J. Y., He, Y., Hor, Y. K., Hou, B. W., Hou, C., Hou, X., Hu, H. B., Hu, Q., Hu, S. C., Huang, D. H., Huang, T. Q., Huang, W. J., Huang, X. T., Huang, X. Y., Huang, Y., Ji, X. L., Jia, H. Y., Jia, K., Jiang, K., Jiang, X. W., Jiang, Z. J., Jin, M., Kang, M. M., Karpikov, I., Kuleshov, D., Kurinov, K., Li, B. B., Li, C. M., Li, Cheng, Li, Cong, Li, D., Li, F., Li, H. B., Li, H. C., Li, Jian, Li, Jie, Li, K., Li, S. D., Li, W. L., Li, X. R., Li, Xin, Li, Y. Z., Li, Zhe, Li, Zhuo, Liang, E. W., Liang, Y. F., Lin, S. J., Liu, B., Liu, C., Liu, D., Liu, D. B., Liu, H., Liu, H. D., Liu, J., Liu, J. L., Liu, M. Y., Liu, R. Y., Liu, S. M., Liu, W., Liu, Y., Liu, Y. N., Luo, Q., Luo, Y., Lv, H. K., Ma, B. Q., Ma, L. L., Ma, X. H., Mao, J. R., Min, Z., Mitthumsiri, W., Mu, H. J., Nan, Y. C., Neronov, A., Ou, L. J., Pattarakijwanich, P., Pei, Z. Y., Qi, J. C., Qi, M. Y., Qiao, B. Q., Qin, J. J., Raza, A., Ruffolo, D., Sáiz, A., Saeed, M., Semikoz, D., Shao, L., Shchegolev, O., Sheng, X. D., Shu, F. W., Song, H. C., Stenkin, Yu. V., Stepanov, V., Su, Y., Sun, D. X., Sun, Q. N., Sun, X. N., Sun, Z. B., Takata, J., Tam, P. H. T., Tang, Q. W., Tang, R., Tang, Z. B., Tian, W. W., Wang, C., Wang, C. B., Wang, G. W., Wang, H. G., Wang, H. H., Wang, J. C., Wang, Kai, Wang, L. P., Wang, L. Y., Wang, P. H., Wang, R., Wang, W., Wang, X. G., Wang, X. Y., Wang, Y., Wang, Y. D., Wang, Y. J., Wang, Z. H., Wang, Z. X., Wang, Zhen, Wang, Zheng, Wei, D. M., Wei, J. J., Wei, Y. J., Wen, T., Wu, C. Y., Wu, H. R., Wu, Q. W., Wu, S., Wu, X. F., Wu, Y. S., Xi, S. Q., Xia, J., Xiang, G. M., Xiao, D. X., Xiao, G., Xin, Y. L., Xing, Y., Xiong, D. R., Xiong, Z., Xu, D. L., Xu, R. F., Xu, R. X., Xu, W. L., Xue, L., Yan, D. H., Yan, J. Z., Yan, T., Yang, C. W., Yang, C. Y., Yang, F., Yang, F. F., Yang, L. L., Yang, M. J., Yang, R. Z., Yang, W. X., Yao, Y. H., Yao, Z. G., Yin, L. Q., Yin, N., You, X. H., You, Z. Y., Yu, Y. H., Yuan, Q., Yue, H., Zeng, H. D., Zeng, T. X., Zeng, W., Zha, M., Zhang, B. B., Zhang, F., Zhang, H., Zhang, H. M., Zhang, H. Y., Zhang, J. L., Zhang, Li, Zhang, P. F., Zhang, P. P., Zhang, R., Zhang, S. B., Zhang, S. R., Zhang, S. S., Zhang, X., Zhang, X. P., Zhang, Y. F., Zhang, Yi, Zhang, Yong, Zhao, B., Zhao, J., Zhao, L., Zhao, L. Z., Zhao, S. P., Zhao, X. H., Zheng, F., Zhong, W. J., Zhou, B., Zhou, H., Zhou, J. N., Zhou, M., Zhou, P., Zhou, R., Zhou, X. X., Zhu, B. Y., Zhu, C. G., Zhu, F. R., Zhu, H., Zhu, K. J., Zou, Y. C., and Zuo, X.
Subjects: Astrophysics - Instrumentation and Methods for Astrophysics, High Energy Physics - Experiment, Physics - Instrumentation and Detectors
Abstract: The KM2A is the largest sub-array of the Large High Altitude Air Shower Observatory (LHAASO). It consists of 5216 electromagnetic particle detectors (EDs) and 1188 muon detectors (MDs). The data recorded by the EDs and MDs are used to reconstruct primary information of cosmic ray and gamma-ray showers. This information is used for physical analysis in gamma-ray astronomy and cosmic ray physics. To ensure the reliability of the LHAASO-KM2A data, a three-level quality control system has been established. It is used to monitor the status of detector units, stability of reconstructed parameters and the performance of the array based on observations of the Crab Nebula and Moon shadow. This paper will introduce the control system and its application on the LHAASO-KM2A data collected from August 2021 to July 2023. During this period, the pointing and angular resolution of the array were stable. From the observations of the Moon shadow and Crab Nebula, the results achieved using the two methods are consistent with each other. According to the observation of the Crab Nebula at energies from 25 TeV to 100 TeV, the time averaged pointing errors are estimated to be $-0.003^{\circ} \pm 0.005^{\circ}$ and $0.001^{\circ} \pm 0.006^{\circ}$ in the R.A. and Dec directions, respectively., Comment: 15 pages, 9 figures
Published: 2024

14. Discovery of Very-high-energy Gamma-ray Emissions from the Low Luminosity AGN NGC 4278 by LHAASO

Author: Cao, Zhen, Aharonian, F., An, Q., Axikegu, Bai, Y. X., Bao, Y. W., Bastieri, D., Bi, X. J., Bi, Y. J., Cai, J. T., Cao, Q., Cao, W. Y., Cao, Zhe, Chang, J., Chang, J. F., Chen, A. M., Chen, E. S., Chen, Liang, Chen, Lin, Chen, Long, Chen, M. J., Chen, M. L., Chen, Q. H., Chen, S. H., Chen, S. Z., Chen, T. L., Chen, Y., Cheng, N., Cheng, Y. D., Cui, M. Y., Cui, S. W., Cui, X. H., Cui, Y. D., Dai, B. Z., Dai, H. L., Dai, Z. G., Danzengluobu, Dong, X. Q., Duan, K. K., Fan, J. H., Fan, Y. Z., Fang, J., Fang, K., Feng, C. F., Feng, L., Feng, S. H., Feng, X. T., Feng, Y. L., Gabici, S., Gao, B., Gao, C. D., Gao, L. Q., Gao, Q., Gao, W., Gao, W. K., Ge, M. M., Geng, L. S., Giacinti, G., Gong, G. H., Gou, Q. B., Gu, M. H., Guo, F. L., Guo, X. L., Guo, Y. Q., Guo, Y. Y., Han, Y. A., He, H. H., He, H. N., He, J. Y., He, X. B., He, Y., Hor, Y. K., Hou, B. W., Hou, C., Hou, X., Hu, H. B., Hu, Q., Hu, S. C., Huang, D. H., Huang, T. Q., Huang, W. J., Huang, X. T., Huang, X. Y., Huang, Y., Huang, Z. C., Ji, X. L., Jia, H. Y., Jia, K., Jiang, K., Jiang, X. W., Jiang, Z. J., Jin, M., Kang, M. M., Ke, T., Kuleshov, D., Kurinov, K., Li, B. B., Li, Cheng, Li, Cong, Li, D., Li, F., Li, H. B., Li, H. C., Li, H. Y., Li, J., Li, Jian, Li, Jie, Li, K., Li, W. L., Li, X. R., Li, Xin, Li, Y. Z., Li, Zhe, Li, Zhuo, Liang, E. W., Liang, Y. F., Lin, J., Liu, B., Liu, C., Liu, D., Liu, H., Liu, H. D., Liu, J., Liu, J. L., Liu, J. Y., Liu, M. Y., Liu, R. Y., Liu, S. M., Liu, W., Liu, Y., Liu, Y. N., Lu, R., Luo, Q., Lv, H. K., Ma, B. Q., Ma, L. L., Ma, X. H., Mao, J. R., Min, Z., Mitthumsiri, W., Mu, H. J., Nan, Y. C., Neronov, A., Ou, Z. W., Pang, B. Y., Pattarakijwanich, P., Pei, Z. Y., Qi, M. Y., Qi, Y. Q., Qiao, B. Q., Qin, J. J., Ruffolo, D., Sáiz, A., Semikoz, D., Shao, C. Y., Shao, L., Shchegolev, O., Sheng, X. D., Shu, F. W., Song, H. C., Stenkin, Yu. V., Stepanov, V., Su, Y., Sun, Q. N., Sun, X. N., Sun, Z. B., Tam, P. H. T., Tang, Q. W., Tang, Z. B., Tian, W. W., Wang, C., Wang, C. B., Wang, G. W., Wang, H. G., Wang, H. H., Wang, J. C., Wang, K., Wang, L. P., Wang, L. Y., Wang, P. H., Wang, R., Wang, W., Wang, X. G., Wang, X. Y., Wang, Y., Wang, Y. D., Wang, Y. J., Wang, Z. H., Wang, Z. X., Wang, Zhen, Wang, Zheng, Wei, D. M., Wei, J. J., Wei, Y. J., Wen, T., Wu, C. Y., Wu, H. R., Wu, S., Wu, X. F., Wu, Y. S., Xi, S. Q., Xia, J., Xia, J. J., Xiang, G. M., Xiao, D. X., Xiao, G., Xin, G. G., Xin, Y. L., Xing, Y., Xiong, Z., Xu, D. L., Xu, R. F., Xu, R. X., Xu, W. L., Xue, L., Yan, D. H., Yan, J. Z., Yan, T., Yang, C. W., Yang, F., Yang, F. F., Yang, H. W., Yang, J. Y., Yang, L. L., Yang, M. J., Yang, R. Z., Yang, S. B., Yao, Y. H., Yao, Z. G., Ye, Y. M., Yin, L. Q., Yin, N., You, X. H., You, Z. Y., Yu, Y. H., Yuan, Q., Yue, H., Zeng, H. D., Zeng, T. X., Zeng, W., Zha, M., Zhang, B. B., Zhang, F., Zhang, H. M., Zhang, H. Y., Zhang, J. L., Zhang, L. X., Zhang, Li, Zhang, P. F., Zhang, P. P., Zhang, R., Zhang, S. B., Zhang, S. R., Zhang, S. S., Zhang, X., Zhang, X. P., Zhang, Y. F., Zhang, Yi, Zhang, Yong, Zhao, B., Zhao, J., Zhao, L., Zhao, L. Z., Zhao, S. P., Zheng, F., Zheng, J. H., Zhou, B., Zhou, H., Zhou, J. N., Zhou, M., Zhou, P., Zhou, R., Zhou, X. X., Zhu, C. G., Zhu, F. R., Zhu, H., Zhu, K. J., Zou, Y. C., and Zuo, X.
Subjects: Astrophysics - High Energy Astrophysical Phenomena
Abstract: The first source catalog of Large High Altitude Air Shower Observatory reported the detection of a very-high-energy gamma ray source, 1LHAASO J1219+2915. In this paper a further detailed study of the spectral and temporal behavior of this point-like source have been carried. The best-fit position of the TeV source ($\rm{RA}=185.05^{\circ}\pm0.04^{\circ}$, $\rm{Dec}=29.25^{\circ}\pm0.03^{\circ}$) is compatible with NGC 4278 within $\sim0.03$ degree. Variation analysis shows an indication of the variability at a few months level in the TeV band, which is consistent with low frequency observations. Based on these observations, we report the detection of TeV $\gamma$-ray emissions from this low-luminosity AGN NGC 4278. The observations by LHAASO-WCDA during active period has a significance level of 8.8\,$\sigma$ with best-fit photon spectral index $\varGamma=2.56\pm0.14$ and a flux $f_{1-10\,\rm{TeV}}=(7.0\pm1.1_{\rm{sta}}\pm0.35_{\rm{syst}})\times10^{-13}\,\rm{photons\,cm^{-2}\,s^{-1}}$, or approximately $5\%$ of the Crab Nebula. The discovery of VHE from NGC 4278 indicates that the compact, weak radio jet can efficiently accelerate particles and emit TeV photons., Comment: 11 pages, 5 figures
Published: 2024
Full Text: View/download PDF

15. The Next Generation of MeV Energy X-ray Sources for use in the Inspection of Additively Manufactured Parts for Industry

Author: Thornton, C., Karimi, S., Glenn, S., Brown, W. D., Draganic, N., Skeate, M., Ferrucci, M., Chen, Q., Jacob, R., Nakamura, K., Ostermayr, T., van Tilborg, J., Armstrong, C., Finlay, O. J., Turner, N., Glanvill, S., Martz, H., and Geddes, C.
Subjects: Physics - Accelerator Physics, Physics - Plasma Physics
Abstract: For the first time, we demonstrate the application of an inverse Compton scattering X-ray Source, driven by a laser-plasma accelerator, to image an additively manufactured component. X-rays with a mean energy of 380 keV were produced and used to image an additively manufactured part made of an Inconel (Nickel 718) alloy. Because inverse Compton scattering driven by laser-plasma acceleration produces high-energy X-rays while maintaining a focal spot size on the order of a micron, the source can provide several benefits over conventional X-ray production methods, particularly when imaging superalloy parts, with the potential to revolutionise what can be inspected.
Published: 2024

16. LHAASO-KM2A detector simulation using Geant4

Author: Cao, Zhen, Aharonian, F., An, Q., Axikegu, Bai, Y. X., Bao, Y. W., Bastieri, D., Bi, X. J., Bi, Y. J., Cai, J. T., Cao, Q., Cao, W. Y., Cao, Zhe, Chang, J., Chang, J. F., Chen, A. M., Chen, E. S., Chen, Liang, Chen, Lin, Chen, Long, Chen, M. J., Chen, M. L., Chen, Q. H., Chen, S. H., Chen, S. Z., Chen, T. L., Chen, Y., Cheng, N., Cheng, Y. D., Cui, M. Y., Cui, S. W., Cui, X. H., Cui, Y. D., Dai, B. Z., Dai, H. L., Dai, Z. G., Danzengluobu, Dong, X. Q., Duan, K. K., Fan, J. H., Fan, Y. Z., Fang, J., Fang, K., Feng, C. F., Feng, L., Feng, S. H., Feng, X. T., Feng, Y. L., Gabici, S., Gao, B., Gao, C. D., Gao, L. Q., Gao, Q., Gao, W., Gao, W. K., Ge, M. M., Geng, L. S., Giacinti, G., Gong, G. H., Gou, Q. B., Gu, M. H., Guo, F. L., Guo, X. L., Guo, Y. Q., Guo, Y. Y., Han, Y. A., He, H. H., He, H. N., He, J. Y., He, X. B., He, Y., Hor, Y. K., Hou, B. W., Hou, C., Hou, X., Hu, H. B., Hu, Q., Hu, S. C., Huang, D. H., Huang, T. Q., Huang, W. J., Huang, X. T., Huang, X. Y., Huang, Y., Huang, Z. C., Ji, X. L., Jia, H. Y., Jia, K., Jiang, K., Jiang, X. W., Jiang, Z. J., Jin, M., Kang, M. M., Ke, T., Kuleshov, D., Kurinov, K., Li, B. B., Li, Cheng, Li, Cong, Li, D., Li, F., Li, H. B., Li, H. C., Li, H. Y., Li, J., Li, Jian, Li, Jie, Li, K., Li, W. L., Li, X. R., Li, Xin, Li, Y. Z., Li, Zhe, Li, Zhuo, Liang, E. W., Liang, Y. F., Lin, J., Liu, B., Liu, C., Liu, D., Liu, H., Liu, H. D., Liu, J., Liu, J. L., Liu, J. Y., Liu, M. Y., Liu, R. Y., Liu, S. M., Liu, W., Liu, Y., Liu, Y. N., Lu, R., Luo, Q., Lv, H. K., Ma, B. Q., Ma, L. L., Ma, X. H., Mao, J. R., Min, Z., Mitthumsiri, W., Mu, H. J., Nan, Y. C., Neronov, A., Ou, Z. W., Pang, B. Y., Pattarakijwanich, P., Pei, Z. Y., Qi, M. Y., Qi, Y. Q., Qiao, B. Q., Qin, J. J., Ruffolo, D., Sáiz, A., Semikoz, D., Shao, C. Y., Shao, L., Shchegolev, O., Sheng, X. D., Shu, F. W., Song, H. C., Stenkin, Yu. V., Stepanov, V., Su, Y., Sun, Q. N., Sun, X. N., Sun, Z. B., Tam, P. H. T., Tang, Q. W., Tang, Z. B., Tian, W. W., Wang, C., Wang, C. B., Wang, G. W., Wang, H. G., Wang, H. H., Wang, J. C., Wang, K., Wang, L. P., Wang, L. Y., Wang, P. H., Wang, R., Wang, W., Wang, X. G., Wang, X. Y., Wang, Y., Wang, Y. D., Wang, Y. J., Wang, Z. H., Wang, Z. X., Wang, Zhen, Wang, Zheng, Wei, D. M., Wei, J. J., Wei, Y. J., Wen, T., Wu, C. Y., Wu, H. R., Wu, S., Wu, X. F., Wu, Y. S., Xi, S. Q., Xia, J., Xia, J. J., Xiang, G. M., Xiao, D. X., Xiao, G., Xin, G. G., Xin, Y. L., Xing, Y., Xiong, Z., Xu, D. L., Xu, R. F., Xu, R. X., Xu, W. L., Xue, L., Yan, D. H., Yan, J. Z., Yan, T., Yang, C. W., Yang, F., Yang, F. F., Yang, H. W., Yang, J. Y., Yang, L. L., Yang, M. J., Yang, R. Z., Yang, S. B., Yao, Y. H., Yao, Z. G., Ye, Y. M., Yin, L. Q., Yin, N., You, X. H., You, Z. Y., Yu, Y. H., Yuan, Q., Yue, H., Zeng, H. D., Zeng, T. X., Zeng, W., Zha, M., Zhang, B. B., Zhang, F., Zhang, H. M., Zhang, H. Y., Zhang, J. L., Zhang, L. X., Zhang, Li, Zhang, P. F., Zhang, P. P., Zhang, R., Zhang, S. B., Zhang, S. R., Zhang, S. S., Zhang, X., Zhang, X. P., Zhang, Y. F., Zhang, Yi, Zhang, Yong, Zhao, B., Zhao, J., Zhao, L., Zhao, L. Z., Zhao, S. P., Zheng, F., Zheng, J. H., Zhou, B., Zhou, H., Zhou, J. N., Zhou, M., Zhou, P., Zhou, R., Zhou, X. X., Zhu, C. G., Zhu, F. R., Zhu, H., Zhu, K. J., and Zuo, X.
Subjects: Astrophysics - Instrumentation and Methods for Astrophysics, Astrophysics - High Energy Astrophysical Phenomena
Abstract: KM2A is one of the main sub-arrays of LHAASO, working on gamma ray astronomy and cosmic ray physics at energies above 10 TeV. Detector simulation is the important foundation for estimating detector performance and data analysis. It is a big challenge to simulate the KM2A detector in the framework of Geant4 due to the need to track numerous photons from a large number of detector units (>6000) with large altitude difference (30 m) and huge coverage (1.3 km^2). In this paper, the design of the KM2A simulation code G4KM2A based on Geant4 is introduced. The process of G4KM2A is optimized mainly in memory consumption to avoid memory overffow. Some simpliffcations are used to signiffcantly speed up the execution of G4KM2A. The running time is reduced by at least 30 times compared to full detector simulation. The particle distributions and the core/angle resolution comparison between simulation and experimental data of the full KM2A array are also presented, which show good agreement.
Published: 2024
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17. Evolution of chirality from transverse wobbling in $^{135}$Pr

Author: Sensharma, N., Garg, U., Chen, Q. B., Frauendorf, S., Zhu, S., Arroyo, J., Ayangeakaa, A. D., Burdette, D. P., Carpenter, M. P., Copp, P., Cozzi, J. L., Ghugre, S. S., Hartley, D. J., Howard, K. B., Janssens, R. V. F., Kondev, F. G., Lauritsen, T., Li, J., Palit, R., Saracino, A., Seweryniak, D., Weyhmiller, S., and Wu, J.
Subjects: Nuclear Experiment, Nuclear Theory
Abstract: Chirality is a distinct signature that characterizes triaxial shapes in nuclei. We report the first observation of chirality in the nucleus $^{135}$Pr using a high-statistics Gammasphere experiment with the $^{123}$Sb($^{16}$O,4n)$^{135}$Pr reaction. Two chiral-partner bands with the configuration $\pi(1h_{11/2})^1\otimes \nu(1h_{11/2})^{-2}$ have been identified in this nucleus. Angular distribution analyses of the $\Delta I = 1$ connecting transitions between the two chiral partners have revealed a dominant dipole character. Quasiparticle triaxial rotor model calculations are in good agreement with the experiment. This is the first time that both signatures of triaxiality--chirality and wobbling--have been observed in the same nucleus., Comment: 11 pages, 11 figures
Published: 2024

18. Measurements of All-Particle Energy Spectrum and Mean Logarithmic Mass of Cosmic Rays from 0.3 to 30 PeV with LHAASO-KM2A

Author: The LHAASO Collaboration, Cao, Zhen, Aharonian, F., An, Q., Axikegu, A., Bai, Y. X., Bao, Y. W., Bastieri, D., Bi, X. J., Bi, Y. J., Cai, J. T., Cao, Q., Cao, W. Y., Cao, Zhe, Chang, J., Chang, J. F., Chen, A. M., Chen, E. S., Chen, Liang, Chen, Lin, Chen, Long, Chen, M. J., Chen, M. L., Chen, Q. H., Chen, S. H., Chen, S. Z., Chen, T. L., Chen, Y., Cheng, N., Cheng, Y. D., Cui, M. Y., Cui, S. W., Cui, X. H., Cui, Y. D., Dai, B. Z., Dai, H. L., Dai, Z. G., Danzengluobu, della Volpe, D., Dong, X. Q., Duan, K. K., Fan, J. H., Fan, Y. Z., Fang, J., Fang, K., Feng, C. F., Feng, L., Feng, S. H., Feng, X. T., Feng, Y. L., Gabici, S., Gao, B., Gao, C. D., Gao, L. Q., Gao, Q., Gao, W., Gao, W. K., Ge, M. M., Geng, L. S., Giacinti, G., Gong, G. H., Gou, Q. B., Gu, M. H., Guo, F. L., Guo, X. L., Guo, Y. Q., Guo, Y. Y., Han, Y. A., He, H. H., He, H. N., He, J. Y., He, X. B., He, Y., Heller, M., Hor, Y. K., Hou, B. W., Hou, C., Hou, X., Hu, H. B., Hu, Q., Hu, S. C., Huang, D. H., Huang, T. Q., Huang, W. J., Huang, X. T., Huang, X. Y., Huang, Y., Huang, Z. C., Ji, X. L., Jia, H. Y., Jia, K., Jiang, K., Jiang, X. W., Jiang, Z. J., Jin, M., Kang, M. M., Ke, T., Kuleshov, D., Kurinov, K., Li, B. B., Li, Cheng, Li, Cong, Li, D., Li, F., Li, H. B., Li, H. C., Li, H. Y., Li, J., Li, Jian, Li, Jie, Li, K., Li, W. L., Li, X. R., Li, Xin, Li, Y. Z., Li, Zhe, Li, Zhuo, Liang, E. W., Liang, Y. F., Lin, S. J., Liu, B., Liu, C., Liu, D., Liu, H., Liu, H. D., Liu, J., Liu, J. L., Liu, J. Y., Liu, M. Y., Liu, R. Y., Liu, S. M., Liu, W., Liu, Y., Liu, Y. N., Lu, R., Luo, Q., Lv, H. K., Ma, B. Q., Ma, L. L., Ma, X. H., Mao, J. R., Min, Z., Mitthumsiri, W., Mu, H. J., Nan, Y. C., Neronov, A., Ou, Z. W., Pang, B. Y., Pattarakijwanich, P., Pei, Z. Y., Qi, M. Y., Qi, Y. Q., Qiao, B. Q., Qin, J. J., Ruffolo, D., Sáiz, A., Semikoz, D., Shao, C. Y., Shao, L., Shchegolev, O., Sheng, X. D., Shu, F. W., Song, H. C., Stenkin, Yu. V., Stepanov, V., Su, Y., Sun, Q. N., Sun, X. N., Sun, Z. B., Tam, P. H. T., Tang, Q. W., Tang, Z. B., Tian, W. W., Wang, C., Wang, C. B., Wang, G. W., Wang, H. G., Wang, H. H., Wang, J. C., Wang, K., Wang, L. P., Wang, L. Y., Wang, P. H., Wang, R., Wang, W., Wang, X. G., Wang, X. Y., Wang, Y., Wang, Y. D., Wang, Y. J., Wang, Z. H., Wang, Z. X., Wang, Zhen, Wang, Zheng, Wei, D. M., Wei, J. J., Wei, Y. J., Wen, T., Wu, C. Y., Wu, H. R., Wu, S., Wu, X. F., Wu, Y. S., Xi, S. Q., Xia, J., Xia, J. J., Xiang, G. M., Xiao, D. X., Xiao, G., Xin, G. G., Xin, Y. L., Xing, Y., Xiong, Z., Xu, D. L., Xu, R. F., Xu, R. X., Xu, W. L., Xue, L., Yan, D. H., Yan, J. Z., Yan, T., Yang, C. W., Yang, F., Yang, F. F., Yang, H. W., Yang, J. Y., Yang, L. L., Yang, M. J., Yang, R. Z., Yang, S. B., Yao, Y. H., Yao, Z. G., Ye, Y. M., Yin, L. Q., Yin, N., You, X. H., You, Z. Y., Yu, Y. H., Yuan, Q., Yue, H., Zeng, H. D., Zeng, T. X., Zeng, W., Zha, M., Zhang, B. B., Zhang, F., Zhang, H. M., Zhang, H. Y., Zhang, J. L., Zhang, L. X., Zhang, Li, Zhang, P. F., Zhang, P. P., Zhang, R., Zhang, S. B., Zhang, S. R., Zhang, S. S., Zhang, X., Zhang, X. P., Zhang, Y. F., Zhang, Yi, Zhang, Yong, Zhao, B., Zhao, J., Zhao, L., Zhao, L. Z., Zhao, S. P., Zheng, F., Zhou, B., Zhou, H., Zhou, J. N., Zhou, M., Zhou, P., Zhou, R., Zhou, X. X., Zhu, C. G., Zhu, F. R., Zhu, H., Zhu, K. J., and Zuo, X.
Subjects: Astrophysics - High Energy Astrophysical Phenomena
Abstract: We present the measurements of all-particle energy spectrum and mean logarithmic mass of cosmic rays in the energy range of 0.3-30 PeV using data collected from LHAASO-KM2A between September 2021 and December 2022, which is based on a nearly composition-independent energy reconstruction method, achieving unprecedented accuracy. Our analysis reveals the position of the knee at $3.67 \pm 0.05 \pm 0.15$ PeV. Below the knee, the spectral index is found to be -$2.7413 \pm 0.0004 \pm 0.0050$, while above the knee, it is -$3.128 \pm 0.005 \pm 0.027$, with the sharpness of the transition measured with a statistical error of 2%. The mean logarithmic mass of cosmic rays is almost heavier than helium in the whole measured energy range. It decreases from 1.7 at 0.3 PeV to 1.3 at 3 PeV, representing a 24% decline following a power law with an index of -$0.1200 \pm 0.0003 \pm 0.0341$. This is equivalent to an increase in abundance of light components. Above the knee, the mean logarithmic mass exhibits a power law trend towards heavier components, which is reversal to the behavior observed in the all-particle energy spectrum. Additionally, the knee position and the change in power-law index are approximately the same. These findings suggest that the knee observed in the all-particle spectrum corresponds to the knee of the light component, rather than the medium-heavy components., Comment: 8 pages, 3 figures
Published: 2024
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19. Risk factors of liver metastasis from advanced pancreatic adenocarcinoma: a large multicenter cohort study

Author: Dong S., Wang L., Guo Y. B., Ying H. F., Shen X. H., Meng Z. Q., Chen Hao, Chen Q. W., and Li Z. S.
Subjects: Surgery, RD1-811, Neoplasms. Tumors. Oncology. Including cancer and carcinogens, RC254-282
Abstract: Abstract Background Clinical prognostic parameters of liver metastasis from pancreatic adenocarcinoma have not been specifically identified.This study is to explore the risk factors of liver metastasis in advanced pancreatic adenocarcinoma (PDAC) patients in China. Methods A multicenter cohort study was conducted to explore whether liver metastasis in locally advanced and metastatic PDAC could be reflected by some common laboratory indexes. We collected 1787 advanced PDAC patients from three participating hospitals between 2004 and 2014. The associations between some laboratory indexes and risks of liver metastases were analyzed. Results Results have shown that 87% of stage IV patients developed synchronous liver metastasis. Primary tumor location (body/tail vs. head/neck, OR 0.55, 95% CI 0.36-0.83), primary tumor diameter (≥20 mm vs.
Published: 2017
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20. An accurate and efficient method for calculating surface waves in defective two–dimensional semi–infinite periodic structures

Author: Yan, B. W., Gao, Q., Jia, H. K., Bi, R., and Chen, Q. W.
Published: 2024
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21. Spin squeezed states and wobbling motion in collective Hamiltonian

Author: Chen, Q. B. and Frauendorf, S.
Subjects: Nuclear Theory, Nuclear Experiment
Abstract: A semiclassical approach is proposed to calculate the collective potential and mass parameters to formulate a collective Hamiltonian capable of describing the wobbling motion in both even-even and odd-mass systems. By diagonalizing the resulting collective Hamiltonian (CH), one can obtain the energies and wave functions associated with the wobbling states. Furthermore, a novel technique called spin squeezed state (SSS) maps is introduced based on the derived wave functions. To validate the results obtained from the collective Hamiltonian, a comparative analysis is conducted against predictions from the triaxial rotor model (TRM) and particle triaxial rotor (PTR) model. Notably, the SSS plots determined using the TRM and PTR models exhibit a strong correlation with the probability density distributions of the wave functions obtained from the CH. This correlation highlights the consistency and coherence between the different theoretical approaches when describing the wobbling phenomenon and associated rotational dynamics., Comment: submitted to PRC
Published: 2024

22. Measurement of flow coefficients in high-multiplicity $p$+Au, $d$+Au and $^{3}$He$+$Au collisions at $\sqrt{s_{_{\mathrm{NN}}}}$=200 GeV

Author: STAR Collaboration, Abdulhamid, M. I., Aboona, B. E., Adam, J., Adamczyk, L., Adams, J. R., Aggarwal, I., Aggarwal, M. M., Ahammed, Z., Aschenauer, E. C., Aslam, S., Atchison, J., Bairathi, V., Cap, J. G. Ball, Barish, K., Bellwied, R., Bhagat, P., Bhasin, A., Bhatta, S., Bhosale, S. R., Bielcik, J., Bielcikova, J., Brandenburg, J. D., Broodo, C., Cai, X. Z., Caines, H., Sánchez, M. Calderón de la Barca, Cebra, D., Ceska, J., Chakaberia, I., Chaloupka, P., Chan, B. K., Chang, Z., Chatterjee, A., Chen, D., Chen, J., Chen, J. H., Chen, Q., Chen, Z., Cheng, J., Cheng, Y., Christie, W., Chu, X., Crawford, H. J., Csanád, M., Dale-Gau, G., Das, A., Deppner, I. M., Deshpande, A., Dhamija, A., Dixit, P., Dong, X., Drachenberg, J. L., Duckworth, E., Dunlop, J. C., Engelage, J., Eppley, G., Esumi, S., Evdokimov, O., Eyser, O., Fatemi, R., Fazio, S., Feng, C. J., Feng, Y., Finch, E., Fisyak, Y., Flor, F. A., Fu, C., Gagliardi, C. A., Galatyuk, T., Gao, T., Geurts, F., Ghimire, N., Gibson, A., Gopal, K., Gou, X., Grosnick, D., Gupta, A., Guryn, W., Hamed, A., Han, Y., Harabasz, S., Harasty, M. D., Harris, J. W., Harrison-Smith, H., Havener, L. B., He, W., He, X. H., He, Y., Herrmann, N., Holub, L., Hu, C., Hu, Q., Hu, Y., Huang, H., Huang, H. Z., Huang, S. L., Huang, T., Huang, Y., Humanic, T. J., Isshiki, M., Jacobs, W. W., Jalotra, A., Jena, C., Jentsch, A., Ji, Y., Jia, J., Jin, C., Ju, X., Judd, E. G., Kabana, S., Kalinkin, D., Kang, K., Kapukchyan, D., Kauder, K., Keane, D., Khanal, A., Khyzhniak, Y. V., Kikoła, D. P., Kincses, D., Kisel, I., Kiselev, A., Knospe, A. G., Ko, H. S., Kołaś, J., Kosarzewski, L. K., Kumar, L., Labonte, M. C., Lacey, R., Landgraf, J. M., Lauret, J., Lebedev, A., Lee, J. H., Leung, Y. H., Li, C., Li, D., Li, H-S., Li, H., Li, W., Li, X., Li, Y., Li, Z., Liang, X., Liang, Y., Licenik, R., Lin, T., Lin, Y., Lisa, M. A., Liu, C., Liu, G., Liu, H., Liu, L., Liu, T., Liu, X., Liu, Y., Liu, Z., Ljubicic, T., Lomicky, O., Longacre, R. S., Loyd, E. M., Lu, T., Luo, J., Luo, X. F., Ma, L., Ma, R., Ma, Y. G., Magdy, N., Mallick, D., Manikandhan, R., Markert, C., Matonoha, O., McNamara, G., Mezhanska, O., Mi, K., Mioduszewski, S., Mohanty, B., Mondal, B., Mondal, M. M., Mooney, I., Mrazkova, J., Nagy, M. I., Naim, C. J., Nain, A. S., Nam, J. D., Nasim, M., Neff, D., Nelson, J. M., Nie, M., Nigmatkulov, G., Niida, T., Nonaka, T., Odyniec, G., Ogawa, A., Oh, S., Okubo, K., Page, B. S., Pal, S., Pandav, A., Panday, A., Pandey, A. K., Pani, T., Paul, A., Pawlik, B., Pawlowska, D., Perkins, C., Pluta, J., Pokhrel, B. R., Pinto, I. D. Ponce, Posik, M., Protzman, T. L., Prozorova, V., Pruthi, N. K., Przybycien, M., Putschke, J., Qin, Z., Qiu, H., Racz, C., Radhakrishnan, S. K., Rana, A., Ray, R. L., Reed, R., Robertson, C. W., Robotkova, M., Aguilar, M. A. Rosales, Roy, D., Chowdhury, P. Roy, Ruan, L., Sahoo, A. K., Sahoo, N. R., Sako, H., Salur, S., Sato, S., Schaefer, B. C., Schmidke, W. B., Schmitz, N., Seck, F-J., Seger, J., Seto, R., Seyboth, P., Shah, N., Shanmuganathan, P. V., Shao, T., Sharma, M., Sharma, N., Sharma, R., Sharma, S. R., Sheikh, A. I., Shen, D., Shen, D. Y., Shen, K., Shi, S. S., Shi, Y., Shou, Q. Y., Si, F., Singh, J., Singha, S., Sinha, P., Skoby, M. J., Smirnov, N., Söhngen, Y., Song, Y., Srivastava, B., Stanislaus, T. D. S., Stefaniak, M., Stewart, D. J., Su, Y., Sumbera, M., Sun, C., Sun, X., Sun, Y., Surrow, B., Svoboda, M., Sweger, Z. W., Tamis, A. C., Tang, A. H., Tang, Z., Tarnowsky, T., Thomas, J. H., Timmins, A. R., Tlusty, D., Todoroki, T., Trentalange, S., Tribedy, P., Tripathy, S. K., Truhlar, T., Trzeciak, B. A., Tsai, O. D., Tsang, C. Y., Tu, Z., Tyler, J., Ullrich, T., Underwood, D. G., Upsal, I., Van Buren, G., Vanek, J., Vassiliev, I., Verkest, V., Videbæk, F., Voloshin, S. A., Wang, G., Wang, J. S., Wang, J., Wang, K., Wang, X., Wang, Y., Wang, Z., Webb, J. C., Weidenkaff, P. C., Westfall, G. D., Wielanek, D., Wieman, H., Wilks, G., Wissink, S. W., Witt, R., Wu, J., Wu, X., Xi, B., Xiao, Z. G., Xie, G., Xie, W., Xu, H., Xu, N., Xu, Q. H., Xu, Y., Xu, Z., Yan, G., Yan, Z., Yang, C., Yang, Q., Yang, S., Yang, Y., Ye, Z., Yi, L., Yu, Y., Zbroszczyk, H., Zha, W., Zhang, C., Zhang, D., Zhang, J., Zhang, S., Zhang, W., Zhang, X., Zhang, Y., Zhang, Z. J., Zhang, Z., Zhao, F., Zhao, J., Zhao, M., Zhou, S., Zhou, Y., Zhu, X., Zurek, M., and Zyzak, M.
Subjects: Nuclear Experiment
Abstract: Flow coefficients ($v_2$ and $v_3$) are measured in high-multiplicity $p$+Au, $d$+Au, and $^{3}$He$+$Au collisions at a center-of-mass energy of $\sqrt{s_{_{\mathrm{NN}}}}$ = 200 GeV using the STAR detector. The measurements utilize two-particle correlations with a pseudorapidity requirement of $|\eta| <$ 0.9 and a pair gap of $|\Delta\eta|>1.0$. The primary focus is on analysis methods, particularly the subtraction of non-flow contributions. Four established non-flow subtraction methods are applied to determine $v_n$, validated using the HIJING event generator. $v_n$ values are compared across the three collision systems at similar multiplicities; this comparison cancels the final state effects and isolates the impact of initial geometry. While $v_2$ values show differences among these collision systems, $v_3$ values are largely similar, consistent with expectations of subnucleon fluctuations in the initial geometry. The ordering of $v_n$ differs quantitatively from previous measurements using two-particle correlations with a larger rapidity gap, which, according to model calculations, can be partially attributed to the effects of longitudinal flow decorrelations. The prospects for future measurements to improve our understanding of flow decorrelation and subnucleonic fluctuations are also discussed., Comment: 29 pages, 25 figures
Published: 2023

23. Diet changes due to urbanization in South Africa are linked to microbiome and metabolome signatures of Westernization and colorectal cancer

Author: Ramaboli, M. C., Ocvirk, S., Khan Mirzaei, M., Eberhart, B. L., Valdivia-Garcia, M., Metwaly, A., Neuhaus, K., Barker, G., Ru, J., Nesengani, L. T., Mahdi-Joest, D., Wilson, A. S., Joni, S. K., Layman, D. C., Zheng, J., Mandal, R., Chen, Q., Perez, M. R., Fortuin, S., Gaunt, B., Wishart, D., Methé, B., Haller, D., Li, J. V., Deng, L., Swart, R., and O’Keefe, S. J. D.
Published: 2024
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24. Improving the degradation and magnetization performance of FePC amorphous alloys by annealing treatment

Author: Qi, Z. G., Chen, Q., Wang, Z. X., Song, Z. Q., Kim, K. B., Pang, J., Zhang, X. H., and Wang, W. M.
Published: 2024
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25. First-principles generated mechanical property database for multi-component Al alloys: Focusing on Al-rich corner

Author: Wang J., Du Y., Tao X., Ouyang Y., Zhang L., Chen Q., and Engström A.
Subjects: Al alloys, elasticity, first-principles, CALPHAD-type database, Mining engineering. Metallurgy, TN1-997
Abstract: Systematic first-principles calculations of the single crystal elastic stiffness constants (cij’s) and the polycrystalline aggregates including bulk modulus (B), shear modulus (G), Young’s modulus (E) have been performed for series binary and ternary Al compounds at 0 K. In addition, the temperature-dependent elastic properties for some technologically important phases are calculated. The cij’s are calculated by means of an efficient strain-stress method. Phonon density of states or Debye model is employed to calculate the linear thermal expansion, which is then used to calculate the temperature dependence of elastic properties. The calculated temperature-dependent elastic properties are compiled in the format of CALPHAD (CALculation of PHAse Diagram) type formula. The presently computed elastic properties for Al compounds are needed for simulation of microstructure evolution of commercial Al alloys during series of processing route.
Published: 2017
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26. Production of Protons and Light Nuclei in Au+Au Collisions at $\sqrt{s_{\mathrm{NN}}}$ = 3 GeV with the STAR Detector

Author: STAR Collaboration, Abdulhamid, M. I., Aboona, B. E., Adam, J., Adamczyk, L., Adams, J. R., Aggarwal, I., Aggarwal, M. M., Ahammed, Z., Aschenauer, E. C., Aslam, S., Atchison, J., Bairathi, V., Cap, J. G. Ball, Barish, K., Bellwied, R., Bhagat, P., Bhasin, A., Bhatta, S., Bhosale, S. R., Bielcik, J., Bielcikova, J., Brandenburg, J. D., Broodo, C., Cai, X. Z., Caines, H., Sánchez, M. Calderón de la Barca, Cebra, D., Ceska, J., Chakaberia, I., Chaloupka, P., Chan, B. K., Chang, Z., Chatterjee, A., Chen, D., Chen, J., Chen, J. H., Chen, Q., Chen, Z., Cheng, J., Cheng, Y., Christie, W., Chu, X., Crawford, H. J., Csanád, M., Dale-Gau, G., Das, A., Deppner, I. M., Deshpande, A., Dhamija, A., Dixit, P., Dong, X., Drachenberg, J. L., Duckworth, E., Dunlop, J. C., Engelage, J., Eppley, G., Esumi, S., Evdokimov, O., Eyser, O., Fatemi, R., Fazio, S., Feng, C. J., Feng, Y., Finch, E., Fisyak, Y., Flor, F. A., Fu, C., Gagliardi, C. A., Galatyuk, T., Gao, T., Geurts, F., Ghimire, N., Gibson, A., Gopal, K., Gou, X., Grosnick, D., Gupta, A., Guryn, W., Hamed, A., Han, Y., Harabasz, S., Harasty, M. D., Harris, J. W., Harrison-Smith, H., He, W., He, X. H., He, Y., Herrmann, N., Holub, L., Hu, C., Hu, Q., Hu, Y., Huang, H., Huang, H. Z., Huang, S. L., Huang, T., Huang, Y., Humanic, T. J., Isshiki, M., Jacobs, W. W., Jalotra, A., Jena, C., Jentsch, A., Ji, Y., Jia, J., Jin, C., Ju, X., Judd, E. G., Kabana, S., Kalinkin, D., Kang, K., Kapukchyan, D., Kauder, K., Keane, D., Khanal, A., Khyzhniak, Y. V., Kikoła, D. P., Kincses, D., Kisel, I., Kiselev, A., Knospe, A. G., Ko, H. S., Kołaś, J., Kosarzewski, L. K., Kumar, L., Labonte, M. C., Lacey, R., Landgraf, J. M., Lauret, J., Lebedev, A., Lee, J. H., Leung, Y. H., Li, C., Li, D., Li, H-S., Li, H., Li, W., Li, X., Li, Y., Li, Z., Liang, X., Liang, Y., Licenik, R., Lin, T., Lin, Y., Lisa, M. A., Liu, C., Liu, G., Liu, H., Liu, L., Liu, T., Liu, X., Liu, Y., Liu, Z., Ljubicic, T., Lomicky, O., Longacre, R. S., Loyd, E. M., Lu, T., Luo, J., Luo, X. F., Ma, L., Ma, R., Ma, Y. G., Magdy, N., Mallick, D., Manikandhan, R., Markert, C., Matonoha, O., McNamara, G., Mezhanska, O., Mi, K., Mioduszewski, S., Mohanty, B., Mondal, B., Mondal, M. M., Mooney, I., Mrazkova, J., Nagy, M. I., Naim, C. J., Nain, A. S., Nam, J. D., Nasim, M., Neff, D., Nelson, J. M., Nie, M., Nigmatkulov, G., Niida, T., Nonaka, T., Odyniec, G., Ogawa, A., Oh, S., Okubo, K., Page, B. S., Pal, S., Pandav, A., Panday, A., Pandey, A. K., Pani, T., Paul, A., Pawlik, B., Pawlowska, D., Perkins, C., Pluta, J., Pokhrel, B. R., Posik, M., Protzman, T. L., Prozorova, V., Pruthi, N. K., Przybycien, M., Putschke, J., Qin, Z., Qiu, H., Racz, C., Radhakrishnan, S. K., Rana, A., Ray, R. L., Reed, R., Robertson, C. W., Robotkova, M., Aguilar, M. A. Rosales, Roy, D., Chowdhury, P. Roy, Ruan, L., Sahoo, A. K., Sahoo, N. R., Sako, H., Salur, S., Sato, S., Schaefer, B. C., Schmidke, W. B., Schmitz, N., Seck, F-J., Seger, J., Seto, R., Seyboth, P., Shah, N., Shanmuganathan, P. V., Shao, T., Sharma, M., Sharma, N., Sharma, R., Sharma, S. R., Sheikh, A. I., Shen, D., Shen, D. Y., Shen, K., Shi, S. S., Shi, Y., Shou, Q. Y., Si, F., Singh, J., Singha, S., Sinha, P., Skoby, M. J., Smirnov, N., Söhngen, Y., Song, Y., Srivastava, B., Stanislaus, T. D. S., Stefaniak, M., Stewart, D. J., Su, Y., Sumbera, M., Sun, C., Sun, X., Sun, Y., Surrow, B., Svoboda, M., Sweger, Z. W., Tamis, A. C., Tang, A. H., Tang, Z., Tarnowsky, T., Thomas, J. H., Timmins, A. R., Tlusty, D., Todoroki, T., Trentalange, S., Tribedy, P., Tripathy, S. K., Truhlar, T., Trzeciak, B. A., Tsai, O. D., Tsang, C. Y., Tu, Z., Tyler, J., Ullrich, T., Underwood, D. G., Upsal, I., Van Buren, G., Vanek, J., Vassiliev, I., Verkest, V., Videbæk, F., Voloshin, S. A., Wang, G., Wang, J. S., Wang, J., Wang, K., Wang, X., Wang, Y., Wang, Z., Webb, J. C., Weidenkaff, P. C., Westfall, G. D., Wielanek, D., Wieman, H., Wilks, G., Wissink, S. W., Witt, R., Wu, J., Wu, X., Xi, B., Xiao, Z. G., Xie, G., Xie, W., Xu, H., Xu, N., Xu, Q. H., Xu, Y., Xu, Z., Yan, G., Yan, Z., Yang, C., Yang, Q., Yang, S., Yang, Y., Ye, Z., Yi, L., Yu, N., Yu, Y., Zbroszczyk, H., Zha, W., Zhang, C., Zhang, D., Zhang, J., Zhang, S., Zhang, W., Zhang, X., Zhang, Y., Zhang, Z. J., Zhang, Z., Zhao, F., Zhao, J., Zhao, M., Zhou, S., Zhou, Y., Zhu, X., Zurek, M., and Zyzak, M.
Subjects: Nuclear Experiment, Nuclear Theory
Abstract: We report the systematic measurement of protons and light nuclei production in Au+Au collisions at $\sqrt{s_{\mathrm{NN}}}$ = 3 GeV by the STAR experiment at the Relativistic Heavy Ion Collider (RHIC). The transverse momentum ($p_{T}$) spectra of protons ($p$), deuterons ($d$), tritons ($t$), $^{3}\mathrm{He}$, and $^{4}\mathrm{He}$ are measured from mid-rapidity to target rapidity for different collision centralities. We present the rapidity and centrality dependence of particle yields ($dN/dy$), average transverse momentum ($\langle p_{T}\rangle$), yield ratios ($d/p$, $t/p$,$^{3}\mathrm{He}/p$, $^{4}\mathrm{He}/p$), as well as the coalescence parameters ($B_2$, $B_3$). The 4$\pi$ yields for various particles are determined by utilizing the measured rapidity distributions, $dN/dy$. Furthermore, we present the energy, centrality, and rapidity dependence of the compound yield ratios ($N_{p} \times N_{t} / N_{d}^{2}$) and compare them with various model calculations. The physics implications of those results on the production mechanism of light nuclei and on QCD phase structure are discussed., Comment: 17 pages, 17 figures
Published: 2023
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27. Random Fields from Quenched Disorder in an Archetype for Correlated Electrons: the Parallel Spin Stripe Phase of La$_{1.6-x}$Nd$_{0.4}$Sr$_x$CuO$_4$ at the 1/8 Anomaly

Author: Chen, Q., Huang, S. H. -Y., Ma, Q., Smith, E. M., Sharron, H., Aczel, A. A., Tian, W., and Gaulin, B. D.
Subjects: Condensed Matter - Superconductivity, Condensed Matter - Strongly Correlated Electrons
Abstract: The parallel stripe phase is remarkable both in its own right, and in relation to the other phases it co-exists with. Its inhomogeneous nature makes such states susceptible to random fields from quenched magnetic vacancies. We argue this is the case by introducing low concentrations of nonmagnetic Zn impurities (0-10%) into La$_{1.6-x}$Nd$_{0.4}$Sr$_x$CuO$_4$ (Nd-LSCO) with $x$ = 0.125 in single crystal form, well below the percolation threshold of $\sim$ 41% for two-dimensional (2D) square lattice. Elastic neutron scattering measurements on these crystals show clear magnetic quasi-Bragg peaks at all Zn dopings. While all the Zn-doped crystals display order parameters that merge into each other and the background at $\sim$ 68 K, the temperature dependence of the order parameter as a function of Zn concentration is drastically different. This result is consistent with meandering charge stripes within the parallel stripe phase, which are pinned in the presence of quenched magnetic vacancies. In turn it implies vacancies that preferentially occupy sites within the charge stripes, and hence that can be very effective at disrupting superconductivity in Nd-LSCO ($x$ = 0.125), and, by extension, in all systems exhibiting parallel stripes.
Published: 2023

28. Does or did the supernova remnant Cassiopeia A operate as a PeVatron?

Author: Cao, Zhen, Aharonian, F., An, Q., Axikegu, Bai, Y. X., Bao, Y. W., Bastieri, D., Bi, X. J., Bi, Y. J., Cai, J. T., Cao, Q., Cao, W. Y., Cao, Zhe, Chang, J., Chang, J. F., Chen, A. M., Chen, E. S., Chen, Liang, Chen, Lin, Chen, Long, Chen, M. J., Chen, M. L., Chen, Q. H., Chen, S. H., Chen, S. Z., Chen, T. L., Chen, Y., Cheng, N., Cheng, Y. D., Cui, M. Y., Cui, S. W., Cui, X. H., Cui, Y. D., Dai, B. Z., Dai, H. L., Dai, Z. G., Danzengluobu, della Volpe, D., Dong, X. Q., Duan, K. K., Fan, J. H., Fan, Y. Z., Fang, J., Fang, K., Feng, C. F., Feng, L., Feng, S. H., Feng, X. T., Feng, Y. L., Gabici, S., Gao, B., Gao, C. D., Gao, L. Q., Gao, Q., Gao, W., Gao, W. K., Ge, M. M., Geng, L. S., Giacinti, G., Gong, G. H., Gou, Q. B., Gu, M. H., Guo, F. L., Guo, X. L., Guo, Y. Q., Guo, Y. Y., Han, Y. A., He, H. H., He, H. N., He, J. Y., He, X. B., He, Y., Heller, M., Hor, Y. K., Hou, B. W., Hou, C., Hou, X., Hu, H. B., Hu, Q., Hu, S. C., Huang, D. H., Huang, T. Q., Huang, W. J., Huang, X. T., Huang, X. Y., Huang, Y., Huang, Z. C., Ji, X. L., Jia, H. Y., Jia, K., Jiang, K., Jiang, X. W., Jiang, Z. J., Jin, M., Kang, M. M., Ke, T., Kuleshov, D., Kurinov, K., Li, B. B., Li, Cheng, Li, Cong, Li, D., Li, F., Li, H. B., Li, H. C., Li, H. Y., Li, J., Li, Jian, Li, Jie, Li, K., Li, W. L., Li, X. R., Li, Xin, Li, Y. Z., Li, Zhe, Li, Zhuo, Liang, E. W., Liang, Y. F., Lin, S. J., Liu, B., Liu, C., Liu, D., Liu, H., Liu, H. D., Liu, J., Liu, J. L., Liu, J. Y., Liu, M. Y., Liu, R. Y., Liu, S. M., Liu, W., Liu, Y., Liu, Y. N., Lu, R., Luo, Q., Lv, H. K., Ma, B. Q., Ma, L. L., Ma, X. H., Mao, J. R., Min, Z., Mitthumsiri, W., Mu, H. J., Nan, Y. C., Neronov, A., Ou, Z. W., Pang, B. Y., Pattarakijwanich, P., Pei, Z. Y., Qi, M. Y., Qi, Y. Q., Qiao, B. Q., Qin, J. J., Ruffolo, D., Sáiz, A., Semikoz, D., Shao, C. Y., Shao, L., Shchegolev, O., Sheng, X. D., Shu, F. W., Song, H. C., Stenkin, Yu. V., Stepanov, V., Su, Y., Sun, Q. N., Sun, X. N., Sun, Z. B., Tam, P. H. T., Tang, Q. W., Tang, Z. B., Tian, W. W., Wang, C., Wang, C. B., Wang, G. W., Wang, H. G., Wang, H. H., Wang, J. C., Wang, K., Wang, L. P., Wang, L. Y., Wang, P. H., Wang, R., Wang, W., Wang, X. G., Wang, X. Y., Wang, Y., Wang, Y. D., Wang, Y. J., Wang, Z. H., Wang, Z. X., Wang, Zhen, Wang, Zheng, Wei, D. M., Wei, J. J., Wei, Y. J., Wen, T., Wu, C. Y., Wu, H. R., Wu, S., Wu, X. F., Wu, Y. S., Xi, S. Q., Xia, J., Xia, J. J., Xiang, G. M., Xiao, D. X., Xiao, G., Xin, G. G., Xin, Y. L., Xing, Y., Xiong, Z., Xu, D. L., Xu, R. F., Xu, R. X., Xu, W. L., Xue, L., Yan, D. H., Yan, J. Z., Yan, T., Yang, C. W., Yang, F., Yang, F. F., Yang, H. W., Yang, J. Y., Yang, L. L., Yang, M. J., Yang, R. Z., Yang, S. B., Yao, Y. H., Yao, Z. G., Ye, Y. M., Yin, L. Q., Yin, N., You, X. H., You, Z. Y., Yu, Y. H., Yuan, Q., Yue, H., Zeng, H. D., Zeng, T. X., Zeng, W., Zha, M., Zhang, B. B., Zhang, F., Zhang, H. M., Zhang, H. Y., Zhang, J. L., Zhang, L. X., Zhang, Li, Zhang, P. F., Zhang, P. P., Zhang, R., Zhang, S. B., Zhang, S. R., Zhang, S. S., Zhang, X., Zhang, X. P., Zhang, Y. F., Zhang, Yi, Zhang, Yong, Zhao, B., Zhao, J., Zhao, L., Zhao, L. Z., Zhao, S. P., Zheng, F., Zhou, B., Zhou, H., Zhou, J. N., Zhou, M., Zhou, P., Zhou, R., Zhou, X. X., Zhu, C. G., Zhu, F. R., Zhu, H., Zhu, K. J., and Zuo., X.
Subjects: Astrophysics - High Energy Astrophysical Phenomena
Abstract: For decades, supernova remnants (SNRs) have been considered the prime sources of Galactic Cosmic rays (CRs). But whether SNRs can accelerate CR protons to PeV energies and thus dominate CR flux up to the knee is currently under intensive theoretical and phenomenological debate. The direct test of the ability of SNRs to operate as CR PeVatrons can be provided by ultrahigh-energy (UHE; $E_\gamma \geq 100$~TeV) $\gamma$-rays. In this context, the historical SNR Cassiopeia A (Cas A) is considered one of the most promising target for UHE observations. This paper presents the observation of Cas A and its vicinity by the LHAASO KM2A detector. The exceptional sensitivity of LHAASO KM2A in the UHE band, combined with the young age of Cas A, enabled us to derive stringent model-independent limits on the energy budget of UHE protons and nuclei accelerated by Cas A at any epoch after the explosion. The results challenge the prevailing paradigm that Cas A-type SNRs are major suppliers of PeV CRs in the Milky Way., Comment: 11 pages, 3 figures, Accepted by the APJL
Published: 2023

29. Very high energy gamma-ray emission beyond 10 TeV from GRB 221009A

Author: Cao, Zhen, Aharonian, F., An, Q., Axikegu, A., Bai, Y. X., Bao, Y. W., Bastieri, D., Bi, X. J., Bi, Y. J., Cai, J. T., Cao, Q., Cao, W. Y., Cao, Zhe, Chang, J., Chang, J. F., Chen, A. M., Chen, E. S., Chen, Liang, Chen, Lin, Chen, Long, Chen, M. J., Chen, M. L., Chen, Q. H., Chen, S. H., Chen, S. Z., Chen, T. L., Chen, Y., Cheng, N., Cheng, Y. D., Cui, M. Y., Cui, S. W., Cui, X. H., Cui, Y. D., Dai, B. Z., Dai, H. L., Dai, Z. G., Danzengluobu, della Volpe, D., Dong, X. Q., Duan, K. K., Fan, J. H., Fan, Y. Z., Fang, J., Fang, K., Feng, C. F., Feng, L., Feng, S. H., Feng, X. T., Feng, Y. L., Gabici, S., Gao, B., Gao, C. D., Gao, L. Q., Gao, Q., Gao, W., Gao, W. K., Ge, M. M., Geng, L. S., Giacinti, G., Gong, G. H., Gou, Q. B., Gu, M. H., Guo, F. L., Guo, X. L., Guo, Y. Q., Guo, Y. Y., Han, Y. A., He, H. H., He, H. N., He, J. Y., He, X. B., He, Y., Heller, M., Hor, Y. K., Hou, B. W., Hou, C., Hou, X., Hu, H. B., Hu, Q., Hu, S. C., Huang, D. H., Huang, T. Q., Huang, W. J., Huang, X. T., Huang, X. Y., Huang, Y., Huang, Z. C., Ji, X. L., Jia, H. Y., Jia, K., Jiang, K., Jiang, X. W., Jiang, Z. J., Jin, M., Kang, M. M., Ke, T., Kuleshov, D., Kurinov, K., Li, B. B., Li, Cheng, Li, Cong, Li, D., Li, F., Li, H. B., Li, H. C., Li, H. Y., Li, J., Li, Jian, Li, Jie, Li, K., Li, W. L., Li, X. R., Li, Xin, Li, Y. Z., Li, Zhe, Li, Zhuo, Liang, E. W., Liang, Y. F., Lin, S. J., Liu, B., Liu, C., Liu, D., Liu, H., Liu, H. D., Liu, J., Liu, J. L., Liu, J. Y., Liu, M. Y., Liu, R. Y., Liu, S. M., Liu, W., Liu, Y., Liu, Y. N., Lu, R., Luo, Q., Lv, H. K., Ma, B. Q., Ma, L. L., Ma, X. H., Mao, J. R., Min, Z., Mitthumsiri, W., Mu, H. J., Nan, Y. C., Neronov, A., Ou, Z. W., Pang, B. Y., Pattarakijwanich, P., Pei, Z. Y., Qi, M. Y., Qi, Y. Q., Qiao, B. Q., Qin, J. J., Ruffolo, D., Sáiz, A., Semikoz, D., Shao, C. Y., Shao, L., Shchegolev, O., Sheng, X. D., Shu, F. W., Song, H. C., Stenkin, Yu. V., Stepanov, V., Su, Y., Sun, Q. N., Sun, X. N., Sun, Z. B., Tam, P. H. T., Tang, Q. W., Tang, Z. B., Tian, W. W., Wang, C., Wang, C. B., Wang, G. W., Wang, H. G., Wang, H. H., Wang, J. C., Wang, K., Wang, L. P., Wang, L. Y., Wang, P. H., Wang, R., Wang, W., Wang, X. G., Wang, X. Y., Wang, Y., Wang, Y. D., Wang, Y. J., Wang, Z. H., Wang, Z. X., Wang, Zhen, Wang, Zheng, Wei, D. M., Wei, J. J., Wei, Y. J., Wen, T., Wu, C. Y., Wu, H. R., Wu, S., Wu, X. F., Wu, Y. S., Xi, S. Q., Xia, J., Xia, J. J., Xiang, G. M., Xiao, D. X., Xiao, G., Xin, G. G., Xin, Y. L., Xing, Y., Xiong, Z., Xu, D. L., Xu, R. F., Xu, R. X., Xu, W. L., Xue, L., Yan, D. H., Yan, J. Z., Yan, T., Yang, C. W., Yang, F., Yang, F. F., Yang, H. W., Yang, J. Y., Yang, L. L., Yang, M. J., Yang, R. Z., Yang, S. B., Yao, Y. H., Yao, Z. G., Ye, Y. M., Yin, L. Q., Yin, N., You, X. H., You, Z. Y., Yu, Y. H., Yuan, Q., Yue, H., Zeng, H. D., Zeng, T. X., Zeng, W., Zha, M., Zhang, B. B., Zhang, F., Zhang, H. M., Zhang, H. Y., Zhang, J. L., Zhang, L. X., Zhang, Li, Zhang, P. F., Zhang, P. P., Zhang, R., Zhang, S. B., Zhang, S. R., Zhang, S. S., Zhang, X., Zhang, X. P., Zhang, Y. F., Zhang, Yi, Zhang, Yong, Zhao, B., Zhao, J., Zhao, L., Zhao, L. Z., Zhao, S. P., Zheng, F., Zhou, B., Zhou, H., Zhou, J. N., Zhou, M., Zhou, P., Zhou, R., Zhou, X. X., Zhu, C. G., Zhu, F. R., Zhu, H., Zhu, K. J., and Zuo, X.
Subjects: Astrophysics - High Energy Astrophysical Phenomena
Abstract: The highest energy gamma-rays from gamma-ray bursts (GRBs) have important implications for their radiation mechanism. Here we report for the first time the detection of gamma-rays up to 13 TeV from the brightest GRB 221009A by the Large High Altitude Air-shower Observatory (LHAASO). The LHAASO-KM2A detector registered more than 140 gamma-rays with energies above 3 TeV during 230$-$900s after the trigger. The intrinsic energy spectrum of gamma-rays can be described by a power-law after correcting for extragalactic background light (EBL) absorption. Such a hard spectrum challenges the synchrotron self-Compton (SSC) scenario of relativistic electrons for the afterglow emission above several TeV. Observations of gamma-rays up to 13 TeV from a source with a measured redshift of z=0.151 hints more transparency in intergalactic space than previously expected. Alternatively, one may invoke new physics such as Lorentz Invariance Violation (LIV) or an axion origin of very high energy (VHE) signals., Comment: 49pages, 11figures
Published: 2023
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30. Areca nut and oral cancer: warnings and prevention in China: Mouth cancer

Author: Chen, Q., Hu, X., Li, D., and Huang, X.
Published: 2025
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31. Dual-species Bose-Einstein condensates of $^{7}$Li and $^{133}$Cs

Author: Chen, Y. -D., Li, W. -X., Sun, Y. -T., Chen, Q. -C., Chang, P. -Y., and Tung, S.
Subjects: Condensed Matter - Quantum Gases, Physics - Atomic Physics
Abstract: We report the creation of dual-species Bose-Einstein condensates (BECs) of $^{7}$Li and $^{133}$Cs. These BECs are formed in a bichromatic optical dipole trap created with 1550-nm and 780-nm laser beams. During the production process, an external magnetic field of 886~G is applied to adjust the scattering lengths to $a_{\rm{Cs}} = 123a_0$, $a_{\rm{Li}} = 484a_0$, and $a_{\rm{LiCs}} = 248a_0$. These scattering lengths allow for efficient evaporation and sympathetic cooling. The dual-species BECs are typically produced with $1.5\times 10^4$ Cs atoms and $6.0\times 10^3$ Li atoms. This quantum degenerate mixture of Li and Cs provides an ideal platform for exploring phenomena such as polarons and Efimov trimers, as well as for creating ground-state LiCs molecules., Comment: 9 pages, 9 figures
Published: 2023

32. High RRM2 Correlates with Mitochondrial and Immune Responses in the Eosinophilic Subtype of Clear Cell Renal Cell Carcinoma

Author: Zhu X, Al-danakh A, Jian Y, Safi M, Luo S, Chen Q, Wang S, and Yang D
Subjects: clear cell renal cell carcinoma subtypes, metabolic reprogramming. mitochondria, tumor microenvironment, rrm2, immune checkpoint inhibitors, Pathology, RB1-214, Therapeutics. Pharmacology, RM1-950
Abstract: Xinqing Zhu,1,&ast; Abdullah Al-danakh,1,&ast; Yuli Jian,2,&ast; Mohammed Safi,3,&ast; Sijie Luo,1,&ast; Qiwei Chen,1 Shujing Wang,2 Deyong Yang1,4 1Department of Urology, First Affiliated Hospital of Dalian Medical University, Dalian, Liaoning, People’s Republic of China; 2Department of Biochemistry and Molecular Biology, Institute of Glycobiology, Dalian Medical University, Dalian, Liaoning People’s Republic of China; 3Thoracic/Head and Neck Medical Oncology Department, MD Anderson Cancer Center, Houston, TX, USA; 4Department of Surgery, Healinghands Clinic, Dalian, Liaoning, People’s Republic of China&ast;These authors contributed equally to this workCorrespondence: Deyong Yang; Shujing Wang, Email yangdeyong@dmu.edu.cn; wangshujing@dmu.edu.cnBackground: Clear cell renal cell carcinoma (ccRCC), the predominant subtype of RCC, is distinguished by unique biological characteristics and heterogeneity, including eosinophilic and clear subtypes. Notwithstanding progress in therapy, immune checkpoint inhibitors (ICIs), and tyrosine kinase inhibitors (TKIs), the prognosis for individuals with metastatic ccRCC remains poor, presumably owing to metabolic alterations leading to mitochondrial dysfunction, which affects treatment response variability.Methods: We analyzed histological and immunohistochemical data from a cohort at Dalian Medical University’s First Affiliated Hospital alongside RNA-sequencing transcriptome data from the TCGA database. Histologically, eosinophilic and clear ccRCC subtypes were evaluated using Kaplan-Meier and Cox proportional hazards models for survival analysis and prognosis. Differential gene expression (DEG) analysis and Gene Set Enrichment Analysis were performed to explore transcriptomic differences and relevant pathways.Results: The study discovered substantial histological and molecular differences between the eosinophilic and clear cell subtypes of ccRCC. The eosinophilic subtype linked with frequent high-grade tumors (69.05% eosinophil vs 35.35% clear) and a poorer prognosis (HR=2.659, 95% CI:1.437– 4.919, P=0.002). DEG analysis revealed distinct expression patterns among subtypes and identified a risk score signature that remained significant even after adjusting for clinical variables (HR=3.967, 95% CI: 1.665– 9.449, P=0.002), showing less favorable survival in the high-risk group (P < 0.0001). RRM2 emerged as the most prognostic gene from this risk score, particularly in the eosinophilic subtype, alongside other clinical variables. By IHC, RRM2 shows high IHC score in eosinophilic compared to clear subtype (P=0.019). In addition, highly expressed RRM2 correlates with poor outcomes and is linked to mitochondrial genes, immunological pathways, and ICIs treatment.Conclusion: These findings show significant differences in prognosis between subtypes. RRM2 was the most prognostic gene from the discovered novel risk score signature associated with subtypes. Future research is essential to validate these insights and their therapeutic implications for ccRCC management.Keywords: clear cell renal cell carcinoma subtypes, metabolic reprogramming. mitochondria, tumor microenvironment, RRM2, immune checkpoint inhibitors
Published: 2024

33. Downregulation of Circular RNA Gla Reduced Astrocyte Inflammatory Status by Regulating miR-488/MEKK1 Levels and Promoted Functional Recovery After Spinal Cord Injury

Author: Shao Q, Zhang Y, Zhang Z, Jiang W, Yin Y, Fang Y, Zhang C, Chen Q, and Ning B
Subjects: non-coding rnas, inflammation, competing endogenous rnas, traumatic spinal cord injury, secondary injury, Pathology, RB1-214, Therapeutics. Pharmacology, RM1-950
Abstract: Qiang Shao,1,2,&ast; Ying Zhang,2,&ast; Zhiyuan Zhang,2 Wei Jiang,2 Yongcheng Yin,3 Yuepeng Fang,1 Ce Zhang,1 Qingfa Chen,2 Bin Ning1,2 1Cheeloo College of Medicine, Jinan Central Hospital, Shandong University, Jinan, People’s Republic of China; 2Central Hospital Affiliated to Shandong First Medical University, Shandong First Medical University & Shandong Academy of Medical Sciences, Jinan, Shandong, People’s Republic of China; 3School of Clinical Medicine, Shandong Second Medical University, Weifang, People’s Republic of China&ast;These authors contributed equally to this workCorrespondence: Bin Ning; Qingfa Chen, Central Hospital Affiliated to Shandong First Medical University, No. 105, Jiefang Road, Jinan, Shandong, 250013, People’s Republic of China, Tel +86-18866510349, Email bning@sdfmu.edu.cn; tsingfachan@163.comBackground: Post-spinal cord injury (SCI) inflammation correlates with neurologic recovery. Through sequencing, we explored the roles of a differentially expressed circRNA in mice after SCI, circGla, on inflammation and recovery of SCI.Methods: The T8–T10 SCI model was established in C57BL6 mice. HE staining and RT-qPCR verified circGla upregulation results after injury obtained through sequencing. RNase R digestion and primer amplification experiments confirmed the circular properties of circGla. Nucleus and cytoplasm isolation experiments and FISH confirmed circGla expression in the astrocyte cytoplasm. AAV was used to establish a circGla knockdown mouse model. Behavioral tests were conducted to assess the recovery of the neurological function. The key inflammatory molecules after SCI were evaluated through MRI, RT-qPCR, and ELISA. For in vitro experiments, circGla was upregulated or knocked down in mouse astrocytes to detect its effect. The binding between miR-488 and circGla was confirmed through RIP and the dual luciferase experiment. RT-qPCR and ELISA confirmed the content correlation of the two molecules and the in vitro inflammatory function of miR-488. The binding experiment in astrocytes confirmed the binding between miR-488 and MEKK1 mRNA. Western blotting of MAPK pathway-related proteins confirmed that MEKK1 is a downstream effector for circGla and miR-488 in astrocytes.Results: Following SCI, the circular RNA circGla levels increased and it existed in the astrocyte cytoplasm. circGla knockdown reduced inflammation and improved neurological recovery in vivo. The correlation between circGla and proinflammatory factors was confirmed in vitro. circGla bound to miR-488, and the high miR-488 level was associated with the low astrocyte inflammatory state. miR-488 bound to MEKK1 mRNA, and upregulation or knockdown of circGla or miR-488 affected MAPK pathway-related protein expression.Conclusion: Following SCI, downregulation of circGla expression in astrocytes can reduce inflammatory manifestations and stimulate long-term functional recovery in mice through miR-488 and MEKK1. Keywords: non-coding RNAs, inflammation, competing endogenous RNAs, traumatic spinal cord injury, secondary injury
Published: 2024

34. Yam Carbon Dots Promote Bone Defect Repair by Modulating Histone Demethylase 4B

Author: Chen Q, Liu S, Wang Y, Tong M, Sun H, Dong M, Lu Y, Niu W, and Wang L
Subjects: traditional chinese medicine, carbon dots, yam, bone defect, kdm4b, Medicine (General), R5-920
Abstract: QianYang Chen,1,&ast; Shuo Liu,1,&ast; Yuhan Wang,1,&ast; MeiChen Tong,2 HaiBo Sun,2 Ming Dong,1 Yun Lu,1 WeiDong Niu,1 LiNa Wang1 1Department of Endodontics and Periodontics, College of Stomatology, Dalian Medical University, Dalian, Liaoning, 116044, People’s Republic of China; 2Academician Laboratory of Immune and Oral Development & Regeneration, Dalian Medical University, Dalian, Liaoning, 116044, People’s Republic of China&ast;These authors contributed equally to this workCorrespondence: WeiDong Niu; LiNa Wang, Email 13840886663@139.com; wanglina@dmu.edu.cnIntroduction: Chronic apical periodontitis is a typical inflammatory disease of the oral cavity, the pathology is characterized by an inflammatory reaction with bone defects in the periapical area. Chinese medicine is our traditional medicine, Carbon Dots (CDs) are a new type of nanomaterials. The purpose of this study was to prepare Yam Carbon Dots (YAM-CDs) to investigate the mechanism of action of YAM-CDs on bone differentiation in vivo and in vitro.Methods: We characterized YAM-CDs using transmission electron microscopy (TEM), Fourier Transform Infrared Spectrometer (FTIR), X-Ray Diffraction (XRD) and photoluminescence (PL). CCK-8 assay, Real-time qPCR, and Western Blot were conducted using bone marrow mesenchymal stem cells (BMSCs) to verify that YAM-CDs promote osteoblast differentiation. In addition, we investigated the role of YAM-CDs in promoting bone formation in an inflammatory setting in an in vivo mouse model of cranial defects.Results: The results of TEM and PL showed that the YAM-CDs mostly consisted of the components C1s, O1s, and N1s. Additionally the average sizes of YAM-CDs were 2– 6 nm. The quantum yield was 4.44%, with good fluorescence stability and biosafety. Real-time qPCR and Western blot analysis showed that YAM-CDs promoted osteoblast differentiation under an inflammatory environment by regulating expression of histone demethylase 4B (KDM4B). In vivo, results showed that YAM-CDs effectively repaired cranial bone defects in a mouse model and reduced the expression of inflammatory factors under the action of lipopolysaccharides (LPS).Conclusion: YAM-CDs promoted the proliferation and differentiation of osteoblasts by regulating the expression of KDM4B to repair cranial bone defects in mice under an LPS-induced inflammatory milieu, which will provide a new idea for the treatment of clinical periapical inflammation and other bone defect diseases.Keywords: traditional Chinese medicine, carbon dots, YAM, bone defect, KDM4B
Published: 2024

35. Trajectories of Short-Term Post-Traumatic Stress Disorder Symptoms in Patients with Post-Intensive Care Syndrome: A Longitudinal Observational Study

Author: Chen Q, Huang Y, Chen X, and Xu L
Subjects: post-traumatic stress disorder, latent growth mixture model, longitudinal study, post-intensive care syndrome, Medicine (General), R5-920
Abstract: Qiong Chen,1,* Yanjin Huang,2,* Xiaomei Chen,3 Limin Xu1 1Department of Intensive Care Medicine, Xiamen Haicang Hospital, Xiamen, Fujian, People’s Republic of China; 2Department of Nursing, Xiamen Haicang Hospital, Xiamen, Fujian, People’s Republic of China; 3Department of Pain, Xiamen Haicang Hospital, Xiamen, Fujian, People’s Republic of China*These authors contributed equally to this workCorrespondence: Qiong Chen, Department of Intensive Care Medicine, Xiamen Haicang Hospital, No. 89 haiyu Road, Haicang District, Xiamen, Fujian, 361026, People’s Republic of China, Tel +86 177 50612461, Email chenqiongfff@163.comPurpose: Post-traumatic stress disorder (PTSD) is a major psychiatric health issue among intensive care unit (ICU) survivors with post-intensive care syndrome (PICS). Although early PTSD intervention has been demonstrated to decrease the risk of progression from acute to chronic PTSD, information on the progression trajectory of short-term PTSD symptoms and modifiable risk factors in PICS patients is limited. This study aimed to explore the clinical progression trajectories of short-term PTSD symptoms and the associated factors in PICS patients by conducting a prospective longitudinal observational study.Patients and Methods: This study was conducted at a tertiary hospital in China. The impact of event scale-revised was used to collect data on the PTSD symptoms of patients at 1, 2, 3, and 4 months post-discharge from the ICU. The latent growth mixture model was used to construct trajectory models for PTSD symptoms and multivariate logistic regression was used to determine the factors associated with the trajectories.Results: A total of 130 ICU survivors with PICS completed the 4-month short-term follow-up. Our results showed that PTSD symptoms in PICS patients manifested as three trajectories, namely, moderate chronic (n = 17, 13.1%), recovery (n = 25, 19.2%), and resilience (n = 88, 67.7%). Compared with the resilience trajectory, age and female were identified as risk factors for the moderate chronic trajectory, while prolonged ICU stay was a risk factor for the recovery trajectory.Conclusion: Our study showed that short-term PTSD symptoms in PICS patients manifested as moderate chronic, recovery, and resilience trajectories. Additionally, our results showed that PTSD screening should be conducted for critically ill patients, especially younger, female, or long-term ICU patients, immediately after their discharge from the ICU.Keywords: post-traumatic stress disorder, latent growth mixture model, longitudinal study, post-intensive care syndrome
Published: 2024

36. In vitro Synergistic and Bactericidal Effects of Aztreonam in Combination with Ceftazidime/ Avibactam, Meropenem/Vaborbactam and Imipenem/Relebactam Against Dual-Carbapenemase-Producing Enterobacterales

Author: Fu Y, Zhu Y, Zhao F, Yao B, Yu Y, Zhang J, and Chen Q
Subjects: dual-carbapenemase-producing enterobacterales, β‑lactam/β‑lactamase inhibitor combinations, aztreonam, checkerboard assay, time-kill curve, Infectious and parasitic diseases, RC109-216
Abstract: Ying Fu,1,2 Yufeng Zhu,1,3 Feng Zhao,1,2,4 Bingyan Yao,1,2 Yunsong Yu,5 Jun Zhang,1,2 Qiong Chen6 1Department of Clinical Laboratory, Sir Run Run Shaw Hospital, School of Medicine, Zhejiang University, Hangzhou, Zhejiang Province, People’s Republic of China; 2Key Laboratory of Precision Medicine in Diagnosis and Monitoring Research of Zhejiang Province, Sir Run Run Shaw Hospital, Hangzhou, Zhejiang Province, People’s Republic of China; 3Department of Clinical Laboratory, Hangzhou Xixi Hospital, School of Medicine, Zhejiang University, Hangzhou, Zhejiang Province, People’s Republic of China; 4Department of Clinical Laboratory, Zhejiang University Sir Run Run Shaw Alar Hospital, Alar, Xinjiang province, People’s Republic of China; 5Department of Infectious Diseases, Zhejiang Provincial People’s Hospital, Hangzhou, Zhejiang Province, People’s Republic of China; 6Department of Clinical Laboratory, Affiliated Hangzhou First People’s Hospital, School of Medicine, Westlake University, Hangzhou, Zhejiang Province, People’s Republic of ChinaCorrespondence: Qiong Chen; Jun Zhang, Email chenqiong2002@126.com; jameszhang2000@zju.edu.cnObjective: Our aim was to elucidate the resistance mechanisms and assess the combined synergistic and bactericidal activities of aztreonam in combination with ceftazidime/avibactam (CZA), meropenem/vaborbactam (MEV), and imipenem/relebactam (IMR) against Enterobacterales strains producing dual carbapenemases.Methods: Species identification, antimicrobial susceptibility testing and determination of carbapenemase type were performed for these strains. Plasmid sizes, plasmid conjugation abilities and the localization of carbapenemase genes were investigated. Whole-genome sequencing was performed for all strains and their molecular characteristics were analyzed. In vitro synergistic and bactericidal activities of the combination of aztreonam with CZA, MEV and IMR against these strains were determined using checkerboard assay and time-kill curve assay.Results: A total of 12 Enterobacterales strains producing dual-carbapenemases were collected, including nine K. pneumoniae, two P. rettgeri, and one E. hormaechei. The most common dual-carbapenemase gene pattern observed was bla(KPC-2+NDM-5) (n=4), followed by blaKPC-2+IMP-26 (n=3), bla(KPC-2+NDM-1) (n=2), bla(KPC-2+IMP-4) (n=1), bla(NDM-1+IMP-4) (n=1) and bla(KPC-2+KPC-2) (n=1). In each strain, the carbapenemase genes were found to be located on two distinct plasmids which were capable of conjugating from the original strain to the receipt strain E. coli J53. The results of the checkerboard synergy analysis consistently revealed good synergistic effects of the combination of ATM with CZA, MEV and IMR. Except for one strain, all strains exhibited significant synergistic activity and bactericidal activity between 2 and 8 hours.Conclusion: Dual-carbapenemase-producing Enterobacterales posed a significant threat to clinical anti-infection treatment. However, the combination of ATM with innovative β-lactam/β-lactamase inhibitor compounds had proven to be an effective treatment option.Keywords: dual-carbapenemase-producing enterobacterales, β‑lactam/β‑lactamase inhibitor combinations, aztreonam, checkerboard assay, time-kill curve
Published: 2024

37. Mechanism of Traditional Chinese Medicine in Treating Migraine: A Comprehensive Review

Author: Chen Q, Wang M, Fu F, Nie L, Miao Q, Zhao L, Liu L, and Li B
Subjects: traditional chinese medicine, migraine, acupuncture, herbs, massage, mechanism, Medicine (General), R5-920
Abstract: Qiuyi Chen,&ast; Mina Wang,&ast; Feiyu Fu,&ast; Limin Nie, Quan Miao, Luopeng Zhao, Lu Liu, Bin Li Department of Acupuncture and Moxibustion, Beijing Hospital of Traditional Chinese Medicine, Capital Medical University, Beijing Key Laboratory of Acupuncture Neuromodulation, Beijing, 100010, People’s Republic of China&ast;These authors contributed equally to this workCorrespondence: Bin Li; Lu Liu, Department of Acupuncture and Moxibustion, Beijing Hospital of Traditional Chinese Medicine, Capital Medical University, Beijing Key Laboratory of Acupuncture Neuromodulation, No. 23 Meishuguan Houjie, Beijing, 100010, People’s Republic of China, Tel +86 18910781852 ; +86 13520788752, Email libin@bjzhongyi.com; lululalavictor1985@126.comAbstract: Migraine is a common neurological illness that causes a great burden on individuals and society. Many migraine patients seek relief through complementary and alternative therapies, with Traditional Chinese medicine (TCM) often being their preferred choice. Acupuncture, Chinese herbal medicine, and massage are important components of TCM, and are commonly used in clinical treatment of migraine. This review aims to consolidate the current knowledge regarding the mechanisms of the three TCM interventions for migraine: acupuncture, herbs, and massage, and how they relieve pain. However, the mechanisms underlying the effectiveness of TCM therapies in treating migraine remain unclear. Therefore, we reviewed the research progress on acupuncture, herbal medicine, and massage as TCM approaches for the treatment of migraine. We conducted a comprehensive search of CNKI, PubMed, Web of Science, and Cochrane databases using keywords such as migraine, acupuncture, needle, herbs, herbal, prescription, decoction, massage, Tuina, and TCM, covering the period from 2000 to 2023. The literature included in the review was selected based on specified exclusion criteria. We discussed the mechanism of TCM therapies on migraine from the perspective of modern medicine, focusing on changes in inflammatory factors, neurotransmitters, and other relevant biomarkers. TCM can relieve migraine by decreasing neuropeptide levels, inhibiting inflammation, modulating neuronal sensitization, changing brain function and structure, changing blood brain barrier permeability, regulating hormone levels, and relieving muscle tension. The purpose of this paper is to provide a basis for improving the clinical strategies of TCM for the treatment of migraine.Keywords: traditional Chinese medicine, migraine, acupuncture, herbs, massage, mechanism
Published: 2024

38. Inflammatory indices—Systemic Immune-Inflammation Index (SII) and Systemic Inflammatory Response Index (SIRI)—during Pregnancy and Associations with Gestational Diabetes Mellitus

Author: Zhang D, Zeng Y, Sun B, Li W, Liu W, Gao H, Zhu Y, Li H, and Chen Q
Subjects: gestational diabetes mellitus, gdm, systemic immune-inflammation index, sii, systemic inflammatory response index, siri, Pathology, RB1-214, Therapeutics. Pharmacology, RM1-950
Abstract: Danwei Zhang,1 Yating Zeng,1 Bin Sun,2 Wei Li,2 Wenjuan Liu,2 Haiyan Gao,2 Yibing Zhu,2 Haibo Li,2,&ast; Qiang Chen1,&ast; 1Department of Cardiac Surgery, Fujian Children’s Hospital (Fujian Branch of Shanghai Children’s Medical Center), College of Clinical Medicine for Obstetrics & Gynecology and Pediatrics, Fujian Medical University, Fuzhou, People’s Republic of China; 2Division of Birth Cohort Study, Fujian Maternity and Child Health Hospital, College of Clinical Medicine for Obstetrics & Gynecology and Pediatrics, Fujian Medical University, Fuzhou, People’s Republic of China&ast;These authors contributed equally to this workCorrespondence: Qiang Chen, Department of Cardiac Surgery, Fujian Children’s Hospital (Fujian Branch of Shanghai Children’s Medical Center), College of Clinical Medicine for Obstetrics & Gynecology and Pediatrics, Fujian Medical University, No. 966 Hengyu Road, Jinan District, Fuzhou, 350014, People’s Republic of China, Email chenqiang2228@163.com Haibo Li, Division of Birth Cohort Study, Fujian Maternity and Child Health Hospital, College of Clinical Medicine for Obstetrics & Gynecology and Pediatrics, Fujian Medical University, No. 18 Daoshan Road, Gulou District, Fuzhou, 350001, People’s Republic of China, Email haiboli89@163.comPurpose: Gestational diabetes mellitus (GDM) is a prevalent complication during pregnancy. This study aimed to explore the associations between inflammatory indices during pregnancy and the development of GDM.Methods: Data from the Fujian Birth Cohort Study between March 2019 and December 2022 were used. Participants who delivered a live-born singleton were included and categorized into GDM and non-GDM groups. Two inflammatory indices, the systemic immune-inflammation index (SII) and systemic inflammatory response index (SIRI), were calculated for each trimester of pregnancy via hematological parameters from complete blood count tests. The distributions of inflammatory indicators across trimesters were compared between the GDM and non-GDM groups. Additionally, multivariable logistic regression models were employed to investigate the associations between inflammatory indices and the incidence of GDM.Results: A total of 17297 participants were included, 21.2% of whom were diagnosed with GDM. In the first trimester, the median SIIs for the GDM and non-GDM groups were 817.7× 109/L and 756.9× 109/L, respectively, whereas the median SIRIs were 1.6× 109/L and 1.5× 109/L, respectively. In both groups, the SII increased to its peak in the second trimester before declining, whereas the SIRI progressively increased throughout pregnancy. The SII and SIRI were greater in the GDM group than in the non-GDM group during the first two trimesters but lower in the third trimester. Nonlinear positive associations between first-trimester SII and SIRI levels and GDM were observed, with extreme quartile odds ratios of 1.32 (95% CI: 1.19, 1.48) and 1.39 (95% CI: 1.24, 1.55), respectively.Conclusion: The SII and SIRI increased and reached their peak values in the second and third trimesters of pregnancy, respectively. Elevated levels of the SII and SIRI in early pregnancy were linked to an increased risk of GDM, suggesting their potential utility as screening tools for GDM.Keywords: gestational diabetes mellitus, GDM, systemic immune-inflammation index, SII, systemic inflammatory response index, SIRI
Published: 2024

39. The First LHAASO Catalog of Gamma-Ray Sources

Author: Cao, Zhen, Aharonian, F., An, Q., Axikegu, Bai, Y. X., Bao, Y. W., Bastieri, D., Bi, X. J., Bi, Y. J., Cai, J. T., Cao, Q., Cao, W. Y., Cao, Zhe, Chang, J., Chang, J. F., Chen, A. M., Chen, E. S., Chen, Liang, Chen, Lin, Chen, Long, Chen, M. J., Chen, M. L., Chen, Q. H., Chen, S. H., Chen, S. Z., Chen, T. L., Chen, Y., Cheng, N., Cheng, Y. D., Cui, M. Y., Cui, S. W., Cui, X. H., Cui, Y. D., Dai, B. Z., Dai, H. L., Dai, Z. G., Danzengluobu, della Volpe, D., Dong, X. Q., Duan, K. K., Fan, J. H., Fan, Y. Z., Fang, J., Fang, K., Feng, C. F., Feng, L., Feng, S. H., Feng, X. T., Feng, Y. L., Gabici, S., Gao, B., Gao, C. D., Gao, L. Q., Gao, Q., Gao, W., Gao, W. K., Ge, M. M., Geng, L. S., Giacinti, G., Gong, G. H., Gou, Q. B., Gu, M. H., Guo, F. L., Guo, X. L., Guo, Y. Q., Guo, Y. Y., Han, Y. A., He, H. H., He, H. N., He, J. Y., He, X. B., He, Y., Heller, M., Hor, Y. K., Hou, B. W., Hou, C., Hou, X., Hu, H. B., Hu, Q., Hu, S. C., Huang, D. H., Huang, T. Q., Huang, W. J., Huang, X. T., Huang, X. Y., Huang, Y., Huang, Z. C., Ji, X. L., Jia, H. Y., Jia, K., Jiang, K., Jiang, X. W., Jiang, Z. J., Jin, M., Kang, M. M., Ke, T., Kuleshov, D., Kurinov, K., Li, B. B., Li, Cheng, Li, Cong, Li, D., Li, F., Li, H. B., Li, H. C., Li, H. Y., Li, J., Li, Jian, Li, Jie, Li, K., Li, W. L., Li, X. R., Li, Xin, Li, Y. Z., Li, Zhe, Li, Zhuo, Liang, E. W., Liang, Y. F., Lin, S. J., Liu, B., Liu, C., Liu, D., Liu, H., Liu, H. D., Liu, J., Liu, J. L., Liu, J. Y., Liu, M. Y., Liu, R. Y., Liu, S. M., Liu, W., Liu, Y., Liu, Y. N., Lu, R., Luo, Q., Lv, H. K., Ma, B. Q., Ma, L. L., Ma, X. H., Mao, J. R., Min, Z., Mitthumsiri, W., Mu, H. J., Nan, Y. C., Neronov, A., Ou, Z. W., Pang, B. Y., Pattarakijwanich, P., Pei, Z. Y., Qi, M. Y., Qi, Y. Q., Qiao, B. Q., Qin, J. J., Ruffolo, D., Sáiz, A., Semikoz, D., Shao, C. Y., Shao, L., Shchegolev, O., Sheng, X. D., Shu, F. W., Song, H. C., Stenkin, Yu. V., Stepanov, V., Su, Y., Sun, Q. N., Sun, X. N., Sun, Z. B., Tam, P. H. T., Tang, Q. W., Tang, Z. B., Tian, W. W., Wang, C., Wang, C. B., Wang, G. W., Wang, H. G., Wang, H. H., Wang, J. C., Wang, K., Wang, L. P., Wang, L. Y., Wang, P. H., Wang, R., Wang, W., Wang, X. G., Wang, X. Y., Wang, Y., Wang, Y. D., Wang, Y. J., Wang, Z. H., Wang, Z. X., Wang, Zhen, Wang, Zheng, Wei, D. M., Wei, J. J., Wei, Y. J., Wen, T., Wu, C. Y., Wu, H. R., Wu, S., Wu, X. F., Wu, Y. S., Xi, S. Q., Xia, J., Xia, J. J., Xiang, G. M., Xiao, D. X., Xiao, G., Xin, G. G., Xin, Y. L., Xing, Y., Xiong, Z., Xu, D. L., Xu, R. F., Xu, R. X., Xu, W. L., Xue, L., Yan, D. H., Yan, J. Z., Yan, T., Yang, C. W., Yang, F., Yang, F. F., Yang, H. W., Yang, J. Y., Yang, L. L., Yang, M. J., Yang, R. Z., Yang, S. B., Yao, Y. H., Yao, Z. G., Ye, Y. M., Yin, L. Q., Yin, N., You, X. H., You, Z. Y., Yu, Y. H., Yuan, Q., Yue, H., Zeng, H. D., Zeng, T. X., Zeng, W., Zha, M., Zhang, B. B., Zhang, F., Zhang, H. M., Zhang, H. Y., Zhang, J. L., Zhang, L. X., Zhang, Li, Zhang, P. F., Zhang, P. P., Zhang, R., Zhang, S. B., Zhang, S. R., Zhang, S. S., Zhang, X., Zhang, X. P., Zhang, Y. F., Zhang, Yi, Zhang, Yong, Zhao, B., Zhao, J., Zhao, L., Zhao, L. Z., Zhao, S. P., Zheng, F., Zhou, B., Zhou, H., Zhou, J. N., Zhou, M., Zhou, P., Zhou, R., Zhou, X. X., Zhu, C. G., Zhu, F. R., Zhu, H., Zhu, K. J., and Zuo., X.
Subjects: Astrophysics - High Energy Astrophysical Phenomena, High Energy Physics - Phenomenology
Abstract: We present the first catalog of very-high energy and ultra-high energy gamma-ray sources detected by the Large High Altitude Air Shower Observatory (LHAASO). The catalog was compiled using 508 days of data collected by the Water Cherenkov Detector Array (WCDA) from March 2021 to September 2022 and 933 days of data recorded by the Kilometer Squared Array (KM2A) from January 2020 to September 2022. This catalog represents the main result from the most sensitive large coverage gamma-ray survey of the sky above 1 TeV, covering declination from $-$20$^{\circ}$ to 80$^{\circ}$. In total, the catalog contains 90 sources with an extended size smaller than $2^\circ$ and a significance of detection at $> 5\sigma$. Based on our source association criteria, 32 new TeV sources are proposed in this study. Among the 90 sources, 43 sources are detected with ultra-high energy ($E > 100$ TeV) emission at $> 4\sigma$ significance level. We provide the position, extension, and spectral characteristics of all the sources in this catalog., Comment: 40 pages, 13 figures, 4 tables
Published: 2023
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40. A note on the image of polynomials and Waring type problems on upper triangular matrix algebras

Author: Chen, Q.
Subjects: Mathematics - Rings and Algebras
Abstract: In the present paper we shall obtain a result on the image of polynomials with zero constant term on upper triangular matrix algebras over an algebraically closed field. This is a supplement to a result obtained by Panja and Prasad recently., Comment: pages 15. arXiv admin note: substantial text overlap with arXiv:2302.14377, arXiv:2304.01591
Published: 2023

41. Measurement of ultra-high-energy diffuse gamma-ray emission of the Galactic plane from 10 TeV to 1 PeV with LHAASO-KM2A

Author: Cao, Zhen, Aharonian, F., An, Q., Axikegu, Bai, Y. X., Bao, Y. W., Bastieri, D., Bi, X. J., Bi, Y. J., Cai, J. T., Cao, Q., Cao, W. Y., Cao, Zhe, Chang, J., Chang, J. F., Chen, A. M., Chen, E. S., Chen, Liang, Chen, Lin, Chen, Long, Chen, M. J., Chen, M. L., Chen, Q. H., Chen, S. H., Chen, S. Z., Chen, T. L., Chen, Y., Cheng, N., Cheng, Y. D., Cui, M. Y., Cui, S. W., Cui, X. H., Cui, Y. D., Dai, B. Z., Dai, H. L., Dai, Z. G., Danzengluobu, della Volpe, D., Dong, X. Q., Duan, K. K., Fan, J. H., Fan, Y. Z., Fang, J., Fang, K., Feng, C. F., Feng, L., Feng, S. H., Feng, X. T., Feng, Y. L., Gabici, S., Gao, B., Gao, C. D., Gao, L. Q., Gao, Q., Gao, W., Gao, W. K., Ge, M. M., Geng, L. S., Giacinti, G., Gong, G. H., Gou, Q. B., Gu, M. H., Guo, F. L., Guo, X. L., Guo, Y. Q., Guo, Y. Y., Han, Y. A., He, H. H., He, H. N., He, J. Y., He, X. B., He, Y., Heller, M., Hor, Y. K., Hou, B. W., Hou, C., Hou, X., Hu, H. B., Hu, Q., Hu, S. C., Huang, D. H., Huang, T. Q., Huang, W. J., Huang, X. T., Huang, X. Y., Huang, Y., Huang, Z. C., Ji, X. L., Jia, H. Y., Jia, K., Jiang, K., Jiang, X. W., Jiang, Z. J., Jin, M., Kang, M. M., Ke, T., Kuleshov, D., Kurinov, K., Li, B. B., Li, Cheng, Li, Cong, Li, D., Li, F., Li, H. B., Li, H. C., Li, H. Y., Li, J., Li, Jian, Li, Jie, Li, K., Li, W. L., Li, X. R., Li, Xin, Li, Y. Z., Li, Zhe, Li, Zhuo, Liang, E. W., Liang, Y. F., Lin, S. J., Liu, B., Liu, C., Liu, D., Liu, H., Liu, H. D., Liu, J., Liu, J. L., Liu, J. Y., Liu, M. Y., Liu, R. Y., Liu, S. M., Liu, W., Liu, Y., Liu, Y. N., Lu, R., Luo, Q., Lv, H. K., Ma, B. Q., Ma, L. L., Ma, X. H., Mao, J. R., Min, Z., Mitthumsiri, W., Mu, H. J., Nan, Y. C., Neronov, A., Ou, Z. W., Pang, B. Y., Pattarakijwanich, P., Pei, Z. Y., Qi, M. Y., Qi, Y. Q., Qiao, B. Q., Qin, J. J., Ruffolo, D., Saiz, A., Semikoz, D., Shao, C. Y., Shao, L., Shchegolev, O., Sheng, X. D., Shu, F. W., Song, H. C., Stenkin, Yu. V., Stepanov, V., Su, Y., Sun, Q. N., Sun, X. N., Sun, Z. B., Tam, P. H. T., Tang, Q. W., Tang, Z. B., Tian, W. W., Wang, C., Wang, C. B., Wang, G. W., Wang, H. G., Wang, H. H., Wang, J. C., Wang, K., Wang, L. P., Wang, L. Y., Wang, P. H., Wang, R., Wang, W., Wang, X. G., Wang, X. Y., Wang, Y., Wang, Y. D., Wang, Y. J., Wang, Z. H., Wang, Z. X., Wang, Zhen, Wang, Zheng, Wei, D. M., Wei, J. J., Wei, Y. J., Wen, T., Wu, C. Y., Wu, H. R., Wu, S., Wu, X. F., Wu, Y. S., Xi, S. Q., Xia, J., Xia, J. J., Xiang, G. M., Xiao, D. X., Xiao, G., Xin, G. G., Xin, Y. L., Xing, Y., Xiong, Z., Xu, D. L., Xu, R. F., Xu, R. X., Xu, W. L., Xue, L., Yan, D. H., Yan, J. Z., Yan, T., Yang, C. W., Yang, F., Yang, F. F., Yang, H. W., Yang, J. Y., Yang, L. L., Yang, M. J., Yang, R. Z., Yang, S. B., Yao, Y. H., Yao, Z. G., Ye, Y. M., Yin, L. Q., Yin, N., You, X. H., You, Z. Y., Yu, Y. H., Yuan, Q., Yue, H., Zeng, H. D., Zeng, T. X., Zeng, W., Zha, M., Zhang, B. B., Zhang, F., Zhang, H. M., Zhang, H. Y., Zhang, J. L., Zhang, L. X., Zhang, Li, Zhang, P. F., Zhang, P. P., Zhang, R., Zhang, S. B., Zhang, S. R., Zhang, S. S., Zhang, X., Zhang, X. P., Zhang, Y. F., Zhang, Yi, Zhang, Yong, Zhao, B., Zhao, J., Zhao, L., Zhao, L. Z., Zhao, S. P., Zheng, F., Zhou, B., Zhou, H., Zhou, J. N., Zhou, M., Zhou, P., Zhou, R., Zhou, X. X., Zhu, C. G., Zhu, F. R., Zhu, H., Zhu, K. J., and Zuo, X.
Subjects: Astrophysics - High Energy Astrophysical Phenomena
Abstract: The diffuse Galactic $\gamma$-ray emission, mainly produced via interactions between cosmic rays and the interstellar medium and/or radiation field, is a very important probe of the distribution, propagation, and interaction of cosmic rays in the Milky Way. In this work we report the measurements of diffuse $\gamma$-rays from the Galactic plane between 10 TeV and 1 PeV energies, with the square kilometer array of the Large High Altitude Air Shower Observatory (LHAASO). Diffuse emissions from the inner ($15^{\circ}10$~TeV). The energy spectrum in the inner Galaxy regions can be described by a power-law function with an index of $-2.99\pm0.04$, which is different from the curved spectrum as expected from hadronic interactions between locally measured cosmic rays and the line-of-sight integrated gas content. Furthermore, the measured flux is higher by a factor of $\sim3$ than the prediction. A similar spectrum with an index of $-2.99\pm0.07$ is found in the outer Galaxy region, and the absolute flux for $10\lesssim E\lesssim60$ TeV is again higher than the prediction for hadronic cosmic ray interactions. The latitude distributions of the diffuse emission are consistent with the gas distribution, while the longitude distributions show clear deviation from the gas distribution. The LHAASO measurements imply that either additional emission sources exist or cosmic ray intensities have spatial variations., Comment: 12 pages, 8 figures, 5 tables; accepted for publication in Physical Review Letters; source mask file provided as ancillary file
Published: 2023
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42. Vaccination compartmental epidemiological models for the delta and omicron SARS-CoV-2 variants

Author: Cuevas-Maraver, J., Kevrekidis, P. G., Chen, Q. Y., Kevrekidis, G. A., and Drossinos, Y.
Subjects: Quantitative Biology - Populations and Evolution
Abstract: We explore the inclusion of vaccination in compartmental epidemiological models concerning the delta and omicron variants of the SARS-CoV-2 virus that caused the COVID-19 pandemic. We expand on our earlier compartmental-model work by incorporating vaccinated populations. We present two classes of models that differ depending on the immunological properties of the variant. The first one is for the delta variant, where we do not follow the dynamics of the vaccinated individuals since infections of vaccinated individuals were rare. The second one for the far more contagious omicron variant incorporates the evolution of the infections within the vaccinated cohort. We explore comparisons with available data involving two possible classes of counts, fatalities and hospitalizations. We present our results for two regions, Andalusia and Switzerland (including the Principality of Liechtenstein), where the necessary data are available. In the majority of the considered cases, the models are found to yield good agreement with the data and have a reasonable predictive capability beyond their training window, rendering them potentially useful tools for the interpretation of the COVID-19 and further pandemic waves, and for the design of intervention strategies during these waves.
Published: 2023

43. Zero field magnetic resonance spectroscopy based on Nitrogen-vacancy centers

Author: Zhao, Linkai and Chen, Q.
Subjects: Quantum Physics
Abstract: We propose a scheme to have zero field magnetic resonance spectroscopy based on a nitrogen-vacancy center and investigate the new applications in which magnetic bias field might disturb the system under investigation. Continual driving with circularly polarized microwave fields is used to selectively address one spin state. The proposed method is applied for single molecule spectroscopy, such as nuclear quadrupole resonance spectroscopy of a $^{11}$B nuclear spin and the detection of the distance of two hydrogen nuclei in a water molecule. Our work extends applications of NV centers as a nanoscale molecule spectroscopy in the zero field regime., Comment: 11 pages, 3 figures
Published: 2023

44. Woven thermoplastic composite forming simulation with solid-shell element method

Author: Chen Q. Q., Saouab A., Boisse P., Park C. H., and Bréard J.
Subjects: solid-shell element, woven thermoplastic composite, forming simulation, Industrial engineering. Management engineering, T55.4-60.8, Industrial directories, T11.95-12.5
Abstract: Textile composites become more and more popular in aeronautic industries, due to their high performances. In this work, based on the model shown in the paper [9], a solid-shell element is used to simulate the woven composite forming. Comparing with other shell elements, a distinctive advantage of solid-shell elements is that the complication on handing finite rotations does not exist. Accounting the specific mechanical behaviors of woven composite material, the tensile, in-plane shear, bending and compressive energies are taken into account depending on the fiber direction, and then the total strain energy is computed to be the sum of those energies. The necessary material data for the simulation come from standard tensile, compressive and bias test experiments. Some forming simulations are performed with this method, and we can observe the wrinkles that exhibit the influence of bending stiffness. The results show the efficiency of the approach.
Published: 2009
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45. iRGD-Guided Silica/Gold Nanoparticles for Efficient Tumor-Targeting and Enhancing Antitumor Efficacy Against Breast Cancer

Author: Hou X, Chen Q, Fang Y, Zhang L, Huang S, Xu M, Ren Y, Shi Z, Wei Y, and Li L
Subjects: silicon dioxide nanoparticles, gold nanoclusters, irgd penetrating peptide, integrin αvβ3, tumor targeting, breast cancer, Medicine (General), R5-920
Abstract: Xuefeng Hou,1– 4,&ast; Qi Chen,1,&ast; Ying Fang,5 Li Zhang,1 Shuoheng Huang,1 Minjie Xu,1 Yaning Ren,1 Zhansen Shi,1 Yan Wei,1 Lihua Li1– 4 1School of Pharmacy, Wannan Medical College, Wuhu, Anhui Province, People’s Republic of China; 2Anhui Provincial Engineering Laboratory for Screening and Re-Evaluation of Active Compounds of Herbal Medicines in Southern Anhui, Wannan Medical College, Wuhu, Anhui Province, People’s Republic of China; 3Anhui Provincial Engineering Research Center for Polysaccharide Drugs, Wannan Medical College, Wuhu, Anhui Province, People’s Republic of China; 4Drug Research and Development Center, Wannan Medical College, Wuhu, Anhui Province, People’s Republic of China; 5School of Pharmacy, Shanghai University of Traditional Chinese Medicine, Shanghai, People’s Republic of China&ast;These authors contributed equally to this workCorrespondence: Yan Wei; Lihua Li, School of Pharmacy, Wannan Medical College, No. 22, West Wenchang Road, Wuhu, 241002, People’s Republic of China, Email yanwei@wnmc.edu.cn; llh05530226@126.comBackground: Breast cancer presents significant challenges due to the limited effectiveness of available treatments and the high likelihood of recurrence. iRGD possesses both RGD sequence and C-terminal sequence and has dual functions of targeting and membrane penetration. iRGD-modified nanocarriers can enhance drug targeting of tumor vascular endothelial cells and penetration of new microvessels, increasing drug concentration in tumor tissues.Methods: The amidation reaction was carried out between SiO2/AuNCs and iRGD/PTX, yielding a conjugated drug delivery system (SiO2/AuNCs-iRGD/PTX, SAIP@NPs). The assessment encompassed the characterization of the morphology, particle size distribution, physicochemical properties, in vitro release profile, cytotoxicity, and cellular uptake of SAIP@NPs. The tumor targeting and anti-tumor efficacy of SAIP@NPs were assessed using a small animal in vivo imaging system and a tumor-bearing nude mice model, respectively. The tumor targeting and anti-tumor efficacy of SAIP@NPs were assessed utilizing a small animal in vivo imaging system and an in situ nude mice breast cancer xenograft model, respectively.Results: The prepared SAIP@NPs exhibited decent stability and a certain slow-release effect in phosphate buffer (PBS, pH 7.4). In vitro studies had shown that, due to the dual functions of transmembrane and targeting of iRGD peptide, SAIP@NPs exhibited strong binding to integrin αvβ 3, which was highly expressed on the membrane of MDA-MB-231 cells, improving the uptake capacity of tumor cells, inhibiting the rapid growth of tumor cells, and promoting tumor cell apoptosis. The results of animal experiments further proved that SAIP@NPs had longer residence time in tumor sites, stronger anti-tumor effect, and no obvious toxicity to major organs of experimental animals.Conclusion: The engineered SAIP@NPs exhibited superior functionalities including efficient membrane permeability, precise tumor targeting, and imaging, thereby significantly augmenting the therapeutic efficacy against breast cancer with a favorable safety profile. Keywords: silicon dioxide nanoparticles, gold nanoclusters, iRGD penetrating peptide, integrin αvβ 3, tumor targeting, breast cancer
Published: 2024

46. Treatment Outcomes and Associated Influencing Factors Among Patients with Rifampicin-Resistant Tuberculosis: A Multicenter, Retrospective, Cohort Study in China

Author: Zou L, Kang W, Guo C, Du J, Chen Q, Shi Z, Tang X, Liang L, Tang P, Pan Q, Zhu Q, Yang S, Chang Z, Guo Z, Wu G, and Tang S
Subjects: rr-tb, treatment outcomes, influencing factors, china, Infectious and parasitic diseases, RC109-216
Abstract: Liping Zou,1,&ast; Wanli Kang,2,&ast; Chunhui Guo,3,&ast; Juan Du,4,&ast; Qing Chen,1 Zhengyu Shi,1 Xianzhen Tang,1 Li Liang,1 Peijun Tang,5 Qing Pan,6 Qingdong Zhu,7 Song Yang,8 Zhanlin Chang,9 Zhouli Guo,1 Guihui Wu,1 Shenjie Tang2 1Department of Tuberculosis, Public Health Clinical Center of Chengdu, Chengdu, People’s Republic of China; 2Beijing Chest Hospital, Capital Medical University, Beijing Tuberculosis and Thoracic Tumor Research Institute, Beijing, People’s Republic of China; 3Department of Tuberculosis, Harbin Chest Hospital, Harbin, People’s Republic of China; 4Department of Tuberculosis, Wuhan Pulmonary Hotel, Wuhan, People’s Republic of China; 5Department of Tuberculosis, the Fifth People’s Hospital of Suzhou, Suzhou, People’s Republic of China; 6Department of Respiratory and Critical Care Medicine, Anqing Municipal Hospital, Anqing, People’s Republic of China; 7Department of Tuberculosis, the Fourth People’s Hospital of Nanning, Nanning, People’s Republic of China; 8General Internal Medicine Department, Chongqing Public Health Medical Center, Chongqing, People’s Republic of China; 9Department of Surgery, the Third People’s Hospital of Tibet Autonomous Region, Lasa, People’s Republic of China&ast;These authors contributed equally to this workCorrespondence: Guihui Wu, Department of Tuberculosis, Public Health Clinical Center of Chengdu, Jingjusi 18 Street, Jingjiang District, Chengdu, Sichuan, People’s Republic of China, Tel +86-28-8455-0573, Email wghwgh2584@sina.com Shenjie Tang, Beijing Chest Hospital, Capital Medical University, Beijing Tuberculosis and Thoracic Tumor Research Institute, Beijing, 101149, People’s Republic of China, Tel +86-13621028338, Email tangsj1106@vip.sina.comObjective: Rifampin-resistant tuberculosis (RR-TB) remains a serious global public health concern. We assessed treatment outcomes and associated influencing factors among RR-TB patients in China.Methods: This research enrolled 1339 patients who started RR-TB treatment between May 2018 and April 2020 in China retrospectively. Data were collected from the electronic medical records. Multivariable logistic regression analysis was used to identify the influencing factors related to unfavorable outcomes.Results: Of the 1339 RR-TB patients, 78.8% (1055/1339) achieved treatment success (cured or treatment completed), 5.1% (68/1339) experienced treatment failure, 1.1% (15/1339) died during treatment, 10.1% (135/1339) were lost to follow-up, and 4.9% (66/1339) were not evaluated. About 67.7% (907/1339) of patients experienced at least one adverse event (AE). The most common AE was hypohepatia (507/1339, 37.9%), followed by hyperuricemia (429/1339, 32.0%), anemia (368/1339, 27.5%), electrolyte disturbance (318/1339, 23.7%), peripheral neuritis (245/1339, 18.3%), and gastrointestinal reactions (203/1339, 15.2%). Multivariate analysis showed that age ≥ 60 years [adjusted odds ratio (aOR): 1.96, 95% confidence interval (CI): 1.39– 2.77], national minority (aOR: 2.36, 95% CI: 1.42– 3.93), smoking (aOR: 1.50, 95% CI: 1.10– 2.04), cardiopathy (aOR: 2.90, 95% CI: 1.33– 6.31), tumors (aOR: 9.84, 95% CI: 2.27– 42.67), immunocompromise (aOR: 2.17, 95% CI: 1.21– 3.91), re-treated TB (aOR: 1.46, 95% CI: 1.08– 1.97), and experienced gastrointestinal reactions (aOR: 2.27, 95% CI: 1.52– 3.40) were associated with unfavorable outcomes. Body mass index (BMI) ≥ 18.5 kg/m2, regimens containing bedaquiline and experienced adverse events (AEs) such as hypohepatia, leukopenia, peripheral neuritis, and optic neuritis were associated with favorable outcomes.Conclusion: High rates of treatment success were achieved for RR-TB patients at tertiary tuberculosis hospitals in China. Age ≥ 60 years, national minority, smoking status, comorbidities, re-treated TB, and experienced gastrointestinal reactions were independent prognostic factors for unfavorable treatment outcomes.Keywords: RR-TB, treatment outcomes, influencing factors, China
Published: 2024

47. Novel Diagnostic Biomarkers Related to Necroptosis and Immune Infiltration in Coronary Heart Disease

Author: Chen Q, Zhang JL, Yang JS, Jin Q, Yang J, Xue Q, and Guang XF
Subjects: atherosclerosis, cell death, inflammations, diagnostic biomarkers, necroptosis, immune infiltration, Pathology, RB1-214, Therapeutics. Pharmacology, RM1-950
Abstract: Qiu Chen,1 Ji-Lei Zhang,1 Jie-Shun Yang,2 Qing Jin,1 Jun Yang,3 Qiang Xue,1 Xue-feng Guang1 1Department of Cardiology, Yan’an Hospital Affiliated to Kunming Medical University, Kunming, People’s Republic of China; 2Department of Pathology, The Second Affiliated Hospital of Kunming Medical University, Kunming, People’s Republic of China; 3Key Laboratory of Cardiovascular Disease of Yunnan Province, Yan’an Hospital Affiliated to Kunming Medical University, Kunming, People’s Republic of ChinaCorrespondence: Qiang Xue; Xue-feng Guang, Department of Cardiology, Yan’ an Hospital Affiliated to Kunming Medical University, No. 245 Renmin Dong Road, Kunming, Yunnan Province, 650051, People’s Republic of China, Email xueqiang3513@126.com; gxfyayy@163.comPurpose: Necroptosis, a monitored form of inflammatory cell death, contributes to coronary heart disease (CHD) progression. This study examined the potential of using necroptosis genes as diagnostic markers for CHD and sought to elucidate the underlying roles.Methods: Through bioinformatic analysis of GSE20680 and GSE20681, we first identified the differentially expressed genes (DEGs) related to necroptosis in CHD. Hub genes were identified using least absolute shrinkage and selection operator (LASSO) regression and random forest analysis after studying immune infiltration and transcription factor-miRNA interaction networks according to the DEGs. Quantitative polymerase chain reaction and immunohistochemistry were used to further investigate hub gene expression in vivo, for which a diagnostic model was constructed and the predictive efficacy was validated. Finally, the CHD group was categorized into high- and low-score groups in accordance with the single-sample gene set enrichment analysis (ssGSEA) score of the necroptosis genes. Gene Ontology, Kyoto Encyclopedia of Genes and Genomes, GSEA, and further immune infiltration analyses were performed on the two groups to explore the possible roles of hub genes.Results: Based on the results of the LASSO regression and random forest analyses, four genes were used to construct a diagnostic model to establish a nomogram. Additionally, an extensive analysis of all seventeen necroptosis genes revealed notable distinctions in expression between high-risk and low-risk groups. Evaluation of immune infiltration revealed that neutrophils, monocytes, B cells, and activated dendritic cells were highly distributed in the peripheral blood of patients with CHD. Specifically, the high CHD score group exhibited greater neutrophil and monocyte infiltration. Conversely, the high-score group showed lower infiltration of M0 and M2 macrophages, CD8+ T, plasma, and resting mast cells.Conclusion: TLR3, MLKL, HMGB1, and NDRG2 may be prospective biomarkers for CHD diagnosis. These findings offer plausible explanations for the role of necroptosis in CHD progression through immune infiltration and inflammatory response.Keywords: atherosclerosis, cell death, inflammations, diagnostic biomarkers, necroptosis, immune infiltration
Published: 2024

48. Comparison of the Effects of Adductor Canal and Femoral Nerve Blocks on Postoperative Opioid Consumption and Inflammatory Factor Levels in Elderly Patients After Total Knee Arthroplasty: A Prospective Observational Study

Author: Han Z, Zhang Y, Xue C, Jin S, and Chen Q
Subjects: adductor canal, femoral nerve, block, opioid, analgesia, tka, Medicine (General), R5-920
Abstract: Zhengyi Han,1,2 Yangyang Zhang,1,2 Chenxi Xue,3 Shiyun Jin,1,2 Qi Chen,1,2 Ye Zhang1,2 1Department of Anesthesiology, The Second Affiliated Hospital of Anhui Medical University, Hefei, 230601, People’s Republic of China; 2Key Laboratory of Anesthesiology and Perioperative Medicine of Anhui Higher Education Institutes, Anhui Medical University, Hefei, 230601, People’s Republic of China; 3Department of Orthopedics, the Second Affiliated Hospital of Anhui Medical University, Hefei, 230601, People’s Republic of ChinaCorrespondence: Ye Zhang, Department of Anesthesiology, The Second Hospital of Anhui Medical University, 678 Furong Road, Hefei, 230601, People’s Republic of China, Tel +86-055163869480, Email zhangye_hassan@sina.comPurpose: Total Knee Arthroplasty (TKA) is a highly invasive procedure causing severe postoperative pain, which hampers early mobility. Effective pain management is crucial for optimal recovery. This study aimed to evaluate how adductor canal block (ACB) and femoral nerve block (FNB) affect opioid use and inflammation factor levels in elderly TKA patients.Methods: This prospective observational study included 120 patients who received TKA, and divided them into three groups, based on the different nerve block technique: ACB, FNB, and no intervention before general anesthesia (CON). Postoperative opioid consumption, pain assessment, inflammation factor, knee function recovery and other clinical indicators were recorded.Results: The CON group had significantly higher cumulative sufentanil consumption compared to the ACB and FNB groups at both 12 h and 48h postoperative (P< 0.001). Compared with the CON group, the ACB and FNB groups persistently had lower pain scores until 12 h at rest and 24 h during motion after surgery. The ACB group showed significantly lower serum concentrations of C-reactive protein (CRP) and interleukin-6 (IL-6) compared to the CON group at 24 h postoperative (P=0.017, P=0.009), and IL-6 levels remained significantly lower at 72 h postoperative (P=0.005). Both ACB and FNB groups achieved earlier ambulation compared to the CON group (P=0.002). On the first day postoperative, both the ACB and FNB groups showed significantly better knee motion (P< 0.001), quadriceps strength (P< 0.001), and daily mobilization (P< 0.001) compared to the CON group. Additionally, the ACB group exhibited superior quadriceps strength (P< 0.001) and daily mobilization (P< 0.001) compared to the FNB group.Conclusion: The ACB and FNB groups exhibited comparable clinical efficacy outcomes in terms of pain scores and opioid consumption. However, the ACB group experienced reduced postoperative inflammation and improved knee recovery, especially in quadriceps strength.Keywords: adductor canal block, femoral nerve block, opioid, analgesia, inflammation, TKA
Published: 2024

49. A Novel Continuous Sleep State Artificial Neural Network Model Based on Multi-Feature Fusion of Polysomnographic Data

Author: Cui J, Sun Y, Jing H, Chen Q, Huang Z, Qi X, and Cui H
Subjects: sleep depth value, sleep continuity, eeg features, timing fitness, ann model, Psychiatry, RC435-571, Neurophysiology and neuropsychology, QP351-495
Abstract: Jian Cui,1 Yunliang Sun,2 Haifeng Jing,3 Qiang Chen,1 Zhihao Huang,1 Xin Qi,1 Hao Cui1 1Department of Big Data and Fundamental Sciences, Shandong Institute of Petroleum and Chemical Technology, Dongying, Shandong, 257061, People’s Republic of China; 2Department of Respiratory and Sleep Medicine, Bin Zhou Medical University Hospital, Binzhou, Shandong, 256600, People’s Republic of China; 3College of Software and Microelectronics, Peking University, Beijing, 100000, People’s Republic of ChinaCorrespondence: Jian Cui, Department of Big Data and Fundamental Sciences, Shandong Institute of Petroleum and Chemical Technology, 271 Bei Er Lu, Dongying City, Shandong Province, 257061, People’s Republic of China, Tel +86-15066073763, Email jian.cui@sdipct.edu.cnPurpose: Sleep structure is crucial in sleep research, characterized by its dynamic nature and temporal progression. Traditional 30-second epochs falter in capturing the intricate subtleties of various micro-sleep states. This paper introduces an innovative artificial neural network model to generate continuous sleep depth value (SDV), utilizing a novel multi-feature fusion approach with EEG data, seamlessly integrating temporal consistency.Methods: The study involved 50 normal and 100 obstructive sleep apnea–hypopnea syndrome (OSAHS) participants. After segmenting the sleep data into 3-second intervals, a diverse array of 38 feature values were meticulously extracted, including power, spectrum entropy, frequency band duration and so on. The ensemble random forest model calculated the timing fitness value for all the features, from which the top 7 time-correlated features were selected to create detailed sleep sample values ranging from 0 to 1. Subsequently, an artificial neural network (ANN) model was trained to delineate sleep continuity details, unravel concealed patterns, and far surpassed the traditional 5-stage categorization (W, N1, N2, N3, and REM).Results: The SDV changes from wakeful stage (mean 0.7021, standard deviation 0.2702) to stage N3 (mean 0.0396, standard deviation 0.0969). During the arousal epochs, the SDV increases from the range (0.1 to 0.3) to the range around 0.7, and decreases below 0.3. When in the deep sleep (≤ 0.1), the probability of arousal of normal individuals is less than 10%, while the average arousal probability of OSA patients is close to 30%.Conclusion: A sleep continuity model is proposed based on multi-feature fusion, which generates SDV ranging from 0 to 1 (representing deep sleep to wakefulness). It can capture the nuances of the traditional five stages and subtle differences in microstates of sleep, considered as a complement or even an alternative to traditional sleep analysis.Keywords: sleep depth value, sleep continuity, EEG features, timing fitness, ANN model
Published: 2024

50. The Benefit of the Optimized Formula of Yinxieling in Psoriasis Vulgaris via Regulation on Autophagy Based on microRNA Expression Profile and Network Pharmacology Analysis

Author: Lu Y, Pan S, Li W, Qi Y, Li L, Yan YH, Wei J, Yao DN, Wu J, Deng H, Ye S, Chen H, Chen Q, Gao H, Han L, and Lu C
Subjects: psori-cm01, psoriasis vulgaris, microrna expression profile, network pharmacology, autophagy, Therapeutics. Pharmacology, RM1-950
Abstract: Yue Lu,1– 3,&ast; Simin Pan,4,&ast; Wenzhen Li,5 Yao Qi,6,7 Li Li,1– 3 Yu-Hong Yan,1– 3 Jianan Wei,1– 3 Dan-Ni Yao,1– 3 Jingjing Wu,1– 3 Hao Deng,1– 3 Shuyan Ye,1– 3 Haiming Chen,1– 3 Qubo Chen,1– 3 Hengjun Gao,6,7 Ling Han,1– 3 Chuanjian Lu1– 3 1State Key Laboratory of Dampness Syndrome of Chinese Medicine, The Second Affiliated Hospital of Guangzhou University of Chinese Medicine (Guangdong Provincial Hospital of Chinese Medicine), Guangzhou, People’s Republic of China; 2Guangdong Provincial Key Laboratory of Clinical Research on Traditional Chinese Medicine Syndrome, Guangzhou, People’s Republic of China; 3Guangdong-Hong Kong-Macau Joint Laboratory on Chinese Medicine and Immune Disease Research, Guangzhou University of Chinese Medicine, Guangzhou, People’s Republic of China; 4The Second Clinical College, Guangzhou University of Chinese Medicine, Guangzhou, People’s Republic of China; 5The Clinical College of Acupuncture Rehabilitation, Guangzhou University of Chinese Medicine, Guangzhou, People’s Republic of China; 6Shanghai Molecular Medicine Engineering Technology Research Center, Shanghai, People’s Republic of China; 7Shanghai National Engineering Research Center of Biochip, Shanghai, People’s Republic of China&ast;These authors contributed equally to this workCorrespondence: Ling Han; Chuanjian Lu, Email linghan99@gzucm.edu.cn; lcj@gzucm.edu.cnBackground: Psoriasis is a widespread chronic, immune-mediated skin disease with frequent recurrences, and is extremely harmful to the physical and mental health of patients, causing enormous suffering and exerting considerable economic burdens on the health care system as a whole. In more than a decade of clinical use, the optimized formula of Yinxieling (PSORI-CM01) has consistently demonstrated its effectiveness for treating psoriasis. However, its underlying mechanism remains largely unexplored.Methods: The network pharmacology analysis was conducted to predict the mechanism and protective effect of PSORI-CM01 in treating psoriasis. Subsequently, we collected blood samples from 21 patients with psoriasis as part of a randomized, double-blind, and double-dummy clinical trial for microRNA expression profiling. Finally, it was experimentally confirmed that PSORI-CM01 improved psoriasis by regulating miR-20a-3p and miR-3184-3p expression.Results: As a result of the network pharmacology analysis, PSORI-CM01 improved psoriasis through the regulation of autophagy, cellular apoptosis, cellular proliferation, and anti-inflammatory processes. In the target-miRNA regulatory network, these key targets were mainly associated with the regulation of hsa-miR-20a-3p, hsa-miR-155-5p, has-miR-3184-3p, hsa-miR-328-3p and hsa-miR-124-3p. Based on the microRNA expression profiling results, the PSORI-CM01 treatment group exhibited five up-regulated genes and 16 down-regulated genes compared with the healthy control group. In particular, miR-20a-3p and miR-3184-3p were the primary differentially expressed microRNAs, and they were significantly enriched in the signaling pathways involving autophagy, apoptosis, proliferation, and anti-inflammation. Further experiments confirmed that PSORI-CM01 effectively regulates miR-20a-3p and miR-3184-3p, resulting in increased autophagy.Conclusion: We demonstrated by combining network pharmacology and clinical studies of miRNA expression profiles in PBMCs that PSORI-CM01 effectively modulated miR-20a-3p and miR-3184-3p, leading to an increase in autophagy and a decrease in keratinocyte proliferation.Keywords: PSORI-CM01, Psoriasis vulgaris, MicroRNA expression profile, network pharmacology, autophagy
Published: 2024

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