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185 results on '"Wang, P.H"'

1. Data quality control system and long-term performance monitor of 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.

Published
2025
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2. Evidence for particle acceleration approaching PeV energies in the W51 complex

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., Zuo, X., and Celli, S.

Published
2024
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3. Method to measure muon content of extensive air showers with LHAASO KM2A-WCDA synergy

Author

Cao, Zhen, Aharonian, F., An, Q., Axikegu, Bai, L.X., 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, 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, H.L., Cheng, N., Cheng, Y.D., 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., Gao, B., Gao, C.D., Gao, L.Q., Gao, Q., Gao, W., Gao, W.K., Ge, M.M., Geng, L.S., 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.S., Liu, J.Y., Liu, M.Y., Liu, R.Y., Liu, S.M., Liu, W., Liu, Y., Liu, Y.N., Long, W.J., Lu, R., Luo, Q., Lv, H.K., Ma, B.Q., Ma, L.L., Ma, X.H., Mao, J.R., Min, Z., Mitthumsiri, W., Nan, Y.C., 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., Shao, C.Y., Shao, L., Shchegolev, O., Sheng, X.D., Song, H.C., Stenkin, Yu.V., Stepanov, V., Su, Y., Sun, Q.N., Sun, X.N., Sun, Z.B., Tam, P.H.T., Tang, Z.B., Tian, W.W., Wang, C., Wang, C.B., Wang, G.W., Wang, H.G., Wang, H.H., Wang, J.C., Wang, J.S., 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., 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., Zeng, Z.K., 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, Y., Zhou, B., Zhou, H., Zhou, J.N., Zhou, P., Zhou, R., Zhou, X.X., Zhu, C.G., Zhu, F.R., Zhu, H., Zhu, K.J., and Zuo, X.

Published
2024
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4. Pointing calibration of LHAASO-WFCTA telescopes using bright stars

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.

Published
2023
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14. 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, M, 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, M, 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, Zuo, X, AstroParticule et Cosmologie (APC (UMR_7164)), Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Institut National de Physique Nucléaire et de Physique des Particules du CNRS (IN2P3)-Observatoire de Paris, Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Centre National de la Recherche Scientifique (CNRS)-Université Paris Cité (UPCité), and LHAASO

Subjects
High Energy Astrophysical Phenomena (astro-ph.HE), FOS: Physical sciences, cosmic radiation: interaction, gamma ray: energy spectrum, gamma ray: emission, gamma ray: UHE, gamma ray: VHE, gas, propagation, TeV, galaxy, cosmic radiation: UHE, gamma ray: flux, [PHYS.ASTR]Physics [physics]/Astrophysics [astro-ph], Astrophysics - High Energy Astrophysical Phenomena, LHAASO
Abstract

The diffuse Galactic $\gamma$-ray emission, mainly produced via interactions between cosmic rays and the diffuse interstellar medium, 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 slight deviation from the gas distribution. The LHAASO measurements imply that either additional emission sources exist or cosmic ray intensities have spatial variations., Comment: 11 pages, 7 figures, 5 tables

Published
2023

18. The burden of olfactory dysfunction during the COVID-19 pandemic in the United Kingdom

Author

Lechner, M., primary, Liu, J., additional, Counsell, N., additional, Gillespie, D., additional, Chandrasekharan, D., additional, Ta, N.H., additional, Jumani, K., additional, Gupta, R., additional, Rocke, J., additional, Williams, C., additional, Tetteh, A., additional, Amnolsingh, R., additional, Khwaja, S., additional, Batterham, R.L., additional, Yan, C.H., additional, Treibel, T.A., additional, Moon, J.C., additional, Woods, J., additional, Brunton, R., additional, Boardman, J., additional, Hatter, M., additional, Abdelwahab, M., additional, Holsinger, F.C., additional, Capasso, R., additional, Nayak, J.V., additional, Wang, P.H., additional, Paun, S., additional, Eynon-Lewis, N., additional, Kumar, B.N., additional, Jayaraj, S., additional, Hopkins, C., additional, Philpott, C., additional, and Lund, V.J., additional

Published
2022
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21. Towards the Measurement of the Mass Modifications of Vector Mesons in a Finite Density Matter

Author

Ozawa, K., primary, Aoki, K., additional, Arimizu, D., additional, Ashikaga, S., additional, Chang, W.C., additional, Chujo, T., additional, Ebata, K., additional, En'yo, H., additional, Esumi, S., additional, Hamagaki, H., additional, Honda, R., additional, Ichikawa, M., additional, Kajikawa, S., additional, Kanno, K., additional, Kimura, Y., additional, Kiyomichi, A., additional, Kondo, T.K., additional, Kyan, S., additional, Lin, C.H., additional, Lin, C.S., additional, Morino, Y., additional, Murakami, H., additional, Murakami, T.N., additional, Muto, R., additional, Nakai, W., additional, Nakasuga, S., additional, Naruki, M., additional, Nonaka, T., additional, Noumi, H., additional, Sakaguchi, T., additional, Sako, H., additional, Sakuma, F., additional, Sato, S., additional, Sawada, S., additional, Sekimoto, M., additional, Shigaki, K., additional, Shirotori, K., additional, Sugimura, H., additional, Takahashi, T.N., additional, Takaura, Y., additional, Tatsumi, R., additional, Tsukui, K., additional, Wang, P.H., additional, Yahiro, K., additional, Yamaguchi, K.H., additional, and Yokkaichi, S., additional

Published
2022
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24. Progress on solid breeder TBM at SWIP

Author

Feng, K.M., Pan, C.H., Zhang, G.S., Luo, T.Y., Zhao, Z., Chen, Y.J., Ye, X.F., Hu, G., Wang, P.H., Yuan, T., Feng, Y.J., Xiang, B., Zhang, L., Wang, Q.J., Cao, Q.X., Li, Z.X., and Wang, F.

Published
2010
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28. Regulation of the expression of angiotensin-converting enzyme 2 by polyunsaturated fatty acids in porcine adipocytes

Author

Tseng, Y.W., Wang, P.H., Lee, H.S., Liu, B.H., Mersmann, H.J., Lin, E.C., and Ding, S.T.

Subjects
Swine -- Physiological aspects, Swine -- Research, Unsaturated fatty acids -- Research, Unsaturated fatty acids -- Properties, Fat cells -- Research, Enzymes -- Research, Zoology and wildlife conservation
Abstract

Using suppression subtractive hybridization technique, we found that 2 novel genes (AEUG1 and AEUG3) were highly expressed in the adipocytes compared with preadipocytes. We then identified that these 2 genes were both angiotensin-converting enzyme 2 (ACE2). We applied 3'RACE (rapid amplification of cDNA end), 5'RACE, and PCR to obtain the full-length porcine ACE2 cDNA sequence. Because eicosanoids derived from PUFA are involved in regulating blood pressure, we hypothesized that PUFA can regulate the expression of the vasodilator ACE2. Preadipocytes from Landrace pigs were induced to differentiate for 4 d, then treated with 50 [micro]M of different PUFA, CLA, arachidonic acid, eicosapentaenoic acid (EPA), docosahexaenoic acid (DHA), or stearic acid (18:0). Addition of EPA or 18:0 for 48 h did not change the ACE2 mRNA abundance, whereas the treatments of arachidonic acid, CLA, and DHA significantly decreased ACE2 mRNA abundance after 48 h (P [less than or equal to] 0.05). Treatment with PUFA did not change (P > 0.05) type I and type II angiotensin receptor mRNA abundance. To further understand how PUFA metabolites affect ACE2 mRNA expression, we inhibited individual enzymes that are involved in eicosanoid production. We found that 3 individual eicosanoid pathway enzyme inhibitors recovered the PUFA effect on the expression of ACE2, indicating these pathways are involved in mediating the PUFA function. In conclusion, we obtained the full-length porcine ACE2 cDNA sequence and found PUFA could downregulate the expression of ACE2 through its metabolites without changing the expression of their receptor in porcine adipocytes. Key words: amplification of complementary deoxyribonucleic acid end, angiotensin-converting enzyme, polyunsaturated fatty acid doi: 10.2527/jas.2010-2905

Published
2010

29. Visfatin regulates genes related to lipid metabolism in porcine adipocytes

Author

Yang, C.C., Deng, S.J., Hsu, C.C., Liu, B.H., Lint, E.C., Cheng, W.T.K., Wang, P.H., and Ding, S.T.

Subjects
Fat cells -- Research, Lipid metabolism -- Research, Cytokines -- Research, Swine -- Genetic aspects, Swine -- Physiological aspects, Zoology and wildlife conservation
Abstract

Visfatin is a visceral adipose tissue-specific adipocytokine that plays a positive role in attenuating insulin resistance by binding to the insulin receptor. Visfatin has been suggested to play a role in the regulation of lipid metabolism and inflammation; however, the mechanism remains unclear. We investigated the effects of visfatin on the regulation of gene expression in cultured porcine preadipocytes and differentiated adipocytes. In preadipocytes, the mRNA abundance of lipoprotein lipase and PPAR[gamma] were significantly increased by visfatin or insulin treatment after 8 d (all P < 0.05). In the presence of insulin, the mRNA abundance of adipocyte fatty acid-binding protein was 24.7-fold greater than in the untreated group (P < 0.05), whereas visfatin alone had no effect on adipocyte fatty acid-binding protein mRNA abundance. Adipocyte differentiation was induced by insulin treatment for 8 d. In differentiated porcine adipocytes, exposure to insulin or visfatin for 24 h increased (P < 0.05) fatty acid synthase mRNA abundance but had no effect on the expression of sterol regulatory element binding-protein 1c mRNA. We also found a 5.8-fold upregulation of IL-6 expression in porcine adipocytes after 24 h of treatment with visfatin (P < 0.05). These results demonstrated that visfatin upregulated lipoprotein lipase expression in preadipocytes, potentially facilitating lipid uptake, and increased the gene expression of fatty acid synthase in differentiated adipocytes to potentially enhance lipogenic activity. Furthermore, visfatin can upregulate IL-6 expression in differentiated porcine adipocytes. The information presented in this study provides insights into the roles of visfatin in lipid metabolism in pigs. Key words: adipocyte, lipid metabolism, visfatin doi: 10.2527/jas.2010-2799

Published
2010

30. Porcine peroxisome proliferator-activated receptor [delta] mediates the lipolytic effects of dietary fish oil to reduce body fat deposition

Author

Yu, Y.H., Wang, P.H., Cheng, W.T.K., Mersmann, H.J., Wu, S.C., and Ding, S.T.

Subjects
Zoology and wildlife conservation
Abstract

Peroxisome proliferator-activated receptor [delta] promotes fatty acid catabolism and energy expenditure in skeletal muscle and adipose tissues. A ligand for PPAR[delta] is required to activate PPAR[delta] function. Polyunsaturated fatty acids are potential ligands for PPAR[delta] activation. The current experiment was designed to determine the potential for PUFA, particularly from dietary fish oil, to activate porcine PPAR[delta] in vivo. Transgenic mice were generated to over-express porcine PPAR5 in the adipose tissue. Mice were fed a high-saturated fat (13% beef tallow), or high-unsaturated fat (13% fish oil) diet, or a diet containing 4 rag/ kg of a PPAR[delta] ligand (L165041) for 4 mo. Compared with beef tallow feeding, fish oil feeding reduced fat mass and decreased (P < 0.05) plasma triacylglycerol and FFA concentrations in the transgenic mice. Adipose tissue expression of genes involved in adipogenesis (i.e., lipoprotein lipase and adipocyte fatty acid-binding protein) was decreased in transgenic mice fed fish oil or the PPAR[delta] ligand. In the same mice, expression of the lipolytic gene, hormone-sensitive lipase was increased (P < 0.05). Fish oil feeding also stimulated expression of genes participating in fatty acid oxidation in the liver of transgenic mice compared with wild-type mice. Overall, these results indicate that PUFA may serve as natural and effective regulators of lipid catabolism in vivo and many of these effects may be generated from activation of PPAR[delta]. Key words: adipose tissue, fish oil, lipid metabolism, peroxisome proliferator-activated receptor [delta], pig, polyunsaturated fatty acid doi: 10.2527/jas.2009-2597

Published
2010

31. Docosahexaenoic acid regulates adipogenic genes in myoblasts via porcine peroxisome proliferator-activated receptor [[gamma].sup.1]

Author

Yu, Y.H., Lin, E.C., Wu, S.C., Cheng, W.T.K., Mersmann, H.J., Wang, P.H., and Ding, S.T.

Subjects
Fat cells -- Properties, Fat cells -- Genetic aspects, Cell differentiation -- Genetic aspects, Binding proteins -- Properties, Binding proteins -- Genetic aspects, Swine -- Physiological aspects, Swine -- Genetic aspects, Peroxisomes -- Genetic aspects, Peroxisomes -- Properties, Fatty acids -- Genetic aspects, Fatty acids -- Properties, Zoology and wildlife conservation
Abstract

The nuclear transcription factor peroxisome proliferator-activated receptor [gamma] (PPAR[gamma]) triggers adipocyte differentiation by regulating lipogenic genes. A ligand for PPAR[gamma] is necessary to activate PPAR[gamma] function. Fatty acids are potential ligands for PPAR[gamma] activation. The current experiment was designed to determine the potential for individual fatty acids to activate porcine PPAR[gamma] ectopically expressed in myoblasts. The expression of adipocyte fatty acid binding protein (aP2) and adiponectin in myoblasts stably expressing porcine PPAR[gamma] was increased when docosahexaenoic acid (DHA) was added to the adipogenic medium. The response was positively related to DHA concentration and suggests that DHA may bind to and activate porcine PPAR[gamma], leading to increased expression of aP2 and adiponectin. The conditioned media collected from myoblasts expressing PPAR[gamma] between d 3 and 6 or between d 6 and 9, but not DHA itself, activated the aP2 gene promoter-driven luciferase activity. These results suggest that a metabolite of DHA is the ligand binding to and activating porcine PPAR[gamma]. The metabolite and pathway for its production are currently unknown. Key words: adipocyte differentiation, adipocyte fatty acid-binding protein, adiponectin, docosahexaenoic acid, peroxisome proliferator-activated receptor [gamma], pig

Published
2008

32. Fasting regulates the expression of adiponectin receptors in young growing pigs

Author

Liu, B.H., Wang, P.H., Wang, Y.C., Cheng, W.M., Mersmann, H.J., and Ding, S.T.

Subjects
Swine -- Physiological aspects, Swine -- Food and nutrition, Fat cells -- Properties, Fat cells -- Influence, Hormones -- Research, Animal development -- Research, Fasting -- Influence, Hormone receptors -- Properties, Zoology and wildlife conservation
Abstract

Adiponectin is an adipocyte-derived hormone that can improve insulin sensitivity. Its functions in regulating glucose utilization and fatty acid metabolism in mammals are mediated by 2 subtypes of adiponectin receptors (AdipoR1 and AdipoR2). This study was conducted to determine the effect of fasting on the expression of adiponectin and its receptors. The expression of adiponectin was not affected in s.c. adipose tissue, but adiponectin expression increased in visceral adipose tissue after fasting. In contrast, expression of both AdipoR mRNA was increased in the liver and s.c. adipose tissue of 24-h-fasted pigs compared with fed pigs, but the mRNA in muscle and visceral adipose tissue was not affected by fasting. A third putative adiponectin receptor, T-cadherin, was cloned and the mRNA expression was determined. T-Cadherin has been recognized to act as a vascular adiponectin receptor in vascular endothelial and smooth muscle cells. Our data showed that the expression of T-cadherin was decreased in the muscle of fasted pigs, suggesting that the expression of T-cadherin can be regulated by feeding status. In summary, in young pigs, adiponectin mRNA was up-regulated by fasting in visceral, but not s.c., adipose tissue, whereas AdipoR1 and AdipoR2 mRNA were increased in s.c., but not visceral, adipose tissue. The adiponectin receptor, T-cadherin, was expressed in s.c. and visceral adipose tissue and in muscle, but only muscle mRNA expression was decreased by fasting. Key words: adiponectin, adiponectin receptor, pig, T-cadherin

Published
2008

34. The effect of dietary docosahexaenoic acid on the expression of porcine lipid metabolism-related genes

Author

Hsu, J.M., Wang, P.H., Liu, B.H., and Ding, S.T.

Subjects
Genetic research -- Physiological aspects, Gene expression -- Physiological aspects, Zoology and wildlife conservation
Abstract

To study the effect of dietary docosahexaenoic acid (DHA) on the expression of adipocyte determination and differentiation-dependent factor 1 (ADD1) mRNA in pig tissues, weaned, crossbred pigs (30 d of age) were fed either 2% (as-fed basis) tallow or DHA oil for 18 d. Body weight of the pigs was not affected by different dietary fatty acid (FA) compositions. The plasma and liver FA composition reflected the composition of the diet. The adipose tissue and skeletal muscle FA composition only partially reflected the diet, indicating either a slower FA turnover or that a greater proportion of the FA in these tissues is from endogenous FA synthesis. The ADD1 is an important transcription factor that modulates transcription of FA synthase to regulate the endogenous FA synthesis in the liver and adipose tissue. The ADD1 mRNA was decreased (P < 0.05) in the liver of DHA-treated pigs compared with that of the tallow-treated pigs. The diets did not have an effect on the ADD1 mRNA in pig adipose tissue. The ADD1 transcript was not detected in pig skeletal muscle. These results indicate that significant enrichment of liver DHA content inhibits the expression of ADD1 mRNA. Such an effect is similar to that reported in porcine differentiating adipocytes cultured with DHA. The liver and muscle acyl CoA oxidase mRNA concentration was increased (P < 0.05) by DHA oil treatment, suggesting that DHA treatment may increase peroxisomal fatty acid oxidation in these two tissues. Our present observations demonstrate that dietary DHA enrichment not only affects tissue DHA concentration but also mildly modifies the expression of genes related to fatty acid metabolism in the porcine liver and skeletal muscle. Key Words: Acyl-CoA Oxidase, Adipocyte Determination and Differentiation-Dependent Factor 1, Docosahexaenoic Acid, Fatty Acid Synthase, Pigs

Published
2004

36. GOG 3016/ENGOT-cx9: An open-label, multi-national, randomized, phase III trial of cemiplimab, an anti-programmed death (PD)-1, versus investigator's choice (IC) chemotherapy in ≥2 line recurrent or metastatic cervical cancer

Author

Tewari, K.S., primary, Monk, B.J., additional, Oaknin, A., additional, Lheureux, S., additional, Zagouri, F., additional, Rischin, D., additional, Hasegawa, K., additional, Kim, Y.M., additional, Pereira, R., additional, Sadozye, A., additional, Wang, P.H., additional, Lisyanskaya, A., additional, Samouëlian, V., additional, Kim, H.S., additional, Zambrano, M., additional, Yi, S., additional, Li, J., additional, Feng, M., additional, Mathias, M., additional, Fury, M.G., additional, Lowy, I., additional, and Vergote, I., additional

Published
2020
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40. Weighted low-rank approximation of general complex matrices and its application in the design of 2-D digital filters

Author

Lu, W.S., Pei, S.-C., and Wang, P.H.

Subjects
Digital filters -- Research, Electric circuits, Linear -- Research, Business, Computers and office automation industries, Electronics, Electronics and electrical industries
Abstract

In this brief we present a method for the weighted low-rank approximation of general complex matrices along with an algorithmic development for its computation. The method developed can be viewed as an extension of the conventional singular value decomposition to include a nontrivial weighting matrix in the approximation error measure. It is shown that the optimal rank-K weighted approximation can be achieved by computing K generalized Schmidt pairs and an iterative algorithm is presented to compute them. Application of the proposed algorithm to the design of FIR two-dimensional (2-D) digital filters is described to demonstrate the usefulness of the algorithm proposed. Index Terms - 2-D digital filters, singular value decomposition.

Published
1997

47. Culturing disc endplates

Author

Potier, E., Wang, P.H., Ito, K., and Orthopaedic Biomechanics

Subjects
ComputingMilieux_LEGALASPECTSOFCOMPUTING
Published
2009

48. Green tea drinking, high tea temperature and esophageal cancer in high- and low-risk areas of Jiangsu Province, China: a population-based case-control study.

Author

Wu, M., Liu, A.M., Kampman, E., Zhang, Z.F., Veer, P. van 't, Wu, D.L., Wang, P.H., Yang, J., Qin, Y., Mu, L.N., Kok, F.J., Zhao, J.K., Wu, M., Liu, A.M., Kampman, E., Zhang, Z.F., Veer, P. van 't, Wu, D.L., Wang, P.H., Yang, J., Qin, Y., Mu, L.N., Kok, F.J., and Zhao, J.K.

Abstract

Contains fulltext : 80598.pdf (publisher's version ) (Closed access), Epidemiological studies suggested drinking green tea is inversely associated with esophageal cancer but results remain inconclusive. Moreover, inconsistent observations found high temperature drinks are associated with esophageal cancer. A population-based case-control study was conducted in a high-risk area (Dafeng) and a low-risk area (Ganyu) of esophageal cancer in Jiangsu province China from 2003 to 2007. It aimed to explore green tea drinking and tea temperature with the risk of esophageal cancer, and to compare the difference between different risk regions. Using identical protocols, 1,520 cases and 3,879 healthy controls were recruited as study subjects in 2 regions. Detailed information was collected to assess green tea drinking habits. Unconditional logistic regression was used to obtain OR and 95% CI. Results showed that ever drinking green tea elevated OR in both counties (Dafeng OR = 1.2, 95% CI = 0.9-1.5; Ganyu: OR = 1.9, 95% CI = 1.4-2.4). Drinking tea at high temperature was found to increase cancer risk in both areas (Dafeng: OR = 1.9, 95% CI = 1.2-2.9; Ganyu OR = 3.1 95% CI = 2.2-4.3). However, after further adjustment for tea temperature, ever drinking tea was not related to cancer in either county (Dafeng: OR = 1.0, 95% CI = 0.7-1.3; Ganyu: OR = 1.3, 95% CI = 0.9-1.7). For dose-response relationships, we observed positive relationship with monthly consumption of tea (p for trend = 0.067) and tea concentration (p for trend = 0.006) after further adjustment for tea temperature. In conclusion, green tea drinking was not inversely associated with esophageal cancer in this study. However, drinking tea at high temperatures significantly increased esophageal cancer risk. There was no obvious difference of green tea drinking between low- and high-risk areas.

Published
2009

50. Retrieving aerosol optical properties from moments of the particle size distributrion

Author

Yue, G.K., Lu, J., Mohnen, V.A., Wang, P.H., Saxena, Y.K., Anderson, J., and Publica

Abstract

The randomized minimization search technique (RMST) is a powerful tool for retrieving aerosol size distribution from a set of aerosol extinction measurements. This technique is now extended to retrieve aerosol optical properties from a known moment sequence. The new usage of the RMST is demonstrated by using particle size distributions obtained from fits to in situ measurements conducted in the troposphere and stratosphere. Good agreements (with differences less than 5 per cent) between some aerosol optical properties calculated from the moments and those calculated directly from the particle size distributions are obtained. Our results illustrate that the RMST can be applied to parameterize particle optical properties from the lower-order moments of an aerosol size distribution.

Published
1997

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