Tang Hanedanlığında Bir Türk: An Lushan (安禄山) by Şükrü Aktaş

Subjects: “…an lushan…”
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Microbial Corrosion in Cooling Water of Lushan Shahid Beheshti Power Plant by Majid Ghahraman Afshar, Hossein Ghaseminejad, Mohsen Esmaeilpour

“…In this work, the aim is to collect information on the microbial corrosion in the cooling water of Shahid Beheshti power plant of Lushan and to provide several solutions to reduce the occurrence of microbial corrosion Therefore, physicochemical properties, microbial tests and ion measurement are conducted in order to investigate the parameters affecting this phenomenon for the cooling tower of Shahid Beheshti power plant of Lushan. …”
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GIS-based Assessment of Vulnerability to Landslide Hazards in Lushan Earthquake-stricken Areas by Mingtao Ding, Cheng Miao

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Using Ecological Footprint Analysis to Evaluate Sustainable Development in Lushan County, China by Huihui Yang, Shuiyu Yan, Na An, Qiang Yao

“…The results show that from 2009 to 2022, the per capita ecological carbon footprint in Lushan County decreased by 48%, and the ecological carrying capacity declined by 9%. …”
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Characterization of Key Odorants in Lushan Yunwu Tea in Response to Intercropping with Flowering Cherry by Yinxiang Gao, Zhiyong Lei, Jigang Huang, Yongming Sun, Shuang Liu, Liping Yao, Jiaxin Liu, Wenxin Liu, Yanan Liu, Yan Chen

Subjects: “…Lushan Yunwu tea…”
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Source characteristics of the aftershocks of the Wenchuan and Lushan earthquake sequences in the Longmen-Shan fault zone by Zibo Wang, Ruifeng Liu, Wei Liu

“…The stress drop level of the Lushan earthquake sequence is higher than the mean value of intraplate earthquakes, while the Wenchuan earthquake sequence is typical of intraplate earthquakes. …”
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Geological Formation Mechanism and Thermal Resources Assessment of Geothermal Spring in Lushan County, China by Qing Zhang, Zhaohui Bin, Yongji Xu, Shaohe Luo, Li Zhao, Jiazhong Qian

“…The research results will be beneficial for the development and utilization of Lushan hot springs. At the same time, it also provides reference for more geothermal research.…”
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Gravity field changes reveal deep mass transfer before and after the 2013 Lushan earthquake by Linhai Wang, Shi Chen, Jiancang Zhuang, Bei Zhang, Wen Shi, Jinling Yang, Weimin Xu

“…Abstract The 2013 M S 7.0 Lushan earthquake, Sichuan, China, occurred on a blind thrust fault in the southern Longmenshan fault belt. …”
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Insights into the Landslides Triggered by the 2022 Lushan Ms 6.1 Earthquake: Spatial Distribution and Controls by Bo Zhao, Weile Li, Lijun Su, Yunsheng Wang, Haochen Wu

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Are There One or More Geophysical Coupling Mechanisms before Earthquakes? The Case Study of Lushan (China) 2013 by Yiqun Zhang, Ting Wang, Wenqi Chen, Kaiguang Zhu, Dedalo Marchetti, Yuqi Cheng, Mengxuan Fan, Siyu Wang, Jiami Wen, Donghua Zhang, Hanshuo Zhang

Subjects: “…2013 Lushan earthquake…”
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Electrical Structure of Southwestern Longmenshan Fault Zone: Insights into Seismogenic Structure of 2013 and 2022 Lushan Earthquakes by Yan Zhan, Xiangyu Sun, Guoze Zhao, Lingqiang Zhao, Xiaoping Yang, Haibo Yang, Dawei Jiang, Xiaoyu Lou

Subjects: “…Lushan earthquake…”
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Tracing biomimicry from the ancient Chinese wisdom: a case study at Lushan UNESCO Global Geopark by Lyuxin Xiong, Mazlan Mohd Tahir, Tanot Anak Unjah, Chao Xu

“…This paper uses the Lushan UNESCO Global Geopark as a sample to explore the application of biomimicry based on ancient Chinese methods. …”
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NIR Spectrometric Approach for Geographical Origin Identification and Taste Related Compounds Content Prediction of Lushan Yunwu Tea by Xiaoli Yan, Yujie Xie, Jianhua Chen, Tongji Yuan, Tuo Leng, Yi Chen, Jianhua Xie, Qiang Yu

Subjects: “…Lushan Yunwu tea…”
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Characterized source model of the 2013 Lushan earthquake (M w 6.6) by the empirical Green’s function method by Hao Wu, Kojiro Irikura, Ken Miyakoshi, Kazuhiro Somei

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Mutual impact of true triaxial stress, borehole orientation and bedding inclination on laboratory hydraulic fracturing of Lushan shale by Yongfa Zhang, Anfa Long, Yu Zhao, Arno Zang, Chaolin Wang

“…In this study, we present a series of true triaxial hydraulic fracturing experiments conducted on Lushan shale to investigate the interplay of internal factors (bedding, natural fractures and in situ stress) and external factors (wellbore orientation) on the growth process of fracture networks in cubic specimens of 200 mm in length. …”
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Rapid report of June 1, 2022 MW 5.9 Lushan earthquake, China with geodetic and teleseismic data by Lei Xie, Nan Fang, Xiaoge Liu, Wenbin Xu

Subjects: “…Lushan earthquake…”
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Brief Communication: Landslides triggered by the <i>M</i>_s = 7.0 Lushan earthquake, China by X. L. Chen, L. Yu, M. M. Wang, C. X. Lin, C. G. Liu, J. Y. Li

“…Also, this study reveals that the empirical relationship between distance and seismic magnitude is more suitable for estimating the landslide concentration area during the Lushan earthquake compared to other methods.…”
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Human–Land Coupling Relationship in Lushan National Park and Its Surrounding Areas: From an Integrated Ecological and Social Perspective by Zihang Zhou, Haotian Li, Jie Li, Yawen Lu, Chi Gao, Diechuan Yang

“…On this basis, we analyzed the spatial coupling characteristics of these indices from 2000 to 2020, preliminarily assessed the human–land relationship in Lushan National Park and its surrounding areas (LNPSA), and then explored the driving factors of the coupling relationship. …”
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Pre-Earthquake and Coseismic Ionosphere Disturbances of the Mw 6.6 Lushan Earthquake on 20 April 2013 Monitored by CMONOC by Kunpeng Shi, Xin Liu, Jinyun Guo, Lu Liu, Xinzhao You, Fangjian Wang

Subjects: “…Lushan earthquake…”
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Predicting deep-seated landslide displacement on Taiwan's Lushan through the integration of convolutional neural networks and the Age of Exploration-Inspired Optimizer by J.-S. Chou, H.-M. Nguyen, H.-P. Phan, K.-L. Wang

“…Our approach demonstrates exceptional forecasting capabilities by utilizing 8 years of comprehensive data – including displacement, groundwater levels, and meteorological information from the Lushan (mountainous) region in Taiwan. The AEIO–MobileNet model precisely predicts imminent deep-seated landslide displacement with a mean absolute percentage error (MAPE) of 2.81 %. …”
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