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John Wohlgemuth, Allison C. Waters, Joohi Jimenez-Shahed
2026-07-08T16:46:01Z
置信度 0.70
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R. Deiva Nayagam, D. Selvathi, S. Shalini
2026-03-23T04:43:04Z
置信度 0.70
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2022-12-01T18:12:48Z
置信度 0.70
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Abstract Deep artificial neural networks have become a good alternative to classical forecasting methods in solving forecasting problems. Popular deep neural networks classically use additive aggregation functions in their cell structures. It is available in t…
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Erol Egrioglu, Eren Bas
2023-05-12T03:49:09Z
置信度 0.70
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Denys Kataiev, Janusz Kacprzyk, Artur Zaporozhets
2026-07-23T11:30:46Z
置信度 0.70
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Fernando Corinto, Alon Ascoli, Valentina Lanza, Marco Gilli
2011-10-06T17:24:17Z
置信度 0.70
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Muhammad Shahan Ibad, Omar Bin Samin, Adnan Amin, Feras Al-Obeidat 等
2026-04-10T15:19:58Z
置信度 0.70
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Jingyuan Shao, Huabao Chen, Qiankun Li, Xiang Huang 等
2025-10-18T14:49:11Z
置信度 0.70
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Denys Kataiev, Janusz Kacprzyk, Artur Zaporozhets
2026-07-23T11:30:36Z
置信度 0.70
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Yuheng Zong, Huaiping Jin, Hao Fang, Chai Hu 等
2025-12-22T16:57:00Z
置信度 0.70
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2009-09-04T20:24:49Z
置信度 0.70
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Jiemei Zhao
2020-06-05T05:02:21Z
置信度 0.70
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Kunpeng Wang, Yuanping Zhu
2026-03-14T01:17:54Z
置信度 0.70
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Alexander W. Denton, Michael P. Riddick, Isaac E. Weintraub, Donald L. Kunz
2026-02-03T07:51:02Z
置信度 0.70
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Hongkai Zhang, Haoming Yuan
2026-05-09T10:50:37Z
置信度 0.70
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Xin Wang, Huaqing Li, Tingwen Huang
2026-01-02T02:56:51Z
置信度 0.70
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Qianhui XU, Ke NIU, Jun LI, Shunzhe Zhu 等
2026-05-11T06:31:01Z
置信度 0.70
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2018-03-24T08:06:28Z
置信度 0.70
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Abstract Lewy body diseases, including Parkinson’s disease and dementia with Lewy bodies, are marked by neuronal α‑synuclein aggregation, motor parkinsonism, cognitive impairment and diverse non‑motor symptoms including communication impairments. Compared to o…
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Daniel Carbol, Lubomira Novakova, Patricia Klobusiakova, Irena Rektorova
2026-03-07T08:02:53Z
置信度 0.70
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Lina Qiu, Minjin Wu, You Hu, Baiqiang Long 等
2026-04-17T15:45:28Z
置信度 0.70
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Peilin Lai, Yang Zhang, Weizhao He, Yu Zeng 等
2026-05-22T15:34:19Z
置信度 0.70
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Rebecca Pattichis, Sebastian Janampa, Constantinos S. Pattichis, Marios S. Pattichis
2026-06-11T19:58:56Z
置信度 0.70
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2007-10-28T00:36:19Z
置信度 0.70
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Zhi-Jun Li, Yi-Cheng Zeng
2013-04-05T19:29:06Z
置信度 0.70
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Karthikeyini S, Revathi. B.S, Ravikumar M, Abishek S 等
2026-05-08T19:37:43Z
置信度 0.70
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Siddharth Roheda, Aniruddha Bala, Rohit Chowdhury, Rohan Jaiswal
2026-04-21T21:25:57Z
置信度 0.70
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Pragya Khanna, Anil Kumar Vuppala
2026-04-21T21:25:28Z
置信度 0.70
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Navneeth Srinivasan, Suo Yang
2026-02-03T08:40:23Z
置信度 0.70
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Yu Li, Xudong Jia, Yu Sun, Yan Cui 等
2025-12-09T16:15:18Z
置信度 0.70
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Titorea veera jothi Archunan Titorea veera jothi Archunan
2026-07-18T14:15:00Z
置信度 0.70
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Abstract The aim of this paper was to analyze the primary and secondary order of influencing factors and to establish a BP neural network prediction model with different hydraulic performance indicators. Particle swarm optimization algorithm was used to test f…
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Zakaria Issaka
2026-04-17T10:42:12Z
置信度 0.70
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2026-05-28T21:09:57Z
置信度 0.70
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2026-05-28T21:09:57Z
置信度 0.70
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Maciej Krzywda, Szymon Łukasik, Amir Gandomi
2026-08-13T20:45:38Z
置信度 0.70
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Cristian Sestito, Weijie Huang, Shady Agwa, Themis Prodromakis
2024-09-17T18:47:00Z
置信度 0.70
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Abdelrahman A. Elmaradny, Aras Vakilimafakheri, Abdelrahman A. Abdelrazek, Haithem E. Taha
2026-01-29T07:09:51Z
置信度 0.70
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Jiale Li, Zhihang Liu, Sean Longyu Ma, Chiu-Wing Sham 等
2025-11-14T18:46:15Z
置信度 0.70
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Yijie Dong, Jianmin Wang
2022-07-29T19:38:39Z
置信度 0.70
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Kai Zhong, Song Zhu, Qiqi Yang
2016-01-21T18:11:03Z
置信度 0.70
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Huiyi Liu
2026-07-09T20:25:48Z
置信度 0.70
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Weiran Cai, Ronald Tetzlaff
2013-12-18T10:27:01Z
置信度 0.70
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Phu Pham
2026-05-25T11:19:07Z
置信度 0.70
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Roger Nick Anaedevha, Alexander Gennadievich Trofimov
2026-08-12T19:19:27Z
置信度 0.70
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Coryn Bailer-Jones, David MacKay, Philip Withers
2002-08-24T22:35:54Z
置信度 0.70
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This study aimed to develop and empirically validate an integrated model for continuous smart government service usage. This model integrates constructs from the unified theory of acceptance and use of the technology framework with the expectation-confirmation…
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Nuseiba Altarawneh, Omar Hujran
2025-11-06T13:50:46Z
置信度 0.70
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Fatemeh Sogandi, Mahdyeh Shiri
2026-03-10T20:15:46Z
置信度 0.70
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Ching-Yuan Chen, Krishnendu Chakrabarty
2021-08-24T22:11:46Z
置信度 0.70
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Bin Hu, Zhi-Hong Guan, Zhi-Wei Liu, Xiao-Wei Jiang
2017-07-25T16:10:21Z
置信度 0.70
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Kyota Hattori, Tomohiro Korikawa, Chikako Takasaki
2026-07-17T19:45:58Z
置信度 0.70
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Igor Livshin
2019-04-12T15:05:25Z
置信度 0.70
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Leon Chua
2013-12-18T10:27:01Z
置信度 0.70
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2022-12-01T06:22:51Z
置信度 0.70
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Jianbo Nie, Hao Feng, Zhou Lan, Kun Wang 等
2026-06-17T07:16:28Z
置信度 0.70
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Environmental, Social, and Governance (ESG) risks increasingly propagate across interconnected supply chains, yet conventional ESG assessment methods remain largely reliant on firm-level disclosures and static ESG ratings that often overlook indirect risk tran…
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Michael A. Aruwaji, Ferina Marimuthu
2026-08-10T09:24:53Z
置信度 0.70
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2022-12-01T18:14:38Z
置信度 0.70
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crossref
2022-12-01T03:35:42Z
置信度 0.70
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Wen-Ya Ma, Zheng-Hua He, Bo Wen
2025-10-23T16:56:46Z
置信度 0.70
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Introduction The ever-increasing complexity of biochemical systems, alongside the rapid growth of pharmaceutical and biomedical data, underscores the urgent need for intelligent, scalable, and interpretable computational models. These models must be capable of…
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Zhongyi Chai, Jing Wang, Huili Du
2026-06-11T22:10:25Z
置信度 0.70
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crossref
2026-05-27T14:48:51Z
置信度 0.70
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Background: The computational identification of drug-target interaction (DTI) is pivotal in drug discovery and chemical genomics. Current network-based approaches model DTI as a link prediction problem utilizing bipartite graphs. However, simplistic representa…
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Ping Zhang, Yongbin Zeng
2026-06-01T07:14:56Z
置信度 0.70
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R. Sathishkumar, Vijayalakshmi R, I. Govindharaj
2026-07-07T19:42:48Z
置信度 0.70
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Ning Li, Wei Xing Zheng
2020-04-23T19:54:14Z
置信度 0.70
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Jiejie Chen, Boshan Chen, Zhigang Zeng
2020-09-23T00:19:28Z
置信度 0.70
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Abstract The rapid development of brain-like neural networks and secure data transmission technologies has placed greater demands on highly complex neural network systems and highly secure encryption methods. To this end, the paper proposes a novel high-dimens…
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Shuang 双 Zhao 赵, Yunzhen 云贞 Zhang 张, Xiangjun 湘军 Chen 陈, Bin 彬 Gao 高 等
2025-11-11T10:47:43Z
置信度 0.70
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Climate change is altering the global hydrological cycle and, when combined with human interventions such as reservoir operations, the river flow regime is further modified. Given the strong spatial heterogeneity of these impacts and the basin-specific nature …
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Helena Barreiro-Fonta, Diego Fernández-Nóvoa
2026-03-14T04:55:57Z
置信度 0.70
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Jinze Wu, Zhi Li, Zhiyun Lin, Hui Cheng 等
2026-04-21T21:24:02Z
置信度 0.70
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Jiaqi Wang, Alexander Serb, Shiwei Wang, Themistoklis Prodromakis
2022-11-11T20:38:08Z
置信度 0.70
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crossref
Ling Chen, Chuandong Li, Tingwen Huang, Xing He 等
2014-09-10T10:30:33Z
置信度 0.70
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Govinda B. Sambare, Mahesh P. Wankhade, Geeta Navale, Snehlata Kapil Wankhade 等
2026-06-08T09:53:37Z
置信度 0.70
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crossref
Ashutosh Bhushan, Navleen Kaur, Monisha, Mini Srivastava 等
2026-04-13T19:34:56Z
置信度 0.70
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crossref
Ouku Bhulakshmi, Nagari Kavya Sree, M. V. Subramanyam, Farooq Sunar Mahammad 等
2026-08-14T19:36:42Z
置信度 0.70
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While the human visual system is known to be highly sensitive to global and configural shape information, deep neural networks models (DNNs) trained on ImageNet seem to favour local shape features.  However, a more exact understanding of these differe…
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Nicholas Baker, John Wilder, James H. Elder
2026-04-27T23:50:08Z
置信度 0.70
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The emergence of sixth-generation (6G) telecommunications at Terahertz (THz) frequencies necessitates advanced computational electromagnetic solvers capable of modeling anomalous dispersion in complex nanomaterials. Traditional grid-based methods, such as Fini…
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Basem Ajarmah, Iyad Odeh
2026-03-10T10:51:21Z
置信度 0.70
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Abstract Physics-informed neural networks (PINNs) provide a promising framework for solving partial differential equations while embedding the underlying physical laws directly into the learning process. This study presents a PINN-based framework for modeling …
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Sonal Ankush Chibire, Jenn-Terng Gau, Bo Zhang
2026-07-14T16:07:33Z
置信度 0.70
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Vikash Panthi, Nikita Kashyap, Manoj Gupta
2026-05-15T03:00:58Z
置信度 0.70
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Islem Touati, Abderraouf Boussif
2026-08-07T19:16:20Z
置信度 0.70
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crossref
Zhiqiang Ning, Jian Zhang, Qiguo Xiao, Zhiqiang Li
2026-06-30T20:33:11Z
置信度 0.70
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ABSTRACT Hyperspectral imaging (HSI) has emerged as a promising technique for microplastic detection through analysis of reflectance variations across multiple wavelengths. Traditional approaches have focused primarily on isolated microplastic particles, requi…
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Nikhita Sai Nayani, Ran Yang, Yue Sun, Lihong Yang 等
2026-01-22T07:02:28Z
置信度 0.70
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crossref
C Fassnacht, A Zippelius
2002-08-24T22:35:54Z
置信度 0.70
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crossref
G Tambouratzis, D Tambouratzis
2002-08-25T02:35:54Z
置信度 0.70
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To solve the network slicing placement problem, the methods based on CNN/RNN were inadequate in handling the randomness of fluctuating channel quality and bandwidth needs for each network slice. While the Monte Carlo Tree Search (MCTS) methodology effectively …
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Liang-Chun Chen
2025-03-16T09:40:10Z
置信度 0.70
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crossref
2025-10-14T17:48:35Z
置信度 0.70
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crossref
Nagarjuna Telagam, Nehru Kandasamy, D. Ajitha
2025-05-24T10:42:07Z
置信度 0.70
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We present a machine learning-based approach for wavefront aberration correction using a single intensity image. Our approach utilizes a trained bias, implemented as a single optical element, to effectively resolve ambiguity issues.
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Sina Moayed Baharlou, Muhammad Waleed Khalid, Alexander V. Sergienko, Abdoulaye Ndao
2025-09-18T17:02:18Z
置信度 0.70
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2025-08-25T21:09:14Z
置信度 0.70
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crossref
2025-10-21T15:02:49Z
置信度 0.70
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crossref
Alexander Yurievich Fonarev
2025-07-05T08:15:56Z
置信度 0.70
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crossref
Sukru Omur, Nilay Ork Efendioglu, Mahmut Sinecen
2025-05-21T17:40:28Z
置信度 0.70
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crossref
2025-01-25T16:09:21Z
置信度 0.70
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crossref
2025-03-31T17:20:23Z
置信度 0.70
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G. Bhavani, C. Jeyalakshmi
2025-03-04T16:54:31Z
置信度 0.70
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İrem Aksoy, Fatma Patlar Akbulut
2025-12-08T18:38:51Z
置信度 0.70
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Ahren Jun Sukirya, Gredion Prajena
2026-04-13T19:35:30Z
置信度 0.70
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Dunhui Xiao, Xinyu Pan, Lihua Wang
2025-07-23T06:08:31Z
置信度 0.70
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Yuxin Fan, Zirui Wang, Qiao Hu, Zhiqiang Wei
2025-04-21T13:52:26Z
置信度 0.70
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G. Donati, S. Biasi, L. Pavesi, A. Hurtado
2025-12-22T18:39:16Z
置信度 0.70
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Su Ziteng
2025-11-04T07:16:37Z
置信度 0.70
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Yunxia Fu
2025-04-24T17:02:57Z
置信度 0.70
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Zhuoxin Lei
2025-07-21T02:33:21Z
置信度 0.70
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R. Stanley Williams
2019-11-12T17:03:43Z
置信度 0.70