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Shasha Dai on Muck Rack

Shasha Dai

New York
As seen in: LinkedIn, The Wall Street Journal, Wiley Online Library, MarketWatch, MDPI, Inc., Nasdaq, Dow Jones Newswires, Financial News, Private Equity News and
Product Marketing Executive @Octus. Formerly @WSJ, @Reuters. Alumna @columbiajourn. Translator and editor of "My Autobiography" (Kerosene Press, Aug. 24).
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Articles

Modification, Structure Identification, and Biological Activities of Phosphorylated Polysaccharides: A Review

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By Yuchao Gao, Zechao Liu, Shasha Dai, Jianduo Zhao
| Wiley Online Library Verified
Conflict of Interest The authors declare that the work has not been published previously. No conflict of interest exits in the submission of this manuscript and the manuscript is approved by all authors for publication. References 1, , , Foods 2021, 10, 3004. 2, , , , , Food. Res. Int. 2021, 150, 110761. 3, , Biomed. Pharmacother. 2022, 150, 113015. 4, , , , , , , Front. Nutr. 2021, 8, 769181. 5, , , , , , Food Funct. 2020, 11, 163. 6, , , , , , , , , Int. J. Biol. Macromol. 2019, 136, 579. 7, , Trends Food Sci.
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Modification, Structural Characterizations, and Biological Activities of Sulfated Polysaccharides: A Review

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By Zitong Hao, Shasha Dai, Jiaqi Tan, Yuchao Gao
| Wiley Online Library Verified
Conflict of Interest The authors declare no conflict of interest. References 1, , , , , , , , , , , , Crit. Rev. Food Sci. 2016, 56, S60. 2, , Prog. Polym. Sci. 2017, 74, 78. 3, , , , , Food Hydrocolloid 2016, 60, 148. 4, , , , Food Res. Int. 2021, 149, 110706. 5, , , , , Postharvest Biol. Tech. 2017, 131, 31. 6, , , , , , , , , , Food Hydrocolloid 2021, 121, 107016. 7, , , , Carbohyd. Polym. 2018, 183, 91. 8, , Int. J. Biol. Macromol. 2018, 120, 1646. 9, , Int. J. Biol. Macromol. 2018, 115, 811. 10, , Int. J. Biol. Macromol.
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Filtering in Triplet Markov Chain Model in the Presence of Non-Gaussian Noise with Application to Target Tracking

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By Guanghua Zhang, Xiqian Zhang, Shasha Dai, Linghao Zeng
| MDPI
1. Introduction Target tracking plays an important role in both civilian and military fields [1,2,3,4]. It aims to estimate the kinematic state (e.g., position, velocity and acceleration) of a target in real time [5]. Target tracking problems are usually solved in the framework of state-space models, and the state estimation can be obtained by solving the filtering problem of a state-space model. The hidden Markov chain (HMC) model is the most commonly used state-space model for target tracking.
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