Publications by authors named "K Y Ding"

The rapid development of the global chemical industry has led to widespread groundwater contamination, with frequent pollution incidents posing severe threats to water safety. However, there has been insufficient assessment of the health risks posed by chlorinated hydrocarbon contamination in groundwater around chemical industrial parks. This study evaluates the chlorinated hydrocarbon contamination in groundwater at a chemical park and conducts a multi-pathway health risk assessment, identifying the key risk pollutants.

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LiCoO2 batteries for 3C electronics demand high charging voltage and wide operating temperature range, which are virtually impossible for existing electrolytes due to aggravated interfacial parasitic reactions and sluggish kinetics. Herein, we report an electrolyte design strategy based on a partially fluorinated ester solvent (i.e.

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Background: Type 2 diabetes mellitus (T2DM) development is closely linked to microbiota, influenced by geography, ethnicity, gender, and age. While the relationship between oral microbiota and T2DM has been explored, specific microbiota associated with T2DM in the Dai and Han populations remains unclear. This study aims to compare oral microbiota differences and identify keystone species between these populations, both with and without T2DM.

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Article Synopsis
  • Ochratoxin A (OTA) is a major global contaminant that affects food safety, and this study focuses on isolating probiotics capable of degrading OTA.
  • MM35, a newly identified strain, achieved an impressive 87.10% degradation of OTA within 48 hours and produced enzymes that contribute to this process.
  • The strain exhibits significant antibacterial properties and tolerance to harsh environments, making it a promising candidate for managing OTA contamination in food and feed industries.
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Certifying robustness against external uncertainties throughout the control process to reduce the risk of instability is very important. Most existing approaches based on adversarial learning use a fixed parameter to adjust the intensity of adversarial perturbations and design these perturbations in a greedy manner without considering future implications. However, they often lead to severe vulnerabilities when attack budgets vary dynamically or under foresighted attacks.

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