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Titlebook: Microbiota of the Human Body; Implications in Heal Andreas Schwiertz Book 2016 The Editor(s) (if applicable) and The Author(s), under exclu

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樓主: Hayes
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發(fā)表于 2025-3-25 07:06:38 | 只看該作者
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發(fā)表于 2025-3-25 08:02:35 | 只看該作者
https://doi.org/10.1007/978-3-319-31248-4Gut microbiota; Oral microbiota; Skin microbiota; Vaginal microbiota; Probiotics; Prebiotics
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發(fā)表于 2025-3-25 14:56:56 | 只看該作者
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發(fā)表于 2025-3-25 15:52:31 | 只看該作者
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發(fā)表于 2025-3-25 23:42:33 | 只看該作者
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發(fā)表于 2025-3-26 01:22:24 | 只看該作者
Andreas Schwiertz,Volker Ruscha result of this process, we obtained 25 articles: 12 in the initial search and 13 in the backward process. The literature review resulted in the collection of 21 influence metrics, 4 influence algorithms, and 8 models of influence analysis. We start by defining influence and presenting its properti
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發(fā)表于 2025-3-26 06:47:00 | 只看該作者
Alan W. Walkera result of this process, we obtained 25 articles: 12 in the initial search and 13 in the backward process. The literature review resulted in the collection of 21 influence metrics, 4 influence algorithms, and 8 models of influence analysis. We start by defining influence and presenting its properti
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發(fā)表于 2025-3-26 08:41:32 | 只看該作者
Mona Mischke,Torsten Pl?schhe fairness of a given incentive scheme. Such fairness is quantified in terms of the employee ordering with respect to a notion of employee utility, as captured through disparate key performance indicators (KPIs, e.g., work duration, work quality). Our approach uses a multi-objective formulation via
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發(fā)表于 2025-3-26 16:38:16 | 只看該作者
Nicole B. Arweiler,Lutz Netuschilhe fairness of a given incentive scheme. Such fairness is quantified in terms of the employee ordering with respect to a notion of employee utility, as captured through disparate key performance indicators (KPIs, e.g., work duration, work quality). Our approach uses a multi-objective formulation via
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發(fā)表于 2025-3-26 19:29:36 | 只看該作者
Markus Egert,Rainer Simmeringacy of 94.67%, precision of 95.00%, recall of 94.67%, and F1 Score of 94.66%. Our proposed novel model produced better results in comparison to similar studies and can be used in the agriculture industry for better decision-making for early detection and prediction of plant leaf diseases.
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