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Article ; Online: H

Huo, Shaohu / Xie, Qianhui / Zhang, Min / Jiang, Zitong / Fu, Ling / Li, Wenhong / Bian, Chenrong / Wu, Kaile / Zhu, Yulin / Nie, Xuan / Ding, Shenggang

Journal of materials chemistry. B

2023 Volume 11, Issue 25, Page(s) 5817–5829

Abstract: Antibiotic tolerance is implicated in difficult-to-treat infections and the development and spread of antibiotic resistance. The high storage capacities and excellent biocompatibilities of UiO-66-based metal-organic frameworks (MOFs) have made them ... ...

Abstract	Antibiotic tolerance is implicated in difficult-to-treat infections and the development and spread of antibiotic resistance. The high storage capacities and excellent biocompatibilities of UiO-66-based metal-organic frameworks (MOFs) have made them emerging candidates as drug-delivery vectors. In view of hydrogen sulfide (H
MeSH term(s)	Metal-Organic Frameworks/pharmacology ; Escherichia coli ; Organometallic Compounds ; Anti-Bacterial Agents/pharmacology
Chemical Substances	UiO-66 ; Metal-Organic Frameworks ; Organometallic Compounds ; Anti-Bacterial Agents
Language	English
Publishing date	2023-06-28
Publishing country	England
Document type	Journal Article ; Research Support, Non-U.S. Gov't
ZDB-ID	2702241-9
ISSN	2050-7518 ; 2050-750X
ISSN (online)	2050-7518
ISSN	2050-750X
DOI	10.1039/d3tb00552f
Database	MEDical Literature Analysis and Retrieval System OnLINE

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Book ; Online: Power Cyber-Physical System Risk Area Prediction Using Dependent Markov Chain and Improved Grey Wolf Optimization

Qu, Zhaoyang / Xie, Qianhui / Liu, Yuqing / Li, Yang / Wang, Lei / Xu, Pengcheng / Zhou, Yuguang / Sun, Jian / Xue, Kai / Cui, Mingshi

2020

Abstract: Existing power cyber-physical system (CPS) risk prediction results are inaccurate as they fail to reflect the actual physical characteristics of the components and the specific operational status. A new method based on dependent Markov chain for power ... ...

Abstract	Existing power cyber-physical system (CPS) risk prediction results are inaccurate as they fail to reflect the actual physical characteristics of the components and the specific operational status. A new method based on dependent Markov chain for power CPS risk area prediction is proposed in this paper. The load and constraints of the non-uniform power CPS coupling network are first characterized, and can be utilized as a node state judgment standard. Considering the component node isomerism and interdependence between the coupled networks, a power CPS risk regional prediction model based on dependent Markov chain is then constructed. A cross-adaptive gray wolf optimization algorithm improved by adaptive position adjustment strategy and cross-optimal solution strategy is subsequently developed to optimize the prediction model. Simulation results using the IEEE 39-BA 110 test system verify the effectiveness and superiority of the proposed method. Comment: Accepted by IEEE Access
Keywords	Computer Science - Networking and Internet Architecture ; Electrical Engineering and Systems Science - Signal Processing
Subject code	620
Publishing date	2020-04-29
Publishing country	us
Document type	Book ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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