Zobrazeno 1 - 10
of 10
pro vyhledávání: '"Xiuyun Zhai"'
Autor:
Xiuyun Zhai, Mingtong Chen
Publikováno v:
High Temperature Materials and Processes. 42
The emission of blast furnace (BF) exhaust gas has been criticized by society. It is momentous to quickly predict the comprehensive coke ratio (CCR) of BF, because CCR is one of the important indicators for evaluating gas emissions, energy consumptio
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::6845e27c13db73c02f6236dac16c2647
https://doi.org/10.1515/htmp-2022-0261
https://doi.org/10.1515/htmp-2022-0261
Autor:
Xiuyun Zhai, Mingtong Chen
Publikováno v:
Nanoscale Advances; 8/21/2023, Vol. 5 Issue 16, p4065-4073, 9p
Publikováno v:
ISIJ International. 60:2471-2476
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::923b53fe0a0da9f6e31da4b0d5399aaf
https://doi.org/10.2355/isijinternational.isijint-2020-238
https://doi.org/10.2355/isijinternational.isijint-2020-238
Autor:
Shuaiyun Gao, Jing Ye, Eric Gilson, Yiming Lu, Bo Wang, Waiian Leong, Cuicui Wang, Wei Xu, Xiuyun Zhai
Publikováno v:
Aging (Albany NY)
Targeting of PP2A suggests a close link to tau-related cognitive and functional declines. However, little is known about how the expression of PP2A subunits and PP2A activity are dysregulated in the course of AD, precluding any specific targeting str
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::cf3e86a6048b33225f499d52441105d2
http://europepmc.org/articles/PMC7202491
http://europepmc.org/articles/PMC7202491
Publikováno v:
Computational Materials Science. 151:41-48
Curie temperature (Tc), the second order phase transition temperature, is also one of the important physical properties of perovskite materials. It is a meaningful work to quickly and efficiently predict Tc of new perovskite materials before doing a
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::4ec55b39597188d84db5598bd682ec01
https://doi.org/10.1016/j.commatsci.2018.04.031
https://doi.org/10.1016/j.commatsci.2018.04.031
Publikováno v:
Chemometrics and Intelligent Laboratory Systems. 177:26-34
With the rapid development of the Materials Genome Initiative (MGI), scientists and engineers are confronted with the need to conduct sophisticated data analytics in modeling the behaviors of materials. Nowadays, it is inconvenient for material resea
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::9dc550408e26554caa665148af1b05c3
https://doi.org/10.1016/j.chemolab.2018.04.004
https://doi.org/10.1016/j.chemolab.2018.04.004
Publikováno v:
Journal of Mathematical Chemistry. 56:1744-1758
The layered double hydroxides (LDHs) with high specific surface areas (SSA) are of great benefit for their practical applications as catalysts, sensors or adsorbents and so on. So, it has instructional significance to effectively predict specific sur
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::5eb5cd3c416f958aa5e6832c553d780e
https://doi.org/10.1007/s10910-018-0889-2
https://doi.org/10.1007/s10910-018-0889-2
Publikováno v:
Materials Research Bulletin. 93:123-129
A Quantitative Structure Property Relationship (QSPR) model for the basal spacing of layered double hydroxide is developed in the present work by using generic algorithms feature selection, and relevance vector machine regression. The relative error
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::af72d11d626053e94b229907d65a3284
https://doi.org/10.1016/j.materresbull.2017.03.045
https://doi.org/10.1016/j.materresbull.2017.03.045
Publikováno v:
Toxicology mechanisms and methods. 28(6)
For safely using the untested metal oxide nanoparticles (MONPs) in industrial and commercial applications, it is important to predict their potential toxicities quickly and efficiently. In this research, the quantitative structure-activity relationsh
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::2aee63fd60727f71afc4c060811c5068
https://pubmed.ncbi.nlm.nih.gov/29644916
https://pubmed.ncbi.nlm.nih.gov/29644916
Publikováno v:
2016 International Conference on Information System and Artificial Intelligence (ISAI).
Near infrared spectroscopy with support vector machine (NIR-SVM) to predict the crude protein (CP) in Alfalfa samples. The R2 of the predicted CP versus the experimental CP of the training data set is 0.983. The R2 of the independent test Data Set is
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::d1a3dc569188c5b03268302c788d770e
https://doi.org/10.1109/isai.2016.0094
https://doi.org/10.1109/isai.2016.0094