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- 저자 : Li Hongpeng (검색결과 5 건)
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( Zhaofeng Li ),( Si-qian Chen ),( Xiao Cao ),( Lin Li ),( Jie Zhu ),( Hongpeng Yu ) 한국미생물생명공학회(구 한국산업미생물학회) 2021 Journal of microbiology and biotechnology Vol.31 No.3
Bacterial cellulose (BC) is widely used in the food industry for products such as nata de coco. The mechanical properties of BC hydrogels, including stiffness and viscoelasticity, are determined by the hydrated fibril network. Generally, Komagataeibacter bacteria produce gluconic acids in a glucose medium, which may affect the pH, structure and mechanical properties of BC. In this work, the effect of pH buffer on the yields of Komagataeibacter hansenii strain ATCC 53582 was studied. The bacterium in a phosphate and phthalate buffer with low ionic strength produced a good BC yield (5.16 and 4.63 g/l respectively), but there was a substantial reduction in pH due to the accumulation of gluconic acid. However, the addition of gluconic acid enhanced the polymer density and mechanical properties of BC hydrogels. The effect was similar to that of the bacteria using glycerol in another carbon metabolism circuit, which provided good pH stability and a higher conversion rate of carbon. This study may broaden the understanding of how carbon sources affect BC biosynthesis.
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Research on a New Method based on Improved ACO Algorithm and SVM Model for Data Classification
Hongpeng Zhu,Xiaohong Li 보안공학연구지원센터 2016 International Journal of Database Theory and Appli Vol.9 No.1
Because the properties of data are becoming more and more complex, the traditional data classification is difficult to realize the data classification according to the complexity characteristic of the data. Support vector machine is a machine learning method with the good generalization ability and prediction accuracy. So an improved ant colony optimization(ACO) algorithm is introduced into the support vector machine(SVM) model in order to propose a new data classification(ERURACO-SVM) method. In the ERURACO-SVM method, the pheromone evaporation rate strategy and pheromone updating rule are introduced into the ACO algorithm to improve the optimization performance of the ACO algorithm, and then the parallelism, global optimization ability, positive feedback mechanism and strong robustness of the improved ACO algorithm is used to find the optimal combination of parameters of the SVM model in order to improve the learning performance and generalization ability of the SVM model and establish the optimal data classification model. Finally, the experimental data from the UCI machine learning database are selected to validate the classification correctness of the ERURACO-SVM method. The experiment results show that the improved ACO(ERURACO) algorithm has better optimization performance for parameters selection of the SVM model and the ERURACO-SVM method has higher classification accuracy and better generalization ability.
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A Novel Model for Mechanics and Safety Calculation of Pipe Roof Based on the Load Transfer Mechanism
Yang Zhou,Hongpeng Lai,Yuanpei Chen,Zhilei Li 대한토목학회 2023 KSCE Journal of Civil Engineering Vol.27 No.12
A novel model is created in this paper to realize the systematic evaluation of the mechanical properties and construction safety of the pipe roof support system. The pipe roof is split into supported area, unloading area, and stable area based on the load distribution characteristics. Combining a statically indeterminate beam (for the area of unloading) with an elastic foundation beam (supported area and stability area) takes into account the mechanical effect of the pipe roof. The new model can better reflect the load transfer mechanism and the stability of the tunnel face under the pre-support. It is demonstrated that the novel model, when compared to the existing theoretical solution, is more closely related to the measured and simulated results than typical engineering cases and is practical for field application. It can provide a new idea for the design of pipe roof support and the safety evaluation of field construction.
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Lin Shen,Shen Runnan,Huang Jingqian,Liu Yanhan,Li Hongpeng,Xu Qingfang 한국유전학회 2023 Genes & Genomics Vol.45 No.12
Background Both epidemiological and clinical studies have suggested the comorbidity between cutaneous melanoma (CM) and obesity-related physical traits. However, it remains unclear about their shared genetic architecture. Objective To determine the shared genetic architecture between CM and obesity-related physical traits through conditional false discovery rate (cFDR) analysis. Method Quantile–quantile plots were firstly built to assess the pleiotropic enrichment of shared single nucleotide polymorphisms between CM and each trait. Then, cFDR and conjunctional cFDR (ccFDR) were used to identify the shared risk loci between CM and each trait. Moreover, the functional evaluation of shared risk genes was carried out through analyses of expression quantitative trait loci (eQTL), Kyoto Encyclopedia of Genes and Genomes and gene ontology, respectively. Finally, single-cell sequence analysis was performed to locate the expression of eQTL-mapped genes in tissues. Results Successive pleiotropic enrichment was found between CM and 5 obesity-related traits or height. 24 shared risk loci were identified between CM and 13 traits except appendicular lean mass using ccFDR analysis, with 17 novel and 4 validated loci. The functions of ccFDR-identified and eQTL-mapped genes were revealed to be mainly involved in cellular senescence, proliferation, meiotic nuclear division, cell cycle, and the metabolism of lipid, cholesterol and glucose. Single-cell sequence analysis showed that keratinocytes contribute to the occurrence and aggressiveness of CM through secreting paracrine cytokines. Conclusion Our findings demonstrate the significant genetic correlation between CM and obesity-related physical traits, which may provide a novel genetical basis for the pathogenesis and treatment of CM.
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