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- 저자 : Jiangang Zheng (검색결과 3 건)
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Jiangang Zheng,Yinlan Xu,Ajab Khan,Shaoyu Wang,Hongquan Li,Na Sun 한국생물공학회 2020 Biotechnology and Bioprocess Engineering Vol.25 No.2
The prevalence of encephalomyocarditis virus has brought about enormous financial losses to the swine industry throughout the globe. Chinese herbal medicines have potential antiviral activity which has been proved. Fifteen traditional Chinese medicine compounds were screened for anti-encephalomyocarditis virus (EMCV) activity. The maximum non-toxic concentration (MNTC), cytotoxic concentration 50% (CC50), maximal inhibition rate (MIR), and effective concentration 50% (EC50) against EMCV were measured using MTT and antiviral assays on baby hamster syrian kidney (BHK-21) cells. Two of the compounds, baicalin and matrine, with MIR > 50% and selective index (SI) > 3 were chosen for further virus load analysis. The results showed that the MIRs of baicalin and matrine were higher than that of positive control ribavirin while the SI values were much smaller than that of the control. Real-time quantitative PCR analysis demonstrated that baicalin and matrine have significant (p < 0.05) anti- EMCV activity compared to the control. It is a baseline study concluded that baicalin and matrine needs further development as an independent drugs or part of a Chinese medicine prescription for the treatment of EMCV infection.
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Kai Jiang,Guanzuo Wu,Guohao Si,Jiangang Li,Boyang Zheng,Qi Shen,Shida Song 한국지질과학협의회 2023 Geosciences Journal Vol.27 No.4
The Fenwei Graben located in the conjoint region between the Neo-Tethys tectonic domain and the western Pacific tectonic domain is key to understanding the tectonic evolution of the East Asian interior. Extensive Late Cretaceous–Paleogene fluvial and lacustrine deposits within the southeastern Fenwei Graben, provide ideal archives of early tectono-sedimentary evolution in Fenwei Graben. Recently, we find two complete Late Cretaceous–Cenozoic sections in the Lingbao Basin. These unique sections provide an excellent opportunity to investigate regional tectono-sedimentary evolution and its potential dynamic mechanisms in Fenwei Graben. The anisotropy of magnetic susceptibility (AMS) is an effective and economic method to obtain information about tectonic and/or sedimentary evolution recorded in deposits. To this end, we present an integrated rock magnetism and AMS study of 36 sampling sites from these two sections that span the interval from the Late Cretaceous to the Early Eocene. Our rock magnetism results suggest that hematite and magnetite are the main magnetic carriers of remanence, and paramagnetic minerals and hematite are major contributors to AMS in the Lingbao Basin. In addition, the reverse, intermediate and anomalous magnetic fabrics are found in these two sections, which may result from the contribution of iron-bearing carbonates in deposits. The AMS of the Lingbao Basin is a primary sedimentary fabric with the K1 direction antiparallel to the preferential paleocurrent direction. During the Paleocene, the clockwise rotation of the paleocurrent directions in both sections is more likely to be caused by the rotation of the Lingbao basin or the relatively rapid uplift of western provenance under the influence of the India–Eurasia convergence. Moreover, the inferred early evolution of the Fenwei Graben is likely controlled by both the western Pacific plate subduction and India–Eurasia convergence.
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Intelligent optimization of axial-flow pump using physics-considering machine learning
KANKANAM GAMAGE PIYUMIKA MADUSHANI,Zhou Jie,Feng Jiangang,XUHUI ZHOU,Yuan Zheng,Chen Huixiang,Chen Jinbo 한국CDE학회 2024 Journal of computational design and engineering Vol.11 No.1
To address the significant energy waste generated by axial flow pumps, this paper proposes an intelligent optimization method based on physics-considering machine learning. First, a highly parameterized geometric design theory is constructed using six featured variables to achieve a complete three-dimensional modeling of the blade geometry. Four hundred preliminary cases are studied using the computational fluid dynamics method with various combinations of these featured variables to obtain a preliminary solution. The best preliminary design has an efficiency of 83.33%, and a head of 5.495 m. To further improve this performance, this paper also presents a high-precision prediction model for the energy performance of axial flow pump based on back-propagation neural network and the encoding layers of random sampling and local feature aggregator network created. Afterwards, a multi-population genetic algorithm is used to quickly find the optimal solution within the prediction mode range. The algorithm achieved a highest efficiency of 86.373% and was validated by numerical simulation with a value of 86.057% and a prediction error of 0.316%. Compared with the preliminary solution, the efficiency of the optimized axial flow pump is increased by 1.615%, with a wider high-efficiency range and an optimal operating point closer to the design conditions. Overall, this intelligent optimization method has the potential to significantly reduce the design time of axial pumps and increase their performance.
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