• Entomological Research
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• v.48 no.5
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• pp.372-383
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• 2018

Leptocimbex formosanus group of Leptocimbex Semenov 1896, Cimbicidae is defined and the relationships and characteristics are provided. Two new species of this group are described from Hunan and Yunnan Provinces in China: Leptocimbex shinoharai Yan & Wei sp. nov. and L. naitoi Yan & Wei sp. nov. Redescriptions of the three known species of L. formosanus group (L. formosanus Enslin 1911, L. dendrobii Rohwer 1915 and L. nigropropodea Wei & Deng 2002) and a key to all known species of this group are provided.

• Natural Product Sciences
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• v.24 no.4
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• pp.284-287
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• 2018

A new isoprenylated acetophenone, acronyculatin P (1) as well as two known compounds, 3',5'-diisoprenyl-2',4'-dihydroxy-6'-methoxyphenylethanone (2) and 3'-isoprenyl-2',4',6'-trihydroxyphenylethanone (3) were isolated from the stem bark of Acronychia pedunculata (L.) Miq. The structures were determined by HRESIMS, 1D and 2D NMR. The inhibitory activity of the isoprenylated acetophenone derivatives against murine leukemia P-388 cells showed compound 1 moderate activity with $IC_{50}$ $15.42{\mu}M$.

• Natural Product Sciences
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• v.28 no.2
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• pp.58-61
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• 2022

Three isoprenylated flavanones were isolated from the leaves of Flemingia lineata (L.) Aiton. Among them are two new flavanones, flemilineatins A and B (1-2), along with 6-isoprenyl eridioctyol (3). Their structures were determined using HRESIMS data and NMR spectra. Flavanones 1 - 3 were assayed in the HeLa cancer cells. Compound 1 showed moderate activity with an IC₅₀ value of 11.2 μM.

• Natural Product Sciences
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• v.28 no.1
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• pp.40-43
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• 2022

Three isoprenylated flavanones were isolated from the leaves of Flemingia lineata (L.) Aiton. Among them are two new flavanones, flemilineatins A and B (1-2), along with 6-isoprenyl eridioctyol (3). Their structures were determined using HRESIMS data and NMR spectra. Flavanones 1-3 were assayed in the HeLa cancer cells. Compound 1 showed moderate activity with an IC₅₀ value of 11.2 μM.

• Elastomers and Composites
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• v.50 no.3
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• pp.189-195
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• 2015

In this study, we prepared epoxidized poly ethylene-ter-1-decene-ter-divinylbenzene (Epo-PEHV) as a reactive compatibilizer to prevent phase separation phenomenon which occurs upon blending polypropylene (PP) and ethylene-vinyl alcohol copolymer (EVOH). Firstly, PEHV was prepared under high catalyst activity according to content of catalyst and cocatalyst. After then, we modified vinyl group of the terpolymer with epoxy group. We observed that the terpolymer was successfully epoxidized by 1H-NMR and FT-IR analysis. The Epo-PEHV was added by 2, 5, 10% in PP/EVOH blends. The morphologies and mechanical properties of PP/Epo-PEHV/EVOH blends were analyzed by SEM and UTM, respectively. Epo-PEHV enhanced the interfacial adhesion of PP and EVOH blends.

• Bulletin of Food Technology
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• v.26 no.2
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• pp.102-108
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• 2013

• Bulletin of Food Technology
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• v.25 no.1
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• pp.44-51
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• 2012

• Bulletin of Food Technology
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• v.22 no.2
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• pp.283-299
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• 2009

• The Bulletin of The Korean Astronomical Society
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• v.25 no.1
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• pp.53-53
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• 2000

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.16 no.12
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• pp.3836-3854
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• 2022

The student group division is benefit for universities to do the student management based on the group profile. With the widespread use of student smart cards on campus, especially where students living in campus residence halls, students' daily activities on campus are recorded with information such as smart card swiping time and location. Therefore, it is feasible to depict the students with the daily activity data and accordingly group students based on objective measuring from their campus behavior with some regular student attributions collected in the management system. However, it is challenge in feature representation due to diverse forms of the student data. To effectively and comprehensively represent students' behaviors for further student group division, we proposed to adopt activity data from student smart cards and student attributes as input data with taking account of activity and attribution relationship types from different perspective. Specially, we propose a novel student group division method based on a multi-view student attribute heterogeneous information network (MSA-HIN). The network nodes in our proposed MSA-HIN represent students with their multi-dimensional attribute information. Meanwhile, the edges are constructed to characterize student different relationships, such as co-major, co-occurrence, and co-borrowing books. Based on the MSA-HIN, embedded representations of students are learned and a deep graph cluster algorithm is applied to divide students into groups. Comparative experiments have been done on a real-life campus dataset collected from a university. The experimental results demonstrate that our method can effectively reveal the variability of student attributes and relationships and accordingly achieves the best clustering results for group division.