A New Sterol from the Soft Coral Lobophytum crassum

Nguyen, Phuong Thao,Nguyen, Hoai Nam,Nguyen, Xuan Cuong,Nguyen, Xuan Nhiem,Pham, The Tung,Tran, Hong Quang,Nguyen, Thi Thanh Ngan,Phan, Van Kiem,Chau, Van Minh,Kim, Young Ho Korean Chemical Society 2013 Bulletin of the Korean Chemical Society Vol.34 No.1

Anti-inflammatory Asterosaponins from the Starfish <i>Astropecten monacanthus</i>

Thao, Nguyen Phuong,Cuong, Nguyen Xuan,Luyen, Bui Thi Thuy,Thanh, Nguyen Van,Nhiem, Nguyen Xuan,Koh, Young-Sang,Ly, Bui Minh,Nam, Nguyen Hoai,Kiem, Phan Van,Minh, Chau Van,Kim, Young Ho American Chemical Society and American Society of 2013 Journal of natural products Vol.76 No.9

<P>Four new asterosaponins, astrosteriosides A–D (<B>1</B>–<B>3</B> and <B>5</B>), and two known compounds, psilasteroside (<B>4</B>) and marthasteroside B (<B>6</B>), were isolated from the MeOH extract of the edible Vietnamese starfish <I>Astropecten monacanthus</I>. Their structures were elucidated by chemical and spectroscopic methods including FTICRMS and 1D and 2D NMR experiments. The effects of the extracts and isolated compounds on pro-inflammatory cytokines were evaluated by measuring the production of IL-12 p40, IL-6, and TNF-α in LPS-stimulated bone marrow-derived dendritic cells. Compounds <B>1</B>, <B>5</B>, and <B>6</B> exhibited potent anti-inflammatory activity comparable to that of the positive control. Further studies are required to confirm efficacy <I>in vivo</I> and the mechanism of effects. Such potent anti-inflammatory activities render compounds <B>1</B>, <B>5</B>, and <B>6</B> important materials for further applications including complementary inflammation remedies and/or functional foods and nutraceuticals.</P><P><B>Graphic Abstract</B> <IMG SRC='http://pubs.acs.org/appl/literatum/publisher/achs/journals/content/jnprdf/2013/jnprdf.2013.76.issue-9/np400492a/production/images/medium/np-2013-00492a_0005.gif'></P><P><A href='http://pubs.acs.org/doi/suppl/10.1021/np400492a'>ACS Electronic Supporting Info</A></P>

Anti-inflammatory tirucallane saponins from Paramignya scandens.

Phan, Nguyen Huu Toan,Thuan, Nguyen Thi Dieu,Ngoc, Ninh Thi,Thao, Nguyen Phuong,Kim, Sohyun,Koh, Young Sang,Thanh, Nguyen Van,Cuong, Nguyen Xuan,Nam, Nguyen Hoai,Kiem, Phan Van,Kim, Young Ho,Minh, Cha Pharmaceutical Society of Japan 2015 Chemical & pharmaceutical bulletin Vol.63 No.7

<P>Five new tirucallane saponins, paramignyosides A-E (1-5), were isolated from the water fraction of the Paramignya scandens stem and leaves. Their structures were elucidated on the basis of spectroscopic evidence including high-resolution electrospray ionization mass spectrometry (HR-ESI-MS) and one dimensional (1D)- and 2D-NMR. The effects of isolated compounds on pro-inflammatory cytokines were evaluated by measuring the production of interleukin (IL)-12 p40, IL-6, and tumor necrosis factor-α (TNF-α) in lipopolysaccharide (LPS)-stimulated bone marrow-derived dendritic cells (BMDCs). Paramignyoside C (3) exhibited selective and potent inhibitory effect (IC50=5.030.19??M) on the production of IL-12 p40 comparable to that of the positive control, SB203580 (IC50=5.000.16??M). Further studies are required to confirm efficacy in vivo and the mechanism of anti-inflammatory effects.</P>

High-Resolution Simulations for Vietnam - Methodology and Evaluation of Current Climate

Jack Katzfey,Kim Nguyen,John McGregor,Peter Hoffmann,Suppiah Ramasamy,Hiep Van Nguyen,Mai Van Khiem,Thang Van Nguyen,Kien Ba Truong,Thang Van Vu,Hien Thuan Nguyen,Tran Thuc,Doan Ha Phong,Bang Thanh Ng 한국기상학회 2016 Asia-Pacific Journal of Atmospheric Sciences Vol.52 No.2

To assist the government of Vietnam in its efforts to better understand the impacts of climate change and prioritise its adaptation measures, dynamically downscaled climate change projections were produced across Vietnam. Two Regional Climate Models (RCMs) were used: CSIRO’s variable-resolution Conformal-Cubic Atmospheric Model (CCAM) and the limited-area model Regional Climate Model system version 4.2 (RegCM4.2). First, global CCAM simulations were completed using bias- and variance-corrected sea surface temperatures as well as sea ice concentrations from six Coupled Model Intercomparison Project Phase 5 (CMIP5) global climate models. This approach is different from other downscaling approaches as it does not use any atmospheric fields from the GCMs. The global CCAM simulations were then further downscaled to 10 km using CCAM and to 20 km using RegCM4.2. Evaluations of temperature and precipitation for the current climate (1980-2000) were completed using station data as well as various gridded observational datasets. The RCMs were able to reproduce reasonably well most of the important characteristics of observed spatial patterns and annual cycles of temperature. Average and minimum temperatures were well simulated (biases generally less than 1oC), while maximum temperatures had biases of around 1oC. For precipitation, although the RCMs captured the annual cycle, RegCM4.2 was too dry in Oct.-Nov. (−60% bias), while CCAM was too wet in Dec.- Mar. (130% bias). Both models were too dry in summer and too wet in winter (especially in northern Vietnam). The ability of the ensemble simulations to capture current climate increases confidence in the simulations of future climate.

Triterpenoids from aerial parts of Glochidion eriocarpum.

Thu, Vu Kim,Kiem, Phan Van,Yen, Pham Hai,Nhiem, Nguyen Xuan,Tung, Nguyen Huu,Cuong, Nguyen Xuan,Minh, Chau Van,Huong, Hoang Thanh,Lau, Trinh Van,Thuan, Ngo Thi,Kim, Young Ho Natural Product Communications 2010 Natural product communications Vol.5 No.3

<P>From the aerial parts of Glochidion eriocarpum, a new triterpene, glochieriol (1), three new triterpenoid saponins, glochieriosides C - E (2 - 4), together with four known triterpenes (glochidonol, glochidiol, lupeol, and 3-epi-lupeol) were isolated by using combined chromatographic separations. The structures of the new compounds were elucidated on the basis of spectroscopic data, including FTICR-MS, 1D and 2D NMR.</P>

Modeling the temperature dependence of the optical properties of anisotropic SnS0.52Se0.48

Nguyen Xuan Au,Kim Bogyu,Kim Kyujin,Lee Wonjun,Kim Young Dong,Kim Tae Jung,Van Long Le,Nguyen Hoang Tung 한국물리학회 2021 THE JOURNAL OF THE KOREAN PHYSICAL SOCIETY Vol.78 No.4

We report the parameter values of the model dielectric function (ε = ε1 + iε2 ) of SnS0.52Se0.48 in the spectral range from 0.74 to 6.42 eV and the temperature range from 27 to 350 K. An analytic representation, obtained using the Tauc–Lorentz (TL) dispersion model, allows interpolation of the parameters with respect to both energy and temperature. We used reported experimental spectra as the basis of our approach and verified that the TL model can parameterize the model dielectric function so as to reproduce the experimental data well and yield values of the dielectric function and the refractive index at arbitrary temperatures that are useful for device applications.

Analysis of the Output Ripple of the DC-DC Boost Charger for Li-Ion Batteries

Nguyen, Van-Sang,Tran, Van-Long,Choi, Woojin,Kim, Dae-Wook The Korean Institute of Power Electronics 2014 JOURNAL OF POWER ELECTRONICS Vol.14 No.1

In the design of battery chargers, limiting the output ripple current according to the manufacturer's recommendation is important for reliable service and extended battery life. Ripple components can cause internal heating of the battery and thus reduce the service life of the battery. Care must be exerted in the design of the switching converter for the charge application through the accurate estimation of the output current ripple value. This study proposes a method to reduce the output current ripple of the converter and presents a detailed analysis of the output current ripple of the DC-DC boost converter to provide a guideline for the design of the battery charger.

Hadoop-based System for Analysis Big Social Sensor Data

Van Quan Nguyen,Linh Van Ma,Jinsul Kim 한국정보기술학회 2018 Proceedings of KIIT Conference Vol.2018 No.6

요즘 많은 분석 데이터가 많은 업무에서 중요 해지고 있습니다. 데이터는 산업 시스템의 과학, 의학, 기상, 금융, 마케팅 또는 센서 데이터 일 수도 있고 소셜 네트워크의 소셜 데이터 일 수도 있습니다. Hadoop 프레임워크는 현재 분산 데이터뿐 아니라 대용량 데이터 처리를위한 최상의 선택이되고 있습니다. 이 백서에서는 Hadoop 분산 파일 시스템 (HDFS)에 MapReduce 기반 아키텍처를 배포하여 여러 사용자가 재난에 대해 수집한 사회적 센서 데이터를 처리합니다. 기상청이 예상하고 예측하고 주민에게 경고문을 게시하려면이 큰 데이터를 실시간으로 수집, 저장 및 처리해야합니다. Nowadays large analysis amount of data has become important for many tasks. Data could be scientific, medical, meteorological, financial, marketing or sensor data from industrial system even social data from the social network. Hadoop framework is currently becoming the best choice for big data processing as well as distributed data. This paper deployed MapReduce based architecture on Hadoop Distributed File System (HDFS) to process social sensor data which is collected from multiple users about the disaster. Real-time collecting, storing and processing of this big data is necessary for the meteorological department to forecast as well as publish the warning to residents.

Social Media based Real-time Event Detection by using Deep Learning Methods

Nguyen, Van Quan,Yang, Hyung-Jeong,Kim, Young-chul,Kim, Soo-hyung,Kim, Kyungbaek THE KOREAN INSTITUTE OF SMART MEDIA 2017 스마트미디어저널 Vol.6 No.3

Event detection using social media has been widespread since social network services have been an active communication channel for connecting with others, diffusing news message. Especially, the real-time characteristic of social media has created the opportunity for supporting for real-time applications/systems. Social network such as Twitter is the potential data source to explore useful information by mining messages posted by the user community. This paper proposed a novel system for temporal event detection by analyzing social data. As a result, this information can be used by first responders, decision makers, or news agents to gain insight of the situation. The proposed approach takes advantages of deep learning methods that play core techniques on the main tasks including informative data identifying from a noisy environment and temporal event detection. The former is the responsibility of Convolutional Neural Network model trained from labeled Twitter data. The latter is for event detection supported by Recurrent Neural Network module. We demonstrated our approach and experimental results on the case study of earthquake situations. Our system is more adaptive than other systems used traditional methods since deep learning enables to extract the features of data without spending lots of time constructing feature by hand. This benefit makes our approach adaptive to extend to a new context of practice. Moreover, the proposed system promised to respond to acceptable delay within several minutes that will helpful mean for supporting news channel agents or belief plan in case of disaster events.

Deep Learning-based Approach to Smart Factory

Van Quan Nguyen,Linh Van Ma,Jinsul Kim 한국정보기술학회 2018 Proceedings of KIIT Conference Vol.2018 No.6

스마트 제조는 수집, 분석, 시각화 및 의사 결정에서의 시스템 성능 향상을 의미합니다. 우리는 장치 기반 및 네트워크 기반 기술뿐만 아니라 센서 데이터 조작에 있어 고급 프로세스를 시작하기 위해 기계 학습을 널리 사용하는 것을 목격 해 왔습니다. 이 글에서는 산업용 시스템의 센서 데이터를 분석하기위한 LSTM 아키텍처 기반의 반복적 인 신경망을 제시합니다. 시계열 데이터는 타임 라인에 따라 개체의 상태에 반영되기 때문에 많은 시스템에서 중요한 부분이 되었습니다. 왜 우리가 반복적 인 신경망을 데이터의 순서를 탐색하는 솔루션으로 사용하는지에 대한 주된 이유. LSTM 신경 회로망의 응용을 증명하기 위해 이상 검출 알고리즘을 제안하고 여러 데이터 집합에서 수행한다. Smart manufacturing refers to using advanced techniques in collecting, analyzing, visualization and decision making in management to improve system’s performance. We have been witnessing the widespread of machine learning to launch advanced process in the manipulation of sensor data as well as managing devices and productions based on network technologies. This article presents recurrent neural network with LSTM architecture-based approach to analyzing sensor data for the industrial system. Using time series data has become a critical part of many systems because this explored information reflect the state of objects according to the timeline. This is the major reason why we use Recurrent Neural Networks as a solution to explore the sequence of data. In order to prove the application of LSTM neural network, anomaly detection algorithm is proposed and perform on time series datasets.