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- 저자 : Thi-Kha Nguyen (검색결과 3 건)
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Effects of pretreatment and air drying temperature on Noni fruit powder
Tuyen Chan Kha,Cong Thanh Nguyen,Luyen Thi Tran,Trung Tan Truong 한국식품과학회 2021 Food Science and Biotechnology Vol.30 No.12
The plant Morinda citrifolia L. (Noni) has beenthe subject of several recent research due to its positiveimpact on the treatment and prevention of a variety ofdiseases. Noni fruits contain a variety of phytochemicals,including flavonoid, polyphenol, and triterpenoid saponin. This study aimed to determine the best pre-treatment (includingblanching, soaking in ascorbic acid solution andmetabisulfite solution) and air-drying temperature (50, 60,70, and 80 C) to maximize the total polyphenol content(TPC), flavonoid content (TFC), and triterpenoid saponincontents (TSC) of the resultant Noni fruit powder. Theresults revealed that pre-soaked Noni fruit samples inascorbic acid or metabisulfite solution before air-drying at60 C were beneficial in preserving TPC, TFC, and TSC. TPC, TFC, and TSC losses increased as drying temperatures(70 and 80 C) rose. The optimum sample was held atfive different relative humidity conditions until theyattained weight equilibrium. The results indicated that thesorption isotherm curve of the Noni powder was the sigmoidshape and fitted with the BET and GAB models.
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ĐÁNH GIÁ KHẢ NĂNG LÀM LÀNH VẾT BỎNG TRÊN CHUỘT CỦA HYDROGEL TỪ AgNP-CHITOSAN-CURCUMIN
Hoang Ngoc Anh Nhan,Nguyen Ba Trung,Giang Thi Kim Lien,Truong Thi My Phuong,Ho Kha Vinh Nhan,Pham Xuan Anh 한국베트남학회 2023 베트남연구 Vol.21 No.1
Skin burn is one of the major causes of morbidity and mortality in burn patients due to its susceptibility to infection. The disruption of the epidermal barrier, combined with the denaturation of proteins and lipids, provides a fertile environment that is rich in bacterial nutrients for microbial growth, making it significantly prone to infection. This study aimed to formulate a thermoresponsive hydrogel containing silver nanoparticles (300 ppm), oligo chitosan 2%, and curcumin (0.1%) in the polymer pluronic F127 matrix with a final concentration of 13% to effectively promote the healing of skin wounds. The prepared thermoresponsive hydrogel was investigated for its physical and chemical stability, gelation temperature, and chemical composition. In addition to in vitro antibacterial activity against Staphylococcus aureus found in burn infections, in vivo burn healing and antibacterial activities were also investigated and compared with those of a commercial product using burn-induced infected wounds in mice. The formulation showed antibacterial activity with effective values for wound healing properties, as shown in vivo and by histopathological studies. This study also demonstrates that the thermoresponsive hydrogel was successful as an antibacterial and burned wound-healing transdermal drug delivery system.
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Machine learning for predicting long-term deflections in reinforce concrete flexural structures
Anh-Duc Pham,Ngoc-Tri Ngo,Thi-Kha Nguyen 한국CDE학회 2020 Journal of computational design and engineering Vol.7 No.1
Prediction of deflections of reinforced concrete (RC) flexural structures is vital to evaluate the workability and safety of structures during its life cycle. Empirical methods are limited to predict a long-term deflection of RC structures because they are difficult to consider all influencing factors. This study presents data-driven machine learning (ML) models to early predict the long-term deflections in RC structures. An experimental dataset was used to build and evaluate single and ensemble ML models. The models were trained and tested using the stratified 10-fold cross-validation algorithm. Analytical results revealed that the ML model is effective in predicting the deflection of RC structures with good accuracy of 0.972 in correlation coefficient (R), 8.190 mm in root mean square error (RMSE), 4.597 mm in mean absolute error (MAE), and 16.749% in mean absolute percentage error (MAPE). In performance comparison against with empirical methods, the prediction accuracy of the ML model improved significantly up to 66.41% in the RMSE and up to 82.04% in the MAE. As a contribution, this study proposed the effective ML model to facilitate designers in early forecasting long-term deflections in RC structures and evaluating their long-term serviceability and safety.
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