Golden camellias or yellow camellias are species belonging to genus Camellia L., family Theaceae. Fifty two species were described in southern China and Vietnam. Active ingredients such as polysaccharides, polyphenols, saponins, and flavonoids are well known characteristics of golden camellias. Its leaves and flowers have been long traditionally used for health improvement. It was found to be able to inhibit transplanted cancer, lower blood pressure, lower blood lipid, lower cholesterol, and prevent atherosclerosis. Currently, it costs 320–700US$ per one kg of dry flowers. Such price attracts many local ethnic people to plant golden camellias for poverty reduction. This work reviews (1) species and natural distribution, (2) uses and healthcare values, (3) techniques for seedling production, planting and tending, and (4) opportunities and challenges for future development of golden camellias.
Bài báo này trình bày về nghiên cứu ảnh hưởng của một số phụ gia khoáng (PGK) đến đặc tính ăn mòn cốt thép trong bê tông chất lượng siêu cao (BTCLSC). Trong nghiên cứu này, PGK sử dụng bao gồm silica fume (SF), tro bay (FA) và xỉ lò cao hạt hóa nghiền mịn (GGBFS) được sử dụng để thay thế xi măng theo tỷ lệ SF sử dụng 10% và 20%; FA sử dụng 10% và 20%; GGBFS sử dụng 20%, 40% theo khối lượng chất kết dính. Kết quả nghiên cứu cho thấy với mẫu BTCLSC sử dụng PGK cho khả năng chống ăn mòn của cốt thép trong bê tông tốt hơn so với mẫu không sử dụng PGK và với mẫu bê tông thường (cường độ nén khoảng 30 MPa). Mức độ ăn mòn cốt thép của mẫu BTCLSC sử dụng GGBFS và SF thấp hơn so với mẫu sử dụng FA.
Nhận ngày 10/01/2018; sửa xong 24/01/2018; chấp nhận đăng 28/02/2018
Over time, machine learning methods have developed, but there have not been many studies comparing how well they predict ignition delays. In this study, a model that forecasts the ignition delay of a diesel engine utilizing diesel fuel and biodiesel fuel was developed using Artificial Neural Network (ANN) and Support Vector Machine (SVM) machine learning techniques. This work has clarified the problems in designing and training the model. The effectiveness of the ANN and SVM machine learning methods' ignition delay prediction models has been evaluated under various input variable conditions. The authors employed a data set of over 700 input data sets from diesel fuel and biodiesel in the B0 to B60 range for this purpose. To evaluate the accuracy of the models, the authors compared the average accuracy of the overall classification as well as the standard deviation. The results after training and verifying the accuracy of the models show that the SVM model has a better ability to predict the fire ignition delay than the ANN model. Specifically, with the test data set and the SVM model at compression ratio (ε) = 15,
This research examines the three-component structure of the concept internal market orientation (IMO) and investigates its impact on frontliners’ organizational commitment (OC) and customer-oriented behavior (COB) in the context of airport service in Vietnam. Based on a sample of 294 frontliners working in various airports, SEM analysis reveals that IMO has a strong impact on OC. It also has direct and indirect impacts on COB. Moreover, the empirical result supports the component structure of IMO. It is, therefore, suggested that IMO is powerful to enhance both internal employee management and external marketing performance in the research context.
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