Đánh giá động lực làm việc của nhân viên y tế Bệnh viện Mắt TP. Đà Nẵng, từ đó làm cơ sở tham mưu cho lãnh đạo bệnh viện trong công tác quản lý bệnh viện.
The current study examined metadiscursive devices in EMI lectures conducted by a non-native English-speaking lecturer at
Duy Tan University, a notable private university in central Vietnam, using the taxonomy developed by Dafouz & Perucha
(2010). It was found that many MDs are employed in three different phases in their EMI classes, including: Discourse
structuring, Interaction and Conclusion phases, aiming to improve the effectiveness of the lectures.
This paper presents the algorithms for training an artificial neural network (ANN) for regression analysis; the algorithm is based on the generalized delta rule. The training method of a simple neuron model and an ANN model are presented and generalized. The models are then programed in Visual C# .NET and applied to predict the compressive strength of concrete mixes. Three datasets, collected from the literature, are used to demonstrate the applications of the models.
The aim of this study is to assess the performance and the efficiency of the aviation industry in the Asia-Pacific region through two-stage Data Envelopment Analysis (DEA). In the first stage, the relative efficiency was measured through a Window model in DEA. Then, at the second stage, the efficiency improvement was examined by the Malmquist Productivity Index (MPI).
To investigate how the incident angle of microwaves affects the microwave absorption properties of SrFe12-xCux/2Cox/2TixO19 samples, the RL curves with the different microwave incident angles were carefully simulated for two different polarization modes: transverse electric (TE) and transverse magnetic (TM).
This paper uses XGBoost to predict bearing capacity of concrete piles. The proposed model is trained and tested against
a dataset of 472 samples collected from static load tests in Vietnam. The results indicate that the default XGBoost model consistently outperforms the Deep Neural Network (DNN) regression. XGBoost is a suitable tool for engineers to predict pile bearing capacity.
This article presents the method for training a deep artificial neural network (DANN) for regression analysis; this method is based on the generalized delta rule. To illustrate the rule, a DANN with two hidden layers is used. The model’s construction is described in the form of mathematical equations. Subsequently, a DANN program is written in Visual C# .NET. This program is tested with the task of estimating the shear strength of soil samples.
These limitations are not exclusive to DQCC but are prevalent in other testing centers as well. The study highlights the need for attention and investment from higher-level organizations to overcome these challenges. By addressing equipment shortages, providing standardized materials and chemicals, and enhancing the overall testing infrastructure, DQCC can improve its capabilities, adhere to international standards, and ensure the quality of pharmaceutical products. Future research should focus on improving testing capacity for tablets, injections, and herbal/traditional drugs to meet the increasingly strict requirements and variety of dosage forms of the pharmaceutical sector.
This paper constructs linear regression models for estimating the compressive strength (CS) of rice husk ash (RHA)- blended concrete. The conventional multiple linear regression model and multivariate power function-based model are employed. Experimental results show that the performance of the latter is better than that of the former. The multivariate power function-based regression model can achieve good prediction results with a mean absolute percentage error of 14%.
Bào chế dạng siro Nhị Trần để thuận tiện cho người dùng, với các mục tiêu bao gồm (1) chuẩn bị và kiểm nghiệm chất lượng nguyên liệu, (2) bào chế siro Nhị Trần, và (3) đánh giá chất lượng sản phẩm.
