Modelling study

The study was conducted to determine the level of awareness, behavior and willingness to pay for reusable bags of students at Thai Nguyen University of Economics and Business Administration. We applied Contingent Valuation Method and conducted a survey of 370 students at Thai Nguyen University of Economics and Business Administration.

8p viengfa 28-10-2024 1 1   Download

This study is designed to validate the impact of entrepreneurial mindset on product innovation outcomes in technology-based enterprises while concurrently examining the mediating influence of business model innovation.

9p viengfa 28-10-2024 2 1   Download

Accurate daily load forecasting is critical for effective energy management planning. In this study, the article proposes a new method for daily load forecasting that takes advantage of load data and weather data over time in Tien Giang.

10p viengfa 28-10-2024 1 1   Download

This study investigates the effect of various experimental conditions, such as initial pH, initial arsenic concentration, shaking temperature, and contact time, as well as thermodynamics models on the ability to remove arsenic from water.

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This study focuses on evaluating short-term deflections of reinforced concrete beams according to the Vietnamese Standard on the Design of Concrete and Reinforced Concrete Structures (TCVN 5574:2018) by comparing them with simulated results obtained from Abaqus software.

9p viengfa 28-10-2024 1 1   Download

Accurate forecasting of the electrical load is a critical element for grid operators to make well-informed decisions concerning electricity generation, transmission, and distribution. In this study, an Extreme Learning Machine (ELM) model was proposed and compared with four other machine learning models including Artificial Neural Networks (ANN), Convolutional Neural Networks (CNN), Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU).

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The process of neural stem cell (NSC) differentiation into neurons is crucial for the development of potential cell-centered treatments for central nervous system disorders. However, predicting, identifying, and anticipating this differentiation is complex. In this study, we propose the implementation of a convolutional neural network model for the predictable recognition of NSC fate, utilizing single-cell brightfield images.

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This paper is structured as follows. The following section presents related work. Section 3 summarizes the characteristics of the two datasets utilized in the model and the system’s overall architecture for image-based disease diagnosis. Section 4 provides our experimental results that compare the performance metrics with other studies.

6p viengfa 28-10-2024 2 2   Download

This study proposes to test a combination model between CNN network and XGBoost algorithm for weather image classification problem. The proposed model uses deep learning network, namely CNN for feature extraction, then feeds the features into the XGBoost classifier to recognize the images.

6p viengfa 28-10-2024 1 1   Download

In this study, the author examined the SERVQUAL model's service quality evaluation elements involving customer satisfaction. More specifically, the hypothetical model was proposed to examine how each component of service quality affects customer satisfaction specifications.

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The paper explores the application of the Modal Truncation algorithm in Model Order Reduction, focusing on its effectiveness in reducing high-dimensional mathematical model. The algorithm identifies dominant modes governing dynamic responses, discards the high-order model, and reconstructs a new model with reduced dimensions.

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The main objective of this study is to predict accurately the loaddeflection of composite concrete bridges using two popular machine learning (ML) models namely Random Tree (RT) and Artificial Neural Network (ANN). Data from 83 track loading tests conducted on various bridges in Vietnam were collected and analyzed.

9p viengfa 28-10-2024 3 2   Download

Predicting the macroscopic permeability of porous media is critical in various scientific and engineering applications. This study proposes a novel model that combines Random Forest (RF) and rime-ice (RIME) optimization algorithm, denoted RIME-RF-RIME, to predict permeability based on six key features covering fluid phase dimensions, geometric characteristics, surrounding phase permeability, and media porosity.

14p viengfa 28-10-2024 2 2   Download

In this study, we aim to delineate landslide susceptibility zones within Dien Bien province, Vietnam, leveraging the capabilities of various machine learning models including Light Gradient Boosting Machine (LGBM), K-Nearest Neighbors (KNN), and Gradient Boosting (GB).

19p viengfa 28-10-2024 4 2   Download

This study delves into the application of machine learning (ML), specifically a Gradient Boosting (GB) model, for predicting the punching shear strength (PSS) of two-way reinforced concrete flat slabs.

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In this study, our primary aim is to assess and compare the efficacy of Support Vector Machines (SVM) employing various kernel functions: linear (LIN), polynomial (POL), Radial Basis Function (RBF), and sigmoid (SIG) in predicting the compressive strength of concrete.

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In this study, we propose a machine learning technique for estimating the shear strength of CRC beams across a range of service periods. To do this, we gathered 158 CRC beam shear tests and used Artificial Neural Network (ANN) to create a forecast model for the considered output.

12p viengfa 28-10-2024 3 2   Download

This paper develops an Artificial Neural Network (ANN) model based on 96 experimental data to forecast the dynamic modulus of asphalt concrete mixtures. This study applied the repeated KFold cross-validation technique with 10 folds on the training data set to make the simulation results more reliable and find a model with more general predictive power.

9p viengfa 28-10-2024 5 2   Download

This study proposes the application of Ensemble Decision Tree Boosted (EDT Boosted) model for forecasting the surface chloride concentration of marine concrete Cs. A database of 386 experimental results was collected from 17 different sources covering twelve variables was used to build and verify the predictive power of the EDT model.

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In this study, an artificial neural networkbased Bayesian regularization (ANN) model is proposed to predict the compressive strength of concrete. The database in this study includes 208 experimental results synthesized from laboratory experiments with 9 input variables related to temperature change and design material composition.

12p viengfa 28-10-2024 2 2   Download