Economic performance of Vietnam, 1976-2000: New evidence from input-output model

This study provides a concise introduction to the economic history of Vietnam from 1976 to present. We identify different phases of the development of the Vietnamese economy, from its unification after a Vietnam war to the current phases of the transition (1989-2000) and propose a specific pattern of transition in the case of Vietnam. This research is the first attempt to make a synthesis quantitative analysis of socio-economic aggregate data during different phases of the Vietnamese economy in 1986-2000, in which different national input-output tables (1989, 1996 and 2000) in constant prices have been employed. The economic performances are investigated from three aspects: (i) evolution of domestic final demand; (ii) evolution of international trade structure and (iii) the technological change. The analysis shows economic history of Vietnam from 1986 up to present as a continuous evolutionary process and integration in to the international market is inevitable. Government programmes only played a vital role of accommodator to the economic changes of the Vietnamese economy.Input-output analysis, Vietnamese economy, Economic history, Transition economy, Macro-economic policy

NAVIGATION STRATEGY FOR MOBILE ROBOT BASED ON COMPUTER VISION AND YOLOV5 NETWORK IN THE UNKNOWN ENVIRONMENT

Intelligent mobile robots must possess the ability to navigate in complex environments. The field of mobile robot navigation is continuously evolving, with various technologies being developed. Deep learning has gained attention from researchers, and numerous navigation models utilizing deep learning have been proposed. In this study, the YOLOv5 model is utilized to identify objects to aid the mobile robot in determining movement conditions. However, the limitation of deep learning models being trained on insufficient data, leading to inaccurate recognition in unforeseen scenarios, is addressed by introducing an innovative computer vision technology that detects lanes in real-time. Combining the deep learning model with computer vision technology, the robot can identify different types of objects, allowing it to estimate distance and adjust speed accordingly. Additionally, the paper investigates the recognition reliability in varying light intensities. The findings of this study offer promising directions for future breakthroughs in mobile robot navigatio

Damage Detection in Structural Health Monitoring using Hybrid Convolution Neural Network and Recurrent Neural Network

The process of damage identification in Structural Health Monitoring (SHM) gives us a lot of practical information about the current status of the inspected structure. The target of the process is to detect damage status by processing data collected from sensors, followed by identifying the difference between the damaged and the undamaged states. Different machine learning techniques have been applied to attempt to extract features or knowledge from vibration data, however, they need to learn prior knowledge about the factors affecting the structure. In this paper, a novel method of structural damage detection is proposed using convolution neural network and recurrent neural network. A convolution neural network is used to extract deep features while recurrent neural network is trained to learn the long-term historical dependency in time series data. This method with combining two types of features increases discrimination ability when compares with it to deep features only. Finally, the neural network is applied to categorize the time series into two states - undamaged and damaged. The accuracy of the proposed method was tested on a benchmark dataset of Z24-bridge (Switzerland). The result shows that the hybrid method provides a high level of accuracy in damage identification of the tested structure

Damage detection in structural health monitoring using combination of deep neural networks

Structural Health Monitoring is a process of continuous evaluation of infrastructure status. In order to be able to detect the damage status, data collected from sensors have to be processed to identify the difference between the damaged and the undamaged states. In recent years, convolution neural network has been applied to detect the structural damage and with positive results. This paper proposes a new method of damage detection using combination of deep neural networks. The method uses a convolution neural network to extract deep features in time series and Long Short Term Memory network to find a statistically significant correlation of each lagged features in time series data. These two types of features are combined to increase discrimination ability compared to deep features only. Finally, the fully connected layer will be used to classify the time series into normal and damaged states. The accuracy of damaged detection was tested on a benchmark dataset from Los Alamos National Laboratory and the result shows that hybrid features provided a highly accurate damage identification

Multi-level damage detection using a combination of deep neural networks

In recent years, bridge damage identification using a convolutional neural network (CNN) has become a hot research topic and received much attention in the field of civil engineering. Although CNN is capable of categorizing damaged and undamaged states from the measured data, the level of accuracy for damage diagnosis is still insufficient due to the tendency of CNN to ignore the temporal dependency between data points. To address this problem, this paper introduces a novel hybrid damage detection method based on the combination of CNN and Long Short-Term Memory (LSTM) to classify and quantify different levels of damage in the bridge structure. In this method, the CNN model will be used to extract the spatial damage features, which will be combined with the temporal features obtained from Long Short-Term Memory (LSTM) model to create the enhanced damage features. The combination successfully strengthened the damage detection capability of the neural network. Moreover, deep learning is also improved in this paper to process the acceleration-time data, which has a different amplitude at short intervals and the same amplitude at long intervals. The empirical result on the Vang bridge shows that our hybrid CNN-LSTM can detect structural damage with a high level of accuracy

FLIGHT-TO-QUALITY OR CONTAGION DURING U.S. SUBPRIME CRISIS: EVIDENCE FROM VIETNAM’S FINANCIAL MARKETS

Abstract: This study investigates whether contagion or flight-to-quality occurred in Vietnam’s financial markets during the US subprime crisis in 2007. We apply the asymmetric dynamic conditional correlation model (ADCC-GARCH (1,1)) to daily stock-index and bond index returns of Vietnam’s and US stock markets. We test for contagion or flight-to-quality by using a test for difference in dynamic conditional correlation means. The results show a contagion between the US and Vietnam’s stock markets, confirming the widespread influence of the US stock market on a young market like Vietnam. This result suggests a low benefit from diversification for investors holding portfolios containing assets in Vietnam’s stock market and US stock market during the crisis. Moreover, the relationship between Vietnam’s stock and bond markets represents a flight-to-quality during the US subprime crisis. This finding shows that the investors tend to hold less risky assets, i.e., bonds, instead of stocks during this turbulent period in Vietnam.Keywords: international financial contagion, flight-to-quality, Vietnam, US subprime crisis, ADCC-GARC

COMMON ERRORS IN PRONOUNCING FINAL CONSONANTS OF ENGLISH-MAJORED SOPHOMORES AT TAY DO UNIVERSITY, VIETNAM

It is not deniable that pronunciation is considered one of the most crucial parts of learning English helping learners enhance their communication in both speaking and listening comprehension. To reach a level of a clear and precise pronunciation has never been an effortless task; however, it is a far more problematic one for English majored students regardless of their learning years. For this reason, the study entitled “Common Errors in Pronouncing Final Consonants of English-Majored Sophomores at Tay Do University” was implemented with the aim at investigating the errors that English-majored students encountered in pronouncing final consonants. 80 English-majored sophomores from course 13 at Tay Do University were selected to participate in the study. Questionnaires and recording tests were delivered to the participants for collecting data and getting more information. The collected data from the two instruments mentioned above were all analyzed afterward. The findings of the research revealed that sophomores of English major often mispronounced the final consonants, particularly /s/, /z/, /ʃ/, /f/ and /v/ in two main mistakes, including omission and substitution. The results of this study may also be useful for those who are interested in this field. Article visualizations

ECONOMIC INTEGRATION AND ENDOGENOUS GROWTH: AN EXPLANATION USING AK MODEL

Abstract: This research investigates the impacts of economic integration on endogenous growth by an application of the AK learning-by-doing model. Assuming that the knowledge that increases the productivity of labor will be created by accumulated capital, we divide economic integration into two different categories: one-way and two-way integration. The results show that two identical countries cannot have any benefits from economic integration. If two countries are different, the domestic country should only integrate with foreign countries that have a lower cost of capital of wage, or higher learning coefficient (the speed of transferring accumulated capital to knowledge) in the case of one-way integration. The same conclusion is still drawn in the case of two-way integration for two similar countries.Keywords: economic integration, endogenous growth, AK mode

A STUDY ABOUT MASTITIS INFECTION CHARACTERISTICS IN DAIRY COW OF BAVI, HANOI, VIETNAM

Objective: The present research was performed to investigate the prevalence of mastitis infection in Bavi, Hanoi, Vietnam and also to study the relationship between places, seasons and cow breeds with the disease occurrence.Methods: Mastitis infection was diagnosed by clinical symptoms observation and California Mastitis Test (CMT). The results of these 2 methods were then analyzed to understand the clinical and subclinical infection. The infected cases were also separated to different places and breeds to analyze the relationship with disease prevalence. In the seasonal investigation, the mastitis infection was diagnosed continuously over one year with the aid of farm managers and local veterinarians. Results: Positive infection detected by CMT kits were significantly higher than that of the clinical symptoms diagnose, suggested the involvement of subclinical infection cases, the infection in which no clinical symptoms could be observed. There was no significance difference between places and seasons, however the occurrence in summer was higher than other seasons. The Holstein Friesian (HF) purebred had significantly higher infection rates compare to crossbreds. In addition, there is a trend of increased percentage of the prevalence of mastitis in higher generations.Conclusion: The prevalence of mastitis in Bavi is lower than other parts of Hanoi and other places in Vietnam. Crossbreds F1HF and F2HF had significantly low sensitivity to mastitis and were recommended for dairy cow husbandry. Summer is the most risky time for mastitis and therefore requires the application of appropriate preventive methods.Key words: clinical mastitis, subclinical mastitis, dairy cows, breed, season, prevalence, Bav