Tien-Thinh Nguyen

Given the context of the COVID-19 pandemic disrupting global logistics, coupled with the Russia–Ukraine war causing global energy price changes, examining both the linear and nonlinear associations between shipping cost and oil price is crucial in a global context. This study empirically exhibits the association among Global Commodity Prices Index (GPI), Oil Price (OP), Gold Future Price (GFP), and Baltic Dry Index (BDI) by employing Linear Autoregressive Distributive Lag (ARDL) as well as Nonlinear Autoregressive Distributive Lag (Nonlinear ARDL) from January 2003 to January 2023. The findings indicate that the influence of OP on BDI has a negative impact in the long run and a positive impact in the short run. Furthermore, the OP has an asymmetric effect on BDI in both the long and short terms. Finally, the predictive performance of the NARDL model outperforms the ARDL model in forecasting OP and BDI. The empirical findings derived from the ARDL and NARDL algorithms offer valuable insights for policymakers in designing public policies and for investors in portfolio construction.

The geological condition of Ho Chi Minh (HCM) City is soft soil and high groundwater and includes two main structural layers such as Pleistocene and Holocene sediments. Therefore, deep excavation of all the high-rise buildings in the city is usually supported by concrete retaining walls such as the diaphragm or bored pile retaining walls. The system limits the excavation wall deflection during the construction process which could pose a potential risk to the construction and neighborhood areas. To estimate wall deformation at a highly accurate and efficient level, this study presents several machine learning models including feed-forward neural network back-propagation (FFNN-BP), long short-term memory (LSTM), bidirectional long short-term memory (Bi-LSTM), and support vector regression (SVR). The database for the experiment was obtained from a high building in HCM City, Vietnam. The database is deployed to implement the proposed algorithms in walk-forward validation technique. As a result, the Bi-LSTM model reduced prediction errors and improved robustness than the LSTM, FFNN-BP, and SVR models. Bi-LSTM, LSTM, and FFNN-PB could be used for predicting deep excavation wall deflection. In the meantime, not only could the estimated results support safety monitoring and early warning during the construction stages but also could contribute to legal guidelines for the architecture of deep excavations in the city’s soft ground.

Labor exports are currently considered among the most important foreign economic sectors, implying that they contribute to a country’s economic development and serve as a strategic solution for employment creation. Therefore, with the support of data collected between 1992 and 2020, this paper proposes that labor exports contribute significantly to Vietnam’s socio-economic development. This study also aims to employ the Backpropagation Neural Network (BPNN), k-Nearest Neighbor (kNN), and Random Forest Regression (RFR) models to analyze labor migration forecasting in Taiwan, Korea, and Japan. The study results indicate that the BPNN model was able to achieve the highest accuracy regarding the actual labor exports. In terms of these accuracy metrics, this study will aid the Vietnamese government in establishing new legislation for Vietnamese migrant workers in order to improve the nation’s economic development.

This study aims to examine the asymmetric relationship between trade openness and FDI (foreign direct investment) inflows to Vietnam by using NARDL (nonlinear autoregressive distributed lag) during the period from 1997 to 2019. Our findings show that the influence of FDI on trade openness is asymmetric in the short-run and long-run. But the influence of trade openness on FDI is symmetric in the short-run and asymmetric in the long run.