This study examines the asymmetric effects of exchange rate on Vietnam's trade balance. Data used in this study consist of monthly trade balance, exchange rate, industrial production index and foreign direct investment series over the period from January 2010 to June 2020. Using the nonlinear autoregressive distributed lag (ARDL) bounds testing approach, the empirical findings confirm that the exchange rate has asymmetric effects on trade balance in both the long-run and short-run, meaning that a decrease in exchange rate has the different effect on trade balance as an increase in exchange rate with the same size. Specifically, in the short-run, one percent increase in the exchange rate (USD/VND) is associated with 4,2607% decrease in the trade balance, while appreciation of VND have no effects on the trade balance. In the long-run, one percent increase in the exchange rate results in 0.902% increase in the trade balance. However, no evidence is found for the effect of the appreciation of VND on the trade balance in the long-run. Moreover, the results derived from the error correction model (ECM) indicate that 89.07% of the disequilibria from the previous month are converged and corrected back to the long-run equilibrium in the current month.
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http://dx.doi.org/10.1016/j.heliyon.2023.e14455 | DOI Listing |
Cell Host Microbe
December 2024
CAS Center for Excellence in Molecular Plant Sciences (CEMPS), Institute of Plant Physiology and Ecology (SIPPE), Chinese Academy of Sciences, Shanghai 200031, People's Republic of China; College of Life Sciences and Oceanography, Shenzhen University, Shenzhen 518060, People's Republic of China. Electronic address:
Plant stomata open in response to blue light, allowing gas exchange and water transpiration. However, open stomata are potential entry points for pathogens. Whether plants can sense pathogens and mount defense responses upon stomatal opening and how blue-light cues are integrated to balance growth-defense trade-offs are poorly characterized.
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December 2024
School of Big Data, Fuzhou University of International Studies and Trade, Fuzhou, 350202, China.
The traditional machine learning methods such as decision tree (DT), random forest (RF), and support vector machine (SVM) have low classification performance. This paper proposes an algorithm for the dry bean dataset and obesity levels dataset that can balance the minority class and the majority class and has a clustering function to improve the traditional machine learning classification accuracy and various performance indicators such as precision, recall, f1-score, and area under curve (AUC) for imbalanced data. The key idea is to use the advantages of borderline-synthetic minority oversampling technique (BLSMOTE) to generate new samples using samples on the boundary of minority class samples to reduce the impact of noise on model building, and the advantages of K-means clustering to divide data into different groups according to similarities or common features.
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December 2024
Key Laboratory of Advanced Manufacturing Technology, Ministry of Education, Guizhou University, Guiyang, 550025, Guizhou, China.
In response to the challenges faced by the Coati Optimization Algorithm (COA), including imbalance between exploration and exploitation, slow convergence speed, susceptibility to local optima, and low convergence accuracy, this paper introduces an enhanced variant termed the Adaptive Coati Optimization Algorithm (ACOA). ACOA achieves a balanced exploration-exploitation trade-off through refined exploration strategies and developmental methodologies. It integrates chaos mapping to enhance randomness and global search capabilities and incorporates a dynamic antagonistic learning approach employing random protons to mitigate premature convergence, thereby enhancing algorithmic robustness.
View Article and Find Full Text PDFBiomimetics (Basel)
December 2024
Air Traffic Management Institute, Civil Aviation Flight University of China, Deyang 618307, China.
This paper proposes an Improved Spider Wasp Optimizer (ISWO) to address inaccuracies in calculating the population (N) during iterations of the SWO algorithm. By innovating the population iteration formula and integrating the advantages of Differential Evolution and the Crayfish Optimization Algorithm, along with introducing an opposition-based learning strategy, ISWO accelerates convergence. The adaptive parameters trade-off probability (TR) and crossover probability (Cr) are dynamically updated to balance the exploration and exploitation phases.
View Article and Find Full Text PDFBioessays
December 2024
Department of Molecular Neurosciences, Center for Brain Research, Medical University of Vienna, Vienna, Austria.
Neuropeptides are key modulators of adult neurocircuits, balancing their sensitivity to both excitation and inhibition, and fine-tuning fast neurotransmitter action under physiological conditions. Here, we reason that transient increases in neuropeptide availability and action exist during brain development for synapse maturation, selection, and maintenance. We discuss fundamental concepts of neuropeptide signaling at G protein-coupled receptors (GPCRs), with a particular focus on how signaling at neuropeptide GPCRs could underpin neuronal morphogenesis.
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