This paper presents results of research of the wave regime in Vietnamese waters (South China Sea) based on the data of numerical modeling data using the WAM model (WAVE Modeling). The model domain covers the basin of the South China Sea (SCS). The bathymetry of the SCS used in the model is based on the ETOPO5 digital database. Wind parameters are based on the six-hour NCEP/NCAR reanalysis data with a resolution of ΔX = ΔY = 0.25° over the period 1979–2021. The field wave data measurements were collected by the Institute of Oceanography of Vietnam Academy of Science and Technology in the southern central Vietnamese waters in 2013. The statistical data of computed wave characteristics for the period of 43 years (1979–2021) illustrate that the main wave direction in Vietnamese waters was NE during the Northeastern (NE) monsoon, and in the opposite direction during the Southwestern (SW) monsoon. The NE monsoon wave was more dominant than that of the SW monsoon wave. Recurrence frequency (%) of significant wave height Hs >1.0 m (Hs – significant wave height is an average of 1/3 the largest of serial waves relative to average seawater level) greater than 50%
covered the northeastern, central region of the SCS, and central Vietnamese coast. The wave recurrence frequency in the Gulf of Tonkin and Gulf of Thailand was <40% and <30%, respectively. The central Vietnamese coast from Ly Son Island to Phu Quy Island was the strongest affected by wave action. The recurrence frequency of the maximum significant wave height Hs >3.5 m was greater than 1.5%. The Gulf of Tonkin (Bach Long Vi Island) and the Gulf of Thailand (Tho Chu Island) were less affected by
wave action than the central Vietnamese coast: the recurrence frequency of the maximum wave height (Hs>3.5 m) was less than 0.1%. Phu Quy and Con Dao Islands were more influenced by wave action during both seasons than the central coast of Vietnam.
We present the in situ synthesis of silver nanoparticles (AgNPs) through ionotropic gelation utilizing the biodegradable saccharides lactose (Lac) and alginate (Alg). The lactose reduced silver ions to form AgNPs. The crystallite structure of the nanocomposite AgNPs@Lac/Alg, with a mean size of 4–6 nm, was confirmed by analytical techniques. The nanocomposite exhibited high catalytic performance in degrading the pollutants methyl orange and rhodamine B. The antibacterial activity of the nanocomposite is pH-dependent, related to the alterations in surface properties of the nanocomposite at different pH values. At pH 6, the nanocomposite demonstrated the highest antibacterial activity. These findings suggest that this nanocomposite has the potential to be tailored for specific applications in environmental and medicinal treatments, making it a highly promising material.
The variability of nonvolatile memory becomes more important as memory capacity increases. This is because, in general, individual device variability increases with scaling down. Using our simulation, we analyzed the intrinsic variability in phasechange memory switching originating from stochastic nucleation for self-heating and heater-based cell architectures. Differential equations of electrothermal and phase-field models were numerically solved in a fully coupled manner to simulate the amorphization and crystallization of the active material in the reset and set operations, respectively. Nuclei were seeded stochastically based on Poisson's probability. The phase distribution and cell resistance vary sample by sample owing to stochastic nucleation. The variability of reset resistance occurs because of nucleation during the falling time (FT) of the reset pulse, which is a short time of 10 ns. The resistance distribution was obtained by collecting data from 100 samples. All cells become reset by the 10 ns FT, with a narrow distribution of the reset resistance. As the FT increases, the resistance distribution becomes wider because some cells obtain more nuclei. As the FT becomes sufficiently long, the resistance distribution narrows again, and almost all cells are in the set state. The reset resistance is better fitted to the Weibull statistics than to the lognormal statistics. The heater-based cell showed less variability, whereas the self-heating cell consumed less energy during switching. Our simulation is useful in estimating the device performance and analyzing the variability as demonstrated in this report.
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