ON SITE WASTEWATER TREATMENT MODEL USED IN URBAN RESIDENTIAL AND TOURISM AREAS

Joint Research on Environmental Science and Technology for the Eart

WATER ENVIRONMENT AND WATER POLLUTION CONTROL IN VIETNAM : OVERVIEW OF STATUS AND MEASURES FOR FUTURE

Joint Research on Environmental Science and Technology for the Eart

A Study of SVC’s Impact Simulation and Analysis for Distance Protection Relay on Transmission Lines

This paper focuses on analyzing and evaluating impact of a Static Var Compensator (SVC) on the measured impedance at distance protection relay location on power transmission lines. The measured impedance at the relay location when a fault occurs on the line is determined by using voltage and current signals from voltage and current transformers at the relay and the type of fault occurred on the line. The MHO characteristic is applied to analyze impact of SVC on the distance protection relay. Based on the theory, the authors in this paper develop a simulation program on Matlab/Simulink software to analyze impact of SVC on the distance protection relay. In the power system model, it is supposed that the SVC is located at mid-point of the transmission line to study impact of SVC on the distance relay. The simulation results show that SVC will impact on the measured impedance at the relay when the fault occurs after the location of the SVC on the power transmission line

Bacteria associated with soft coral from Mot island - Nha Trang bay and their antimicrobial activities

Microbial communities associated with invertebrates had been considered as a new source of bioactive compounds. The soft coral associated bacteria in Mot island, Nha Trang bay were isolated, extracted and assessed for antagonistic activity against human and coral pathogens, the strongly active strains were identified by 16S rRNA analysis. The soft coral associated bacterium SCN10 had abcd antibacterial pattern which was named for inhibition towards Bacillus subtilis (pattern a), Escherichia coli (pattern b), Salmonella typhimurium (pattern c) and Serratia marcescens (pattern d). It was the nearest strain to the well-known antibiotic producer Bacillus amyloliquefaciens with 99% sequence similarity. Whereas strain SCL19 had abde pattern which means inhibition of the growth of B. subtilis, E. coli, S. marcescens and Vibrio parahaemolyticus (pattern e). This strain SCL19 affiliated with Bacillus sp. strain A-3-23B with 99.8% identity. In addition to antimicrobial activity to the aforementioned tested bacteria, the isolate SCX15 also inhibited Vibrio alginolyticus (pattern f) and Candida albicans (pattern g), so this isolate possessed abcdefg antimicrobial pattern. The coral associated isolate SCX15 was identified as Bacillus velezensis with 99% sequence similarity. Among the 78 screened strains, 25 isolates possessed antibacterial activity against at least one of seven tested microorganisms and exhibited 12 different types of antimicrobial activities, suggesting that they can produce many different natural substances with antibacterial activity

Efficient Integration of Multi-Order Dynamics and Internal Dynamics in Stock Movement Prediction

Advances in deep neural network (DNN) architectures have enabled new prediction techniques for stock market data. Unlike other multivariate time-series data, stock markets show two unique characteristics: (i) \emph{multi-order dynamics}, as stock prices are affected by strong non-pairwise correlations (e.g., within the same industry); and (ii) \emph{internal dynamics}, as each individual stock shows some particular behaviour. Recent DNN-based methods capture multi-order dynamics using hypergraphs, but rely on the Fourier basis in the convolution, which is both inefficient and ineffective. In addition, they largely ignore internal dynamics by adopting the same model for each stock, which implies a severe information loss. In this paper, we propose a framework for stock movement prediction to overcome the above issues. Specifically, the framework includes temporal generative filters that implement a memory-based mechanism onto an LSTM network in an attempt to learn individual patterns per stock. Moreover, we employ hypergraph attentions to capture the non-pairwise correlations. Here, using the wavelet basis instead of the Fourier basis, enables us to simplify the message passing and focus on the localized convolution. Experiments with US market data over six years show that our framework outperforms state-of-the-art methods in terms of profit and stability. Our source code and data are available at \url{https://github.com/thanhtrunghuynh93/estimate}.Comment: Technical report for accepted paper at WSDM 202

FIRST RESULTS ON NITROGEN AMMONIA REMOVAL FROM GROUND WATER BY NITRIFICATION AT CEETIA LAB

Joint Research on Environmental Science and Technology for the Eart