ESTIMATION OF WAVE CHARACTERISTICS IN EAST VIETNAM SEA USINGWAM MODEL

WAM (WaveModeling) is a third generation wave model developed by WAMDI Group which describes the evolution of a two-dimensional ocean wave spectrum under the effects of winds, currents, bottom and non-linear wave-wave interactions. The model runs for deep and shallow waters and includes depth and current refraction. This study used the WAM cycle 4.5 with model domain which is covered from 990E to 1210E and 00N to 250N with a resolution of ∆X = ∆Y = 0.250. Bathymetry of East Vietnam Sea (EVS) was taken from ‘ETOPO5’ data set of National Geophysical Data Center, Colorado, USA with resolution of 5’ (≈ 9 km). Wind velocities were obtained from 6 hourly NCEP/NCAR reanalysis data, USA with resolution of ∆X = ∆Y = 0.250. Study results show that during NE monsoon period, the main wave direction in EVS was NE and vice versa during SW monsoon period. Regions of greatest wave height were in the central and northern part of the EVS. Statistic of computed wave characteristics from 1987 to 2011 shows that wave regime in the offshore region of Nhatrang coast has two main wave directions that are NE with 40.82% of occurrence, SSW with 20.15% of occurrence. NE monsoon wave dominated from October to April of the next year, SW monsoon wave dominated from June to August. May and September are transitional periods. Assimilation of wind data with resolution of ∆X = ∆Y = 0.250 permits the model to be used to simulate the wave field during typhoon activity in EVS

A research on the performance of down-flow hanging sponge (DHS) reactor treating domestic wastewater

The aim of this study was to evaluate the performance of a down-flow hanging sponge (DHS) system in treating domestic wastewater. A pilot-scale of DHS system with a capacity of 60 L was designed and fabricated from polyvinyl chloride (PVC). The dimensions of DHS system are 1.5 m in height and square surface with 0.2 m in width, consists of three identical segments connected vertically in series. Each segment was filled by polyurethane sphere containing sponge. The total area of sponge and polyurethane sphere was 3,300 m2 m-3, density at 150 kg m-3, void ratio at 90%. DHS system was operated at ambient temperature within 82 days and stepwise increased of organic f rate from 0.5 to 1 and 1.5 kg COD m-3 d-1. The results showed that, this system performed well throughout the operational period and achieve the maximum removal of COD, BOD5, NH4+-N, and TN as 80%, 83%, 65% and 60%. The effluent of wastewater from DHS system achieved the requirement for National technical regulation on domestic wastewater of Vietnam type B QCVN 14:2008/BTNMT. In conclusion, the performance of DHS system indicated a high potential for application in removing organic matter and converting nitrogen ammonia to nitrogen nitrate, however it did not perform well for the removal of total nitrogen, it is necessary to study further by providing an anoxic zone in the system to enhance the treatment of nutrient in wastewater

Assessment of Radioactive Gaseous Effluent Released from Ninh Thuan 1 Nuclear Power Plant under Scenario of INES-level 7 Nuclear Accident

Based on guidance RG 1.109, RG 1.111 published by United States Nuclear Regulatory Commission (USNRC) our research concentrates on assessing radiation doses caused by radioactive substances released from the Ninh Thuan 1 nuclear power plant (NPP) to the environment under scenario of an INES-level 7 nuclear accident caused by two incidents: Station Black Out (SBO) and Loss of Coolant Accident (LOCA) using software RASCAL4.3 provided by the Emergency Operations Center of USNRC. The NPP Ninh Thuan 1 is assumed to use the VVER-1200 technology with a total power of 2400 MWe from two units. The input data for the model calculations is built based on the accident scenario, the technical parameters of VVER-1200 technology and the meteorology. In this work the meteorological data on dry and rainy seasons which are typical for the Ninh Thuan region was considered. The maximum dose distributions were calculated within 40 km from the NPP site. The distributions are strongly affected by meteorological conditions. In the rainy season the dose values near the plant are higher than those in the dry season due to deposition effect of the radioactive substances. The calculation results show that consequences of the accident are very serious. A total radioactivity of radiological equivalence 225,000 TBq to 131I released to the atmosphere. Within 20km the Total Effective Dose Equivalence (TEDE) values are very high, about several tens of times greater than the dose limit. It is requested to establish National Steering Board for Accident Response to direct the relevant authorities in response for the accident consequences and ensure for security in the area of NPP. The public communication, emergency preparedness plan, people evacuation must be implemented under the guidance of Circular 25/2014/TT-BKHC

Explain by Evidence: An Explainable Memory-based Neural Network for Question Answering

Interpretability and explainability of deep neural networks are challenging due to their scale, complexity, and the agreeable notions on which the explaining process rests. Previous work, in particular, has focused on representing internal components of neural networks through human-friendly visuals and concepts. On the other hand, in real life, when making a decision, human tends to rely on similar situations and/or associations in the past. Hence arguably, a promising approach to make the model transparent is to design it in a way such that the model explicitly connects the current sample with the seen ones, and bases its decision on these samples. Grounded on that principle, we propose in this paper an explainable, evidence-based memory network architecture, which learns to summarize the dataset and extract supporting evidences to make its decision. Our model achieves state-of-the-art performance on two popular question answering datasets (i.e. TrecQA and WikiQA). Via further analysis, we show that this model can reliably trace the errors it has made in the validation step to the training instances that might have caused these errors. We believe that this error-tracing capability provides significant benefit in improving dataset quality in many applications.Comment: Accepted to COLING 202

Unveiling the role of artificial intelligence for wound assessment and wound healing prediction

Wound healing is a very dynamic and complex process as it involves the patient, wound-level parameters, as well as biological, environmental, and socioeconomic factors. Its process includes hemostasis, inflammation, proliferation, and remodeling. Evaluation of wound components such as angiogenesis, inflammation, restoration of connective tissue matrix, wound contraction, remodeling, and re-epithelization would detail the healing process. Understanding key mechanisms in the healing process is critical to wound research. Elucidating its healing complexity would enable control and optimize the processes for achieving faster healing, preventing wound complications, and undesired outcomes such as infection, periwound dermatitis and edema, hematomas, dehiscence, maceration, or scarring. Wound assessment is an essential step for selecting an appropriate treatment and evaluating the wound healing process. The use of artificial intelligence (AI) as advanced computer-assisted methods is promising for gaining insights into wound assessment and healing. As AI-based approaches have been explored for various applications in wound care and research, this paper provides an overview of recent studies exploring the application of AI and its technical developments and suitability for accurate wound assessment and prediction of wound healing. Several studies have been done across the globe, especially in North America, Europe, Oceania, and Asia. The results of these studies have shown that AI-based approaches are promising for wound assessment and prediction of wound healing. However, there are still some limitations and challenges that need to be addressed. This paper also discusses the challenges and limitations of AI-based approaches for wound assessment and prediction of wound healing. The paper concludes with a discussion of future research directions and recommendations for the use of AI-based approaches for wound assessment and prediction of wound healing

A Status Data Transmitting System for Vessel Monitoring

This paper presents a status data transmitting system suitable for vessel monitoring. The system consists of four main parts, which are a status data module, a frequency synthesizer, a power amplifier and a horn antenna. The status data module packs information of the identification, longitude, latitude and state of the vessel into data frames. FSK/MSK/GMSK schemes were used to modulate the data. The frequency synthesizer was designed with very high stability over temperature and very low frequency tolerance. The power amplifier provides 130 W output power at S band. The impedance bandwidth of the horn antenna can be controlled using the beveling technique

An Updated Loop-Mediated Isothermal Amplification Method for Rapid Diagnosis of H5N1 Avian Influenza Viruses

We designed a new set of primers for reverse transcriptase loop-mediated isothermal amplification (RTLAMP) to specifically amplify the HA gene of avian influenza viruses subtype H5N1. By testing nine H5N1 virus strains and 41 clinical samples collected in Northern Vietnam, we found that the new primers showed higher sensitivity and specificity than the previously published RT-LAMP primers and were comparable to the RT-PCR method currently recommended by WHO. These results suggest that our RT-LAMP assay may be a better choice as a diagnostic tool for current H5N1 influenza virus infection