ANTIBACTERIAL ACTIVITY OF SILVER NANOPARTICLES AGAINST Aeromonas spp. AND Vibrio spp. ISOLATED FROM AQUACULTURE WATER ENVIRONMENT IN THUA THIEN HUE

Abstract: A silver nanoparticle solution prepared at the Center for Incubation and Technology Transfers was used in the current study. The nanoparticles have an average size of 15.0 nm. The silver nanoparticle solution exhibits an antibacterial activity to Aeromonas hydrophyla and Aeromonas caviae isolated from fresh water fish ponds and Vibrio harveyi and Vibrio alginoliticus isolated from white shrimp ponds. The silver nanoparticle solution at a concentration of 25 ppm inhibits A. caviae and A. hydrophila, and the peak attenuation time was 24 hours after exposure to the bacteria. The solution at a concentration of 12.5 ppm also inhibits Vibrio harveyi and Vibrio alginoliticus, and the peak attenuation time was 48 hours after exposure to the bacteria.Keywords: antibacterial activity, silver nanoparticles, Aeromonas spp. and Vibrio spp

RESEARCH ON NEARSHORE WAVE CONDITIONS AT NHAT LE COASTAL AREA (QUANG BINH PROVINCE) BY USING MIKE21-SW

Research on marine dynamics, including coastal wave motions, is a concern of countries in the world in general and Vietnam in particular. Coastal wave dynamics has a direct impact on human activities including coastal construction, shipping, irrigation, aquatic resources exploitation, etc. The coastal area of Nhat Le, Quang Binh is one of the areas strongly influenced by the coastal wave regime which increases the risk of coastal erosion, estuarine sedimentation, destroys the economic life, affects marine fishing and directly affects the tourist beach area. This article aims to introduce some research results based on the application of MIKE21-SW model of the Danish Hydraulic Institute (DHI) to simulate coastal wave regime in Nhat Le coastal zone, Quang Binh province. The model results are verified by real-time wave data in long-term from the WaMoS® II Radar System at Quang Binh station. The results show that there are many similarities in wave height and direction between the computational model and the actual observation data from the radar system. This result will be an important basis for research and application for coastal protection, reduction in river mouth sedimentation, clearing and flood drainage in the study area

Learning efficient temporal information in deep networks: From the viewpoints of applications and modeling

With the introduction of deep learning, machine learning has dominated several technology areas, giving birth to high-performance applications that can even challenge human-level accuracy. However, the complexity of deep models is also exploding as a by-product of the revolution of machine learning. Such enormous model complexity has raised the new challenge of improving the efficiency in deep models to reduce deployment expense, especially for systems with high throughput demands or devices with limited power. The dissertation aims to improve the efficiency of temporal-sensitive deep models in four different directions. First, we develop a bandwidth extension mapping to avoid deploying multiple speech recognition systems corresponding to wideband and narrowband signals. Second, we apply a multi-modality approach to compensate for the performance of an excitement scoring system, where the input video sequences are aggressively down-sampled to reduce throughput. Third, we formulate the motion feature in the feature space by directly inducing the temporal information from intermediate layers of deep networks instead of relying on an additional optical flow stream. Finally, we model a spatiotemporal sampling network inspired by the human visual perception mechanism to reduce input frames and regions adaptively

HYDROGEN-PLASMA-TREATED NANO TIO2 FOR PHOTOCATALYTIC OXIDATION OF VOCS IN AIR STREAM

Unlike water treatment processes, the photocatalytic oxidation of VOCs in air stream exhibits many challenges. This study will develop the hydrogen-plasma-treated TiO2 with improvement in photocatalytic activity. The hydrogen-plasma-treatment was carried out in the non-thermal atmospheric pressure reactor at room temperature or above. The catalysts were prepared and analyzed by advanced techniques such as X-ray diffraction (XRD), scanning electro-microscopy (SEM) and transmission electro-microscopy (TEM). The photocatalytic activity of the catalyst has been investigated under UV light with various reaction conditions such as different initial toluene/formaldehyde concentrations and water content. Significantly, the conversion of toluene by a plasma-treated sample was 1.5 times higher than the bare TiO2 in a similar reaction condition

Innovative Firm Performance Management Using a Recommendation System Based on Fuzzy Association Rules: The Case of Vietnam’s Apparel Small and Medium Enterprises

Purpose: This study aims to apply a classification algorithm based-on fuzzy association rules (FARs) to improve the effectiveness of firms' performance prediction problem. Particularly, this study investigates potential FARs exists between inputs and outputs of firms' performance management process. These extracted FARs could be used to help firm’s managers make better dicision to improve firm’s performance.   Theoretical framework: Private enterprise development has been identified as key to Vietnam's economy that was commonly depended on state enterprise. For that, understanding and improving firms' performance and productivity is one of the most important tasks, from both macro and micro perspectives. There have been many studies on Vietnam's firm performance, but mostly relying on econometric methods that limit the understanding with structural equations. This study, instead, attempts to utilize new achievements of Artificial Intelligence (AI) for this task. Among AI techniques, fuzzy association rule is able to address the relationship between input factors and firm performance indicators. For each company, the finding FARs can be used to predict its performance and then change the business plan or react to improve weekness of organization.   Design/Methodology/Approach: The proposal model is applied on data of small and medium-sized enterprises (SMEs) of the apparel industry in Vietnam in the period 2010-2015. The sample consist of a total of 23637 observation of  Vietnam firms in apparel and textile industry and contains 16 main criterias for those firms.   Finding: A recommendation system (RS) is constructed from disclosed FARs and is a key factor in a novel innovative firms' performance management process. The percentage of classified instances using the mining FARs is not quite high (about 82%), but it is not always the case. Vietnam’s apparel dataset includes rare classes of ROA, therefore applying only frequent FARs is not enough. This issue can be fixed by using both frequent and infrequent FARs.       Research, practical & social implications: The proposed model has a great opportunity to use not only in the small and medium-sized enterprises (SMEs) of the apparel industry but other industrial sectors. FARs support the well-understand of firm performance to firm’s manager and help them better to react. Besides, FARs could be used to create RSs that makes alerts about risk automatically.   Originality/Value: The fact, our current study is the first to inspect the ability of FARs on SMEs of the apparel industry in Vietnam. This study provides theoritical potential knowledge and empirical evidence in the application of FARs technology in innovative firm’s management

Evolutionary testing of autonomous software agents

A system built in terms of autonomous software agents may require even greater correctness assurance than one that is merely reacting to the immediate control of its users. Agents make substantial decisions for themselves, so thorough testing is an important consideration. However, autonomy also makes testing harder; by their nature, autonomous agents may react in different ways to the same inputs over time, because, for instance they have changeable goals and knowledge. For this reason, we argue that testing of autonomous agents requires a procedure that caters for a wide range of test case contexts, and that can search for the most demanding of these test cases, even when they are not apparent to the agents’ developers. In this paper, we address this problem, introducing and evaluating an approach to testing autonomous agents that uses evolutionary optimisation to generate demanding test cases. We propose a methodology to derive objective (fitness) functions that drive evolutionary algorithms, and evaluate the overall approach with two simulated autonomous agents. The obtained results show that our approach is effective in finding good test cases automatically

Alumni survey of Masters of Public Health (MPH) training at the Hanoi School of Public Health

© 2007 Le et al; licensee BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution Licens

Using multi-temporal satellite images to evaluate the changes of vegetation index of land cover in Thai Binh Province

224pThis chapter describes the current status of Thai Binh province in Vietnam and its agricultural development plans for 2010. The environmental and economic impacts of pig production are discussed. The various stakeholders and their active involvement in agricultural production are analysed. In addition, an innovative approach to sustainable development of animal produce commodity chains in northern Vietnam, is described. The 12-month E3P Project (Environmental Protection and Pig Production) was aimed to establish baseline work for designing and implementing a geographical information system. A large proportion of unknown factors concerning the issue of effluents in the province was studied at the farm, communal and district, and on a scientific levels. These unknown factors justify the regional diagnosis presented by the E3P Project

Dynamic Structural Neural Network

The file attached to this record is the author's final peer reviewed version.Artificial neural network (ANN) has been well applied in pattern recognition, classification and machine learning thanks to its high performance. Most ANNs are designed by a static structure whose weights are trained during a learning process by supervised or unsupervised methods. These training methods require a set of initial weights values, which are normally randomly generated, with different initial sets of weight values leading to different convergent ANNs for the same training set. Dealing with these drawbacks, a trend of dynamic ANN was invoked in the past year. However, they are either too complex or far from practical applications such as in the pathology predictor in binary multi-input multi-output (MIMO) problems, when the role of a symptom is considered as an agent, a pathology predictor’s outcome is formed by action of active agents while other agents’ activities seem to be ignored or have mirror effects. In this paper, we propose a new dynamic structural ANN for MIMO problems based on the dependency graph, which gives clear cause and result relationships between inputs and outputs. The new ANN has the dynamic structure of hidden layer as a directed graph showing the relation between input, hidden and output nodes. The properties of the new dynamic structural ANN are experienced with a pathology problem and its learning methods’ performances are compared on a real well known dataset. The result shows that both approaches for structural learning process improve the quality of ANNs during learning iteration