Development of freshwater prawn (Macrobrachium rosenbergii) seed production and culture technology in the Mekong Delta Region of Vietnam: A review of the JIRCAS Project at Cantho University.

The Mekong Delta of Vietnam is a region rich in aquatic resources having high potential for aquaculture development. Inland aquaculture in the Mekong Delta has greatly increased since the last decade. Fish culture carried out in combination with other agricultural activities such as animal husbandry and rice cultivation, and intensive aquaculture in ponds and cages have been the dominant forms of fish production. However, the giant freshwater prawn, Macrobrachium rosenbergii, has recently become a species of economic significance and the target of aquaculture activity in the Mekong Delta. M. rosenbergii is cultured throughout the region in the rice fields, ponds, orchard gardens and in pens along river banks. The major constraints in this industry are seed supply and culture techniques, becoming the major obstacles for the further development of the culture of this species. In a collaborative research project implemented between the Japan International Research Center for Agricultural Sciences (JIRCAS) and Cantho University (CTU) since 1994, studies have been carried out on various aspects relating to the establishment of M. rosenbergii seed production and culture technology. The project is now in the middle of its second phase and has generated a great deal of scientific and practical information. This paper presents an overview of the achievements of this project

The Vietnamese shrimp trade: livelihoods analysis of stakeholders and market chain analysis

Aquaculture and capture fisheries in Vietnam have been increasing fast in the last decade, especially aquaculture growth rate is 12% for the 1999 – 2003 period, contributing a significant part into the hunger eradication and poverty reduction1. Vietnam is to be ranked into one of the countries potential to produce the aquatic economic in the world, and the fact is that, after 40 years of establishing, the fisheries sector has made remarkable contributions to the country. By the list, at the moment the aquatic products make up about 4 - 5% of GDP and create job opportunities for over 3 three million employees (VASEP, 2004), in which the largest contribution is from shrimp farming. [PDF contains 124 pages.

Comparative study of the analgesic effects of Bungarus fasciatus snake venom from Vinh Phuc and Tien Giang Provinces of Vietnam

Purpose: To determine the analgesic activity of Bungarus fasciatus venoms and their fractions from two Vietnamese Provinces. Methods: Male Swiss Albino mice were randomly divided into three groups containing 8 to 10 mice each. Control group was injected subcutaneously with normal saline, standard group received aspirin solution (50 mg/kg) perorally, and study group received a solution of crude venom or isolated fractions in physiological saline. To determine analgesic activity, acetic acid writhing and tail immersion tests were used. The venoms were separated by liquid chromatography and the analgesic activity of the fractions was analyzed. Results: Both venoms showed analgesic effect in the acetic acid writhing test, but only the venom from Tien Giang showed analgesic effect in the tail immersion test. The bioactive fractions of Vinh Phuc and Tien Giang venoms were significantly different, with most of Vinh Phuc venom fractions being more active (p < 0.05). Thus, 35 min after the injection, the number of writhings decreased from 15 - 16 in the control to 0.85 ± 0.34 for the BF-4VS (Vinh Phuc) fraction compared to 2.67 ± 1.20 (p < 0.05) for the BF-4DT (Thien Giang) fraction. Two proteins with analgesic activity were isolated from Vinh Phuc venom, and one with greater activity matched the known B. fasciatus phospholipase A2. Conclusion: The analgesic activity of two samples of B. fasciatus venom from two different provinces in Vietnam reveal that their pharmacological profiles differ. The isolates can be explored as leads in the development of new analgesic agents

Electrical Power Exchange in GMS and Its Influence on Power Systems in Vietnam and Thailand

The paper aims to identify the development of power interconnection network in the Greater Mekong Sub-region (GMS) which is a part of the major energy infrastructure mandated by ASEAN delegates in 1997. An overview of power systems in the region is introduced. The combined load curve for Vietnam and Thailand are formed to show the benefit of power grid interconnection of GMS. The paper also concentrates on simulation, analysis and evaluation of power transfer in 500kV and 220kV interconnection transmission lines in GMS for the planning horizon of 2010-2020. Reliability and environmental benefits of the interconnection are discussed due to interconnection. Based on the simulation results few recommendations are given

Deep Transfer Learning: A Novel Collaborative Learning Model for Cyberattack Detection Systems in IoT Networks

Federated Learning (FL) has recently become an effective approach for cyberattack detection systems, especially in Internet-of-Things (IoT) networks. By distributing the learning process across IoT gateways, FL can improve learning efficiency, reduce communication overheads and enhance privacy for cyberattack detection systems. Challenges in implementation of FL in such systems include unavailability of labeled data and dissimilarity of data features in different IoT networks. In this paper, we propose a novel collaborative learning framework that leverages Transfer Learning (TL) to overcome these challenges. Particularly, we develop a novel collaborative learning approach that enables a target network with unlabeled data to effectively and quickly learn knowledge from a source network that possesses abundant labeled data. It is important that the state-of-the-art studies require the participated datasets of networks to have the same features, thus limiting the efficiency, flexibility as well as scalability of intrusion detection systems. However, our proposed framework can address these problems by exchanging the learning knowledge among various deep learning models, even when their datasets have different features. Extensive experiments on recent real-world cybersecurity datasets show that the proposed framework can improve more than 40% as compared to the state-of-the-art deep learning based approaches.Comment: 12 page