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.

Assessment of Shear Strength Models of Reinforced Concrete Columns

Shear strength is a crucial parameter in designing Reinforced Concrete (RC) columns considering the effects of lateral loads such as wind or earthquakes. Numerous design codes and published studies have proposed equations for calculating the shear strength of RC columns. However, a discrepancy exists between the calculated models and the experimental results. The aim of this study is to evaluate the calculated models for the shear strength of rectangular RC columns based on 735 data sets, obtained from the literature. Six code-based and empirical models are investigated in this paper. The four code-based models include ACI 318 (2014), CSA (2014), Eurocode 8 (2005), and FEMA 273 (1997), and the two empirical models are proposed by Ascheim & Moehle (1992) [8] and Sezen & Moehle (2004) [9]. The shear strengths of RC columns are calculated for the six models using inputs from the experimental database. Finally, the results are evaluated using statistical indicators, including coefficient of determination and root-mean-squared error. The results reveal that Eurocode 8 (2005) is the best model, followed by Sezen & Moehle (2004) and Canada CSA (2014) since the results of those models are close to the experimental ones and shown to be more conservative than the others

UIT-Saviors at MEDVQA-GI 2023: Improving Multimodal Learning with Image Enhancement for Gastrointestinal Visual Question Answering

In recent years, artificial intelligence has played an important role in medicine and disease diagnosis, with many applications to be mentioned, one of which is Medical Visual Question Answering (MedVQA). By combining computer vision and natural language processing, MedVQA systems can assist experts in extracting relevant information from medical image based on a given question and providing precise diagnostic answers. The ImageCLEFmed-MEDVQA-GI-2023 challenge carried out visual question answering task in the gastrointestinal domain, which includes gastroscopy and colonoscopy images. Our team approached Task 1 of the challenge by proposing a multimodal learning method with image enhancement to improve the VQA performance on gastrointestinal images. The multimodal architecture is set up with BERT encoder and different pre-trained vision models based on convolutional neural network (CNN) and Transformer architecture for features extraction from question and endoscopy image. The result of this study highlights the dominance of Transformer-based vision models over the CNNs and demonstrates the effectiveness of the image enhancement process, with six out of the eight vision models achieving better F1-Score. Our best method, which takes advantages of BERT+BEiT fusion and image enhancement, achieves up to 87.25% accuracy and 91.85% F1-Score on the development test set, while also producing good result on the private test set with accuracy of 82.01%.Comment: ImageCLEF2023 published version: https://ceur-ws.org/Vol-3497/paper-129.pd

Aquatic emergency preparedness and response system in Viet Nam

Viet Nam is one of the top worldwide producers of aquaculture products which accounts for about 22 percent of total agricultural GDP of Viet Nam. Recently, diseases have become the biggest challenge for global aquaculture development therefore the Vietnamese government has paid close attention to develop an effective aquatic emergency preparedness and response system to timely deal with disease introduction and outbreaks. The Department of Animal Health (DAH), under the Ministry of Agriculture and Rural Development (MARD), which is the competent authority of aquatic animal health management. To monitor transboundary diseases (especially the OIE-listed diseases), the current Vietnamese regulations only allow import of aquatic animals and its products which are certified as disease-free by competent authority of exporting country, and export aquatic animals and its products complying with importing conditions of importing country. Regional Animal Health Offices (belong to DAH) shall carry out sampling for testing pathogens and isolation for imported aquatic animals and its products as regulated in Circular 26/2016/TT-BNNPTNT dated 30 June 2016 before granting permit to import or export. For domestic transportation of aquatic animals, provincial sub DAH is responsible for monitoring infectious pathogens to certify disease-free status of aquatic animals before issuing health certificate for movement. In addition, a reporting and response system to aquatic animal diseases was established in the country from farm level to central level (DAH). Early detection and warning of diseases is critical for disease prevention and control, thus since 2014, the DAH has implemented national surveillance programs focusing on dangerous diseases in the key farming species (brackish-water shrimps, pangasius catfish) according to Circular 04/2016/TT-BNNPTNT dated 10 May 2016 of MARD and support exportation of aquatic animals and its products complying with international regulations and importing countries based on OIE recommendations and Circular 14/2016/TT-BNNPTNT dated 2 June 2016

Load Shedding in Microgrids with Dual Neural Networks and AHP Algorithm

This paper proposes a new load shedding method based on the application of a Dual Neural Network (NN). The combination of a Back-Propagation Neural Network (BPNN) and of Particle Swarm Optimization (PSO) aims to quickly predict and propose a load shedding strategy when a fault occurs in the microgrid (MG) system. The PSO algorithm has the ability to search and compare multiple points, so the proposed NN training method helps determine the link weights faster and stronger. As a result, the proposed method saves training time and achieves higher accuracy. The Analytic Hierarchy Process (AHP) algorithm is applied to rank the loads based on their importance factor. The results of the ratings of the loads serve as a basis for constructing the load shedding strategies of a NN combined with the PSO algorithm (ANN-PSO). The proposed load shedding method is tested on an IEEE 25-bus 8-generator MG power system. The simulation results show that the frequency recovery of the power system is positive. The proposed neural network adapts well to the simulated data of the system and achieves high performance in fault prediction

Response surface modeling and optimizing conditions for anthocyanins extraction from purple sweet potato (Ipomoea batatas (L.) Lam) grown in Lam Dong province, Vietnam

Anthocyanin is increasingly used as a natural and safe coloring agent. In this paper, the extraction of purple sweet potato anthocyanin (PSPAs) was investigated by using response surface methodology (RSM). Different extraction temperatures of solvent ethanol (60 - 70 °C), duration of extraction (35 - 45 min) and solid-liquid ratios (4:1 - 6:1) were selected in order to extract PSPAs. The highest anthocyanin content of 206.019 mg/L of PSPAs was collected at the solid liquid ratio 6:1, extraction time 39.61 min, and temperature 67.38°C. PSPAs yield detailed significant correlation with high F values, low P values (<0.0001), the determination coefficient (R2=0.9986) and a high desirability 93.5%