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In response to the reform in education proposed by the Ministry of Education and Training (MoET) of Vietnam, the pendulum of most teaching and learning activities has swung their concentration to communicative competence-based approaches. Developing communicative competence amongst students of English has received tons of effort from related stakeholders such as teachers, students, educators, researchers and policy makers. The number of studies investigating this shift to date, however, has remained scared. The current study, therefore, aims to investigate the perception of English as a Foreign Language (EFL) students about the difficulties they encounter in achieving English oral communicative competence. Sixty-five students volunteered to participate in the study. The study employed a questionnaire as an instrument for data collection. The results show that the students underwent a certain number of difficulties related to linguistic, sociocultural, discourse, formulaic, interactional and strategic knowledge in the process of achieving oral communicative competence.

Rainfall–runoff modelling is complicated due to numerous complex interactions and feedback in the water cycle among precipitation and evapotranspiration processes, and also geophysical characteristics. Consequently, the lack of geophysical characteristics such as soil properties leads to difficulties in developing physical and analytical models when traditional statistical methods cannot simulate rainfall–runoff accurately. Machine learning techniques with data-driven methods, which can capture the nonlinear relationship between prediction and predictors, have been rapidly developed in the last decades and have many applications in the field of water resources. This study attempts to develop a novel 1D convolutional neural network (CNN), a deep learning technique, with a ReLU activation function for rainfall–runoff modelling. The modelling paradigm includes applying two convolutional filters in parallel to separate time series, which allows for the fast processing of data and the exploitation of the correlation structure between the multivariate time series. The developed modelling framework is evaluated with measured data at Chau Doc and Can Tho hydro-meteorological stations in the Vietnamese Mekong Delta. The proposed model results are compared with simulations of long short-term memory (LSTM) and traditional models. Both CNN and LSTM have better performance than the traditional models, and the statistical performance of the CNN model is slightly better than the LSTM results. We demonstrate that the convolutional network is suitable for regression-type problems and can effectively learn dependencies in and between the series without the need for a long historical time series, is a time-efficient and easy to implement alternative to recurrent-type networks and tends to outperform linear and recurrent models.

Monthly changes of hepatosomatic index (HSI), gonadosomatic index (GSI), and plasma testosterone (T) levels in the reproductive season of male golden rabbitfish (Siganus guttatus) in captivity were investigated by sampling the liver, testis, and blood. HSI and GSI were estimated as percentage of relative weight of liver and testis to total body weight, respectively. Plasma T levels were measured using enzyme immune assay (EIA). Fragments of the testis were embedded in paraffin, cut to 5 μm thickness, and stained with Harris' hematoxylin and eosin. The results included seasonal changes in HSI, GSI, T, and testicular stages. The plasma T levels were observed high at spermation stage and during February-March and May-June. The values of HSI were monthly fluctuations, but no significant differences between testicular stages were observed. The GSI values continuously exhibited a significant increase from December to April and maintained at high levels until June. GSI continuously increased from stage II (immature testis) to stage V (spawning testis). There was correlation between GSI and testicular stages. Histological sections showed that male golden rabbitfish are multiple spawners and spawning season starts from March to June. These results contribute to further understanding of reproductive biology in golden rabbitfish, which can be implicated for artificial propagation.

The effects of tuna viscera hydrolysate (TVH) on juvenile pompano Trachinotus blochii, growth performance, nutritional response, intestinal and liver health, and resistance to Streptococcus iniae were investigated in this study. Five isonitrogenous and isocaloric diets (protein 46.0%, lipid 10.0%) were formulated in which TVH was added to replace fishmeal protein at levels of 0 (control), 30, 60, 90, and 120 g kg-1, labelled as TVH0, TVH05, TVH10, TVH15, and TVH20, respectively. Triplicate groups of pompano were fed the respective diets for ten weeks. The results showed that fish fed diets containing TVH10 produced significantly higher final body weight and specific growth rate in comparison to the fishmeal control ( P < 0.05 ). Dietary TVH did not produce any effect on feed utilisation, somatic indices, and proximate composition of juvenile pompano ( P > 0.05 ). While most amino acids were unchanged by the dietary inclusion of TVH, phenylalanine and valine levels were significantly lower in the fish fed TVH20 diet compared to the control. Fish fed the TVH20 diet had significantly lowered total serum protein compared to the TVH10 treatment, whereas other biochemical parameters in the blood did not show any difference among treatments. The intestinal histology indicated a significant increase in goblet cell numbers in fish fed TVH10 diet. Fish fed diet supplemented with TVH showed the highest disease resistance against Streptococcus iniae after 14 days of challenge. Based on a quadratic regression between final body weight and dietary TVH levels, the optimum TVH was calculated to be 10% or 60.0 g kg-1 for maximum growth performance when fed to pompano.