The correlation between learning styles and self–directed learning of fifth graders

Vietnam education has been directed into a learner-centered approach and fostering competencies for students, especially self-directed learning. Thus, understanding how students’ learning styles impact self-directed learning are crucial for the new direction of Vietnam education. This research employed the survey method by questionnaire and presents the results of practical research on the correlation between styles of learning and self–directed learning of fifth-grade students at elementary schools in District 10, Ho Chi Minh City, Vietnam. The sample was 364 fifth graders voluntarily participating in the survey with the consent of parents. The results indicated that learning styles are strongly correlated with self–directed learning competency of fifth graders. The data revealed that students at District 10 not only attained high self-directed learning levels but also their learning styles attributing impactfully on their self-directed learning competency

Parallel circuit - a modular neural network architecture

One of the obstacles that hinder the development of Artificial Neural Networks (ANNs) is the heavy computational cost of the training process. In an attempt to address this problem, I proposed a lightweight model named Parallel Circuits (PCs), with an emphasis on modularity. One of the key inspirations for the proposed model is the human retina, which consists of various cell types that only respond to particular visual stimuli. Similarly, conventional ANNs with high redundancy are decomposed into semi-independent modules, which is deemed to provide more efficient learning, both in terms of speed and generalizability. Owing to the benefits of having fewer connections, the PC models were empirically shown to be considerably faster, especially when implemented in larger models. I also pursued the ability of automatic problem decomposition, and discovered that diversifying the learning process in each circuit strongly benefits the generalization of the proposed model. PC was shown to be advantageous in term of sparsity, which is highly correlated to modularity. DropCircuit, a regularizer that targets circuits, was introduced to further enhance their specialities. Together with PCs, DropCircuit outperformed models with dense connectivity in several experiments. The circuit-level DropCircuit also exhibited better performance compared to conventional DropOut in conjunction with both PC and non-PC configurations, demonstrating the benefits of modularity. The modularity was further enhanced by imposing a set of biologically inspired constraints. Circuits are modelled as either excitatory or inhibitory types with contrastive properties. Modified PC networks were shown to discover sparse and part-based representations, showing further improvement in generalization

Application of HEC-HMS model and satellite precipitation products to restore runoff in Laigiang river basin in Vietnam

The Laigiang river basin in the South Central Coast of Vietnam plays an important role in the socio-economic development of Binhdinh Province. In recent decades, the region has experienced commonly flooding in vast areas. This research aims to simulate event-based rainfall-runoff modelling, a historical flood event in December 2016, by applying the HEC-HMS model and rainfall data from CHIRPS. The CHIRPS data is an acceptable potential data input of the hydrology model. These have been confirmed through reliable validation indexes: The peak flood flow rate of 2,542.6 m3/s corresponds to the flood frequency of 5%; NSE with the value at 0.95; R2 coefficient reached 0.87; PBIAS being around 0.45, and PFC being at 0.89. It shows better performance in the rainy season than in the dry season. Inclusive, the CHIRPS rainfall data set and the HEC model could be used for some operational purposes in weather forecasting, especially for flood warnings in river basins in the South Central Coast, Vietnam

ẢNH HƯỞNG CỦA ASTAXANTHIN BỔ SUNG TRONG THỨC ĂN LÊN TĂNG TRƯỞNG, TỶ LỆ SỐNG VÀ MÀU SẮC DA CÁ KHOANG CỔ NEMO, Amphiprion ocellaris THƯƠNG MẠI

This study examined the influence of astaxanthin added to food on growth, survival rate and pigmentation of commercial false clownfish, Amphiprion ocellaris. Five experiments were performed with synthetic astaxanthin contents: 0; 50; 100; 150 and 200 mg/kg diets. Mean weight and mean length of fish were 1.16 ± 0.22 gand and 33.05 ± 3.29 mm respectively. Fish were fed by 5% of their live weight during the examination of 8 weeks. After 56 days of experiments, the skin pigmentation levels were analysed using Clownfish Exercise pigmentation chart which has a scale from 1 to 10. Color scales of 5 experiments: 0; 50; 100; 150 and 200 mg Astaxanthin/kg diets were 2.12 ± 0.08, 3.79 ± 0.1, 5.31 ± 0.14, 7.78 ± 0.09, 8.04 ± 0.12 respectively. The result showed that the dietary astaxanthin could increase coloration of skin compared with the control group which had the lightest color (P 0.05) but there were no significant effects on growth and survival rate of clownfish (P 0.05).Nghiên cứu này đánh giá ảnh hưởng của Astaxanthin bổ sung trong thức ăn lên tăng trưởng, tỷ lệ sống và màu sắc da của cá khoang cổ Nemo Amphiprion ocellaris thương mại. Năm lô thí nghiệm được thực hiện với hàm lượng astaxanthin tổng hợp (Carophyll Pink 10% CWS) bổ sung vào trong thức ăn là: 0, 50, 100, 150 và 200 mg/kg. Cá thí nghiệm có khối lượng và chiều dài trung bình ban đầu tương ứng là 1,16 ± 0,22 g và 33,05 ± 3,29 mm. Cá được cho ăn với khẩu phần 5% khối lượng thân trong 8 tuần. Sau 56 ngày nuôi màu sắc da của cá được đánh giá bằng phương pháp cho điểm sử dụng thước so màu Clownfish Exercise có thang điểm từ 1 tới 10. Thang điểm màu sắc của 5 lô bổ sung 0, 50, 100, 150 và 200 mg Astaxanthin/kg thức ăn lần lượt là: 2,12 ± 0,08; 3,79 ± 0,1; 5,31 ± 0,14; 7,78 ± 0,09; 8,04 ± 0,12. Kết quả cho thấy những lô thí nghệm có bổ sung Astaxanthin làm tăng màu sắc da của cá so với lô đối chứng (P0,05) nhưng không có sự khác biệt có ý nghĩa về tăng trưởng và tỷ lệ sống giữa các lô thí nghiệm với nhau (P 0,05)

LEARNING IDIOMS FOR ENGLISH MAJORS: VIETNAMESE STUDENTS’ PERCEPTIONS OF DIFFICULTIES AND LEARNING STRATEGIES

Learning idioms play an influential role in language generally and in English notably. Comprehending idioms assists language learners in integrating culture, enhancing skills, and ameliorating English levels. Numerous studies have analyzed the function of idioms in second language acquisition (Cieślicka, 2015). This study investigates the difficulties and strategies used in learning idioms by English-majored students at a regional public university (PU) in the south of Vietnam. This paper furnishes data showing learners’ perceptions of facing complications and learning methods. The samples consisted of 150 undergraduate EFL students from English-medium instruction programs. The data was analyzed by utilizing descriptive and inferential statistics. The findings reveal that students struggle to understand idiomatic terms without specific, understandable contexts. Furthermore, the results indicate that the most frequently employed strategies are guessing the meaning of idioms, learning idioms through keywords, and learning from a range of sources, particularly via media. The findings also mentioned that low-proficiency and high-proficiency students encounter identical challenges, with no significant differences. The study's results revealed that the majority of students have difficulty acquiring, recognizing, and interpreting idioms. The findings indicated that guessing the implication of idioms is the most used strategy.  Article visualizations

RADIFUSION: A multi-radiomics deep learning based breast cancer risk prediction model using sequential mammographic images with image attention and bilateral asymmetry refinement

Breast cancer is a significant public health concern and early detection is critical for triaging high risk patients. Sequential screening mammograms can provide important spatiotemporal information about changes in breast tissue over time. In this study, we propose a deep learning architecture called RADIFUSION that utilizes sequential mammograms and incorporates a linear image attention mechanism, radiomic features, a new gating mechanism to combine different mammographic views, and bilateral asymmetry-based finetuning for breast cancer risk assessment. We evaluate our model on a screening dataset called Cohort of Screen-Aged Women (CSAW) dataset. Based on results obtained on the independent testing set consisting of 1,749 women, our approach achieved superior performance compared to other state-of-the-art models with area under the receiver operating characteristic curves (AUCs) of 0.905, 0.872 and 0.866 in the three respective metrics of 1-year AUC, 2-year AUC and > 2-year AUC. Our study highlights the importance of incorporating various deep learning mechanisms, such as image attention, radiomic features, gating mechanism, and bilateral asymmetry-based fine-tuning, to improve the accuracy of breast cancer risk assessment. We also demonstrate that our model's performance was enhanced by leveraging spatiotemporal information from sequential mammograms. Our findings suggest that RADIFUSION can provide clinicians with a powerful tool for breast cancer risk assessment.Comment: v

Factors Affect Students’ Satisfaction In Blended Learning Courses In A Private University In Vietnam

Blended learning, a combination of online and offline learning, is believed to enhance students’ self-learning, and help increase their learning performances. To successfully operate a blended learning system, increasing the learners’ satisfaction seems to be an important task. Moreover, there should be a duty to understand the self-efficacy of a student to encourage them to participate in this course (Chen & Yao, 2016). As a result, knowing the internal or external factors that influence student satisfaction in blended learning is critical for the effective design of blended learning courses in the future (Graham, Henrie, & Gibbons, 2013). In this study, a 76-item survey questionnaire with a five-level Likert scale was administered to 2403 students, in which 453 returned but just 345 responses were qualified for data analysis. The questionnaire was adapted from the previous studies by Reid (1984), Wu, Hsia, Liao, & Tennyson (2008), Ali (2011), Azawei (2017). The results divulged that a) social environment and cognitive factors had significantly positive correlations with students’ satisfaction in a BL course, in which social factors have a higher relation, b) learning climate and perceived usefulness are the two factors having the most significant impact on student satisfaction, while c) students’ learning styles have the lowest correlation, but positive to the other variables. The pedagogical implications and limitations of study are also discussed

ANALYSIS OF THE POPULARITY OF VOCABULARY USED WHEN PERFORMING SPEAKING ACTIVITIES IN THE CLASS OF FIRST-YEAR ENGLISH LANGUAGE STUDENTS IN THE DIRECTION OF DISCOURSE ANALYSIS

Vocabulary learning is extremely important when learning a foreign language. Fluency in a language depends on vocabulary and its use in specific situations. Speaking well is using vocabulary flexibly and speaking fluently. Researching the popularity of vocabulary is analyzing the prevalence of vocabulary used by linguistics students in communication from discourse analysis. This is a topic the research team is working on. This project will help the researchers learn about common vocabulary that students often use to communicate outside or in the classroom. Thereby understanding whether the vocabulary that students use is diverse, rich, and for the right purpose or not. This study will help students have a more comprehensive view of the ways to use words in communication. In addition, it also helps students improve their communication vocabulary, helps in exams and can be useful for later work. In this study, the research team will investigate the students' ability to use spoken vocabulary, i.e., frequency and extent of vocabulary usage.  Article visualizations

Characterization of arsenic-resistant endophytic Priestia megaterium R2.5.2 isolated from ferns in an arsenic-contaminated multi-metal mine in Vietnam

Bioremediation is a biological process to remove or neutralize environmental pollutants. This study was carried out to investing at the efficacy of arsenic resistant endophytic bacteria isolated from Pteris vittata, Pityrogramma calomelanos, Blenchum orientale, and Nephrolepis exaltata, which grow in a highly arsenic (As) contamination mining site in Vietnam. Their segmented roots, stems, and leaves were homogenized separately and inoculated on LB agar plates containing 5mM As(III) and As(V). A total of 31 arsenic resistant endophytic strains were selected, in which strain R2.5.2 isolated from the root of P. calomelanos had the highest arsenic resistant capability. Strain R2.5.2 tolerated up to 320 mM and 160 mM of arsenate and arsenite, respectively. The strain developed well on a media of 0.1 5% NaCl, at 20-40ºC and pH 5 9, and actively utilized most of the sugar sources. It had a high IAA biosynthesis capacity with an average concentration of 19.14 mg/L, tolerated to 0.5-16 mM concentration of Ag+, Hg2+, Co2+, Ni2+, Cu2+, Cr4+, and reduced As(V). Based on 16s rDNA, R2.5.2 was identified as Priestia megaterium. The ars C gene coding for arsenate reductase catalyzing reduction of As(V) was successfully amplified in P. megaterium R2.5.2.  The selected strain may have potential use for bioremediation practice