The Relationship between Teachers\u27 Interaction Strategies and Student Oral Involvement

A lot of research has focused on exploring reasons for and solutions to Asian students’ reticence in speaking. It is found that their unwillingness to speak is affected not only by the students themselves but also by the situations they are placed in. However, there is still space to explore how teachers use interaction strategies to enhance students’ speaking involvement, especially in Vietnam. This paper examines the relationship between teacher interaction strategies and student oral involvement. The data were collected via audio-recording and class observation. Five experienced teachers and their respective classes at a center for foreign languages were invited to participate in the study. The data were analyzed qualitatively based on the three interaction strategies proposed by Lee and Ng (2010). The findings show that three types of interaction strategies were used by the teachers in the classroom and had a positive effect on student oral involvement. The extent to which students are involved orally in response to these strategies was discrepant. More importantly, there are others elements related to pedagogical factors such as lesson objectives, task type, activities used, classroom management and the proficiency level of the students which were also identified to impact a teacher’s interaction strategy decision making

Students’ Perceptions on Blended Synchronous Learning in the Postcrisis Era

With the severe impacts of the Covid-19 pandemic, the educational systems have to be reformed and evolved. Blended synchronous learning has become an attractive tendency in education worldwide as the technology has mushroomed recently and attracts a vast number of users and researchers. Therefore, the current study was conducted to investigate students’ overall perceptions of blended synchronous learning as well as its benefits and challenges. 163 participants in the study have experienced ENT courses in a blended synchronous learning environment for 105 hours within 7 weeks. The instrument employed in the quantitative phase was 27 items adapted from studies by Rahman et al. (2015), López-Pérez et al. (2011), and Wu et al. (2010). Additionally, semi-structured interviews were used to have a deeper understanding of the research issues. Results indicate that more than half of participants had good perceptions about the blended synchronous learning environment and perceived various benefits as well as challenges of it. Moreover, these findings are supplemented with illustrative quotes from interview transcripts to compare and contrast with previous findings reported in the literature, and therefore this study contributes to the field by offering the learners\u27 voices

Machine Learning Models for Inferring the Axial Strength in Short Concrete-Filled Steel Tube Columns Infilled with Various Strength Concrete

Concrete-filled steel tube (CFST) columns are used in the construction industry because of their high strength, ductility, stiffness, and fire resistance. This paper developed machine learning techniques for inferring the axial strength in short CFST columns infilled with various strength concrete. Additive Random Forests (ARF) and Artificial Neural Networks (ANNs) models were developed and tested using large experimental data. These data-driven models enable us to infer the axial strength in CFST columns based on the diameter, the tube thickness, the steel yield stress, concrete strength, column length, and diameter/tube thickness. The analytical results showed that the ARF obtained high accuracy with the 6.39% in mean absolute percentage error (MAPE) and 211.31 kN in mean absolute error (MAE). The ARF outperformed significantly the ANNs with an improvement rate at 84.1% in MAPE and 65.4% in MAE. In comparison with the design codes such as EC4 and AISC, the ARF improved the predictive accuracy with 36.9% in MAPE and 22.3% in MAE. The comparison results confirmed that the ARF was the most effective machine learning model among the investigated approaches. As a contribution, this study proposed a machine learning model for accurately inferring the axial strength in short CFST columns

Physiochemical properties, antibacterial and antioxidant activities of Terminalia catappa seed oils from two extracting processes

Terminalia catappa is a widespread medium tree species in many tropical countries. While the majority of the studies up to date focuses on the aerial part of the plant such as leaf, stem bark and fruit, information about the phytochemical property as well as the biological property of the edible seed is still scarce. This study was the first to explore the fatty acid composition, antibacterial and antioxidant activities of the seed oil from T. catappa grown in Vietnam. The results showed that both the hot-pressed and cold-pressed oils contained a high level of unsaturated fatty acids such as oleic (~32%) and linoleic acids (28.38%-29.2%), as well as saturated fatty acids such as palmitic acid (~33.3%-33.61%). The presence of eicosadienoic acid in T. catappa seed oils was reported in this study for the first time. These oils displayed antibacterial activity against 5 out of 12 tested strains such as Bacillus cereus, Staphylococcus aureus, Escherichia coli, Pseudomonas aeruginosa and Vibrio parahaemolyticus. The antioxidant activity of the oils was also recorded by DPPH radical scavenging assays with IC50 values of 950 µg/ml and 2529 µg/ml for cold-pressed oil and hot-pressed oil respectively. This study has provided promising extracting methods and resulted in oils that could be good candidates for developing food sources with valuable fatty acids, antioxidant and antibacterial capacities against both Gram-positive and negative bacteria in the human diet

The cryptocurrency market in transition before and after COVID-19: an opportunity for investors?

We analyze the correlation between different assets in the cryptocurrency market throughout different phases, specifically bearish and bullish periods. Taking advantage of a fine-grained dataset comprising 34 historical cryptocurrency price time series collected tick-by-tick on the HitBTC exchange, we observe the changes in interactions among these cryptocurrencies from two aspects: time and level of granularity. Moreover, the investment decisions of investors during turbulent times caused by the COVID-19 pandemic are assessed by looking at the cryptocurrency community structure using various community detection algorithms. We found that finer-grain time series describes clearer the correlations between cryptocurrencies. Notably, a noise and trend removal scheme is applied to the original correlations thanks to the theory of random matrices and the concept of Market Component, which hasnever been considered in existing studies in quantitative finance. To this end, we recognized that investment decisions of cryptocurrency traders vary between bearish and bullish markets. The results of our work can help scholars, especially investors, better understand the operation of the cryptocurrency market, thereby building up an appropriate investment strategy suitable to the prevailing certain economic situation

Volatility and returns connectedness in cryptocurrency markets: insights from graph-based methods

We employ graph-based methods to examine the connectedness between cryptocurrencies of different market caps over time. By applying denoising and detrending techniques inherited from Random Matrix Theory and the concept of the so-called Market Component, we are able to extract new insights from historical return and volatility time series. Notably, our analysis reveals that changes in volatility-based network structure can be used to identify major events that have, in turn, impacted the cryptocurrency market. Additionally, we find that these structures reflect investors’ sentiments, including emotions like fear and greed. Using metrics such as PageRank, we discover that certain minor coins unexpectedly exert a disproportionate influence on the market, while the largest cryptocurrencies such as BTC and ETH seem less influential. We suggest that our findings have practical implications for investors in different ways: Firstly, helping them to avoid major market disruptions such as crashes, to safeguard their investments, and to capitalize on opportunities for high returns; Secondly, sharpening and optimizing the portfolios thanks to the understanding of cryptocurrencies’ connectedness

Chemical profiles and biological activities of acetone extracts of nine Annonaceae plants

This study investigated the chemical components and bioactivities of acetone leaf extracts of nine Annonaceae plants collected in the Binh Chau-Phuoc Buu Nature Reserve, Vietnam. A total of 182 constituents were identified, with linolenic acid, diaeudesmin, germacrene D, 1-octadecenoic acid, 8-(3-octyl-2-oxiranyl)-1-octanol, oleic acid, and phenylmethyl ester being the major compounds. The antimicrobial activity of the extracts was evaluated using a disc diffusion assay. Eight of the nine extracts, except for the Mitrephora thorelii extract, showed an inhibition effect against Bacillus cereus and Staphylococcus aureus. The antioxidant activity of the extracts was determined using DPPH assay, and the cytotoxic activity was deter mined using SRB assay. The results showed that the acetone extracts of Artabotrys hexapetalus, Uvularia grandiflora, Polyalthia luensis, Xylopia pierrei, Sphaerocoryne affinis, Desmos cochinchinensis, Uvaria littoralis, Mitrephora thorelii, and Goniothalamus touranensis had significant activity with IC50 for the DPPH radical scavenging activity ranging from 18.56 to 702.33 μg/mL, and the IC50 for the cytotoxic effects ranged from 5.39 to 251.77 μg/mL. Overall, the results obtained provide experimental evidence for the potential use of these plants in medicine and other related fields

Groundwater simulation in Dak Lak province based on MODFLOW model and climate change scenarios

Purpose – Groundwater plays a critical part in both natural and human existence. When surface water is scarce in arid climates, groundwater becomes an immensely valuable resource. Dak Lak is an area that frequently lacks water resources for everyday living and production, and the scarcity of water resources is exacerbated during the dry season. As a result, it is critical to do study and understand about groundwater to meet the region's water demand. This study aims to extend the use of the MODFLOW model for groundwater simulation and assess the overall groundwater reserves and water demand in the highland province Dak Lak. Design/methodology/approach – The MODFLOW model is used in this work to compute and analyze the flow, prospective reserves of groundwater from which to plan extraction and estimate groundwater variation in the future. Findings – The application of the MODFLOW model to Dak Lak province demonstrates that, despite limited data, particularly drilling hole data for subterranean water research, the model's calculation results have demonstrated its reliability and great potential for use in other similar places. The use of the model in conjunction with other data extraction modules is a useful input for creating underground flow module maps for various time periods. The large impact of recharge and evaporation on groundwater supplies and water balance in the research area is demonstrated by simulations of climate change scenarios RCP4.5 and RCP8.5. Originality/value – None of the studies has been done previously to analyze water resources of Dak Lak and the scarcity of water resources is exacerbated during the dry season. Therefore, this study will provide useful insights in the water resource management and the conservation of Dak Lak. The groundwater in Dak Lak can meet the area's water demand, according to the results obtained and water balance in the study area. However, the management of water resources and rigorous monitoring of groundwater extraction activities in the area should receive more attention