STUDENTS’ ATTITUDES TOWARDS DRAMA-BASED ROLE PLAY IN ORAL PERFORMANCE

Speaking is widely recognized as being the most important tool for communication that influences how students succeed in foreign language learning. In particular, given its importance to enhancing students’ oral performance, drama-based role plays are strongly connected to this language learning skill through English. Recent reforms in higher education in Vietnam have stressed the increased demands on universities to promote the quality of teaching and learning foreign languages at all levels to meet learners’ needs, particularly students’ capacity of interacting with others using English. However, research into the effects of drama-based role play activities on English as a foreign language (EFL) students’ oral performance is limited in the Mekong Delta. Moreover, students’ reluctance to interact in the target language is largely influenced by traditional speaking instruction, whereas speaking requires a more interactive and communicative learning environment. This paper therefore provides insights into students’ attitudes towards the use of drama-based role-play activities in EFL speaking classes. Using a descriptive design, interviews were undertaken with freshmen who were currently learning at a Vietnamese university in the Mekong Delta. The findings show positive attitudes of the students towards drama-based role play activities. Pedagogical implications for productive instructional practice to advance students’ oral performance are also discussed.         Article visualizations

Design an Intelligent System to automatically Tutor the Method for Solving Problems

Nowadays, intelligent systems have been applied in many real-word domains. The Intelligent chatbot is an intelligent system, it can interact with the human to tutor how to work some activities. In this work, we design an architecture to build an intelligent chatbot, which can tutor to solve problems, and construct scripts for automatically tutoring. The knowledge base of the intelligent tutoring chatbot is designed by using the requirements of an Intelligent Problem Solver. It is the combination between the knowledge model of relations and operators, and the structures of hint questions and sample problems, which are practical cases. Based on the knowledge base and tutoring scripts, a tutoring engine is designed. The tutoring chatbot plays as an instructor for solving real-world problems. It simulates the working of the instructor to tutor the user for solving problems. By utilizing the knowledge base and reasoning, the architecture of the intelligent chatbot are emerging to apply in the real-world. It is used to build an intelligent chatbot to support the learning of high-school mathematics and a consultant system in public administration. The experimental results show the effectiveness of the proposed method in comparison with the existing systems

A Unified Query-based Paradigm for Camouflaged Instance Segmentation

Due to the high similarity between camouflaged instances and the background, the recently proposed camouflaged instance segmentation (CIS) faces challenges in accurate localization and instance segmentation. To this end, inspired by query-based transformers, we propose a unified query-based multi-task learning framework for camouflaged instance segmentation, termed UQFormer, which builds a set of mask queries and a set of boundary queries to learn a shared composed query representation and efficiently integrates global camouflaged object region and boundary cues, for simultaneous instance segmentation and instance boundary detection in camouflaged scenarios. Specifically, we design a composed query learning paradigm that learns a shared representation to capture object region and boundary features by the cross-attention interaction of mask queries and boundary queries in the designed multi-scale unified learning transformer decoder. Then, we present a transformer-based multi-task learning framework for simultaneous camouflaged instance segmentation and camouflaged instance boundary detection based on the learned composed query representation, which also forces the model to learn a strong instance-level query representation. Notably, our model views the instance segmentation as a query-based direct set prediction problem, without other post-processing such as non-maximal suppression. Compared with 14 state-of-the-art approaches, our UQFormer significantly improves the performance of camouflaged instance segmentation. Our code will be available at https://github.com/dongbo811/UQFormer.Comment: This paper has been accepted by ACM MM202

NUTRIENT CYCLING IN EUCALYPTUS DUNNII: MICRONUTRIENTS IN THE LITTERFALL

The evaluation of litterfall and nutrient return is important for understanding the dynamics of nutrient cycling. Although required in smaller quantities by plants, micronutrients have unique importance in biogeochemical regulation. The objective of the present study was to quantify the litterfall and the concentration of micronutrients in the different fractions and seasons of the year in Eucalyptus dunnii stand. Four plots of 20 m x 21 m were demarcated. The collection of leaf litter, twigs (diameter 0.5 cm), four useful areas of medium-diameter trees were demarcated in each plot. The leaf fraction represented 59% of litterfall and the transfer order was Mn> Fe> B> Zn> Cu, totaling 8.04 kg ha-1. The leaf fraction presented the highest concentrations for B and Mn. The litterfall was seasonal with summer and spring differing statistically from winter and the temperature variable explains the deposition pattern of the same

Solving the Traveling Salesman Problem with Ant Colony Optimization: A Revisit and New Efficient Algorithms

Ant colony optimization (ACO) techniques are known to be efficient for combinatorial optimization. The traveling salesman problem (TSP) is the benchmark used for testing new combinatoric optimization algorithms. This paper revisits the application of ACO techniques to the TSP and discuss some general aspects of ACO that have been previously overlooked. In fact, it is observed that the solution length does not reflect exactly the quality of a particular edge belong to the solution, but it is only used for relatively evaluating whether the edge is good or bad in the process of reinforcement learning. Based on this observation, we propose two algorithms– Smoothed Max-Min Ant System and Three-Level Ant System– which not only can be easily implemented but also provide better performance, as compared to the well-known Max-Min Ant System. The performance is evaluated by numerical simulation using benchmark datasets

The role of N-terminal pro-B-type natriuretic peptide in prognostic evaluation of heart failure

Heart failure (HF) is a growing challenge in the Asia Pacific region. N-terminal pro-B-type natriuretic peptide (NT-proBNP) is a well-established tool for diagnosis of HF; however, it is relatively underutilized in predicting adverse outcomes in HF. Multiple studies have demonstrated the prognostic role of NT-proBNP in HF. A single value of NT-proBNP >5000 pg/mL predicts a worse outcome in hospitalized patients with HF with reduced ejection fraction (HFrEF). In stable outpatients with HFrEF, NT-proBNP > 1000 pg/mL predicts a poorer prognosis. NT-proBNP provides the same prognostic information in patients with HF with preserved ejection fraction (HFpEF) as in those with HFrEF. An expert panel composed of cardiologists mainly from Asia Pacific region was convened to discuss the utility of NT-proBNP in HF prognostication. This article summarizes available scientific evidence and consensus recommendations from the meeting

Prophylactic and Therapeutic Efficacy of Avian Antibodies against Influenza Virus H5N1 and H1N1 in Mice

Background: Pandemic influenza poses a serious threat to global health and the world economy. While vaccines are currently under development, passive immunization could offer an alternative strategy to prevent and treat influenza virus infection. Attempts to develop monoclonal antibodies (mAbs) have been made. However, passive immunization based on mAbs may require a cocktail of mAbs with broader specificity in order to provide full protection since mAbs are generally specific for single epitopes. Chicken immunoglobulins (IgY) found in egg yolk have been used mainly for treatment of infectious diseases of the gastrointestinal tract. Because the recent epidemic of highly pathogenic avian influenza virus (HPAIV) strain H5N1 has resulted in serious economic losses to the poultry industry, many countries including Vietnam have introduced mass vaccination of poultry with H5N1 virus vaccines. We reasoned that IgY from consumable eggs available in supermarkets in Vietnam could provide protection against infections with HPAIV H5N1. Methods and Findings: We found that H5N1-specific IgY that are prepared from eggs available in supermarkets in Vietnam by a rapid and simple water dilution method cross-protect against infections with HPAIV H5N1 and related H5N2 strains in mice. When administered intranasally before or after lethal infection, the IgY prevent the infection or significantly reduce viral replication resulting in complete recovery from the disease, respectively. We further generated H1N1 virus-specific IgY by immunization of hens with inactivated H1N1 A/PR/8/34 as a model virus for the current pandemic H1N1/09 and found that such H1N1-specific IgY protect mice from lethal influenza virus infection. Conclusions: The findings suggest that readily available H5N1-specific IgY offer an enormous source of valuable biological material to combat a potential H5N1 pandemic. In addition, our study provides a proof-of-concept for the approach using virus-specific IgY as affordable, safe, and effective alternative for the control of influenza outbreaks, including the current H1N1 pandemic