The Relationship Between Emotional Intelligence And Instructor Performance in Ho Chi Minh City Univeristy of Foreign Languages and Information Technology (Huflit), Vietnam

Emotional intelligence represents the combination of heart and mind, which has been determined as an indispensable activator and enhancer of intellectual prowess (Cooper & Sawaf, 1997). Many studies have concluded that IQ is a necessary condition but emotional intelligence is a sufficient condition to make a performance star at work (Goleman, 2001). This research aimed to investigate if emotional intelligence really helps HUFLIT instructors perform at their best. EQ Map (Q-Metrics, 1997) was employed to measure emotional intelligence of instructors in four levels: optimal, proficient, vulnerable, and caution. Besides determining the relationship between emotional intelligence and instructor performance, the research indicated the differences of emotional intelligence relative to selected demographic factors. The findings were discussed based on Vietnam’s cultural and social perspectives. Some recommendations were given for emotional intelligence and instructor performance improvement as well as for further research relating to the topic

Fragmented implementation of maternal and child health home-based records in Vietnam: need for integration

Background: Home-based records (HBRs) are globally implemented as the effective tools that encourage pregnant women and mothers to timely and adequately utilise maternal and child health (MCH) services. While availability and utilisation of nationally representative HBRs have been assessed in several earlier studies, the reality of a number of HBRs subnationally implemented in a less coordinated manner has been neither reported nor analysed. Objectives: This study is aimed at estimating the prevalence of HBRs for MCH and the level of fragmentation of and overlapping between different HBRs for MCH in Vietnam. The study further attempts to identify health workers’ and mothers’ perceptions towards HBR operations and utilisations. Design: A self-administered questionnaire was sent to the provincial health departments of 28 selected provinces. A copy of each HBR available was collected from them. A total of 20 semi-structured interviews with health workers and mothers were conducted at rural communities in four of 28 selected provinces. Results: Whereas HBRs developed exclusively for maternal health and exclusively for child health were available in four provinces (14%) and in 28 provinces (100%), respectively, those for both maternal health and child health were available in nine provinces (32%). The mean number of HBRs in 28 provinces (=5.75) indicates over-availability of HBRs. All 119 minimum required items for recording found in three different HBRs under nationwide scale-up were also included in theMaternal and Child Health Handbook being piloted for nationwide scaling-up. Implementation of multiple HBRs is likely to confuse not only health workers by requiring them to record the same data on several HBRs but also mothers about which HBR they should refer to and rely on at home. Conclusions: To enable both health workers and pregnant women to focus on only one type of HBR, province-specific HBRs for maternal and/or child health need to be nationally standardised. Moreover, to ensure a continuum of maternal, newborn, and child health care, the HBRs currently fragmented into different MCH stages (i.e. pregnancy, delivery, child immunisation, child growth, and child development) should be integrated. Standardisation and integration of HBRs will help increase technical efficiency and financial sustainability of HBR operations

The Relationship between Political Institutional Factors and Internal Audit Effectiveness in Vietnamese Enterprises

Using qualitative research methods, we have clarified the content of internal audits and the effectiveness of internal audits. A literature review has shown that the Political Institutions factor has not received much research attention in Vietnam. That's why we identified the gap and conducted this research. Data collected from interviews with 20 experts working in the field of accounting and auditing were analyzed using King's (2004) format and Cresswell's (2003) analysis process. The analysis results have shown that the Political Institutions factor, and its measurement factors are regulatory capacity, political stability, legal effectiveness, police accountability, and corruption control impact on internal audit effectiveness. Next, the study conducted a survey with 80 employees working in the field of accounting and auditing. This is a research step to ensure transparency in determining influencing factors. The results of this survey are consistent with the direct interview method. This means that the effectiveness of internal audits is affected by political and institutional factors

An Efficient Method for Generating Synthetic Data for Low-Resource Machine Translation – An empirical study of Chinese, Japanese to Vietnamese Neural Machine Translation

Data sparsity is one of the challenges for low-resource language pairs in Neural Machine Translation (NMT). Previous works have presented different approaches for data augmentation, but they mostly require additional resources and obtain low-quality dummy data in the low-resource issue. This paper proposes a simple and effective novel for generating synthetic bilingual data without using external resources as in previous approaches. Moreover, some works recently have shown that multilingual translation or transfer learning can boost the translation quality in low-resource situations. However, for logographic languages such as Chinese or Japanese, this approach is still limited due to the differences in translation units in the vocabularies. Although Japanese texts contain Kanji characters that are derived from Chinese characters, and they are quite homologous in sharp and meaning, the word orders in the sentences of these languages have a big divergence. Our study will investigate these impacts in machine translation. In addition, a combined pre-trained model is also leveraged to demonstrate the efficacy of translation tasks in the more high-resource scenario. Our experiments present performance improvements up to +6.2 and +7.8 BLEU scores over bilingual baseline systems on two low-resource translation tasks from Chinese to Vietnamese and Japanese to Vietnamese

Efficient prediction of axial load-bearing capacity of concrete columns reinforced with FRP bars using GBRT model

The behavior of concrete columns reinforced with fiber reinforced polymer (FRP) bars is different from conventional reinforced concrete columns due to the mechanical properties of FRP bars. This study develops a novel machine learning (ML) model, namely gradient boosting regression tree (GBRT), for efficiently predicting the axial load-bearing capacity (ALC) of concrete columns reinforced with FRP bars. A data base containing 283 experimental results is collected to develop the ML model. Seven code-based and empirical-based equations are also included in comparison with the developed ML models. Moreover, we also propose a multiple linear regression (MLR)-based formula for calculating the ALC of the FRP-concrete column. The performance results of GBRT model are compared with those of published formulas and the proposed MLR-based formula. Statistical properties including , , and  are calculated to evaluate the accuracy of those predictive models. The comparisons demonstrate that GBRT outperforms other models with very high  values and small . Moreover, the influence of input parameters on the predicted ALC isevaluated. Finally, an efficient graphical user interface tool is developed to simplify the practical design process of FRP-concrete columns

Efficient prediction of axial load-bearing capacity of concrete columns reinforced with FRP bars using GBRT model

The behavior of concrete columns reinforced with fiber reinforced polymer (FRP) bars is different from conventional reinforced concrete columns due to the mechanical properties of FRP bars. This study develops a novel machine learning (ML) model, namely gradient boosting regression tree (GBRT), for efficiently predicting the axial load-bearing capacity (ALC) of concrete columns reinforced with FRP bars. A data base containing 283 experimental results is collected to develop the ML model. Seven code-based and empirical-based equations are also included in comparison with the developed ML models. Moreover, we also propose a multiple linear regression (MLR)-based formula for calculating the ALC of the FRP-concrete column. The performance results of GBRT model are compared with those of published formulas and the proposed MLR-based formula. Statistical properties including , , and  are calculated to evaluate the accuracy of those predictive models. The comparisons demonstrate that GBRT outperforms other models with very high  values and small . Moreover, the influence of input parameters on the predicted ALC isevaluated. Finally, an efficient graphical user interface tool is developed to simplify the practical design process of FRP-concrete columns

Investigation of Ultrasonic Pulse Velocity Reduction in Reinforced Concrete Members Exposed to High Temperature

Nowadays, the fire resistance of reinforced concrete members is generally defined by material characteristics at elevated temperatures and temperature functions. However, the influence of steel reinforcement in concrete members exposed to high temperatures on the ultrasonic pulse velocity (UPV) measurements has still been limited. In this paper, the quality of concrete and steel reinforcement/concrete interface was assessed under high temperatures using UPV measurements. The specimens were classified into four categories: the control tested cubes without rebar; tested cubes with plain and ribbed steel rebars. Tested cubes with dimensions of 100x100x100 mm were cast and cured for 28 days at room temperature (20oC). After drying all specimens at 105oC for 48 hours, these cubes were subjected to four different temperature levels ranging from 150oC to 400oC for 4 hours before being cooled to room temperature. According to the measured values of UPV, the higher the temperature attained in specimens, the greater the following changes occurred in concrete: (i) the degradation within the concrete; (ii) the debonding of steel reinforcements in concrete

The causal relationships between components of customer-based brand equity for a destination: Evidence from South Korean tourists in Danang city, Vietnam

The main purpose of this study is to examine the causal relationships between components of customer-based brand equity for a tourist destination. We have collected data from 252 South Korean tourists in Danang City and tested some hypotheses by applying structural equation modeling (SEM). Results show that: (1) destination brand awareness has a significant and positive effect on destination brand image, but not on destination perceived quality and destination brand loyalty; (2) destination brand image has positive and direct influences on destination perceived quality and destination brand loyalty; and (3) destination perceived quality has significant positive impacts on destination brand loyalty. Lastly, these findings have managerial implications for decision makers