Impacts of Economic Development on the Living Conditions of Ethnic Minority People in the Border Region of Northern Vietnam

Purpose:   In this study, we examine the effects of the economic development policies of the Vietnamese government on the living conditions of ethnic minority people in the border region of Northern Vietnam.   Theoretical framework: We examine the impact of economic changes, societal changes, and environmental improvements on the living conditions of ethnic minority people in the border region of Northern Vietnam.   Design/Methodology/Approach: The collected data was subsequently cleaned and analyzed using Partial Least Squares Structural Equation Modelling (PLS-SEM) with the aid of SPSS 20 software.     Findings: The results show that the current Vietnamese government’s policies on developing agriculture, industry, trade, and service for ethnic minority people have a diverse impact on their living conditions. In general, agriculture, trade, and service are major fields that benefit the living conditions of ethnic minorities. In contrast, the industry seems to have no effects on improving the standard of living of ethnic minority people in Northern Vietnam.   Research, practical & social implications: The results of this study provide suggestion to Vietnamese government on promoting living conditions of ethnic minority people in the border region of Northern Vietnam.   Originality/Value: This is the first paper evaluates the impact of the economic development policies of the Vietnamese government on the living conditions of ethnic minority people in the border region of Northern Vietnam

HMU fluorinze mouthwash enhances enamel remineralization: An in vitro study

BACKGROUND: Fluoride therapy has long been used extensively to prevent dental caries. Fluoride appears in variety of dental care products such as mouthrinse, dentifrice, gel, etc. HMU fluorinze is the first mouthwash containing fluoride in Vietnam. AIM: This research was conducted to evaluate the efficacy of HMU Fluorinze mouthwash on remineralizing enamel in laboratory conditions. METHODS: 20 third molars teeth were cleaned and covered with nail polish , except for a 3x3 mm square on their buccal surfaces. These teeth went through two steps: demineralization using Coke and remineralization for 20 days: 1) using standard calcifying solution (control group) and 2) using standard calcifying solution + HMU Fluorinze mouthwash 2 times/day (experimental group). The mineralization index of enamel structure after demineralization and remineralization was assessed by DIAGNOdent pen 2190. RESULTS: The mineralization indexes of the control group and experimental group at baseline were 3.65 ± 0.76 and 3.35 ± 0.64, after demineralization were in turn of 21.78 ± 4.48 and 20.25 ± 2.26; and after remineralization were 6.30 ± 1.03 and 3.90 ± 1.24. The different figures  between the two groups after remineralization shows statistical significance (p<0.01). Group B using HMU fluorinze mouthwash after 20 days did not differ from the original results (p = 0.272), in contrast with the control group (p<0.01). CONCLUSIONS: HMU fluorinze mouthwash has better mineralization effect than standard calcifying solution

Deep Transfer Learning: A Novel Collaborative Learning Model for Cyberattack Detection Systems in IoT Networks

Federated Learning (FL) has recently become an effective approach for cyberattack detection systems, especially in Internet-of-Things (IoT) networks. By distributing the learning process across IoT gateways, FL can improve learning efficiency, reduce communication overheads and enhance privacy for cyberattack detection systems. Challenges in implementation of FL in such systems include unavailability of labeled data and dissimilarity of data features in different IoT networks. In this paper, we propose a novel collaborative learning framework that leverages Transfer Learning (TL) to overcome these challenges. Particularly, we develop a novel collaborative learning approach that enables a target network with unlabeled data to effectively and quickly learn knowledge from a source network that possesses abundant labeled data. It is important that the state-of-the-art studies require the participated datasets of networks to have the same features, thus limiting the efficiency, flexibility as well as scalability of intrusion detection systems. However, our proposed framework can address these problems by exchanging the learning knowledge among various deep learning models, even when their datasets have different features. Extensive experiments on recent real-world cybersecurity datasets show that the proposed framework can improve more than 40% as compared to the state-of-the-art deep learning based approaches.Comment: 12 page

Catalytic Dye Oxidation over CeO2 Nanoparticles Supported on Regenerated Cellulose Membrane

A novel regenerated cellulose (RC) membrane containing cerium oxide (CeO2) nanoparticles is described in detail. In this work, CeO2 nanoparticles with high surface area and mesoporosity were prepared by a modified template-assisted precipitation method. Successful synthesis was achieved using cerium nitrate as a precursor, adjusting the final pH solution to around 11 by ammonium hydroxide and ethylene diamine, and annealing at 550 °C for 3 hours under a protective gas flow. This resulted in a surface area of 55.55 m².g–1 for the nanoparticles. The regenerated cellulose membrane containing CeO2 particles was synthesized by the novel and environmentally friendly method. The catalyst CeO2 and cellulose/CeO2 membrane were characterized by Fourier transform infrared spectroscopy (FTIR), X-ray diffraction (XRD), Electron paramagnetic resonance (EPR), and Brunauer-Emmett-Teller (BET) measurements. The g-value of 2.276 has confirmed the presence of the surface superoxide species of CeO2 nanoparticles in EPR. The photocatalytic activity of the catalyst and the membrane containing the catalyst was evaluated through the degradation of methylene blue under visible light irradiation by UV-VIS measurements. The cellulose/CeO2 membrane degraded 80% of the methylene blue solution in 120 minutes, showing a better photocatalytic activity than the CeO2 catalyst, which degraded approximately 62% in the same period. It has been proven that the RC membrane is not only a good transparent supporting material but also a good adsorption for high-performance of CeO2 catalyst. Copyright © 2022 by Authors, Published by BCREC Group. This is an open access article under the CC BY-SA License (https://creativecommons.org/licenses/by-sa/4.0).

Survey on Vietnamese teachers’ perspectives and perceived support during COVID-19

The COVID-19 pandemic has caused unprecedented damage to the educational system worldwide. Besides the measurable economic impacts in the short-term and long-term, there is intangible destruction within educational institutions. In particular, teachers – the most critical intellectual resources of any schools – have to face various types of financial, physical, and mental struggles due to COVID-19. To capture the current context of more than one million Vietnamese teachers during COVID-19, we distributed an e- survey to more than 2,500 randomly selected teachers from two major teacher communities on Facebook from 6th to 11th April 2020. From over 373 responses, we excluded the observations which violated our cross-check questions and retained 294 observations for further analysis. This dataset includes: (i) Demographics of participants; (ii) Teachers' perspectives regarding the operation of teaching activities during the pandemic; (iii) Teachers' received support from their schools, government bodies, other stakeholders such as teacher unions, and parents' associations; and (iv) teachers' evaluation of school readiness toward digital transformation. Further, the dataset was supplemented with an additional question on the teachers' primary source of professional development activities during the pandemic

A novel ontology framework supporting model-based tourism recommender

In this paper, we present a tourism recommender framework based on the cooperation of ontological knowledge base and supervised learning models. Specifically, a new tourism ontology, which not only captures domain knowledge but also specifies knowledge entities in numerical vector space, is presented. The recommendation making process enables machine learning models to work directly with the ontological knowledge base from training step to deployment step. This knowledge base can work well with classification models (e.g., k-nearest neighbours, support vector machines, or naıve bayes). A prototype of the framework is developed and experimental results confirm the feasibility of the proposed framework. © 2021, Institute of Advanced Engineering and Science. All rights reserved