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Nguyen Trung Dung,Nguyen Van Hiep,Manh B. Nguyen,Vu Dinh Thao,Nguyen Nhat Huy 한국화학공학회 2021 Korean Journal of Chemical Engineering Vol.38 No.10
Photocatalysis is usually considered as one of the most effective methods for treating non-biodegradable pollutants commonly found in textile wastewater. In this study, the photocatalyst of g-C3N4/MIL-53(Fe) was synthesized by the hydrothermal method and applied for the removal of Rhodamine B (RhB) in water. The photocatalytic material was characterized by X-ray diffraction, Fourier-transform infrared spectroscopy, scanning electron microscopy, energy-dispersive X-ray spectroscopy, Brunauer-Emmett-Teller analysis, UV-Vis diffuse reflectance spectroscopy, and X-ray photoelectron spectroscopy. The results showed that the g-C3N4 doped MIL-53(Fe) with 97 wt% of MIL- 53(Fe) works effectively under visible light and the presence of oxidants (Na2S2O8). RhB removal efficiency can be more than 99% with 20mg/L of RhB, 300mg/L of catalyst, 200mg/L of Na2S2O8, and pH 3. In addition, the photocatalytic degradation mechanism of RhB with g-C3N4/MIL-53(Fe) was also proposed, which could be improved and studied for a wide range of applications in textile wastewater treatment.
Determinants Influencing Information Transparency in Vietnamese Commercial Banks
NGUYEN, Minh Phuong,NGUYEN, Thi Hong Hai,HOANG, Phuong Dung,TRAN, Manh Dung,PHAM, Quang Trung Korea Distribution Science Association 2020 The Journal of Asian Finance, Economics and Busine Vol.7 No.12
Information transparency ensures that market players all have the opportunity to access the same information to come up with their assessment of the banks' financial situation, performance and risks to reach effective investment decisions. This research is conducted to investigate the levels of impact of determinants on information transparency by examining the case studies of Vietnamese commercial banks. This study combines both qualitative and quantitative research methods, based on interviews of 32 specialists in banking, accounting and auditing fields, which were conducted to explore determinants influencing information transparency and to develop measurement scales. Then, a survey of 160 managers of commercial banks, audit firms, and accounting managers of firms who frequently had transactions with banks was carried out to investigate the statistical significance of these determinants. The results show that, out of seven determinants that have significant impacts on the banks' information transparency, commitment from banks' senior management regarding transparency in information disclosure has the highest impact, followed by state governance, auditing, information infrastructure, credit rating agencies, personnel and bank performance. Accordingly, we provide some recommendations for improving information transparency in the Vietnamese banking industry context as a case study and in emerging countries context in general.
PSI-rooted subgraph: A novel feature for IoT botnet detection using classifier algorithms
Huy-Trung Nguyen,Quoc-Dung Ngo,Doan-Hieu Nguyen,Van-Hoang Le 한국통신학회 2020 ICT Express Vol.6 No.2
It is obvious that IoT devices are widely used more and more in many areas. However, due to limited resources (e.g., memory, CPU), the security mechanisms on many IoT devices such as IP-Camera, router are low. Therefore, botnets are an emerging threat to compromise IoT devices recently. To tackle this, a novel method for IoT botnets detection plays a crucial role. In this paper, we have some contributions for IoT botnet detection: first, we present a novel high-level PSI-rooted subgraph-based feature for the detection of IoT botnets; second, we generate a limited number of features that have precise behavioral descriptions, which require smaller space and reduce processing time; third, The evaluation results show the effectiveness and robustness of PSI-rooted subgraph-based features, as with five machine classifiers consisting of Random Forest, Decision Tree, Bagging, k-Nearest Neighbor, and Support Vector Machine, each classifier achieves more than 97% detection rate and low time-consuming. Moreover, compared to other work, our proposed method obtains better performance. Finally, we publicize all our materials on Github, which will benefit future research (e.g., IoT botnet detection approach).
An Efficient Pedestrian Detection Approach Using a Novel Split Function of Hough Forests
Trung Dung Do,Thi Ly Vu,Van Huan Nguyen,Hakil Kim,Chongho Lee 한국정보과학회 2014 Journal of Computing Science and Engineering Vol.8 No.4
In pedestrian detection applications, one of the most popular frameworks that has received extensive attention in recent years is widely known as a ‘Hough forest’ (HF). To improve the accuracy of detection, this paper proposes a novel split function to exploit the statistical information of the training set stored in each node during the construction of the forest. The proposed split function makes the trees in the forest more robust to noise and illumination changes. Moreover, the errors of each stage in the training forest are minimized using a global loss function to support trees to track harder training samples. After having the forest trained, the standard HF detector follows up to search for and localize instances in the image. Experimental results showed that the detection performance of the proposed framework was improved significantly with respect to the standard HF and alternating decision forest (ADF) in some public datasets.
Chiral Separation of Fluvastatin Enantiomers by Capillary Electrophoresis
Trung, Tran Quoc,Dung, Phan Thanh,Hoan, Nguyen Ngoc,Kim, Dae-Joong,Lee, Joo-Huyn,Kim, Kyeong-Ho 대한약학회 2008 Archives of Pharmacal Research Vol.31 No.8
An analytical CE method was developed for the enantiomeric purity determination of fluvastatin enantiomers. Fluvastatin enantiomers were separated on an uncoated fused silica with 100 mM-borate solution containing 30 mg/mL of (2-hydroxypropyl)-$\beta$-cyclodextrin (HP-$\beta$-CD) as running buffer and fenoprofen as an internal standard. The linearity was observed within a $400-700\;{\mu}g/mL$ concentration range ($r^2{\geq}0.995$) for both fluvastatin enantiomers. The repeatability expressed as coefficient of variation (CV) of the method were 0.96 and 0.92% for (+)-3R, 5S and (-)-3S, 5R-fluvastatin, respectively. The limit of detection and quantification for both fluvastatin enantiomers were $1.5\;{\mu}g/mL$ and $2.5\;{\mu}g/mL$, respectively.
The Impact of Human Resource Management Activities on the Compatibility and Work Results
NGUYEN, Duc Trung,HA, Van Dung,DANG, Truong Thanh Nhan Korea Distribution Science Association 2020 The Journal of Asian Finance, Economics and Busine Vol.7 No.9
This research focuses on determining the impact of human resource management activities on the compatibility and work results of employees of Ho Chi Minh Stock Exchange (HOSE) listed companies. The paper includes five parts: introduction, literature review, research methodology, research results, and conclusion and policy implications. The data are collected from the survey of 350 listed companies in HOSE, in which 315 survey notes filled with sufficient information are used for analysis. The paper employs both qualitative method and quantitative method. Group discussion of 10 experts is for qualitative research. Quantitative method performs analysis of Statistics, Cronbach's Alpha, EFA analysis, CFA analysis and SEM model. The results of the research clearly indicate that human resource management (HRM) activities are measured through improving the ability, improving the motivation and improving the opportunity. While compatibility is measured through suitability, connection and sacrifice; whereby HRM activities of ability improvement have a positive effect on the job suitability and connection; HRM activities of motivation improvement have a positive effect on the job suitability, connection and sacrifice; and HRM activities of opportunity improvement have a positive effect on the job suitability, sacrifice and connection; Finally, the job suitability, sacrifice and connection positively affect the work results of employees.
A survey of IoT malware and detection methods based on static features
Quoc-Dung Ngo,Huy-Trung Nguyen,Van-Hoang Le,Doan-Hieu Nguyen 한국통신학회 2020 ICT Express Vol.6 No.4
Due to a lack of security design as well as the specific characteristics of IoT devices such as the heterogeneity of processor architecture, IoT malware detection has to deal with very unique challenges, especially on detecting cross-architecture IoT malware. Therefore, the IoT malware detection domain is the focus of research by the security community in recent years. There are many studies taking advantages of well-known dynamic or static analysis for detecting IoT malware; however, static-based methods are more effective when addressing the multi-architecture issue. In this paper, we give a thorough survey of static IoT malware detection. We first introduce the definition, evolution and security threats of IoT malware. Then, we summarize, compare and analyze existing IoT malware detection methods proposed in recent years. Finally, we carry out exactly the methods of existing studies based on the same IoT malware dataset and an experimental configuration to evaluate objectively and increasing the reliability of these studies in detecting IoT malware.
An Efficient Pedestrian Detection Approach Using a Novel Split Function of Hough Forests
Do, Trung Dung,Vu, Thi Ly,Nguyen, Van Huan,Kim, Hakil,Lee, Chongho Korean Institute of Information Scientists and Eng 2014 Journal of Computing Science and Engineering Vol.8 No.4
In pedestrian detection applications, one of the most popular frameworks that has received extensive attention in recent years is widely known as a 'Hough forest' (HF). To improve the accuracy of detection, this paper proposes a novel split function to exploit the statistical information of the training set stored in each node during the construction of the forest. The proposed split function makes the trees in the forest more robust to noise and illumination changes. Moreover, the errors of each stage in the training forest are minimized using a global loss function to support trees to track harder training samples. After having the forest trained, the standard HF detector follows up to search for and localize instances in the image. Experimental results showed that the detection performance of the proposed framework was improved significantly with respect to the standard HF and alternating decision forest (ADF) in some public datasets.