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Duy Toan Pham, Thi My Huong Vo, Phuong Truong, Phuoc Tinh Ho and Manh Quan Nguyen



Infectious diseases, especially those caused by multidrug-resistant bacteria, are becoming a serious problem worldwide because of the lack of effective therapeutic agents. Moreover, most antifungal drugs exhibit low efficacy and high toxicity because of the similarity between fungal and human cells. These issues warrant the search for potential new agents.


To synthesize potent 2-(2-iodophenylimino)-5-arylidenethiazolidin-4-one derivatives, improve the synthetic process, elucidate their structures, and determine their antimicrobial activity.


2-Iodoaniline was used as an initial reactant in a 3-step process for the synthesis of 2-(2-iodophenylimino)-5-arylidenethiazolidin-4-one derivatives, including an acylation reaction, a cyclization reaction, and aldol condensation reactions. The structures of the obtained derivatives were investigated and elucidated using spectral methods. Antimicrobial activity toward 5 bacterial strains and 2 fungal strains was determined using Kirby–Bauer and agar dilution methods.


We successfully synthesized 12 novel compounds and elucidated their structures. The derivatives had no antifungal activities. By contrast, they showed remarkable antibacterial activities. Some of them with minimum inhibitory concentrations (MICs) ≤8 μg/mL in both Staphylococcus aureus and methicillin-resistant S. aureus.


A simple and flexible way to synthesize new compounds with a thiazolidin-4-one ring was determined. Some of these new compounds have outstanding effects with low MICs for bacteria. Their further investigation as therapeutic agents is warranted.

Open access

Truong Duc Phuong, Do Van Thanh and Nguyen Duc Dung


The main objective of this paper is to introduce fuzzy sequential patterns with fuzzy time-intervals in quantitative sequence databases. In the fuzzy sequential pattern with fuzzy time-intervals, both quantitative attributes and time distances are represented by linguistic terms. A new algorithm based on the Apriori algorithm is proposed to find the patterns. The mined patterns can be applied to market basket analysis, stock market analysis, and so on.

Open access

Thi-Oanh Tran, Hai-Trieu Dang, Viet-Thuong Dinh, Thi-Minh-Ngoc Truong, Thi-Phuong-Thao Vuong and Xuan-Hieu Phan


This paper presents a study on Predicting Student Performance (PSP) in academic systems. In order to solve the task, we have proposed and investigated different strategies. Specifically, we consider this task as a regression problem and a rating prediction problem in recommender systems. To improve the performance of the former, we proposed the use of additional features based on course-related skills. Moreover, to effectively utilize the outputs of these two strategies, we also proposed a combination of the two methods to enhance the prediction performance. We evaluated the proposed methods on a dataset which was built using the mark data of students in information technology at Vietnam National University, Hanoi (VNU). The experimental results have demonstrated that unlike the PSP in e-Learning systems, the regression-based approach should give better performance than the recommender system-based approach. The integration of the proposed features also helps to enhance the performance of the regression-based systems. Overall, the proposed hybrid method achieved the best RMSE score of 1.668. These promising results are expected to provide students early feedbacks about their (predicted) performance on their future courses, and therefore saving times of students and their tutors in determining which courses are appropriate for students’ ability.