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Hoa Thi Tran,Giang Thu Nguyen,Hong Ha Thi Nguyen,Huyen Thi Tran,Quang Hong Tran,Quang Ho Tran,Ngoc Thi Ninh,Phat Tien Do,Ha Hoang Chu,Ngoc Bich Pham 한국균학회 2022 Mycobiology Vol.50 No.5
Endophytic fungi are promising sources for the production of podophyllotoxin-an important anticancer compound, replacing depleted medical plants. In this study, the endophytes asso- ciated with Dysosma difformis-an ethnomedicinal plant species were isolated to explore novel sources of podophyllotoxin. Fifty-three endophytic fungi were isolated and identified by morphological observation and ITS-based rDNA sequencing, assigning them to 27 genera in 3 divisions. Fusarium was found the most prevalent genus with a colonization frequency of 11.11%, followed by Trametes (9.26%) and Penicillium (7.41%). Phylogenetic trees were constructed for the endophytic fungi community in two collection sites, Ha Giang and Lai Chau, revealing the adaptation of the species to the specific tissues and habitats. Cytotoxic activity of endophytic fungal extracts was investigated on cancer cell lines such as SK-LU-1, HL-60, and HepG2, demonstrating strong anti-cancer activity of six isolates belonging to Penicillium, Trametes, Purpureocillium, Aspergillus, and Ganoderma with IC50 value of lower than 10 mg/mL. The presence of podophyllotoxin was indicated in Penicillium, Trametes, Aspergillus and for the first time in Purpureocillium and Ganoderma via high-performance liquid chromatography, which implied them as a potential source of this anti- cancer compound.
Tran Trung Tien,Jinwoo Lee(이진우),Jonghwan Suhr(서종환) 한국자동차공학회 2021 한국자동차공학회 부문종합 학술대회 Vol.2021 No.6
Additive manufacturing (AM) defined as the method of building parts by adding material in layer-by-layer fashion to create an object. Fused deposition modeling (FMD) is a subset of AM using filament shaped thermoplastic polymer the material, computer aided design model then analyzed by the program and command are send to the 3D printer for building the model. Ease of use is the main strong point that make 3D printing become the rapid prototyping for industrial application and for hobbies or school teaching. FDM 3D printed structures can be used in automobile industry in application that required low volume high complexity part, but 3D printed part suffered low mechanical properties, highly anisotropic mechanical strength, low reliabilities due to imperfection. Therefore, research is needed to best taking advantages of 3D printing and increase parts reliabilities so more and more 3D printed part can be implemented in real world uses in the future. Tensile strength and print time are the two properties that get the most attention when using FDM printing to make part since it will decide whether or not the designed parts are suitable 3D printing. Build orientation, layer thickness, print speed and other overlooked printing parameters can influence tensile strength and print time drastically. Experiments are conducted to find the most affective parameters to each response properties, the result is later interpreted to optimize the print process for each response (faster print time or stronger part). However, considering many parameters and level in each parameter will increase the number of experiments exponentially. Taguchi experiment design method and Analysis of variance (ANOVA) are widely used not only to reduces the amount of experiment needed, but also have the superior benefit of identifying the relationship between inputs parameters on each other. This experimental study will investigate the affect of 3D printing parameters on print time and part tensile strength, using Taguchi method to create the set of experiments and ANOVA to identify the optimal combination of parameters to achieve the desired property.
Electrooxidation of tannery wastewater with continuous flow system: Role of electrode materials
Tran Tan Tien,Tran Le Luu 대한환경공학회 2020 Environmental Engineering Research Vol.25 No.3
Tannery wastewater is known to contain high concentrations of organic compounds, pathogens, and other toxic inorganic elements such as heavy metals, nitrogen, sulfur, etc. Biological methods such as aerobic and anaerobic processes are unsuitable for tannery wastewater treatment due to its high salinity, and electrochemical oxidation offers a promising method to solve this problem. In this study, raw tannery wastewater treatment using DSA® Ti/RuO₂, Ti/IrO₂ and Ti/BDD electrodes with continuous flow systems was examined. Effects of current densities and electrolysis times were investigated, to evaluate the process performance and energy consumption. The results showed that a Ti/BDD electrode is able to reach higher treatment efficiency than Ti/IrO₂, and Ti/RuO₂ electrodes across all parameters, excluding Total Nitrogen. The main mechanism of tannery wastewater oxidation at a Ti/BDD electrode is based on direct oxidation on the electrode surface combined with the generation of oxidants such as °OH and Cl₂, while at DSA® Ti/RuO₂ and Ti/IrO₂ electrodes, the oxidation mechanisms are based on the generation of chlorine. After treatment, the effluents can be discharged to the environment after 6-12 h of electrolysis. Electrooxidation thus offers a promising method for removing the nutrients and non-biodegradable organic compounds in tannery wastewater.
Combination of Deep Learner Network and Transformer for 3D Human Pose Estimation
Tien-Dat Tran,Xuan-Thuy Vo,Duy-Linh Nguyen,Kang-Hyun Jo 제어로봇시스템학회 2022 제어로봇시스템학회 국제학술대회 논문집 Vol.2022 No.11
Deep neural networks (DNNs) have attained the maximum performance today not just for human pose estimation but also for other machine vision applications (e.g., semantic segmentation, object detection, image classification). Besides, the Transformer shows its good performance for extracting the information in temporal information for video challenges. As a result, the combination of deep learner and transformer gains a better performance than only the utility one, especially for 3D human pose estimation. At the start point, input the 2D key point into the deep learner layer and transformer and then use the additional function to combine the extracted information. Finally, the network collects more data in terms of using the fully connected layer to generate the 3D human pose which makes the result increased precision efficiency. Our research would also reveal the relationship between the use of the deep learner and transformer. When compared to the baseline-DNNs, the suggested architecture outperforms the aseline-DNNs average error under Protocol 1 and Protocol 2 in the Human3.6M dataset, which is now available as a popular dataset for 3D human pose estimation.
Tran, Nguyen Tien,Kim, Jinsoo,Othman, Mohd Roslee Elsevier 2019 Microporous and mesoporous materials Vol.285 No.-
<P><B>Abstract</B></P> <P>Microporous ZIF-8 membrane has great potential to separate propylene from propane effectively by molecular sieving due to the theoretical ZIF-8 aperture of 0.4 nm, which lies in between the kinetic diameter of propylene (0.4 nm) and propane (0.43 nm). In this work, defect free ZIF-8 membranes were successfully developed from the secondary growth seeding technique with sodium formate as deprotonating agent that facilitated a continuous, well-intergrown ZIF-8 crystal layer on α-Al<SUB>2</SUB>O<SUB>3</SUB> support. The defects formed by the crack formation in the membrane were steadily removed by the uniquely discovered rapid recrystallization property. The ZIF-8 membranes demonstrated excellent molecular sieve separation capability for equal molar propylene/propane mixture with the highest separation factor of 115 and average propylene permeance of 50.40 × 10<SUP>−10</SUP> mol/m<SUP>2</SUP> s Pa.</P> <P><B>Graphical abstract</B></P> <P>[DISPLAY OMISSION]</P>