https://jos.hueuni.edu.vn/index.php/hujos-tt/issue/feed Hue University Journal of Science: Techniques and Technology 2024-06-12T03:01:48+00:00 Tạp chí Khoa học Đại học Huế ddhung@hueuni.edu.vn Open Journal Systems <p><strong>ISSN (Print) 2588-1175 </strong></p> <p><strong>ISSN (Online) 2615-9732</strong></p> <p><strong>Editor in chief: </strong>Do Thi Xuan Dung</p> <p><strong>Chair Editor: </strong>Vo Viet Minh Nhat</p> <p><strong>Managing Editor: </strong>Tran Xuan Mau</p> <p><strong>Technical Editor: </strong>Duong Duc Hung</p> <p><strong>Phone:</strong> 02343845658 | <strong>Email: </strong>ddhung@hueuni.edu.vn</p> https://jos.hueuni.edu.vn/index.php/hujos-tt/article/view/7366 Enhancing the Self-Assembled Monolayer Formation for Protein Detection Platform through L-Cysteine Utilization 2024-01-07T22:44:48+00:00 Thi Thuy Linh Huynh thuylinh@hueuni.edu.vn Xuân Cường Ngô ngoxuancuong@hueuni.edu.vn Quang Nhã Võ voquangnha@hueuni.edu.vn <p>Electrochemical immunosensing has emerged as a contemporary sensing strategy based on the principles of specific antigen-antibody recognition, offering exceptional specificity, remarkable sensitivity, and seamless integration. In this study, we present a rapid, three-step and cost-effective modification process to establish an immunosensing platform using self-assembled monolayer (SAM) of L-Cysteine. This approach was experimentally implemented through quantitative BSA protein detection experiments spanning a concentration range from 0.5 µM to 8µM. Optical signals, along with observable changes in electrical signals from cyclic voltammetry (CV), square wave voltammetry (SWV) and electrochemical impedance spectroscopy (EIS), confirmed the formation of monolayers on the electrode surface and detection signals for BSA protein. The characteristic curve, employing ΔR<sub>ct</sub> as a function of BSA protein concentration, was plotted with a coefficient of determination (R²) value of 0.95136. These findings underscore the potential of L-Cysteine-based SAMs in electrochemical biosensing applications for highly sensitive and cost-efficient protein detection.</p> 2024-06-12T00:00:00+00:00 Copyright (c) 2023 Hue University Journal of Science: Techniques and Technology https://jos.hueuni.edu.vn/index.php/hujos-tt/article/view/7450 Direct contact membrane distillation – a potential technology for treating saline water in Quang Dien and Phu Vang district, Thua Thien Hue province 2024-04-11T08:46:28+00:00 Quoc Linh Ve vqlinh@hueuni.edu.vn Minh Cuong Do dmcuong@hueuni.edu.vn Thanh Cuong Nguyen nguyenthanhcuong@hueuni.edu.vn Quoc Huy Nguyen nguyenquochuy@hueuni.edu.vn Ton Thanh Tam Phan phantonthanhtam@huaf.edu.vn Quang Lich Nguyen nguyenquanglich@hueuni.edu.vn <p>This study aims to clarify the salinization degree of irrigation water in Quang Dien and Phu Vang districts in Winter-Spring crop season and to propose a potential technology to treat saline water on lab-scale. The majority of irrigation water was brackish water (70%) at Quang Phuoc, Quang Loi, and Phu Dien villages with water concentration of up to nearly 7.1‰. For Quang Thai and Phu An villages, the salinization degree is much lower when the percentage of brackish water was from 30% to 40%. Direct contact membrane distillation (DCMD) was implemented to treat 20‰ - 40‰ concentrations of saline water. The experimental results revealed that the freshwater production by DCMD met the requirements of irrigation water when the salinity was under 0.1‰. Additionally, feed inlet temperature was the most effective factor to produce the highest amount of freshwater compared to volume flowrate and feed concentration factors.</p> 2024-06-12T00:00:00+00:00 Copyright (c) 2024 Hue University Journal of Science: Techniques and Technology https://jos.hueuni.edu.vn/index.php/hujos-tt/article/view/7495 Crowd Counting using Deep Learning Model on FPGA card 2024-06-11T06:06:31+00:00 Thi Thu Thao Khong kttthao@hueuni.edu.vn Van Loc Tran vltran@gmail.com Hai Phong Phan phphong@hueuni.edu.vn Duc Hung Duong ddhung@hueuni.edu.vn <p>Machine learning and deep learning are becoming important tools for processing video in artificial intelligence applications, especially real-time tasks that require speed, accuracy, and flexibility. For this reason, we introduce a crowd counting and detecting system from RTSP video streams using a deep learning model. Our system uses FPGA cards, i.e. Xilinx Alveo U30 and U200, to accelerate the transmission of video streams and the deep learning inference. In the input and output stream, Vitis Video Analysis SDK GStreamer is utilized to leverage the features of Alveo U30 for streaming RTSP videos. In the deep learning inference, we apply the trained YOLOX model to detect and count people from video frames. YOLOX is accelerated by Alveo U200 based on the Mipsology Zebra framework. The proposed system not only processes multiple streams but also achieves faster inference and lower CPU usage than the system that just uses CPU for deep learning inference.</p> 2024-06-12T00:00:00+00:00 Copyright (c) 2024 Hue University Journal of Science: Techniques and Technology