Assessment of Water Quality During 2018-2022 in the Vam Co River Basin, Vietnam

Water pollution in the Vam Co River basin is becoming more complicated due to untreated wastewater being directly discharged into rivers and canals from agricultural, industrial, and domestic activities. To assess the water quality in this area, this study conducted monitoring at ten sampling locations (S1-S10) from 2018 to 2022, calculated the Water Quality Index (WQI) for each parameter, and simulated water quality in 2022 using the 1D- MIKE 11 model developed by DHI with two main modules including HD and AD. The findings showed that most parameters did not surpass the allowable limits per QCVN 08-MT:2015/BTNMT on Vietnam National Technical Regulation on Surface Water Quality. However, organic and microbial pollution led to certain parameters, such as BOD5, COD, and Coliform, exceeding the limits. The lowest water quality was recorded in Long An province, especially at sampling locations S3, S4, and S6, with the average WQI for nine water quality parameters from February to July 2022 being 58.4, 67.8, and 21.1, respectively. Additionally, the simulation outcomes of the MIKE 11 model salinity, BOD5, DO, and NH4 aligned with the real measurements taken. It has been observed that the southern area of the Vam Co River Basin possesses poorer water quality than the northern part, with Long An province located downstream of the Vam Co River basin being the primary source of pollution. The development of this hydraulic model signifies a crucial milestone in comprehending and regulating the effects of pollution in monitoring and managing water management systems, controlling saline intrusion, and ensuring water supply for agricultural production and daily use in the Vam Co River basin

Sematic understanding of large-scale outdoor web images: From emotion recognition to scene classification

Facial expression recognition and scene-based image clustering are very popular topics in the fields of human-computer interaction and computer vision. Their relationship has been rarely investigated but is a very attractive topic that has many potential applications, such as landscape design, instructions for vacation choices, or plant layout design in the public space. In this research, we use the existing deep learning algorithms to study two issues, i.e., facial expression recognition and scene-based image clustering for large scale outdoor web images. This research paves a path for a future attempt that explores their relationship in real-world images. First, we concentrate on emotion recognition and investigate the performance of the well-known algorithms including Visual Geometry Group Network (VGG network) and Residual Net (ResNet) on the emotions in images captured from a public park. Then we introduce some approaches to address the challenges of the occluded or children's faces. Our proposed pre-processing schemes not only allow the algorithm to detect more faces but also to increase the rate of recognition accuracy under the complex environment. We also investigate the visual analysis of landscape by introducing a set of scene labels for a large set of natural scene images collected from an online source. Then the weakly supervised method - Curriculum Net is applied for scene labeling of our dataset. In Curriculum Net, the training dataset is split into two parts, clean (easy) and noisy (hard) datasets by using a Density Peak Clustering algorithm, from which Curriculum Net is trained from easy to hard data. Particularly, we adopt a more effective density clustering method, Hierarchical Density-Based Spatial Clustering of Applications with Noise (HDBSCAN), to improve the clean-noisy separation of training images that leads to the improved scene labeling performance. By summarizing the work in emotion recognition and scene-based image clustering, we prepare the future research to reveal the relationship between the two aspects in real-world scenarios

Examining university student satisfaction and barriers to taking online remote exams

Recent years have seen a surge in the popularity of online exams at universities, due to the greater convenience and flexibility they offer both students and institutions. Driven by the dearth of empirical data on distance learning students' satisfaction levels and the difficulties they face when taking online exams, a survey with 562 students at The Open University (UK) was conducted to gain insights into their experiences with this type of exam. Satisfaction was reported with the environment and exams, while work commitments and technical difficulties presented the greatest barriers. Gender, race and disability were also associated with different levels of satisfaction and barriers. This study adds to the increasing number of studies into online exams, demonstrating how this type of exam can still have a substantial effect on students experienced in online learning systems and technologies