CHARACTERISTICS OF QUATERNARY SEDIMENTARY FACIES IN RELATION TO WATER BEARING CAPACITY OF AQUIFERS AND AQUICLUDES IN RED RIVER DELTA, VIETNAM

Joint Research on Environmental Science and Technology for the Eart

Physical security with power beacon assisted in half-duplex relaying networks over Rayleigh fading channel: performance analysis

In this research, we proposed and investigated physical security with power beacon assisted in half-duplex relaying networks over a Rayleigh fading channel. In this model, the source (S) node communicates with the destination (D) node via the helping of the intermediate relay (R) node. The D and R nodes harvest energy from the power beacon (PB) node in the presence of a passive eavesdropper (E) node. Then we derived the integral form of the system outage probability (OP) and closed form of the intercept probability (IP). The correctness of the analytical of the OP and IP is verified by the Monte Carlo simulation. The influence of the main system parameters on the OP and IP also is investigated. The research results indicated that the analytical results are the same as the simulation ones

SEASONAL VARIATION OF PHYTOPLANKTON FUNCTIONAL GROUPS IN TUYEN LAM RESERVOIR, CENTRAL HIGHLANDS, VIETNAM

Seasonal changes in freshwater phytoplankton assemblages at Tuyen Lam Reservoir in the Central Highlands of Vietnam were classified into 23 functional groups based on physiological, morphological, and ecological characteristics. A total of 168 species were recorded during 10 surveys from 2015 to 2019 at 7 sampling sites, with Chlorophyta dominating in number of species. Phytoplankton abundance varied from 0.18×105 to 21.2×105 cells/L during the study period, mainly due to cyanobacteria. Seven of the 23 functional groups were considered to be dominant (relative density > 5%).  The dominant functional groups were groups M and G in the dry season and groups M, G, P, and E in the rainy season. Group M (Microcystis aeruginosa) was the most common in both seasons, while group P (Closterium, Staurastrum, Aulacoseira), group E (Dinobryon, Synura), and group G (Sphaerocystis, Eudorina) were more common in the rainy season. The Shannon diversity index (H¢) showed that phytoplankton communities were relatively diverse and that most of the study sites were lightly polluted. However, the ecological status has deteriorated at some locations due to the overgrowth of group M, leading to eutrophication in this reservoir. This study highlights the usefulness of functional groups in the study of seasonal changes in phytoplankton dynamics. Functional groups are applied for the first time at Tuyen Lam Reservoir and can be used to predict early-stage cyanobacterial blooms in future studies

Beyond Domain Adaptation: Unseen Domain Encapsulation via Universal Non-volume Preserving Models

Recognition across domains has recently become an active topic in the research community. However, it has been largely overlooked in the problem of recognition in new unseen domains. Under this condition, the delivered deep network models are unable to be updated, adapted or fine-tuned. Therefore, recent deep learning techniques, such as: domain adaptation, feature transferring, and fine-tuning, cannot be applied. This paper presents a novel Universal Non-volume Preserving approach to the problem of domain generalization in the context of deep learning. The proposed method can be easily incorporated with any other ConvNet framework within an end-to-end deep network design to improve the performance. On digit recognition, we benchmark on four popular digit recognition databases, i.e. MNIST, USPS, SVHN and MNIST-M. The proposed method is also experimented on face recognition on Extended Yale-B, CMU-PIE and CMU-MPIE databases and compared against other the state-of-the-art methods. In the problem of pedestrian detection, we empirically observe that the proposed method learns models that improve performance across a priori unknown data distributions

VIETNAMESE STUDENT RESEARCHERS’ EXPECTATIONS OF THEIR SUPERVISOR AND SUPERVISION PROCESS

In Vietnam, scientific research is no longer just the work of scientists, graduate students, scholars, or lecturers; tertiary students are also encouraged to conduct scientific research. Therefore, the scientific research work of students receives more attention from educators. The research on carrying out scientific research of students is also therefore increasing. However, researchers do not seem to have paid enough attention to the role of supervisors during the supervision process. The evidence is that there are few studies on supervisors in Vietnam. Therefore, this study was conducted to learn about the role of supervisors from the student's perspective and expectations. Specifically, this study was conducted quantitatively with the use of a questionnaire consisting of 49 questions with a 5-point Likert scale. A total of 100 English-major students at a university in Southwest Vietnam participated in this study by answering the questionnaire. The results from the questionnaire show that students had high expectations from their supervisors. Specifically, students expect their supervisor to be someone who respects their opinions, has good scientific research knowledge, can give constructive comments, and is always willing to help them when needed. Based on research findings, supervisors are encouraged to participate in professional development training related to scientific research to improve their research knowledge and skills. Along with that, supervisors need to be aware of their role during the process of guiding students to do scientific research.  Article visualizations

Factors affecting career turnover intention after graduation among nursing students: A cross-sectional study in Central Vietnam

Background & Aim: Turnover intention can predict the actual turnover behavior of nurses. Previous studies identified a variety of factors influencing nurses' turnover intention. However, few studies investigate nursing students' career turnover intention. This study aimed to examine turnover intention and associated factors among nursing students in Central Vietnam. Methods & Materials: A cross-sectional study was implemented among 425 nursing students recruited through a multistage sampling technique from April to May 2022. Data were collected using a questionnaire including demographic characteristics, fear of COVID-19, perceived academic stress, and turnover intention. Descriptive statistics were used to describe demographic characteristics and study variables. Independent t-test, one-way analysis of variance, and Pearson's correlation coefficients were computed to examine the association between variables. Results: Approximately one-third (32.5%) of the respondents will look for jobs without patient contact, 32.2% would not study nursing if given a choice, and 31.1% often think of not staying in the nursing profession. The sum scores of turnover intention ranged from 3 to 15 with a mean of 9.19 (SD= 2.49). The turnover intention was associated with the year of study, the reason to study nursing, and preparation for nursing school (p<.05). Perceived academic stress had a moderate correlation with turnover intention (r= -.325, p<.05). In contrast, the association between fear of COVID-19 and turnover intention was not significant. Conclusion: A considerable number of nursing students had turnover intention upon graduation. Factors affecting turnover intention should be considered to retain students in educational programs and avoid a future nursing shortage

DyGLIP: A Dynamic Graph Model with Link Prediction for Accurate Multi-Camera Multiple Object Tracking

Multi-Camera Multiple Object Tracking (MC-MOT) is a significant computer vision problem due to its emerging applicability in several real-world applications. Despite a large number of existing works, solving the data association problem in any MC-MOT pipeline is arguably one of the most challenging tasks. Developing a robust MC-MOT system, however, is still highly challenging due to many practical issues such as inconsistent lighting conditions, varying object movement patterns, or the trajectory occlusions of the objects between the cameras. To address these problems, this work, therefore, proposes a new Dynamic Graph Model with Link Prediction (DyGLIP) approach to solve the data association task. Compared to existing methods, our new model offers several advantages, including better feature representations and the ability to recover from lost tracks during camera transitions. Moreover, our model works gracefully regardless of the overlapping ratios between the cameras. Experimental results show that we outperform existing MC-MOT algorithms by a large margin on several practical datasets. Notably, our model works favorably on online settings but can be extended to an incremental approach for large-scale datasets.Comment: accepted at CVPR 202