Introduction: The influenza A(H1N1)pdm09 virus arrived in Vietnam in May 2009 via the United States and rapidly spread throughout the country. This study provides data on the viral diagnosis and molecular epidemiology of influenza A(H1N1)pdm09 virus isolated in Thua Thien Hue Province, central Vietnam. Methodology: Nasopharyngeal swabs and throat swabs from 53 clinically infected patients in the peak of the outbreak were processed for viral diagnosis by culture and RT-PCR. Sequencing of entire HA and NA genes of representative isolates and molecular epidemiological analysis were performed. Results: A total of 32 patients were positive for influenza A virus by virus culture and/or RT-PCR; of these 22 were positive both by viral isolation and RT-PCR, 2 only by virus culture and 8 only by RT-PCR. The novel subtype of influenza A(H1N1)pdm09 was present in 93.4% of the isolates. Phylogenetic analysis of the HA and NA gene sequences showed identities higher than 99.50% in both genes. They were also similar to reference isolates in HA sequences (> 99% identity) and in NA sequences (>98.50% identity). Amino acid sequences predicted for the HA gene were highly identical to reference strains. The NA amino acid substitutions identified did not include the oseltamivir-resistant H275Y substitution. Conclusion: viral isolation and RT-PCR together were useful for diagnosis of the influenza A(H1N1)pdm09 virus. Variations in HA and NA sequences are similar to those identified in worldwide reference isolates and no drug resistance was found.
A comprehensive approach in water resources management, referred to as integrated water resources management (IWRM), is required for successful water resource management. This study determined the degree of IWRM implementation in the Mekong River Delta in Viet Nam using indicator 6.5.1 from the United Nation's Sustainable Development Goal 6. A Delphi questionnaire approach was used in the study with a set of Knowledge Acquisition for Multiple Experts with Time Scales. The questionnaire was conducted with 21 selected experts for three rounds. The results showed that the degree of IWRM implementation in the Mekong River Delta in Viet Nam is at a medium-high level. Enabling environment scores significantly better than the three other components, while the finance dimension falls short. This reflects the government's efforts to effectively manage water resources in the region. However, given the limitations in technical and financial capacity for IWRM implementation, this effort is deemed inadequate. The results thus provide an assessment baseline to assist decision makers in addressing the shortcomings of the current state of IWRM.
Recently, thermal imaging modules equipped for infantry soldiers have been a trend to improve the combat ability of soldiers. Soldiers have to perform many different tasks at the same time, so it is necessary to equip them with the tools of automatic target detection, especially human objects detection, in practice. Hence, there is a need to intelligently optimize the effectiveness of thermal imaging equipment. New artificial intelligence and deep learning(DL) approaches are applicable methods that show superior accuracy compared to previous methods. However, state-of-the-art DL methods depend on the generality and diversity of the training data set. To address this issue, our paper presents the DeepThermal Outdoor thermal imaging data set, which is collected from equipment mounted on the body of infantry at various terrain locations. The labeled dataset focuses on human objects with different locomotion postures, and it contains 10,190 images and 22,464 labeled human-objects. Finally, the experiment is conducted with several DL methods using the proposed dataset, and the results show its contribution to the improvement of the performance of DL methods to detect humans on thermal images as well as to evaluate the practical applicability of a DL.
Hand action recognition in rehabilitation exercises is to automatically recognize what exercises the patient has done. This is an important step in an AI system to assist doctors to handle, monitor and assess the patient’s rehabilitation. The expected system uses videos obtained from the patient's body-worn camera to recognize hand action automatically. In this paper, we propose a model to recognize the patient's hand action in rehabilitation exercises, which is a combination of the results of a deep learning network recognizing actions on Video RGB, R(2+1)D, and a main interactive object in the exercises detection algorithm. The proposed model is implemented, trained, and tested on a dataset of rehabilitation exercises collected from wearable cameras of patients. The experimental results show that the accuracy in exercise recognition is practicable, averaging 88.43% on the test data independent of the training data. The action recognition results of the proposed method outperform the results of a single R(2+1)D network. Furthermore, the better results show the reduced rate of confusion between exercises with similar hand gestures. They also prove that the combination of interactive object information and the action recognition improve the accuracy significantly.
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