NEW RURAL CONSTRUCTION IN HO CHI MINH CITY, VIETNAM

Ho Chi Minh City is Vietnam's the largest city with potentials, strengths on the development of industry, construction and service. The proportion of these sectors accounts for more than 99% of the city's GDP structure. Moreover, the city also has a large rural area with 05 suburban districts. In the past years, the city has also focused on strong investment to build and develop rural areas, contributing to changing the face of rural areas of the city; and simultaneously narrowing the gap between the urban and suburban areas. Since the day of national reunification up to now, Ho Chi Minh City has focused resources to invest in developing rural areas under the policy of new rural construction of the Communist Party of Vietnam, especially investment in building a synchronous and completed socio-economic infrastructure system, contributing to rural development, increasing incomes and improving people's lives. Article visualizations

Factors affecting to digital skills and adaptability of students in the context of digital transformation at the Ho Chi Minh city University of Technology and Education

The article focuses on analyzing the factors affecting students' digital skills and adaptability in the context of digital transformation. The study identified influencing factors: Environment, Behavior, Individuals, Teachers, Time of use, and access. However, the results of an online survey of 1.282 students of the Ho Chi Minh City University of Technology and Education (HCMUTE) with Cronbach's Alpha test method, EFA analysis, correlations, and regression analysis, showed that there are 3/5 factors identified in the hypothesis that affect digital skills and adaptability of HCMUTE students in the context of digital transformation, specifically: behavior (Beta = 0.177, Sig. = 0.000); individuals (Beta = 0.181, Sig. = 0.027); teacher (Beta = 0.547, Sig. = 0.000). Besides, the environment does not affect digital skills and adaptability (KNSKNTU) due to Beta = 0.017 and Sig.=0.384>0.05. Sig does not involve usage and access time factors. >0.05 in the table Correlations not be further analyzed in the regression model. The research results are expected to help provide a more objective view of the reality of digital adoption and student adaptability in the digital transformation context at HCMUTE

Factors affecting to digital skills and adaptability of students in the context of digital transformation at the Ho Chi Minh city University of Technology and Education

The article focuses on analyzing the factors affecting students' digital skills and adaptability in the context of digital transformation. The study identified influencing factors: Environment, Behavior, Individuals, Teachers, Time of use, and access. However, the results of an online survey of 1.282 students of the Ho Chi Minh City University of Technology and Education (HCMUTE) with Cronbach's Alpha test method, EFA analysis, correlations, and regression analysis, showed that there are 3/5 factors identified in the hypothesis that affect digital skills and adaptability of HCMUTE students in the context of digital transformation, specifically: behavior (Beta = 0.177, Sig. = 0.000); individuals (Beta = 0.181, Sig. = 0.027); teacher (Beta = 0.547, Sig. = 0.000). Besides, the environment does not affect digital skills and adaptability (KNSKNTU) due to Beta = 0.017 and Sig.=0.384>0.05. Sig does not involve usage and access time factors. >0.05 in the table Correlations not be further analyzed in the regression model. The research results are expected to help provide a more objective view of the reality of digital adoption and student adaptability in the digital transformation context at HCMUTE

Emerging Role of Circulating Tumor Cells in Gastric Cancer

With over 1 million incidence cases and more than 780,000 deaths in 2018, gastric cancer (GC) was ranked as the 5th most common cancer and the 3rd leading cause of cancer deaths worldwide. Though several biomarkers, including carcinoembryonic antigen (CEA), cancer antigen 19-9 (CA19-9), and cancer antigen 72-4 (CA72-4), have been identified, their diagnostic accuracies were modest. Circulating tumor cells (CTCs), cells derived from tumors and present in body fluids, have recently emerged as promising biomarkers, diagnostically and prognostically, of cancers, including GC. In this review, we present the landscape of CTCs from migration, to the presence in circulation, biologic properties, and morphologic heterogeneities. We evaluated clinical implications of CTCs in GC patients, including diagnosis, prognosis, and therapeutic management, as well as their application in immunotherapy. On the one hand, major challenges in using CTCs in GC were analyzed, from the differences of cut-off values of CTC positivity, to techniques used for sampling, storage conditions, and CTC molecular markers, as well as the unavailability of relevant enrichment and detection techniques. On the other hand, we discussed future perspectives of using CTCs in GC management and research, including the use of circulating tumor microembolies; of CTC checkpoint blockade in immunotherapy; and of organoid models. Despite the fact that there are remaining challenges in techniques, CTCs have potential as novel biomarkers and/or a non-invasive method for diagnostics, prognostics, and treatment monitoring of GC, particularly in the era of precision medicine

Demographic Factors Affecting the Level of Financial Literacy in Rural Areas: The Case of Vietnam

This article measures the level of financial literacy in the rural areas of Vietnam. The financial literacy is usually concerned by financial institutions and government organizations. This is considered to be an indicator that contributes to the assessment of the quality and potential growth of the financial system. In the article the determinants of financial literacy in Vietnam are identified. In result the authors propose a designed financial literacy enhancement programme for implementation

Developing a restaurant recommended system via the Vietnamese food image classification

A recommendation system is a system that recommends products and services to users based on daily online searching habits. The recommender system is applied in many fields such as job searching, health care, education, music, and tourism. However, few studies have combined computer vision and collaborative filtering to build a restaurant recommendation system in the tourism sector. In this study, we presented a solution to build a restaurant recommendation system through Vietnamese food image classification. First, we used ResNet-34 which is a variant of the convolutional neural network to classify Vietnamese food images. Then, the system applied the alternative least square technique in matrix factorization and Apache Spark in distributed computing to train the restaurant location dataset. The output was the most relevant restaurant places list to show many choices to users. The experimental datasets included the Vietnamese image and the restaurant location datasets that were collected from kaggle.com and foody.vn websites. For image classification task evaluation, we compared ResNet-34 to variants of ResNet. For the restaurant recommendation task evaluation, we compared alternative least squares with k-nearest neighbor. The comparison results show that the proposed solution is better than traditional popular models

Class Similarity Weighted Knowledge Distillation for Continual Semantic Segmentation

Deep learning models are known to suffer from the problem of catastrophic forgetting when they incrementally learn new classes. Continual learning for semantic segmentation (CSS) is an emerging field in computer vision. We identify a problem in CSS: A model tends to be confused between old and new classes that are visually similar, which makes it forget the old ones. To address this gap, we propose REMINDER - a new CSS framework and a novel class similarity knowledge distillation (CSW-KD) method. Our CSW-KD method distills the knowledge of a previous model on old classes that are similar to the new one. This provides two main benefits: (i) selectively revising old classes that are more likely to be forgotten, and (ii) better learning new classes by relating them with the previously seen classes. Extensive experiments on Pascal-Voc 2012 and ADE20k datasets show that our approach outperforms state-of-the-art methods on standard CSS settings by up to 7.07% and 8.49%, respectively

A Simple, Rapid, and Cost-Effective PCR Procedure for Detection of NUDT15 Gene Variants in Vietnamese Patients with Acute Lymphoblastic Leukemia

Objective The NUDT15 variants impact thiopurine dose selection in acute lymphoblastic leukemia patients. The ability to rapidly detect variants is important in clinical practice. This study aims to develop a simple polymerase chain reaction (PCR) procedure for detecting NUDT15 variants in Vietnamese patients. Materials and Methods Sanger sequencing was used to determine NUDT15 variants from 200 patients. We designed primers and optimized the PCR procedure for detection of wild-type and variant alleles and compared with Sanger sequencing results. Results The inserted variant c.55_56insGAGTCG was detected by differences in size through conventional PCR. The tetra-primer amplification refractory mutation system PCR was successful in detecting two variations, c.52G > A and c.415C > T. The sensitivity and specificity of PCR procedure achieved 100% when compared to 200 Sanger sequencing results. Conclusion Our PCR procedure is suitable for replacing Sanger sequencing to detect the NUDT15 variants in clinical setting

Class similarity weighted knowledge distillation for continual semantic segmentation

Deep learning models are known to suffer from the problem of catastrophic forgetting when they incrementally learn new classes. Continual learning for semantic segmentation (CSS) is an emerging field in computer vision. We identify a problem in CSS: A model tends to be confused between old and new classes that are visually similar, which makes it forget the old ones. To address this gap, we propose REMINDER - a new CSS framework and a novel class similarity knowledge distillation (CSW-KD) method. Our CSW-KD method distills the knowledge of a previous model on old classes that are similar to the new one. This provides two main benefits: (i) selectively revising old classes that are more likely to be forgotten, and (ii) better learning new classes by relating them with the previously seen classes. Extensive experiments on Pascal-VOC 2012 and ADE20k datasets show that our approach outperforms state-of-the-art methods on standard CSS settings by up to 7.07% and 8.49%, respectively