Preservice Teachers’ Identity Development during the Teaching Internship

This article reports the analysis of two preservice teachers’ narratives to highlight the process of teacher identity development during their teaching internship. The analysis showed that their teacher identities had been shaped before they entered the teacher education program where it continued to be shaped by educational experts. In that way, they formed expectations or imaginations of their professional roles and responsibilities prior to the teaching internships. When entering the teaching internships, these pre-existing expectations or imaginations were challenged by the reality they faced. Their engagement with the internship, resilience and negotiations of professional practices were found to be significant for the development of their teacher identities. The article discusses some important implications for teacher education programs

New criteria for exponential stability of linear neutral differential systems with distributed delays

summary:We present new explicit criteria for exponential stability of general linear neutral time-varying differential systems. Particularly, our results give extensions of the well-known stability criteria reported in [3,11] to linear neutral time-varying differential systems with distributed delays

CHOOSE REMEMBERSHIP REMEDIES TO IMPROVE THE HAPPINESS OF FEMALE OFFICERS AND OFFICERS UNIVERSITY OF FITNESS AND SPORTS DA NANG, VIETNAM

Happiness has long been a veritable area of scientific research in most industrialized countries. Using the method of synthesis of documents and interview method, the study investigates the current state of happiness of 30 female officers and employees of Danang University of Physical Education and Sports using the author's happiness scale. Michael Argyle and Peter Hills (2002) have shown: 30.0% less are happy; 43.33% are happy on average; 26.67% are moderately happy; the most important factor in happiness is health. Subject-selected measures that can increase happiness are finding moments with loved ones in the family; practising more positive thinking; accepting the reality of the situation; join arts and sports clubs

Impacts of flood on health: epidemiologic evidence from Hanoi, Vietnam

Vietnam is one of the most disaster-prone countries in the world. The country suffers from many kinds of natural disasters, of which the most common and serious one is flooding. Long and heavy rainfall during the last days of October and the first week of November 2008 resulted in a devastating flood unseen for over three decades in the capital city of Hanoi. It caused a substantial health impact on residents in and around the city and compromised the capacity of local health services. The aim of this study is to ascertain the vulnerability and health impacts of the devastating flood in Hanoi by identifying the differences in mortality, injuries, and morbidity patterns (dengue, pink eye, dermatitis, psychological problems, and hypertension) between flood affected and non-affected households. A cross-sectional study was carried out involving 871 households in four selected communes (two heavily flood affected and two comparatively less affected) from two severely flooded districts of Hanoi. Participants were interviewed and information collected on the social, economic, and health impacts of the devastation within 1 month after the flood. The self-reported number of deaths and injuries reported in this study within 1 month after the heavy rainfall were a bit higher in severely affected communes as compared to that of the less affected communes of our study. The findings showed higher incidences of dengue fever, pink eye, dermatitis, and psychological problems in communes severely affected by flood as compared to that of the controlled communes. For people in flood prone areas (at risk for flooding), flood prevention and mitigation strategies need to be seriously thought through and acted upon, as these people are exposed to greater health problems such as psychological issues and communicable diseases such as pink eye or dermatitis

Central limit theorem for the functional of jump Markov process

Central limit theorem for the functional of jump Markov process. Nguyễn Văn Hữu, Vương Quân Hoàng và Trần Minh Ngọc. Báo cáo: Hội nghị toàn quốc lần thứ III “Xác suất - Thống kê: Nghiên cứu, ứng dụng và giảng dạy” (tr. 34). Ba Vì, Hà Tây, ngày 12-14 tháng 05 năm 2005. Viện Toán học / Trường Đại học Khoa học tự nhiên / Đại học Quốc gia Hà Nộ

Linear Approximation and Asymptotic Expansion of Solutions for a Nonlinear Carrier Wave Equation in an Annular Membrane with Robin-Dirichlet Conditions

This paper is devoted to the study of a nonlinear Carrier wave equation in an annular membrane associated with Robin-Dirichlet conditions. Existence and uniqueness of a weak solution are proved by using the linearization method for nonlinear terms combined with the Faedo-Galerkin method and the weak compact method. Furthermore, an asymptotic expansion of a weak solution of high order in a small parameter is established

DeepNC: a framework for drug-target interaction prediction with graph neural networks

The exploration of drug-target interactions (DTI) is an essential stage in the drug development pipeline. Thanks to the assistance of computational models, notably in the deep learning approach, scientists have been able to shorten the time spent on this stage. Widely practiced deep learning algorithms such as convolutional neural networks and recurrent neural networks are commonly employed in DTI prediction projects. However, they can hardly utilize the natural graph structure of molecular inputs. For that reason, a graph neural network (GNN) is an applicable choice for learning the chemical and structural characteristics of molecules when it represents molecular compounds as graphs and learns the compound features from those graphs. In an effort to construct an advanced deep learning-based model for DTI prediction, we propose Deep Neural Computation (DeepNC), which is a framework utilizing three GNN algorithms: Generalized Aggregation Networks (GENConv), Graph Convolutional Networks (GCNConv), and Hypergraph Convolution-Hypergraph Attention (HypergraphConv). In short, our framework learns the features of drugs and targets by the layers of GNN and 1-D convolution network, respectively. Then, representations of the drugs and targets are fed into fully-connected layers to predict the binding affinity values. The models of DeepNC were evaluated on two benchmarked datasets (Davis, Kiba) and one independently proposed dataset (Allergy) to confirm that they are suitable for predicting the binding affinity of drugs and targets. Moreover, compared to the results of baseline methods that worked on the same problem, DeepNC proves to improve the performance in terms of mean square error and concordance index

Gestational diabetes diagnosed in third trimester of pregnancy: an observation at a Hospital of Women and Children in Vietnam

Background. Gestational diabetes mellitus (GDM) remains a significant concern within the medical community due to its high risk, as well as its serious side effects on both the mothers and the fetuses. This study aims to assess the prevalence and the risk factors of gestational diabetes mellitus in pregnant women at Da Nang Hospital for Women and Children.Methods. A cross-sectional study was conducted on 706 pregnant women at 2428 weeks of gestation at Da Nang hospital to determine the prevalence of gestational diabetes. Multivariate regression analysis was used to clarify the independent risk factors associated with gestational diabetes. All participants were interviewed and tested for the oral glucose tolerance test (OGTT) to identify the number of gestational diabetes, which was diagnosed according to the American Diabetes Association (ADA) diagnostic criteria in 2014.Results. Gestational diabetes prevalence was 10.2%; categorized by the number of matched diagnostic criteria: 1 criterion: 7.1%; 2 criteria: 2.1%; 3 criteria: 1.0%. There are four independent risk factors for gestational diabetes determined through multivariate regression analysis: maternal age > 30 years (OR = 2.376), a history of gestational diabetes (OR = 12.211), pre-pregnancy BMI ≥ 23 kg/m2 (OR = 10.775), a history of fetal macrosomia > 3800 g (OR = 4.655). The risk of gestational diabetes in the group with risk factors was 6.21 times higher than that in the group with no risk factors.Conclusion. More attention should be paid to the risk factors for gestational diabetes, such as maternal age > 30 years, a history of gestational diabetes, pre-pregnancy BMI ≥ 23 kg/m2, a history of fetal macrosomia > 3800 g in all pregnant women