Factors Affecting Vietnamese Higher Education Quality in the Context of Industry 4.0

Industry 4.0 has challenged the quality of higher education by demanding more employability besides the academic and vocational skills of undergraduate laborers. Previous studies have addressed this issue unidimensionally. This study explores the measurement model of higher education quality multidimensionally under this circumstance by (1) Confirming the factors to measure higher education quality functionally and technically and (2) Ranking the factors in the quality measurement model. The qualitative Delphi method based on twenty in-depth interviews was conducted to fulfill the study’s objectives. The findings show that both the functional and technical dimensions of education quality have been integrated into the Vietnamese Higher Education Institution (HEIs) quality model including: (1) output; (2) critical thinking and problem-solving; (3) organizing and managing ability; (4) adaptability; (5) lifelong learning; (6) teaching process; (7) creativity and innovation; (8) expertise and digitalization; (9) administrative process; (10) learning process; (11) foreign language; and (12) input. The priority of output and learners’ competencies over input and education process in the model highlights the need for proper policies to effectively improve Vietnamese HEIs quality

Prognostic Values of Serum Lactate-to-Bicarbonate Ratio and Lactate for Predicting 28-Day IN-Hospital Mortality in Children With Dengue Shock Syndrome

This study aimed to assess the clinical utility of blood lactate-to-bicarbonate (L/B) ratio, as a prognostic factor for 28-day in-hospital mortality in children with dengue shock syndrome (DSS), admitted to the pediatric intensive care unit (PICU). This single-center retrospective study was conducted at a tertiary children hospital in southern Vietnam from 2013 to mid-2022. Prognostic models for DSS mortality were developed, using a predefined set of covariates in the first 24 hours of PICU admission. Area under the curves (AUCs), multivariable logistic and Least Absolute Shrinkage and Selection Operator (LASSO) regressions, bootstrapping and calibration slope were performed. A total of 492 children with DSS and complete clinical and biomarker data were included in the analysis, and 26 (5.3%) patients died. The predictive values for DSS mortality, regarding lactate showing AUC 0.876 (95% CI, 0.807-0.944), and that of L/B ratio 0.867 (95% CI, 0.80-0.934) (P values of both biomarkers \u3c .001). The optimal cutoff point of the L/B ratio was 0.25, while that of lactate was 4.2 mmol/L. The multivariable model showed significant clinical predictors of DSS fatality including severe bleeding, cumulative amount of fluid infused and vasoactive-inotropic score (\u3e30) in the first 24 hours of PICU admission. Combined with the identified clinical predictors, the L/B ratio yielded higher prognostic values (odds ratio [OR] = 8.66, 95% confidence interval [CI], 1.96-38.3; P \u3c .01) than the lactate-based model (OR = 1.35, 95% CI, 1.15-1.58; P \u3c .001). Both the L/B and lactate models showed similarly good performances. Considering that the L/B ratio has a better prognostic value than the lactate model, it may be considered a potential prognostic biomarker in clinical use for predicting 28-day mortality in PICU-admitted children with DSS

E-learning Quality and the Learners’ Choice Using Spherical Fuzzy Analytic Hierarchy Process Decision-making Approach

E-learning in the context of Industry 4.0 and the outbreak of the COVID-19 pandemic has transformed traditional education. However, the smooth transition from face-to-face education to e-learning remains a challenging task, given concerns about e-learning quality. This study aims to explore the quality criteria and the adoption of e-learning via the spherical fuzzy analytic hierarchy process (SF-AHP). The extended technical acceptance model is used as a theoretical framework for constructing quality in an adoption hierarchical model. The input data derived from in-depth interviews of 20 experts in the field and the SF-AHP calculator have generated the priority weights of quality criteria in the model of e-learning adoption. The findings confirm the role of three major criteria, in order of importance, as follows: system, resources and core factors. The results highlight system factors as most crucial, including aspects such as governmental policies and institutional leadership, which are essential for setting a conducive environment for e-learning. Resource factors are ranked second, emphasizing the importance of IT applications, human capital and facilities to support e-learning infrastructure. Core factors, though ranked lower, are vital in ensuring the effectiveness of e-learning through course materials, instruction, and learner support. The weights of 14 sub-criteria have further shed light on policies to promote e-learning quality and its adoption. The implied priority of each weight a valuable guideline for the stakeholders’ actions to reach the targeted goals under the constraint resources

Assistance to assessing rating students by language tuple- 4 scale

In this paper, we introduce an assistance to assessing rating the annual learning and process training of students in the opinion of experts, the approach of hedge algebra. It is advisary to make optimally fuzzy parameters with neural network in order to scale tuple-4 in accordance with current regulations on student assessment annual ranking including 7 levels

Predicting SNPs in Mature MicroRNAs Dysregulated in Breast Cancer

Breast cancer (BC) is the leading type of cancer among women. Findings have revolutionized current knowledge of microRNA (miRNA) in breast tumorigenesis. The seed region of miRNA regulates the process of gene expression negatively. The presence of SNPs in the seed regions of miRNA dramatically alters the mature miRNA function. Additionally, SNPs in the out-seed region of miRNAs have a significant impact on miRNA targeting. This study focuses on the in silico analysis procedure of mature miRNA SNPs and their impact on BC risk. The database annotated SNPs on mature miRNAs was used. Also, target gene alterations, miRNAs function in BC, and the interaction of miRNAs with targets were predicted. A list of 101 SNPs in 100 miRNAs with functional targets in BC was indicated. Under the SNPs allele variation, 10 miRNAs changed function, 6 miRNAs lost targets, 15 miRNAs gained targets, 48 onco-miRNAs remained unchanged, and 21 tumor suppressor miRNAs remained unchanged. At last, a list of 89 SNPs, which alter miRNA function and miRNA-mRNA interaction, were shown to be potentially associated with BC risk. This research theoretically generated a list of possible causative SNPs in the mature miRNA gene that might be used in future BC management studies

Application of supply chain management in construction industry

The application of the supply chain management (SCM) in industry has achieved much success, especially in the manufacturing sector. In the current market, the Vietnamese construction companies must compete not only with domestic companies but also with foreign ones, the application of SCM is essential to improve efficiency and increase their competitive advantage. In this paper, a survey was carried out to identify factors that cause limitation in applying SCM to the Vietnamese construction industry. A qualitative approach was based on prime contractor’s perspective at the construction phase of the project. The survey questionnaire was designed by synthesizing and inheriting the previous studies and consultation with experts. The survey participants are those who have had working experience with main contractors and joined in the construction projects. The face-to-face interviews were conducted to collect data. Descriptive statistics analysis and Exploratory Factor Analysis (EFA) were used to analyze data. The results indicated seven leading causes which limited the application of SCM in the construction industry

Quadcopter UAVs Extended States/Disturbance Observer-Based Nonlinear Robust Backstepping Control

A trajectory tracking control for quadcopter unmanned aerial vehicle (UAV) based on a nonlinear robust backstepping algorithm and extended state/disturbance observer (ESDO) is presented in this paper. To obtain robust attitude stabilization and superior performance of three-dimension position tracking control, the construction of the proposed algorithm can be separated into three parts. First, a mathematical model of UAV negatively influenced by exogenous disturbances is established. Following, an extended state/disturbance observer using a general second-order model is designed to approximate undesirable influences of perturbations on the UAVs dynamics. Finally, a nonlinear robust controller is constructed by an integration of the nominal backstepping technique with ESDO to enhance the performance of attitude and position control mode. Robust stability of the closed-loop disturbed system is obtained and guaranteed through the Lyapunov theorem without precise knowledge of the upper bound condition of perturbations. Lastly, a numerical simulation is carried out and compared with other previous controllers to demonstrate the great advantage and effectiveness of the proposed control method