Management of university-level training programs according to the AUN-QA approach: theoretical basis, management content, and influencing factors

The issue of education quality in general and university training program management, in particular, is one of the major concerns of current education systems in the world and in Vietnam. Many studies have shown a dialectical relationship between education quality and training program management. The university training program is to train human resources with high skills, thinking abilities, and creative abilities. In training activities, it is necessary to innovate contents, programs, teaching, and learning methods, build a list of training occupations, and a system of assurance and accreditation of college training quality, towards integration with the university educational community of countries in the region and the world. The identification and clarification of the theoretical basis of training program management according to the AUN-QA approach are even more urgent when many Vietnamese universities are conducting training quality accreditation under this system. Along with that, it is necessary to clarify the management contents and impact factors of the management process of university-level training programs according to the AUN-QA approach. Qualitative analyzes were used as one of the main tools of this study. However, in some important contents, some important issues need to be clarified, this study will conduct some surveys to create more objectivity and accuracy in the conclusions

Management of university-level training programs according to the AUN-QA approach: theoretical basis, management content, and influencing factors

The issue of education quality in general and university training program management, in particular, is one of the major concerns of current education systems in the world and in Vietnam. Many studies have shown a dialectical relationship between education quality and training program management. The university training program is to train human resources with high skills, thinking abilities, and creative abilities. In training activities, it is necessary to innovate contents, programs, teaching, and learning methods, build a list of training occupations, and a system of assurance and accreditation of college training quality, towards integration with the university educational community of countries in the region and the world. The identification and clarification of the theoretical basis of training program management according to the AUN-QA approach are even more urgent when many Vietnamese universities are conducting training quality accreditation under this system. Along with that, it is necessary to clarify the management contents and impact factors of the management process of university-level training programs according to the AUN-QA approach. Qualitative analyzes were used as one of the main tools of this study. However, in some important contents, some important issues need to be clarified, this study will conduct some surveys to create more objectivity and accuracy in the conclusions

A low-cost system for monitoring pH, dissolved oxygen and algal density in continuous culture of microalgae

In a continuous and closed system of culturing microalgae, constantly monitoring and controlling pH, dissolved oxygen (DO) and microalgal density in the cultivation environment are paramount, which ultimately influence on the growth rate and quality of the microalgae products. Apart from the pH and DO parameters, the density of microalgae can be used to contemplate what light condition in the culture chamber is or when nutrients should be supplemented, which both also decide productivity of the cultivation. Moreover, the microalgal density is considered as an indicator indicating when the microalgae can be harvested. Therefore, this work proposes a low-cost monitoring equipment that can be employed to observe pH, DO and microalgal density over time in a culture environment. The measurements obtained by the proposed monitoring device can be utilized for not only real-time observations but also controlling other sub-systems in a continuous culture model including stirring, ventilating, nutrient supplying and harvesting, which leads to more efficiency in the microalgal production. More importantly, it is proposed to utilize the off-the-shelf materials to fabricate the equipment with a total cost of about 513 EUR, which makes it practical as well as widespread. The proposed monitoring apparatus was validated in a real-world closed system of cultivating a microalgae strain of Chlorella vulgaris. The obtained results indicate that the measurement accuracies are 0.3%, 3.8% and 8.6% for pH, DO and microalgae density quantities, respectively. © 2022 The Author(s

A systematic review of effort-reward imbalance among health workers

The purpose of this article is to systematically collate effort-reward imbalance (ERI) rates among health workers internationally and to assess gender differences. The effort-reward (ER) ratio ranges quite widely from 0.47 up to 1.32 and the ERI rate from 3.5% to 80.7%. Many studies suggested that health workers contribute more than they are rewarded, especially in Japan, Vietnam, Greece, and Germany—with ERI rates of 57.1%, 32.3%, 80.7%, and 22.8% to 27.6%, respectively. Institutions can utilize systems such as the new appraisal and reward system, which is based on performance rather than the traditional system, seniority, which creates a more competitive working climate and generates insecurity. Additionally, an increased workload and short stay patients are realities for workers in a health care environment, while the structure of human resources for health care remains inadequate. Gender differences within the ER ratio can be explained by the continued impact of traditional gender roles on attitudes and motivations that place more pressure to succeed for men rather than for women. This systematic review provides some valued evidence for public health strategies to improve the ER balance among health workers in general as well as between genders in particular. An innovative approach for managing human resources for health care is necessary to motivate and value contributions made by health workers. Copyright © 2018 John Wiley & Sons, Ltd

3D Transformer based on deformable patch location for differential diagnosis between Alzheimer's disease and Frontotemporal dementia

Alzheimer's disease and Frontotemporal dementia are common types of neurodegenerative disorders that present overlapping clinical symptoms, making their differential diagnosis very challenging. Numerous efforts have been done for the diagnosis of each disease but the problem of multi-class differential diagnosis has not been actively explored. In recent years, transformer-based models have demonstrated remarkable success in various computer vision tasks. However, their use in disease diagnostic is uncommon due to the limited amount of 3D medical data given the large size of such models. In this paper, we present a novel 3D transformer-based architecture using a deformable patch location module to improve the differential diagnosis of Alzheimer's disease and Frontotemporal dementia. Moreover, to overcome the problem of data scarcity, we propose an efficient combination of various data augmentation techniques, adapted for training transformer-based models on 3D structural magnetic resonance imaging data. Finally, we propose to combine our transformer-based model with a traditional machine learning model using brain structure volumes to better exploit the available data. Our experiments demonstrate the effectiveness of the proposed approach, showing competitive results compared to state-of-the-art methods. Moreover, the deformable patch locations can be visualized, revealing the most relevant brain regions used to establish the diagnosis of each disease

Deep Grading based on Collective Artificial Intelligence for AD Diagnosis and Prognosis

Accurate diagnosis and prognosis of Alzheimer's disease are crucial to develop new therapies and reduce the associated costs. Recently, with the advances of convolutional neural networks, methods have been proposed to automate these two tasks using structural MRI. However, these methods often suffer from lack of interpretability, generalization, and can be limited in terms of performance. In this paper, we propose a novel deep framework designed to overcome these limitations. Our framework consists of two stages. In the first stage, we propose a deep grading model to extract meaningful features. To enhance the robustness of these features against domain shift, we introduce an innovative collective artificial intelligence strategy for training and evaluating steps. In the second stage, we use a graph convolutional neural network to better capture AD signatures. Our experiments based on 2074 subjects show the competitive performance of our deep framework compared to state-of-the-art methods on different datasets for both AD diagnosis and prognosis.Comment: arXiv admin note: substantial text overlap with arXiv:2206.0324

Creating Fatigue Curve for Steel Machine Elements Using Fatigue Test Method with Gradually Increasing Stress Amplitude

In order to create a fatigue curve, the traditional fatigue test method is applied to specimens using a cyclic stress with constant amplitude. However, this method has disadvantages such as the experimental results could not be used because of specimens broken before reaching the expected stress amplitude, or the tests may be stopped before specimen broken because of limitation of time. To overcome this hurdle of the traditional method, a new experimental method using cyclic stress with gradually increasing amplitude was proposed to build the fatigue curve for steel machine elements

La eficiencia de aplicar muestreo comprimido y resolución múltiple en tomografía por ultrasonido

Introduction: This publication is the product of  research developed within the research lines of the Smart Sensing, Signal Processing, and Applications (3SPA)  research  group  throughout  2018,  which  supports  the  work  of  a  doctor’s degree at VNU University of Engineering & Technology, Vietnam. Problem: The limitations of diagnostic ultrasound techniques using echo information has motivated the study of new imaging models in order to create additional quantitative ultrasound information in multi-model imaging devices. A promising solution is to use image sound contrast because it is capable of detecting changes in diseased tissue structures. Ultrasound tomography shows speed-of-sound changes in the propagation medium of sound waves. This technique is primarily used for imaging cancer-causing cells in womens’ breasts. The Distorted Born Iterative Method (DBIM), based on the first-order Born approximation, is an efficient diffraction tomography approach. The compressed sensing technique is utilized for DBIM to obtain the high-quality ultrasound image, although the image reconstruction process is quite long. Objective: The objective of the research is to propose an combined method for the efficient ultrasound tomography. Methodology: In this paper, we proposed an approach to enhance the imaging quality and to reduce the imaging time by applying the compressed sensing technique along with the multi-resolution technique for the DBIM. Results: The simulation results indicate that the imaging time is reduced by 33% and the imaging quality is improved by 83%. Conclusion: This project seeks to propose an improvement in ultrasound tomography. The simulated results confirmed the realibility of the propsed method. Originality: Through this research, a combined method of compressed sensing and multiple resolution are formulated for the first time in ultrasound tomography. Limitations: The lack of experiments to confirm the proposed method.Introducción: esta publicación es el producto de la investigación desarrollada dentro de las líneas del grupode investigación Detección Inteligente, Procesamiento de Señales y Aplicaciones (3SPA, Smart Sensing, Signal Processing, and Applications) a lo largo de 2018, que respalda el trabajo de un doctorado en la Universidad deIngeniería y Tecnología de VNU, Vietnam. Problema: las limitaciones de las técnicas de diagnóstico por ultrasonido que utilizan información de eco han motivado el estudio de nuevos modelos de imágenes para crear información cuantitativa adicional de ultrasonidos en dispositivos de imágenes de modelos múltiples. Una solución prometedora es utilizar el contraste de sonido de la imagen porque es capaz de detectar cambios en las estructuras de los tejidos enfermos. La tomografía por ultrasonido muestra los cambios en la velocidad del sonido en el medio de propagación de las ondas sonoras. Esta técnica se usa principalmente para obtener imágenes de células que causan cáncer en los senos de las mujeres.  Objetivo: el objetivo de la investigación es proponer un método combinado para la tomografía de ultrasonido eficiente. Metodología: en este documento se propuso un enfoque para mejorar la calidad de la imagen y reducir el tiempo diante la aplicación de la técnica de detección comprimida junto con la técnica de resolución múltiplepara el DBIM. Resultados: los resultados de la simulación indican que el tiempo de imagen se reduce en un 33 % y la calidad de imagen se mejora en un 83 %. Conclusión: este proyecto busca proponer una mejora en la tomografía por ultrasonido. Los resultados simulados confirmaron la viabilidad del método sugerido. Originalidad: a través de esta investigación, se formula por primera vez un método combinado de detección comprimida y resolución múltiple en la tomografía por ultrasonido. Limitaciones: la falta de experimentos para confirmar el método propuesto