Factors Influencing the Consumer Adoption of Digital Banking Services During the Covid-19 Pandemic in Vietnam

The Covid-19 pandemic has created an opportunity for Vietnam to digitalize economy faster in many industries, especially banking service. Hence, this research paper aims to discovery the main determinants affecting the digital banking service adoption of Vietnamese people during the Covid-19 time. Conducting an online survey with 513 respondents during the social distancing period owing to the 4th wave of corona virus spreading in Vietnam, the researchers analyze the role of Covid-19 as a reason to create a dilemma situation pushing people to choose digital banking services to keep them be safe in the pandemic. Basing on the theory of consumer behavior models, the study indicates five determinants which influence the adoption of digital banking services in Vietnam in the context of Covid-19 spreading, as follow: banking service safety, online shopping preference, recommendation, bank marketing and acceptant of perceived risk. Basing on the results of data analyzation, the affection of banking service safety and online shopping preference contributes significantly to the rise of digital banking service adoption during the corona pandemic. And to be avoiding to the threat of covid-19 infected, people surveyed intend to accept the risk caused by digital banking services. These findings can contribute to understanding consumer behavior under the affection of situation (such as disaster or pandemic) comprehensively, help Vietnamese banks to build stronger strategies to enlarge their digital banking service market shares. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

Synchronous learning and teaching in engineering education in response to COVID situations

COVID-19 and the government restrictions in place have seriously affected the face-to-face (F2F) mode of delivery in education and higher education has been one of the hardest hit. Universities around the world had to implement the transition from F2F to online on very short notice. In this paper, the author presents a case study that demonstrates the challenges, steps, and adjustments taken to bring a course that has significant components of communication, teamwork, and project management from a full F2F mode to fully online whilst maintaining as much learner-learner and learner-teacher interactions and students engagement as possible. This has been achieved, not without drawbacks and challenges, via synchronous delivery mode and with private channels and breakout room function to promote intra-team and inter-team communications, and teamwork.

Mask or Non-Mask? Robust Face Mask Detector via Triplet-Consistency Representation Learning

In the absence of vaccines or medicines to stop COVID-19, one of the effective methods to slow the spread of the coronavirus and reduce the overloading of healthcare is to wear a face mask. Nevertheless, to mandate the use of face masks or coverings in public areas, additional human resources are required, which is tedious and attention-intensive. To automate the monitoring process, one of the promising solutions is to leverage existing object detection models to detect the faces with or without masks. As such, security officers do not have to stare at the monitoring devices or crowds, and only have to deal with the alerts triggered by the detection of faces without masks. Existing object detection models usually focus on designing the CNN-based network architectures for extracting discriminative features. However, the size of training datasets of face mask detection is small, while the difference between faces with and without masks is subtle. Therefore, in this article, we propose a face mask detection framework that uses the context attention module to enable the effective attention of the feed-forward convolution neural network by adapting their attention maps' feature refinement. Moreover, we further propose an anchor-free detector with Triplet-Consistency Representation Learning by integrating the consistency loss and the triplet loss to deal with the small-scale training data and the similarity between masks and occlusions. Extensive experimental results show that our method outperforms the other state-of-the-art methods. The source code is released as a public download to improve public health at https://github.com/wei-1006/MaskFaceDetection.

Continuous physiological monitoring using wearable technology to inform individual management of infectious diseases, public health and outbreak responses.

Optimal management of infectious diseases is guided by up-to-date information at the individual and public health levels. For infections of global importance, including emerging pandemics such as COVID-19 or prevalent endemic diseases such as dengue, identifying patients at risk of severe disease and clinical deterioration can be challenging, considering that the majority present with a mild illness. In our article, we describe the use of wearable technology for continuous physiological monitoring in healthcare settings. Deployment of wearables in hospital settings for the management of infectious diseases, or in the community to support syndromic surveillance during outbreaks, could provide significant, cost-effective advantages and improve healthcare delivery. We highlight a range of promising technologies employed by wearable devices and discuss the technical and ethical issues relating to implementation in the clinic, focusing on low- and middle- income countries. Finally, we propose a set of essential criteria for the rollout of wearable technology for clinical use.