Blockchain-based digital trust mechanism: a use case of cloud manufacturing of LDS syringes for COVID-19 vaccination

Trust is essential in the digital world. It is a critical task to build digital trust for the ongoing digital engineering transformation. Aiming at developing a blockchain-based digital trust mechanism for Cloud Manufacturing or Manufacturing-as-a-Service (MaaS), in this paper, we use the manufacturing of low dead space (LDS) medical syringes through Cloud Manufacturing as a motivating scenario to develop a basic framework. To meet the need of optimally saving COVID-19 vaccine doses to save more lives, the medical device manufacturing community needs to make a swift move to meet the surged need for LDS syringes. Cloud Manufacturing is a form of emerging Digital Manufacturing facilitated with Cloud/Edge Computing, the Internet of Things, and other digital technologies. Cloud manufacturing allows quickly establishing a digital virtual enterprise that pools together various manufacturing resources worldwide to meet the surged needs of products and save cost and time. Trusting the product quality and safety is a significant challenge when using Cloud Manufacturing to manufacture the products. This paper proposes a schema of blockchain-based digital trust mechanisms with examples of using Cloud Manufacturing of medical LDS syringes for the urgent needs of catering COVID-19 vaccination. [ABSTRACT FROM AUTHOR] Copyright of Journal of Integrated Design & Process Science is the property of IOS Press and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Design of real-time ECG monitoring wearable equipment based on the Holter system

The COVID-19 epidemic has largely restricted the traditional offline medical treatment model. In this study, we designed ECG monitoring smart clothing based on the Holter system after identifying and analyzing the needs of patients and doctors. This clothing is a wearable device that integrates monitoring and remote diagnosis, building a general network platform to realize remote data transfer sharing and online interactive auxiliary diagnosis. Wearable clothing that can monitor ECG in real time is designed and developed by intelligently integrating limb lead wires, conductive fiber fabrics, lead interfaces, and electrode signal storage receivers by using the human body sensing conduction principle of real-time ECG monitoring. Wearable real-time ECG monitoring clothing can help patients achieve fast virtual medical care and auxiliary diagnosis, and solve the design issues with electrode signal storage receivers. © 2022 IEEE.

Refused-knowledge during the COVID-19 Pandemic: Mobilising Experiential Expertise for Care and Well-being

Since the early period of the COVID-19 pandemic concerned groups of people have produced knowledge refused by institutional science of how to manage public health and individual well-being in everyday pandemic life. Research in science and technology studies seeks to understand the social and cultural conditions under which contestation over scientific knowledge claims occurs. In the Italian case, ‘refused' knowledge claims emerging outside institutionalised science play a performative role in questioning the current models for managing individual and public health. Such refused claims ascribe novel meanings to the COVID-19 pandemic and orient the ways in which people manage their own health and well-being during their everyday life. Two interrelated dimensions are at stake in the production and enactment of refused knowledge: (1) how experiential expertise is mobilised to reframe one's body in a process of self-care, thus validating a corpus of refused knowledge through personal experience, and (2) how narratives demarcate between a body of refused knowledge and the prevalent biomedical paradigms as a way of gaining experiential epistemic autonomy.

Masked Face Recognition System Based on Attention Mechanism

With the continuous development of deep learning, the face recognition field has also developed rapidly. However, with the massive popularity of COVID-19, face recognition with masks is a problem that is now about to be tackled in practice. In recognizing a face wearing a mask, the mask obscures most of the facial features of the face, resulting in the general face recognition model only capturing part of the facial information. Therefore, existing face recognition models are usually ineffective in recognizing faces wearing masks. This article addresses this problem in the existing face recognition model and proposes an improvement of Facenet. We use ConvNeXt-T as the backbone of the network model and add the ECA (Efficient Channel Attention) mechanism. This enhances the feature extraction of the unobscured part of the face to obtain more useful information, while avoiding dimensionality reduction and not increasing the model complexity. We design new face recognition models by investigating the effects of different attention mechanisms on face mask recognition models and the effects of different data set ratios on experimental results. In addition, we construct a large set of faces wearing masks so that we can efficiently and quickly train the model. Through experiments, our model proved to be 99.76% accurate for real faces wearing masks. A combined accuracy of 99.48% for extreme environments such as too high or lousy contrast and brightness.

An IoT-enabled smart health care system for screening of COVID-19 with multi layers features fusion and selection

Advancement of smart medical sensors, devices, cloud computing, and health care technologies is getting remarkable attention from academia and the health care industry. As, Internet of things (IoT) has been recognized as one of the promising research topics in the domain of health care, particularly in medical image processing. Researchers utilized various machine and deep learning techniques along with artificial intelligence for analyzing medical images. These developed techniques are used to detect diseases that might assist medical experts in diagnosing diseases at early stages and providing accurate, consistent, effective, and speedy results, and decrease the mortality rate. Nowadays, Coronavirus (COVID-19) has been growing as one of the most rigorous and severe infections and spreading globally. Consequently, an intelligent automated system is required as an active diagnostic choice that might be used to prevent the spread of COVID-19. Thus, this work presented an IoT-enabled smart health care system for the automatic screening and classification of contagious diseases (Pneumonia, COVID-19) in Chest X-ray images. The developed system is based on two different deep learning architectures used with a multi-layers feature fusion and feature selection approach to classify X-ray images of infectious diseases. This work comprises the following steps: to enhance the diversity of the data set, data augmentation is performed, while for feature extraction, deep learning architectures, i.e., VGG-19 and Inception-V3, are used along with transfer learning. For the fusion of extracted features obtained from deep learning architectures, a parallel maximum covariance, and for feature selection, the multi-logistic regression controlled entropy variance approach is applied. For experimentation, a data set is customized containing Chest X-ray images using various publicly available resources. The system provides an overall classification accuracy of 97%. [ FROM AUTHOR] Copyright of Computing is the property of Springer Nature and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full . (Copyright applies to all s.)

Telemedicine applications in diabetology: Digital consulting hours and online training-implementation in daily practice.

Since the start of the coronavirus disease 2019 (COVID-19) pandemic-related restrictions, digital consultation hours and online training have become an integral part of the care of people with diabetes in both in- and outpatient practices specializing in diabetes. Despite advancing digitalization and the use of modern diabetes technology, only a few model projects and specialized practices with dedicated diabetes teams were previously using these new communication options. With the improvement of the technology, the framework conditions, the expansion of the information technology (IT) infrastructure and, above all, the expectations of the patients, the digital consultation hour is also, even after COVID-19, a good addition and alternative to in-person consultations. To start digital consultations requires careful preparation. In addition to personal willingness to expand patient communication, appropriate structural and process quality standards are necessary in medical practice. Legal requirements, technical prerequisites, and integration into the existing practice workflow must be implemented. Digital consultation hours and online training are presented from the perspective of a diabetes consultant and diabetologist from a practice specializing in diabetes. Difficulties at the beginning, practical tips for a successful start, the advantages of video consultation, and problems in daily practice routine are presented. Demographic developments, increasing prevalence of diabetes, fewer treatment facilities, diabetologists and diabetic teams, location-independent consultation, and also the expectations of our patients support the implementation of digital consultation hours in daily practice. Although not yet optimal, the significantly improved remuneration is also an incentive for the new developments in communication. Treatment structures in diabetology practice and treatment success are improved by digital consultation hours.Copyright © 2023, The Author(s), under exclusive licence to Springer Medizin Verlag GmbH, ein Teil von Springer Nature.

A Sylvatic or Syncretic Approach to Healthcare Innovation?

A personal narrative is presented which explores the authors experience of having healthcare organizations with sylvatic approach to innovation.

Emerging technologies in paediatric laboratory medicine

An editorial is presented on improving paediatric healthcare including prenatal and perinatal care. Topics include public health measures resulted in remarkable improvements in childhood survival, nutrition, and general health;and focusing on the use of science and technology for detecting and measuring biomarkers for clinical care.