Research on fatigue identification methods based on low-load wearable ECG monitoring devices

The identification of fatigue in personal workers in particular environments can be achieved through early warning techniques. In order to prevent excessive fatigue of medical workers staying in infected areas in the early phase of the coronavirus disease pandemic, a system of low-load wearable electrocardiogram (ECG) devices was used as intelligent acquisition terminals to perform a continuous measurement ECG collection. While machine learning (ML) algorithms and heart rate variability (HRV) offer the promise of fatigue detection for many, there is a demand for ever-increasing reliability in this area, especially in real-life activities. This study proposes a random forest-based classification ML model to identify the four categories of fatigue levels in frontline medical workers using HRV. Based on the wavelet transform in ECG signal processing, stationary wavelet transform was applied to eliminate the main perturbation of ECG in the motion state. Feature selection was performed using ReliefF weighting analysis in combination with redundancy analysis to optimize modeling accuracy. The experimental results of the overall fatigue identification achieved an accuracy of 97.9% with an AUC value of 0.99. With the four-category identification model, the accuracy is 85.6%. These results proved that fatigue analysis based on low-load wearable ECG monitoring at low exertion can accurately determine the level of fatigue of caregivers and provide further ideas for researchers working on fatigue identification in special environments. © 2023 Author(s).

Intelligent Pneumonia Detection Model of Focal Transformer and Multi-scale-input SPD

The clinical diagnosis results based on lung X-rays provide important evidence in the COVID-19 pneumonia diagnosis process and for some other disease. However, due to the similarity of the lesions among many types of pneumonia displayed by X-rays, and due to the huge amount of X-ray readings of a doctor's daily work, traditional reading and identification method purely by human have problems of diagnosis mistakes, missed diagnosis and huge time consumption. Therefore, an intelligent detection model of pneumonia with multi-scale-input Focal Transformer integrated with SPD module is proposed to automatically identify various types of pneumonia including COVID-19 pneumonia. The method can pay attention to the multi-scale characteristic features of pneumonia lesions, and then make improved classification among COVID-19 pneumonia, cases with lung opacity, viral pneumonia and normal cases, providing stronger support for radiologists in medical diagnosis. The experiment results show that the proposed model has advantages in comparison to the traditional network models ResNet-50 and Swin Transformer in aspects of accuracy, recall, F1-Measure and other indicators. © 2022 IEEE.

From COVID-19 to Green Recovery with natural capital accounting

The COVID-19 pandemic and related social and economic emergencies induced massive public spending and increased global debt. Economic recovery is now an opportunity to rebuild natural capital alongside financial, physical, social and human capital, for long-term societal benefit. Yet, current decision-making is dominated by economic imperatives and information systems that do not consider society's dependence on natural capital and the ecosystem services it provides. New international standards for natural capital accounting (NCA) are now available to integrate environmental information into government decision-making. By revealing the effects of policies that influence natural capital, NCA supports identification, implementation and monitoring of Green Recovery pathways, including where environment and economy are most positively interlinked. © 2022, The Author(s).

The Effect of Source Location on the Effectiveness of Source Detection in Finite Networks

The topic of source identification has attracted wide attention from researchers. In practice, the source identification method aims to locate the sources of rumors, computer viruses, and epidemics, such as COVID-19. However, there are two main problems with existing propagation source detection methods. First, most source detection methods are based on infinite networks, not in line with reality. Second, sources are often randomly selected in simulations, but different sources often cause significantly different detection results in real-world applications. To this end, we study how does the source location impact the effectiveness of source detection in finite networks. This paper first proposes a diameter-based node division method to classify the nodes based on their structural location. We further offer different evaluation indicators to measure the effectiveness of source detection methods. Then, we conduct systematic experiments on three synthetic networks and two real-world networks. Our experiments demonstrate that the location of the source directly effects detection effectiveness in finite networks for all source detection methods. Specifically, sources closer to the network boundary will lead to worse detection performance. It means that attackers can choose sources close to the network boundary to reduce the probability of detection to achieve a larger spreading scale. © 2022 IEEE.

Identifying and Averting the Difficulties in Converting Biotechnological Innovations into Successful Commerce: Case of Bahrain

The purpose of this study is to examine the difficulties encountered by Bahraini entrepreneurs in converting innovative ideas in the biotechnology field to a profitable trade. Methodology - The study applies a qualitative approach. Semistructured, one-to-one interviews with four managers have been done to identify the obstacles they faced. Findings - Initial results prove that some factors, including funding, workforce skills, government regulations, and the Bahraini market environment, significantly affect success in the biotechnology industry. Further analysis suggests that the Bahraini market environment is the more fundamental obstacle compared to the others. Two additional factors, namely time management and infrastructure, have an insignificant effect on Bahrain’s progress in the biotech industry. Practical implications - This study is targeted at regulatory bodies in Bahrain and entrepreneurs to assist them in pinpointing the obstacles faced in the biotech industry and address them subsequently. Originality– This research contributes to innovative entrepreneurship literature relating to the biotech industry in the Gulf Cooperation Council (GCC) countries, primarily in Bahrain. As the 2030 vision for Bahrain depends on diversification of income sources, it is interesting to examine the difficulties that challenge entrepreneurs once they try to convert innovative ideas in the field of biotechnology into a successful trade story. Recommendation - improving the workforce skills to be competitive in the market with current impacts of the COVID-19 pandemic and validating these results quantitively are recommended. © 2020, Arabian Gulf University. All rights reserved.