Impact of the COVID-19 pandemic on blood services operations: Korean experience

The coronavirus disease 19 (COVID-19) pandemic had a profound impact on blood services operations in Korea. Blood collection was affected due to decrease in donor availability caused by avoidance of public places, social distancing policies, and cancellation of blood drives. The negative impact on blood collection was more pronounced with the COVID-19 pandemic than with other outbreaks experienced previously such as the influenza (H1N1) outbreak or the Middle East respiratory virus (MERS) pandemic. To cope with the blood shortage, campaigns to appeal for blood donation, raise public awareness on the importance of blood donation and gain donor's confidence in safe blood donation were implemented using mass communication media such as TV and radio broadcasting as well as postings on various social media platforms. Upon Korean Red Cross Blood Services's (KRCBS) request, the Ministry of Health and Welfare (MoHW) approved the relaxation of the geographical restrictions regarding indigenous malaria thus enabling collection of more than 23,000 units of whole blood. To mitigate even a theoretical risk of transfusion-transmission of SARS-CoV-2 via blood donation from pre-symptomatic COVID-19 donors, the KRCBS received the data on COVID-19 identified cases from the Korean Disease Control and Prevention Agency (KDCA) from the early get-go of the pandemic for cross referencing to donors for further recipient investigation and recall of blood products not transfused. Communication with donors, staff members, national health authorities, hospital customers and other stakeholders was and remains of utmost importance to respond to this unprecedented situation which is still ongoing.Copyright © Annals of Blood. All rights reserved.

Signal Preprocessing Technique With Noise-Tolerant for RF-Based UAV Signal Classification

Since the beginning of the COVID-19 pandemic, the demand for unmanned aerial vehicles (UAVs) has surged owing to an increasing requirement of remote, noncontact, and technologically advanced interactions. However, with the increased demand for drones across a wide range of fields, their malicious use has also increased. Therefore, an anti-UAV system is required to detect unauthorized drone use. In this study, we propose a radio frequency (RF) based solution that uses 15 drone controller signals. The proposed method can solve the problems associated with the RF based detection method, which has poor classification accuracy when the distance between the controller and antenna increases or the signal-to-noise ratio (SNR) decreases owing to the presence of a large amount of noise. For the experiment, we changed the SNR of the controller signal by adding white Gaussian noise to SNRs of -15 to 15 dB at 5 dB intervals. A power-based spectrogram image with an applied threshold value was used for convolution neural network training. The proposed model achieved 98% accuracy at an SNR of -15 dB and 99.17% accuracy in the classification of 105 classes with 15 drone controllers within 7 SNR regions. From these results, it was confirmed that the proposed method is both noise-tolerant and scalable.