Currently, the only widely available tool for controlling the SARS-CoV-2 pandemic is nonpharmacological interventions (NPIs). Coronavirus aerosols are around 0.3–2 microns in diameter (0.9 m in mass). The present study used artificial intelligence such as gene expression programming (GEP) and genetic algorithms (GA) were used to predict and optimize the diameter of Nylon-6,6 nanofibers via electrospinning for protection against coronavirus. It is suggested that using the controlled experimental conditions such as concentration of nylon-6,6 (16 % wt/v), applied voltage (26 kV), working distance (18 cm) and injection rate (0.2 mL/h) have resulted the diameter of nylon-6,6 nanofibers about 55.8 nm. Coronavirus face masks could use the obtained diameter and electrostatic interaction between viral particles and naofibers as active layers.

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In blockchain technology, all registered information, from the place of production of the product to its point of sale, is recorded as permanent and unchangeable, and no intermediary has the ability to change the data of other members and even the data registered by them without public consensus. In this way, users can trust the accuracy of the data. Blockchain systems have a wide range of applications in the medical and health sectors, from creating an integrated system for recording and tracking patients’ medical records to creating transparency in the drug supply chain and medical supplies. However, implementing blockchain technology in the supply chain has limitations and sometimes has risks. In this study, BWM methods and VIKORSort have been used to classify the risks of implementing blockchain in the drug supply chain. The results show that cyberattacks, double spending, and immutability are very dangerous risks for implementation of blockchain technology in the drug supply chain. Therefore, the risks of blockchain technology implementation in the drug supply chain have been classified based on a literature review and opinions of the experts. The risks of blockchain technology implementation in the supply chain were determined from the literature review.

As the COVID-19 pandemic started triggering widespread lockdowns across the globe, cybercriminals did not hesitate to take advantage of users' increased usage of the Internet and their reliance on it. In this paper, we carry out a comprehensive measurement study of online social engineering attacks in the early months of the pandemic. By collecting, synthesizing, and analyzing DNS records, TLS certificates, phishing URLs, phishing website source code, phishing emails, web traffic to phishing websites, news articles, and government announcements, we track trends of phishing activity between January and May 2020 and seek to understand the key implications of the underlying trends.We find that phishing attack traffic in March and April 2020 skyrocketed up to 220% of its pre-COVID-19 rate, far exceeding typical seasonal spikes. Attackers exploited victims' uncertainty and fear related to the pandemic through a variety of highly targeted scams, including emerging scam types against which current defenses are not sufficient as well as traditional phishing which outpaced the ecosystem's collective response.• Record-breaking attack volume. We observed that traffic to phishing websites reached record levels in March

Iris segmentation and localization in unconstrained environments are challenging due to long distances, illumination variations, limited user cooperation, and moving subjects. Some existing methods in the literature have somehow mitigated some of the above-mentioned issues. In this paper, motivated by these weaknesses, we propose a framework that employs a deep neural network-based approach to iris segmentation and localization. The proposed framework is based on a U-Net architecture initialized with a pre-trained MobileNetV2 model. In addition, to better study the detectors in iris recognition scenarios, we have collected 1000 images. The provided dataset (KartalOl) is made publicly available for the research community. In the proposed framework, to have better generalization, we fine-tuned the MobileNetV2 model on the provided data for NIR-ISL 2021 from the CASIA-Iris-Asia, CASIA-Iris-M1, and CASIA-Iris-Africa and our dataset. Likewise, data augmentation techniques are applied on images. We chose the binarization threshold for the binary masks by iterating over the images in the provided dataset. The proposed framework is trained and tested in CASIA-Iris-Asia, CASIA-Iris-M1, and CASIA-Iris-Africa, along with the KartalOl dataset. The experimental results highlight that our method surpasses state-of-the-art methods on mobile-based benchmarks. The implementation source code of KartalOl is made publicly available at https://github.com/Jalilnkh/KartalOl-NIR-ISL2021031301.