It is undisputable fact that Coronavirus pandemic will go into the annals of history as one of the devastating plagues. From the healthcare perspective, a lot of efforts are underway geared towards testing, management and vaccination whereas industry and research communities explore innovative solutions. Quite a number of solutions have emerged zooming in on contact tracing, combating misinformation, data aggregation and analysis as well as test result certification with blockchain technology been the core. Aside the reliance on centralized architectures and use of permissioned/consortium blockchain, conspicuously missing in existing solutions based on blockchain is the work around the immutability feature of the technology given the fact that a person's test result is not static but dynamic. In this paper, we propose a solution using blockchain and smart contract that allows for state changes to be made by authorized entities. We leverage distributed storage technology using InterPlanetary File System (IPFS) for storage of user encrypted records and subsequent retrieval for verification purposes. We extend our solution by incorporating vaccination status to provide comprehensive source of information and show proof of concept. The full code of our proposed solution is made publicly available on GitHub.

As a harmless detection method, terahertz has become a new trend in security detection. However, there are inherent problems such as the low quality of the images collected by terahertz equipment and the insufficient detection accuracy of dangerous goods. This work advances BiFPN at the neck of YOLOv5 of the deep learning model as a mechanism to improve low resolution. We also perform transfer learning, thereby fine-tuning the pre-training weight of the backbone for migration learning in our model. Results from experimental analysis reveal that mAP@0.5 and mAP@0.5:0.95 values witness a percentage increase of 0.2% and 1.7%, respectively, attesting to the superiority of the proposed model to YOLOv5, which is the state-of-the-art model in object detection.

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