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.

A novel design of a nearly perfect metamaterial absorber based on square split-ring resonators for terahertz sensing applications is proposed and analyzed. The design in this report is simulated and analyzed by using standard numerical simulation software. Magnetic and electric resonant field enhancement in the impedance matched absorber cavity enables stronger interaction with the dielectric analyte. The proposed structure is based on the simultaneous increase in the electromagnetic field and the surface current distribution at the resonance frequency. An absorptivity of 99% is achieved at 0.53 THz with a narrow resonance peak and a Q-factor of 44.17. At a fixed analyte thickness, the resonance frequency is sensitive to the refractive index of the surrounding medium. The influence of the thickness of the covering sample on the sensitivity and absorption coefficient of the absorber is comprehensively analyzed, and the reported design can be used as a refractive index sensor with a high sensitivity of 126.0 GHz/RIU and a figure of merit of 10.5 in the refractive index range from 1.0 to 2.0 at an analyte thickness of 15.0 µm. The results show that the sensor has high sensitivity to the analyte covering it. The sensor not only exhibits good sensitivity to thin analytes but also shows high sensitivity to analytes more than 10 µm thick in the terahertz low frequency band. Specifically, the sensitivity changes rapidly when the thickness of the sample changes in the range of 0-6 µm, but slowly in the range of 6-16 µm. In general, the response of the resonance frequency to changes in the refractive index of the sample becomes more sensitive as the thickness of the sample is increased from 0 to 16 µm . The reported terahertz sensor of a metamaterial absorber has potential applications in biomedical sensing and trace detection of substances.

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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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