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Significant growth of Unmanned Aerial Vehicles (UAV) has unlocked many services and applications opportunities in the healthcare sector. Aerial transportation of medical cargo delivery can be an effective and alternative way to ground-based transport systems in times of emergency. To improve the security and the trust of such aerial transportation systems, Blockchain can be used as a potential technology to manage, operate and monitor the entire process. In this paper, we present a blockchain network solution based on Ethereum for the transportation of medical cargo such as blood, medicines, vaccines, etc. The smart contract solution developed in solidity language was tested using the Truffle program. Ganache blockchain test network was employed to host the blockchain network and test the operation of the proposed blockchain model. The suitability of the model is validated in real-time using a UAV and all the flight data are captured and uploaded into the blockchain.

Mass is one important suspicious object for breast cancer diagnosis in mammograms. Computer-aided detection (CAD) based on fully supervised deep learning achieves high performance for mass detection in mammograms. The lack of fine-grained expert labels becomes the bottleneck for the large-scale application of CAD to achieve detection in mammograms. Weakly supervised methods provide a solution to tackle the annotation problems, including in the application for mass detection. However, previous works face the problem of insufficient localization information, which affect the ability of mass detection. In this paper, we propose a multi-view enhancing mass detection network (MVMDNet) with dual view inputs that contains craniocaudal (CC) and mediolateral oblique (MLO) view of mammograms, where different view features are interacted and fused to enhance localization information.

Abstract The demand for contactless, rapid manufacturing has increased over the years, especially during the COVID-19 pandemic. Additive manufacturing (AM), a type of rapid manufacturing, is a computer-based system that precisely manufactures products. It proves to be a faster, cheaper, and more efficient production system when integrated with cloud-based manufacturing (CBM). Similarly, the need for semiconductors has grown exponentially over the last five years. Several companies could not keep up with the increasing demand for many reasons. One of the main reasons is the lack of a workforce due to the COVID-19 protocols. This article proposes a novel technique to manufacture semiconductor chips in a fast-paced manner. An algorithm is integrated with cloud, machine vision, sensors, and email access to monitor with live feedback and correct the manufacturing in case of an anomaly.