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Abstract The use of nanomaterials and nanostructures have been revolutionizing the advancements of science and technology in various engineering and medical fields. As an example, Carbon Nanotubes (CNTs) have been extensively used for the improvement of mechanical, thermal, electrical, magnetic, and deteriorative properties of traditional composite materials for applications in high-performance structures. The exceptional materials properties of CNTs (i.e., mechanical, magnetic, thermal, and electrical) have introduced them as promising candidates for reinforcement of traditional composites. Most structural configurations of CNTs provide superior material properties; however, their geometrical shapes can deliver different features and characteristics. As one of the unique geometrical configurations, helical CNTs have a great potential for improvement of mechanical, thermal, and electrical properties of polymeric resin composites.

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.

Abstract The hot corrosion studies for the die-casted magnesium (Mg) silver (Ag) alloys are carried out through the steam heating route. The Magnesium Silver (QE22A) alloy is fixed under the top lid of the pressure cooker (2 liters) and filled with water and 5% salt (NaCl) solution. The specimens are treated with different time intervals (10, 20, and 30 minutes), with the steam temperature maintained at 100°C around the specimen. The results showed an increase in the corrosion rate with the increase in the steaming time. Further, after the specimens have cooled down to room temperature, similar experiments are repeated for the second and third cycles. Here the formation of the oxide layers over the specimen has reduced the corrosion rate. The structural, surface study was carried out through scanning electron microscopy (SEM), X-ray diffraction (XRD), and energy-dispersive spectroscopy (EDS) to know the corrosion behavior on the specimen.