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The class activation map (CAM) represents the neural-network-derived region of interest, which can help clarify the mechanism of the convolutional neural network’s determination of any class of interest. In medical imaging, it can help medical practitioners diagnose diseases like COVID-19 or pneumonia by highlighting the suspicious regions in Computational Tomography (CT) or chest X-ray (CXR) film. Many contemporary deep learning techniques only focus on COVID-19 classification tasks using CXRs, while few attempt to make it explainable with a saliency map. To fill this research gap, we first propose a VGG-16-architecture-based deep learning approach in combination with image enhancement, segmentation-based region of interest (ROI) cropping, and data augmentation steps to enhance classification accuracy. Later, a multi-layer Gradient CAM (ML-Grad-CAM) algorithm is integrated to generate a class-specific saliency map for improved visualization in CXR images. We also define and calculate a Severity Assessment Index (SAI) from the saliency map to quantitatively measure infection severity. The trained model achieved an accuracy score of 96.44% for the three-class CXR classification task, i.e., COVID-19, pneumonia, and normal (healthy patients), outperforming many existing techniques in the literature. The saliency maps generated from the proposed ML-GRAD-CAM algorithm are compared with the original Gran-CAM algorithm. 

Manufacturing and production systems have become increasingly complex in the past decade to meet the competitive demand in a growing industry. As these systems grow in complexity and flexibility, there is a need for efficient management and analysis of these systems. Model-based systems engineering (MBSE) addresses the complexity inherent with systems development with a model-centric approach that supported tailored modeling languages, methods and tools. This paper identifies the thematic evolution and trends and relationships found in the use and application of MBSE specifically in the manufacturing and production engineering domain. A collection of 471 published article from Institute of Electrical and Electronics Engineers (IEEE) and Science Direct over the past decade were used for the analysis using text mining techniques. Due to the limitation on the access to full text information of all the articles identified, only abstracts were considered for analysis. This effort helps the researchers across the domain to explore the reason behind and understand the change of the thematic perspectives of MBSE application over the last decade. In addition, the finding of the growing interest in addressing the aspects of complexity and systems requirements, and on the aspects of the use of MBSE for identifying and addressing the challenges related to Cyber Physical Systems help in paving a path for future research. 

Model-Based Systems Engineering (MBSE) is the formalized application of modeling to support various system evolving stages starting from the conceptual design phase to all the life cycle phases that follow. To facilitate in an efficient system behavior design process, in this paper, a Design Structure Matrix (DSM) based approach is developed and illustrated for determining the operational sequence of activities relevant to requirements of the system and in identifying the concurrent activities as well. A triangularization algorithm method is extended especially for application on activity diagrams to determine knowledge activities in an interaction graph to identify groups of activities and arrange them concurrently. The findings through the DSM based approach are validated by a system engineering expert and are implemented to construct the MBSE activity diagram to facilitate an enhanced behavior design of the system. This paper illustrates the use of Design Structure Matrix to facilitate modeling interdependencies between activities and the approach to aggregate the resulted sequential and concurrent activities with the activity diagram, applied to a case study of Execute Hohmann Transfer based on DellSat-77 Satellite System. In addition, the potentials benefits of using a Design Structure Matrix methodology for assisting Model Based Systems Engineering activities for enhanced systems behavior design is portrayed.