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This laboratory experiment is designed for Research Experiences for Undergraduates (REU) programs, offering students immersive, hands-on research opportunities in the synthesis and characterization of magnetic materials. It emphasizes the foundational principles of magnetism, explores the essential properties of magnetic materials, and introduces various characterization techniques. The protocol highlights the significance of magnetite-based materials in diverse applications, providing a focused investigation into magnetic exchange coupling and enabling students to connect fundamental magnetic phenomena with cutting-edge research. Students conduct four experiments to prepare magnetite-based composites that incorporate both titanium and cobalt oxides. This approach allows them to explore magnetic exchange coupling and examine the resulting magnetic properties. By combining magnetite (Fe3O4), a well-known magnetic material, with titanium dioxide (TiO2), a diamagnetic oxide, and cobalt ferrite (CoFe2O4), a strong ferrimagnetic oxide with high coercivity, students investigate how the interaction between soft and hard magnetic phases affects overall magnetization behavior and magnetic coupling efficiency. Students then characterize these composites using techniques such as X-ray diffraction and vibrating sample magnetometry to study their magnetic properties and chemical structure, deepening their understanding of how these factors influence material behavior. This integrated approach reinforces core concepts of magnetism, materials science, and engineering while equipping students with practical skills in material preparation and characterization. 

Among the non-invasive Colorectal cancer (CRC) screening approaches, Computed Tomography Colonography (CTC) and Virtual Colonoscopy (VC), are much more accurate. This work proposes an AI-based polyp detection framework for virtual colonoscopy (VC). Two main steps are addressed in this work: automatic segmentation to isolate the colon region from its background, and automatic polyp detection. Moreover, we evaluate the performance of the proposed framework on low-dose Computed Tomography (CT) scans. We build on our visualization approach, Fly-In (FI), which provides “filet”-like projections of the internal surface of the colon. The performance of the Fly-In approach confirms its ability with helping gastroenterologists, and it holds a great promise for combating CRC. In this work, these 2D projections of FI are fused with the 3D colon representation to generate new synthetic images. The synthetic images are used to train a RetinaNet model to detect polyps. The trained model has a 94% f1-score and 97% sensitivity. Furthermore, we study the effect of dose variation in CT scans on the performance of the the FI approach in polyp visualization. A simulation platform is developed for CTC visualization using FI, for regular CTC and low-dose CTC. This is accomplished using a novel AI restoration algorithm that enhances the Low-Dose CT images so that a 3D colon can be successfully reconstructed and visualized using the FI approach. Three senior board-certified radiologists evaluated the framework for the peak voltages of 30 KV, and the average relative sensitivities of the platform were 92%, whereas the 60 KV peak voltage produced average relative sensitivities of 99.5%.