Carrot industry processing outputs 50% waste from raw materials; this waste contains polyphenols and carotenoids, which are a significant natural source of pro-vitamin A. Also, yogurt's high consumption globally allows for designing a new functional product. So the goal is to enhance the functionality of fortified stirred yogurt by incorporating carotenoid beads. The carotenoids were extracted from carrot waste using ultrasonication. Then nanoemulsion carotenoids incorporating with alginate to produce beads by extrusion technique. Measurement of carotenoid stability to nanoemulsion and beads. Manufactured five treatments of orange-flavored stirred yogurt and investigated its physicochemical properties, LAB survival, viscosity, and sensory acceptability. Findings – Carrot waste extract had about 44.75 ± 3.15 mg/g of β-carotene. The mean particle size of the nanoemulsion decreased with the increasing carotenoid addition (0.5%, 1%, and 1.5%) of carrot waste extract. The mean diameters of the alginate beads with nanoemulsions were 1.498 ± 0.245, 1.654 ± 0.310, and 1.792 ± 0.454 mm, respectively. The highest chemical stability of carotenoids showed with the alginate beads after Storage at 55°C to 14 days, compared with free or nanoemulsion carotenoids. Yogurt's physicochemical properties, viscosity, and LAB count improve when double-encapsulated carotenoids are added. Carotenoid double-encapsulation appeared to have a high ability to protect carotenoids from degradation and the ability to be applied in dairy and pharmaceutical products. Also, the resultant stirred yogurt with carotenoids-loaded beads gave carotenoids high stability and sensory acceptability.

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Anatomical information in ultrasound (US) imaging has not been exploited fully because its wave interference pattern (WIP) has been viewed as speckle noise. We tested the idea that more information can be retrieved by disentangling the WIP rather than discarding it as noise. We numerically solved the forward model of generating US images from computed tomography (CT) images by solving wave-equations using the Stride library. By doing so, we have paved the way for using deep neural networks to be trained on the data generated by the forward model to simulate the solution of the inverse problem, which is generating the CT-style and CT-quality images from a real US image. We demonstrate qualitative features of the generated images that are rich in anatomical details and realism.

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