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Human skin contains highly specialized deformation receptors that allow us to intuitively and effortlessly interpret our surroundings. These sensors help us to localize touch and determine the degree of contact pressure. In addition, the innate understanding of our own body posture is also due to these mechanoreceptors. This work demonstrates a synthetic sensory–motor analog that can be 3D printed, using direct ink writing (DIW) onto soft, fluidic elastomer actuators (FEAs). This 3D printing technique uses two inks–one that is an ionically conductive hydrogel and another that is an electrically insulating silicone–which is then patterned and photopolymerized into stretchable capacitive sensors. In this paper, these sensors are used to enable tactile sensing and kinesthetic feedback in a pneumatically actuated haptic device. This capacitive skin enabled the device to detect a compressive force from a finger press of ∼ 2 N, and an internal pressurization of as low as ∼ 10 kPa.

The quantitative assessment of structures from Cardiac Computed Tomography Angiography (CCTA) usually requires manual segmentation and adjustment which can be time-consuming. We used Deep Learning to segment the proximal ascending and descending aorta (PAA, DA), superior and inferior vena cavae (SVC

Discordance between the physiological diagnostic parameters like fractional flow reserve (FFR) and coronary flow reserve (CFR) is related to the presence of diffuse and/or focal stenoses and the associated pressure loss due to flow disturbances. In this study, using an in silico approach, we non