Soft machines typically exhibit slow locomotion speed and low manipulation strength because of intrinsic limitations of soft materials. Here, we present a generic design principle that harnesses mechanical instability for a variety of spine-inspired fast and strong soft machines. Unlike most current soft robots that are designed as inherently and unimodally stable, our design leverages tunable snap-through bistability to fully explore the ability of soft robots to rapidly store and release energy within tens of milliseconds. We demonstrate this generic design principle with three high-performance soft machines: High-speed cheetah-like galloping crawlers with locomotion speeds of 2.68 body length/s, high-speed underwater swimmers (0.78 body length/s), and tunable low-to-high-force soft grippers with over 1 to 103 stiffness modulation (maximum load capacity is 11.4 kg). Our study establishes a new generic design paradigm of next-generation high-performance soft robots that are applicable for multifunctionality, different actuation methods, and materials at multiscales.

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Wireless capsule endoscopy (WCE) is relatively a new device which investigates the entire gastrointestinal (GI). About 55000 frames are captured during an examination (two frames per second). Thus, it is bene icial to ind an automatic method to detect diseases frames or regions of a frame. The WCE videos have lots of uninformative regions (such as intestinal juice, bubbled, and dark regions); therefore, preprocessing is useful and necessary in diseases detection. In this paper, three practical methods are introduced to detect the informative and uninformative regions in a frame. In order to achieve this goal, morphological operations, fuzzy k-means, sigmoid function, statistic features, Gabor ilters, Fisher test, neural network, and discriminators in the HSV color space are used to detect uninformative regions (do not carry clinical information) in a frame. Our experimental results indicate that precision, sensitivity, accuracy, and speci icity are respectively 97.76%, 97.80%, 98.15%, and 98.40% in the irst method, 93.32%, 84.60%, 91.05%, and 95.67%, respectively in the second one, and 93.32%, 84.60%, 91.05%, and 95.67%, respectively in the third method.

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Image correlation and cross-correlation spectroscopy are most appropriate to unravel the motion of the PLLA nanocarrier and to demonstrate that the rate of the directed motion of organelles is influenced by their interaction with the nanocarriers. Not all PLLA-hypericin NPs are associated with organelles.

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