The pectoral fins of manta rays are driven by bilateral muscles to generate up-down flapping movements that allow for efficient and flexible swimming. However, current soft underwater biomimetic flapping robots are mostly driven by unilateral muscle actuators, and the pectoral fin can only realize simple downward bending motion, the biomimicry, motion flexibility, and swimming speed of these robots need to be further enhanced. Here, a soft manta ray robot driven by bilateral dielectric elastomer bionic muscle actuators is proposed. The bilateral muscle-driven pectoral fin can not only achieve a larger bending angle (37°), but also present a 3D morphology similar to that of a natural manta ray during the swimming process. Compared to the simple downward flapping used by most robots, the simulation shows that the pectoral fins driven by bilateral muscles can obtain larger vortex intensity, flow field velocity, and thrust. The fastest swimming speed of the designed manta ray robot driven by a bilateral bionic actuator is 42 mm/s (0.76 body length per second BL/s), which is about 0.7 times higher than the swimming speed of the robot driven by a unilateral bionic muscle actuator (25 mm/s). What's more, by strategically controlling multiple actuating units of the actuators, the manta ray robot can achieve more flexible swimming motions, including continuous turns, C-shaped, and S-shaped swimming trajectories. This study not only reveals the effectiveness of the bidirectional flapping mode in improving the performance of underwater swimming robots but also the proposed bilateral muscle-driven mode can be applied to other soft robots and flexible electronic devices.

The increasing consumption of ducks and chickens in China demands characterizing carcasses of domestic birds efficiently. Most existing methods, however, were developed for characterizing carcasses of pigs or cattle. Here, we developed a noncontact and automated weighing method for duck carcasses hanging on a production line. A 2D camera with its facilitating parts recorded the moving duck carcasses on the production line. To estimate the weight of carcasses, the images in the acquired dataset were modeled by a convolution neuron network (CNN). This model was trained and evaluated using 10-fold cross-validation. The model estimated the weight of duck carcasses precisely with a mean abstract deviation ( MAD ) of 58.8 grams and a mean relative error ( MRE ) of 2.15% in the testing dataset. Compared with 2 widely used methods, pixel area linear regression and the artificial neural network (ANN) model, our model decreases the estimation error MAD by 64.7 grams (52.4%) and 48.2 grams (45.0%). We release the dataset and code at https://github.com/RuoyuChen10/Image_weighing.

Various robot platforms have been developed for investigating new algorithms for swarm robotics. Morphogenetic engineering in robot swarms, however, proposes new requirements for platforms: precise motion control, physical interactions with the environment or neighbor robots, and functionalized shells. Few current platforms fulfill all the above characteristics. Here, we present Morphobot, a robot platform for morphogenetic engineering in swarm robotics. Its direct current coreless motors provide physical support and strong power, meanwhile, these needle-like motors also enhance the physical interactions among robots. Each Morphobot has a changeable shell. It is functionalized for programming local interactions, through physical contact or communication, among Morphobots. We characterized the mobility of Morphobot to test its capability of moving and physically interacting with its neighbors. To demonstrate its advantages in the morphogenesis of robot swarms, we designed two morphogenetic engineering experiments. The results revealed that swarms of Morphobots can form patterns via physical interactions and optical communications.

The cytochrome P450 (Cyp) proteins Cyp1A1 and Cyp1A2 are strongly induced in the mouse liver by the potent environmental toxicant 2, 3, 7, 8‐tetrachlorodibenzo‐p‐dioxin (TCDD), acting through the aryl hydrocarbon receptor (AHR). The induction of Cyp1A1 is localized within the centrilobular regions of the mouse liver at low doses of TCDD, progressing to pan‐lobular induction at higher doses. Even without chemical perturbation, metabolic functions and associated genes are basally zonated in the liver lobule along the central‐to‐portal axis. To investigate the mechanistic basis of spatially restricted gene induction by TCDD, we have developed a multiscale computational model of the mouse liver lobule with single‐cell resolution. The spatial location of individual hepatocytes in the model was calibrated from previously published high‐resolution images. A systems biology model of the network of biochemical signaling pathways underlying Cyp1A1 and Cyp1A2 induction was then incorporated into each hepatocyte in the model. Model simulations showed that a negative feedback loop formed by binding of the induced Cyp1A2 protein to TCDD, together with cooperative gene induction by the β‐catenin/AHR/TCDD transcription factor complex and β‐catenin, help produce the spatially localized induction pattern of Cyp1A1. Although endogenous WNT regulates the metabolic zonation of many genes, it was not a driver of zonal Cyp1A1 induction in our model. Conclusion: In this work, we used data‐driven computational modeling to identify the mechanistic basis of zonally restricted gene expression induced by the potent and persistent environmental pollutant TCDD. The multiscale model and derived results clarify the mechanisms of dose‐dependent hepatic gene induction responses to TCDD. Additionally, this work contributes to our broader understanding of spatial gene regulation along the liver lobule.