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We present a new method to estimate the boundary of extended sources in high-energy photon lists and to quantify the uncertainty in the boundary. This method extends the graphed seeded region growing method developed by M. Fan et al. Here, we describe how an unambiguous boundary of a centrally concentrated astronomical source may be defined by first spatially segmenting the photon list, then forcibly merging the segments until only two segments—an extended source and its background—remain, and finally constructing a boundary as the connected outer edges of the Voronoi tessellation of the photons included in the source segment. The resulting boundary is then modeled using Fourier descriptors to generate a smooth curve, and this curve is bootstrapped to generate uncertainties. We apply the method to photon event lists obtained during the observations of galaxies NGC 2300 and Arp 299. We demonstrate how the derived extent and enclosed flux of NGC 2300 obtained with Chandra and XMM-Newton are comparable. We also show how complex internal structure, as in the case of Arp 299, may be subsumed to construct a compact boundary of the object. 

Shape‐morphing devices, a crucial branch in soft robotics, hold significant application value in areas like human–machine interfaces, biomimetic robotics, and tools for biological systems. To achieve 3D programmable shape morphing (PSM), the deployment of array‐based actuators is essential. However, a critical knowledge gap in 3D PSM is controlling the complex systems formed by these soft actuator arrays to mimic the morphology of the target shapes. This study, for the first time, represents the configuration of shape‐morphing devices using point cloud data and employing deep learning to map these configurations to control inputs. Shape Morphing Net (SMNet), a method that realizes the regression from point cloud to high‐dimensional control input vectors, is proposed. It has been applied to 3D PSM devices with three different actuator mechanisms, demonstrating its universal applicability to inversely reproduce the target shapes. Further, applied to previous 2D PSM devices, SMNet significantly enhances control precision from 82.23% to 97.68%. In the demonstrations of morphology mimicking, 3D PSM devices successfully replicate arbitrary target shapes obtained either through 3D scanning of physical objects or via 3D modeling software. The results show that within the deformable range of 3D PSM devices, accurate reproduction of the desired shapes is achievable. 

In the field of biomechanics, customizing complex strain fields according to specific requirements poses an important challenge for bioreactor technology, primarily due to the intricate coupling and nonlinear actuation of actuator arrays, which complicates the precise control of strain fields. This paper introduces a bioreactor designed with a 9 × 9 array of independently controllable dielectric elastomer actuators (DEAs), addressing this challenge. We employ image regression-based machine learning for both replicating target strain fields through inverse control and rapidly predicting feasible strain fields generated by the bioreactor in response to control inputs via forward control. To generate training data, a finite element analysis (FEA) simulation model was developed. In the FEA, the device was prestretched, followed by the random assignment of voltages to each pixel, yielding 10,000 distinct output strain field images for the training set. For inverse control, a multilayer perceptron (MLP) is utilized to predict control inputs from images, whereas, for forward control, MLP maps control inputs to low-resolution images, which are then upscaled to high-resolution outputs through a super-resolution generative adversarial network (SRGAN). Demonstrations include inputting biomechanically significant strain fields, where the method successfully replicated the intended fields. Additionally, by using various tumor–stroma interfaces as inputs, the bioreactor demonstrated its ability to customize strain fields accordingly, showcasing its potential as an advanced testbed for tumor biomechanics research.