From Folding Mechanics to Robotic Function: A Unified Modeling Framework for Compliant Origami

Origami inspired architectures offer a powerful route toward lightweight, reconfigurable, and programmable robotic systems. Yet, a unified mechanics framework capable of seamlessly bridging rigid folding, elastic deformation, and stability driven transitions in compliant origami remains lacking. Here, we introduce a geometry consistent modeling framework based on discrete differential geometry (DDG) that unifies panel elasticity and crease rotation within a single variational formulation. By embedding crease panel coupling directly into a mid edge geometric discretization, the framework naturally captures rigid folding limits, distributed bending, multistability, and nonlinear dynamic snap through within one mechanically consistent structure. This unified description enables programmable control of stability and deformation across rigid and compliant regimes, allowing origami structures to transition from static folding mechanisms to active robotic modules. An implicit dynamic formulation incorporating gravity, contact, friction, and magnetic actuation further supports strongly coupled multiphysics simulations. Through representative examples spanning single fold bifurcation, deployable Miura membranes, bistable Waterbomb modules, and Kresling based crawling robots, we demonstrate how geometry driven mechanics directly informs robotic functionality. This work establishes discrete differential geometry as a foundational design language for intelligent origami robotics, enabling predictive modeling, stability programming, and mechanics guided robotic actuation within a unified computational platform.

Magnetic Resonance Spectroscopy Quantification Aided by Deep Estimations of Imperfection Factors and Overall Macromolecular Signal

Magnetic Resonance Spectroscopy (MRS) is an important non-invasive technique for in vivo biomedical detection. However, it is still challenging to accurately quantify metabolites with proton MRS due to three problems: Serious overlaps of metabolite signals, signal distortions due to non-ideal acquisition conditions and interference with strong background signals including macromolecule signals. The most popular software, LCModel, adopts the non-linear least square to quantify metabolites and addresses these problems by introducing regularization terms, imperfection factors of non-ideal acquisition conditions, and designing several empirical priors such as basissets of both metabolites and macromolecules. However, solving such a large non-linear quantitative problem is complicated. Moreover, when the signal-to-noise ratio of an input MRS signal is low, the solution may have a large deviation. In this work, deep learning is introduced to reduce the complexity of solving this overall quantitative problem. Deep learning is designed to predict directly the imperfection factors and the overall signal from macromolecules. Then, the remaining part of the quantification problem becomes a much simpler effective fitting and is easily solved by Linear Least Squares (LLS), which greatly improves the generalization to unseen concentration of metabolites in the training data. Experimental results show that compared with LCModel, the proposed method has smaller quantification errors for 700 sets of simulated test data, and presents more stable quantification results for 20 sets of healthy in vivo data at a wide range of signal-to-noise ratio. Qnet also outperforms other deep learning methods in terms of lower quantification error on most metabolites. Finally, QNet has been deployed on a cloud computing platform, CloudBrain-MRS, which is open accessed at https://csrc.xmu.edu.cn/CloudBrain.html.

Physics-informed deep diffusion MRI reconstruction: break the bottleneck of training data in artificial intelligence

In this work, we propose a Physics-Informed Deep Diffusion magnetic resonance imaging (DWI) reconstruction method (PIDD). PIDD contains two main components: The multi-shot DWI data synthesis and a deep learning reconstruction network. For data synthesis, we first mathematically analyze the motion during the multi-shot data acquisition and approach it by a simplified physical motion model. The motion model inspires a polynomial model for motion-induced phase synthesis. Then, lots of synthetic phases are combined with a few real data to generate a large amount of training data. For reconstruction network, we exploit the smoothness property of each shot image phase as learnable convolution kernels in the k-space and complementary sparsity in the image domain. Results on both synthetic and in vivo brain data show that, the proposed PIDD trained on synthetic data enables sub-second ultra-fast, high-quality, and robust reconstruction with different b-values and undersampling patterns.