This paper presents the design of a flexible bending actuator using shape memory alloy (SMA) and its integration in attitude control for solar sailing. The SMA actuator has advantages in its power-to-weight ratio and light weight. The bending mechanism and models of the actuator were designed and developed. A neural network based adaptive controller was implemented to control the non-linear nature of the SMA actuator. The actuator control modules were integrated into the solar sail attitude model with a quaternion PD controller that formed a cascade control. The feasibility and performance of the proposed actuator for attitude control were investigated and evaluated, showing that the actuator could generate 1.5 × 10⁻³ Nm torque which maneuvered a 1600 m² CubeSat based solar sail by 45° in 14 h. The results demonstrate that the proposed SMA bending actuator can be effectively integrated in attitude control for solar sailing under moderate external disturbances using an appropriate controller design, indicating the potential of a lighter solar sail for future missions.

Ischemic stroke triggered by carotid atherosclerosis remains unpredictable because the thrombosis embolization is governed by patient-specific vascular geometry and hemodynamics that cannot be recapitulated in conventional models. Here, we engineered "Physical Twin" artery-on-chip platforms that reproduce individualized carotid anatomy with humanized subendothelial matrix composition and arterial endothelial phenotype under physiological flow patterns. We then established thrombotic microenvironments following laser-induced injury. Computational fluid dynamics-guided experiments across distinct patient geometries reveal that local shear dictates a mechanobiological hierarchy: high-shear bifurcations (∼3,000 s⁻¹) produce von Willebrand factor (VWF) A1-dependent thrombi suppressible by conformationally sensitive inhibitors, whereas low-shear stenoses generate fragile aggregates where VWF-integrin α _IIb β₃ coupling governs embolization overgrowth. Pulsatile flow suppresses thrombotic growth independent of mean shear. Molecular dynamics simulations reveal why conformationally sensitive nanobodies (caplacizumab) outperform shear-independent aptamers (ARC1172) in high-shear bifurcation flow zones. This Physical Twin platform provides a mechanistic blueprint for geometry-informed, personalized antithrombotic therapy selection.

We developed an integrated platform combining high-throughput automated biofabrication, systematic patient-derived tissue experiments, and specialized artificial intelligence to enable patient-specific computational “digital twins” for thrombosis prediction. Our automated manufacturing platform fabricates 80 fully assembled, patient-specific vessel-on-chips within 10 hours from clinical imaging—a ∼100-fold improvement over manual methods—achieving sub-micron precision through novel two-stage pneumatic motion control and integrated optical feedback. Using these chips, we systematically captured thrombosis across 491 high-fidelity videos spanning 6 patient-derived vascular geometries, 5 distinct anatomical injury sites, and 14 anticoagulant/antiplatelet interventions, establishing a “physical twin” experimental corpus. We trained CLoT (Cascade Learner of Thrombosis), a conditional video diffusion model efficiently adapted via lightweight Low-Rank Adaptation (LoRA) to generate realistic thrombosis videos conditioned on patient-specific geometry, injury location, and drug treatment. Rigorous benchmarking against state-of-the-art commercial models (Sora, Wan, Kling, Seedance, Hailuo, Hunyuan) reveals CLoT achieves 7.38-fold superior temporal biological consistency and 5.3-fold higher spatial morphological fidelity. Prospective validation on unseen patients demonstrates >90% temporal accuracy. This integrated paradigm—combining automated fabrication with domain-specialized generative AI—establishes proof-of-concept for personalized medicine enabled by digital twins trained on human-derived vascular anatomy, enabling pre-treatment antithrombotic evaluation while providing a replicable template for translating tissue engineering into clinical practice.

Cerebral venous sinus thrombosis (CVST) and carotid web (CW) are distinct cerebrovascular disorders where vascular geometry, disturbed hemodynamics, and endothelial activation interact to drive thrombosis. Both conditions disproportionately affect women, yet remain understudied because of the inability of traditional platforms to replicate complex three-dimensional lumen profiles. This perspective positions light-based lithography as a powerful biofabrication strategy for patient-specific modeling. Digital light processing and volumetric bioprinting render enclosed, perfusable hydrogel networks with high-fidelity surface topography. When integrated into dynamic vessel-on-chip perfusion loops guided by clinical imaging and computational fluid dynamics, these platforms simulate pathological flow stasis and recirculation zones, heterogeneous shear-stress gradients, and site-specific cellular aggregation. This biofabrication approach enables personalized disease modeling and translationally relevant risk assessment, providing a critical platform for advancing women’s cerebrovascular health.