Incorporating multiple robotic manipulators into large-scale manufacturing systems enhances production efficiency and expands manufacturing capabilities beyond those of single-robot systems. Workpiece positioners in robotic manufacturing have demonstrated significant benefits for process optimization, but coordination strategies for multi-robot systems with shared positioners have received limited attention. This work presents a task-space coordinated trajectory-tracking control framework for multi-robot manufacturing systems, in which robots coordinate their motions within a shared, dynamic workpiece positioning frame. A workpiece positioner actively adjusts the pose of the manufactured component to enable greater operational concurrency and improve overall production efficiency. The proposed motion-coordination scheme employs a distributed and scalable architecture, supporting coordination across heterogeneous multi-robot systems. Two optimization methodologies are introduced to manage kinematic redundancies and maintain continuous, near-optimal operation throughout the manufacturing process. The first strategy exploits a task-space dimensionality reduction to achieve locally optimal configurations by leveraging symmetry-axis rotations of the tool. The second strategy utilizes the workpiece positioner to drive the coordinated robots toward stable and kinematically favorable configurations. For both optimization strategies, multiple objectives are defined to improve key performance metrics, including manipulability, configuration consistency, proximity to mechanical limits, and motion efficiency. Addressing a key limitation of existing coordination approaches, the framework is designed around online setpoint modification, allowing coordinated robots to respond effectively to in-situ process feedback. The proposed control framework is validated using the Robot Operating System (ROS) middleware on a combination of physical and simulated multi-robot system hardware.
Short-fiber thermoplastic (SFT) composites are increasingly employed in lightweight aerospace and automotive structures owing to their favorable strength-to-weight ratio, high production rates, and recyclability. Unlike continuous-fiber systems, the mechanical response of SFTs is governed by mesoscale interactions among fiber orientation, spatial clustering, and manufacturing-induced porosity. These features exhibit significant spatial variability in manufactured components and influence stiffness, damage initiation, and nonlinear deformation. Although mesoscale finite element (FE) models can resolve such heterogeneity, their application to realistic three-dimensional microstructures remains computationally intractable. A data-driven surrogate framework is proposed to predict the mechanical behavior of additively manufactured, compression-molded (AM-CM) SFTs. Microstructures reconstructed from micro-computed tomography data were discretized into Voronoi-based cells representing distinct fiber-interaction neighborhoods. Each cell was homogenized via nonlinear FE simulations incorporating matrix damage, and the resulting stress-strain responses trained a hybrid Graph Neural Network-Long Short-Term Memory (GNN-LSTM) architecture encoding microstructural topology and history-dependent mechanical evolution. The surrogate accurately predicts stiffness and stress-strain behavior of unseen microstructures, achieving R^2≈ 0.98 relative to high-fidelity FE simulations with over two orders-of-magnitude reduction in computational cost. Coupling the framework with experimentally calibrated damage laws demonstrates that fiber orientation, clustering, and porosity collectively govern local effective stiffness. The approach provides a physics-informed, data-efficient pathway to identify mechanically weak microstructural cells and accelerate digital-twin development for SFT components.
Reliable simulation of graphite-moderated pebble bed reactors requires validation against integral experiments. This work validates the SCALE development version (SCALE 7.0 Beta 12) against the HTR-PROTEUS benchmark suite from the International Handbook of Evaluated Reactor Physics Benchmark Experiments (IRPhE). Eleven critical configurations were analyzed, including hexagonal close-packed (HCP), columnar hexagonal point-on-point (CHPOP), and nominally random pebble-bed arrangements, with variations in moderator-to-fuel ratio and simulated water-ingress conditions. Criticality calculations were performed with SCALE’s Shift Monte Carlo code using multiple nuclear data libraries in both continuous-energy and multigroup modes and with explicit pebble modeling. Recent Shift enhancements in the mesh acceleration for such stochastic geometries made the simulations efficient. In addition to criticality, the effective delayed neutron fraction and selected control rod worths were evaluated. Nuclear data sensitivity and uncertainty analyses were also performed. The calculations reproduced the main benchmark trends, with both ENDF/B-VIII.0 and the preliminary SCALE-processed ENDF/B-VIII.1 continuous-energy libraries yielding smaller criticality biases than ENDF/B-VII.1. For ENDF/B-VII.1 and ENDF/B-VIII.0, the propagated nuclear-data-induced uncertainty in keff remained near 0.7%. The ENDF/B-VIII.1 analysis was limited to the continuous-energy keff comparison. Overall, these results support the use of SCALE for high-fidelity analysis of graphite-moderated pebble bed reactor systems.
Membrane-active peptides (MAPs) have garnered significant attention as potential alternatives to conventional cancer therapies, which are frequently limited by severe side effects. Among them, antimicrobial peptides (AMPs) that leverage differences between the plasma membranes of cancer cells and healthy cells are particularly attractive. While several AMPs have demonstrated anticancer potency, structure-function relationship studies are lacking to explain the molecular basis of their selectivity and to help design improved analogs. Here, we contribute to filling this gap by investigating Nile tilapia piscidin 4 (TP4), an AMP with demonstrated activity against several solid organ cancers. First, we discover through biological assays that the anticancer activity of the peptide, which underscores a promising therapeutic window, is associated with increased plasma membrane permeability in cancer cells compared to normal cells and positively (negatively) correlated with enzymes that enrich (deplete) anionic PS in the outer leaflet. Next, we utilize a suite of complementary techniques on model membranes to investigate the interactions of TP4 with membranes, uncovering behaviors not previously observed in related AMPs. Circular dichroism experiments reveal that TP4 preferentially binds to zwitterionic phosphatidylcholine (PC) membranes enriched in anionic PS, while cholesterol markedly impairs binding. X-ray diffraction demonstrates that TP4 disrupts PC-PS membranes by inducing lipid segregation. Covering a range of biologically relevant peptide concentrations with neutron diffraction and reflectometry measurements in fluid bilayers and MD simulations, we unveil how TP4 and associated water gradually insert into the hydrocarbon region and cause convoluted membrane deformations to breach the membrane barriers. These studies highlight the pivotal role of the TP4 polyarginine tail in driving selective membrane binding and disruption on membranes enriched with the anionic lipid PS. Together, our results elucidate the molecular determinants underpinning the selective anticancer effects of TP4, providing a strategic framework for the rational design of advanced membrane-active therapeutics.
