VLM-integrated 3D perception model for robust robotic grasping adapted to deformable sacks with arbitrary shapes

This study presents a data-informed convolutional neural network (CNN) for identifying fluid-filled (e.g., water) inclusions in two-dimensional (2D) solids. To generate training data, we simulate the propagation of elastic waves in structures containing fluid inclusions using Ansys Mechanical. To provide structural details, we introduce a gridwise classification method that reconstructs the targeted domain containing arbitrary fluid-filled inclusions by creating a 2D grid-structured image. This work produces training data comprising input-layer features (i.e., measured wave responses) and output-layer features (i.e., gridwise maps labeling each grid element as solid or fluid). The CNN is trained on these simulated data sets to classify grid elements based on wave responses recorded at surface sensors, and clusters of predicted fluid grid elements are used to reconstruct fluid-filled inclusions. Numerical results show that the proposed CNN can effectively detect fluid inclusions of various locations, sizes, and shapes. We also present parametric studies of factors such as the number of sensors, the size of the training data sets, and excitation frequency, providing a comprehensive analysis of their effect on detection performance.

A navigation-guided 3D breast ultrasound scanning and reconstruction system for automated multi-lesion spatial localization and diagnosis.

The spatiotemporal dynamics and density of actin networks are key determinants of actin cytoskeleton-mediated cellular functions. In vitro reconstitution systems have been widely used to study actin cytoskeletal dynamics; however, many existing approaches offer limited flexibility in controlling the geometry, thickness, and density of the assembled actin networks. Here, we present an in vitro optogenetic protocol that enables precise control of actin network assembly on supported lipid bilayers using an improved light-induced dimer (iLID)-SspB-based light-inducible dimerization system. In this system, His-mEGFP-iLID is anchored to a Ni-NTA-containing lipid bilayer, while SspB-mScarlet-I-VCA, a nucleation-promoting factor fused with SspB, together with other actin cytoskeletal proteins, is supplied in bulk solution. Upon blue light illumination, SspB-mScarlet-I-VCA is recruited to the membrane in a spatially and temporally defined manner, inducing localized actin polymerization. By tuning illumination patterns and duration, actin networks with defined density, thickness, and geometry can be generated, and polymerization can be rapidly halted by stopping illumination. This protocol provides a versatile platform for reconstructing actin networks with controlled spatial organization and density, enabling quantitative analysis of density-dependent interactions between actin networks and actin-binding proteins. Key features • Actin networks with varying densities and arbitrary shapes can be formed on the same supported lipid bilayer by controlling blue light illumination through the objective lens. • Actin polymerization can be stopped simply by turning off blue light illumination, enabling the formation of actin networks with defined thicknesses. • This protocol requires purified actin and actin-binding proteins.

ABSTRACT In this proceeding, a phase‐field model to describe the evolution of size, shape, and composition of volcanic crystals is introduced. It is built on top of an existing model for idealized spherical crystals but uses the phase‐field approach to investigate arbitrary initial shapes and considers the diffusion of diffusion elements as well as heat. Furthermore, it also takes the development of the dislocation density into account, which makes it possible to make predictions about the stability of the crystals. In this proceeding, the effects of different initial configurations and the behavior of the model under instability are investigated.

This work establishes a generalized framework for predicting effectiveness factors (E) of catalysts with any types of kinetics using a modified Thiele modulus λp for catalysts of arbitrary geometries and assemblages with mixed properties. It is verified that the most appropriate definition of the Thiele modulus is given by λ p = V p A p R ( C AS ) 2 D e ∫ C Ae C AS R ( C A ) d C A which unifies diffusion-reaction phenomena for catalyst pellets of arbitrary shapes and property distributions. Numerical validations for various catalyst geometries (spheres, finite cylinder, hollow cylinders, cones, parallelepipeds) confirmed that E = 1 1 + λ p 2 closely predicts the effectiveness factor for different values of λp . The model resolves the limitations of classical approaches that are difficult and complex for pellets of non-basic shapes. For catalyst pellet assemblages of mixed properties, the overall modulus λb defined as follows was proved to be appropriate for the above equation for E: λ b = ( ∑ i = 1 N c v c i 1 + λ p c i 2 ) − 2 − 1 Validated for catalyst assemblages and fixed-bed reactor descriptions, this general definition of the Thiele modulus for any kinetics expression, together with the simple relation E = 1 1 + λ p 2 , enables optimization of industrial catalysts with complex geometries or property distributions. It also provides unified criteria for the asymptotic regimes of effectiveness factor; one for the regime in which pore diffusion does not affect the overall rate (λp < 0.1 within ∼ 1% error and λp < 0.3 within ∼ 10% error) so that E ≈ 1 and the other in which pore diffusion is strong enough to make the reaction occur mainly near the external surface (λp > 10 within ∼ 1% error and λp > 3 within ∼ 10% error) so that E ≈ 1 / λ p .

ABSTRACT Liquid crystal elastomers (LCEs) with integrated functionalities are critical for developing advanced soft actuators. However, challenges remain in simultaneously achieving reprogrammability, chemical recyclability, and intrinsic luminescence within existing exchangeable LCE systems. Herein, we develop a new class of LCE based on dynamic vinylogous urethane (VU) bonds to address these challenges, where VU moieties serve as both crosslinking points and luminescent clusters. The optimal polydomain vinylogous urethane‐based LCE (VULCE) material exhibits excellent mechanical properties, with a Young's modulus of 1.45 ± 0.08 MPa and a toughness of 148.26 ± 0.11 KJ m −3 . Thanks to the catalyst‐free transamination of VU bonds, the VULCE enables reprogramming into diverse three dimensional (3D) actuators with reversible actuation at 110°C for 10 min. Furthermore, the VULCE network can be depolymerized into LC oligomers and small molecules in an organic solvent under excess bifunctional amines at 80°C for 6 h. The recycled solution can be repolymerized into new VULCE with arbitrary shapes by reintroducing chain extenders, mesogenic monomers, and crosslinkers, achieving polymer‐oligomer/small molecule‐polymer recycling. More importantly, the VULCE emits intrinsic blue fluorescence owing to the clustering‐triggered emission (CTE) effect. This work demonstrates unprecedented multifunctionality, providing new insights for the development of multifunctional integrated LCE materials.

Liquid crystal elastomers (LCEs) with integrated functionalities are critical for developing advanced soft actuators. However, challenges remain in simultaneously achieving reprogrammability, chemical recyclability, and intrinsic luminescence within existing exchangeable LCE systems. Herein, we develop a new class of LCE based on dynamic vinylogous urethane (VU) bonds to address these challenges, where VU moieties serve as both crosslinking points and luminescent clusters. The optimal polydomain vinylogous urethane-based LCE (VULCE) material exhibits excellent mechanical properties, with a Young's modulus of 1.45 ± 0.08MPa and a toughness of 148.26 ± 0.11 KJ m-3. Thanks to the catalyst-free transamination of VU bonds, the VULCE enables reprogramming into diverse three dimensional (3D) actuators with reversible actuation at 110°C for 10min. Furthermore, the VULCE network can be depolymerized into LC oligomers and small molecules in an organic solvent under excess bifunctional amines at 80°C for 6h. The recycled solution can be repolymerized into new VULCE with arbitrary shapes by reintroducing chain extenders, mesogenic monomers, and crosslinkers, achieving polymer-oligomer/small molecule-polymer recycling. More importantly, the VULCE emits intrinsic blue fluorescence owing to the clustering-triggered emission (CTE) effect. This work demonstrates unprecedented multifunctionality, providing new insights for the development of multifunctional integrated LCE materials.

Comparison of rate models for gradient elution chromatography and experimental evaluation for IEC, HIC, and RPC systems.

• A novel metal additive manufacturing method is presented in which molten metal droplets are ejected from an array of nozzles to form the 3D part geometry. • High material deposition rates are possible without any compromise in feature size due to the small droplet sizes. • Spacing between concurrently printed tracks of material can be varied by changing the printing skew angle. • Preliminary results with the proposed method are demonstrated with 2D and 3D shapes. Molten metal droplet jetting (MMJ) is an emerging metal additive manufacturing (AM) technology that can use low-cost wire, rod, or even ingot feedstock material. This paper describes the architecture and preliminary implementation of what is believed to be among the first demonstrations of MMJ with a multi-nozzle array that is akin to inkjet printing using molten metal as the ink. A multi-nozzle printhead with a communal reservoir and three piezoelectric actuator pistons is presented. The drive waveforms for each nozzle are independently addressable, thus enabling precise control over drop placement for raster printing of arbitrary layer shapes. A jetting strategy is described in which variable track spacing is achieved by altering the yaw angle of the printhead. This yaw angle method allows printed row pitches that are less than or equal to the nozzle pitch. The printhead and build strategy are applied to demonstrate feasibility of the method with single and multi-layer test sample geometries. The influence of these initial results on future multi-nozzle systems is discussed.

Level-Set Particle And Geometry GEnerator (LS-PAGGE): Accurate representations of arbitrary particle shapes

A sharp cartesian grid method for simulating flow past viscous droplets of arbitrary shape and viscosity

With the exponential growth of Big Data in domains such as healthcare, genomics, and sensor networks, computationally efficient and effective clustering techniques have become essential for uncovering meaningful patterns. Traditional clustering methods face fundamental limitations in Big Data analysis. K-means is among the fastest known approaches, but it fails to capture non-spherical clusters. Hierarchical clustering can detect arbitrary shapes but suffers from sub-cubic complexity, while many state-of-the-art methods still incur quadratic complexity. Moreover, most existing approaches fail to capture the intrinsic structure of data. In this context, graph-based clustering has emerged as a powerful alternative due to its ability to model geometric relationships and reveal underlying structures. However, existing graph-based techniques typically incur quadratic complexity, limiting their scalability. The objective of this work is to develop a scalable graph-based clustering framework that reduces complexity while preserving clustering quality in large, noisy, and high-dimensional datasets. To achieve this, we propose a fast graph clustering framework with overall complexity <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\mathcal {O}(N \lg N)$</tex-math></inline-formula>, where <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$N$</tex-math></inline-formula> denotes the number of data points. The method employs a two-stage dispersion-based partitioning to generate cohesive sub-clusters, followed by the construction of a sparse graph on sub-cluster centers to efficiently capture adjacency. Sub-clusters are then merged iteratively using a gravitational-force-inspired attraction model, enabling the discovery of coherent structures with reduced computation. Extensive experiments on 41 multi-scale datasets demonstrate that our method consistently outperforms traditional and state-of-the-art approaches, achieving average 27.33% higher clustering accuracy while reducing runtime by more than 86.64% on average. These results highlight both the innovation and the effectiveness of the proposed approach, making it highly suitable for Big Data analytics.

In this paper, an analytical solution for the scattering of plane SH waves by arbitrarily shaped tunnels in a nonlocal fractional-order viscoelastic half-space is presented by using complex variable theory. First, the complex dynamical boundary science problem is simplified by introducing a conformal mapping function that maps an arbitrarily shaped tunnel to a unit circle. Then, a fractional-order viscoelastic model is introduced to portray the viscoelastic properties of the soil, and nonlocal effects, such as the particle scale of the soil, are considered in conjunction with nonlocal theory. The wave function expansion method constructs the scattered field displacement potential function that satisfies the zero-stress condition at the ground-surface boundary. Finally, the effects of incident wave property, tunnel shape, and soil behaviors on the dynamic stress concentration factor of the tunnel are analyzed using numerical examples. The results show that the incident-wave frequency, tunnel shape, and soil properties have a significant effect on the distribution of dynamic concentration factors in tunnels, noncircular tunnels have noticeable scattering and interfering effects on plane SH waves, and the distribution of dynamic stress concentration factors on the inner surfaces of the tunnel is more complicated.

Generic polychromatic light scattering theories for particles of arbitrary shapes and morphologies illuminated by laser pulses

We study decision problems on geometric tilings. First, we study a variant of the Domino problem where square tiles are replaced by geometric tiles of arbitrary shape. We show that this variant is undecidable regardless of the shapes, extending the results of [1] on rhombus tiles. This result holds even when the geometric tiling is forced to belong to a fixed set. Second, we consider the problem of deciding whether a geometric subshift has finite local complexity, which is a common assumption when studying geometric tilings. We show that this problem is undecidable even in a simple setting (square shapes with small modifications).

Redirected Walking (RDW) enables navigation in large virtual worlds by manipulating a user's viewpoint, yet the perceptual thresholds governing these manipulations are often treated as fixed sensory limits. In this work, we propose the concept of Semantic Anchors: objects that stabilise self-motion perception through high-level cognitive priors. In a psychophysical experiment (N = 22), participants judged virtual rotations while viewing familiar or arbitrary objects of varying sizes and rotational reference frames. Results reveal that scene semantics do not merely modulate thresholds but fundamentally alter the integration of spatial cues. When a familiar object's canonical size was violated to create a specific size-congruency conflict, the window for imperceptible redirection expanded significantly, allowing rotation gains of up to 30% to remain imperceptible. Crucially, familiarity inverted the geometric dependence observed with arbitrary shapes: while geometric frames dictated sensitivity for abstract objects, semantic stability dominated for familiar ones. These findings demonstrate that RDW thresholds are dynamically co-constructed by sensory input and scene plausibility, offering a new principle for designing semantically informed locomotion systems that leverage cognitive expectations to expand explorable space.

In this paper, the effects of gravity-gradient potential on a spacecraft of arbitrary shape are outlined. The potential expressing the planet’s gravity-gradient torque on a triaxial spacecraft is formed. The planet’s shape is considered oblate spheroidal, and the dimensions of the spacecraft are assumed small compared to its distance from the center of the planet. The radial, transverse and normal components of the Lorentz force, in terms of orbital elements, are constructed. The variations in the orbital elements due to both gravity-gradient potential and Lorentz force are derived. The charges per unit mass needed to balance such perturbation are obtained. The symmetrical results in mathematical equations are obvious. The International Space Station (ISS) is used as an example to test our model. A three-dimensional diagram was plotted to illustrate the charge per unit mass with the shape and size of the orbits.

Abstract A framework for encoding the boundary of any arbitrary binary shape in a high dimensional digital image is described, with special attention to the 3D case. The voxels of the outer shell of the shape are treated as vertices in a connected subgraph of an undirected cubic grid graph which has at least a spanning tree. A traversal of this spanning tree visits each voxel in a repeatable order. The sequence of traversed edges is coded into a chain that constitutes a high dimensional chain code, analogous to the classical Freeman chain code for 2D. The outer surface of the shape can be unambiguously reconstructed from this chain code. The framework is general in the sense that it can be used under any connectivity rule in any practical number of dimensions and also with noncubic voxels. Explicit algorithms for the computation of the chain codes and the reconstruction of the digital shapes in 3D and 4D are detailed. These are based on a hybrid graph traversal algorithm. The algorithms are illustrated with some simple digital shapes and tested with a benchmark dataset of medical images of intervertebral discs.

We propose a generalization of molecular density functional theory to describe inhomogeneous solvent mixtures, to model electrolytic solutions. Two electrolytic models are presented, both within the HNC approximation. The first one is a two-component mixture representing a primitive-like model of sodium chloride, where the solvent is described as a dielectric continuum. This popular model has the advantage of simplicity, as the ion densities solely depend on spatial coordinates. In addition, we develop a realistic three-component electrolyte model, in which water solvent is described by a third density field that depends on both spatial and orientational coordinates. The proposed methodology and its tridimensional implementation (three spatial coordinates and three Euler angles) are validated by comparing the solvation properties of a sodium cation with the predictions of integral equation theory solved in 1D (one intermolecular distance and five Euler angles), showing near-perfect agreement. This methodology enables the study of solvation properties of solutes of arbitrary shapes in electrolytic solutions, as demonstrated with the prototypical N-methyl acetamide molecule immersed in both electrolytic solution models.