BackgroundCardiac magnetic resonance imaging (CMR) is the gold standard for analysis of myocardial function. Volumetric parameters like ejection fraction (EF) give a general overview of cardiac performance whereas myocardial strain allows detection of early dysfunction. However, both methods focus mainly on contraction from end-diastole to end-systole whereby information about intermediate patterns of contraction and relaxation are not accounted for. For temporal analysis and visualization of cardiac motion, we propose an automated vector-based approach. By integrating two AI models - one for supervised segmentation and another for self-supervised registration our method characterizes cardiac motion as an angle α between motion direction of the myocardium and a focus point over a full cardiac cycle.PurposeTo characterize left (LV) and right ventricular (RV) myocardial motion using the cosine of α (cos(α)) as an indicator of direction, and the vector norm |v| as a measure of motion magnitude, and to investigate differences in healthy patients (NOR) and those with hypertrophic (HCM), dilated (DCM) or right heart cardiomyopathy, as well as myocardial infarction (MINF).MethodsA biventricular segmentation model was trained on an open-source CMR dataset (1) to generate LV and RV myocardial masks, and a second deformable image registration CNN model was trained to generate dense vector fields ϕ for subsequent direction calculation. Based on another method (2), a direction module was used to calculate the relative direction of the motion, αi, from the displacement vector vi in relation to a focus point derived from the respective segmentation mask, for each voxel xi in the masked LV and RV. By anatomical mapping and spatial aggregation, a 1-dimensional (D) direction curve cos(α) is generated over a full cardiac cycle, as well as a magnitude curve |v| from the norm of the vector field ϕ. LV and RV motion and magnitude curves of an independent dataset (3) were evaluated.ResultsThe model generated distinct LV and RV cos(α) motion curves, showing peak motion towards focus point in systole, reversal of direction while transitioning into diastole and bimodal course in diastole representing early ventricular relaxation and late filling by atrial contraction. Corresponding |v| curves depicted myocardial deformation magnitude across time. Extrema of cos(α) (LV cos(α) minimum NOR vs. DCM: U = 26, p<0,001; NOR vs. HCM: U = 27, p<0,001; NOR vs. MINF: U = 30, p<0,001) and several other motion features (Figure 2B) showed significant differences between NOR and patients with disease independent of LV-EF.ConclusionMyocardial motion—a complex 4D deformation process—was reduced to two interpretable 1D curves describing direction and magnitude, who can complement traditional strain analysis. While general differences between cohorts were observed, more comprehensive datasets are required to assess the relevance of the proposed method for improved diagnosis.Schematic illustration of cos(a)NOR vs. DCM diagramm and tests

The efficiencies of additive manufacturing (AM) over conventional processes have enabled the rapid production of aluminum (Al) alloys with AM. Because laser beam powder bed fusion (LB-PBF) parts do not offer the surface quality and geometrical accuracy for direct use, the functional surfaces of LB-PBF parts are usually machined by subtractive machining. The machinability of LB-PBF AlSi10Mg was studied in dry, MQL (used corn oil), and cryo-LN2 cutting environments across distinct speed–feed combinations using CVD-AlTiN-coated carbide inserts, and surface integrity and tool life were quantified in terms of surface roughness (Ra) and flank wear (Vb), respectively. The lowest Ra (0.98–1.107 μm) was obtained with cryo-LN2, followed by MQL and dry cutting environments, because the trends observed were consistent with the surface mechanisms observed in 3D topography and bearing curves. Similarly, the tool wear results mirrored the Ra results, lowest with LN2 (0.087–0.110 mm), due to improved thermal management, reduced adhesion and abrasion, and shorter contact length. Cryo-LN2 provided the best surface finish and tool life among all tested environments. To enable data-driven prediction, the limited dataset was augmented using SMOTE, and machine learning (ML) models were trained to predict Ra and Vb. CatBoost was found to yield the best Ra predictions (R2 = 0.9090), while Random Forest and XGBoost yielded the best Vb predictions (R2 ≈ 0.878).

Topological data analysis (TDA) is a rapidly evolving field in applied mathematics and data science that leverages tools from topology to uncover robust, shape-driven, and explainable insights in complex datasets. The main workhorse is persistent homology, a technique rooted in algebraic topology. Paired with topological deep learning (TDL) or topological machine learning, persistent homology has achieved tremendous success in a wide variety of applications in science, engineering, medicine, and industry. However, persistent homology has many limitations due to its high-level abstraction, insensitivity to non-topological changes, and restriction to point cloud data. This paper presents a comprehensive review of TDA and TDL beyond persistent homology. It analyzes how persistent topological Laplacians and Dirac operators provide spectral representations to capture both topological invariants and homotopic evolution. Other formulations are presented in terms of sheaf theory, Mayer topology, and interaction topology. For data on differentiable manifolds, techniques rooted in differential topology, such as persistent de Rham cohomology, persistent Hodge Laplacian, and Hodge decomposition, are reviewed. For one-dimensional (1D) curves embedded in 3-space, approaches from geometric topology are discussed, including multiscale Gauss-link integrals, persistent Jones polynomials, and persistent Khovanov homology. This paper further discusses the appropriate selection of topological tools for different input data, such as point clouds, sequential data, data on manifolds, curves embedded in 3-space, and data with additional non-geometric information. A review is also given of various topological representations, software packages, and machine learning vectorizations. Finally, this review ends with concluding remarks.

Abstract Prominence seismology, applied to large-amplitude longitudinal oscillations, is used to indirectly diagnose the geometry and strength of the magnetic fields inside the prominence. In this paper, combining imaging and spectroscopic data, the magnetic field configuration of a quiescent prominence is revealed by large-amplitude longitudinal oscillations observed in end view on 2023 December 4. In particular, the prominence oscillation involved blueshift velocities in Dopplergrams and horizontal motions in extreme-ultraviolet images. Originally, the prominence oscillation was triggered by the collision and heating of an adjoining hot structure associated with two coronal jets. The oscillation involved two groups of signals with similar oscillatory parameters, a three-dimensional (3D) initial amplitude of ∼40 Mm and a 3D velocity amplitude of ∼48 km s −1 , both lasting for ∼4 cycles with a period of ∼77 minutes, with a phase difference of ∼ π /8. The angle between the 3D velocities and the prominence axis ranges from 10 ∘ to 30 ∘ . Two methods, utilizing time–distance diagrams and velocity fields, are employed to calculate the curvature radius of magnetic dips supporting the prominence materials. Both methods yield similar value ranges and trends from the bottom to the top of magnetic dips, with the curvature radius increasing from ∼90 Mm to ∼220 Mm, then decreasing to ∼10 Mm, with transverse magnetic field strength ≥25 Gauss. From this, the realistic 3D geometry of the prominence magnetic dips is determined to be sinusoidal. To the best of our knowledge, we present the first accurate calculation of the 3D curvature radius and geometry of the prominence magnetic dips based on longitudinal oscillatory motions.

Neural implicit shape representation has drawn significant attention in recent years due to its smoothness, differentiability, and topological flexibility. However, directly modeling the shape of a neural implicit surface, especially as the zero-level set of a neural signed distance function (SDF), with sparse geometric control is still a challenging task. Sparse input shape control typically includes 3D curve networks or, more generally, 3D curve sketches, which are unstructured and cannot be connected to form a curve network, and therefore more difficult to deal with. While 3D curve networks or curve sketches provide intuitive shape control, their sparsity and varied topology pose challenges in generating high-quality surfaces to meet such curve constraints. In this paper, we propose NeuVAS, a variational approach to shape modeling using neural implicit surfaces constrained under sparse input shape control, including unstructured 3D curve sketches as well as connected 3D curve networks. Specifically, we introduce a smoothness term based on a functional of surface curvatures to minimize shape variation of the zero-level set surface of a neural SDF. We also develop a new technique to faithfully model G 0 sharp feature curves as specified in the input curve sketches. Comprehensive comparisons with the state-of-the-art methods demonstrate the significant advantages of our method.

Hair cards remain a widely used representation for hair modeling in real-time applications, offering a practical trade-off between visual fidelity, memory usage, and performance. However, generating high-quality hair card models remains a challenging and labor-intensive task. This work presents an automated pipeline for converting strand-based hair models into hair card models with a limited number of cards and textures while preserving the hairstyle appearance. Our key idea is a novel differentiable representation where each strand is encoded as a projected 2D curve in the texture space, which enables end-to-end optimization with differentiable rendering while respecting the structures of the hair geometry. Based on this representation, we develop a novel algorithm pipeline, where we first cluster hair strands into initial hair cards and project the strands into the texture space. We then conduct a two-stage optimization, where our first stage optimizes the orientation of each hair card separately, and after strand projection, our second stage conducts joint optimization over the entire hair card model for fine-tuning. Our method is evaluated on a range of hairstyles, including straight, wavy, curly, and coily hair. To capture the appearance of short or coily hair, our method comes with support for hair caps and cross-card.

The programmable properties of polylactic acids and shape memory polymers in 4D printing enable time-dependent shape transformations, allowing the fabrication of 3D shells with zero material waste. However, achieving target geometries requires inverse design, often constrained by slow evolutionary algorithms or complex analytical models. Herein, we present a 2D curve matrix, tuned by material ratios and arc angles, to enable contraction or elongation and thereby reproduce protruding features such as noses. A fully convolutional network (FCN) directly generates design patterns with high accuracy from depth images in a single step, with multi-task learning predicting rib composition and curvature. In parallel, we refine the inverse design of the line matrix and utilize transfer learning to accurately reconstruct human facial geometries, while the FCN also performs well in forward prediction to bypass computational costs. Furthermore, the fabricated 3D shells closely match target facial features in both scale and geometry, with minimal deviation between simulations and experiments, demonstrating the method’s potential for scalable, customizable 4D-printed applications.

Abstract Background Impairment of ventricular interdependence may determine the typical exertional dyspnea in heart failure (HF). The implications of the pathophysiological insights of biventricular interaction has been poorly understood over time. We hypothesized that a comprehensive analysis of the interventricular septum (IVS) adaptations during exercise performance in HF may unlock the related mechanisms to the limited O2 uptake and impaired cardiac reserve. Aim Study objectives were to study the pathophysiology of biventricular interaction during exercise in HF exploring how the IVS curvature changes during exercise may impact on exercise performance Methods 33 HF patients (20 HFpEF and 13 HFrEF) were prospectively enrolled, who underwent a combined cardiopulmonary exercise testing imaging (echoCPET) with RV 3D-imaging analysis and were compared with a control population. The RV chamber was assessed by 3D analysis and images were examined off-line using the 4D RV TomTec software. 3D septal curvature reconstruction was obtained by a 3D mesh of the mean curvature value, using a custom RV model software. The degree of IVS curvature was examined in 4 regions of the RV - inflow tract (RVIT), outflow tract (RVOT), apical, and body - and curvature measurements were acquired during end-diastole (ED) and end systole (ES) phases using a parametric curvature map. Results An abnormal septal curve was found in patients with both HF phenotypes (HFpEF mean age 74.5 ± 7.5, 60% female; HFrEF mean age 64.6 ± 11.4, 21% female), typically with a more leftward configuration either at rest and during exercise (HFpEF: rest= −0.014±0.006 at ED, and −0.014±0.009 at ES; peak exercise= −0.011±0.01 at ED; HFrEF: rest: −0.011±0.008 at ED, −0.013±0.009 at ES; peak exercise= -0.012 ± 0.003 at ED, -0.012 ± 0.006 at ES) compared to controls (rest=−0.02±0.002 at ED, and −0.02±0.006 at ES; peak exercise −0.02±0.0.006 at ED, and −0.02±0.01 at ES, Figure 1). Furthermore, the degree of IVS curvature impairment showed a linear correlation with an impaired gas exchange performance as reflected in a lower peak VO2 in HF (HFpEF: r=0.54, p&amp;lt;0.001; HFrEF: r=0.60, p&amp;lt;0.001 at ED during exercise, Figure 2). Conclusions In HF, different IVS curvatures characterize HF patients when compared to controls, with differences related to HF phenotypes. The evidence of a right to left IVS displacement appears worth exploring, and it is strongly associated with reduced VO2 peak during exercise performance. These findings suggest the usefulness of evaluating how new and current therapeutic approaches may modify the negative septum displacement and overall cardiac hemodynamics.Figure 1 Figure 2

In this study, we consider new optical soliton solutions of one of the most important non-integrable model arising in optical fibres, namely Nonlinear Helmholtz equations (NHEs) that describes transverse interactions, transmission of coupled waves and optical solitons’ propagation in the field of fiber optics. We apply an adapted method to obtain some novel plethora of optical quasi-periodic soliton solutions. These solutions are presented in the shape of exponential, hyperbolic, trigonometric and rational functions. A set of 3D visualization, contour plots and 2D curves of these solutions physical relevance are presented with implications for the nonlinear optics. These figures also reveal that the established optical solitons exhibit quasi-periodicity due to the combination of linear periodic and axial perturbations, and that the presence of quasi-periodical perturbations of the solitons leads to the formation of the fractal-like structures. We also study the chaotic/periodic and bifurcation behavior, associated with the model, in the light of Hamiltonian analysis, as a consequence, we find positive results of the quasi-periodicity and fractal-like structures in the systems under consideration. Apart from offering novel analytical perspectives for dealing with the coupled NHEs, the present results would also be a concrete contribution to the understanding the soliton wave dynamics in complicated nonlinear media.

Interfacial tension and growth both contribute to mechanical cell competition.

Purpose This paper aims to develop a new 3D electromagnetic analytical model in cylindrical coordinates to study the transient and steady-state dynamic performance of radial flux permanent magnet couplers. Design/methodology/approach The magneto-dynamic problem is addressed by coupling the proposed 3D electromagnetic model with the equations of motion and the circuit model of the drive motor. The electromagnetic model is developed by solving Maxwell’s equations in three-dimensional cylindrical coordinates using a magnetic scalar potential approach. The static torque expression is then derived from the Lorentz force, based on the electrostatic-magnetostatic analogy. Findings The obtained results demonstrate the accuracy of the proposed method, which accounts for magnetic edge effects without the need for correction factors. The magneto-dynamic model accurately predicts transient and steady-state performance while ensuring a good compromise between accuracy and computation time. Practical implications The 3D analytical model significantly reduces computation time compared to 3D finite element simulations, making it an efficient and accurate tool for designing and optimizing radial flux permanent magnet couplers. Originality/value A new 3D analytical model in cylindrical coordinates has been developed to compute the electromagnetic torque in radial flux permanent magnet couplers. This model inherently accounts for 3D magnetic edge and curvature effects without requiring correction factors. The 3D electromagnetic model is coupled with the dynamic equations to analyze both transient and steady-state performance.

The curvature of cell adhesion substrates has emerged as a critical geometric parameter influencing cellular fate determination. While its regulatory role is increasingly recognized, the osteogenic effects of complex three-dimensional (3D) curved surfaces remain insufficiently explored. In this study, high-precision two-photonic polymerization 3D printing was utilized to fabricate scaffolds with controlled curvature distributions, achieving unprecedented fidelity between manufactured surfaces and their digital models. Comparative analysis of triply periodic minimal surface (TPMS) scaffolds and conventional truss scaffolds revealed distinct osteogenic mechanisms: zero mean curvature enhanced osteogenic differentiation through improved scaffold permeability, while negative Gaussian curvature promoted bone formation through combined effects of permeability controlling and guided cellular organization. Notably, scaffolds exhibiting broader ranges of negative Gaussian curvature demonstrated superior osteogenesis inductive capacity, as evidenced by enhanced new bone formation in bothin vitroandin vivomodels. These findings provide mechanistic insights into curvature-dependent osteogenesis, quantitative design principles for TPMS-based bone scaffolds, and experimental validation of curvature optimization strategies. The study establishes a geometric framework for rational scaffold design, advancing the development of high-performance regenerative implants.Keyworks.TPMS, Gaussian curvature, two-photonic polymerization, osteogenesis, bone regeneration.

Soft X-ray tomography (SXT) is an invaluable tool for quantitatively analyzing cellular structures at suboptical isotropic resolution. However, it has traditionally depended on manual segmentation, limiting its scalability for large datasets. Here, we leverage a deep learning-based autosegmentation pipeline to segment and label cellular structures in hundreds of cells across three Saccharomyces cerevisiae strains. This task-based pipeline uses manual iterative refinement to improve segmentation accuracy for key structures, including the cell body, nucleus, vacuole, and lipid droplets, enabling high-throughput and precise phenotypic analysis. Using this approach, we quantitatively compared the three-dimensional (3D) whole-cell morphometric characteristics of wild-type, VPH1-GFP, and vac14 strains, uncovering detailed strain-specific cell and organelle size and shape variations. We show the utility of SXT data for precise 3D curvature analysis of entire organelles and cells and detection of fine morphological features using surface meshes. Our approach facilitates comparative analyses with high spatial precision and statistical throughput, uncovering subtle morphological features at the single-cell and population level. This workflow significantly enhances our ability to characterize cell anatomy and supports scalable studies on the mesoscale, with applications in investigating cellular architecture, organelle biology, and genetic research across diverse biological contexts.

Thermoelectric generators (TEGs) demonstrate significant potential for sustainable energy harvesting through direct heat-to-electricity conversion. Nevertheless, conventional fully encapsulated designs face critical limitations including heat dissipation inefficiencies and restricted conformability to complex curved surfaces. This investigation proposes a breakthrough bidirectional π-structured (BDπ-structure) that achieves enhanced mechanical compliance while establishing a mechano-electrical coupling criterion for abrupt curvature transitions. Through implementing a reverse design framework integrating 3D scanning and curvature distribution analysis, customized topological configurations are specifically developed and adapted to target heat source geometries. Concurrently, a novel photocurable composite with enhanced thermal conductivity (0.213 W·m-1·K-1) is designed through 3D-printed structural optimization, achieving 59.1% power enhancement compared to conventional encapsulated modules. Experimental validation demonstrates remarkable surface fit tightness of 90.7% (positive Gaussian) and 80.2% (negative Gaussian), translating to exceptional power output improvements of 432.7% and 253.2% relative to non-optimized counterparts. This work establishes a comprehensive framework encompassing material innovation, structural design, and system integration strategies, significantly advancing flexible thermoelectric technology for high-efficiency energy harvesting from geometrically complex thermal sources.

ABSTRACT We investigate the motion of closed smooth curves that evolve in space . The governing evolutionary equation for the evolution of the curve is accompanied by a parabolic equation for the scalar quantity evaluated over the evolving curve. We apply the direct Lagrangian approach to describe the flow of 3D curves, resulting in a system of degenerate parabolic equations. We prove the local existence and uniqueness of classical Hölder smooth solutions to the governing system of nonlinear parabolic equations. A numerical discretization scheme is constructed using the method of flowing finite volumes. We present several numerical examples of the evolution of curves in 3D with a scalar quantity. We consider the flow of curves with zero torsion evolving in rotating and parallel planes. Next, we present examples of the evolution of curves with initially knotted and unknotted curves.

A simulator for the validation of tractography-based cortical surface parcellations.

Agomelatine (AGT) is used for the treatment of major depressive disorder in adults. Agomelatine is highly susceptible to first-pass metabolism, and it has less than 5% oral bioavailability. Therapy for major depressive disorder extends for a long period and every time, additional caregivers are required to remind and manage the timely dosing of oral medicine to patients. In such cases, once a week, administration of agomelatine via transdermal patch dosage form provides major patient benefits and lowers overall therapy costs. An agomelatine transdermal patch was prepared using the solvent evaporation method using the LTE-S Werner Mathis AG coater and dryer. A patch was prepared using silicon adhesive after screening different pressure-sensitive adhesives like acrylate, polyisobutylene, and silicon. To make a crystal-free patch, the concentration of povidone k-29/32 was optimized in preliminary trials. To deliver the drug over a 7-day period, propylene glycol monolaurate (PGML) was identified from different penetration enhancers. Three factors optimization was carried out, like the concentration of povidone k-29/32, the concentration of PGML, and the mixing time of the blend using the Box Behnken design. 3D surface response curves and contour plots were derived using Design Expert and Minitab software. From overlay plots, design spaces were identified. The optimized AGT patch has good adhesion properties along with a desirable flux of 4.63 μg/cm2/h on human cadaver skin along with a lower residual drug. There was no impact of heat flux studies on normal conditions, hence justifying the in-use condition of the patient population during hot showers, baths, and saunas. AGT Patch was also non-irritating in skin irritation studies performed on Wistar albino rats. It was concluded that agomelatine transdermal patches can be manufactured using silicon adhesive, povidone k-29/32, and propylene glycol monolaurate for the treatment of major depressive disorder and will be the most convenient and cost-effective therapy for the patient.

BackgroundThe rapid development of artificial intelligence has permeated many fields, with its application in drug discovery becoming increasingly mature. Machine learning, particularly deep learning, has significantly improved the efficiency of drug discovery. In the core task of predicting drug–target affinity (DTA), deep learning enhances predictive performance by automatically extracting complex features from compounds and proteins.MethodsTraditional approaches often rely heavily on sequence and two-dimensional structural information, overlooking critical three-dimensional and physicochemical properties. To address this, we propose a novel model—Cross Attention Fusion based on Information Enhancement for Drug–Target Affinity Prediction (CAFIE-DTA)—which incorporates protein 3D curvature and electrostatic potential information. The model approximates protein surface curvature using Delaunay triangulation, calculates total electrostatic potential via Adaptive Poisson-Boltzmann Solver (APBS) software, and employs cross multi-head attention to fuse physicochemical and sequence information of proteins. Simultaneously, it integrates graph-based and physicochemical features of compounds using the same attention mechanism. The resulting protein and compound vectors are concatenated for affinity prediction.ResultsCross-validation and comparative evaluations on the benchmark Davis and KIBA datasets demonstrate that CAFIE-DTA outperforms existing methods. On the Davis dataset, it achieved improvements of 0.003 in confidence interval (CI) and 0.022 in R2. On the KIBA dataset, it improved MSE by 0.008, CI by 0.005, and R2 by 0.017. Compared to traditional models relying on 2D structures and sequence data, CAFIE-DTA shows superior performance in DTA prediction. The source code is available at: https://github.com/NTU-MedAI/CAFIE-DTA.

3D curve weld seam path and posture planning based on line laser sensors

Precise and quantitative fluid addition is essential for maintaining consistency in volume and concentration across various applications including pharmaceuticals, food production, and biochemical research. Microfluidic droplet technology has emerged as a versatile microreactor for manipulating nanoliter- to picoliter-scale droplets, offering advantages such as reduced reagent consumption, faster reactions, and enhanced sensitivity. Among these, microfluidic droplet injection technology has shown promise for precise reagent addition. However, passive droplet injection methods as well as active injection methods driven by pneumatic, electrical, and acoustic forces often suffer from volume deviation due to external disturbance. Additionally, traditional two-dimensional (2D) measurement methods overlook the droplet's three-dimensional (3D) curvature, leading to insufficient data capture and significant measurement errors. This study introduces a novel on-demand microfluidic droplet injection system integrating quantitative phase imaging (QPI) for 3D visualization serving as feedback with a dual-pressure-pulse (DPP) method for fluid actuation. Comparative experiments confirmed that our approach significantly improves injection precision, achieving a coefficient of variation (CV) of 7.03%, which represents a 4.5-fold improvement over passive methods. Dynamic response experiments further verified the system's capability to adapt to target volume changes rapidly, maintaining deviations below 2% across varying conditions. As a proof of concept, the system effectively compensates for initial volume fluctuations, ensuring consistent final droplet volumes and enabling controlled isoconcentration of selenium-containing droplets, with deviations of 1.17% and 2.5%, respectively. These findings showcase the system's potential for applications requiring stringent control of volume and concentration, such as single-cell analysis, enzyme kinetics, drug delivery, and food production.