Subject-specific finite element (FE) modeling enables detailed evaluation of cartilage and labral contact mechanics in the hip; however, the computational demands of this approach limit its use in large cohorts. Soft tissue overlap (STO) modeling is a simplified alternative that estimates strain from geometric overlap between articulating surfaces; however, its predictive accuracy remains unclear. The objective of this study was to evaluate the accuracy of STO models for predicting acetabular cartilage and labral strains during simulated walking by direct comparison with subject-specific FE models. Eight individuals with radiographically normal hips and eight patients with cam-type femoroacetabular impingement syndrome were evaluated using subject-specific anatomy, kinematics, and joint reaction forces. STO-predicted strains were compared with FE-predicted compressive strains using correspondence-based Network Analysis and Bland–Altman analyses at heel-strike and heel-off. No group-dependent differences in agreement were observed, so the results for the two cohorts were pooled. STO models captured the general spatial patterns and locations of peak strain in both cartilage and labrum. However, significant differences between STO and FE predictions were present across 1% to 75% of the gait cycle, particularly during higher magnitudes of joint loading. STO systematically underpredicted strain at higher magnitudes, with disagreement increasing as strain magnitude increased. Discrepancies were most pronounced in the anterosuperior cartilage and labrum near heel-off, where FE models predicted higher, more localized strains. These findings indicate that while STO modeling provides qualitative insight into strain patterns and contact locations, its ability to quantify strain magnitude, particularly under higher loading conditions and in the labrum, is limited.

Scientists face significant visualization challenges as time-varying datasets grow in speed and volume, often requiring specialized infrastructure and expertise to handle massive datasets. Petascale climate models generated in NASA laboratories require a dedicated group of graphics and media experts and access to high-performance computing resources. Scientists may need to share scientific results with the community iteratively and quickly. However, the time-consuming trial-and-error process incurs significant data transfer overhead and far exceeds the time and resources allocated for typical post-analysis visualization tasks, disrupting the production workflow. Our paper introduces a user-friendly framework for creating 3D animations of petascale, time-varying data on a commodity workstation. Our contributions: (i) Generalized Animation Descriptor (GAD) with a keyframe-based adaptable abstraction for animation, (ii) efficient data access from cloud-hosted repositories to reduce data management overhead, (iii) tailored rendering system, and (iv) an LLM-assisted conversational interface as a scripting module to allow domain scientists with no visualization expertise to create animations of their region of interest. We demonstrate the framework's effectiveness with two case studies: first, by generating animations in which sampling criteria are specified based on prior knowledge, and second, by generating AI-assisted animations in which sampling parameters are derived from natural-language user prompts. In all cases, we use large-scale NASA climate-oceanographic datasets that exceed 1PB in size yet achieve a fast turnaround time of 1 minute to 2 hours. Users can generate a rough draft of the animation within minutes, then seamlessly incorporate as much high-resolution data as needed for the final version.

Hybrid cooling systems that combine wet and dry cooling have drawn growing attention, as they can leverage ambient air temperature to enable broader regional deployment while mitigating water consumption. However, hybrid cooling remains insufficiently explored because the water associated with power generation is often overlooked. Moreover, distributing water is also challenging when water is scarce. In this paper, we present WaterSplit, a water-aware optimization framework that minimizes the water footprint to enable sustainable datacenter operations with hybrid cooling under limited water budgets.

Hydrothermal plumes are turbulent structures of intense heat and mineral smoke that rise and disperse into the deep ocean. Existing models generally characterize these systems as a single axisymmetric plume originating from a point source. However, this assumption breaks down in weakly venting, spatially distributed systems, where low-flux discharge and strong background currents produce faint and distorted plumes that traditional centerline-based diagnostics fail to characterize. We present a topology-based visual analysis framework that treats plumes as time-varying three-dimensional scalar fields and captures their full structural variability. Using merge trees, we cluster plume snapshots into representative morphologies and uncover a clear coupling between plume structure and phases of the tidal cycle. To support exploration, we introduce a radial merge tree visualization that arranges plume similarity on a tidal clock, revealing periodic behaviors not discernible with existing techniques. We further develop an extremum graph based workflow that identifies discrete surface venting regions within a reconstructed porous rock, and use streamline visualizations to link subsurface transport pathways to the overlying plume.

Statistical shape modeling (SSM) is central to population level analysis of anatomical variability, yet most existing approaches rely on densely annotated segmentations and fixed latent representations. These requirements limit scalability and reduce flexibility when modeling complex anatomical variation. We introduce MorphoFlow, a sparse supervised generative shape modeling framework that learns compact probabilistic shape representations directly from sparse surface annotations. MorphoFlow integrates neural implicit shape representations with an autodecoder formulation and autoregressive normalizing flows to learn an expressive probabilistic density over the latent shape space. The neural implicit representation enables resolution-agnostic modeling of 3D anatomy, while the autodecoder formulation supports direct optimization of per-instance latent codes under sparse supervision. The autoregressive flow captures the distribution of latent anatomical variability providing a tractable, likelihood-based generative model of shapes. To promote compact and structured latent representations, we incorporate adaptive latent relevance weighting through sparsity-inducing priors, enabling the model to regulate the contribution of individual latent dimensions according to their relevance to the underlying anatomical variation while preserving generative expressivity. The resulting latent space supports uncertainty quantification and anatomically plausible shape synthesis without manual latent dimensionality tuning. Evaluation on publicly available lumbar vertebrae and femur datasets demonstrates accurate high-resolution reconstruction from sparse inputs and recovery of structured modes of anatomical variation consistent with population level trends.

The conformational activation of α_IIbβ₃integrin is crucial for platelet aggregation, a central event in hemostasis and thrombosis. Although activation can be triggered by extracellular arginine-glycine-aspartic acid (RGD)-containing ligands as well as mechanical forces, how these biochemical and mechanical cues exactly govern the structural dynamics of α_IIbβ₃remains unclear. Here, using all-atom molecular dynamics simulations, we show that mechanical force and RGD binding promote activation α_IIbβ₃through distinct mechanisms. Mechanical force applied to the RGD-binding site induces long-range, correlated motions of distant parts of the receptor, facilitating head–leg separation. In contrast, RGD binding increases localized, non-correlated fluctuations that weaken leg coordination but do not generate long-range motions. Despite these differences, both cues stabilize the open, extended conformation of α_IIbβ₃. Together, these findings suggest that mechanical and biochemical stimuli play complementary yet distinct roles in integrin conformational activation. A balance between global coordination and local fluctuations likely governs integrin activation in complex environments where the dominance of mechanical or biochemical cues could lead to distinct activation pathways and functional outcomes.

This study validates a finite deformation, nonlinear viscoelastic constitutive model for the collagen matrix of immature bovine articular cartilage, using reactive viscoelasticity. Tissue samples underwent proteoglycan (PG) digestion, losing more than 98% of their initial PG content to increase their hydraulic permeability. To verify that PG-digestion eliminated flow-dependent viscoelasticity, samples were subjected to a gravitational permeation experiment, demonstrating that their hydraulic permeability, k=268±152 mm⁴/N⋅s (n=8), was five orders of magnitude greater than reported for untreated cartilage. Digested cartilage plugs were subjected to unconfined compression stress relaxation (four consecutive 10% strain ramp-hold profiles) to fit the load response and extract material properties (RMSE_fit=1.86±0.61 kPa, n=8). Successful curve-fitting served as a necessary condition for validating the model. Then, a separate unconfined compression stress-relaxation test was performed on the same samples, to 40% compressive strain at the same ramp rate. The model was able to faithfully predict this experimental response using fitted material properties (RMSE_pred=3.95±1.33 kPa, with 0=stresses=155±37 kPa), providing a sufficient condition for validation in unconfined compression stress-relaxation. A computational model showed that flow-independent viscoelasticity of cartilage collagen can enhance the stress response by ~15% at fast strain rates, over flow-dependent effects. However, we estimate from prior studies that flow-independent viscoelasticity may enhance the stress response of cartilage by up to 200%, implying that PGs probably contribute significantly to the tissue?s flow-independent viscoelasticity.

Generative AI has demonstrated significant potential in creative design, enabling the rapid generation of visual content and imaginative concepts. Although deep AI models achieve effective featurization in the latent space, navigating the space remains a challenge. Current techniques, such as GANSlider and SliderSpace, use multiple sliders to generate high-dimensional vectors in generative AI's latent space. Despite applying (global) PCA to reduce the number of sliders, these approaches struggle with scalability and usability as the number of control dimensions increases. In this paper, we introduce LatentGandr, a visual analytics technique that facilitates latent space exploration by extracting locally linear dimensions from embeddings in high-dimensional latent spaces. By analyzing the topology and local curvature of the embeddings, LatentGandr automatically identifies local neighborhoods and computes their principal components using localized PCA. These local principal components are visualized as interactive image grids, allowing users to efficiently explore and control the generative process, providing an intuitive means to refine the generation of novel content and concepts. To evaluate the effectiveness of LatentGandr, we conducted a study comparing it to GANSlider, the current state-of-the-art visualization interface for generative AI models. The results offer insights into how localized exploration techniques can enhance user interaction with these models.

Neural networks are used as generative surrogate models for scientific discovery, which are trainable approximations of scientific simulations. These models enable users to replace time-consuming numerical simulations with learned alternatives, providing quick solutions. However, high-fidelity generative surrogate models require massive training datasets, which can create storage and I/O challenges. Lossy compression is a promising way to reduce this burden, but compression errors may affect the model quality in subtle ways, making it challenging to quantify their impact. In this work, we examine how lossy compression of training data impacts the quality of generative surrogate models. We begin by characterizing the uncertainty inherent in training neural networks, showing that identical training configurations can produce different models. By exploiting this variability, we propose a method to estimate how much compression-induced error a surrogate model can tolerate without affecting its accuracy. Evaluation of two application simulations demonstrates that our approach significantly reduces memory/storage requirements and speeds up training while producing high-quality surrogate models. These results show that lossy compression saves data storage up to 23.7x and 39x with negligible impact on the quality of the surrogate model. Meanwhile, reducing the size of the training data set also enhances the data loading speed and reduces the training time by up to 3x.

Idling Vehicle Detection (IVD) seeks to determine, at the final frame of a video clip, whether any vehicle is idling, meaning the vehicle is stationary with its engine running, using synchronized video from a remote surveillance camera and multichannel audio captured by spatially distributed wireless microphones along the roadside. Prior full-image, clip-level fusion approaches tend to overfit scene background and full-frame context, produce unstable temporal decisions, and lack an explicit spatial prior to align vehicles with microphones, which makes them brittle under domain shift and data inefficient. Instead, we introduce TAVR-IVD, an audio-visual framework guided by multi-object tracking. Our method detects vehicles, links detections into tracklets, and classifies each vehicle by operating on its tracklet. This design raises the effective signal-to-noise ratio, stabilizes temporal decisions through tracklets, enforces an explicit spatial prior to align vehicles with microphones, and adapts across domains with limited calibration annotations while remaining detector agnostic and efficient. To evaluate deployment robustness, we further curate two evaluation extensions, AVIVD-LT and AVIVD-M, covering inter-day and cross-site shifts.

Computational biomechanics increasingly requires models that combine mechanics, transport, chemistry, and biological regulation across different spatial and temporal scales. The FEBio simulation software (Finite Elements for Biomechanics and Biophysics) provides extensive open-source capabilities for modeling these processes using monolithic approaches. However, assembling independently developed physics models into reproducible coupled workflows remains challenging. Existing approaches often require custom scripts or external software pipelines, which can limit model reuse and complicate development. We present FUSE, the FEBio Unified Simulation and Exchange framework, a partitioned coupling plugin that enables separately defined FEBio models to communicate through structured field exchange. FUSE is designed for problems that are best solved independently, particularly when fast mechanical responses influence slower biological or chemical evolution. The framework uses a time-decoupled strategy in which a primary model advances on the longer time scale, while one or more secondary models are repeatedly initialized, supplied with updated fields, solved over shorter time horizons, with results returned to the primary model. Field exchange utilizes existing FEBio data maps, output fields, and user-specified filters, allowing coupled workflows to be constructed without modifying the underlying solvers. The framework was able to reproduce reference coupled solutions while handling bidirectional transfer, spatial field mapping, and filtered exchange of model variables. Example applications demonstrated coupling between mechanical loading and chemical degradation in injured cartilage and interaction between biological tissue formation and mechanical feedback during bone healing. By separating coupling logic from physics implementation, FUSE provides a practical mechanism for building maintainable multiphysics workflows within FEBio.

Finite element (FE) simulations emulating transcatheter pulmonary valve (TPV) system deployment in patient-specific right ventricular outflow tracts (RVOT) assume material properties for the RVOT and adjacent tissues. Sensitivity of the deployment to variation in RVOT material properties is unknown. Moreover, the effect of a transannular patch stiffness and location on simulated TPV deployment has not been explored. A sensitivity analysis on the material properties of a patient-specific RVOT during TPV deployment, modeled as an uncoupled HGO material, was conducted using FEBioUncertainSCI. Further, the effects of a transannular patch during TPV deployment were analyzed by considering two patch locations and four patch stiffnesses. Visualization of results and quantification were performed using custom metrics implemented in SlicerHeart and FEBio. Sensitivity analysis revealed that the shear modulus of the ground matrix (c), fiber modulus (k1), and fiber mean orientation angle (gamma) had the greatest effect on 95th %ile stress, whereas only c had the greatest effect on 95th %ile Lagrangian strain. First-order sensitivity indices contributed the greatest to the total-order sensitivity indices. Simulations using a transannular patch revealed that peak stress and strain were dependent on patch location. As stiffness of the patch increased, greater stress was observed at the interface connecting the patch to the RVOT, and stress in the patch itself increased while strain decreased. The total enclosed volume by the TPV device remained unchanged across all simulated patch cases. This study highlights that while uncertainties in tissue material properties and patch locations may influence functional outcomes, FE simulations provide a reliable framework for evaluating these outcomes in TPVR. 

READMEs shape first impressions of software projects, yet what constitutes a good README varies across audiences and contexts. Research software needs reproducibility details, while open-source libraries might prioritize quick-start guides. Through a design probe, LintMe, we explore how linting can be used to improve READMEs given these diverse contexts, aiding style and content issues while preserving authorial agency. Users create context-specific checks using a lightweight DSL that uses a novel combination of programmatic operations (e.g., for broken links) with LLM-based content evaluation (e.g., for detecting jargon), yielding checks that would be challenging for prior linters. Through a user study (N=11), comparison with naive LLM usage, and an extensibility case study, we find that our design is approachable, flexible, and well matched with the needs of this domain. This work opens the door for linting more complex documentation and other culturally mediated text-based documents.

Ventricular tachycardia is a life-threatening rhythm disorder and a major cause of sudden cardiac death. Pace-mapping is a clinical procedure for identifying the intervention target during catheter ablation of VT. It requires clinicians to pace different sites in the ventricles and rapidly interpret the resulting electrocardiograms to determine where to pace next or whether a target site has been identified. Active learning AI models have been proposed to guide clinicians to the next pacing site, showing promise in reducing the number of pacing sites and improving the efficiency of pace-mapping. Existing methods require retraining each target without the ability to transfer knowledge across multiple VTs within the same patient or across patients. We introduce cAPM for continuous AI-assisted pace-mapping to capture and transfer knowledge accumulated from past pace-mapping data to reduce the number of pace-mapping data needed for future target VTs. This is made possible by a task-agnostic surrogate neural network that learns the mapping from pacing sites to 12-lead ECG morphology, an active-learning strategy that refines this surrogate model by selecting the most informative pacing site for each target, and a continual learning strategy to do so sequentially while retaining knowledge from prior targets. Evaluated on an in-silico testbed consisting of sequentially-presented localization tasks across different physiological conditions and ventricular geometries, cAPM with and without replay of past data samples achieved an 81% probability of localizing within clinical tolerance (5 mm accuracy) using 4.5 pace-mapping sites, compared to the state-of-the-art active-learning method achieving 38% probability using 13.7 pacing sites. These results provide a strong basis for preparing cAPM towards in-vivo preclinical and clinical studies where it can be used to guide pace-mapping.

Synchrotron facilities like the Cornell High Energy Synchrotron Source (CHESS) generate massive data volumes from complex beamline experiments, but face challenges such as limited access time, the need for on-site experiment monitoring, and managing terabytes of data per user group. We present the design, deployment, and evaluation of a framework that addresses CHESS's data acquisition and management issues. Deployed on a secure CHESS server, our system provides real time, web-based tools for remote experiment monitoring and data quality assessment, improving operational efficiency. Implemented across three beamlines (ID3A, ID3B, ID4B), the framework managed 50-100 TB of data and over 10 million files in late 2024. Testing with 43 research groups and 86 dashboards showed reduced overhead, improved accessibility, and streamlined data workflows. Our paper highlights the development, deployment, and evaluation of our framework and its transformative impact on synchrotron data acquisition.

We are witnessing urgent events, including extreme weather events, wildfires, natural disasters, cyberattacks, pandemics, and infrastructure failures, with increasing frequency and impact. At the same time, technological advances and strategic investments have resulted in the pervasive availability of a continuum of unparalleled computational and data capabilities, services, and expertise, which, if harnessed, make it possible to move beyond ad hoc judgments to respond to such events and enable rigorous, data-driven decision making to save lives, protect property, and safeguard public health. Urgent science refers to science that supports rigorous, data-driven decision making in response to emergencies while effectively managing inherent uncertainties. Urgent computing refers to computing that supports urgent science. It can be defined as computing under strict time and quality constraints to support decision making with the desired confidence, within a specified time interval. The goal is to effectively leverage a diverse set of computing and data resources distributed across the end-to-high-performance computing (HPC)/cloud digital continuum to detect events, develop responses, and trigger actions. Several computational science and engineering research challenges underlie the vision of urgent science and the realization of urgent computing. This special issue of Computing in Science & Engineering focuses on articles that address various aspects of urgent computing and urgent science, including use cases, mathematical foundations, computational frameworks and services, policy and governance structures, and end-to-end experiences.

Predicting the risk of distant metastasis from primary tumor tissue histology is a critical yet challenging task in computational pathology. Multiple Instance Learning (MIL) approaches can attend to subdomains in tumor regions that harbor features of metastatic cancer progression. However MIL models treat tissue patches as unordered bags, discarding the spatial layout that defines the metastatic potential. We propose that metastatic risk is inherently dictated by the geometric arrangement of the tumor microenvironment at the interface with tumor cells. Our model is designed to explicitly capture the spatial relationships between tumor cells, tumor associated fibroblasts and infiltrating lymphocytes. For this purpose, we propose Distance aware Tissue Modeling for Multiple Instance Learning(DTMf-MIL), a novel method that reinforces visual features with explicit spatial priors. By computing signed distance functions (SDF) relative to tissue phenotypes, our model learns to recognize structural signatures of metastatic risk. This geometric awareness translates directly to superior clinical performance as DTMf-MIL significantly outperforms state-of-the-art methods that ignore spatial layout on metastasis prediction from tissue in the primary tumor. We further validate our approach on public benchmarks, demonstrating that spatial awareness consistently improves diagnostic accuracy across diverse clinical tasks.

A new release of ChemNetworks (Journal of Computational Chemistry, 2013, 35, 495-505), is described. Updates have made the software performant in supercomputing environments, capable of real-time graph construction and analysis with running simulations, and incorporate graph theory libraries for diverse and customizable analysis workflows. Here, we describe newly implemented algorithms (and examples) in ChemNetworks that include a recursive Z-matrix algorithm for structure identification and graph construction, the incorporation of the DataSpaces data staging framework as one of its optional I/O engines, and user-contributed graph theory analyses that leverage the igraph library. The new release of ChemNetworks better enables ongoing development through community contributions.

Annotation is a central mechanism in visualization design that enables people to communicate key insights. Prior research has provided essential accounts of the visual forms annotations take, but less attention has been paid to the decisions behind them. This paper examines how annotations are designed in practice and how educators reflect on those practices. We conducted a two-phase qualitative study: interviews with ten practitioners from diverse backgrounds revealed the heuristics they draw on when creating annotations, and interviews with seven visualization educators offered complementary perspectives situated within broader concerns of clarity, guidance, and viewer agency. These studies provide a systematic account of annotation design knowledge in professional settings, highlighting the considerations, trade-offs, and contextual judgments that shape the use of annotations. By making this tacit expertise explicit, our work complements prior form-focused studies, strengthens understanding of annotation as a design activity, and points to opportunities for improved tool and guideline support.