Search

Background: Patient-reported survey data are used to train prognostic models aimed at improving healthcare. However, such data are typically available multi-centric and, for privacy reasons, cannot easily be centralized in one data repository. Models trained locally are less accurate, robust, and generalizable. We present and apply privacy-preserving federated machine learning techniques for prognostic model building, where local survey data never leaves the legally safe harbors of the medical centers. Methods: We used centralized, local, and federated learning techniques on two healthcare datasets (GLA:D data from the five health regions of Denmark and international SHARE data of 27 countries) to predict two different health outcomes. We compared linear regression, random forest regression, and random forest classification models trained on local data with those trained on the entire data in a centralized and in a federated fashion. Results: In GLA:D data, federated linear regression (R2 0.34, RMSE 18.2) and federated random forest regression (R2 0.34, RMSE 18.3) models outperform their local counterparts (i.e., R2 0.32, RMSE 18.6, R2 0.30, RMSE 18.8) with statistical significance. We also found that centralized models (R2 0.34, RMSE 18.2, R2 0.32, RMSE 18.5, respectively) did not perform significantly better than the federated models. In SHARE, the federated model (AC 0.78, AUROC: 0.71) and centralized model (AC 0.84, AUROC: 0.66) perform significantly better than the local models (AC: 0.74, AUROC: 0.69). Conclusion: Federated learning enables the training of prognostic models from multi-center surveys without compromising privacy and with only minimal or no compromise regarding model performance.

Fast-Fourier Transform (FFT) methods have been widely used in solid mechanics to address complex homogenization problems. However, current FFT-based methods face challenges that limit their applicability to intricate material models or complex mechanical problems. These challenges include the manual implementation of constitutive laws and the use of computationally expensive and complex algorithms to couple microscale mechanisms to macroscale material behavior. Here, we incorporate automatic differentiation (AD) within the FFT framework to mitigate these challenges. We demonstrate that AD-enhanced FFT-based methods can derive stress and tangent stiffness directly from energy density functionals, facilitating the extension of FFT-based methods to more intricate material models. Additionally, AD simplifies the calculation of homogenized tangent stiffness for microstructures with complex architectures and constitutive properties. This enhancement renders current FFT-based methods more modular, enabling them to tackle homogenization in complex multiscale systems, especially those involving multiphysics processes. Our work will simplify the numerical implementation of FFT-based methods for complex solid mechanics problems.

Accurately modeling the mechanical behavior of materials is crucial for numerous engineering applications. The quality of these models depends directly on the accuracy of the constitutive law that defines the stress-strain relation. Discovering these constitutive material laws remains a significant challenge, in particular when only material deformation data is available. To address this challenge, unsupervised machine learning methods have been proposed. However, existing approaches have several limitations: they either fail to ensure that the learned constitutive relations are consistent with physical principles, or they rely on a predefined library of constitutive relations or manually crafted input features. These dependencies require significant expertise and specialized domain knowledge. Here, we introduce a machine learning approach called uLED, which overcomes the limitations by using the input convex neural network (ICNN) as the surrogate constitutive model. We improve the optimization strategy for training ICNN, allowing it to be trained end-to-end using direct strain invariants as input across various materials. Furthermore, we utilize the nodal force equilibrium at the internal domain as the training objective, which enables us to learn the constitutive relation solely from temporal displacement recordings. We validate the effectiveness of the proposed method on a diverse range of material laws. We demonstrate that it is robust to a significant level of noise and that it converges to the ground truth with increasing data resolution. We also show that the model can be effectively trained using a displacement field from a subdomain of the test specimen and that the learned constitutive relation from one material sample is transferable to other samples with different geometries. The developed methodology provides an effective tool for discovering constitutive relations.

Fault zones mature through the accumulation of earthquakes and the wearing of contact asperities at multiple scales. This study examines how wear-induced gouge production affects the evolution of fault seismicity, focusing on earthquake nucleation, recurrence, and moment partitioning. Using 2D quasi-dynamic simulations integrating rate-and-state friction with Archard's law of wear, we model the space-time distribution of gouge and its effect on the critical slip distance. The study reveals a shift from single to multi-rupture nucleation, marked by increased foreshock activity. The recurrence interval undergoes two separate phases: an initial phase of steady increase followed by a secondary phase of unpredictable behavior. Finally, we observe a transition in the moment partitioning from faster to slower slip rates and a decrease in the moment released per cycle relative to the case where no gouge formation is simulated. This research sheds light on wear-driven mechanisms affecting fault slip behavior, offering valuable insights into how the evolution of gouge along a fault affects its seismic potential.

Cold joints in extruded concrete structures form once the exposed surface of a deposited filament dries prematurely and gets sequentially covered by a layer of fresh concrete. This creates a material heterogeneity which lowers the structural durability and shortens the designed service life. Many factors concurrently affect cold joint formation, yet a suitable tool for their categorization is missing. Here, we present a computational model that simulates the drying kinetics at the exposed structural surface, accounting for cement hydration and the resulting microstructural development. The model provides a time estimate for cold joint formation as a result. It allows us to assess the drying severity for a given structure's geometry, its interaction with the environment, and ambient conditions. We evaluate the assessed factors and provide generalized recommendations for cold joint mitigation., Comment: 31 pages, 12 figures

Statistical models for predicting lung cancer have the potential to facilitate earlier diagnosis of malignancy and avoid invasive workup of benign disease. Many models have been published, but comparative studies of their utility in different clinical settings in which patients would arguably most benefit are scarce. This study retrospectively evaluated promising predictive models for lung cancer prediction in three clinical settings: lung cancer screening with low-dose computed tomography, incidentally detected pulmonary nodules, and nodules deemed suspicious enough to warrant a biopsy. We leveraged 9 cohorts (n=898, 896, 882, 219, 364, 117, 131, 115, 373) from multiple institutions to assess the area under the receiver operating characteristic curve (AUC) of validated models including logistic regressions on clinical variables and radiologist nodule characterizations, artificial intelligence on chest CTs, longitudinal imaging AI, and multi-modal approaches. We implemented each model from their published literature, re-training the models if necessary, and curated each cohort from primary data sources. We observed that model performance varied greatly across clinical use cases. No single predictive model emerged as a clear winner across all cohorts, but certain models excelled in specific clinical contexts. Single timepoint chest CT AI performed well in lung screening, but struggled to generalize to other clinical settings. Longitudinal imaging and multimodal models demonstrated comparatively promising performance on incidentally-detected nodules. However, when applied to nodules that underwent biopsy, all models underperformed. These results underscore the strengths and limitations of 8 validated predictive models and highlight promising directions towards personalized, noninvasive lung cancer diagnosis., Comment: Submitted to Radiology: AI

Structural build-up in fresh cement paste at rest is characterized by time evolutions of storage modulus and yield stress, which both increase linearly in time during the induction period of hydration, followed by an exponential evolution after entering the acceleration period. While it is understood that C-S-H formation at contact points between cement particles dictates build-up in the acceleration period, the mechanism in the induction period lacks consensus. Here, we provide experimental evidence that, at least in absence of admixtures, structural build-up at rest originates in both periods from the same mechanism. We couple calorimetry and oscillatory shear measurements of OPC at different w/c ratios, capturing how the storage modulus evolves with changes in cumulative heat. We obtain an exponential relation between stiffness and heat, with the same exponent in both the induction and acceleration periods. This suggests that C-S-H formation dictates build-up at rest in both periods., Comment: 14 pages, 4 figures

The mineralized collagen fibril is the main building block of hard tissues and it directly affects the macroscopic mechanics of biological tissues such as bone. The mechanical behavior of the fibril itself is determined by its structure: the content of collagen molecules, minerals, and cross-links, and the mechanical interactions and properties of these components. Advanced-Glycation-Endproducts (AGEs) cross-linking between tropocollagen molecules within the collagen fibril is one important factor that is believed to have a major influence on the tissue. For instance, it has been shown that brittleness in bone correlates with increased AGEs densities. However, the underlying nano-scale mechanisms within the mineralized collagen fibril remain unknown. Here, we study the effect of mineral and AGEs cross-linking on fibril deformation and fracture behavior by performing destructive tensile tests using coarse-grained molecular dynamics simulations. Our results demonstrate that after exceeding a critical content of mineral, it induces stiffening of the collagen fibril at high strain levels. We show that mineral morphology and location affect collagen fibril mechanics: The mineral content at which this stiffening occurs depends on the mineral's location and morphology. Further, both, increasing AGEs density and mineral content lead to stiffening and increased peak stresses. At low mineral contents, the mechanical response of the fibril is dominated by the AGEs, while at high mineral contents, the mineral itself determines fibril mechanics., Comment: 11 pages, 8 figures

Earthquakes are rupture-like processes that propagate along tectonic faults and cause seismic waves. The propagation speed and final area of the rupture, which determine an earthquake's potential impact, are directly related to the nature and quantity of the energy dissipation involved in the rupture process. Here we present the challenges associated with defining and measuring the energy dissipation in laboratory and natural earthquakes across many scales. We discuss the importance and implications of distinguishing between energy dissipation that occurs close to and far behind the rupture tip and we identify open scientific questions related to a consistent modeling framework for earthquake physics that extends beyond classical Linear Elastic Fracture Mechanics., Comment: 8 pages, 2 figures

Symbolic regression holds great potential for uncovering underlying mathematical and physical relationships from empirical data. While existing transformer-based models have recently achieved significant success in this domain, they face challenges in terms of generalizability and adaptability. Typically, in cases where the output expressions do not adequately fit experimental data, the models lack efficient mechanisms to adapt or modify the expression. This inflexibility hinders their application in real-world scenarios, particularly in discovering unknown physical or biological relationships. Inspired by how human experts refine and adapt expressions, we introduce Symbolic Q-network (Sym-Q), a novel reinforcement learning-based model that redefines symbolic regression as a sequential decision-making task. Sym-Q leverages supervised demonstrations and refines expressions based on reward signals indicating the quality of fitting precision. Its distinctive ability to manage the complexity of expression trees and perform precise step-wise updates significantly enhances flexibility and efficiency. Our results demonstrate that Sym-Q excels not only in recovering underlying mathematical structures but also uniquely learns to efficiently refine the output expression based on reward signals, thereby discovering underlying expressions. Sym-Q paves the way for more intuitive and impactful discoveries in physical science, marking a substantial advancement in the field of symbolic regression.

Building prediction models from mass-spectrometry data is challenging due to the abundance of correlated features with varying degrees of zero-inflation, leading to a common interest in reducing the features to a concise predictor set with good predictive performance. In this study, we formally established and examined regularized regression approaches, designed to address zero-inflated and correlated predictors. In particular, we describe a novel two-stage regularized regression approach (ridge-garrote) explicitly modelling zero-inflated predictors using two component variables, comprising a ridge estimator in the first stage and subsequently applying a nonnegative garrote estimator in the second stage. We contrasted ridge-garrote with one-stage methods (ridge, lasso) and other two-stage regularized regression approaches (lasso-ridge, ridge-lasso) for zero-inflated predictors. We assessed the predictive performance and predictor selection properties of these methods in a comparative simulation study and a real-data case study to predict kidney function using peptidomic features derived from mass-spectrometry. In the simulation study, the predictive performance of all assessed approaches was comparable, yet the ridge-garrote approach consistently selected more parsimonious models compared to its competitors in most scenarios. While lasso-ridge achieved higher predictive accuracy than its competitors, it exhibited high variability in the number of selected predictors. Ridge-lasso exhibited slightly superior predictive accuracy than ridge-garrote but at the expense of selecting more noise predictors. Overall, ridge emerged as a favourable option when variable selection is not a primary concern, while ridge-garrote demonstrated notable practical utility in selecting a parsimonious set of predictors, with only minimal compromise in predictive accuracy.

Temporal graphs are graphs where the edge set can change in each time step, and the vertex set stays the same. Exploration of temporal graphs whose snapshot in each time step is a connected graph, called connected temporal graphs, has been widely studied. We extend the concept of graph automorphisms from static graphs to temporal graphs and show that symmetries enable faster exploration: We prove that a connected temporal graph with $n$ vertices and orbit number $r$ (i.e., $r$ is the number of automorphism orbits) can be explored in $O(r n^{1+\epsilon})$ time steps, for any fixed $\epsilon>0$. For $r=O(n^c)$ for constant $c<1$, this is a significant improvement over the known tight worst-case bound of $\Theta(n^2)$ time steps for arbitrary connected temporal graphs. We also give two lower bounds for exploration, showing that $\Omega(n \log n)$ time steps are required for some inputs with $r=O(1)$ and that $\Omega(rn)$ time steps are required for some inputs for any $r$ with $1\le r\le n$. The techniques we develop for fast exploration are used to derive the following result for rendezvous in connected temporal graphs: Two agents are placed by an adversary at arbitrary vertices and given full information about the temporal graph, except that they do not have consistent vertex labels. The agents can meet at a common vertex after $O(n^{1+\epsilon})$ time steps, for any $\epsilon>0$. For some connected temporal graphs with constant orbit number we present a complementary lower bound of $\Omega(n\log n)$ time steps. Finally, we give a randomized algorithm to construct a temporal walk $W$ that visits all vertices of a given orbit with probability at least $1-\epsilon$ for any $0<\epsilon<1$ such that $W$ spans $O((n^{5/3}+rn)\log n)$ time steps. The runtime of this algorithm consists of $O(n^{1/3} \log (n/\epsilon))$ linear-time scans of the snapshots that exist in this time span.

SQL is a central component of any database course. Despite the small number of SQL commands, students struggle to practice the concepts. To overcome this challenge, we developed an intelligent tutoring system (ITS) to guide the learning process with a small effort by the lecturer. Other systems often give only basic feedback (correct or incorrect) or require hundreds of instance specific rules defined by a lecturer. In contrast, our system can provide individual feedback based on a semi-automatically/intelligent growing pool of reference solutions, i.e., sensible approaches. Moreover, we introduced the concept of good and bad reference solutions. The system was developed and evaluated in three steps based on Design Science research guidelines. The results of the study demonstrate that providing multiple reference solutions are useful with the support of harmonization to provide individual and real-time feedback and thus improve the learning process for students.

Spreadsheets are one of the most widely used tools for end users. As a result, spreadsheets such as Excel are now included in many curricula. However, digital solutions for assessing spreadsheet assignments are still scarce in the teaching context. Therefore, we have developed an Intelligent Tutoring System (ITS) to review students' Excel submissions and provide individualized feedback automatically. Although the lecturer only needs to provide one reference solution, the students' submissions are analyzed automatically in several ways: value matching, detailed analysis of the formulas, and quality assessment of the solution. To take the students' learning level into account, we have developed feedback levels for an ITS that provide gradually more information about the error by using one of the different analyses. Feedback at a higher level has been shown to lead to a higher percentage of correct submissions and was also perceived as well understandable and helpful by the students.

Topologically interlocked structures are assemblies of interlocking blocks that hold together solely through contact. Such structures have been shown to exhibit high strength, energy dissipation, and crack arrest properties. Recent studies on topologically interlocked structures have shown that both the peak strength and work-to-failure saturate with increasing friction coefficient. However, this saturated structural response is only achievable with nonphysically high values of the friction coefficient. For beam-like topologically interlocked structures, non-planar blocks provide an alternate approach to reach similar structural response with friction properties of commonly used materials. It remains unknown whether non-planar blocks have similar effects for slab-like assemblies, and what the achievable structural properties are. Here, we consider slab-like topologically interlocked structures and show, using numerical simulations, that non-planar blocks with wave-like surfaces allow for saturated response capacity of the structure with a realistic friction coefficient. We further demonstrate that non-planar morphologies cause a non-linear scaling of the work-to-failure with peak strength and result in significant improvements of the work-to-failure and ultimate deflection - values that cannot be attained with planar-faced blocks. Finally, we show that the key morphology parameter responsible for the enhanced performance of non-planar blocks with wave-like surfaces is the local angle of inclination at the hinging points of the loaded block. These findings shed new light on topologically interlocked structures with non-planar blocks, allowing for a better understanding of their strengths and energy absorption.

Mechanical contact between solids is almost exclusively modeled in Lagrangian frameworks. While these frameworks have been developed extensively and applied successfully to numerous contact problems, they generally require complex algorithms for contact detection and resolution. These challenges become particularly important when contact appears between solids with evolving boundaries, such as in systems where crystals grow in a constrained space. In this work, we introduce a fully Eulerian finite element framework for modeling contact between elastic solids tailored towards problems including evolving and intricate surfaces. The proposed approach uses a phase-field method that involves a diffuse representation of geometries on a fixed mesh, simplifying the modeling of evolving surfaces. Our methodology introduces a novel volumetric contact constraint based on penalty body forces, efficiently resolving the interpenetration of solids. We showcase the validity and versatility of our method through numerical examples, highlighting its ability to accurately capture complex solid-solid interactions. The Eulerian phase-field formulation greatly simplifies contact detection and its resolution. Furthermore, the framework can be straightforwardly coupled with other physical phenomena through the inclusion of multiple energy terms in the evolution of the phase-field. This enables multiphysics modeling, potentially providing a valuable tool for a wide range of applications involving chemically or physically evolving deformable solids in contact, as they commonly occur in deterioration processes of porous media.

Linear elastic fracture mechanics theory predicts that the speed of crack growth is limited by the Rayleigh wave speed. Although many experimental observations and numerical simulations have supported this prediction, some exceptions have raised questions about its validity. The underlying reasons for these discrepancies and the precise limiting speed of dynamic cracks remain unknown. Here, we demonstrate that tensile (mode~I) cracks can exceed the Rayleigh wave speed and propagate at supershear speeds. We show that taking into account geometric non-linearities, inherent in most materials, is sufficient to enable such propagation modes. These geometric non-linearities modify the crack-tip singularity, resulting in different crack-tip opening displacements, cohesive zone behavior, and energy flows towards the crack tip., Comment: 8 pages, 5 figures

Advanced-Glycation-Endproducts (AGEs) are known to be a major cause of impaired tissue material properties. In collagen fibrils, the main building component of human tissue, these AGEs appear as fibrillar cross-links. When AGEs accumulate in collagen fibrils, a process often caused by diabetes and aging, the mechanical properties of the collagen fibril are altered. However, current knowledge about the mechanical properties of different types of AGEs, and their quantity in collagen fibrils is limited owing to the scarcity of available experimental data. Consequently, the precise relationship between the nano-scale cross-link properties, their density in collagen fibrils, and the mechanical properties of the collagen fibrils at larger scales remains poorly understood. In our study, we use coarse-grained molecular dynamics simulations and perform destructive tensile tests on collagen fibrils to evaluate the effect of different cross-link densities and their mechanical properties on collagen fibril deformation and fracture behavior. We observe that the collagen fibril stiffens at high strain levels when either the AGEs density or the loading energy capacity of AGEs are increased. We demonstrate that this stiffening is caused by a mechanism that favors energy absorption via stretching rather than inter-molecular sliding. Hence, in cross-linked collagen fibrils, the absorbed energy is stored rather than dissipated through friction, resulting in brittle fracture upon fibrillar failure. Further, by varying multiple AGEs nano-scale parameters, we show that the AGEs loading energy capacity is, aside from their density in the fibril, the unique factor determining the effect of different types of AGEs on the mechanical behavior of collagen fibrils. Our results show that knowing AGEs properties is crucial for understanding of the origin of impaired tissue behavior., Comment: 19 pages, 10 figures

Topologically Interlocked Structures (TIS) are structural assemblies that achieve stability and carrying capacity through the geometric arrangement of interlocking blocks, relying solely on normal and friction forces for load transfer. Unlike beam-like TIS, whose deflection never exceeds the height of the blocks, the deflection of slab-like TIS often does. Yet, the upper limit of deflection of slab-like TIS, a key parameter defining their loading energy capacity, remains unexplored. Here, we establish a theoretical upper bound for the deflection capacity of slab-like TIS and outline a systematic design strategy to approach this upper bound. This strategy is based on engineering the contact interfaces such that the non-central blocks are more engaged in the structural response, leading to a more global and holistic deformation mode with higher deflections. We demonstrate the application of this strategy in a numerical case study on a typical slab-like TIS. We show that it leads to a 350$\%$ increase in deflection, yielding a value closer to the upper bound than previously reported in the literature. We also show that the resulting deflection mode engages all the blocks equally, avoids localized sliding modes, and resembles that of monolithic equivalents. Lastly, we show that the strategy not only maximizes the assembly's deflection capacity but also its loading energy capacity.

Understanding fracture in cementitious materials caused by the deposition and growth of corrosion products requires scale-bridging approaches due to the large length-scale difference between the micro-pores, where deposition occurs, and the structure, where deterioration manifests. Cementitious materials bear a highly heterogeneous micro-structure owing to the fractal nature of micro-pores. Simultaneously, a corrosion-driven fracture is a multi-physics problem involving ionic diffusion, chemical reactions, and stress development. This multi-scale and multi-physical character makes scale-bridging studies computationally costly, often leading to the use of simplified fractal porous media, which has important consequences for the quantitative interpretation of the results. Recent advances in homogenization approaches using Fast-Fourier-Transform (FFT) based methods have raised interest due to their ease of implementation and low computational cost. This paper presents an FFT-based framework for solving corrosion-driven fractures within fractal porous media. We demonstrate the effectiveness of the Fourier-based spectral method in resolving the multiple corrosion-driven mechanisms such as ionic diffusion, stress development, and damage within a fractal porous microstructure. Based on the presented methodology, we analyze the impact of simplifying fractal porous media with simple Euclidean geometry on corrosion-driven fracture. Our results demonstrate the importance of preserving both the porosity and fractal nature of pores for precise and reliable modeling of corrosion-driven failure mechanisms.

Interacting systems are ubiquitous in nature and engineering, ranging from particle dynamics in physics to functionally connected brain regions. These interacting systems can be modeled by graphs where edges correspond to the interactions between interactive entities. Revealing interaction laws is of fundamental importance but also particularly challenging due to underlying configurational complexities. The associated challenges become exacerbated for heterogeneous systems that are prevalent in reality, where multiple interaction types coexist simultaneously and relational inference is required. Here, we propose a novel probabilistic method for relational inference, which possesses two distinctive characteristics compared to existing methods. First, it infers the interaction types of different edges collectively by explicitly encoding the correlation among incoming interactions with a joint distribution, and second, it allows handling systems with variable topological structure over time. We evaluate the proposed methodology across several benchmark datasets and demonstrate that it outperforms existing methods in accurately inferring interaction types. We further show that when combined with known constraints, it allows us, for example, to discover physics-consistent interaction laws of particle systems. Overall the proposed model is data-efficient and generalizable to large systems when trained on smaller ones. The developed methodology constitutes a key element for understanding interacting systems and may find application in graph structure learning., Comment: Under review. Links to the supporting code can be found at the end of the main content

Jupiter's satellite auroral footprints are a consequence of the interaction between the Jovian magnetic field with co-rotating iogenic plasma and the Galilean moons. The disturbances created near the moons propagate as Alfv\'en waves along the magnetic field lines. The position of the moons is therefore "Alfv\'enically" connected to their respective auroral footprint. The angular separation from the instantaneous magnetic footprint can be estimated by the so-called lead angle. That lead angle varies periodically as a function of orbital longitude, since the time for the Alfv\'en waves to reach the Jovian ionosphere varies accordingly. Using spectral images of the Main Alfv\'en Wing auroral spots collected by Juno-UVS during the first forty-three orbits, this work provides the first empirical model of the Io, Europa and Ganymede equatorial lead angles for the northern and southern hemispheres. Alfv\'en travel times between the three innermost Galilean moons to Jupiter's northern and southern hemispheres are estimated from the lead angle measurements. We also demonstrate the accuracy of the mapping from the Juno magnetic field reference model (JRM33) at the completion of the prime mission for M-shells extending to at least 15RJ . Finally, we shows how the added knowledge of the lead angle can improve the interpretation of the moon-induced decametric emissions., Comment: 20 pages, 8 figures, Accepted for publication in Journal of Geophysical Research: Space Physics on 20 April 2023

ESA's Jupiter Icy Moons Explorer (JUICE) will provide a detailed investigation of the Jovian system in the 2030s, combining a suite of state-of-the-art instruments with an orbital tour tailored to maximise observing opportunities. We review the Jupiter science enabled by the JUICE mission, building on the legacy of discoveries from the Galileo, Cassini, and Juno missions, alongside ground- and space-based observatories. We focus on remote sensing of the climate, meteorology, and chemistry of the atmosphere and auroras from the cloud-forming weather layer, through the upper troposphere, into the stratosphere and ionosphere. The Jupiter orbital tour provides a wealth of opportunities for atmospheric and auroral science: global perspectives with its near-equatorial and inclined phases, sampling all phase angles from dayside to nightside, and investigating phenomena evolving on timescales from minutes to months. The remote sensing payload spans far-UV spectroscopy (50-210 nm), visible imaging (340-1080 nm), visible/near-infrared spectroscopy (0.49-5.56 $\mu$m), and sub-millimetre sounding (near 530-625\,GHz and 1067-1275\,GHz). This is coupled to radio, stellar, and solar occultation opportunities to explore the atmosphere at high vertical resolution; and radio and plasma wave measurements of electric discharges in the Jovian atmosphere and auroras. Cross-disciplinary scientific investigations enable JUICE to explore coupling processes in giant planet atmospheres, to show how the atmosphere is connected to (i) the deep circulation and composition of the hydrogen-dominated interior; and (ii) to the currents and charged particle environments of the external magnetosphere. JUICE will provide a comprehensive characterisation of the atmosphere and auroras of this archetypal giant planet., Comment: 83 pages, 24 figures, accepted to Space Science Reviews special issue on ESA's JUICE mission

The accuracy of predictive models for solitary pulmonary nodule (SPN) diagnosis can be greatly increased by incorporating repeat imaging and medical context, such as electronic health records (EHRs). However, clinically routine modalities such as imaging and diagnostic codes can be asynchronous and irregularly sampled over different time scales which are obstacles to longitudinal multimodal learning. In this work, we propose a transformer-based multimodal strategy to integrate repeat imaging with longitudinal clinical signatures from routinely collected EHRs for SPN classification. We perform unsupervised disentanglement of latent clinical signatures and leverage time-distance scaled self-attention to jointly learn from clinical signatures expressions and chest computed tomography (CT) scans. Our classifier is pretrained on 2,668 scans from a public dataset and 1,149 subjects with longitudinal chest CTs, billing codes, medications, and laboratory tests from EHRs of our home institution. Evaluation on 227 subjects with challenging SPNs revealed a significant AUC improvement over a longitudinal multimodal baseline (0.824 vs 0.752 AUC), as well as improvements over a single cross-section multimodal scenario (0.809 AUC) and a longitudinal imaging-only scenario (0.741 AUC). This work demonstrates significant advantages with a novel approach for co-learning longitudinal imaging and non-imaging phenotypes with transformers. Code available at https://github.com/MASILab/lmsignatures., Comment: Accepted to MICCAI 2023

Ultraviolet spectroscopy is a powerful method to study planetary surface composition through reflectance measurements and atmospheric composition through stellar/solar occultations, transits of other planetary bodies, and direct imaging of airglow and auroral emissions. The next generation of ultraviolet spectrographs (UVS) on board ESA's JUICE (Jupiter Icy Moons Explorer) and NASA's Europa Clipper missions will perform such measurements of Jupiter and its moons in the early 2030's. This work presents a compilation of a detailed UV stellar catalog, named CUBS, of targets with high intensity in the 50-210 nm wavelength range with applications relevant to planetary spectroscopy. These applications include: 1) Planning and simulating occultations, including calibration measurements; 2) Modeling starlight illumination of dark, nightside planetary surfaces primarily lit by the sky; and 3) Studying the origin of diffuse galactic UV light as mapped by existing datasets from Juno-UVS and others. CUBS includes information drawn from resources such as the International Ultraviolet Explorer (IUE) catalog and SIMBAD. We have constructed model spectra at 0.1 nm resolution for almost 90,000 targets using Kurucz models and, when available, IUE spectra. CUBS also includes robust checks for agreement between the Kurucz models and the IUE data. We also present a tool for which our catalog can be used to identify the best candidates for stellar occultation observations, with applications for any UV instrument. We report on our methods for producing CUBS and discuss plans for its implementation during ongoing and upcoming planetary missions., Comment: 13 pages, 4 figures, submitted to JATIS

Reflected sunlight observations from the Ultraviolet Spectrograph (UVS) on the Juno spacecraft were used to study the distribution of acetylene (C$_2$H$_2$) at Jupiter's south pole. We find that the shape of the C$_2$H$_2$ absorption feature varies significantly across the polar region, and this can be used to infer spatial variability in the C$_2$H$_2$ abundance. There is a localized region of enhanced C$_2$H$_2$ absorption which coincides with the location of Jupiter's southern polar aurora; the C$_2$H$_2$ abundance poleward of the auroral oval is a factor of 3 higher than adjacent quiescent, non-auroral longitudes. This builds on previous infrared studies which found enhanced C$_2$H$_2$ abundances within the northern auroral oval. This suggests that Jupiter's upper-atmosphere chemistry is being strongly influenced by the influx of charged auroral particles and demonstrates the necessity of developing ion-neutral photochemical models of Jupiter's polar regions., Comment: Accepted in JGR: Planets

Cross-links are considered to be a key component of collagen fibrils as they can change the fibrillar behavior in various ways. Advanced-Glycation Endproducts (AGEs), one particular type of cross-links, have been shown to accumulate and impair the mechanical properties of collageneous tissues, whereas enzymatic cross-links (ECLs) are known for stabilizing the structure of the fibril. However, the reasons for whether a given type of cross-link improves or impairs the material properties remain unknown. Here, we use coarse-grained steered molecular models to evaluate the effect of AGEs and ECLs cross-links content on the deformation and failure properties of collagen fibrils. Our simulations show that the collagen fibrils stiffen at high strain levels when the AGEs content exceeds a critical value. In addition, the strength of the fibril increases with AGEs accumulation. By analyzing the forces within the different types of cross-links (AGEs and ECLs) as well as their failure, we demonstrate that a change of deformation mechanism is at the origin of these observations. A high AGEs content reinforces force transfer through AGEs cross-links rather than through friction between sliding tropocollagen molecules. We show that this failure mechanism, which is associated with lower energy dissipation, results in more abrupt failure of the collagen fibril. Our results provide a direct and causal link between increased AGEs content, inhibited intra-fibrillar sliding, increased stiffness, and abrupt fibril fracture. Therefore, they explain the mechanical origin of bone brittleness as commonly observed in elderly and diabetic populations. Our findings contribute to a better understanding of the mechanisms underlying impaired tissue behaviour due to elevated AGEs content and could enable targeted measures regarding the reduction of specific collagen cross-linking levels.

The failure of heterogeneous materials with microstructures is a complex process of damage nucleation, growth and localisation. This process spans multiple length scales and is challenging to simulate numerically due to its high computational cost. One option is to use domain decomposed multi-scale methods with dynamical refinement. If needed, these methods refine coarse regions into a fine-scale representation to explicitly model the damage in the microstructure. However, damage evolution is commonly restricted to fine-scale regions only. Thus, they are unable to capture the full complexity and breath of the degradation process in the material. In this contribution, a generic procedure that allows to account for damage in all representations is proposed. The approach combines a specially designed orthotropic damage law, with a scheme to generate pre-damaged fine-scale microstructures. Results indicate that the damage approximation for the coarse representation works well. Furthermore, the generated fine-scale damage patterns are overall consistent with explicitly simulated damage patterns. Minor discrepancies occur in the generation but subsequently vanish when explicit damage evolution continuous; for instance under increased load. The presented approach provides a methodological basis for adaptive multi-scale simulation schemes with consistent damage evolution.

Let $G$ be an unlabeled planar and simple $n$-vertex graph. Unlabeled graphs are graphs where the label-information is either not given or lost during the construction of data-structures. We present a succinct encoding of $G$ that provides induced-minor operations, i.e., edge contractions and vertex deletions. Any sequence of such operations is processed in $O(n)$ time in the word-RAM model. At all times the encoding provides constant time (per element output) neighborhood access and degree queries. Optional hash tables extend the encoding with constant expected time adjacency queries and edge-deletion (thus, all minor operations are supported) such that any number of edge deletions are computed in $O(n)$ expected time. Constructing the encoding requires $O(n)$ bits and $O(n)$ time. The encoding requires $\mathcal{H}(n) + o(n)$ bits of space with $\mathcal{H}(n)$ being the entropy of encoding a planar graph with $n$ vertices. Our data structure is based on the recent result of Holm et al. [ESA 2017] who presented a linear time contraction data structure that allows to maintain parallel edges and works for labeled graphs, but uses $\Theta(n \log n)$ bits of space. We combine the techniques used by Holm et al. with novel ideas and the succinct encoding of Blelloch and Farzan [CPM 2010] for arbitrary separable graphs. Our result partially answers the question raised by Blelloch and Farzan whether their encoding can be modified to allow modifications of the graph. As a simple application of our encoding, we present a linear time outerplanarity testing algorithm that uses $O(n)$ bits of space.

Topological Interlocking Structures (TIS) have been increasingly studied in the past two decades. However, some fundamental questions concerning the effects of Young's modulus and the friction coefficient on the structural mechanics of the most common type of TIS application - centrally loaded slabs - are not yet clear. Here, we present a first-of-its-kind parametric study based on the Level-Set-Discrete-element-Method that aims to clarify how these two parameters affect multiple aspects of the behavior and failure of centrally-loaded TIS slabs. This includes the evolution of the structural response up to and including failure, the foremost structural response parameters, and the residual carrying capacity. We find that the structural response parameters in TIS slabs scale linearly with Young's modulus, that they saturate with the friction coefficient, and that the saturated response provides an upper-bound on the capacity of centrally loaded TIS slabs reported in the literature. This, together with additional findings, insights, and observations, comprise a novel contribution to our understanding of the interlocked structural form.

Topologically interlocked materials and structures, which are assemblies of unbonded interlocking building blocks, are promising concepts for versatile structural applications. They have been shown to exhibit exceptional mechanical properties, including outstanding combinations of stiffness, strength, and toughness, beyond those achievable with common engineering materials. Recent work has established a theoretical upper limit for the strength and toughness of beam-like topologically interlocked structures. However, this theoretical limit is only achievable for structures with unrealistically high friction coefficients; therefore, it remains unknown whether it is achievable in actual structures. Here, we demonstrate that a hierarchical approach for topological interlocking, inspired by biological systems, overcomes these limitations and provides a path toward optimized mechanical performance. We consider beam-like topologically interlocked structures that present a sinusoidal surface morphology with controllable amplitude and wavelength and examine the properties of the structures using numerical simulations. The results show that the presence of surface morphologies increases the effective frictional strength of the interfaces and, if well-designed, enables us to reach the theoretical limit of the structural carrying capacity with realistic friction coefficients. Furthermore, we observe that the contribution of the surface morphology to the effective friction coefficient of the interface is well described by a criterion combining the surface curvature and surface gradient. Our study demonstrates the ability to architecture the surface morphology in beam-like topological interlocked structures to significantly enhance its structural performance.

Topological Interlocking Structures (TIS) are assemblies of interlocking building blocks that hold together solely through contact and friction at the blocks' interfaces, and thus do not require any connective elements. This salient feature makes them highly energy-absorbing, resistant to crack propagation, geometrically versatile, and re-usable. It also gives rise to failure mechanisms that, differently from ordinary structures, are governed by multiple contact interactions between blocks and frictional slip at their interfaces. Commonly-used modeling tools for structural analysis severely struggle to capture and quantify these unusual failure mechanisms. Here, we propose a different approach that is well suited to model the complex failure of TIS. It is based on the Level-Set-Discrete-Element-Method, originally developed for granular mechanics applications. After introducing the basic assumptions and theoretical concepts underlying our model, we show that it well-captures experimentally observed slip-governed failure in TIS slabs and that it estimates the force-displacement curves better than presently available modeling tools. The theoretical foundation together with the results of this study provide a proof-of-concept for our new approach and point to its potential to improve our ability to model and to understand the behavior of interlocked structural forms.