Open-source Implementation Research Articles

A unified phase field solution to fracture analyses on (pseudo) elastoplastic solids and structures

Despite that phase field to brittle fracture has been extensively studied and extension towards solving ductile fracture has been reported, crack evolution in pseudo elastoplastic solids is still an on-going research area. Taking engineered cementitious composites (ECC) as example, it is featured with pseudo ductility, by developing multiple fine cracks to withstand excessive deformation, causing significant numerical difficulties. In this regard, pseudo plasticity is introduced into phase field method to form a novel approach, guided by degrading fracture toughness, to ductile failure analyses. Moreover, a yield surface decay model is proposed to consider the progressive loss of strain-bearing capacity in pseudo elastoplastic solids under damage accumulation by multi-cracking development. Based on the proposed model, a unified solution is achieved for fracture analyses on both conventional and pseudo elastoplastic solids. The model robustness is first verified by a numerical benchmark and an in-situ experiment on metallic materials under quasi-static regime, before applying to study the fracture mechanics of bulk ECCs under both direct tension and flexure. The proposed method is presented with an open-source implementation in COMSOL Multiphysics, and is demonstrated to be capable of assisting future analyses and designs on (pseudo) elastoplastic solids and structures.

Replication of the bSTAR sequence and open-source implementation.

The reproducibility of scientific reports is crucial to advancing human knowledge. This paper is a summary of our experience in replicating a balanced SSFP half-radial dual-echo imaging technique (bSTAR) using open-source frameworks as a response to the 2023 ISMRM "repeat it with me" Challenge. We replicated the bSTAR technique for thoracic imaging at 0.55T. The bSTAR pulse sequence is implemented in Pulseq, a vendor neutral open-source rapid sequence prototyping environment. Image reconstruction is performed with the open-source Berkeley Advanced Reconstruction Toolbox (BART). The replication of bSTAR, termed open-source bSTAR, is tested by replicating several figures from the published literature. Original bSTAR, using the pulse sequence and image reconstruction developed by the original authors, and open-source bSTAR, with pulse sequence and image reconstruction developed in this work, were performed in healthy volunteers. Both echo images obtained from open-source bSTAR contain no visible artifacts and show identical spatial resolution and image quality to those in the published literature. A direct head-to-head comparison between open-source bSTAR and original bSTAR on a healthy volunteer indicates that open-source bSTAR provides adequate SNR, spatial resolution, level of artifacts, and conspicuity of pulmonary vessels comparable to original bSTAR. We have successfully replicated bSTAR lung imaging at 0.55T using two open-source frameworks. Full replication of a research method solely relying on information on a research paper is unfortunately rare in research, but our success gives greater confidence that a research methodology can be indeed replicated as described.

Two different micro-polar phase-field models for brittle fracture and their open-source finite element implementation

Realizing the limitations of classical phase-field fracture models and the need for the development of non-local phase-field models that can capture the experimentally observed size effects in brittle and quasi-brittle materials, we propose thermodynamically consistent volumetric-deviatoric and spectral decomposition-based phase-field models (PFMs) for brittle fracture in micro-polar continua. For developing the proposed models, we have considered the micro-rotation as an additional kinematic descriptor, and have derived the balance equations by invoking the virtual power principle. On satisfying the thermodynamic laws, we have determined the constitutive relations for the thermodynamic fluxes and demonstrated that any dissipative effect can easily be incorporated into the proposed models. We have provided an open-source finite element (FE) implementation of the micro-polar PFMs using Gridap in Julia. The implementation includes various benchmark problems to illustrate how the responses in a specimen change with the variations in micro-polar material parameters. Through a series of numerical examples with different values of micro-polar material parameters, we have showcased both the capabilities and limitations of the proposed volumetric-deviatoric and spectral decomposition-based micro-polar PFMs.

A Statistical Interpolation of Satellite Data with Rain Gauge Data over Papua New Guinea

Abstract Satellites provide a useful way of estimating rainfall where the availability of in situ data is low but their indirect nature of estimation means there can be substantial biases. Consequently, the assimilation of in situ data is an important step in improving the accuracy of the satellite rainfall analysis. The effectiveness of this step varies with gauge density, and this study investigated the effectiveness of statistical interpolation (SI), also known as optimal interpolation (OI), on a monthly time scale when gauge density is extremely low using Papua New Guinea (PNG) as a study region. The topography of the region presented an additional challenge to the algorithm. An open-source implementation of SI was developed on Python 3 and confirmed to be consistent with an existing implementation, addressing a lack of open-source implementation for this classical algorithm. The effectiveness of the analysis produced by this algorithm was then compared to the pure satellite analysis over PNG from 2001 to 2014. When performance over the entire study domain was considered, the improvement from using SI was close to imperceptible because of the small number of stations available for assimilation and the small radius of influence of each station (imposed by the topography present in the domain). However, there was still value in using OI as performance around each of the stations was noticeably improved, with the error consistently being reduced along with a general increase in the correlation metric. Furthermore, in an operational context, the use of OI provides an important function of ensuring consistency between in situ data and the gridded analysis. Significance Statement The blending of satellite and gauge rainfall data through a process known as statistical interpolation (SI) is known to be capable of producing a more accurate dataset that facilitates better estimation of rainfall. However, the performance of this algorithm over a domain such as Papua New Guinea, where gauge density is extremely low, is not often explored. This study reveals that, although an improvement over the entire Papua New Guinea domain was slight, the algorithm is still valuable as there was a consistent improvement around the stations. Additionally, an adaptable and open-source version of the algorithm is provided, allowing users to blend their own satellite and gauge data and create better geospatial datasets for their own purposes.

TubULAR: tracking in toto deformations of dynamic tissues via constrained maps.

A common motif in biology is the arrangement of cells into tubes, which further transform into complex shapes. Traditionally, analysis of dynamic tissues has relied on inspecting static snapshots, live imaging of cross-sections or tracking isolated cells in three dimensions. However, capturing the interplay between in-plane and out-of-plane behaviors requires following the full surface as it deforms and integrating cell-scale motions into collective, tissue-scale deformations. Here, we present an analysis framework that builds in toto maps of tissue deformations by following tissue parcels in a static material frame of reference. Our approach then relates in-plane and out-of-plane behaviors and decomposes complex deformation maps into elementary contributions. The tube-like surface Lagrangian analysis resource (TubULAR) provides an open-source implementation accessible either as a standalone toolkit or as an extension of the ImSAnE package used in the developmental biology community. We demonstrate our approach by analyzing shape change in the embryonic Drosophila midgut and beating zebrafish heart. The method naturally generalizes to in vitro and synthetic systems and provides ready access to the mechanical mechanisms relating genetic patterning to organ shape change.

SQAT: a MATLAB-based toolbox for quantitative sound quality analysis

Literature suggests that psychoacoustic metrics, which are established by models firmly based on the human auditory system, are able to better approximate perceptual attributes of sounds in a quantitative manner than conventional metrics based on the sound pressure level. Therefore, psychoacoustic metrics are commonly used as sound quality indicators in practical applications, such as product design or environmental noise assessment, in which human perception plays an important role. However, their calculation is not straightforward and the non-availability of verified, well-documented, open-source implementations in a single environment poses a challenge to reproducible research. In this contribution, we introduce the sound quality analysis toolbox (SQAT), an open-source compilation of MATLAB codes containing the implementation of the following key psychoacoustic metrics: loudness (ISO 532-1:2017), sharpness (DIN 45692:2009), roughness (Daniel & Weber's model), fluctuation strength (Osses et al. model), and tonality (Aures' model). A comprehensive description of the implemented psychoacoustic metrics is provided. Furthermore, a verification study is presented in order to demonstrate the reliability of the implementations, supporting the use of SQAT as a reliable tool for future works in the field of sound quality assessment based on psychoacoustic metrics.

Open-source software to calculate industrial, railway, and road traffic noise

Dutch noise legislation provides instructions and formulas to calculate noise exposure levels on a similar level to Cnossos or ISO 9613-2. The instructions are implemented in commercially available software packages. Each implementation includes a pathfinder and a sound propagation/attenuation module. Whilst the attenuation is described in detail in the legislation, the pathfinder is not and its design is left to the software company. Currently there are only proprietary, closed-source implementations available. That is going to change. Based on a previously closed-source solution, we are working towards an open-source, national implementation to calculate noise levels based on 3D model input. We implemented the updated Dutch calculation method and improved the pathfinder. For example, this now includes sloped barriers. Only the calculation core is part of this project: the development of a graphical user interface is not planned, but can be provided by others. Also, other implementations of the Dutch noise calculation directive can still co-exist next to our open-source solution, since the legislation only specifies the instructions and has no requisite for a specific implementation. In this paper we will discuss the open-source implementation, the impact on noise modelling in the Netherlands and its potential applications outside of the Netherlands.

SciKit digital health package for accelerometry-measured physical activity: comparisons to existing solutions and investigations of age effects in healthy adults

IntroductionAccelerometry has become increasingly prevalent to monitor physical activity due to its low participant burden, quantitative metrics, and ease of deployment. Physical activity metrics are ideal for extracting intuitive, continuous measures of participants’ health from multiple days or weeks of high frequency data due to their fairly straightforward computation. Previously, we released an open-source digital health python processing package, SciKit Digital Health (SKDH), with the goal of providing a unifying device-agnostic framework for multiple digital health algorithms, such as activity, gait, and sleep.MethodsIn this paper, we present the open-source SKDH implementation for the derivation of activity endpoints from accelerometer data. In this implementation, we include some non-typical features that have shown promise in providing additional context to activity patterns, and provide comparisons to existing algorithms, namely GGIR and the GENEActiv macros. Following this reference comparison, we investigate the association between age and derived physical activity metrics in a healthy adult cohort collected in the Pfizer Innovation Research Lab, comprising 7–14 days of at-home data collected from younger (18–40 years) and older (65–85 years) healthy volunteers.ResultsResults showed that activity metrics derived with SKDH had moderate to excellent ICC values (0.550 to 1.0 compared to GGIR, 0.469 to 0.697 compared to the GENEActiv macros), with high correlations for almost all compared metrics (>0.733 except vs GGIR sedentary time, 0.547). Several features show age-group differences, with Cohen’s d effect sizes >1.0 and p-values < 0.001. These features included non-threshold methods such as intensity gradient, and activity fragmentation features such as between-states transition probabilities.DiscussionThese results demonstrate the validity of the implemented SKDH physical activity algorithm, and the potential of the implemented PA metrics in assessing activity changes in free-living conditions.

Overcome the Fear Of Missing Out: Active sensing UAV scanning for precision agriculture

This paper deals with the problem of informative path planning for a UAV deployed for precision agriculture applications. First, we observe that the “fear of missing out” data lead to uniform, conservative scanning policies over the whole agricultural field. Consequently, employing a non-uniform scanning approach can mitigate the expenditure of time in areas with minimal or negligible real value, while ensuring heightened precision in information-dense regions. Turning to the available informative path planning methodologies, we discern that certain methods entail intensive computational requirements, while others necessitate training on an ideal world simulator. To address the aforementioned issues, we propose an active sensing coverage path planning approach, named OverFOMO, that regulates the speed of the UAV in accordance with both the relative quantity of the identified classes, i.e. crops and weeds, and the confidence level of such detections. To identify these instances, a robust Deep Learning segmentation model is deployed. The computational needs of the proposed algorithm are independent of the size of the agricultural field, rendering its applicability on modern UAVs quite straightforward. The proposed algorithm was evaluated with a simu-realistic pipeline, combining data from real UAV missions and the high-fidelity dynamics of AirSim simulator, showcasing its performance improvements over the established state of affairs for this type of missions. An open-source implementation of the algorithm and the evaluation pipeline is also available: https://github.com/emmarapt/OverFOMO.

Codebase release 1.0 for Nevanlinna.jl

We introduce a Julia implementation of the recently proposed Nevanlinna analytic continuation method. The method is based on Nevanlinna interpolants and inherently preserves the causality of a response function due to its construction. For theoretical calculations without statistical noise, this continuation method is a powerful tool to extract real-frequency information from numerical input data on the Matsubara axis. This method has been applied to first-principles calculations of correlated materials. This paper presents its efficient and full-featured open-source implementation of the method including the Hamburger moment problem and smoothing.

Nevanlinna.jl: A Julia implementation of Nevanlinna analytic continuation

We introduce a Julia implementation of the recently proposed Nevanlinna analytic continuation method. The method is based on Nevanlinna interpolants and inherently preserves the causality of a response function due to its construction. For theoretical calculations without statistical noise, this continuation method is a powerful tool to extract real-frequency information from numerical input data on the Matsubara axis. This method has been applied to first-principles calculations of correlated materials. This paper presents its efficient and full-featured open-source implementation of the method including the Hamburger moment problem and smoothing.

High level GPU-accelerated 2D PIV framework in Python

The Particle Image Velocimetry (PIV) method is widely used for optical measurment of flow velocity fields. This paper demonstrates the possibilities of using high-level libraries for GPU-accelerated PIV data analysis in Python. The Torch PIV library for the analysis of 2D PIV experiments based on the deep learning framework PyTorch with CUDA support was developed. The library implements a multi pass cross-correlation FFT PIV algorithm with an interrogation window shift. The chosen implementation does not require compilation from the user, has a compact codebase, is able to run both on the CPU and the GPU depending on the user choice, and also it is as flexible as the Python module. In this work, the performance of the CPU version of the developed method was compared with existing open source implementations. It is shown that the main functions of the developed module can be executed on the GPU at the speed of CUDA implementations. The developed library is tested on synthetic images and experimental data. Program SummaryProgram title: TorchPIV.CPC Library link to program files: https://doi.org/10.17632/yw43vjc36h.1.Developer's repository link: https://github.com/NikNazarov/TorchPIV.Licensing provisions: MIT license.Programming language: Python.Nature of problem: PIV experiments often require analyzing a large number of images in order to determine the statistical characteristics of the flow. GPUs are actively used in this field to speed up the data analysis process. Open-source software solutions to this problem usually require build and are difficult to integrate into the analysis pipelines.Solution method: The main feature of the developed module is its flexibility and simple distribution. The module is cross-platform, and installation does not require compilation from the user. The developed library can be imported as an ordinary Python module, at the same time it allows to get a significant performance gain when analyzing PIV experiments by using NVIDIA GPUs. Implementation in pure Python allows the module to serve as a backend for more complex experimental data processing systems. The core library of this method is one of the most reliable and widespread in the field of machine learning.

Scalable generalized screening for high-order terms in the many-body expansion: Algorithm, open-source implementation, and demonstration.

The many-body expansion lies at the heart of numerous fragment-based methods that are intended to sidestep the nonlinear scaling of abinitio quantum chemistry, making electronic structure calculations feasible in large systems. In principle, inclusion of higher-order n-body terms ought to improve the accuracy in a controllable way, but unfavorable combinatorics often defeats this in practice and applications with n ≥ 4 are rare. Here, we outline an algorithm to overcome this combinatorial bottleneck, based on a bottom-up approach to energy-based screening. This is implemented within a new open-source software application ("Fragme∩t"), which is integrated with a lightweight semi-empirical method that is used to cull subsystems, attenuating the combinatorial growth of higher-order terms in the graph that is used to manage the calculations. This facilitates applications of unprecedented size, and we report four-body calculations in (H2O)64 clusters that afford relative energies within 0.1 kcal/mol/monomer of the supersystem result using less than 10% of the unique subsystems. We also report n-body calculations in (H2O)20 clusters up to n = 8, at which point the expansion terminates naturally due to screening. These are the largest n-body calculations reported to date using ab initio electronic structure theory, and they confirm that high-order n-body terms are mostly artifacts of basis-set superposition error.

Improving Existing Segmentators Performance with Zero-Shot Segmentators.

This paper explores the potential of using the SAM (Segment-Anything Model) segmentator to enhance the segmentation capability of known methods. SAM is a promptable segmentation system that offers zero-shot generalization to unfamiliar objects and images, eliminating the need for additional training. The open-source nature of SAM allows for easy access and implementation. In our experiments, we aim to improve the segmentation performance by providing SAM with checkpoints extracted from the masks produced by mainstream segmentators, and then merging the segmentation masks provided by these two networks. We examine the "oracle" method (as upper bound baseline performance), where segmentation masks are inferred only by SAM with checkpoints extracted from the ground truth. One of the main contributions of this work is the combination (fusion) of the logit segmentation masks produced by the SAM model with the ones provided by specialized segmentation models such as DeepLabv3+ and PVTv2. This combination allows for a consistent improvement in segmentation performance in most of the tested datasets. We exhaustively tested our approach on seven heterogeneous public datasets, obtaining state-of-the-art results in two of them (CAMO and Butterfly) with respect to the current best-performing method with a combination of an ensemble of mainstream segmentator transformers and the SAM segmentator. The results of our study provide valuable insights into the potential of incorporating the SAM segmentator into existing segmentation techniques. We release with this paper the open-source implementation of our method.

General, open-source vertex modeling in biological applications using Tissue Forge

Vertex models are a widespread approach for describing the biophysics and behaviors of multicellular systems, especially of epithelial tissues. Vertex models describe a wide variety of developmental scenarios and behaviors like cell rearrangement and tissue folding. Often, these models are implemented as single-use or closed-source software, which inhibits reproducibility and decreases accessibility for researchers with limited proficiency in software development and numerical methods. We developed a physics-based vertex model methodology in Tissue Forge, an open-source, particle-based modeling and simulation environment. Our methodology describes the properties and processes of vertex model objects on the basis of vertices, which allows integration of vertex modeling with the particle-based formalism of Tissue Forge, enabling an environment for developing mixed-method models of multicellular systems. Our methodology in Tissue Forge inherits all features provided by Tissue Forge, delivering open-source, extensible vertex modeling with interactive simulation, real-time simulation visualization and model sharing in the C, C++ and Python programming languages and a Jupyter Notebook. Demonstrations show a vertex model of cell sorting and a mixed-method model of cell migration combining vertex- and particle-based models. Our methodology provides accessible vertex modeling for a broad range of scientific disciplines, and we welcome community-developed contributions to our open-source software implementation.

Scalable methods for computing sharp extreme event probabilities in infinite-dimensional stochastic systems

We introduce and compare computational techniques for sharp extreme event probability estimates in stochastic differential equations with small additive Gaussian noise. In particular, we focus on strategies that are scalable, i.e. their efficiency does not degrade upon temporal and possibly spatial refinement. For that purpose, we extend algorithms based on the Laplace method for estimating the probability of an extreme event to infinite dimensional path space. The method estimates the limiting exponential scaling using a single realization of the random variable, the large deviation minimizer. Finding this minimizer amounts to solving an optimization problem governed by a differential equation. The probability estimate becomes sharp when it additionally includes prefactor information, which necessitates computing the determinant of a second derivative operator to evaluate a Gaussian integral around the minimizer. We present an approach in infinite dimensions based on Fredholm determinants, and develop numerical algorithms to compute these determinants efficiently for the high-dimensional systems that arise upon discretization. We also give an interpretation of this approach using Gaussian process covariances and transition tubes. An example model problem, for which we provide an open-source python implementation, is used throughout the paper to illustrate all methods discussed. To study the performance of the methods, we consider examples of stochastic differential and stochastic partial differential equations, including the randomly forced incompressible three-dimensional Navier–Stokes equations.

Feasibility assessments of a dynamical approach to compartmental modelling on graphs: Scaling limits and performance analysis

Sharkey, Kiss and others developed a dynamical approach to modelling epidemic disease on a contact graph by generating systems of first-order ordinary differential equations expressing the model dynamics [1,2], which are solved to yield exact and deterministic modelling results. However, they left algorithmic generation (and solving) of systems and runtime assessment of the approach as an open question. To address this, we give an open source implementation that takes both a compartmental model and a contact graph as input and then generates and solves a system of equations exactly describing the dynamics of the system. Our implementation uses a moment closure result on single-vertex cutsets in the contact graph to reduce the number of equations required. In runtime experiments, we find that the implementation of the dynamical approach is almost always slower than a comparable Monte Carlo simulation in finding the expected state of the modelling system at a specified time. To complement our runtime evaluations, we give results and bounds on the number of equations required to describe a system as a function of the size of the compartmental model and input graph. We show that a natural extension of the moment closure result on single-vertex cutsets to larger cutsets is only possible for restricted projections of the model states on the cutset. We conclude that the dynamical approach is unlikely to be suitable unless exact, deterministic (rather than simulated) results are essential.

Controllability and accessibility analysis of nonlinear biosystems

Background: We address the problem of determining the controllability and accessibility of nonlinear biosystems. We consider models described by affine-in-inputs ordinary differential equations, which are adequate for a wide array of biological processes. Roughly speaking, the controllability of a dynamical system determines the possibility of steering it from an initial state to any point in its neighbourhood; accessibility is a weaker form of controllability.Methods: While the methodology for analysing the controllability of linear systems is well established, its generalization to the nonlinear case has proven elusive. Thus, a number of related but different properties – including different versions of accessibility, reachability or weak local controllability – have been defined to approach its study, and several partial results exist in lieu of a general test. Here, leveraging the applicable results from differential geometric control theory, we source sufficient conditions to assess nonlinear controllability, as well as a necessary and sufficient condition for accessibility.Results: We develop an algorithmic procedure to evaluate these conditions efficiently, and we provide its open source implementation. Using this software tool, we analyse the accessibility and controllability of a number of models of biomedical interest. While some of them are fully controllable, we find others that are not, as is the case of some models of EGF and NFκB signalling networks.Conclusions: The contributions in this paper facilitate the accessibility and controllability analysis of nonlinear models, not only in biomedicine but also in other areas in which they have been rarely performed to date.

Recommended Practices in Latent Class Analysis Using the Open-Source R-Package tidySEM

Latent class analysis (LCA) refers to techniques for identifying groups in data based on a parametric model. Examples include mixture models, LCA with ordinal indicators, and latent class growth analysis. Despite its popularity, there is limited guidance with respect to decisions that must be made when conducting and reporting LCA. Moreover, there is a lack of user-friendly open-source implementations. Based on contemporary academic discourse, this paper introduces recommendations for LCA which are summarized in the SMART-LCA checklist: Standards for More Accuracy in Reporting of different Types of Latent Class Analysis. The free open-source R-package package tidySEM implements the practices recommended here. It is easy for beginners to adopt thanks to user-friendly wrapper functions, and yet remains relevant for expert users as its models are integrated within the OpenMx structural equation modeling framework and remain fully customizable. The Appendices and tidySEM package vignettes include tutorial examples of common applications of LCA.

Estimating fractal dimensions: A comparative review and open source implementations.

The fractal dimension is a central quantity in nonlinear dynamics and can be estimated via several different numerical techniques. In this review paper, we present a self-contained and comprehensive introduction to the fractal dimension. We collect and present various numerical estimators and focus on the three most promising ones: generalized entropy, correlation sum, and extreme value theory. We then perform an extensive quantitative evaluation of these estimators, comparing their performance and precision using different datasets and comparing the impact of features like length, noise, embedding dimension, and falsify-ability, among many others. Our analysis shows that for synthetic noiseless data, the correlation sum is the best estimator with extreme value theory following closely. For real experimental data, we found the correlation sum to be more strongly affected by noise vs the entropy and extreme value theory. The recent extreme value theory estimator seems powerful as it has some of the advantages of both alternative methods. However, using four different ways for checking for significance, we found that the method yielded "significant" low-dimensional results for inappropriate data like stock market timeseries. This fact, combined with some ambiguities we found in the literature of the method applications, has implications for both previous and future real-world applications using the extreme value theory approach, as, for example, the argument for small effective dimensionality in the data cannot come from the method itself. All algorithms discussed are implemented as performant and easy to use open source code via the DynamicalSystems.jl library.