Open-source Framework Research Articles

Dynamic Modeling of a Thermochemical System for Solar Fuel Production Based on an Open-Source Framework

This paper describes the dynamic process model for a solar fuels synthesis plant currently being built in Germany. The open-source, equation-based modelling language Modelica is used as the foundation. In this plant, biogas and steam are reformed, producing synthesis gas and subsequently turned into synthetic crude oil. This work contains the necessary model setups for the thermal energy storage, solar-absorbing gas receiver and reforming reactor to consider the thermohydraulic, thermochemical and radiative interactions occurring in the process. The remaining infrastructure is modeled with the Modelica Standard Library. A way to fit these models with experimental data for validation is also outlined.

Transport and clogging dynamics of flexible rods in pore constrictions.

The transport and clogging behavior of flexible particles in confined flows is a complex interplay between elastic and hydrodynamic forces and wall interactions. While the motion of non-spherical particles in unbounded flows is well understood, their behavior in confined spaces remains less explored. This study introduces a coupled computational fluid dynamics-discrete element method (CFD-DEM) approach to investigate the transport and clogging dynamics of flexible rod-shaped particles in confined pore constrictions. The spatio-temporal analysis reveals the influence of the rod's initial conditions and flexibility on its transport dynamics through a pore constriction. The simulation results demonstrate an increase in the lateral drift of the rod upon exiting the pore that can be scaled with channel height confinement. The clogging dynamics are explored based on hydrodynamic and mechanical forces, unveiling conditions for mechanical clogging through sieving. The developed method allows for the deconvolution of the forces that contribute to particle trajectories in confined flow, which is highly relevant in particle separation processes, fibrous-shaped virus filtration, biological flows, and related applications. The method is embedded into the open-source CFDEM framework, facilitating future extensions to explore multiple particle dynamics, intermolecular forces, external influences, and complex geometries.

GraphiQ: Quantum circuit design for photonic graph states

GraphiQ is a versatile open-source framework for designing photonic graph state generation schemes, with a particular emphasis on photon-emitter hybrid circuits. Built in Python, GraphiQ consists of a suite of design tools, including multiple simulation backends and optimization methods. The library supports scheme optimization in the presence of circuit imperfections, as well as user-defined optimization goals. Our framework thus represents a valuable tool for the development of practical schemes adhering to experimentally-relevant constraints. As graph states are a key resource for measurement-based quantum computing, all-photonic quantum repeaters, and robust quantum metrology, among others, we envision GraphiQ's broad impact for advancing quantum technologies.

Open-Source Data Formalization through Model-Based Systems Engineering for Concurrent Preliminary Design of CubeSats

Market trends in the space sector suggest a notable increase in satellite operations and market value for the coming decade. In parallel, there has been a shift in the industrial and academic sectors from traditional Document-Based System Engineering to Model-based systems engineering (MBSE) combined with Concurrent engineering (CE) practices. Due to growing demands, the drivers behind this change have been the need for quicker and more cost-effective design processes. A key challenge in this transition remains to determine how to effectively formalize and exchange data during all design stages and across all discipline-specific tools; as representing systems through models can be a complex endeavor. For instance, during the Preliminary design (PD) phase, the integration of system models with external mathematical models for simulations, analyses, and system budgeting is crucial. The introduction of CubeSats and their standard has partly addressed the question of standardization and has aided in reducing overall development time and costs in the space sector. Nevertheless, questions about how to successfully exchange data endure. This paper focuses on formalizing a CubeSat model for use across various stages of the PD phase. The entire process is conducted with the exclusive use of open-source tools, to facilitate the transparency of data integration across the PD phases, and the overall life cycle of a CubeSat. The paper has two primary outcomes: (i) developing a generic CubeSat model using Systems modeling language (SysML) that includes data storage and visualization through the application of Unified modeling language (UML) stereotypes, streamlining in parallel information exchange for integration with various simulation and analysis tools; (ii) creating an end-to-end use case scenario within the Nanostar software suite (NSS), an open-source framework designed to streamline data exchange across different software during CE sessions. A case study from a theoretical academic space mission concept is presented as the illustration of how to utilize the proposed formalization, and it serves as well as a preliminary validation of the proposed formalization. The proposed formalization positions the CubeSat SysML model as the central data source throughout the design process. It also supports automated trade-off analyses by combining the benefits of SysML with effective data instantiating across all PD study phases.

Bayesian Optimization for Anything (BOA): An open-source framework for accessible, user-friendly Bayesian optimization

We introduce Bayesian Optimization for Anything (BOA), a high-level Bayesian Optimization (BO) framework and model wrapping toolkit, which presents a novel approach to simplifying BO, with the goal of making it more accessible and user-friendly, particularly for those with limited expertise in the field. BOA addresses common barriers in implementing BO, focusing on ease of use, reducing the need for deep domain knowledge, and cutting down on extensive coding requirements. A notable feature of BOA is its language-agnostic architecture, which facilitates broader application in various fields and to a wider audience. We showcase BOA's application through three examples: a high-dimensional optimization with 184 parameters of the SWAT + watershed model, a highly parallelized optimization of this intrinsically non-parallel model, and a multi-objective optimization of the FETCH Tree-Crown Hydrodynamics model. These test cases illustrate BOA's effectiveness in addressing complex optimization challenges in diverse scenarios.

Inferring histology-associated gene expression gradients in spatial transcriptomic studies

Spatially resolved transcriptomics has revolutionized RNA studies by aligning RNA abundance with tissue structure, enabling direct comparisons between histology and gene expression. Traditional approaches to identifying signature genes often involve preliminary data grouping, which can overlook subtle expression patterns in complex tissues. We present Spatial Gradient Screening, an algorithm which facilitates the supervised detection of histology-associated gene expression patterns without prior data grouping. Utilizing spatial transcriptomic data along with single-cell deconvolution from injured mouse cortex, and TCR-seq data from brain tumors, we compare our methodology to standard differential gene expression analysis. Our findings illustrate both the advantages and limitations of cluster-free detection of gene expression, offering more profound insights into the spatial architecture of transcriptomes. The algorithm is embedded in SPATA2, an open-source framework written in R, which provides a comprehensive set of tools for investigating gene expression within tissue.

Benchmark of mixed-integer linear programming formulations for district heating network design

Optimal routing and investment decisions are key design criteria to reduce the high investment costs of district heating systems. However, these optimization problems have prohibitively high computational costs for large districts. Four different mixed-integer linear optimization frameworks are benchmarked in this study in order to compare their computational scaling. The frameworks exhibit significant differences in solving times for synthetic benchmarks and real-world urban districts of up to 9587 potential edges. The new open-source framework topotherm, developed for this work, exhibits the best computational performance when only one time step is optimized. The comparison between the models Résimont, DHmin, DHNx, and topotherm shows two main trends. First, fewer integer variables do not necessarily translate to lower solving times, and second, using redundant binary variables, which introduce symmetries into the constraints, leads to higher solving times. None of the considered optimization frameworks is able to solve the largest benchmark problems for five time steps within the allowed time limit and tolerance. These findings highlight the challenges of and pressing need to develop efficient models for simultaneous optimization of district heating network topology, pipe sizing, and operation.

A tutorial on open-source large language models for behavioral science.

Large language models (LLMs) have the potential to revolutionize behavioral science by accelerating and improving the research cycle, from conceptualization to data analysis. Unlike closed-source solutions, open-source frameworks for LLMs can enable transparency, reproducibility, and adherence to data protection standards, which gives them a crucial advantage for use in behavioral science. To help researchers harness the promise of LLMs, this tutorial offers a primer on the open-source Hugging Face ecosystem and demonstrates several applications that advance conceptual and empirical work in behavioral science, including feature extraction, fine-tuning of models for prediction, and generation of behavioral responses. Executable code is made available at github.com/Zak-Hussain/LLM4BeSci.git . Finally, the tutorial discusses challenges faced by research with (open-source) LLMs related to interpretability and safety and offers a perspective on future research at the intersection of language modeling and behavioral science.

From Lustre to Graphical Models and SCCharts

We introduce a systematic approach for automatically creating a visual diagram, akin to the graphical Safety Critical Application Development Environment (SCADE) model, from a Lustre program. This not only saves tedious manual drawing effort but also enables modeling software to automatically provide the developer with different meaningful views for the same program. We also extend the Sequentially Constructive Charts (SCCharts) language with data-flow constructs that adhere to the Lustre semantics, which permits a translation from Lustre to graphical SCCharts. This allows using the SCCharts code generation, simulation, and visualization tooling also for Lustre programs, in addition to the already existing Lustre compilation techniques. Furthermore, we investigate how the sequentially constructive model of computation, used in SCCharts and other synchronous languages, can be used to conservatively extend Lustre. We have implemented and validated this work with the Eclipse-based open-source Kiel Integrated Environment for Layout Eclipse Rich Client (KIELER) framework.

Galaxy Helm chart: a standardized method for deploying production Galaxy servers.

The Galaxy application is a popular open-source framework for data intensive sciences, counting thousands of monthly users across more than 100 public servers. To support a growing number of users and a greater variety of use cases, the complexity of a production-grade Galaxy installation has also grown, requiring more administration effort. There is a need for a rapid and reproducible Galaxy deployment method that can be maintained at high-availability with minimal maintenance. We describe the Galaxy Helm chart that codifies all elements of a production-grade Galaxy installation into a single package. Deployable on Kubernetes clusters, the chart encapsulates supporting software services and implements the best-practices model for running Galaxy. It is also the most rapid method available for deploying a scalable, production-grade Galaxy instance on one's own infrastructure. The chart is highly configurable, allowing systems administrators to swap dependent services if desired. Notable uses of the chart include on-demand, fully-automated deployments on AnVIL, providing training infrastructure for the Bioconductor project, and as the AWS-recommended solution for running Galaxy on the Amazon cloud. The source code for Galaxy Helm is available at https://github.com/galaxyproject/galaxy-helm, the corresponding Helm package at https://github.com/CloudVE/helm-charts, and the required Galaxy container image https://github.com/galaxyproject/galaxy-docker-k8s.

The Design and Real-Time Optimization of an EtherCAT Master for Multi-Axis Motion Control

To address the issues of low bandwidth, weak real-time performance, and poor synchronization in traditional fieldbuses for multi-axis motion control, a solution for the implementation of an EtherCAT master based on the IgH EtherCAT Master open-source software framework and an embedded hardware platform is proposed. On a hardware platform centered around the AM64x Sitara processor, a Linux real-time operating system based on the Xenomai real-time kernel is constructed, and the IgH master framework is ported to realize a high-performance EtherCAT master. The configuration process of the EtherCAT bus is detailed, a master application program is developed, and methods for the real-time performance optimization of the master—such as exclusive CPU usage by the master process and the optimization of the network card driver—are proposed. Finally, experiments are conducted on a six-axis servo control platform, with the packet analysis of the periodic EtherCAT data frames sent by the master. The experimental results show that the optimized master, under a high-speed communication cycle of 500 microseconds, maintains maximum jitter within 20 microseconds and average jitter within 1 microsecond, meeting the requirements for high-precision multi-axis motion control.

Tc-llama 2: fine-tuning LLM for technology and commercialization applications

This paper introduces TC-Llama 2, a novel application of large language models (LLMs) in the technology-commercialization field. Traditional methods in this field, reliant on statistical learning and expert knowledge, often face challenges in processing the complex and diverse nature of technology-commercialization data. TC-Llama 2 addresses these limitations by utilizing the advanced generalization capabilities of LLMs, specifically adapting them to this intricate domain. Our model, based on the open-source LLM framework, Llama 2, is customized through instruction tuning using bilingual Korean-English datasets. Our approach involves transforming technology-commercialization data into formats compatible with LLMs, enabling the model to learn detailed technological knowledge and product hierarchies effectively. We introduce a unique model evaluation strategy, leveraging new matching and generation tasks to verify the alignment of the technology-commercialization relationship in TC-Llama 2. Our results, derived from refining task-specific instructions for inference, provide valuable insights into customizing language models for specific sectors, potentially leading to new applications in technology categorization, utilization, and predictive product development.

Automated Extraction of Patient-Centered Outcomes After Breast Cancer Treatment: An Open-Source Large Language Model-Based Toolkit.

Patient-centered outcomes (PCOs) are pivotal in cancer treatment, as they directly reflect patients' quality of life. Although multiple studies suggest that factors affecting breast cancer-related morbidity and survival are influenced by treatment side effects and adherence to long-term treatment, such data are generally only available on a smaller scale or from a single center. The primary challenge with collecting these data is that the outcomes are captured as free text in clinical narratives written by clinicians. Given the complexity of PCO documentation in these narratives, computerized methods are necessary to unlock the wealth of information buried in unstructured text notes that often document PCOs. Inspired by the success of large language models (LLMs), we examined the adaptability of three LLMs, GPT-2, BioGPT, and PMC-LLaMA, on PCO tasks across three institutions, Mayo Clinic, Emory University Hospital, and Stanford University. We developed an open-source framework for fine-tuning LLM that can directly extract the five different categories of PCO from the clinic notes. We found that these LLMs without fine-tuning (zero-shot) struggle with challenging PCO extraction tasks, displaying almost random performance, even with some task-specific examples (few-shot learning). The performance of our fine-tuned, task-specific models is notably superior compared with their non-fine-tuned LLM models. Moreover, the fine-tuned GPT-2 model has demonstrated a significantly better performance than the other two larger LLMs. Our discovery indicates that although LLMs serve as effective general-purpose models for tasks across various domains, they require fine-tuning when applied to the clinician domain. Our proposed approach has the potential to lead more efficient, adaptable models for PCO information extraction, reducing reliance on extensive computational resources while still delivering superior performance for specific tasks.

Embedded FPGA developments in 130 nm and 28 nm CMOS for machine learning in particle detector readout

Embedded field programmable gate array (eFPGA) technology allows the implementation of reconfigurable logic within the design of an application-specific integrated circuit (ASIC). This approach offers the low power and efficiency of an ASIC along with the ease of FPGA configuration, particularly beneficial for the use case of machine learning in the data pipeline of next-generation collider experiments. An open-source framework called “FABulous” was used to design eFPGAs using 130 nm and 28 nm CMOS technology nodes, which were subsequently fabricated and verified through testing. The capability of an eFPGA to act as a front-end readout chip was assessed using simulation of high energy particles passing through a silicon pixel sensor. A machine learning-based classifier, designed for reduction of sensor data at the source, was synthesized and configured onto the eFPGA. A successful proof-of-concept was demonstrated through reproduction of the expected algorithm result on the eFPGA with perfect accuracy. Further development of the eFPGA technology and its application to collider detector readout is discussed.

ViewR: Architectural-Scale Multi-User Mixed Reality With Mobile Head-Mounted Displays.

The emergence of mobile head-mounted displays with robust "inside-out" markerless tracking and video-passthrough permits the creation of novel mixed reality (MR) experiences in which architectural spaces of arbitrary size can be transformed into immersive multi-user visualisation arenas. Here we outline ViewR, an open-source framework for rapidly constructing and deploying architectural-scale multi-user MR experiences. ViewR includes tools for rapid alignment of real and virtual worlds, tracking loss detection and recovery, user trajectory visualisation and world state synchronisation between users with persistence across sessions. ViewR also provides control over the blending of the real and the virtual, specification of site-specific blending zones, and video-passthrough avatars, allowing users to see and interact with one another directly. Using ViewR, we explore the transformation of large architectural structures into immersive arenas by creating a range of experiences in various locations, with a particular focus on architectural affordances such as mezzanines, stairs, gangways and elevators. Our tests reveal that ViewR allows for experiences that would not be possible with pure virtual reality, and indicate that, with certain strategies for recovering from tracking errors, it is possible to construct large scale multi-user MR experiences using contemporary consumer virtual reality head-mounted displays.

Creating spatially complete zoning maps using machine learning

Zoning regulates land use and intensity of urban development at the county and municipal level in the United States, promoting economic growth, community health, and environmental preservation. However, limited availability of zoning data at scale hinders regional assessments of regulations and coordinated resilience planning efforts. In this study, we developed an open-source, replicable, and transferable framework to predict spatially complete zoning in areas where zoning information is publicly unavailable. We applied a Hierarchical Random Forest algorithm to predict multilevel zoning districts, including three core districts (residential, non-residential, mixed use) and 13 sub-districts. To mimic real-world data accessibility challenges, we evaluated two models: one filling gaps within a county (within-county) and the other extrapolating for counties with no available data (between-county). We tested our models statewide in North Carolina (NC), USA, and developed the State's first comprehensive zoning map. We found strong predictive performance for our within-county model (∼99% accuracy; macro averaged F1 score of ∼0.97) irrespective of district breakdown (i.e., core and sub). However, our between-county model performance was lower and varied depending on the training counties sampled and the district breakdown considered (19–90% accuracy; macro averaged F1 score of 0.105–0.451). Our framework provides spatially complete zoning maps for previously inaccessible locations, enabling researchers and planners to conduct large-scale comprehensive zoning assessments.

The MEMENTO code for modeling of macroscopic melt motion in fusion devices

The MEMENTO (MEtallic Melt Evolution in Next-step TOkamaks) code is a new numerical implementation of the physics model originally developed for the MEMOS-U code with the objective to self-consistently describe the generation of melt and its subsequent large scale dynamics in fusion devices and to assess the damage of metallic reactor armor under powerful normal and off-normal plasma events. The model has been validated in multiple dedicated EUROfusion experiments. MEMENTO solves the heat and phase transfer problem coupled with the incompressible Navier–Stokes equations in the shallow water approximation for the thin liquid film over the solid metal and with the current propagation equations on a domain that features a time-evolving deforming metal-plasma interface. The code utilizes non-uniform and adaptive meshing along with sub-cycling in time facilitated by the AMReX open-source framework as well as AMReX’s built-in parallelization capabilities.

Trame: an Open Source Framework for Efficiently Building Interactive Visualization and Analysis Applications

trame: an Open Source Framework for Efficiently Building Interactive Visualization and Analysis Applications

FetMRQC: A robust quality control system for multi-centric fetal brain MRI

Fetal brain MRI is becoming an increasingly relevant complement to neurosonography for perinatal diagnosis, allowing fundamental insights into fetal brain development throughout gestation. However, uncontrolled fetal motion and heterogeneity in acquisition protocols lead to data of variable quality, potentially biasing the outcome of subsequent studies. We present FetMRQC, an open-source machine-learning framework for automated image quality assessment and quality control that is robust to domain shifts induced by the heterogeneity of clinical data. FetMRQC extracts an ensemble of quality metrics from unprocessed anatomical MRI and combines them to predict experts’ ratings using random forests. We validate our framework on a pioneeringly large and diverse dataset of more than 1600 manually rated fetal brain T2-weighted images from four clinical centers and 13 different scanners. Our study shows that FetMRQC’s predictions generalize well to unseen data while being interpretable. FetMRQC is a step towards more robust fetal brain neuroimaging, which has the potential to shed new insights on the developing human brain.

Biophysical essentials – A full stack open-source software framework for conserved and advanced analysis of patch-clamp recordings

Background and ObjectivesPatch-Clamp recordings allow for in depth electrophysiological characterization of single cells, their general biophysical properties as well as characteristics of voltage- and ligand-gated ionic currents. Different acquisition modes, such as whole-cell patch-clamp recordings in the current or voltage clamp configuration, capacitance measurements or single channel recordings from cultured cells as well as acute brain slices are routinely performed for these purposes. Nevertheless, multipurpose transparent and adaptable software tools to perform reproducible state-of-the-art analysis of multiple experiment types and to manage larger sets of experimental data are currently unavailable. MethodsBiophysical Essentials (BPE) was developed as an open-source full stack python software for transparent and reproducible analysis of electrophysiological recordings. For validation, BPE results were compared with manually analyzed single-cell patch-clamp data acquired from a human in vitro nociceptor-model and mouse dorsal root ganglia neurons. ResultsWhile initially designed to improve time consuming and repetitive analysis steps, BPE was further optimized as a technical software solution for entire workflow processing including data acquisition, data preprocessing, normalization and visualization and of single recordings up to stacked calculations and statistics of multiple experiments. BPE can operate with different file formats from different amplifier systems and producers. An in-process database logs all analysis steps reproducible review and serves as a central storage point for recordings. Statistical testing as well as advanced analysis functions like Boltzmann-fitting and dimensional reduction methods further support the researchers' needs in projects involving electrophysiology techniques. ConclusionsBPE extends beyond available patch-clamp specific, open source – and commercial analysis tools in particular because of reproducible and sharable analysis workflows. BPE enables full analysis from raw data acquisition to publication ready result visualizations – all within one single program. Thereby, BPE significantly enhances transparency in the analytical process of patch-clamp data analysis. BPEs function scope is completely accessible through an easy-to-use graphical user interface eliminating the need for programing language proficiency as required by many community patch-clamp analysis frameworks and algorithms.