Open Source Software Research Articles

Paradoxes of Openness: Trans Experiences in Open Source Software

In recent years, concerns have increased over the lack of contributor diversity in open source software (OSS), despite its status as a paragon of open collaboration. OSS is an important form of digital infrastructure and part of a career path for many developers. While there exists a growing body of literature on cisgender women's under-representation in OSS, the experiences of contributors from other marginalized groups are comparatively absent from the literature. Such is the case for trans contributors, a historically influential group in OSS. In this study, we interviewed 21 trans participants to understand and represent their experiences in the OSS literature. From their experiences, we theorize two related paradoxes of openness in OSS: the paradox of openness and display and the paradox of openness and governance. In an increasingly violent world for trans people, we draw on our theorizing to build recommendations for more inclusive and safer OSS projects for contributors.

Development of an ultra-low-cost wireless data acquisition system for monitoring strain and deflection

Strain gauges are gaining popularity for monitoring stress and strain within structural elements and for detecting and identifying structural damage. Although they are relatively inexpensive, they are not as widely used as accelerometers in structural health monitoring. This is due to several factors, such as the high cost of data acquisition systems required to collect strain data and the need for a large number of gauges to monitor a structure. Therefore, this research aims to develop a low-cost wireless strain gauge data acquisition system with acceptable accuracy and a high sampling rate. This system is composed of commonly available electronic components and open-source software, with a cost of $20. The system’s precision in measuring displacement was assessed in a laboratory experiment, and further experiments were conducted on an aluminum beam to investigate its response under both static and dynamic conditions. Additionally, the accuracy of the system for measuring deflection and strain in reinforced concrete elements was assessed through a bending test on a reinforced concrete beam. The findings indicated that the developed data acquisition system can accurately measure displacement, with a root mean square error of 0.1 mm, as well as strain values within the range of a few micro-strains.

A Streamlined Workflow for Microscopy-Driven MALDI Imaging Mass Spectrometry Data Collection.

Matrix-assisted laser desorption/ionization imaging mass spectrometry (MALDI IMS) is a rapidly advancing technology for biomedical research. As spatial resolution increases, however, so do acquisition time, file size, and experimental cost, which increases the need to perform precise sampling of targeted tissue regions to optimize the biological information gleaned from an experiment and minimize wasted resources. The ability to define instrument measurement regions based on key tissue features and automatically sample these specific regions of interest (ROIs) addresses this challenge. Herein, we demonstrate a workflow using standard software that allows for direct sampling of microscopy-defined regions by MALDI IMS. Three case studies are included, highlighting different methods for defining features from common sample types─manual annotation of vasculature in human brain tissue, automated segmentation of renal functional tissue units across whole slide images using custom segmentation algorithms, and automated segmentation of dispersed HeLa cells using open-source software. Each case minimizes data acquisition from unnecessary sample regions and dramatically increases throughput while uncovering molecular heterogeneity within targeted ROIs. This workflow provides an approachable method for spatially targeted MALDI IMS driven by microscopy as part of multimodal molecular imaging studies.

"I inherently just trust that it works": Investigating Mental Models of Open-Source Libraries for Differential Privacy

Differential privacy (DP) is a promising framework for privacy-preserving data science, but recent studies have exposed challenges in bringing this theoretical framework for privacy into practice. These tensions are particularly salient in the context of open-source software libraries for DP data analysis, which are emerging tools to help data stewards and analysts build privacy-preserving data pipelines for their applications. While there has been significant investment into such libraries, we need further inquiry into the role of these libraries in promoting understanding of and trust in DP, and in turn, the ways in which design of these open-source libraries can shed light on the challenges of creating trustworthy data infrastructures in practice. In this study, we use qualitative methods and mental models approaches to analyze the differences between conceptual models used to design open-source DP libraries and mental models of DP held by users. Through a two-stage study design involving formative interviews with 5 developers of open-source DP libraries and user studies with 17 data analysts, we find that DP libraries often struggle to bridge the gaps between developer and user mental models. In particular, we highlight the tension DP libraries face in maintaining rigorous DP implementations and facilitating user interaction. We conclude by offering practical recommendations for further development of DP libraries.

C Language Teaching Platform Design Based on Blockly Open-Source Software

C Language Teaching Platform Design Based on Blockly Open-Source Software

Lightweight aircraft design: Integrating biomechanics and algorithm optimization for drone construction using paper materials

Lightweight aircraft design emphasizes materials like composites, alloys, and advanced polymers to reduce weight while ensuring structural integrity. Streamlined aerodynamics, efficient propulsion systems, and optimized component layout further enhance performance. The purpose of this research is to develop an innovative aircraft design model for drones, integrating algorithm optimization techniques and lightweight materials to enhance the performance and efficiency of drones with the utilization of open-source software. We apply this structure to the construction of drones with fixed-wing (FW) and vertical take-off and landing (VTOL) configurations. The aircraft’s body is constructed using lightweight materials such as glass fiber fabric (Gff) and extruded polystyrene foam (XPS), employing a vacuum-assisted wet layup technique for fabrication. Multi-objective Co-evolving Ant Colony Optimization (MC-ACO) was used to improve the construction of drones with VTOL and FW capabilities. This strategy enabled it to be easier to achieve the trade-offs required for a generalist drone design, such as range, payload capabilities, and swarm operations, which were then evaluated against commercially available software to evaluate the effectiveness. Incorporating biomechanics into the design method permits higher consideration of user interaction with drones. The findings indicate that low-fidelity architecture is a suitable starting point for prototyping under limited time frames. The paper concludes with a discussion of the technical constraints of employing free software, as well as some practical concerns for flight testing drones with hybrid configurations.

The efficacy and safety of ketorolac for postoperative pain management in lumbar spine surgery: a meta-analysis of randomized controlled trials.

Ketorolac is widely utilized for postoperative pain management, including back pain after lumbar spinal surgery. Several trials have assessed the efficacy of Ketorolac alone and in combination with other analgesics such as bupivacaine, morphine, epinephrine, paracetamol, and pregabalin. However, the effects and safety profile of ketorolac in these contexts remain controversial. We conducted a systematic review and meta-analysis of randomized controlled trials (RCTs) to evaluate the efficacy and safety of Ketorolac administration, both as a monotherapy and in combination with other analgesics, for managing postoperative pain in adults undergoing lumbar spinal surgery. We searched PubMed, EMbase, Web of Science, EBSCO, CNKI, WanFang, VIP, and Cochrane library databases through July 2024 for randomized controlled trials (RCTs) assessing the analgesic efficacy of Ketorolac administration for postoperative pain of lumbar surgery. The meta-analysis was conducted following the Preferred Reporting Items for Systematic reviews and Meta-Analyses (PRISMA) statements. Data were extracted and analyzed using open-source meta-analysis software OpenMeta-Analyst, focusing on outcomes such as VAS pain scores, postoperative morphine requirements (PMR), length of hospital stay (LOS), and adverse effects, such as nausea, vomiting, pruritus, and constipation. The quality of evidence was assessed using the Jada scale. Thirteen RCTs comprising a total of 938 patients were included. The methodological quality of the studies was high, with three studies scoring 5, six studies scoring 4, and four studies scoring 3 on the Jadad scale. Ketorolac significantly reduced pain compared to controls at 0-6h, with a mean difference (MD) of - 1.42 (95% CI: - 2.03 to - 0.80; P < 0.0001), exceeding the Minimal Clinically Important Difference (MCID) of 1.2 to 2.0 points on the Visual Analog Scale (VAS), indicating clinically meaningful pain relief. During the 6-12-h period, the pain reduction was significant (MD = - 0.58; 95% CI: - 0.80 to - 0.35; P < 0.0001), though below the MCID threshold. In the 12-24-h period, Ketorolac continued to show significant pain reduction (MD = - 0.48; 95% CI: - 0.68 to - 0.28; P < 0.0001), but this reduction was also below the MCID. Heterogeneity was low in the 12-24-h period (I2 = 13%), indicating consistent results across studies. There was a significant reduction in PMR (SMD = - 1.83; 95% CI = - 3.42 to - 0.23; P < 0.0001), although with considerable heterogeneity among the studies (I2 = 93%, heterogeneity P < 0.01). Ketorolac administration also significantly reduced the LOS compared to controls (MD = - 0.45days; 95% CI = - 0.74 to - 0.16; P = 0.0001), though this reduction, which is less than a full day (0.45days), may have limited clinical significance. The findings suggest that Ketorolac effectively reduces pain and opioid use postoperatively, supporting its role in multimodal analgesia for lumbar spinal surgery. The significant reduction in PMR indicates a beneficial opioid-sparing effect, crucial in the context of reducing opioid-related complications. The observed reduction in LOS, while statistically significant, may not translate into substantial clinical benefit due to its limited magnitude. No significant increase in common adverse effects was noted, indicating Ketorolac's safety profile. Ketorolac administration, either alone or in combination with other analgesics, effectively reduces postoperative pain and opioid consumption in adults following lumbar spinal surgery. And Ketorolac did not significantly increase the incidence of postoperative nausea and vomiting relative to other analgesics or placebos. While it also decreases LOS, the clinical relevance of this reduction is modest. However, the variability in study designs, dosages, and combination therapies contribute to significant heterogeneity in outcomes. Future research should focus on standardizing protocols and exploring optimal dosing strategies. Additionally, long-term safety and effectiveness studies are needed to better understand Ketorolac's role in postoperative pain management.

Konnektor: A Framework for Using Graph Theory to Plan Networks for Free Energy Calculations.

Alchemical free energy campaigns can be planned using graph theory by building networks that contain nodes representing molecules that are connected by possible transformations as edges. We introduce Konnektor, an open-source Python package, for systematically planning, modifying, and analyzing free energy calculation networks. Konnektor is designed to aid in the drug discovery process by enabling users to easily setup free energy campaigns using complex graph manipulation methods. The package contains functions for network operations including concatenation of networks, deletion of transformations, and clustering of molecules along with a framework for combining these tools with existing network generation algorithms to enable the development of more complex methods for network generation. A comparison of the various network layout features offered is carried out using toy data sets. Additionally, Konnektor contains visualization and analysis tools, making the investigation of network features much simpler. Besides the content of the package, the paper also offers application examples, demonstrating how Konnektor can be used and how the different networks perform from a graph theory perspective. Konnektor is freely available via GitHub at https://github.com/OpenFreeEnergy/konnektor under the permissive MIT License.

Anisotropic induced polarization modeling with neural networks and effective medium theory

Interpreting three-dimensional induced polarization (IP) data requires rock physics models that reflect the omnipresent anisotropy of the Earth’s crust. The Generalized Effective Medium Theory of Induced Polarization (GEMTIP) can model the IP signatures of rocks with polarizable mineral inclusions. However, it is computationally demanding because it requires numerically solving the depolarization tensors of each inclusion. We aim to streamline GEMTIP simulations by (1) integrating anisotropic background conductivity and triaxial ellipsoidal inclusions in the model and (2) estimating the depolarization tensors with a neural network. We validate the neural network predictions against known solutions for spherical and spheroidal inclusions, and we test our method using data from an actual rock sample. The neural network shows a relative sensitivity of 56% to inclusion shape and 44% to host rock anisotropy and is up to 100,000 times faster than numerical integration. We release the pre-trained neural network implementation as an open-source Python package, thereby providing a new method to interpret the IP signatures of anisotropic rocks.

Measurable morphological features of single circulating tumor cells in selected solid tumors-A pilot study.

Liquid biopsies developed into a range of sensitive technologies aiming to analyze for example, circulating tumor cells (CTCs) in peripheral blood, which significantly deepens understanding of the metastatic process. Nevertheless, examination of CTCs is mostly limited to their enumeration and usually only 2-3 markers-based phenotyping, not offering yet sufficient insight into their biology. In contrast, quantitative analysis of their morphological details might extend our knowledge about dissemination and even improve CTC isolation or label-free identification methods dependent on their physical features such as size, and deformability. Current study was conducted to describe CTCs' and their size, shape, presence of protrusions, and micronuclei across various types of cancers (lung, n = 29; ovarian, n = 24, breast, n = 54; and prostate, n = 33). Epithelial (pan-keratins), mesenchymal (vimentin), and two exclusion markers were used to identify CTCs and classify them into four epithelial and epithelial-mesenchymal transition-related phenotypes using standardized and throughput method, imaging flow cytometry. The morphological characteristics of CTCs, including their nuclei, such as circularity, the maximum, and minimum diagonal values were determined using an open-source software QuPath. On average, detected CTCs (n = 1156) were larger, and more irregular in shape compared to leukocytes/endothelial cells (n = 400). Epithelial and mesenchymal CTCs had the largest (median = 18.2 μm) and the smallest diameter (median = 10.4 μm), respectively. In terms of cancer-specific variations, the largest CTCs were identified in lung cancer, whereas the smallest-in prostate and breast cancers. Epithelial CTCs and those negative for both epithelial and mesenchymal markers exhibited the highest degree of elongation, whereas mesenchymal CTCs were the most irregular in shape. Protrusions and micronuclei were observed extremely rarely within CTCs of breast and prostate cancer (0.6%-0.8% of CTCs). Micronuclei were observed only in epithelial and epithelial-mesenchymal CTCs. This study underscores the significant variability in the morphological features of CTCs in relation to their phenotypic classification or even the particular organ of origin, potentially influencing for example, size-dependent CTC isolation methods. It demonstrates for the first time the morphological measurements of CTCs undergoing epithelial-mesenchymal transition, and some specific morphological details (i.e., protrusions, micronuclei) within CTCs in general.

Video Abstracts in Research.

Video abstracts can be useful in health research. A video abstract provides key messages about a research article and can increase public engagement, spark conversations, and may increase academic attention. A growing number of open source software programs make it easier to develop a video abstract. This viewpoint provides practical tips for creating a video abstract for health research.

AnACor2.0: a GPU-accelerated open-source software package for analytical absorption corrections in X-ray crystallography

Analytical absorption corrections are employed in scaling diffraction data for highly absorbing samples, such as those used in long-wavelength crystallography, where empirical corrections pose a challenge. AnACor2.0 is an accelerated software package developed to calculate analytical absorption corrections. It accomplishes this by ray-tracing the paths of diffracted X-rays through a voxelized 3D model of the sample. Due to the computationally intensive nature of ray-tracing, the calculation of analytical absorption corrections for a given sample can be time consuming. Three experimental datasets (insulin at λ = 3.10 Å, thermolysin at λ = 3.53 Å and thaumatin at λ = 4.13 Å) were processed to investigate the effectiveness of the accelerated methods in AnACor2.0. These methods demonstrated a maximum reduction in execution time of up to 175× compared with previous methods. As a result, the absorption factor calculation for the insulin dataset can now be completed in less than 10 s. These acceleration methods combine sampling, which evaluates subsets of crystal voxels, with modifications to standard ray-tracing. The bisection method is used to find path lengths, reducing the complexity from O(n) to O(log2 n). The gridding method involves calculating a regular grid of diffraction paths and using interpolation to find an absorption correction for a specific reflection. Additionally, optimized and specifically designed CUDA implementations for NVIDIA GPUs are utilized to enhance performance. Evaluation of these methods using simulated and real datasets demonstrates that systematic sampling of the 3D model provides consistently accurate results with minimal variance across different sampling ratios. The mean difference of absorption factors from the full calculation (without sampling) is at most 2%. Additionally, the anomalous peak heights of sulfur atoms in the Fourier map show a mean difference of only 1% compared with the full calculation. This research refines and accelerates the process of analytical absorption corrections, introducing innovative sampling and computational techniques that significantly enhance efficiency while maintaining accurate results.

A study on an efficient OSS inspection scheme based on encrypted GML

The importance of Open Source Software (OSS) has increased in recent years. OSS is software that is jointly developed and maintained globally through open collaboration and knowledge sharing. OSS plays an important role, especially in the Information Technology (IT) field, by increasing the efficiency of software development and reducing costs. However, licensing issues, security issues, etc., may arise when using OSS. Some services analyze source code and provide OSS-related data to solve these problems, a representative example being Blackduck. Blackduck inspects the entiresource code within the project and provides OSS information and related data included in the whole project. Therefore, there are problems such as inefficiency due to full inspection of the source code and difficulty in determining the exact location where OSS is identified. This paper proposes a scheme to intuitively analyze source code through Graph Modelling Language (GML) conversion to solve these problems. Additionally, encryption is applied to GML to performsecure GML-based OSS inspection. The study explains the process of converting source code to GML and performing OSS inspection. Afterward, we compare the capacity and accuracy of text-based OSS inspection and GML-based OSS inspection. Signcryption is applied to performsafe, GML-based, efficient OSS inspection.

Analysis of the Effect of Ferromagnetic Materials on the Flux Density of a Solenoid, Using Open-Source Software and Applications

Analysis of the Effect of Ferromagnetic Materials on the Flux Density of a Solenoid, Using Open-Source Software and Applications

Automated Design Process of a Fixed Wing UAV Maximizing Endurance

Unmanned aerial vehicle (UAV) design necessitates significant effort in prototyping, testing, and design iterations. To reduce design time and improve wing performance, an automated design and optimization framework is proposed utilizing open-source software, including OpenVSP: VSPAERO &amp; Parasite Drag Tool, XFOIL, and Python. This study presents a preliminary UAV wing design methodology, emphasizing weight estimation, drag analysis, stall prediction, and endurance optimization. The maximum takeoff weight of the UAV was calculated after estimating the empty weight using a linear regression from data from 20 existing similar UAVs. The wing and engine sizing were determined using the matching plot technique. A solver with low-fidelity models, combining the Vortex Lattice Method (VLM) and analytical expressions, was used to predict the drag coefficient and maximum lift coefficient of the designed wing. An optimization process using a genetic algorithm was applied to maximize endurance while satisfying requirements such as rate of climb, stall, and maximum speeds. The optimized wing was analyzed with computational fluid dynamics (CFD), and its aerodynamic characteristics were compared with those obtained using VLM and the suggested aerodynamic solver. According to the CFD results, the proposed aerodynamic solver estimated the drag coefficient at zero angle of attack with an error of 17.2% compared to 63.1% using the VLM classic method. The error on the maximum lift coefficient estimation was limited to 5.3%. In terms of optimization, the framework showed an increase in the endurance ratio of up to 2% compared to the Artificial Neural Network method coupled with XFLR5. The primary advantage of the suggested framework is the utilization of open-source software, giving a cost-effective and accessible solution for small and medium-sized startups to design and optimize UAVs to achieve mission objectives.

Simulating breakage by compression of iron ore pellets using the discrete breakage model

Simulation of breakage embedded in the discrete element method (DEM) has evolved significantly in recent years, taking advantage of both greater computing power and novel approaches that have become available in both commercial and open-source packages. This work analyzes in detail the simulation of breakage using the discrete breakage model (DBM) in the commercial software Ansys Rocky. Breakage of iron ore pellets under slow compression was studied. After the selection of suitable contact parameters for the pellets, described as polyhedral particles, the model is used to describe their breakage considering fully resolved fragments from the beginning of the simulation. The effects of contact model, time step and number of elements on the ability of the model to represent average breakage response of iron ore pellets under compression is analyzed. An approach was then used to account for the variability in the breakage characteristics of the pellets, which provided a valid description when applied to another pellet sample, also showing capability to describe the size-scale effect on the breakage energy of pellets when applied to pellets of different sizes.

Unveiling the multifaceted public interest in ChatGPT

The present study was aimed at identifying key topics in online discussions about the use of ChatGPT by examining a large dataset extracted from Reddit social media using natural language processing. A corpus of 159,971 posts about ChatGPT were extracted from a custom-made python-coded Reddit content scraper for posts in the r/ChatGPT subreddit discussions. After cleaning the data, the sample was reduced to 119,853 posts which was subjected to cluster analysis using the open-source IRaMuTeQ software to identify main topics based on the cooccurrence of texts. These clusters were named by a panel of social psychology experts (n=3) by reading typical text segments within each cluster. Four thematic clusters emerged, categorized into two main topics: “Society and AI Integration”, focusing on ethical concerns (32.1%), and “Operational Aspects and Applications”, which delves into technical and practical facets (67.9%). The latter includes clusters like “AI Technical Framework”, “Casual AI Interactions”, and “Human-AI Etiquette”. The Reddit discourse provides a comprehensive understanding of ChatGPT, revealing user priorities like system capabilities and ethical considerations. Notably, the “Human-AI Etiquette” cluster is a new topic less covered in existing literature. The findings underscore the importance of effective prompting for meaningful user engagement with ChatGPT.

Simulating the Cellular Context in Synthetic Datasets for Cryo-Electron Tomography.

Cryo-electron tomography (cryo-ET) allows to visualize the cellular context at macromolecular level. To date, the impossibility of obtaining a reliable ground truth is limiting the application of deep learning-based image processing algorithms in this field. As a consequence, there is a growing demand of realistic synthetic datasets for training deep learning algorithms. In addition, besides assisting the acquisition and interpretation of experimental data, synthetic tomograms are used as reference models for cellular organization analysis from cellular tomograms. Current simulators in cryo-ET focus on reproducing distortions from image acquisition and tomogram reconstruction, however, they can not generate many of the low order features present in cellular tomograms. Here we propose several geometric and organization models to simulate low order cellular structures imaged by cryo-ET. Specifically, clusters of any known cytosolic or membrane-bound macromolecules, membranes with different geometries as well as different filamentous structures such as microtubules or actin-like networks. Moreover, we use parametrizable stochastic models to generate a high diversity of geometries and organizations to simulate representative and generalized datasets, including very crowded environments like those observed in native cells. These models have been implemented in a multiplatform open-source Python package, including scripts to generate cryo-tomograms with adjustable sizes and resolutions. In addition, these scripts provide also distortion-free density maps besides the ground truth in different file formats for efficient access and advanced visualization. We show that such a realistic synthetic dataset can be readily used to train generalizable deep learning algorithms.

A micropolar phase-field model for size-dependent electro-mechanical fracture

This article proposes a micropolar phase-field model for size-dependent brittle fracture in solids under electro-mechanical loading conditions. Considering displacement, micro-rotation, electric potential, and phase-field variable as the kinematic descriptors and employing the virtual power principle, we derive a set of coupled governing partial differential equations (PDEs) for size-dependent solids. Invoking the first and second laws of thermodynamics, we determine the constitutive relations for the thermodynamic fluxes. Carrying out the finite element implementation of the derived governing PDEs using the open-source Gridap package in Julia, we demonstrate the efficacy of the proposed phase-field model through a few representative numerical examples. Especially the importance of the proposed model in incorporating the effect of relative rotation, i.e., the difference between macro- and micro-rotation, on the response of solids under electro-mechanical loading is shown that may not be possible with the existing non-local models such as strain-gradient or couple-stress approaches. To capture the experimentally observed size effects in solids under electro-mechanical loading, the proposed model does not demand higher-order continuity of finite element shape functions, unlike a typically used strain gradient model. To demonstrate the efficacy of the proposed model, we have compared our results against demanding experimental and numerical benchmark results available in the literature. We provide a parametric study to unravel the effect of different micropolar material parameters on the electro-mechanical response of a brittle solid. Interestingly, the proposed micropolar model is less sensitive to the phase-field length scale than the conventional non-polar phase-field models.

Automated identification of soil functional components based on NanoSIMS data

NanoSIMS technique allows to investigate the micro-spatial organization in complex structures in multiple scientific fields such as material science, cosmochemistry, and biogeochemistry. In soil biogeochemistry applications, NanoSIMS-based approaches aim to disentangle the interactions of organic matter (OM) and mineral phases in the heterogeneous soil microstructure. Investigating the spatial arrangement of distinct organic and mineral functional components is necessary to understand how these components interact and contribute to biogeochemical processes in soil systems. Identifying soil functional components within NanoSIMS measurements necessitates advanced and efficient data processing tools capable of accessibility and automation. We have developed a pre-processing tool to streamline NanoSIMS data preparation and handling. The tool is provided as an open-source software toolbox (NanoT). In addition, a two-step unsupervised segmentation method was developed to identify soil functional components based on NanoSIMS analyses. To illustrate the segmentation method, here we describe its application to two exemplary NanoSIMS measurements. This allows to distinguish mineral- and OM-dominated regions, as well as different mineral phases. To improve the detection of iron oxides and aluminosilicates, the 56Fe16O− channel was separately processed. The presented NanoSIMS-based processing workflow helps to disentangle functional components within a biogeochemically-diverse microstructure in soils and further warrants applications to a wide range of complex environmental samples.