Open Source Software Research Articles

Advancing open education through open-source software: examining UTAUT 2 factors in adoption and implementation

PurposeThis study aims to examine the factors influencing university students' adoption of open-source software (OSS) within the context of open educational practices (OEP) by applying an extended Unified Theory of Acceptance and Use of Technology (UTAUT 2) model.Design/methodology/approachThe research employs a quantitative approach, gathering data from 156 students at Hong Kong Metropolitan University through an online survey. The survey was designed to test nine hypotheses derived from the UTAUT 2 model, incorporating additional constructs relevant to OSS. Structural equation modelling (SEM) was used to analyse the data and test the relationships between constructs.FindingsThe results indicate that Performance Expectancy (PE), Effort Expectancy (EE), Price Value (PV), Self-Efficacy (SE) and Value Alignment (VA) significantly influence students' Behavioral Intention (BI) to adopt OSS. Conversely, Social Influence (SI), Facilitating Conditions (FC), Hedonic Motivation (HM) and Habit (HT) were not significant predictors. The findings suggest that while UTAUT 2 provides a useful framework for understanding OSS adoption, it requires adaptation to fully capture the unique characteristics of OSS in educational settings.Originality/valueThis study contributes to the literature by extending the UTAUT 2 model to the context of OSS adoption in higher education, highlighting the importance of economic factors and user alignment with OSS values. The results offer practical insights for higher education institutions aiming to promote OSS, emphasizing the need for support structures, training, and the promotion of OSS’s economic and collaborative benefits.

A multi-objective approach for timber harvest scheduling to include management of at-risk species and spatial configuration objectives.

Sustainable forestry typically involves integration of several economic and ecological objectives which, at times, may not be compatible with one another. Multi-objective prioritization via harvest scheduling programs can be used to elucidate these relationships and explore solutions. One such program is a spatially explicit harvest scheduler that adopts the Metropolis-Hastings algorithm to iteratively find management solutions to achieve multiple objectives (Habplan). Although this program has been used to address forest management scheduling and simulation-based tasks, its utility is constrained by time-intensive data preparation and challenges with incorporating spatial configuration objectives. To address these shortcomings, we introduce an open-source software package, HabplanR, streamlines data preparation, sets parameters, visualizes results, and assesses spatial components of ecological objectives. We developed four example objectives to incorporate into a multi-objective management problem: habitat quality indices for three species "types" (open, closed, and intermediate-canopy-associated species), and harvested pine pulpwood (revenue). We demonstrate the utility of this package to find management schedules that can accommodate potentially conflicting habitat needs of species, while achieving economic targets. We produced 100 software runs and prioritized individual objectives to select four management schedules for further comparisons. We compared outcome differences of the four schedules, including a spatial comparison of two high performing schedules. The software package makes costs and benefits of different schedules explicit and allows for consideration of the spatial configuration of management outcomes in decision-making.

Study While You Sleep: Using Targeted Memory Reactivation as an Independent Research Project for Undergraduates.

Newly acquired information is stabilized into long-term memory through the process of consolidation. Memories are not static; rather, they are constantly updated via reactivation, and this reactivation occurs preferentially during Slow-Wave Sleep (SWS, also referred to as N3 in humans). Here we present a scalable neuroscience research investigation of memory reactivation using low-cost electroencephalogram (EEG) recording hardware and open-source software, for students and educators across the K-12 and higher education spectrum. The investigation uses a method called targeted memory reactivation (TMR), whereby auditory cues that were previously associated with learning are re-presented during sleep, triggering the recall of stored memories and (through this) strengthening these memories. We demonstrated the efficacy of this technique on seven healthy human subjects (ages 19-35). The subjects learned to play a spatial memory game on an app where they associated pictures (e.g., a clock) with locations on a grid while they listened to picture-appropriate sounds (e.g., "tic-toc"); next, they took a nap while undergoing EEG recordings. During SWS, half of the sounds from the game were replayed by the app, while half were substituted with non-learned sounds. Subjects then played the memory game again after waking. Results showed that spatial recall was improved more for cued than uncued memories, demonstrating the benefits of memory replay during sleep and suggesting that one may intervene in this process to boost recall of specific memories. This research investigation takes advantage of the importance of sleep for memory consolidation and demonstrates improved memory performance by cueing sounds during SWS.

Musculoskeletal surgeons have implicit bias towards the biomedical paradigm of human illness.

Evidence is mounting that the biopsychosocial paradigm is more accurate and useful than the biomedical paradigm of care. Habits of thought can hinder the implementation of this knowledge into daily care strategies. To understand and lessen these potential barriers, we asked: 1) What is the relative implicit and explicit attitudes of musculoskeletal surgeons towards the biomedical or biopsychosocial paradigms of medicine? 2) What surgeon factors are associated with these attitudes? An online survey-based experiment was distributed to members of the Science of Variation Group (SOVG) with a total of 163 respondents. Implicit bias towards the biomedical or biopsychosocial paradigms was measured using an Implicit Association Test (IAT) designed by our team using open-source software; explicit preferences were measured using ordinal scales. On average, surgeons demonstrated a moderate implicit bias towards the biomedical paradigm (d-score: -0.21; Interquartile range [IQR]: -0.56 to 0.19) and a moderate explicit preference towards the biopsychosocial paradigm (mean: 14; standard deviation: 14). A greater implicit bias towards the biomedical paradigm was associated with male surgeons (d-score: -0.30; IQR: -0.57 to 0.14; P = 0.005). A greater explicit preference towards the biomedical paradigm was independently associated with a European practice location (Regression coefficient: -9.1; 95% CI: -14 to -4.4; P <0.001) and trauma subspecialty (RC: -6.2; 95% CI: -11 to -1.0; P <0.001). The observation that surgeons have an implicit bias favoring the biomedical paradigm might inform strategies for implementation of care strategies based on evidence favoring the biopsychosocial paradigm.

A leap forward in chemical process design: Introducing an automated framework for integrated AI and CFD simulations

Despite the numerous possibilities of integrating AI and CFD simulations for chemical process design, researchers often rely on manual techniques, resulting in suboptimal models and time-consuming processes. To address these challenges, we propose an automated framework that combines high-fidelity AI modeling with hyperparameter optimization, automated CFD simulations using OpenFOAM, and effortless post-processing for data extraction. This framework was tested on a reactor scale-up process involving 51 different configurations for butadiene synthesis, achieving 98.8 % accuracy in CFD validation and over 99 % accuracy in AI models. The automation pipeline streamlines geometry generation, meshing, simulation, data extraction, and AI-driven optimization, significantly reducing manual effort. Our framework is versatile, customizable for various types of process equipment design, and employs open-source software for ease of adoption and reproducibility. This approach not only enhances accuracy and efficiency but also opens up AI and CFD integration to a broader range of researchers.

Smartphone Magnetometers for Assessing Programmable Shunt Valve Settings: A Proof-of-Concept Study.

Programmable shunt valves (PSVs) used to treat hydrocephalus require specialized valve-specific devices that determine their setting. However, these devices are typically only accessible in major medical centers. This study explores the feasibility of using smartphone magnetometers to assess PSV settings. We investigated 2 PSVs, the Codman Certas Plus and Medtronic Strata II, using 3 different smartphones. Free open-source software was used to measure magnetic flux densities of the shunts, and PSV-specific algorithms were developed to interpret these measurements. All measurements were performed ex vivo. We demonstrated that this method could accurately determine PSV settings with 100% accuracy in ex vivo blinded trials. The technique was effective even with a scalp analog simulating different skin thicknesses. This proof-of-concept study suggests that smartphones can be used for PSV assessment. However, further development and validation are needed to refine this method for clinical use. This technology could enhance PSV management accessibility, allowing for remote or at-home evaluations by trained individuals or caregivers, thus improving patient care in underserved areas.

Open-Source Solutions in Archival Institutions

Open-source solutions offer a cost-effective alternative for archival institutions facing budget constraints. The absence of licensing fees and the ability to modify and customize the software without vendor lock-in make open source particularly appealing. This financial flexibility allows institutions to allocate resources to other critical aspects of preservation and access. In the article the author presents various advantages and disadvantages of using open-source software.

Clinical Phenotypes Associated With Cerebral Small Vessel Disease: A Study of 45,013 UK Biobank Participants.

Cerebral small vessel disease (cSVD) is the most common pathology underlying vascular cognitive impairment. Although other clinical features of cSVD are increasingly recognized, it is likely that certain symptoms are being overlooked. A comprehensive description of cSVD associations with clinical phenotypes at scale is lacking. The objective of this study was to conduct a large-scale, hypothesis-free study of associations between cSVD and clinical phenotypes in UK Biobank (UKB). We included participants from the UKB imaging study who had available information on total volume of white matter hyperintensities (WMHs), the most common cSVD neuroimaging feature. We included various UKB variables describing clinical phenotypes, defined as observable signs and symptoms of individuals with concurrent neuroimaging evidence of cSVD. We conducted a phenome scan using the open-source PHESANT software package. Total volume of WMHs was introduced as the independent variable and clinical phenotypes as the dependent variables in the regression model. The association of each phenotype with total volume of WMHs was tested using one of several regression analyses (all age at recruitment and sex-adjusted). All associations were corrected for multiple comparisons using the false discovery rate (FDR) correction method. We included 45,013 participants in the analysis (mean age = 54.97 years, SD = 7.55). We confirm previously reported associations with depression (odds ratio [OR] = 1.07 [95% CI 1.05-1.10]), apathy (OR = 1.11 [95% CI 1.08-1.14]), falls (OR = 1.11 [95% CI 1.09-1.13]), respiratory problems (OR = 1.14 [95% CI 1.04-1.25]), and sleep disturbance (OR = 1.07 [95% CI 1.04-1.09], all FDR-adjusted p < 0.001). We further identified associations with all-cause dental issues (OR = 0.94 [95% CI 0.96-0.92]), hearing problems (OR = 1.06 [95% CI 1.03-1.08]), and eye problems (OR = 0.93 [95% CI 0.91-0.95], all FDR-adjusted p < 0.001). Our findings suggest that presence of cSVD associates with concurrent clinical phenotypes across several body systems. We have corroborated established associations of cSVD and present novel ones. While our results do not provide causality or direction of association because of the cross-sectional nature of our study, they support the need for a more holistic view of cSVD in research, practice, and policy.

ExoLyn: A golden mean approach to multispecies cloud modeling in atmospheric retrieval

Clouds are ubiquitous in exoplanets' atmospheres and play an important role in setting the opacity and chemical inventory of the atmosphere. Understanding clouds is a critical step in interpreting exoplanets' spectroscopic data. The aim is to model the multispecies nature of clouds in atmospheric retrieval studies. To this end, we developed -- a 1D cloud model that balances physical consistency with computational efficiency. solves the transport equation of cloud particles and vapor under cloud condensation rates that are self-consistently calculated from thermodynamics. is a standalone, open source package capable of being combined with optool to calculate solid opacities and with petitRADTRANS to generate transmission or emission spectra. With we find that the compositional structure of clouds in hot Jupiter planets' atmospheres is layered with a cloud dominated by magnesiumsilicates on top of an iron cloud. This finding is consistent with more complex cloud formation models but can be obtained with in only a few seconds. The composition of the cloud particles can be constrained from the spectrum, for example MgSiO3 and Mg2SiO4 components give rise to an absorption feature at $8-10\ m $. We investigate the dependence of the cloud structure on the bulk elemental composition of the planet and find that SiO2 -dominated clouds form on metal-rich planets and Fe clouds with a strong extinction effect form on C-rich planets. Designed toward maximum flexibility can also be used in retrieval analysis of sub-Neptunes and self-luminous planets. The efficiency of opens the possibility of joint retrieval of exoplanets' gas and cloud components.

AM3: An Open-source Tool for Time-dependent Lepto-hadronic Modeling of Astrophysical Sources

We present the Astrophysical Multimessenger Modeling (AM 3 ) software. AM 3 is a documented open-source software (source code at https://gitlab.desy.de/am3/am3; user guide and documentation at https://am3.readthedocs.io/en/latest/) that efficiently solves the coupled integro-differential equations describing the temporal evolution of the spectral densities of particles interacting in astrophysical environments, including photons, electrons, positrons, protons, neutrons, pions, muons, and neutrinos. The software has been extensively used to simulate the multiwavelength and neutrino emission from active galactic nuclei (including blazars), gamma-ray bursts, and tidal disruption events. The simulations include all relevant nonthermal processes, namely synchrotron emission, inverse Compton scattering, photon–photon annihilation, proton–proton and proton–photon pion production, and photo-pair production. The software self-consistently calculates the full cascade of primary and secondary particles, including nonlinear feedback processes and predictions in the time domain. It also allows the user to track separately the particle densities produced by means of each distinct interaction process, including the different hadronic channels. With its efficient hybrid solver combining analytical and numerical techniques, AM 3 combines efficiency and accuracy at a user-adjustable level. We describe the technical details of the numerical framework and present three examples of applications to different astrophysical environments.

SMARTER-database: a tool to integrate SNP array datasets for sheep and goat breeds.

The code is available as open-source software under the MIT license at https://github.com/cnr-ibba/SMARTER-database.

EarthCODE – ESA’s Earth Science Collaborative Open Development Environment

Abstract. Earth Observations (EO) have become crucial for advancing climate and Earth System research, enabling significant scientific discoveries. Advanced EO missions, such as ESA’s Earth Explorers and the EU's Copernicus Programme, provide vast data volumes and continuous and global observations with cutting-edge technology, a vital resource for understanding processes and interactions within Earth's sub-systems, offering critical evidence of climate change impacts on society and ecosystems. Open access to such data products has been instrumental in facilitating global scientific collaboration and innovation. Reproducibility in Earth Science is essential for validating discoveries, enabling the scientific community to trust and build upon each other’s work. Open data, coupled with open-source software, is key to achieving this reproducibility, ensuring transparency, and facilitating peer review. Addressing scientific challenges requires collaborative efforts supported by fit-for-purpose technology to enable new insights and ensuring they are FAIR (findable, accessible, interoperable, and reusable), trustable and up to date, supporting long-term climate studies and action-driven science. Several initiatives within ESA’s Earth Observation Programme are fostering FAIR and Open Science. EarthCODE is one such initiative, focusing on implementing ESA’s vision of EO Open Science and Innovation by adopting FAIR and Open principles in Earth Science activities funded by ESA’s FutureEO Programme. EarthCODE leverages existing European platform solutions for effective cloud-based analyses of EO data and higher-level geospatial products. It aims to ensure the persistence of end-to-end scientific workflows and provide means for managing open research data, including data, code, and documentation. EarthCODE aims to make Earth Science research more reproducible, transparent, and collaborative, driving progress in understanding and addressing global environmental challenges.

SensorHUB – a Novel, Open-source Software Stack for Enhanced Accessibility and Secure Interoperability in IoT Project Management

Abstract. The Internet of Things (IoT) seeks to achieve seamless device connectivity and data sharing, thereby enhancing automation, efficiency, and insights in various fields of application. By delivering real-time data, IoT is essential for systems that aim to model and map the physical world to digital formats, such as digital twins or metaverses. In the recent years, there has been a surge in high-quality commercial IoT products available to various sectors. However, these solutions often come with high costs and proprietary interfaces, which hinder seamless interoperability. For users seeking cost-effective and flexible solutions, open-source software and international standards present viable alternatives. Nonetheless, despite technological progress, the adoption of open and standardized IoT software solutions is still predominantly seen among technology-affine groups or within research and development initiatives. We propose the sensorHUB, a novel IoT stack, which builds upon the Open Geospatial Consortium’s (OGC) data standard SensorThings API and enhances existing solutions towards more accessible, production-oriented solutions for diverse application domains and user groups. Key features include the provision of a new, state-of-the-art graphical user interface for managing data flows and the access to related software modules, and a secure and flexible user and access management.

Perceptions and responses to diseases among patients with inflammatory bowel disease: text-mining analysis of posts on a Japanese patient community website

Introduction: Patients with inflammatory bowel disease (IBD) are increasingly using online platforms to communicate with other patients and healthcare professionals seeking disease-related information and support. Free-text posts on these platforms could provide insights into patients’ everyday lives, which could help to improve patient care. In this proof-of-concept study, we applied text-mining to extract patient needs from free-text posts on a community forum in Japan, and holistically visualized the patients’ perceptions and their connections, and explored the patient characteristic-dependent trends in the use of words. Methods: Free-text posts written between May 11, 2020 and May 31, 2022 on the community forum were retrieved and subjected to text-mining analysis. Trends in the use of words were extracted from the posts for correspondence and co-occurrence network analyses using KH Coder open-source text-mining software. Results: Seventy-four posts were analyzed. Using text mining methods, we successfully extracted and visualized a variety of patient concerns and their connections. The correspondence and co-occurrence analyses revealed patient segment-dependent trends in the use of words. For example, patients with a disease duration of ≤5 yrs were more likely to use words related to emotions or their desire to change or quit their job, such as ‘anxiety’ and ‘resignation.’ Patients with a disease duration of &gt;10 yrs were more likely to use words showing that they are finding ways to live with or accept their disease, and are getting used to the lifestyle but some patients continued to experience worsening disease. Conclusions: We found that free texts posted on an IBD community forum can be a useful source of information to capture the wide variety of thoughts of patients. Text mining procedures can help to visualize the relative importance of the topics identified from free text posts. Our findings of this proof-of-concept study will be useful for generating new hypotheses to better understand and address the needs of patients with IBD.

Leveraging cloud compute and open source software to generate 3D models from drone photography

Abstract. Modern drone platforms provide the opportunity to capture large quantities of high quality, geotagged aerial imagery quickly and over large distances, at a relatively low cost. As organisations increasingly turn to digital twin technology to manage their operations, rich, photorealistic three dimensional (3D) models of their assets become increasingly important. This paper describes how cloud computing, and container based serverless compute in particular, can be used together with open source software to generate a 3D model, converted to 3D tiles for optimised transmission, from images of large outdoor assets captured by drone. This approach offers a flexible and cost effective way to processing drone imagery into 3D models whilst allowing you to maintain ownership and control of your data.

Protein A-like Peptide Design Based on Diffusion and ESM2 Models

Proteins are the foundation of life, and designing functional proteins remains a key challenge in biotechnology. Before the development of AlphaFold2, the focus of design was primarily on structure-centric approaches such as using the well-known open-source software Rosetta3. Following the development of AlphaFold2, deep-learning techniques for protein design gained prominence. This study proposes a new method to generate functional proteins using the diffusion model and ESM2 protein language model. Diffusion models, which are widely used in image and natural language generation, are used here for protein design, facilitating the controlled generation of new sequences. The ESM2 model, trained on the basis of large-scale protein sequence data, provides a deep understanding of the context of the sequence, thus improving the model’s ability to generate biologically relevant proteins. In this study, we used the Protein A-like peptide as a model study object, combined the diffusion model and the ESM2 model to generate new peptide sequences from minimal input data, and verified their biological activities through experiments such as the BLI affinity test. In conclusion, we developed a new method for protein design that provides a novel strategy to meet the challenges of generic protein generation.

QArray: A GPU-accelerated constant capacitance model simulator for large quantum dot arrays

Semiconductor quantum dot arrays are a leading architecture for the development of quantum technologies. Over the years, the constant capacitance model has served as a fundamental framework for simulating, understanding, and navigating the charge stability diagrams of small quantum dot arrays. However, while the size of the arrays keeps growing, solving the constant capacitance model becomes computationally prohibitive. This paper presents an open-source software package able to compute a 100 × 100100×100 pixels charge stability diagram of a 16-dot array in less than a second. Smaller arrays can be simulated in milliseconds - faster than they could be measured experimentally, enabling the creation of diverse datasets for training machine learning models and the creation of digital twins that can interface with quantum dot devices in real-time. Our software package implements its core functionalities in the systems programming language Rust and the high-performance numerical computing library JAX. The Rust implementation benefits from advanced optimisations and parallelisation, enabling the users to take full advantage of multi-core processors. The JAX implementation allows for GPU acceleration.

Codebase release 1.3 for QArray

Semiconductor quantum dot arrays are a leading architecture for the development of quantum technologies. Over the years, the constant capacitance model has served as a fundamental framework for simulating, understanding, and navigating the charge stability diagrams of small quantum dot arrays. However, while the size of the arrays keeps growing, solving the constant capacitance model becomes computationally prohibitive. This paper presents an open-source software package able to compute a 100 × 100100×100 pixels charge stability diagram of a 16-dot array in less than a second. Smaller arrays can be simulated in milliseconds - faster than they could be measured experimentally, enabling the creation of diverse datasets for training machine learning models and the creation of digital twins that can interface with quantum dot devices in real-time. Our software package implements its core functionalities in the systems programming language Rust and the high-performance numerical computing library JAX. The Rust implementation benefits from advanced optimisations and parallelisation, enabling the users to take full advantage of multi-core processors. The JAX implementation allows for GPU acceleration.

Creating a biomedical knowledge base by addressing GPT inaccurate responses and benchmarking context.

We created GNQA, a generative pre-trained transformer (GPT) knowledge base driven by a performant retrieval augmented generation (RAG) with a focus on aging, dementia, Alzheimer's and diabetes. We uploaded a corpus of three thousand peer reviewed publications on these topics into the RAG. To address concerns about inaccurate responses and GPT 'hallucinations', we implemented a context provenance tracking mechanism that enables researchers to validate responses against the original material and to get references to the original papers. To assess the effectiveness of contextual information we collected evaluations and feedback from both domain expert users and 'citizen scientists' on the relevance of GPT responses. A key innovation of our study is automated evaluation by way of a RAG assessment system (RAGAS). RAGAS combines human expert assessment with AI-driven evaluation to measure the effectiveness of RAG systems. When evaluating the responses to their questions, human respondents give a "thumbs-up" 76% of the time. Meanwhile, RAGAS scores 90% on answer relevance on questions posed by experts. And when GPT-generates questions, RAGAS scores 74% on answer relevance. With RAGAS we created a benchmark that can be used to continuously assess the performance of our knowledge base. Full GNQA functionality is embedded in the free GeneNetwork.org web service, an open-source system containing over 25 years of experimental data on model organisms and human. The code developed for this study is published under a free and open-source software license at https://git.genenetwork.org/gn-ai/tree/README.md .

AI-based open-source software for cephalometric analysis from limited FOV radiographs

BackgroundArtificial Intelligence (AI) in dental diagnostics is evolving, offering innovative approaches for conducting cephalometric analysis with less manual input and overcoming the limitations of traditional imaging methods. To enhance the diagnostic processes in dentistry, an open-source software that utilises AI to improve the extraction of cephalometric values from limited field of view (FOV) images was created. Material and MethodsReduced FOV images lack several vital cephalometric landmarks, prompting the creation of predictive models to estimate missing values. The GridSearchCV algorithm and other algorithms were used to construct predictive models using software and to select the best models. The software was validated by comparing the predicted values with the actual measurements and calculating the mean squared error using Excel. Further validation involved a randomly selected cohort of 25 untreated orthodontic cases. ResultsEvaluation of the software showed that it was effective in accurately predicting key cephalometric measurements, suggesting that it could be a reliable tool for clinical use. However, some variations were noted in its predictive accuracy across different measurements, indicating areas that could benefit from further development. The software could align closely with the actual cephalometric measurements through detailed statistical analysis. ConclusionsThe integration of AI into cephalometric analysis with the software could represent progress, potentially leading to more efficient dental diagnostics and a reduction in the need for additional X-rays. This study aimed to advance the integration and refinement of AI in healthcare, focusing on minimising bias and understanding its impact on clinical decisions. In future studies, the application of AI in dental practice should be expanded to address these challenges. Clinical Significance StatementThis software integrates AI into clinical practice to enhance the diagnostic and therapeutic phases for patient benefit. It enables precise and comprehensive cephalometric analyses using data previously considered insufficient, thereby reducing the need for X-rays and improving patient care.