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Accessible and easy-to-use standardized bioinformatics workflows are necessary to advance microbiome research from observational studies to large-scale, data-driven approaches. Standardized multi-omics data enables comparative studies, data reuse, and applications of machine learning to model biological processes. To advance broad accessibility of standardized multi-omics bioinformatics workflows, the National Microbiome Data Collaborative (NMDC) has developed the Empowering the Development of Genomics Expertise (NMDC EDGE) resource, a user-friendly, open-source web application (https://nmdc-edge.org). Here, we describe the design and main functionality of the NMDC EDGE resource for processing metagenome, metatranscriptome, natural organic matter, and metaproteome data. The architecture relies on three main layers (web application, orchestration, and execution) to ensure flexibility and expansion to future workflows. The orchestration and execution layers leverage best practices in software containers and accommodate high-performance computing and cloud computing services. Further, we have adopted a robust user research process to collect feedback for continuous improvement of the resource. NMDC EDGE provides an accessible interface for researchers to process multi-omics microbiome data using production-quality workflows to facilitate improved data standardization and interoperability.

BackgroundThe impression section integrates key findings of a radiology report but can be subjective and variable. We sought to fine-tune and evaluate an open-source Large Language Model (LLM) in automatically generating impressions from the remainder of a radiology report across different imaging modalities and hospitals.MethodsIn this institutional review board-approved retrospective study, we collated a dataset of CT, US, and MRI radiology reports from the University of California San Francisco Medical Center (UCSFMC) (n = 372,716) and the Zuckerberg San Francisco General (ZSFG) Hospital and Trauma Center (n = 60,049), both under a single institution. The Recall-Oriented Understudy for Gisting Evaluation (ROUGE) score, an automatic natural language evaluation metric that measures word overlap, was used for automatic natural language evaluation. A reader study with five cardiothoracic radiologists was performed to more strictly evaluate the model's performance on a specific modality (CT chest exams) with a radiologist subspecialist baseline. We stratified the results of the reader performance study based on the diagnosis category and the original impression length to gauge case complexity.ResultsThe LLM achieved ROUGE-L scores of 46.51, 44.2, and 50.96 on UCSFMC and upon external validation, ROUGE-L scores of 40.74, 37.89, and 24.61 on ZSFG across the CT, US, and MRI modalities respectively, implying a substantial degree of overlap between the model-generated impressions and impressions written by the subspecialist attending radiologists, but with a degree of degradation upon external validation. In our reader study, the model-generated impressions achieved overall mean scores of 3.56/4, 3.92/4, 3.37/4, 18.29 s,12.32 words, and 84 while the original impression written by a subspecialist radiologist achieved overall mean scores of 3.75/4, 3.87/4, 3.54/4, 12.2 s, 5.74 words, and 89 for clinical accuracy, grammatical accuracy, stylistic quality, edit time, edit distance, and ROUGE-L score respectively. The LLM achieved the highest clinical accuracy ratings for acute/emergent findings and on shorter impressions.ConclusionsAn open-source fine-tuned LLM can generate impressions to a satisfactory level of clinical accuracy, grammatical accuracy, and stylistic quality. Our reader performance study demonstrates the potential of large language models in drafting radiology report impressions that can aid in streamlining radiologists' workflows.

Genomic-assisted breeding has transitioned from theoretical concepts to practical applications in breeding. Genomic selection (GS) predicts genomic breeding values (GEBV) using dense genetic markers. Single-stage models predict GEBVs from phenotypic observations in one step, fully accounting for the entire variance-covariance structure among genotypes, but face computational challenges. Two-stage models, preferred for their simplicity and efficiency, first calculate adjusted genotypic means accounting for spatial variation within each environment, then use these means to predict GEBVs. However, unweighted (UNW) two-stage models assume independent errors among adjusted means, neglecting correlations among estimation errors. Here, we show that fully-efficient two-stage models perform similarly to UNW models for randomized complete block designs but substantially better for augmented designs. Our simulation studies demonstrate the impact of the fully-efficient methodology on prediction accuracy across different implementations and scenarios. Incorporating non-additive effects and augmented designs significantly improved accuracy, emphasizing the synergy between design and model strategy. Consistent performance requires the estimation error covariance to be incorporated into a random effect (Full_R model) rather than into the residuals. Our results suggest that the fully-efficient methodology, particularly the Full_R model, should be more prevalent, especially as GS increases the appeal of sparse designs. We also provide a comprehensive theoretical background and open-source R code, enhancing understanding and facilitating broader adoption of fully-efficient two-stage models in GS. Here, we offer insights into the practical applications of fully-efficient models and their potential to increase genetic gain, demonstrating a 13.80 % improvement after five selection cycles when moving from UNW to Full_R models. [ABSTRACT FROM AUTHOR]

The elastic modulus evolution of cementitious materials since early ages can be experimentally measured with the EMM-ARM (Elastic Modulus Measurement through Ambient Response Method) test. This methodology has been extensively validated, but its original implementation relied on costly proprietary platforms. A minimalist, open-source test system may provide a benchmark that sets minimum requirements and performance to enable the development of tailor-made systems. This work aimed to develop, characterise, and validate such system, composed of data-acquisition hardware and post-processing software. Characterisation results showed the system had a spectral noise floor of 2.1 × 10−8 g2/Hz, an RMS noise of 0.003 g, a sampling frequency variability of ± 0.006%, and an average power consumption of 41.53 mAh. Compared to an original EMM-ARM system, validation results indicated the proposed system had an absolute error lower than 0.3% from 1 day of testing onwards, with a similar coefficient of variation of 1.2%. [ABSTRACT FROM AUTHOR]

Smartphones and wearable sensors offer an unprecedented ability to collect peripheral psychophysiological signals across diverse timescales, settings, populations, and modalities. However, open-source software development has yet to keep pace with rapid advancements in hardware technology and availability, creating an analytical barrier that limits the scientific usefulness of acquired data. We propose a community-driven, open-source peripheral psychophysiological signal pre-processing and analysis software framework that could advance biobehavioral health by enabling more robust, transparent, and reproducible inferences involving autonomic nervous system data. [ABSTRACT FROM AUTHOR]

[Display omitted] Investigating solid–liquid interactions to determine advancing and receding contact angles, and consequently contact angle hysteresis, is crucial for understanding material wetting properties. A reliable, automated, and possibly open-source tool is desirable, to standardize and automatize the measurement and make it user-independent. This study introduces an open-source software, DropenVideo, as an extension of Dropen. DropenVideo automates frame-by-frame video analysis for the advancing and receding contact angle determination, by considering needle presence, contrast tuning, and compensating for missing drop edge data. Contact angles are calculated using convolution mask, circle, and polynomial fittings. An innovative feature in DropenVideo is the automatic protocol for identifying advancing and receding contact angles: (i) the advancing contact angle is determined as the average value during drop inflation; and (ii) the receding contact angle is determined from the frame of incipient motion during drop deflation. Exploring the application of DropenVideo across a range of complex surfaces as representative test cases, we highlight existing challenges in interpreting wetting measurements by addressing different wetting scenarios. Our study demonstrates that employing frame-by-frame automatic analysis of contact angle measurement videos using DropenVideo significantly mitigates the potential risks of subjective bias associated with manual interpretation and enhances the precision of identified wetting characteristics. [ABSTRACT FROM AUTHOR]

AbstractAnatomy learning has traditionally relied on drawings, plastic models, and cadaver dissections/prosections to help students understand the three-dimensional (3D) relationships within the human body. However, the landscape of anatomy education has been transformed with the introduction of digital media. In this light, the Open Anatomy Explorer (OPANEX) was developed. It includes two user interfaces (UI): one for students and one for administrators. The administrator UI offers features such as uploading and labelling of 3D models, and customizing 3D settings. Additionally, the OPANEX facilitates content sharing between institutes through its import-export functionality. To evaluate the integration of OPANEX within the existing array of learning resources, a survey was conducted as part of the osteology course at Ghent University, Belgium. The survey aimed to investigate the frequency of use of five learning resources, attitudes towards 3D environments, and the OPANEX user experience. Analysis revealed that the OPANEX was the most frequently used resource. Students’ attitudes towards 3D learning environments further supported this preference. Feedback on the OPANEX user experience indicated various reasons for its popularity, including the quality of the models, regional annotations, and customized learning content. In conclusion, the outcomes underscore the educational value of the OPANEX, reflecting students’ positive attitudes towards 3D environments in anatomy education. [ABSTRACT FROM AUTHOR]

The paradigms of Industry 4.0 and Industrial Internet of Things (IIoT) require functional architectures to deploy and organize hardware and software taking advantage of modern digital technologies in industrial systems. In this sense, a lot of the literature proposes and describes this type of architecture with a conceptual angle, without providing experimental validation or with scarce details about the involved equipment under real operation. Aiming at overcoming these limitations, this paper presents the experimental application of an IIoT architecture divided into four functional layers, namely, Sensing, Network, Middleware and Application layers. Automation and IoT hardware and software are used to implement and apply the architecture. Special attention is put on the software Grafana, chosen in the top layer to deploy graphical user interfaces that are remotely accessible via web. A pilot microgrid integrating photovoltaic energy and hydrogen served as scenario to test and prove the suitability of the architecture in four application cases. [ABSTRACT FROM AUTHOR]

Location Based Games (LBGs) can support various learning activities in both formal and informal settings. Furthermore, the fact that they can utilize players' physical movement for players' advancement in the game world makes them an appealing alternative for promoting physical activity in students. However, LBGs are not sufficiently used in education with one reason being that their development requires advanced technical skills that school teachers often lack. The aim of this study was twofold: (a) the design and development of an open-source platform for the simplified creation of multi-player educational LBGs that require players' physical activity, by persons that possess basic computer or smartphone use skills, such as school teachers, and (b) the evaluation of the platform, and specifically of its game design environment, by school teachers as to its perceived usability and acceptance. YOUth Go comprises a Game Designer Environment (GDE), that the designer uses on any device to create a game, and a Game Player Environment (GPE), that the player uses on a GPS enabled Android or iOS device to play the game outdoors. Eighty-four (84) randomly selected in-service teachers attended a three-hour online seminar on mobile learning, LBGs and the use of the GDE. During it, they started creating their own games, that they could complete in their free time for two weeks, after which, they answered an online questionnaire on the usability and acceptance of the GDE. The usability of the GDE was perceived to be good by the teachers, who found it to be user-friendly and did not encounter particular difficulties with it. They were highly satisfied from the GDE, which they perceived as a useful tool for their work, did not feel anxiety about, but had very positive attitudes towards using it, with their intention to use it in the future being high. The findings suggest that teachers are likely to adopt YOUth Go as a tool for promoting students' learning and physical activity. [ABSTRACT FROM AUTHOR]

Reactor automation is a transformative force for chemical processes, but the potential of reaction monitoring for machine‐assisted autonomous biocatalytic reaction optimization is still largely unexplored. To address this gap, we report on automated reactor optimization for biocatalytic flow‐through microreactors. For this purpose, the inline NMR analysis of an enzymatically catalyzed stereoselective reduction of a prochiral diketone was combined with a self‐developed open‐source analysis and control software. The algorithm is continuously fed with spectra from a benchtop NMR instrument acquired from a reaction solution from a microreactor filled with biocatalytically active materials and adjusts the flow rate of the pumps to achieve predetermined target concentrations of the product. We show that through this automated coupling of data analysis and process parameterization, for example, maximum conversion efficiency can be achieved for a given bioreactor. This work illustrates the potential of inline NMR reaction monitoring for biocatalytic processes and provides a starting point for innovation to develop automated processes for precision biocatalysis through integrated data analysis. [ABSTRACT FROM AUTHOR]

In this article, we propose a novel homogenization method for piezoelectric composites with periodic microstructures. In the proposed homogenization method, one can find the analytical expressions for the material properties of the composites in the representative volume element (RVE) via diffused material interface method. The availability of analytical expressions for the material properties in the entire domain of the RVE enables one to determine the effective homogenized material properties with the standard finite element method. As the proposed homogenization method regularizes the discontinuity in material properties across the interfaces, there is no need to explicitly track the material interfaces while implementing the finite element method for determining the effective material properties. Hence, one can implement the proposed homogenized method for any piezoelectric composites with complicated material interfaces and determine the effective material properties. In this study, we have considered the piezoelectric composites to be comprised of periodic microstructures where the RVE constitutes a matrix with an inclusion of a specific shape. We have carried out a study on the effect of the shape of inclusion viz. square-shaped, I-shaped, T-shaped, and plus-shaped, and the size of inclusion on the effective piezoelectric material properties. We have also studied the influence of shape and size of inclusion on the electro-mechanical response of a homogenized piezoelectric continuum. To implement the proposed homogenization method, we have used Gridap, an open-source finite element toolbox in Julia that provides very compact codes freely available to all the researchers and makes a third-party verification of the proposed method straightforward. [ABSTRACT FROM AUTHOR]

In recent years, we have witnessed increased interest in alternatives to the dominant corporate social media sites, such as Facebook, Twitter (now X), and TikTok. Tired of disinformation, harassment, privacy violations, and the general degradation of platforms, users and technologists have looked for non-corporate alternatives. Not-for-profit social media platforms emerging from free/libre and open-source software (FLOSS) communities based on non-centralized infrastructure have emerged as promising alternatives. For applied epistemology of the internet, these alternative social media platforms present an opportunity to study different ways of producing knowledge together online. This paper evaluates the epistemic potential for such alternative, non-corporate social media. I present an epistemological framework for analyzing the epistemic promises and perils of alternative social media. Then I apply this framework to the case of Mastodon, a federated, open-source microblogging platform. Mastodon's structure and culture of openness present opportunities to avoid many of the epistemic perils of biased and untrustworthy large corporate platforms. However, Mastodon's risks include techno-elitism, white ignorance, and isolated, epistemically toxic communities. [ABSTRACT FROM AUTHOR]

This study delves into the risks associated with 3D-printed hand prostheses lacking mechanical feasibility studies. Such unvalidated prostheses may exhibit various issues like durability shortcomings, anatomical incompatibility, functional safety concerns, manufacturing quality deficiencies, and health risks due to inappropriate materials. These issues can lead to damage or premature failure during use, discomfort, skin irritation, injuries, inability to withstand functional loads, and health hazards from toxic or allergenic substances. Therefore, ensuring the safety, quality, and effectiveness of these prostheses is crucial. The study focuses on a mechanical feasibility study conducted through Finite Element Analysis (FEA) simulations on an open-source hand prosthesis model. It evaluates mechanical properties, stress concentration areas, and displacement on the prosthesis surface. The methodology comprises three key steps: acquiring the virtual model, conducting computational simulations, and selecting the 3D printing material. The simulations assess the prosthesis's ability to withstand compressive forces and identify stress concentration areas. Results from the study indicate that using PETG as the constituent material demonstrates mechanical viability and satisfactory performance under static force conditions. This finding underscores the importance of rigorous testing and adherence to standards in developing 3D-printed hand prostheses. Such studies contribute significantly to enhancing these devices' safety and effectiveness, facilitating their broader adoption in healthcare settings. In summary, this study highlights the critical need for mechanical feasibility studies in the development of 3D-printed hand prostheses. It emphasizes the significance of following strict standards and regulations to ensure these devices' safety, quality, and functionality. By doing so, it paves the way for the widespread use of these prostheses in healthcare, benefiting users and advancing the field of prosthetic technology. [ABSTRACT FROM AUTHOR]

Introduction: Three-dimensional measurements of intracranial volume (ICV) can guide clinical management of brain and skull disorders. However, widespread clinical access is limited by the scarcity of software methods for analyzing CT scans, which are more available than MRI images, and the inaccessibility of proprietary and expensive commercial software. The presented method can calculate ICV from CT scans, using an open-source software, 3D Slicer. Methods: The open-source workflow was optimized with a data-driven approach to find the optimal parameters for ICV accuracy. The accuracy of the open-source method was determined by comparing it to commercial and proprietary software with CT scans of pediatric hydrocephalic macrocephaly patients with craniosynostosis undergoing total vault reconstruction (N = 5 patients,15 scans). Results: An open-source pipeline that combines an initial semi-automatic segmentation of a coronal CT reconstruction with a fully automatic segmentation minimizes the ICV error. The open-source method shows excellent agreement with both the commercial and proprietary software methods (R2 = 0.998 and 95% confidence interval of best-fit line slope: [0.986; 1.047], [0.985;1.066] respectively). The mean percent difference of ICV measurements of the open-source software from the commercial software was −0.56% [95% CI: −1.08%, −0.028%] and from the proprietary software was −0.07% [95% CI: −1.26%, 1.1%]. The mean percent difference of ICV measurements of the commercial software from the proprietary software was 0.36% [95% Confidence Interval: −0.61%, 1.32%]. Conclusions: This is the first study comparing an open-source method for measuring ICV with commercial and proprietary options. A high degree of fidelity was found, confirming this open-source method as a viable option for clinicians who are looking to incorporate ICV measurements into their practice. [ABSTRACT FROM AUTHOR]

This article presents a new open‐source solver within the OpenFOAM framework, to provide a cost‐free alternative to commercial software for simulating blood flows and the transport of low‐density lipoproteins (LDL) in arteries. The proposed algorithm utilizes the velocity field obtained from the hemodynamics computation to solve an advection‐diffusion equation governing a passive scalar variable, that represents the cholesterol concentration in blood. Moreover, two customized boundary conditions, namely periodic pulsatile inflow and LDL blood‐to‐wall transfer law, as well as a non‐Newtonian viscosity model, are included in the code to achieve more realistic results. The solver is first validated by reproducing two benchmark tests, the classical lid‐driven cavity experiment including heat transport, and a constricted tube simulating a stenosed artery. The results obtained were in good agreement with existing literature and experimental measurements, thus confirming the accuracy and robustness of the proposed open‐source solver. Finally, hemodynamics and LDL transport are computed in two arteries, one of them obtained by segmentation from an anonymized clinical patient. Stress and LDL concentration at the vessel's wall are employed to calculate significant descriptors revealing dangerous areas where atherosclerotic plaques could emerge. In the studied cases, the main branch of the artery, and especially the vicinity of the bifurcation, seem to be candidates to develop the illness. This conclusion is in line with medical in‐vivo studies evincing that bifurcations are an usual place where plaques grow. [ABSTRACT FROM AUTHOR]

This paper explores the design and implementation of a voice-controlled smart home system utilizing the Raspberry Pi platform and Node.js framework. The system aims to provide users with an intuitive method for managing household devices through voice commands while enhancing functionality with integrated sensors. Leveraging the Hidden Markov Model Toolkit (HTK) for speech recognition, the system accurately interprets user commands, facilitating control over lighting, temperature, and various IoT devices. In addition to voice activation, the system incorporates multiple sensors: a temperature sensor for monitoring ambient conditions, a motion sensor (PIR) for detecting occupancy, and a light sensor for assessing natural light levels. These sensors work in harmony with the voice control features, enabling automated responses such as adjusting the heating or cooling based on temperature readings, activating lights upon detecting movement, and regulating artificial lighting according to available daylight. Comprehensive testing demonstrated the system's high accuracy in command recognition and its responsiveness to user inputs, as well as its robust integration with additional smart devices. User feedback was instrumental in refining the system, leading to improvements in command clarity and operational efficiency. This research highlights the potential of combining voice control and sensor technology to create a more responsive and user-friendly smart home environment. [ABSTRACT FROM AUTHOR]

This article contributes to recent scholarship on platform, software and media studies by critically engaging with the 'social coding' platform GitHub, one of the most prominent actors in the online proprietary and F/OSS (free and/or open-source software) code hosting space. It examines the platformisation of software and project development on GitHub by combining institutional and cultural analysis. The institutional analysis focuses on critically examining the platform from a material-economic perspective to understand how it configures contemporary software and project development work. It proposes the concept of 'connective coding' to characterise how software intermediaries such as GitHub configure, valorise and capitalise on public repositories, developer and organisation profiles. This institutional perspective is complemented by a case study analysing cultural practices mediated by the platform. The case study examines the platform vernaculars of news media and journalism initiatives highlighted by Source, a key publication in the newsroom software development space, and how GitHub modulates visibility in this space. It finds that the high-visibility platform vernacular of this news media and journalism space is dominated by a mix of established actors such as the New York Times, the Guardian and Bloomberg, as well as more recent actors and initiatives such as ProPublica and Document Cloud. This high-visibility news media and journalism platform vernacular is characterised by multiple F/OSS and F/OSS-inspired practices and styles. Finally, by contrast, low-visibility public repositories in this space may be seen as indicative of GitHub's role in facilitating various kinds of 'post-F/OSS' software development cultures. [ABSTRACT FROM AUTHOR]

This paper reviews the privacy and security challenges posed by open-source artificial intelligence (AI) models. The increased use of open-source machine learning models, while beneficial for resource efficiency and collaboration, has introduced significant privacy risks and security vulnerabilities. Key threats include model inversion, membership inference, data leakage, and backdoor attacks, which could expose sensitive data or compromise system integrity. Our review highlights that many open-source models are vulnerable to these attacks due to their transparency and accessibility. We also identify that adversarial training, differential privacy (DP), and model sanitization techniques can effectively mitigate some of these risks, though achieving a balance between transparency and security remains a challenge. The findings highlight the need for continuous research and innovation to ensure that open-source AI models remain both secure and privacy-compliant in increasingly critical applications across various industries. [ABSTRACT FROM AUTHOR]

Estimating the optical power distribution in a room in order to assess the performance of a visible light communication (VLC) system is nothing new. It can be estimated using a Monte Carlo optical ray tracing algorithm that sums the contribution of each ray on the reception plane. For now, research has focused on rectangular parallelepipedic rooms with single-textured walls, when studying indoor applications. This article presents a new open-source simulator that answers the case of more complex rooms by analysing them using a 3D STL (stereolithography) model. This paper describes this new tool in detail, with the material used, the software architecture, the ray tracing algorithm, and validates it against the literature and presents new use cases. To the best of our knowledge, this simulator is the only free and open-source ray tracing analysis for complex 3D rooms for VLC research. In particular, this simulator is capable of studying any room shape, such as an octagon or an L-shape. The user has the opportunity to control the number of emitters, their orientation, and especially the number of rays emitted and reflected. The final results are detailed heat maps, enabling the visualization of the optical power distribution across any 3D room. This tool is innovative both visually (using 3D models) and mathematically (estimating the coverage of a VLC system). [ABSTRACT FROM AUTHOR]

In this paper, we introduce InternVL 1.5, an open-source multimodal large language model (MLLM) to bridge the capability gap between open-source and proprietary commercial models in multimodal understanding. We introduce three simple improvements. (1) Strong vision encoder: we explored a continuous learning strategy for the large-scale vision foundation model — InternViT-6B, boosting its visual understanding capabilities, and making it can be transferred and reused in different LLMs. (2) Dynamic high-resolution: we divide images into tiles ranging from 1 to 40 of 448×448 pixels according to the aspect ratio and resolution of the input images, which supports up to 4K resolution input. (3) High-quality bilingual dataset: we carefully collected a high-quality bilingual dataset that covers common scenes, document images, and annotated them with English and Chinese question-answer pairs, significantly enhancing performance in optical character recognition (OCR) and Chinese-related tasks. We evaluate InternVL 1.5 through a series of benchmarks and comparative studies. Compared to both open-source and proprietary commercial models, InternVL 1.5 shows competitive performance, achieving state-of-the-art results in 8 of 18 multimodal benchmarks. Code and models are available at . [ABSTRACT FROM AUTHOR]

Abstract Background Platform trials are innovative clinical trials governed by a master protocol that allows for the evaluation of multiple investigational treatments that enter and leave the trial over time. Interest in platform trials has been steadily increasing over the last decade. Due to their highly adaptive nature, platform trials provide sufficient flexibility to customize important trial design aspects to the requirements of both the specific disease under investigation and the different stakeholders. The flexibility of platform trials, however, comes with complexities when designing such trials. In the past, we reviewed existing software for simulating clinical trials and found that none of them were suitable for simulating platform trials as they do not accommodate the design features and flexibility inherent to platform trials, such as staggered entry of treatments over time. Results We argued that simulation studies are crucial for the design of efficient platform trials. We developed and proposed an iterative, simulation-guided “vanilla and sprinkles” framework, i.e. from a basic to a more complex design, for designing platform trials. We addressed the functionality limitations of existing software as well as the unavailability of the coding therein by developing a suite of open-source software to use in simulating platform trials based on the R programming language. To give some examples, the newly developed software supports simulating staggered entry of treatments throughout the trial, choosing different options for control data sharing, specifying different platform stopping rules and platform-level operating characteristics. The software we developed is available through open-source licensing to enable users to access and modify the code. The separate use of two of these software packages to implement the same platform design by independent teams obtained the same results. Conclusion We provide a framework, as well as open-source software for the design and simulation of platform trials. The software tools provide the flexibility necessary to capture the complexity of platform trials.

The Radio Access Network (RAN) of Fifth Generation (5G) communication systems critically influences the overall performance and cost of a 5G system. 5G RANs allow for a gamut of implementations ranging from closed (carrier-grade) implementations by traditional vendors that are expensive and configured by the vendor to RANs that are compliant with the specifications of the O-RAN ALLIANCE, i.e., to so-called O-RANs. O-RANs are available in closed-source and open-source implementations; O-RANs can be configured by the operator and are cheaper than the closed RANs. Understanding the packet-level performance characteristics of these different 5G RAN implementations is a prerequisite for informed network planning and deployment, particularly for Non-Public Networks (NPNs) for specific use cases. Existing measurement studies have mainly considered a single User Equipment (UE); however, practical networks typically operate with multiple UEs. We conduct packet-level measurements for concurrent over-the-air transmissions to (downstream) and from (upstream) multiple UEs for a wide range of packet transmission rates. We consider current representative implementations of three RAN architectural options: closed RAN (based on Nokia system), closed-source O-RAN (based on Airspan system), and open-source O-RAN (based on srsRAN Project system). We find that the one-way packet delays and the packet loss probabilities can vary sharply among the multiple concurrently operating UEs (which are identical in model and configuration) in the closed RAN and open-source O-RAN; whereas, the packet-level performance tends to be more consistent among the UEs in the closed-source O-RAN. Our measurement results provide a baseline for future research on refining RAN operations for multiple concurrent packet flows.

Ensuring the general efficacy and benefit for human beings from medical Large Language Models (LLM) before real-world deployment is crucial. However, a widely accepted and accessible evaluation process for medical LLM, especially in the Chinese context, remains to be established. In this work, we introduce “MedBench”, a comprehensive, standardized, and reliable benchmarking system for Chinese medical LLM. First, MedBench assembles the currently largest evaluation dataset (300901 questions) to cover 43 clinical specialties, and performs multi-faceted evaluation on medical LLM. Second, MedBench provides a standardized and fully automatic cloud-based evaluation infrastructure, with physical separations between question and ground truth. Third, MedBench implements dynamic evaluation mechanisms to prevent shortcut learning and answer memorization. Applying MedBench to popular general and medical LLMs, we observe unbiased, reproducible evaluation results largely aligning with medical professionals’ perspectives. This study establishes a significant foundation for preparing the practical applications of Chinese medical LLMs. MedBench is publicly accessible at https://medbench.opencompass.org.cn.

Quantifying volatile concentrations in magmas is critical for understanding magma storage, phase equilibria, and eruption processes. We present PyIRoGlass, an open-source Python package for quantifying concentrations of H2O and CO2 species in the transmission FTIR spectra of basaltic to andesitic glasses. We leverage a dataset of natural melt inclusions and back-arc basin basalts with volatiles below detection to delineate the fundamental shape and variability of the baseline underlying the CO23− and H2Om,1635 peaks, in the mid-infrared region. All Beer-Lambert Law parameters are examined to quantify associated uncertainties. PyIRoGlass employs Bayesian inference and Markov Chain Monte Carlo sampling to fit all probable baselines and peaks, solving for best-fit parameters and capturing covariance to offer robust uncertainty estimates. Results from PyIRoGlass agree with independent analyses of experimental devolatilized glasses (within 6 %) and interlaboratory standards (10 % for H2O, 6 % for CO2). We determine new molar absorptivities for basalts, [Formula presented] and [Formula presented]; we additionally update the composition-dependent parameterizations of molar absorptivities, with their uncertainties, for all H2O and CO2 species peaks. The open-source nature of PyIRoGlass ensures its adaptability and evolution as more data become available.

Abstract Background The impression section integrates key findings of a radiology report but can be subjective and variable. We sought to fine-tune and evaluate an open-source Large Language Model (LLM) in automatically generating impressions from the remainder of a radiology report across different imaging modalities and hospitals. Methods In this institutional review board-approved retrospective study, we collated a dataset of CT, US, and MRI radiology reports from the University of California San Francisco Medical Center (UCSFMC) (n = 372,716) and the Zuckerberg San Francisco General (ZSFG) Hospital and Trauma Center (n = 60,049), both under a single institution. The Recall-Oriented Understudy for Gisting Evaluation (ROUGE) score, an automatic natural language evaluation metric that measures word overlap, was used for automatic natural language evaluation. A reader study with five cardiothoracic radiologists was performed to more strictly evaluate the model’s performance on a specific modality (CT chest exams) with a radiologist subspecialist baseline. We stratified the results of the reader performance study based on the diagnosis category and the original impression length to gauge case complexity. Results The LLM achieved ROUGE-L scores of 46.51, 44.2, and 50.96 on UCSFMC and upon external validation, ROUGE-L scores of 40.74, 37.89, and 24.61 on ZSFG across the CT, US, and MRI modalities respectively, implying a substantial degree of overlap between the model-generated impressions and impressions written by the subspecialist attending radiologists, but with a degree of degradation upon external validation. In our reader study, the model-generated impressions achieved overall mean scores of 3.56/4, 3.92/4, 3.37/4, 18.29 s,12.32 words, and 84 while the original impression written by a subspecialist radiologist achieved overall mean scores of 3.75/4, 3.87/4, 3.54/4, 12.2 s, 5.74 words, and 89 for clinical accuracy, grammatical accuracy, stylistic quality, edit time, edit distance, and ROUGE-L score respectively. The LLM achieved the highest clinical accuracy ratings for acute/emergent findings and on shorter impressions. Conclusions An open-source fine-tuned LLM can generate impressions to a satisfactory level of clinical accuracy, grammatical accuracy, and stylistic quality. Our reader performance study demonstrates the potential of large language models in drafting radiology report impressions that can aid in streamlining radiologists’ workflows.

Social interactions are fundamental for our survival as a predominately social species. We need and seek positive social interactions to navigate the world. However, when social interactions are negative, and occur in the presence of an aversive event, learning occurs to associate such social interactions as threatening. Gaining insight into the neural circuits that drive social threat learning is crucial for understanding social interactions. Animal models can be leveraged to employ technologies that allow us to track neuronal processes with very high resolution to obtain a better understanding of the neural circuits involved. To accomplish this, we need robust behavioral models that are replicable and high throughput. Here, we present an open-source social interaction chamber that detects social interaction and automatically pairs it with foot shock. The social interaction chamber is designed to easily integrate into modular chambers commonly used for auditory and context threat learning. It contains an array of optical gates that precisely track mouse-to-mouse interactions in real time with digital triggers that can communicate with external devices to deliver a foot shock. We find that pairing social interactions with electric foot shock using our fully automated social interaction chamber is optimal for social threat associations. We further demonstrate that timing of social contact with foot shock produces optimal learning. [ABSTRACT FROM AUTHOR]

The COVID-19 pandemic caught the world unprepared. Large-scale testing efforts were urgently needed, and diagnostic strategies had to rapidly evolve in response to unprecedented worldwide demand. However, the rollout of diagnostic testing and screening for SARS-CoV-2 was often impeded by logistical challenges, including regulatory delays, workforce shortages, laboratory bottlenecks, and supply chain disruptions. Recognizing these hurdles early on, we developed a testing approach that supported frequent, repeat testing, particularly as communities reopened. We hypothesized and experimentally demonstrated that saliva was a suitable specimen for the detection of SARS-CoV-2. This finding was advanced into the development of open-source, extraction-free reverse transcription polymerase chain reaction protocols using readily available, "off-the-shelf" reagents and equipment for the direct detection of SARS-CoV-2 in saliva ("SalivaDirect"). Working with the US Food and Drug Administration (FDA), we established a novel regulatory framework wherein the FDA granted Emergency Use Authorization to Yale University to offer the SalivaDirect test protocol to high-complexity diagnostic laboratories (as designated by the Clinical Laboratory Improvement Amendments) with quality oversight provided by Yale. This grew into a network of more than 200 labs across the United States that, as of May 2024, resulted in over 6.5 million SARS-CoV-2 tests. By making the protocol flexible and open-source, laboratories were able to rapidly and economically scale testing using a simple, self-collected saliva specimen. Additionally, fostering a national network of laboratories enabled real-time exchanges, problem solving, and the development of community best practices. Preparing for the next pandemic, or simply the next seasonal epidemic, the SalivaDirect model of deploying a readily available, expandable solution and accompanying network provides a proven method for the successful implementation of pathogen testing in the United States and globally. [ABSTRACT FROM AUTHOR]

Background: Geometric distortion is a serious problem in MRI, particularly in MRI guided therapy. A lack of affordable and adaptable tools in this area limits research progress and harmonized quality assurance. Purpose: To develop and test a suite of open‐source hardware and software tools for the measurement, characterization, reporting, and correction of geometric distortion in MRI. Methods: An open‐source python library was developed, comprising modules for parametric phantom design, data processing, spherical harmonics, distortion correction, and interactive reporting. The code was used to design and manufacture a distortion phantom consisting of 618 oil filled markers covering a sphere of radius 150 mm. This phantom was imaged on a CT scanner and a novel split‐bore 1.0 T MRI magnet. The CT images provide distortion‐free dataset. These data were used to test all modules of the open‐source software. Results: All markers were successfully extracted from all images. The distorted MRI markers were mapped to undistorted CT data using an iterative search approach. Spherical harmonics reconstructed the fitted gradient data to 1.0 ± 0.6% of the input data. High resolution data were reconstructed via spherical harmonics and used to generate an interactive report. Finally, distortion correction on an independent data set reduced distortion inside the DSV from 5.5 ± 3.1 to 1.6 ± 0.8 mm. Conclusion: Open‐source hardware and software for the measurement, characterization, reporting, and correction of geometric distortion in MRI have been developed. The utility of these tools has been demonstrated via their application on a novel 1.0 T split bore magnet. [ABSTRACT FROM AUTHOR]

Originally developed for application in dissimilar joints, the dynamic feeding technology applied to the GMAW process is gaining popularity with applications in wire arc additive manufacturing (WAAM). Given the growing importance of additive manufacturing (AM) in industrial production, it is essential to better understand the main tool of WAAM technology. Acknowledging the limitations imposed on current waveform parameterization in commercial welding sources, this work used a proprietary welding source, named MIG-AD, which allows full control of parameters through dedicated hardware and open-source software. This study focuses on the impact of arc phase parameters on metal transfer, namely arc pulse time, arc pulse current level, and arc base current level. The results show the impact of altering each parameter on droplet formation, wire retraction, detachment frequency, and metal bridge elongation, as well as the stability and quality of the weld bead. This research offers valuable insights for optimizing welding parameters, aiming for greater control and process customization, making it a viable alternative for advanced research in WAAM, cladding, computational simulation, and artificial intelligence applied to welding. [ABSTRACT FROM AUTHOR]

The use of adaptive comparative judgement (ACJ) for assessment in technology education has been topical since its introduction to the field through the e-scape project coordinated by the Technology Education Research Unit in the United Kingdom. In the last decade, however, there has been an increasing volume of research examining how ACJ can and should be used in the technology classroom. This research has grown in volume to the point where there are now systematic reviews being conducted on the topic. There is a limitation in the use of ACJ within the field in that there does not exist an open-source tool to facilitate its widespread interrogation. Existing proprietary solutions exist and offer exceptional functionality and user experience, but they cannot be easily responsive to needs within the technology education community because they serve a much wider audience, and they cannot be easily used to experiment on algorithm optimization as to do so would be costly. In response to this need, an ACJ shinyapp has been developed. It is presented in this article from a technical perspective with a view that this description can afford needed transparency in the use of ACJ and that having such a tool now permits more systematic investigation into the impactful pedagogical usage of ACJ. [ABSTRACT FROM AUTHOR]

In this paper, we present the development of an open-source discrete event simulator (DES) using the cross-platform game engine Unity and C#. Our goal is to provide a robust DES engine that can be seamlessly integrated with a game engine, leveraging its modularity and reusability. We built a standalone DES simulator in C# and integrated it with Unity through a custom code layer. This integration allowed us to leverage Unity's capabilities and explore potential applications while providing a basic set of assets tailored for manufacturing processes. Verification tests against commercial DES software and experiments validated our approach. Additionally, we showcase examples that explore the advantages and limitations of using game engines as platforms for manufacturing digital twins. While further development is required to transform the simulator into a ready-to-use OS simulator, our approach enables future developments and seamless integration with other tools while maintaining the project's open-source nature. [ABSTRACT FROM AUTHOR]

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