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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.

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 growing environmental challenges, including biodiversity loss, necessitate new tools to enhance collaboration and transparency in conservation efforts. This study introduces the CONSCIOUS framework, a platform built on blockchain technology. The framework's potential to revolutionize communication and cooperation between individuals, organizations, and communities is immense. By leveraging blockchain's ability to create secure and transparent records, the framework ensures clear tracking of project progress and funding. The study demonstrates how this approach can significantly improve stakeholder participation, resource management transparency, and project outcomes accountability. The results suggest that the CONSCIOUS framework has the potential to not just strengthen, but transform biodiversity conservation efforts by uniting diverse groups more transparently and efficiently. [ABSTRACT FROM AUTHOR]

Recent advancements in vehicle technology have stimulated innovation across the automotive sector, from Advanced Driver Assistance Systems (ADAS) to autonomous driving and motorsport applications. Modern vehicles, equipped with sensors for perception, localization, navigation, and actuators for autonomous driving, generate vast amounts of data used for training and evaluating autonomous systems. Real-world testing is essential for validation but is complex, expensive, and time-intensive, requiring multiple vehicles and reference systems. To address these challenges, computer graphics-based simulators offer a compelling solution by providing high-fidelity 3D environments to simulate vehicles and road users. These simulators are crucial for developing, validating, and testing ADAS, autonomous driving systems, and cooperative driving systems, and enhancing vehicle performance and driver training in motorsport. This paper reviews computer graphics-based simulators tailored for automotive applications. It begins with an overview of their applications and analyzes their key features. Additionally, this paper compares five open-source (CARLA, AirSim, LGSVL, AWSIM, and DeepDrive) and ten commercial simulators. Our findings indicate that open-source simulators are best for the research community, offering realistic 3D environments, multiple sensor support, APIs, co-simulation, and community support. Conversely, commercial simulators, while less extensible, provide a broader set of features and solutions. [ABSTRACT FROM AUTHOR]

Lockdowns and social progress have increased hours of work from home, often requiring remote methods of communication. Agricultural organizations from associations to cooperatives to joint stock companies must prepare to carry out more activities online. This article proposes a procedure for the possible implementation of a remote electronic election in personnel matters of the organization using the Belenios system, based on an evaluation of expectations from a questionnaire survey of agricultural college students and graduates. The proposed procedure is subsequently verified based on an evaluation of compliance with the Council of Europe recommendation on standards for electronic voting. [ABSTRACT FROM AUTHOR]

Objectives The study developed framework that leverages an open-source Large Language Model (LLM) to enable clinicians to ask plain-language questions about a patient's entire echocardiogram report history. This approach is intended to streamline the extraction of clinical insights from multiple echocardiogram reports, particularly in patients with complex cardiac diseases, thereby enhancing both patient care and research efficiency. Materials and Methods Data from over 10 years were collected, comprising echocardiogram reports from patients with more than 10 echocardiograms on file at the Mount Sinai Health System. These reports were converted into a single document per patient for analysis, broken down into snippets and relevant snippets were retrieved using text similarity measures. The LLaMA-2 70B model was employed for analyzing the text using a specially crafted prompt. The model's performance was evaluated against ground-truth answers created by faculty cardiologists. Results The study analyzed 432 reports from 37 patients for a total of 100 question-answer pairs. The LLM correctly answered 90% questions, with accuracies of 83% for temporality, 93% for severity assessment, 84% for intervention identification, and 100% for diagnosis retrieval. Errors mainly stemmed from the LLM's inherent limitations, such as misinterpreting numbers or hallucinations. Conclusion The study demonstrates the feasibility and effectiveness of using a local, open-source LLM for querying and interpreting echocardiogram report data. This approach offers a significant improvement over traditional keyword-based searches, enabling more contextually relevant and semantically accurate responses; in turn showing promise in enhancing clinical decision-making and research by facilitating more efficient access to complex patient data. [ABSTRACT FROM AUTHOR]

Maintenance of mechanical elements is considered to be the most crucial task in industry and affects the economics, predominantly. There are many techniques available for fault diagnosis and condition monitoring of rotatory machine elements and one of the popular tools is vibration monitoring. Analyzing vibrations would be useful in supervising equipment/machine components in an attempt to detect the significant change which is the cause of growing failures or faults. Broadly speaking, it comes under prognostics and health monitoring (PHM) and prevents unexpected failure and costly repair. Proficient predictive monitoring and on-board recognition of failures is a need of the Fourth Industrial Revolution. Several types of vibration analyzers and sensors are commercially available, however, their total budget is not affordable. This motivates reviewing the suitability of low-cost sensors for vibration monitoring. An inclusive review of a variety of applications wherein microelectromechanical system (MEMS)-based accelerometer is used is presented in this paper. This would support an open-design movement for creating open. [ABSTRACT FROM AUTHOR]

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.

The integration of digital technologies into political processes represents a pivotal shift in the operational dynamics of political parties, highlighting an evolution from traditional to digital platforms. This transformation offers greater member engagement and decision-making transparency while also introducing new challenges in terms of security and control. This study presents an analytical overview of the Helios voting system as implemented by the Czech Pirate Party, used not only for primary elections, offering a critical examination of its operational framework, security measures, verification processes, and implications for intra-party democracy and member engagement. The Czech Pirate Party's engagement with Helios exemplifies a broader trend toward digitalization within political organizations, reflecting both the potential benefits and inherent challenges of such technological adoption. Drawing on theoretical insights from the literature on political party transformation in the digital age, this article assesses how i-voting systems like Helios can potentially enhance or detract from internal democracy. The discourse around digital platforms is nuanced, revealing opportunities for increased democratic engagement alongside risks of enhanced centralization and control. Empirical data from a survey of the Czech Pirate Party's membership base provides insights into perceptions of the Helios system's usability, security, and its impact on democratic processes within the party. The study further investigates Helios's technical aspects, focusing on its security protocols, verification mechanisms, and governance structures related to data and platform ownership, to understand its implications for electoral integrity and party governance. The research aims to contribute to the dialogue on e-voting platforms, highlighting the need for a careful balance between embracing technological advancements and upholding democratic principles.

In this paper, a model order reduction strategy is adopted for the static and dynamic behaviour simulation of a high-speed tracked vehicle. The total number of degree of freedom of the structure is condensed through a selection of interface degrees of freedom and significant global mode shapes, for an approximated description of vehicle dynamic behaviour. The methodology is implemented in a customised open-source software to reduce the computational efforts. The modelled tracked vehicle includes the sprung mass, the unsprung masses, connected by means of torsional bars, and all the track assemblies, composing the track chain. The proposed research activity presents a comprehensive investigation of the influence of the track chain, combined with longitudinal vehicle speed, on statics and vehicle dynamics, focusing on vertical dynamics. The vehicle response has been investigated both in frequency and time domain. In this last case road-wheel displacements are assumed as inputs for the model, under different working conditions, hence considering several road profiles with different amplitudes and characteristic excitation frequencies. Simulation results have proven a high fidelity in model order reduction approach and a significant contribution of the track chain in the global dynamic behaviour of the tracked vehicle.

Abstract Our prototype system designed for clinical data acquisition and recording of studies is a novel electronic data capture (EDC) software for simple and lightweight data capture in clinical research. Existing software tools are either costly or suffer from very limited features. To overcome these shortcomings, we designed an EDC software together with a mobile client. We aimed at making it easy to set-up, modifiable, scalable and thereby facilitating research. We wrote the software in R using a modular approach and implemented existing data standards along with a meta data driven interface and database structure. The prototype is an adaptable open-source software, which can be installed locally or in the cloud without advanced IT-knowledge. A mobile web interface and progressive web app for mobile use and desktop computers is added. We show the software’s capability, by demonstrating four clinical studies with over 1600 participants and 679 variables per participant. We delineate a simple deployment approach for a server-installation and indicate further use-cases. The software is available under the MIT open-source license. Conclusively the software is versatile, easily deployable, highly modifiable, and extremely scalable for clinical studies. As an open-source R-software it is accessible, open to community-driven development and improvement in the future.

Abstract Background Use of Continuous Subcutaneous Insulin Infusion (CSII) has been shown to improve glycemic outcomes in Type 1 Diabetes (T1D), but high costs limit accessibility. To address this issue, an inter-operable, open-source Ultra-Low-Cost Insulin Pump (ULCIP) was developed and previously shown to demonstrate comparable delivery accuracy to commercial models in standardised laboratory tests. This study aims to evaluate the updated ULCIP in-vivo, assessing its viability as an affordable alternative for those who cannot afford commercially available devices. Methods This first-in-human feasibility study recruited six participants with T1D. During a nine-hour inpatient stay, participants used the ULCIP under clinical supervision. Venous glucose, insulin, and β-Hydroxybutyrate were monitored to assess device performance. Results Participants displayed expected blood glucose and blood insulin levels in response to programmed basal and bolus insulin dosing. One participant developed mild ketosis, which was treated and did not recur when a new pump reservoir was placed. All other participants maintained β-Hydroxybutyrate

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 CO32- 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, εH2Ot,3550 = 63.03 ± 4.47 L/mol · cm and εCO2−3,1515,1430 = 303.44 ± 9.20 L/mol · cm; 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.

In response to the growing cyber-attack threat, incident response teams have become a critical component of an organization's cybersecurity strategy. These teams are responsible for detecting, analyzing, and responding to security incidents promptly and effectively. However, detecting code injection attacks can be particularly challenging, as they can be difficult to detect and often go unnoticed until it is too late. Cybersecurity professionals use detection tools to detect and respond to DLL injection attacks that monitor system activity and detect unusual behavior. A large portion of the related literature focuses on the use of commercial DLL injection tools. In contrast, little attention has been paid to the effectiveness of using open-source DLL injection detection tools. Thus, this research project aims to evaluate the effectiveness of three widely used open-source tools, VirusTotal, Sysinternals, and Yara, in detecting DLL injection incidents. This study's findings highlight each tool's strengths and limitations, which in turn enables cybersecurity professionals to make informed decisions when selecting the most suitable tool for DLL injection detection. Furthermore, the study emphasizes the importance of continuous tool development and updates to keep pace with evolving malware techniques and emerging threats. By highlighting the effectiveness of the tools, this research enhances the overall security posture of organizations and individuals, empowering them to mitigate the risks associated with DLL injection attacks proactively. The outcomes of this research project also underscore the significance of leveraging advanced tools to fortify cybersecurity defenses and safeguard critical systems and data.

Abstract LLMs can accomplish specialized medical knowledge tasks, however, equitable access is hindered by the extensive fine-tuning, specialized medical data requirement, and limited access to proprietary models. Open-source (OS) medical LLMs show performance improvements and provide the transparency and compliance required in healthcare. We present OpenMedLM, a prompting platform delivering state-of-the-art (SOTA) performance for OS LLMs on medical benchmarks. We evaluated OS foundation LLMs (7B-70B) on medical benchmarks (MedQA, MedMCQA, PubMedQA, MMLU medical-subset) and selected Yi34B for developing OpenMedLM. Prompting strategies included zero-shot, few-shot, chain-of-thought, and ensemble/self-consistency voting. OpenMedLM delivered OS SOTA results on three medical LLM benchmarks, surpassing previous best-performing OS models that leveraged costly and extensive fine-tuning. OpenMedLM displays the first results to date demonstrating the ability of OS foundation models to optimize performance, absent specialized fine-tuning. The model achieved 72.6% accuracy on MedQA, outperforming the previous SOTA by 2.4%, and 81.7% accuracy on MMLU medical-subset, establishing itself as the first OS LLM to surpass 80% accuracy on this benchmark. Our results highlight medical-specific emergent properties in OS LLMs not documented elsewhere to date and validate the ability of OS models to accomplish healthcare tasks, highlighting the benefits of prompt engineering to improve performance of accessible LLMs for medical applications.

We present DiadFit—an open-source Python3 tool for efficient processing of Raman spectroscopy data collected from fluid inclusions, melt inclusions and silicate melts. DiadFit is optimized to fit the characteristic peaks from CO2 fluids (Fermi diads, hot bands, 13C), gas species such as SO2, N2, solid precipitates (e.g. carbonates), and Ne emission lines with easily tweakable background positions and peak shapes. DiadFit's peak fitting functions are used as part of a number of workflows optimized for quantification of CO2 in melt inclusion vapour bubbles and fluid inclusions. DiadFit can also convert between temperature, pressure and density using various CO2 and CO2-H2O equations of state (EOS), allowing calculation of fluid inclusion pressures (and depths in the crust), conversion of homogenization temperatures from microthermometry to CO2 density, and propagation of uncertainties associated with EOS calculations using Monte Carlo methods. There are also functions to quantify the area ratio of the silicate vs. H2O region of spectra collected on silicate glasses to determine H2O contents in glasses and melt inclusions. Documentation and worked examples are available (https://bit.ly/DiadFitRTD, https://bit.ly/DiadFitYouTube).

Our prototype system designed for clinical data acquisition and recording of studies is a novel electronic data capture (EDC) software for simple and lightweight data capture in clinical research. Existing software tools are either costly or suffer from very limited features. To overcome these shortcomings, we designed an EDC software together with a mobile client. We aimed at making it easy to set-up, modifiable, scalable and thereby facilitating research. We wrote the software in R using a modular approach and implemented existing data standards along with a meta data driven interface and database structure. The prototype is an adaptable open-source software, which can be installed locally or in the cloud without advanced IT-knowledge. A mobile web interface and progressive web app for mobile use and desktop computers is added. We show the software's capability, by demonstrating four clinical studies with over 1600 participants and 679 variables per participant. We delineate a simple deployment approach for a server-installation and indicate further use-cases. The software is available under the MIT open-source license. Conclusively the software is versatile, easily deployable, highly modifiable, and extremely scalable for clinical studies. As an open-source R-software it is accessible, open to community-driven development and improvement in the future. [ABSTRACT FROM AUTHOR]

Energy modelling plays a crucial role in assisting governmental and policymaking bodies to strategise long-term investments within the context of energy transition. Among the well-established open-source optimisation models, OSeMOSYS—the Open-Source Energy Modelling System—stands out. This paper introduces clicSAND, a novel user interface designed for OSeMOSYS, aimed at reducing the learning curve and supporting novice energy modelers in efficiently conducting long-term investment analyses. clicSAND, freely available and open-source, features a user-friendly Excel interface for data input, integrated solvers, and a visualisation dashboard for result interpretation. The outcomes, projected up to 2070, hold the potential to inform policy decisions and mobilise financial resources for sustainable development endeavors, such as ensuring affordable and secure energy supply and mitigating climate change impacts. This advancement not only democratises access to energy modelling tools but also empowers policymakers and stakeholders to conduct thorough long-term investment analyses with ease. This paper elaborates on clicSAND's key advantages, architecture, and functionalities. Additionally, it discusses the evolutionary journey from clicSAND 1.0 to 3.0, emphasising a commitment to continuous improvement and user-centric adaptation, thereby enhancing its utility and relevance. The inclusion of a South African case study, conducted during the EMP-A (Energy Modelling Platform for Africa) 2021 international capacity-building event, showcases clicSAND's efficacy in facilitating knowledge transfer and skill development among inexperienced users, while providing a tangible example of its application in addressing specific regional energy challenges and policy contexts. Finally, current applications and future extensions of the software are also presented. [ABSTRACT FROM AUTHOR]

Precise point positioning (PPP) is a prevalent, high-precision spatial absolution positioning method, and its performance can be enhanced by ambiguity resolution (AR). To fulfill the growing need for high-precision positioning, we developed an open-source GNSS data processing package based on the decoupled clock model called Cube, which integrates decoupled clock offset estimation and precise point positioning with ambiguity resolution (PPP-AR). Cube is a secondary development based on RTKLIB. Besides the decoupled clock model, Cube can also estimate legacy clocks for the International GNSS Service (IGS), as well as clocks with satellite code bias extraction, and perform PPP-AR using the integer-recovered clock model. In this work, we designed satellite clock estimation and PPP-AR experiments with one week of GPS data to validate Cube's performance. Results show that the software can produce high-precision satellite clock products and positioning results that are adequate for daily scientific study. With Cube, researchers do not need to rely on public PPP-AR products, and they can estimate decoupled clock products and implement PPP-AR anytime. [ABSTRACT FROM AUTHOR]

Background: Use of Continuous Subcutaneous Insulin Infusion (CSII) has been shown to improve glycemic outcomes in Type 1 Diabetes (T1D), but high costs limit accessibility. To address this issue, an inter-operable, open-source Ultra-Low-Cost Insulin Pump (ULCIP) was developed and previously shown to demonstrate comparable delivery accuracy to commercial models in standardised laboratory tests. This study aims to evaluate the updated ULCIP in-vivo, assessing its viability as an affordable alternative for those who cannot afford commercially available devices. Methods: This first-in-human feasibility study recruited six participants with T1D. During a nine-hour inpatient stay, participants used the ULCIP under clinical supervision. Venous glucose, insulin, and β-Hydroxybutyrate were monitored to assess device performance. Results: Participants displayed expected blood glucose and blood insulin levels in response to programmed basal and bolus insulin dosing. One participant developed mild ketosis, which was treated and did not recur when a new pump reservoir was placed. All other participants maintained β-Hydroxybutyrate < 0.6 mmol/L throughout. Conclusion: The ULCIP safely delivered insulin therapy to users in a supervised inpatient environment. Future work should focus on correcting a pump hardware issue identified in this trial and extending device capabilities for use in closed loop control. Longer-term outpatient studies are warranted. Trial Registration: The trial was prospectively registered with the Australian New Zealand Clinical Trials Registry (ACTRN12623001288617) on the 11 December 2023. [ABSTRACT FROM AUTHOR]

The Smoothed Particle Finite Element Method (SPFEM) has gained popularity as an effective numerical method for modelling geotechnical problems involving large deformations. To promote the research and application of SPFEM in geotechnical engineering, we present ESPFEM2D, an open-source two-dimensional SPFEM solver developed using MATLAB. ESPFEM2D discretizes the problem domain into computable particle clouds and generates the finite element mesh using Delaunay triangulation and the α -shape technique to resolve mesh distortion issues. Additionally, it incorporates a nodal integration technique based on strain smoothing, effectively eliminating defects associated with the state variable mapping after remeshing. Furthermore, the solver adopts a simple yet robust approach to prevent the rank-deficiency problem due to under-integration by using only nodes as integration points. The Drucker-Prager model is adopted to describe the soil's constitutive behavior as a demonstration. Implemented in MATLAB, this open-source solver ensures easy accessibility and readability for researchers interested in utilizing SPFEM. ESPFEM2D can be easily extended and effectively coupled with other existing codes, enabling its application to simulate a wide range of large geomechanical deformation problems. Through rigorous validation using four numerical examples, namely the oscillation of an elastic cantilever beam, non-cohesive soil collapse, cohesive soil collapse, and slope stability analysis, the accuracy, effectiveness and stability of this open-source solver have been thoroughly confirmed. [ABSTRACT FROM AUTHOR]

Quantitative assessment of biotribological properties requires expensive specialized equipment. The aim was to: i) adapt an open-source load cell-based platform (PASTA) for biotribometric analysis; ii) study the effects of oxidation on the water-based lubricant using PASTA. Water-based lubricant was treated with 2,2'-azobis(2-amidinopropane) dihydrochloride and/or glutathione. The samples were analyzed with the ORP-146S redox microsensor and PASTA using a modified HX711 integrated circuit bord, NodeMCU ESP-32S, and an open-source Python script. PASTA can be adapted for affordable and reliable quantitative biotribometric assessment. Glutathione can prevent the loss of lubrication capacity of a water-based lubricant upon exposure to air. [ABSTRACT FROM AUTHOR]

This paper introduces ESMira, a server and mobile app (Android, iOS) developed for research projects using experience sampling method (ESM) designs. ESMira offers a very simple setup process and ease of use, while being free, decentralized, and open-source (source code is available on GitHub). The ongoing development of ESMira started in early 2019, with a focus on scientific requirements (e.g., informed consent, ethical considerations), data security (e.g., encryption), and data anonymity (e.g., completely anonymous data workflow). ESMira sets itself apart from other platforms by both being free of charge and providing study administrators with full control over study data without the need for specific technological skills (e.g., programming). This means that study administrators can have ESMira running on their own webspace without needing much technical knowledge, allowing them to remain independent from any third-party service. Furthermore, ESMira offers an extensive list of features (e.g., an anonymous built-in chat to contact participants; a reward system that allows participant incentivization without breaching anonymity; live graphical feedback for participants) and can deal with complex study designs (e.g., nested time-based sampling). In this paper, we illustrate the basic structure of ESMira, explain how to set up a new server and create studies, and introduce the platform's basic functionalities. [ABSTRACT FROM AUTHOR]

Purpose: To develop an open‐source prototype of myocardial T1 mapping (Open‐MOLLI) to improve accessibility to cardiac T1 mapping and evaluate its repeatability. With Open‐MOLLI, we aim to enable faster implementation and testing of sequence modifications and to facilitate inter‐scanner and cross‐vendor reproducibility studies. Methods: Open‐MOLLI is an inversion‐recovery sequence using a balanced SSFP (bSSFP) readout, with inversion and triggering schemes based on the 5(3)3 MOLLI sequence, developed in Pulseq. Open‐MOLLI and MOLLI sequences were acquired in the ISMRM/NIST phantom and 21 healthy volunteers. In 18 of those subjects, Open‐MOLLI and MOLLI were repeated in the same session (test–retest). Results: Phantom T1 values were comparable between methods, specifically for the vial with reference T1 value most similar to healthy myocardium T1 (T1vial3 = 1027 ms): T1MOLLI = 1011 ± 24 ms versus T1Open‐MOLLI = 1009 ± 20 ms. In vivo T1 estimates were similar between Open‐MOLLI and MOLLI (T1MOLLI = 1004 ± 33 ms vs. T1Open‐MOLLI = 998 ± 52 ms), with a mean difference of −17 ms (p = 0.20), despite noisier Open‐MOLLI weighted images and maps. Repeatability measures were slightly higher for Open‐MOLLI (RCMOLLI = 3.0% vs. RCOpen‐MOLLI = 4.4%). Conclusion: The open‐source sequence Open‐MOLLI can be used for T1 mapping in vivo with similar mean T1 values to the MOLLI method. Open‐MOLLI increases the accessibility to cardiac T1 mapping, providing also a base sequence to which further improvements can easily be added and tested. [ABSTRACT FROM AUTHOR]

Flow-mediated dilation (FMD) is a common measure of endothelial function and an indicator of vascular health. Automated software methods exist to improve the speed and accuracy of FMD analysis. Compared with commercial software, open-source software offers similar capabilities at a much lower cost while allowing for increased customization specific to users' needs. We introduced modifications to an existing open-source software, FloWave.us to better meet FMD analysis needs. The purpose of this study was to compare the repeatability and reliability of the modified FloWave.us software to the original software and to manual measurements. To assess these outcomes, duplex ultrasound imaging data from the popliteal artery in older adults were analyzed. The average percent FMD for the modified software was 6.98 ± 3.68% and 7.27 ± 3.81% for observer 1 and 2 respectively, compared with 9.17 ± 4.91% and 10.70 ± 4.47% with manual measurements and 5.07 ± 31.79% with the original software for observer 1. The modified software and manual methods demonstrated higher intraobserver intraclass correlation coefficients (ICCs) for repeated measures for baseline diameter, peak diameter, and percent FMD compared with the original software. For percent FMD, the interobserver ICC was 0.593 for manual measurements and 0.723 for the modified software. With the modified method, an average of 97.7 ± 2.4% of FMD videos frames were read, compared with only 17.9 ± 15.0% frames read with the original method when analyzed by the same observer. Overall, this work further establishes open-source software as a robust and viable tool for FMD analysis and demonstrates improved reliability compared with the original software. NEW & NOTEWORTHY: This study improves edge detection capabilities and implements noise reduction strategies to optimize an existing open-source software's suitability for flow-mediated dilation (FMD) analysis. The modified software improves the precision and reliability of FMD analysis compared with the original software algorithm. We demonstrate that this modified open-source software is a robust tool for FMD analysis. [ABSTRACT FROM AUTHOR]

Copyright of Przegląd Elektrotechniczny is the property of Przeglad Elektrotechniczny and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

In this paper, a model order reduction strategy is adopted for the static and dynamic behaviour simulation of a high-speed tracked vehicle. The total number of degree of freedom of the structure is condensed through a selection of interface degrees of freedom and significant global mode shapes, for an approximated description of vehicle dynamic behaviour. The methodology is implemented in a customised open-source software to reduce the computational efforts. The modelled tracked vehicle includes the sprung mass, the unsprung masses, connected by means of torsional bars, and all the track assemblies, composing the track chain. The proposed research activity presents a comprehensive investigation of the influence of the track chain, combined with longitudinal vehicle speed, on statics and vehicle dynamics, focusing on vertical dynamics. The vehicle response has been investigated both in frequency and time domain. In this last case road-wheel displacements are assumed as inputs for the model, under different working conditions, hence considering several road profiles with different amplitudes and characteristic excitation frequencies. Simulation results have proven a high fidelity in model order reduction approach and a significant contribution of the track chain in the global dynamic behaviour of the tracked vehicle. [ABSTRACT FROM AUTHOR]

Optical projection tomography (OPT) is a threedimensional mesoscopic imaging modality that can use absorption or fluorescence contrast, and is widely applied to fixed and live samples in the mm-cm scale. For fluorescence OPT, we present OPT implemented for accessibility and low cost, an open-source research-grade implementation of modular OPT hardware and software that has been designed to be widely accessible by using low-cost components, including light-emitting diode (LED) excitation and cooled complementary metal-oxidesemiconductor (CMOS) cameras. Both the hardware and software are modular and flexible in their implementation, enabling rapid switching between sample size scales and supporting compressive sensing to reconstruct images from undersampled sparse OPT data, e.g. to facilitate rapid imaging with low photobleaching/phototoxicity. We also explore a simple implementation of focal scanning OPT to achieve higher resolution, which entails the use of a fan-beam geometry reconstruction method to account for variation in magnification. This article is part of the Theo Murphy meeting issue 'Open, reproducible hardware for microscopy'. [ABSTRACT FROM AUTHOR]

Intent-Based Networking (IBN) is an important step towards achieving network automation. Many challenges of today’s complex network management systems can be tackled by the solutions proposed by IBN. However, although IBN has gained a lot of attention from the academic and industrial community in the second half of the last decade leading to many scientific publications and research papers, there has been little effort made on proposing a comprehensive framework for IBN, which converts system-level IBN concepts and theories into a fully featured software implementation. This paper presents such framework. Its implementation is standards-based and open-source. The framework can be used to facilitate and validate novel research ideas and test cases. The paper discusses relevant IBN design concepts and theories, how the framework’s software architecture is derived from those concepts, and the technical and implementation details on key IBN aspects and features including Intent life-cycle, Intent definition and translation, Intent orchestration, and Intent assurance using closed-loops. We also demonstrate a real intent-based use case realized by the framework in order to show and validate the proof-of-concept (PoC). The Future work of this project is also discussed. [ABSTRACT FROM AUTHOR]

Health innovation is mainly envisioned in direct connection to medical research institutions or pharmaceutical and technology companies. Yet, these types of innovation often do not meet the needs and expectations of individuals affected by health conditions. With the emergence of digital health technologies and social media, we can observe a shift, which involves people living with illness modifying and improving medical and health devices outside of the formal research and development sector, figuring both as users and innovators. This patient‐led innovation has been celebrated in innovation studies and economics as a "bottom‐up" type of innovation. In this article, we take a closer look at open‐source patient‐led innovation in the context of type 1 diabetes care. In our inquiry, we pay particular attention to the social and ethical dimensions of this innovation, building on empirical material. Upon exploring the notion of patient‐led innovation and its socio‐political context through the lens of intersectional and global health justice, we argue that a proactive strategy is needed to ensure that open‐source patient‐led innovation will be more globally accessible, center the health needs of the most underserved populations, as well as facilitate equitable and just health benefits. To support this aim, we provide a range of examples of different initiatives addressing the persistent inequalities that have so far inhibited patient‐led innovation from more fully materializing its innovative potential. [ABSTRACT FROM AUTHOR]

Accurate and timely reporting of adverse events (AEs) in clinical trials is crucial to ensuring data integrity and patient safety. However, AE under-reporting remains a challenge, often highlighted in Good Clinical Practice (GCP) audits and inspections. Traditional detection methods, such as on-site investigator audits via manual source data verification (SDV), have limitations. Addressing this, the open-source R package {simaerep} was developed to facilitate rapid, comprehensive, and near-real-time detection of AE under-reporting at each clinical trial site. This package leverages patient-level AE and visit data for its analyses. To validate its efficacy, three member companies from the Inter coMPany quALity Analytics (IMPALA) consortium independently assessed the package. Results showed that {simaerep} consistently and effectively identified AE under-reporting across all three companies, particularly when there were significant differences in AE rates between compliant and non-compliant sites. Furthermore, {simaerep}'s detection rates surpassed heuristic methods, and it identified 50% of all detectable sites as early as 25% into the designated study duration. The open-source package can be embedded into audits to enable fast, holistic, and repeatable quality oversight of clinical trials. [ABSTRACT FROM AUTHOR]

The climatic emergency that involves the globe has led targets of greenhouse gas reduction in the EU and all over the world. In this scenario, recent advances in renewable renewable energy sources (RESs) have focused interest on the diffusion of power supplies that are produced by photovoltaic and wind plants. The non-programmable nature of these energy sources has led recent studies to consider the power-to-gas (P2G) solution as an opportunity for employing the curtailed electric energy by converting it into hydrogen. The localizations of P2G plants depend on several factors regarding power production, distances, and population distributions. The necessity of integrating these factors led this work to study the development of a cost function that is hosted by a webbased GIS (geographic information system) platform, thus allowing for the storage, elaboration, and web fruition of an entire data set that is related to the possible new localizations of P2G plants. The structure is based on open-source technology and creates a solution that is easily employable by specialists. The developed platform is composed of different remotely connected blocks that are solely based on open-source technology and is focused the interest on the territory of Sicily (Italy). GIS software, a RDBMS database, a geospatial server (Geoserver), a Python optimization module, and a WebGIS visualizer are integrated. This work represents a scientific contribution to the management of energy sources, with a particular focus on policies that are based on hydrogen technology. In fact, different data sets that contain several levels of information that are related to the management and the localization of P2G plants will be even further employed in the future. [ABSTRACT FROM AUTHOR]

Purpose: To reduce the inter‐scanner variability of diffusion MRI (dMRI) measures between scanners from different vendors by developing a vendor‐neutral dMRI pulse sequence using the open‐source vendor‐agnostic Pulseq platform. Methods: We implemented a standard EPI based dMRI sequence in Pulseq. We tested it on two clinical scanners from different vendors (Siemens Prisma and GE Premier), systematically evaluating and comparing the within‐ and inter‐scanner variability across the vendors, using both the vendor‐provided and Pulseq dMRI sequences. Assessments covered both a diffusion phantom and three human subjects, using standard error (SE) and Lin's concordance correlation to measure the repeatability and reproducibility of standard DTI metrics including fractional anisotropy (FA) and mean diffusivity (MD). Results: Identical dMRI sequences were executed on both scanners using Pulseq. On the phantom, the Pulseq sequence showed more than a 2.5× reduction in SE (variability) across Siemens and GE scanners. Furthermore, Pulseq sequences exhibited markedly reduced SE in‐vivo, maintaining scan‐rescan repeatability while delivering lower variability in FA and MD (more than 50% reduction in cortical/subcortical regions) compared to vendor‐provided sequences. Conclusion: The Pulseq diffusion sequence reduces the cross‐scanner variability for both phantom and in‐vivo data, which will benefit multi‐center neuroimaging studies and improve the reproducibility of neuroimaging studies. [ABSTRACT FROM AUTHOR]

This paper examines open-source migrations from the early career librarian perspective. After being on the implementation team for two open-source migrations in two years, the author noticed stark similarities in the two experiences. The paper will discuss the challenges all migrations face and challenges more specific to open-source systems. These include lack of resources, changing timelines, and lack of system sustainability. The paper will then move on to provide recommendations for other early career librarians who are implementing open-source systems. The paper will also offer ways colleagues and supervisors who are not working on the implementation can support these migrations and the people. [ABSTRACT FROM AUTHOR]

LLMs can accomplish specialized medical knowledge tasks, however, equitable access is hindered by the extensive fine-tuning, specialized medical data requirement, and limited access to proprietary models. Open-source (OS) medical LLMs show performance improvements and provide the transparency and compliance required in healthcare. We present OpenMedLM, a prompting platform delivering state-of-the-art (SOTA) performance for OS LLMs on medical benchmarks. We evaluated OS foundation LLMs (7B-70B) on medical benchmarks (MedQA, MedMCQA, PubMedQA, MMLU medical-subset) and selected Yi34B for developing OpenMedLM. Prompting strategies included zero-shot, few-shot, chain-of-thought, and ensemble/self-consistency voting. OpenMedLM delivered OS SOTA results on three medical LLM benchmarks, surpassing previous best-performing OS models that leveraged costly and extensive fine-tuning. OpenMedLM displays the first results to date demonstrating the ability of OS foundation models to optimize performance, absent specialized fine-tuning. The model achieved 72.6% accuracy on MedQA, outperforming the previous SOTA by 2.4%, and 81.7% accuracy on MMLU medical-subset, establishing itself as the first OS LLM to surpass 80% accuracy on this benchmark. Our results highlight medical-specific emergent properties in OS LLMs not documented elsewhere to date and validate the ability of OS models to accomplish healthcare tasks, highlighting the benefits of prompt engineering to improve performance of accessible LLMs for medical applications. [ABSTRACT FROM AUTHOR]

Global Navigation Satellite System (GNSS) signals experience delays when passing through the atmosphere due to the presence of free electrons in the ionosphere and air density in the non-ionized part of the atmosphere, known as the troposphere. The Precise Point Positioning (PPP) technique demonstrates highly accurate positioning along with Zenith Tropospheric Delay (ZTD) estimation. ZTD estimation is valuable for various applications including climate modelling and determining atmospheric water vapor. Current GNSS network resolutions are not completely sufficient for the scale of a few kilometres that regional climate and weather models are increasingly adopting. The Centipede-RTK network is a low-cost option for increasing the spatial resolution of tropospheric monitoring. This study is motivated by the question of whether low-cost GNSS networks can provide a viable alternative without compromising data quality or precision. This study compares the performance of the low-cost Centipede-RTK network in calculating the Zenith Tropospheric Delay (ZTD) to that of the existing EUREF Permanent Network (EPN), using two alternative software packages, RTKLIB demo5 version and CSRS-PPP version 3, to ensure robustness and software independence in the findings. This investigation indicated that the ZTD estimations from both networks are almost identical when processed by the CSRS-PPP software, with the highest mean difference being less than 3.5 cm, confirming that networks such as Centipede-RTK could be a reliable option for dense precise atmospheric monitoring. Furthermore, this study revealed that the Centipede-RTK network, when processed using CSRS-PPP, provides ZTD estimations that are very similar and consistent with the EUREF ZTD product values. These findings suggest that low-cost GNSS networks like Centipede-RTK are viable for enhancing network density, thus improving the spatial resolution of tropospheric monitoring and potentially enriching climate modelling and weather prediction capabilities, paving the way for broader application and research in GNSS meteorology. [ABSTRACT FROM AUTHOR]

The Software-Defined Networking concept plays an important role in network management. The central controller, which is the main element of SDN, allows the provision of traffic engineering and security solutions in single- and multiple-layer networks based on optical transmission. In this work, we compare selected open-source implementations of SDN controllers. Throughput and latency measurements were analyzed using the CBench program. The simulation of a link failure and a controller failure were conducted using the API provided by the Mininet network simulator. To detect security vulnerabilities, a dedicated program, called sdnpwn, was used. This work provides an overview of the selected controllers, indicating their strengths and weaknesses. Moreover, some implemention suggestions and recommendations are presented. [ABSTRACT FROM AUTHOR]

In the face of rising population, erratic climate, resource depletion, and increased exposure to natural hazards, environmental monitoring is increasingly important. Satellite data form most of our observations of Earth. On-the-ground observations based on in situ sensor systems are crucial for these remote measurements to be dependable. Providing open-source options to rapidly prototype environmental datalogging systems allows quick advancement of research and monitoring programs. This paper introduces Loom, a development environment for low-power Arduino-programmable microcontrollers. Loom accommodates a range of integrated components including sensors, various datalogging formats, internet connectivity (including Wi-Fi and 4G Long Term Evolution (LTE)), radio telemetry, timing mechanisms, debugging information, and power conservation functions. Additionally, Loom includes unique applications for science, technology, engineering, and mathematics (STEM) education. By establishing modular, reconfigurable, and extensible functionality across components, Loom reduces development time for prototyping new systems. Bug fixes and optimizations achieved in one project benefit all projects that use Loom, enhancing efficiency. Although not a one-size-fits-all solution, this approach has empowered a small group of developers to support larger multidisciplinary teams designing diverse environmental sensing applications for water, soil, atmosphere, agriculture, environmental hazards, scientific monitoring, and education. This paper not only outlines the system design but also discusses alternative approaches explored and key decision points in Loom's development. [ABSTRACT FROM AUTHOR]

This article reports on an open-source ontology that has been developed to establish an industry-wide consensus on wind lidar concepts and terminology. The article provides an introduction to wind lidar ontology, provides an overview of its development, and provides a summary of its aims and achievements. The ontology serves both reference and educational purposes for wind energy applications and lidar technology. The article provides an overview of the creation process, the outcomes of the project, and the proposed uses of the ontology. The ontology is available online and provides standardisation of terminology within the lidar knowledge domain. The open-source framework provides the basis for information sharing and integration within remote sensing science and fields of application. [ABSTRACT FROM AUTHOR]

We developed the open-source bIUreactor research platform for studying 3D structured tissues. The versatile and modular platform allows a researcher to generate 3D tissues, culture them with oxygenated perfusion, and provide cyclic loading, all in their own lab (in laboratorium) for an all in cost of $8,000 including 3D printer, printing resin, and electronics. We achieved this by applying a design philosophy that leverages 3D printing, open-source software and hardware, and practical techniques to produce the following: 1. perfusible 3D tissues, 2. a bioreactor chamber for tissue culture, 3. a module for applying cyclic compression, 4. a peristaltic pump for providing oxygenated perfusion to 3D tissues, 5. motor control units, and 6. open-source code for running the control units. By making it widely available for researchers to investigate 3D tissue models and easy for them to use, we intend for the bIUreactor to democratize 3D tissue research, therefore increasing the pace and scale of biomedical research discoveries using 3D tissue models. [ABSTRACT FROM AUTHOR]

Laboratory automation effectively increases the throughput in sample analysis, reduces human errors in sample processing, as well as simplifies and accelerates the overall logistics. Automating diagnostic testing workflows in peripheral laboratories and also in near-patient settings -like hospitals, clinics and epidemic control checkpoints- is advantageous for the simultaneous processing of multiple samples to provide rapid results to patients, minimize the possibility of contamination or error during sample handling or transport, and increase efficiency. However, most automation platforms are expensive and are not easily adaptable to new protocols. Here, we address the need for a versatile, easy-to-use, rapid and reliable diagnostic testing workflow by combining open-source modular automation (Opentrons) and automation-compatible molecular biology protocols, easily adaptable to a workflow for infectious diseases diagnosis by detection on paper-based diagnostics. We demonstrated the feasibility of automation of the method with a low-cost Neisseria meningitidis diagnostic test that utilizes magnetic beads for pathogen DNA isolation, isothermal amplification, and detection on a paper-based microarray. In summary, we integrated open-source modular automation with adaptable molecular biology protocols, which was also faster and cheaper to perform in an automated than in a manual way. This enables a versatile diagnostic workflow for infectious diseases and we demonstrated this through a low-cost N. meningitidis test on paper-based microarrays. [ABSTRACT FROM AUTHOR]

Data-driven computational analysis is becoming increasingly important in biomedical research, as the amount of data being generated continues to grow. However, the lack of practices of sharing research outputs, such as data, source code and methods, affects transparency and reproducibility of studies, which are critical to the advancement of science. Many published studies are not reproducible due to insufficient documentation, code, and data being shared. We conducted a comprehensive analysis of 453 manuscripts published between 2016–2021 and found that 50.1% of them fail to share the analytical code. Even among those that did disclose their code, a vast majority failed to offer additional research outputs, such as data. Furthermore, only one in ten articles organized their code in a structured and reproducible manner. We discovered a significant association between the presence of code availability statements and increased code availability. Additionally, a greater proportion of studies conducting secondary analyses were inclined to share their code compared to those conducting primary analyses. In light of our findings, we propose raising awareness of code sharing practices and taking immediate steps to enhance code availability to improve reproducibility in biomedical research. By increasing transparency and reproducibility, we can promote scientific rigor, encourage collaboration, and accelerate scientific discoveries. We must prioritize open science practices, including sharing code, data, and other research products, to ensure that biomedical research can be replicated and built upon by others in the scientific community. [ABSTRACT FROM AUTHOR]

Copyright of Studia Universitatis Babes-Bolyai, Educatio Artis Gymnasticae is the property of Babes-Bolyai University, Cluj-Napoca, Romania and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

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.

This paper examines open-source migrations from the early career librarian perspective. After being on the implementation team for two open-source migrations in two years, the author noticed stark similarities in the two experiences. The paper will discuss the challenges all migrations face and challenges more specific to open-source systems. These include lack of resources, changing timelines, and lack of system sustainability. The paper will then move on to provide recommendations for other early career librarians who are implementing open-source system. The paper will also offer ways colleagues and supervisors who are not working on the implementation can support these migrations and the people.

Background Energy communities and local electricity markets (e.g., as peer-to-peer trading) are on the rise due to increasingly decentralized electricity generation and favorable adjustment of the legal framework in many European countries. Methods This work applies a bi-level optimization model for dynamic participation in peer-to-peer electricity trading to determine the optimal parameters of new participants who want to join an energy community, based on the preferences of the members of the original community (e.g., environmental, economic, or mixed preference). The upper-level problem chooses optimal parameters by minimizing an objective function that includes the prosumers’ cost-saving and emission-saving preferences, while the lower level problem maximizes community welfare by optimally allocating locally generated photovoltaic (PV) electricity between members according to their willingness-to-pay. The bi-level problem is solved by transforming the lower level problem by its corresponding Karush-Kuhn-Tucker (KKT) conditions. Results The results demonstrate that environment-oriented prosumers opt for a new prosumer with high PV capacities installed and low electricity demand, whereas profit-oriented prosumers prefer a new member with high demand but no PV system capacity, presenting a new source of income. Sensitivity analyses indicate that new prosumers’ willingness-to-pay has an important influence when the community must decide between two new members. Conclusions The added value of this work is that the proposed method can be seen as a basis for a selection process between a large number of potential new community members. Most important future work will include optimization of energy communities over the horizon several years.

The development and improvement of electromagnetic underground metal target detection methods can be implemented by a framework that is experimental supporting, modular, and extensible. In this paper, we organize the components of electromagnetic underground metal target detection in a comprehensive, modular, and extensible framework. Furthermore, we present an open-source toolbox in Python called MicEMD (Modeling, Inversion, and Classification in ElectroMagnetic Detection, https://github.com/UndergroundDetection/MICEMD). The graphical user interface (GUI) and the library with a Python application programming interface (API) are contained in MicEMD. Included in MicEMD are staggered frequency-domain and time-domain electromagnetic forward modeling, least-squares inversion, and data-based classification methods at present. MicEMD’s capabilities are presented by two synthetic case studies. The first example shows the application of frequency-domain inversion. The second example shows the application of time-domain classification. It is anticipated that MicEMD offers a flexible tool in electromagnetic underground metal target detection.